len(set(l2_target_queries)):\\n # raise DASConfigValdationError(f\\\"Some queries {l2_target_queries} are targeted in L2 optimization more than once\\\",\\n # section=CC.BUDGET, options=options_list)\\n # # NOTE: this is no longer a requirement with constrain-to config specification.\\n # l2_target_queries = sortMarginalNames(l2_target_queries)\\n\\n measured_dp_queries = sortMarginalNames(self.budget.query_budget.dp_query_names[geolevel])\\n\\n if len(set(measured_dp_queries) - set(l2_target_queries)) > 0:\\n raise ValueError(\\n f\\\"In query ordering {query_ordering_name}, geolevel {geolevel}, some of the measured DP queries ({measured_dp_queries}) are not targeted in L2 optimization {l2_target_queries}\\\")\\n\\n print(f\\\"Detected {geolevel} l2_dp_query_ordering: {query_ordering[geolevel][CC.L2_QUERY_ORDERING]}\\\")\\n print(f\\\"Detected {geolevel} l2_ConstrainTo_dp_query_ordering: {query_ordering[geolevel][CC.L2_CONSTRAIN_TO_QUERY_ORDERING]}\\\")\\n print(f\\\"Detected {geolevel} rounder_dp_query_ordering: {query_ordering[geolevel][CC.ROUNDER_QUERY_ORDERING]}\\\")\\n\\n return optimizers, query_ordering, rounder_query_names\",\n \"def lookup(self, entry):\\n entry.add_lazy_fields(self.lazy_loader, self.field_map)\",\n \"def _build_optimizer(self, optimizer_to_use=tf.train.AdamOptimizer, tpu_support=False):\\n self.optimize_ops = []\\n for loss in self.losses['train']: # TODO Create apropoiate external training scheme\\n optimize_op = optimizer_to_use(\\n learning_rate=self.learning_rate\\n )\\n if tpu_support:\\n optimize_op = tpu.CrossShardOptimizer(optimize_op)\\n optimize_op = optimize_op.minimize(\\n loss=loss,\\n global_step=tf.train.get_global_step()\\n )\\n self.optimize_ops.append(optimize_op)\\n logging.info('Optimizers built')\",\n \"def inject_middleware():\\n if 'appmap.django.Middleware' not in settings.MIDDLEWARE:\\n settings.MIDDLEWARE.insert(0, 'appmap.django.Middleware')\",\n \"def _get_slot_and_set_to_optimizer(self, layer_name):\\n for slot_name in self._allowed_slot_names:\\n param_name = get_slot_table_name(layer_name, slot_name)\\n indices = self._tls._unique_ids_all_layers[layer_name]\\n slot_value = self._lookup_embedding_func(param_name, indices)\\n # self._create_slot_variable creates a slot variable in tf\\n # optimizer and set slot_value to it.\\n self._create_slot_variable(layer_name, slot_name, slot_value)\",\n \"def test_multiple_optimizers(self):\\n self.run_subtests(\\n {\\n \\\"sharding_strategy\\\": [\\n ShardingStrategy.FULL_SHARD,\\n ShardingStrategy.SHARD_GRAD_OP,\\n ]\\n },\\n self._test_multiple_optimizers,\\n )\",\n \"def dynCache(*args, **kwargs)->None:\\n pass\",\n \"def Optimization(*args, **kwargs):\\n from warnings import warn\\n\\n warn(\\n \\\"Optimization has been renamed to OptimizationResult and will be removed as soon as v0.13.0\\\", DeprecationWarning\\n )\\n return OptimizationResult(*args, **kwargs)\",\n \"def cache_test_sources(self):\\n self.cache_extra_test_sources(\\\"pdb\\\")\",\n \"def configure_optimizers(self):\\n allowed = list(OPTIM_LOOKUP.keys())\\n if self.optimizer not in allowed:\\n raise ValueError(\\n f\\\"Illegal optimizer given. Got {self.optimizer}. Allowed: {allowed}.\\\"\\n )\\n\\n allowed = list(SCHED_LOOKUP.keys())\\n if self.scheduler not in allowed:\\n raise ValueError(\\n f\\\"Illegal scheduler given. Got {self.scheduler}. Allowed: {allowed}.\\\"\\n )\\n\\n if self.optim_params is None:\\n self.optim_params = {\\n \\\"encoder\\\": {\\\"lr\\\": 0.00005, \\\"weight_decay\\\": 0.00005},\\n \\\"decoder\\\": {\\\"lr\\\": 0.0005, \\\"weight_decay\\\": 0.0005},\\n }\\n\\n params = adjust_optim_params(self.model, self.optim_params)\\n optimizer = OPTIM_LOOKUP[self.optimizer](params)\\n\\n if self.lookahead:\\n optimizer = OPTIM_LOOKUP[\\\"lookahead\\\"](optimizer, k=5, alpha=0.5)\\n\\n if self.scheduler_params is None:\\n self.scheduler_params = {}\\n\\n scheduler = {\\n \\\"scheduler\\\": SCHED_LOOKUP[self.scheduler](\\n optimizer, **self.scheduler_params\\n ),\\n \\\"monitor\\\": \\\"val_loss\\\",\\n \\\"interval\\\": \\\"epoch\\\",\\n \\\"frequency\\\": 1,\\n }\\n\\n return [optimizer], [scheduler]\",\n \"def _store_interpolator_kwargs(self, **kwargs):\\n self._interpolator_kwargs = copy.deepcopy(kwargs)\",\n \"def optimize(self):\\n seq = transform.Sequential([transform.SimplifyInference(),\\n transform.FuseOps(0),\\n transform.ToANormalForm(),\\n transform.InferType()])\\n return seq(self.mod)\",\n \"def add_latesettings_aliases(self):\\n aliases = {\\n mconst.DEF_SETTINGNAME_siteurl_absolute: self.settings.get_subvalue(mconst.DEF_SETTINGSEC_config, mconst.DEF_SETTINGNAME_siteurl_absolute),\\n mconst.DEF_SETTINGNAME_siteurl_relative: self.settings.get_subvalue(mconst.DEF_SETTINGSEC_config, mconst.DEF_SETTINGNAME_siteurl_relative,''),\\n mconst.DEF_SETTINGNAME_sitefilepath: self.settings.get_subvalue(mconst.DEF_SETTINGSEC_config, mconst.DEF_SETTINGNAME_sitefilepath),\\n mconst.DEF_SETTINGNAME_sitename: self.settings.get_subvalue(mconst.DEF_SETTINGSEC_config, mconst.DEF_SETTINGNAME_sitename),\\n }\\n self.settings.merge_settings_key(mconst.DEF_SETTINGSEC_aliases, aliases)\\n self.alias_settings_change()\",\n \"def add_optimizer_op(self, scope):\\r\\n optimizer = tf.train.AdamOptimizer(learning_rate=self.lr)\\r\\n variables = tf.get_collection(key=tf.GraphKeys.TRAINABLE_VARIABLES, scope=scope)\\r\\n\\r\\n grads_vars = optimizer.compute_gradients(loss=self.loss, var_list=variables)\\r\\n if self.config.grad_clip: # clip by global norm\\r\\n grads_vars = [(tf.clip_by_norm(grad, self.config.clip_val), var) if grad is not None else (grad, var) for grad, var in grads_vars]\\r\\n\\r\\n self.train_op = optimizer.apply_gradients(grads_and_vars=grads_vars)\\r\\n self.grad_norm = tf.global_norm([grad for grad, _ in grads_vars]) # we log the clipped norm here\\r\",\n \"def analysis_rule_plug_ins(self, analysis_rule_plug_ins):\\n\\n self._analysis_rule_plug_ins = analysis_rule_plug_ins\",\n \"def SetPersistentCache(ambler, suggestions):\\n for suggestion in suggestions:\\n suggestion_object = models.CachedPlace()\\n suggestion_object.lat = suggestion['lat']\\n suggestion_object.lng = suggestion['lng']\\n suggestion_object.name = suggestion['name']\\n suggestion_object.food_type = suggestion['food_type']\\n suggestion_object.cost = suggestion['cost']\\n suggestion_object.why_description1 = suggestion['why_description1']\\n suggestion_object.why_description2 = suggestion['why_description2']\\n suggestion_object.cache_timestamp = suggestion['cache_timestamp']\\n suggestion_object.address = suggestion['address']\\n ambler.persistent_suggestion_cache.append(suggestion_object)\\n ambler.put()\",\n \"def cache_results(self, results):\\r\\n # cache the results \\r\\n invalidation_dict = {}\\r\\n if cache.add(self.result_key, results, conf.CACHE_SECONDS):\\r\\n \\r\\n invalidation_dict.update(dict([(key, self.result_key) for key in self.get_invalidation_keys(results)]))\\r\\n \\r\\n for child, negate in self.queryset._get_where_clause(self.queryset.query.where): \\r\\n constraint, lookup_type, value_annotation, params = child \\r\\n for model_class, accessor_path in self._get_join_paths(constraint.alias, constraint.col):\\r\\n if self._is_valid_flush_path(accessor_path): \\r\\n invalidation_key = self._register_signal(model_class, accessor_path, lookup_type, negate, params)\\r\\n invalidation_dict[invalidation_key] = self.result_key\\r\\n \\r\\n for join_tuple in self.queryset.query.join_map.keys():\\r\\n if join_tuple[0] == model_class._meta.db_table and self._is_valid_flush_path(accessor_path): \\r\\n model_klass, m2m = self.queryset._get_model_class_from_table(join_tuple[1]) \\r\\n invalidation_key = self._register_signal(model_klass, join_tuple[3], lookup_type, negate, params)\\r\\n invalidation_dict[invalidation_key] = self.result_key\\r\\n \\r\\n # need to add and append to prevent race conditions\\r\\n # replace this with batch operations later\\r\\n for flush_key, flush_list in invalidation_dict.iteritems():\\r\\n added = cache.add(flush_key, self.result_key, 0)\\r\\n if not added:\\r\\n cache.append(flush_key, ',%s' % self.result_key)\",\n \"def optimize(self) -> TileableGraph:\\n if self._config.optimize_tileable_graph:\\n # enable optimization\\n self.tileable_optimization_records = optimize_tileable_graph(\\n self.tileable_graph\\n )\\n return self.tileable_graph\",\n \"def _add_variable_proxy_methods(var, proxy_tensor):\\n proxy_tensor.read_value = lambda: tf.identity(proxy_tensor)\\n proxy_tensor.assign_sub = var.assign_sub\\n proxy_tensor.assign = var.assign\\n proxy_tensor.initialized_value = var.initialized_value\",\n \"def setup_optimiser(self):\\n self.optimiser = ScheduledOptim(\\n optim.Adam(\\n filter(lambda x: x.requires_grad, self.model.parameters()),\\n betas=(0.9, 0.98), eps=1e-09, lr=self.opt.learning_rate),\\n self.opt.d_model, self.opt.n_warmup_steps)\\n if self.opt.verbose:\\n print(\\\"[Info] optimiser configured.\\\")\",\n \"def initializeProxy(self, *args):\\n return _SALOMERuntime.OptimizerAlgSync_initializeProxy(self, *args)\",\n \"def setup(self):\\n\\n for name, infos in Rt.geom_dict.items():\\n if name in Rt.optim_var_dict:\\n self.add_input(name, val=infos[1][0])\",\n \"def configure_optimizers(\\n self,\\n ) -> Dict[str, Union[torch.optim.Optimizer, Dict[str, Union[str, _LRScheduler]]]]:\\n optimizer = instantiate(self.optimizer_cfg, params=self.encoder.parameters())\\n scheduler = {\\n \\\"scheduler\\\": instantiate(self.scheduler_cfg, optimizer),\\n \\\"monitor\\\": \\\"train_err1\\\",\\n }\\n\\n return dict(optimizer=optimizer, lr_scheduler=scheduler)\",\n \"def enable_cache(self, **kwargs: Dict[str, Any]) -> None:\\n pass\",\n \"def get_oracle_adversary_optimizers(\\n self, adversaries: List[torch.nn.Module], mode: str\\n ) -> List[torch.optim.Optimizer]:\\n pass\",\n \"def _hook_then_optimizer(\\n hook: Callable[[Any, dist.GradBucket], torch.futures.Future[torch.Tensor]],\\n optimizer_state: _OptimizerHookState,\\n) -> Callable[[Any, dist.GradBucket], torch.futures.Future[torch.Tensor]]:\\n\\n def hook_then_optimizer_wrapper(\\n hook_state, bucket: dist.GradBucket\\n ) -> torch.futures.Future[torch.Tensor]:\\n # Run original hook\\n fut = hook(hook_state, bucket)\\n\\n def optimizer_step(fut):\\n gradient_tensors = bucket.gradients()\\n model_params = bucket.parameters()\\n for grad_tensor, model_param in zip(gradient_tensors, model_params):\\n optimizer_state.functional_optimizer.step_param(\\n model_param,\\n grad_tensor,\\n )\\n return bucket.buffer()\\n\\n return fut.then(optimizer_step)\\n\\n return hook_then_optimizer_wrapper\",\n \"def make_fp32_optimiser(optimiser_class):\\n class Fp32Optimiser(optimiser_class):\\n def __init__(self, *args, **kwargs, ):\\n super().__init__(*args, **kwargs)\\n\\n def _create_slots(self, var_list):\\n new_var_list = []\\n for v in var_list:\\n if v.dtype.base_dtype != dtypes.float32:\\n new_var_list.append(self._get_or_make_slot(v,\\n math_ops.cast(v.initialized_value(),\\n dtypes.float32),\\n \\\"fp32\\\", \\\"fp32\\\"))\\n else:\\n new_var_list.append(v)\\n return super()._create_slots(new_var_list)\\n\\n def _apply_weight_update(self, grad, var):\\n if var.dtype.base_dtype == dtypes.float32:\\n return super()._apply_weight_update(grad, var)\\n else:\\n orig_var = var\\n var = self.get_slot(var, \\\"fp32\\\")\\n updated_var = super()._apply_weight_update(\\n math_ops.cast(grad, dtypes.float32), var)\\n apply_fp32 = var.assign(updated_var)\\n with tf.control_dependencies([apply_fp32]):\\n return math_ops.cast(updated_var, orig_var.dtype.base_dtype)\\n\\n return Fp32Optimiser\",\n \"def setResolver(self, resolver):\\n pass;\",\n \"def optimizer(func):\\n from shenfun.config import config\\n mod = config['optimization']['mode']\\n verbose = config['optimization']['verbose']\\n\\n if mod.lower() not in ('cython', 'numba'):\\n # Use python function\\n if verbose:\\n print(func.__qualname__ + ' not optimized')\\n return func\\n mod = importlib.import_module('shenfun.optimization.'+mod.lower())\\n fun = getattr(mod, func.__name__, func)\\n if fun is func:\\n fun = getattr(mod, func.__qualname__.replace('.', '_'), func)\\n if verbose:\\n if fun is func:\\n print(fun.__qualname__ + ' not optimized')\\n\\n @wraps(func)\\n def wrapped_function(*args, **kwargs):\\n u0 = fun(*args, **kwargs)\\n return u0\\n\\n return wrapped_function\",\n \"def setup_ng(self, var_manager, budget):\\r\\n\\r\\n for var_name, var_dict in var_manager.variable_info.items():\\r\\n\\r\\n if var_dict['grad_free'] is False:\\r\\n continue\\r\\n\\r\\n if type(var_dict['grad_free']) == tuple:\\r\\n mu, sigma = var_dict['grad_free']\\r\\n\\r\\n if mu is None:\\r\\n mu = np.zeros(var_dict['shape'])\\r\\n\\r\\n if sigma is None:\\r\\n sigma = 1.\\r\\n\\r\\n cma_opt = CMA(mu, sigma=sigma)\\r\\n\\r\\n else:\\r\\n mu = np.zeros(var_dict['shape'])\\r\\n sigma = 1.0\\r\\n\\r\\n opt_fn = ng.optimizers.registry[self.method]\\r\\n p = ng.p.Array(init=mu)#.set_mutation(sigma=sigma)\\r\\n ng_opt = opt_fn(parametrization=p, budget=budget)\\r\\n\\r\\n self.ng_optimizers[(var_dict['var_type'], var_name)] = ng_opt\\r\\n\\r\\n assert len(self.ng_optimizers.keys()) == 1, \\\\\\r\\n 'currently only a single input variable can be optimized via '+\\\\\\r\\n 'Nevergrad but got: {}'.format(self.ng_optimizers.keys())\\r\\n return\",\n \"def _app_state(unit: AppStateMixin) -> Dict[str, Any]:\\n app_state = unit.app_state()\\n tracked_optimizers = _construct_tracked_optimizers(unit) # handles fsdp\\n app_state.update(tracked_optimizers)\\n return app_state\",\n \"def _should_optimize(self, query):\\n optimizations = []\\n\\n if query['operation_type'] == 'query':\\n nscanned = self._get_property('nscanned', query['org_info'])\\n nreturned = self._get_property('nreturned', query['org_info'])\\n if (nreturned and nscanned) and \\\\\\n (nscanned > nreturned * self.nscanned_vs_nreturned_ratio):\\n optimizations.append(\\\"index\\\")\\n\\n reslen = self._get_property('reslen', query['org_info'])\\n if reslen > 100 * 1024:\\n optimizations.append(\\\"select members\\\")\\n\\n elif query['operation_type'] == 'update':\\n nscanned = self._get_property('nscanned', query['org_info'])\\n\\n if nscanned and nscanned > self.nscanned_update_limit:\\n optimizations.append(\\\"index\\\")\\n\\n if query['millis'] > self.slow_query_limit:\\n optimizations.append(\\\"slow\\\")\\n\\n return optimizations\",\n \"def test_custom_cache(self, mocker):\\n dev = qml.device(\\\"default.qubit\\\", wires=1)\\n spy = mocker.spy(qml.interfaces, \\\"cache_execute\\\")\\n\\n def cost(a, cache):\\n with qml.queuing.AnnotatedQueue() as q:\\n qml.RY(a[0], wires=0)\\n qml.RX(a[1], wires=0)\\n qml.expval(qml.PauliZ(0))\\n\\n tape = qml.tape.QuantumScript.from_queue(q)\\n\\n return execute(\\n [tape],\\n dev,\\n gradient_fn=param_shift,\\n cache=cache,\\n )[0]\\n\\n custom_cache = {}\\n params = jax.numpy.array([0.1, 0.2])\\n jax.grad(cost)(params, cache=custom_cache)\\n\\n cache = spy.call_args[0][1]\\n assert cache is custom_cache\",\n \"def initialize(self, *args, **kwargs): \\n super().initialize(*args, **kwargs)\\n self.updates_per_optimize = 1\",\n \"def optimize_layers_and_transparency(self) -> None:\\n self.optimize_layers()\\n self.optimize_transparency()\",\n \"def configure_optimizers(self):\\n return torch.optim.Adam(self.parameters(), lr=0.02)\",\n \"def configure_optimizers(self) -> Union[Optimizer, List[Optimizer]]:\\n optimizers = []\\n\\n for modality in range(self.num_modalities):\\n optimizers.append(\\n torch.optim.Adam(\\n list(self.unimodal_encoder[modality].parameters())\\n + list(self.unimodal_decoder[modality].parameters()),\\n lr=self.gcn_lr,\\n )\\n )\\n\\n if self.multimodal_decoder is not None:\\n optimizers.append(torch.optim.Adam(self.multimodal_decoder.parameters(), lr=self.vcdn_lr))\\n\\n return optimizers\",\n \"def configure_optimizers(self) -> dict[str, Any]:\\n optimizer = torch.optim.Adam(\\n self.model.parameters(), lr=self.hyperparams[\\\"learning_rate\\\"]\\n )\\n return {\\n \\\"optimizer\\\": optimizer,\\n \\\"lr_scheduler\\\": {\\n \\\"scheduler\\\": ReduceLROnPlateau(\\n optimizer,\\n patience=self.hyperparams[\\\"learning_rate_schedule_patience\\\"],\\n ),\\n \\\"monitor\\\": \\\"val_loss\\\",\\n },\\n }\",\n \"def optimize(self, path: str) -> None:\\n self._check_connection()\\n self._dispatch(\\\"post\\\", self._optimize_url(path))\",\n \"def handleRepackSettings(self):\\n self.logger.info(\\\"Hardcoding read/cache strategies for repack\\\")\\n self._enableLazyDownload()\\n return\",\n \"def register():\\n signals.initialized.connect(image_optimizer_initialized)\\n signals.finalized.connect(image_optimizer_finalized)\",\n \"def configure_optimizers(self) -> Dict[str, Any]:\\n optimizer = torch.optim.AdamW(\\n self.model.parameters(), lr=self.hparams[\\\"learning_rate\\\"]\\n )\\n return {\\n \\\"optimizer\\\": optimizer,\\n \\\"lr_scheduler\\\": {\\n \\\"scheduler\\\": ReduceLROnPlateau(\\n optimizer, patience=self.hparams[\\\"learning_rate_schedule_patience\\\"]\\n ),\\n \\\"monitor\\\": \\\"val_loss\\\",\\n },\\n }\",\n \"def warmup_cache(self):\\n self.get_whitespace_changes()\\n self.get_cvsheader_changes()\\n self.get_unmodified_changes()\\n self.get_used_changes()\\n self.get_zapped_changes()\\n self.get_undecided_changes()\",\n \"def register_middleware(self, middleware, opts={}):\\n self._middleware.append((middleware, opts))\",\n \"def _set_proxies_in_storage(self) -> None:\\n\\n data_raw = Froxy._get_data_in_api(API_URL)\\n\\n self.storage.insert(\\n Froxy._data_normalization(data_raw)\\n )\",\n \"def add_settings(context):\\n portal_props = getToolByName(context, 'portal_properties')\\n props = portal_props.sharerizer\\n wanted = [\\n # (propname, proptype, default)\\n ('restrict', 'boolean', False),\\n ('allowed_types', 'lines', []),\\n ]\\n for propname, proptype, default in wanted:\\n if not props.hasProperty(propname):\\n props._setProperty(propname, default, proptype)\\n logger.info('sharerizer: added %s property %r with default '\\n 'value %r', proptype, propname, default)\",\n \"def add_uses(\\n self, context: SlicingContext, traced_instr: ExecutedInstruction\\n ) -> None:\\n if isinstance(traced_instr, ExecutedMemoryInstruction):\\n self._add_variable_uses(context, traced_instr)\\n\\n # Add attribute uses\\n if isinstance(traced_instr, ExecutedAttributeInstruction):\\n self._add_attribute_uses(context, traced_instr)\",\n \"def register_opts():\\n _register_api_opts()\\n _register_db_opts()\",\n \"def _get_optimizer(self):\\n raise NotImplementedError\",\n \"def _prep_cache(xs_cache, E_g=None, phi_g=None):\\n if E_g is not None:\\n xs_cache[\\\"E_g\\\"] = E_g\\n\\n if phi_g is not None:\\n xs_cache[\\\"phi_g\\\"] = phi_g\",\n \"def _optimize(self):\\n # Retrieve all trainable variables\\n train_variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)\\n\\n # Compute the gradient (return a pair of variable and their respective gradient)\\n grads = self.optimizer.compute_gradients(loss=self.loss, var_list=train_variables)\\n self.train_dis = self.optimizer.apply_gradients(grads, global_step=self.global_step)\",\n \"def useHint(self):\\n self.hints = self.hints - 1\",\n \"def _optimize_gql_selections(self, selected_fields: List[SelectedField], graphql_type,\\n store: QueryOptimizerStore = None) -> QueryOptimizerStore:\\n _logger.info('_optimize_gql_selections %r %r', graphql_type, selected_fields)\\n if not store:\\n store = QueryOptimizerStore(disable_abort_only=self.disable_abort_only)\\n if not selected_fields:\\n return store\\n optimized_fields_by_model = {}\\n # schema = self.root_info.schema\\n possible_types = self._get_possible_types(graphql_type)\\n for selected_field in selected_fields:\\n if isinstance(selected_field, InlineFragment):\\n # Inline Fragment e.g. `... on Droid {}`\\n # ToDo\\n # self.handle_inline_fragment(selected_field, schema, possible_types, store)\\n continue\\n name = selected_field.name\\n if name == '__typename':\\n continue\\n if type(selected_field) is FragmentSpread:\\n self._optimize_gql_selections(selected_field.selections, graphql_type, store=store)\\n continue\\n for type_ in possible_types:\\n if isinstance(type_, LazyType):\\n type_ = type_.resolve_type()\\n if selected_field.name == 'rows' and selected_field.selections:\\n # Cursor pagination - optimize the selected fields in `rows`\\n self._optimize_gql_selections(selected_field.selections, graphql_type, store=store)\\n continue\\n selection_field_def = next(\\n (field for field in type_._type_definition.fields if to_camel_case(field.name) == name),\\n None)\\n if not selection_field_def:\\n continue\\n model = type_._django_type.model\\n if model and name not in optimized_fields_by_model:\\n optimized_fields_by_model[name] = model\\n self._optimize_field(store, model, selected_field, selection_field_def, type_)\\n return store\",\n \"def initialize_optimization(self):\\n\\n if self.FLAGS.optimizer == \\\"Adam\\\" :\\n self.solver = tf.train.AdamOptimizer(\\n learning_rate = self.learning_rate,\\n beta1 = self.FLAGS.beta1,\\n beta2 = self.FLAGS.beta2)\\n else:\\n print(\\\"ERROR: Cannot handle optimizer type {}!!!\\\".format(self.FLAGS.optimizer))\\n raise RuntimeError\\n \\n # batch normalization in tensorflow requires this extra dependency\\n # this is required to update the moving mean and moving variance variables\\n extra_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)\\n with tf.control_dependencies(extra_update_ops):\\n self.update = self.solver.minimize(self.loss, global_step=self.global_step)\",\n \"def init_agentpool_decorator_context(self) -> None:\\n self.agentpool_decorator = AKSAgentPoolAddDecorator(\\n self.cmd, self.client, self.__raw_parameters, self.resource_type, self.agentpool_decorator_mode\\n )\\n self.agentpool_context = self.agentpool_decorator.context\\n self.context.attach_agentpool_context(self.agentpool_context)\",\n \"def memoization_prepare(self):\\n if not isinstance(\\n getattr(self, constants.CONST_MEMOIZATION, None),\\n dict,\\n ):\\n setattr(self, constants.CONST_MEMOIZATION, {})\",\n \"def make_optimizer(self):\\r\\n # parameters = [self.encoder.parameters(), self.decoder.parameters(), self.spec_enc.parameters()]\\r\\n if self.flags.optim == 'Adam':\\r\\n op = torch.optim.Adam(self.model.parameters(), lr=self.flags.lr, weight_decay=self.flags.reg_scale)\\r\\n elif self.flags.optim == 'RMSprop':\\r\\n op = torch.optim.RMSprop(self.model.parameters(), lr=self.flags.lr, weight_decay=self.flags.reg_scale)\\r\\n elif self.flags.optim == 'SGD':\\r\\n op = torch.optim.SGD(self.model.parameters(), lr=self.flags.lr, weight_decay=self.flags.reg_scale)\\r\\n else:\\r\\n raise Exception(\\\"Your Optimizer is neither Adam, RMSprop or SGD, please change in param or contact Ben\\\")\\r\\n return op\",\n \"def register(self, *, model, optimizer, criterion, **kwargs):\\n raise NotImplementedError()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6120944","0.5490147","0.54176563","0.52781653","0.51241463","0.50488275","0.49040857","0.49034598","0.4894648","0.4883001","0.48744258","0.48651642","0.48068908","0.47903302","0.47814724","0.473934","0.47289","0.46801856","0.4662181","0.46440312","0.46211925","0.461332","0.459949","0.4594107","0.45690128","0.45690128","0.45690128","0.45575118","0.45556295","0.4554593","0.45510957","0.45438987","0.45409814","0.45369112","0.45265728","0.45005164","0.44905323","0.44885555","0.4473725","0.44490954","0.44323602","0.44317764","0.44312423","0.44274634","0.44142243","0.44140658","0.4409066","0.44015592","0.43960968","0.4393739","0.4378838","0.43747988","0.43721274","0.43666115","0.43464947","0.4344052","0.4340748","0.43375507","0.43329608","0.43278643","0.4321934","0.4318118","0.43154874","0.4313142","0.43018597","0.42996138","0.42971042","0.4294629","0.42936045","0.42904946","0.42670283","0.42611307","0.42608446","0.42503467","0.42486086","0.424836","0.42419052","0.4240652","0.42299393","0.4211903","0.42062005","0.4202492","0.4200524","0.4199194","0.41980633","0.41882193","0.41873184","0.41853806","0.41849783","0.418416","0.41805044","0.41800845","0.417523","0.41679212","0.4164361","0.41640514","0.4158475","0.41553813","0.4152088","0.4146311"],"string":"[\n \"0.6120944\",\n \"0.5490147\",\n \"0.54176563\",\n \"0.52781653\",\n \"0.51241463\",\n \"0.50488275\",\n \"0.49040857\",\n \"0.49034598\",\n \"0.4894648\",\n \"0.4883001\",\n \"0.48744258\",\n \"0.48651642\",\n \"0.48068908\",\n \"0.47903302\",\n \"0.47814724\",\n \"0.473934\",\n \"0.47289\",\n \"0.46801856\",\n \"0.4662181\",\n \"0.46440312\",\n \"0.46211925\",\n \"0.461332\",\n \"0.459949\",\n \"0.4594107\",\n \"0.45690128\",\n \"0.45690128\",\n \"0.45690128\",\n \"0.45575118\",\n \"0.45556295\",\n \"0.4554593\",\n \"0.45510957\",\n \"0.45438987\",\n \"0.45409814\",\n \"0.45369112\",\n \"0.45265728\",\n \"0.45005164\",\n \"0.44905323\",\n \"0.44885555\",\n \"0.4473725\",\n \"0.44490954\",\n \"0.44323602\",\n \"0.44317764\",\n \"0.44312423\",\n \"0.44274634\",\n \"0.44142243\",\n \"0.44140658\",\n \"0.4409066\",\n \"0.44015592\",\n \"0.43960968\",\n \"0.4393739\",\n \"0.4378838\",\n \"0.43747988\",\n \"0.43721274\",\n \"0.43666115\",\n \"0.43464947\",\n \"0.4344052\",\n \"0.4340748\",\n \"0.43375507\",\n \"0.43329608\",\n \"0.43278643\",\n \"0.4321934\",\n \"0.4318118\",\n \"0.43154874\",\n \"0.4313142\",\n \"0.43018597\",\n \"0.42996138\",\n \"0.42971042\",\n \"0.4294629\",\n \"0.42936045\",\n \"0.42904946\",\n \"0.42670283\",\n \"0.42611307\",\n \"0.42608446\",\n \"0.42503467\",\n \"0.42486086\",\n \"0.424836\",\n \"0.42419052\",\n \"0.4240652\",\n \"0.42299393\",\n \"0.4211903\",\n \"0.42062005\",\n \"0.4202492\",\n \"0.4200524\",\n \"0.4199194\",\n \"0.41980633\",\n \"0.41882193\",\n \"0.41873184\",\n \"0.41853806\",\n \"0.41849783\",\n \"0.418416\",\n \"0.41805044\",\n \"0.41800845\",\n \"0.417523\",\n \"0.41679212\",\n \"0.4164361\",\n \"0.41640514\",\n \"0.4158475\",\n \"0.41553813\",\n \"0.4152088\",\n \"0.4146311\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5351505"},"document_rank":{"kind":"string","value":"3"}}},{"rowIdx":22469,"cells":{"query":{"kind":"string","value":"Perform an upload of the given file to the current user's account The upload process consists of three steps and a loop. First, we request and receive an \"upload ticket\" from Vimeo. This ticket represents our place in the queue of videos waiting to upload. After successfully obtaining this ticket, we upload the binary data of the video. To do this, we make an HTTP request whose body contains some amount of the video's binary data. In response to a subsequent request, Vimeo tells us how much of the binary data was successfully uploaded. We repeat this process until Vimeo tells us the entirety of the file has been uploaded successfully. Once the entire video has been uploaded, the last step is to delete the upload ticket with another HTTP request. This action finalizes the upload process."},"document":{"kind":"string","value":"def __call__(self, name, post_check_hook=None):\n def do_upload():\n video_data, filetype = self.read_file(name)\n\n ticket_id, upload_uri, complete_uri = self.get_upload_ticket()\n log.info(\"Ticket ID: %s\" % ticket_id)\n\n _range = 0\n hook_break = False\n while _range < len(video_data) and hook_break != True:\n self.upload_segment(upload_uri, _range, video_data, filetype or 'mp4')\n _range = self.get_last_uploaded_byte(upload_uri)\n # hook is passed the range, breaks retry cycle if it returns True\n if post_check_hook:\n hook_break = post_check_hook(_range)\n\n log.info(\"Upload completed\")\n return self.delete_upload_ticket(complete_uri)\n\n return do_upload()"},"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 upload_file(self, file_upload_parameters, progress=None):\n\n file_upload_parameters._submit_upload_parameters.timeout_in_milliseconds = file_upload_parameters.timeout_in_milliseconds\n operation = self.submit_upload(file_upload_parameters._submit_upload_parameters)\n return self.download_upload_result(operation, file_upload_parameters, progress)","def upload_file(self, f):\n return self._telegraph.upload_file(f)","def upload(self, filename, file_path):\n return","def upload_file(self, file, max_chunk_size=1024 * 1024, parallelism=10):\n\n upload_key = self.internal.media_and_files.GetFileUploadUrl(\n media_and_files_pb2.RequestGetFileUploadUrl(\n expected_size=os.path.getsize(file)\n )\n ).upload_key\n\n with ThreadPoolExecutor(max_workers=parallelism) as executor:\n result = list(\n executor.map(\n lambda x: self.upload_file_chunk(*x),\n (\n (part_number, chunk, upload_key) for part_number, chunk in enumerate(\n read_file_in_chunks(file, max_chunk_size)\n )\n )\n\n )\n )\n\n if not all(result):\n return None\n\n return self.internal.media_and_files.CommitFileUpload(\n media_and_files_pb2.RequestCommitFileUpload(\n upload_key=upload_key,\n file_name=os.path.basename(file)\n )\n ).uploaded_file_location","def put_upload(self):\n # print \"starting upload...\", self.current_upload['filepath']\n self.touch()\n self.log(\"STARTING_UPLOAD\", level=INFO)\n try:\n Backend.put_file(self.fileobj, self.current_upload[\"gcs_url\"])\n except exceptions.FilePutError as err:\n self.handle_put_error(err, self.fileobj)\n raise","def upload_file(self):\n request = copy.deepcopy(self.request_template)\n data = json.dumps(request)\n curr_file = {\n 'request': data,\n 'file': open(self.file_path, 'rb')\n }\n print(\"Sending Upload request of av for file {}\".format(self.file_name))\n try:\n response = requests.post(url=self.url + \"upload\", files=curr_file, verify=False)\n except Exception as E:\n print(\"Upload file failed. file: {} , failure: {}\".format(self.file_name, E))\n raise\n response_j = response.json()\n print(\"av Upload response status for file {} : {}\".format(self.file_name,\n response_j[\"response\"][0][\"status\"][\"label\"]))\n return response_j","def upload_file(self, filename: str) -> SlackResponse:\n try:\n # Send Slack Message\n response = self.client.files_upload(\n file=filename,\n )\n return response\n except SlackApiError as e:\n logging.error(f\"Error uploading file: {e}\")","def upload_file(self, file_name, content):\n return self.files.upload(file_name, content)","def upload_file(\n self, bucket_id: uplink.Path, filename: uplink.Path, file: uplink.Body\n ):\n pass","def upload(self, asset, file):\n uploader = FrameioUploader(asset, file)\n uploader.upload()","def post(self):\n filename = str(time.time())\n filepath = os.path.join(\n os.path.join(current_app.config['UPLOAD_FOLDER'], filename))\n with open(filepath, 'bw') as uploadfile:\n chunk_size = 1024\n while True:\n chunk = request.stream.read(chunk_size)\n if len(chunk) == 0:\n break\n uploadfile.write(chunk)\n current_app.logger.info('file %s upload successfully', filename)\n return {'timestamp': filename}, http.HTTPStatus.CREATED","async def _upload(self) -> None:\n\n # filename given?\n filename = str(uuid.uuid4()) if self.filename is None else self.filename\n\n # check\n if self._upload_path is None:\n raise ValueError(\"No upload URL given.\")\n\n # send data and return image ID\n async with aiohttp.ClientSession() as session:\n data = aiohttp.FormData()\n data.add_field(\"file\", self._buffer, filename=self.filename)\n async with session.post(self._upload_path, auth=self._auth, data=data, timeout=self._timeout) as response:\n if response.status == 401:\n log.error(\"Wrong credentials for uploading file.\")\n raise FileNotFoundError\n elif response.status != 200:\n log.error(f\"Could not upload file to filecache: {response.status} {response.reason}\")\n raise FileNotFoundError","def upload_file(self, file_path, file_name, output_path):","async def upload(self, request):\n\n userid = await authenticated_userid(request)\n project = await request.app.context_project(request, userid)\n\n payload = await request.post()\n\n filename = payload['file'].filename\n upload_stream = payload['file'].file\n\n ext = os.path.splitext(filename)[1]\n\n if not re_filename_ext.match(ext):\n # paranoid check in case a script doesn't protect from code injection\n raise web.HTTPBadRequest(text='file extension not supported: %s' % filename)\n\n camera_id = uuid.uuid1().hex\n\n log = request['slog']\n log.debug('request: camera upload', filename=filename)\n\n config = request.app.config\n\n tmppath = dump_stream(config['media']['tempdir'], upload_stream)\n\n log.debug('file dump', camera_id=camera_id, tmppath=tmppath)\n\n await Camera.insert(request,\n camera_id=camera_id,\n filename=filename,\n project_id=project.project_id)\n\n await request.app.task_broker.publish('camera_upload', {\n 'userid': userid,\n 'project_id': project.project_id,\n 'camera_id': camera_id,\n 'tmppath': tmppath,\n 'filename': filename\n }, log=log)\n\n response_js = {\n 'camera_file_id': camera_id\n }\n\n return web.json_response(response_js, status=HTTPStatus.CREATED)","def upload():\n global video_file_name\n\n if request.method == 'POST' and 'file' in request.files:\n\n # clear uploads folder\n cleanup_uploads()\n\n # Video has been uploaded\n filename = video.save(request.files['file'])\n video_file_name[0] = filename\n\n # Process video on a new thread\n # threading.Thread(target=process_video, args=[os.path.join('uploads', filename)]).start()\n # threading.Thread(target=process_audio, args=[os.path.join('uploads', filename)]).start()\n threading.Thread(target=process_video, args=[os.path.join(basedir, 'static', 'data', 'uploads', filename)]).start()\n threading.Thread(target=process_audio, args=[os.path.join(basedir, 'static', 'data', 'uploads', filename)]).start()\n\n return jsonify({'success': True}), 200, {'ContentType': 'application/json'}\n\n elif request.method == 'GET':\n return render_template('home.html')","def upload(self, fullfilename, remotefolder=None):\n print(\"[Remote Server] Uploading %s to %s:%s\" %(fullfilename, self.server, self.remotefolder))\n\n if not self.status:\n return 0\n\n if remotefolder == None:\n remotefolder = self.remotefolder\n\n if not self.cd(remotefolder):\n return 0\n\n if not self.sendFile(fullfilename):\n print(\"[Remote Server] Error uploading file %s\" %fullfilename)\n return 0\n\n print(\"[Remote Server] upload finished successfully\")\n\n return 1","def upload(upload_url: str, file_path: str) -> None:\n with open(file_path, 'r') as data:\n try:\n r = requests.put(\n upload_url,\n data=data,\n headers={\"Content-Type\": \"application/octet-stream\"},\n )\n r.raise_for_status()\n except requests.exceptions.HTTPError as err:\n print('Error uploading file')\n raise err","def UploadFile(filename, file_id, content, is_binary, status, is_base):\r\n file_too_large = False\r\n if is_base:\r\n type = \"base\"\r\n else:\r\n type = \"current\"\r\n if len(content) > MAX_UPLOAD_SIZE:\r\n print (\"Not uploading the %s file for %s because it's too large.\" %\r\n (type, filename))\r\n file_too_large = True\r\n content = \"\"\r\n checksum = md5(content).hexdigest()\r\n if options.verbose > 0 and not file_too_large:\r\n print \"Uploading %s file for %s\" % (type, filename)\r\n url = \"/%d/upload_content/%d/%d\" % (int(issue), int(patchset), file_id)\r\n form_fields = [(\"filename\", filename),\r\n (\"status\", status),\r\n (\"checksum\", checksum),\r\n (\"is_binary\", str(is_binary)),\r\n (\"is_current\", str(not is_base)),\r\n ]\r\n if file_too_large:\r\n form_fields.append((\"file_too_large\", \"1\"))\r\n if options.email:\r\n form_fields.append((\"user\", options.email))\r\n ctype, body = EncodeMultipartFormData(form_fields,\r\n [(\"data\", filename, content)])\r\n response_body = rpc_server.Send(url, body,\r\n content_type=ctype)\r\n if not response_body.startswith(\"OK\"):\r\n StatusUpdate(\" --> %s\" % response_body)\r\n sys.exit(1)","def _upload_file(self, file, container):\r\n try:\r\n cnt = self.get_container(container)\r\n obj = cnt.get_object(file.filename)\r\n obj.delete()\r\n return self._upload_file_to_rackspace(file, container)\r\n except pyrax.exceptions.NoSuchObject:\r\n return self._upload_file_to_rackspace(file, container)\r\n except pyrax.exceptions.UploadFailed:\r\n return False","def upload_file(Filename=None, Bucket=None, Key=None, ExtraArgs=None, Callback=None, Config=None):\n pass","def UploadFile(filename, file_id, content, is_binary, status, is_base):\n file_too_large = False\n if is_base:\n type = \"base\"\n else:\n type = \"current\"\n if len(content) > MAX_UPLOAD_SIZE:\n print (\"Not uploading the %s file for %s because it's too large.\" %\n (type, filename))\n file_too_large = True\n content = \"\"\n checksum = md5(content).hexdigest()\n if options.verbose > 0 and not file_too_large:\n print \"Uploading %s file for %s\" % (type, filename)\n url = \"/%d/upload_content/%d/%d\" % (int(issue), int(patchset), file_id)\n form_fields = [(\"filename\", filename),\n (\"status\", status),\n (\"checksum\", checksum),\n (\"is_binary\", str(is_binary)),\n (\"is_current\", str(not is_base)),\n ]\n if file_too_large:\n form_fields.append((\"file_too_large\", \"1\"))\n if options.email:\n form_fields.append((\"user\", options.email))\n ctype, body = EncodeMultipartFormData(form_fields,\n [(\"data\", filename, content)])\n response_body = rpc_server.Send(url, body,\n content_type=ctype)\n if not response_body.startswith(\"OK\"):\n StatusUpdate(\" --> %s\" % response_body)\n sys.exit(1)","def upload(self, file_path):\n LOG.debug((\"Uploading file to mattermost: %s\", file_path))\n filename = os.path.basename(file_path)\n post_data = {'channel_id': self.channel_id,\n 'client_ids': filename}\n file_data = {'files': (filename, open(file_path, 'rb'))}\n url = '%s/api/v4/files' % self.server_url\n response = self._request(self._session.post, url, data=post_data,\n files=file_data)\n LOG.debug(\"Received: %s - %s\", response.status_code, response.text)\n\n if response.status_code != 201:\n if response.status_code == 400:\n msg = \"Type of the uploaded file doesn't match its file \" \\\n \" extension or uploaded file is an image that \" \\\n \"exceeds the maximum dimensions\"\n elif response.status_code == 401:\n msg = \"User is not logged in\"\n elif response.status_code == 403:\n msg = \"User does not have permission to upload file to \" \\\n \"the provided team/channel\"\n elif response.status_code == 413:\n msg = \"Uploaded file is too large\"\n elif response.status_code == 500:\n msg = \"File storage is disabled\"\n else:\n msg = response\n raise RuntimeError(\"Server unhappy with request, reports: %s\"\n % msg)\n\n file_id = response.json()[\"file_infos\"][0][\"id\"]\n return file_id","def run(self):\r\n m = hashlib.md5\r\n self.sock.connect((IP, self.port))\r\n #the uploaded file\r\n f = open(self.path, 'rb')\r\n dat = f.read(1024)\r\n print 'uploading...'\r\n\r\n while dat != '':\r\n hashed = m(dat)\r\n self.sock.send(hashed.hexdigest())\r\n self.data = self.sock.recv(2)\r\n\r\n if not self.data == 'ok':\r\n #somthing went wrong and the thread instantly stops\r\n return\r\n\r\n self.sock.send(dat)\r\n self.data = self.sock.recv(2)\r\n #in case the data didn't and the hash sent didn't match\r\n while self.data == 'sa':\r\n #sends again the hash and data\r\n hashed = m(dat)\r\n self.sock.send(hashed.hexdigest())\r\n print hashed.hexdigest()\r\n self.data = self.sock.recv(2)\r\n print self.data\r\n self.sock.send(dat)\r\n #print dat\r\n self.data = self.sock.recv(2)\r\n #can keep sending\r\n if self.data == 'kg':\r\n self.progress += 1024\r\n dat = f.read(1024)\r\n else:\r\n return\r\n\r\n if not self.sending:\r\n\r\n self.sock.send('canceled')\r\n self.sock.close()\r\n f.close()\r\n return\r\n\r\n\r\n f.close()\r\n self.sock.send('end-of-upload-d-o-n-e-now-what-?')\r\n print 'processing video... this may take a few minutes'\r\n self.data = self.sock.recv(1024)\r\n if self.data == 'complete':\r\n self.sock.close()\r\n tasks.complete_upload()\r\n else:\r\n pass","def upload(self, requests, file):\n # Set the source and dest paths\n dest_url = self.base_url + '/upload'\n source_path = os.path.join(self.data_dir, file)\n\n # Get the sha256 hash of the file\n with open(source_path, 'rb') as afile:\n beforeDigest = hashlib.sha256(afile.read()).hexdigest()\n\n print(\"Generated a hash of the temp file: \" + beforeDigest)\n\n # Upload the file and time it\n with open(source_path, 'rb') as f:\n startTime = time.time()\n r = requests.post(dest_url, files={'file': (file, f)}, max_price=5)\n endTime = time.time()\n uploadElapsedTime = endTime - startTime\n\n print(\"Uploaded the file. Elapsed time: \" + str(uploadElapsedTime))\n\n # Verify the upload was successful\n if r.json()['success'] is not True:\n return {'success': False}\n\n retVal = {\n 'success': True,\n 'time': uploadElapsedTime,\n 'digest': beforeDigest,\n 'upload_filename': r.json()['filename']\n }\n\n return retVal","def upload_file(file_name, file_id, model, workspace, header_put):\n filename = \"data_files/{}\".format(file_name)\n data_file = open(filename, \"r\")\n data = data_file.read()\n data_file.close()\n\n uri = (\"https://api.anaplan.com/1/3/workspaces/{}/models/{}/\"\n \"files/{}/chunks/0\").format(workspace, model, file_id)\n response = requests.put(uri, headers = header_put, data = data)\n return response.status_code","def UploadFile(self, request_iterator, context):\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')","def upload_file(self, f):\n with FilesOpener(f) as files:\n response = self.session.post(\n 'https://{}/upload'.format(self.domain),\n files=files\n ).json()\n\n if isinstance(response, list):\n error = response[0].get('error')\n else:\n error = response.get('error')\n\n if error:\n if isinstance(error, str) and error.startswith('FLOOD_WAIT_'):\n retry_after = int(error.rsplit('_',1)[-1])\n raise RetryAfterError(retry_after)\n else:\n raise TelegraphException(error)\n\n return response","def upload_finish(self, cloud_file):","def post_channel_file_upload(\n self,\n channel_id: str,\n file_obj: IO,\n content_type: str,\n metadata: dict=None,\n lifetime: str=None,\n conflict_target: str=None) -> dict:\n path = '/channels/{}/upload'.format(channel_id)\n if not metadata:\n metadata = {}\n headers = {\n 'Content-Type': content_type\n }\n encoded_metadata = encode_metadata(metadata)\n headers.update(encoded_metadata)\n\n params = {}\n if lifetime is not None:\n params['lifetime'] = lifetime\n if conflict_target is not None:\n params['conflict_target'] = conflict_target\n res = self._connection.api_request(\n method='POST',\n headers=headers,\n path=path,\n params=params,\n data=file_obj)\n return decode_file_metadata_if_exist(res)","async def put_file(object_name: str, file: File, **kwargs) -> str:\n # TODO: Do not read file but rather stream content as it comes\n await file.read()\n # Get the synchronous file interface from the asynchronous file\n file_obj = file.file\n # Store position of cursor (number of bytes read)\n file_size = file_obj.tell()\n # Reset cursor at start of file\n file_obj.seek(0)\n # Trace file upload with its size\n logger.debug(f\"Uploading file: {object_name} with {file_size} bytes\")\n # Time file upload for debug\n start = time.time()\n # Store object on s3 storage\n client.put_object(\n bucket_name=DATASETS_BUCKET,\n object_name=object_name,\n length=file_size,\n data=file_obj,\n )\n end = time.time()\n # Log time spent\n logger.debug(f\"Took {end - start} seconds to upload {file_size} bytes\")","def upload():\n\n # TODO: decorator to check token\n token = request.headers.get(\"Authorization\")\n\n has_text = bool(request.get_json())\n has_file = request.files and request.files[\"file\"]\n if not has_text and not has_file:\n error = \"No text input and no file provided\"\n return jsonify({\"success\": False, \"message\": error})\n\n filename, error = save_text(request)\n if error:\n return jsonify({\"success\": False, \"message\": error})\n\n job_id = schedule(filename, token)\n add_user_job(job_id, token)\n\n return jsonify({\"success\": True, \"data\": {\"jobId\": job_id}})","def node_file_upload(ctx, file, filename):\n filepath = click.format_filename(file)\n filename = filename if filename else filepath\n\n with open(file, \"r\") as f:\n content = f.read()\n\n try:\n ctx.obj['node'].create_file(filename, content=content)\n except TimeoutError as e:\n logger.error('Error: %s' % e)\n exit(1)","def upload_file(filename, server, account, projname, language=None,\n username=None, password=None,\n append=False, stage=False, date_format=None):\n stream = transcode_to_stream(filename, date_format)\n upload_stream(stream_json_lines(stream),\n server, account, projname, language=language,\n username=username, password=password,\n append=append, stage=stage)","def upload(filename):\n client = connect()\n for _ in range(RETRIES):\n description = str(time.asctime()).replace(' ', '_')\n data = client.upload_archive(\n vaultName=VAULT_NAME,\n archiveDescription=description,\n body=open(filename))\n print(\"Success at \" + time.asctime() + \": ID \" + data['archiveId'])\n return","def _upload_file(cluster_connection, girder_client, file, path):\n\n r = requests.get(\n '%s/file/%s/download' % (girder_client.urlBase, file['_id']),\n headers={'Girder-Token': girder_client.token}, stream=True)\n check_status(r)\n cluster_connection.put(r.raw, os.path.join(path, file['name']))","def _upload(url, data_file, username, password):\n url_match = '(http(s)?)\\:\\/\\/localhost'\n if re.search(url_match, url):\n print(\"Please configure url settings.\")\n exit(1)\n\n polarion_request = post(url,\n data=data_file,\n auth=auth.HTTPBasicAuth(username,\n password))\n status_code = polarion_request.status_code\n if status_code == codes.ok:\n return status_code\n else:\n print(\"Results upload failed with the follow: {}\".format(\n polarion_request.status_code))\n raise exceptions.RequestException","async def upload_file(\n location_id: LocationID,\n file_id: StorageFileID,\n file_size: ByteSize | None,\n link_type: LinkType = LinkType.PRESIGNED,\n is_directory: bool = False,\n):","def upload(self):\n\n # Try to retrieve the file from the request\n file = None\n try:\n file = request.files[\"file\"]\n except KeyError as e:\n logging.warning(f\"File was not found in request: {e}.\")\n flash(\"No file given.\", \"error\")\n return redirect(request.url)\n except AttributeError as e:\n logging.warning(f\"Error: Request did not contain any files: {e}.\")\n flash(\"No file given.\", \"error\")\n return redirect(request.url)\n\n # Check if file was correctly uploaded\n if not file or len(file.filename) == 0:\n flash(\"No file selected for upload.\", \"message\")\n return redirect(request.url)\n\n \"\"\" Check if file has correct extension. Allowed extensions depend on\n the connector. To make the code more readable, group connectors\n with the same allowed file extensions together like this:\n if connector in ['someconnector', 'someotherconnector']:\n extensions = [...] \"\"\"\n\n if self.connector in [\"overtime\"]:\n allowed_extensions = OVERTIME_SUPPORTED_EXTENSIONS\n else:\n allowed_extensions = []\n\n if not allowed_file(file, allowed_extensions=allowed_extensions):\n flash(\"File extension not allowed.\", \"warning\")\n return redirect(request.url)\n\n \"\"\" File seems uploaded correctly and has correct extension.\n Generate a new record ID to keep track of the uploaded file.\n \"\"\"\n rec_id = generate_temp_record_id()\n\n # Save file to disk\n path = store_temp_file(file, record_id=rec_id)\n\n if not path:\n flash(\"Error saving file!\", \"error\")\n return redirect(request.url)\n\n \"\"\" If everything ended successfully, send the user to the\n confirmation page so he can review his changes \"\"\"\n\n return redirect(url_for(f\"{self.endpoint}.upload_confirm\", rec_id=rec_id))","def upload(self, filepath):\n if self.ver is not None and LooseVersion(str(self.ver)) < LooseVersion('1.4.0'):\n raise VersionMismatchError('File upload')\n\n try:\n with open(filepath, 'rb') as stream:\n url = '{0}{1}'.format(self.url, '/uploads.json')\n response = self.request('post', url, data=stream, headers={'Content-Type': 'application/octet-stream'})\n except IOError:\n raise NoFileError()\n\n return response['upload']['token']","def upload():\n file = None\n if 'file' in request.files:\n file = request.files['file']\n if file and allowed_file(file.filename):\n filename = secure_filename(file.filename)\n file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))\n return json_response(\n message=\"Upload successful\",\n result=\"/v/{}\".format(filename)\n )\n return json_response(\n message=\"Invalid filename or extension (jpg, png, gif)\",\n status_code=500\n )","def uploadFile(self, filename, name=\"Dummy name\", type=\"DummyType\"):\n\n with open(filename, 'rb') as f:\n data = f.read()\n\n if (name == \"Dummy name\"):\n name = filename\n\n data = {'name': name,\n 'type': type,\n 'bits': xmlrpclib.Binary(data),\n 'overwrite': True}\n\n try:\n r = self.server.wp.uploadFile(\n self.blogid, self.username, self.password, data)\n except xmlrpclib.Fault as fault:\n display_XMLRPC_errors(\"upload file \" + filename, fault)\n\n #FIXME: do we really need to split the url ?\n try:\n r['url'] = r['url'].split('?')[1]\n except IndexError:\n from urlparse import urlparse\n r['url'] = urlparse(r['url']).path\n\n print \"uploaded file file =\", r['file']\n print \"uploaded file url =\", r['url']\n print \"uploaded file type =\", r['type']","def upload_file(self, file_name, bucket, destination_name):\n try:\n not self.client.upload_file(file_name, bucket, destination_name)\n except Exception as ex:\n raise ex","def upload_video(self, video_file):\r\n part = \"snippet,status\"\r\n metadata = self.get_metadata(video_file)\r\n body = {\r\n \"snippet\": {\r\n \"title\": metadata['title'],\r\n \"description\": metadata['description'],\r\n \"tags\": metadata['categoryId'],\r\n \"categoryId\": metadata['categoryId']\r\n },\r\n \"status\": {\r\n \"privacyStatus\": \"public\",\r\n \"license\": \"youtube\", # temporary, see gh#414\r\n \"embeddable\": True,\r\n \"publicStatsViewable\": True\r\n }\r\n }\r\n # This is to fix a bug, the API thinks our .ogg files are audio/ogg\r\n mimetype = \"video/{}\".format(video_file.split(\".\")[-1])\r\n media_body = MediaFileUpload(video_file, chunksize=-1, resumable=True, mimetype=mimetype)\r\n insert_request = self.service.videos().insert(part=part, body=body, media_body=media_body)\r\n response = None\r\n error = None\r\n retry = 0\r\n sleep_seconds = 5.0\r\n while response is None:\r\n try:\r\n log.info(\"Uploading %s\" % video_file)\r\n (status, response) = insert_request.next_chunk()\r\n if 'id' in response:\r\n return (Response.SUCCESS, response)\r\n else:\r\n return (Response.UNEXPECTED_FAILURE, response)\r\n except HttpError as e:\r\n if e.resp.status in self.RETRIABLE_STATUS_CODES:\r\n error = \"A retriable HTTP error {} occurred:\\n{}\".format(e.resp.status, e.content)\r\n else:\r\n return (Response.UNRETRIABLE_ERROR, {\"status\": e.resp.status, \"content\": e.content})\r\n except self.RETRIABLE_EXCEPTIONS as e:\r\n error = \"A retriable error occurred: {}\".format(e)\r\n except client.AccessTokenRefreshError:\r\n return (Response.ACCESS_TOKEN_ERROR, None)\r\n if error is not None:\r\n log.error(error)\r\n retry += 1\r\n if retry > self.MAX_RETRIES:\r\n return (Response.MAX_RETRIES_REACHED, None)\r\n log.info(\"Sleeping %s seconds and then retrying...\" % sleep_seconds)\r\n time.sleep(sleep_seconds)","def upload(request):\n # We pass the 'file_id' in the query string as a GET parameter. If\n # we read it from the POSTed data, WebOb would read all POSTed\n # data, which has various features and traps (like setting the\n # \"Content-Length\" header to 0) that we do not need since we are\n # going to read the data ourselves anyway.\n file_id = request.GET['X-Progress-ID']\n input_file, file_size, filename = get_file_from_request(request)\n session = DBSession()\n u = session.query(Upload).filter_by(id=file_id).one()\n upload_dir = request.registry.settings['poulda.upload_dir']\n user_id = authenticated_userid(request)\n # We use a temporary path to detect unfinished uploads (post\n # mortem, not in the application itself).\n path = os.path.join(upload_dir, '_'.join((user_id, file_id)))\n u.tmp_path = path\n u.started = int(time.time())\n u.size = file_size\n u.state = u'uploading'\n session.flush()\n # We need to commit the transaction so that changes to the Upload\n # object can be seen by the other threads (which will serve the\n # 'progress' JSON view called by the upload page).\n transaction.commit()\n with open(path, 'w') as output:\n # We must read only 'file_size' bytes from the 'input_file',\n # not all of it since it also contains the MIME boundary.\n copy_to_file(input_file, file_size, output)\n final_path = filename[1 + filename.rfind(os.sep):]\n final_path = os.path.join(upload_dir, final_path)\n os.rename(path, final_path)\n session = DBSession()\n u = session.query(Upload).filter_by(id=file_id).one()\n u.state = u'done'\n u.final_path = unicode(final_path, 'utf-8')\n return HTTPFound(location='success')","def _upload_file_to_rackspace(self, file, container):\r\n chksum = pyrax.utils.get_checksum(file)\r\n self.cf.upload_file(container,\r\n file,\r\n obj_name=secure_filename(file.filename),\r\n etag=chksum)\r\n return True","def upload_file(file_directory, file_path, login_request, user_id):\n\n data, headers, server_host, server_port = process_request_header(file_directory, file_path, login_request, user_id)\n\n request = requests.post(\"http://\" + server_host + \":\" + server_port + \"/fileOperations/uploadFile\", data=data,\n headers=headers)\n return request.text","def upload(self, dest, overwrite=False):\n dest = normpath(dest)\n try:\n remote = get_remote(dest)\n except ValueError: # Nothing exists at dest, nothing to worry about.\n pass\n else: # Something exists here.\n if isinstance(remote, RemoteFile) and self.hash() == remote.hash:\n # Nothing to update.\n pdbox.info(\"%s and %s are identical\" % (self.path, remote.uri))\n return\n if not overwrite:\n raise ValueError(\"%s exists\" % remote.uri)\n\n # Uploading can either happen all at once (with a 150 MB limit),\n # or in chunks. If the file is smaller than the selected chunk size,\n # then try to upload in one go.\n chunksize = min(pdbox._args.get(\"chunksize\", 149.0), 149.0)\n pdbox.debug(\"Chunk size: %.2f MB\" % chunksize)\n if pdbox._args.get(\"dryrun\"):\n pdbox.info(\"Uploaded %s to %s\" % (self.path, dbx_uri(dest)))\n return None\n\n # Set the write mode.\n if overwrite:\n mode = dropbox.files.WriteMode.overwrite\n else:\n mode = dropbox.files.WriteMode.add\n\n chunk = int(chunksize * 1024 * 1024) # Convert B to MB.\n\n with open(self.path, \"rb\") as f:\n data = f.read()\n sz = len(data)\n\n # TODO: Progress bars.\n if sz < chunk: # One-shot upload.\n meta = execute(pdbox.dbx.files_upload, data, dest, mode)\n else: # Multipart upload.\n nchunks = math.ceil(sz / chunk)\n # Initiate the upload with just the first byte.\n start = execute(pdbox.dbx.files_upload_session_start, f[0])\n cursor = dropbox.files.UploadSessionCursor(start.session_id, 1)\n\n # Now just add each chunk.\n while sz - cursor.offset > chunk:\n pdbox.debug(\n \"Uploading chunk %d/%d\" % (cursor.offset % chunk, nchunks),\n )\n execute(\n pdbox.dbx.files_upload_session_append_v2,\n data[cursor.offset:cursor.offset + chunk],\n cursor,\n )\n cursor.offset += chunk\n\n # Upload the remaining to finish the transaction.\n meta = execute(\n pdbox.dbx.files_upload_session_finish,\n data[cursor.offset:],\n dropbox.files.CommitInfo(dest, mode),\n )\n\n pdbox.info(\"Uploaded %s to %s\" % (self.path, dbx_uri(dest)))\n return RemoteFile(None, meta=meta)","def resumable_upload(insert_request):\r\n response = None\r\n error = None\r\n retry = 0\r\n while response is None:\r\n try:\r\n print(\"Uploading file...\")\r\n status, response = insert_request.next_chunk()\r\n if 'id' in response:\r\n print(\"Video id '%s' was successfully uploaded.\" % response['id'])\r\n f=open(\"uploadedIDs.txt\",\"a+\")\r\n f.write(\"\\n\"+response['id'])\r\n f.close()\r\n else:\r\n exit(\"The upload failed with an unexpected response: %s\" % response)\r\n except HttpError:\r\n import sys\r\n e = sys.exc_info()[1]\r\n if e.resp.status in RETRIABLE_STATUS_CODES:\r\n error = \"A retriable HTTP error %d occurred:\\n%s\" % (e.resp.status,\r\n e.content)\r\n else:\r\n raise\r\n except RETRIABLE_EXCEPTIONS:\r\n import sys\r\n e = sys.exc_info()[1]\r\n error = \"A retriable error occurred: %s\" % e\r\n\r\n if error is not None:\r\n print(error)\r\n retry += 1\r\n if retry > MAX_RETRIES:\r\n exit(\"No longer attempting to retry.\")\r\n\r\n max_sleep = 2 ** retry\r\n sleep_seconds = random.random() * max_sleep\r\n print(\"Sleeping %f seconds and then retrying...\" % sleep_seconds)\r\n time.sleep(sleep_seconds)","def upload_file(self, source, dest):\n print(f\"Uploading {source} to {dest}\")\n with open(source, \"rb\") as data:\n self.client.upload_blob(name=dest, data=data)","def upload():\n\n # Get CAS user roles from session\n cas_attributes = session.get('CAS_ATTRIBUTES', None)\n\n # login_required should mean we're authenticated, but check this\n if not cas_attributes:\n abort(403)\n\n roles = cas_attributes['cas:authority'].split(',')\n\n APP.logger.debug(\n f'User has CAS roles: {roles} ')\n\n # Stop unauthorized users\n if CONFIG.UPLOAD_ROLE not in roles:\n abort(403)\n\n form = UploadForm()\n\n if request.method != 'POST':\n return render_template('upload.html', form=form, upload_error=False)\n\n # User has tried to submit file (POST)\n # Check if it passes (simple) validation in forms.py\n if not form.validate_on_submit():\n return render_template('upload.html', form=form)\n\n # Get filename\n f = form.file.data\n filename = secure_filename(f.filename)\n # Add user email & time\n email = cas_attributes['cas:email']\n upload_time = dt.now().strftime(\"%y%m%d-%H%M%S\")\n ext_filename = email + '_' + upload_time + '_' + filename\n\n # Save file\n try:\n f.save(os.path.join(CONFIG.UPLOAD_PATH, ext_filename))\n except Exception as ex:\n APP.logger.error(\n f'File {ext_filename} could not be saved due to {ex}')\n else:\n APP.logger.info(f'Successfully uploaded file {ext_filename}')\n\n # Notify admin\n msg = Message('New file upload',\n sender=APP.mail.username,\n recipients=CONFIG.UPLOAD_EMAIL)\n msg.body = f\"\"\"\n Hello SBDI-MOL colleagues,\n\n A new file has been uploaded to the ASV portal:\n\n Provider email: {email}\n Upload time: {upload_time}\n Original filename: {filename}\n Saved as: {ext_filename}\n\n Have a nice day!\n\n / Swedish ASV portal\n \"\"\"\n try:\n APP.mail.send(msg)\n except Exception as ex:\n APP.logger.error(f\"Could not send upload notification due to {ex}\")\n else:\n APP.logger.info('Successfully sent upload notification.')\n # Display 'success page' only if upload AND notification worked\n return render_template('uploaded.html', filename=filename)\n\n # Display error msg if EITHER upload OR email failed, so that data\n # providers get a chance to tell us about uploaded files\n return render_template('upload.html', form=form, upload_error=True)","def upload(self):\n # TODO: Should CD to the working directory set by the robscript.\n src = self.state_frame[0]\n dest = self.state_frame[1]\n self.send_upload(src, dest, True, None)\n self.state = STATE_READ_LINE","def upload_local_file_to_oh(file_path, file_name, file_metadata, access_token, member_id):\r\n try:\r\n with open(file_path, 'rb') as fs:\r\n upload_stream(fs, file_name, file_metadata, access_token)\r\n return True\r\n except:\r\n print(f'Failed to upload {file_path} to OH for OH member {member_id}')\r\n return False","def upload(upload_type):\n env_banner()\n\n if upload_type not in Upload.ACCEPTED_TYPES:\n click.echo('Upload of only certain types are accepted.')\n click.echo('UPLOAD_TYPE can be [users | players | scores | all]')\n return\n\n upload_data = Upload()\n if upload_type == 'users':\n upload_data.file_name = config.USER_FILE_NAME\n if upload_type == 'all':\n result = Upload.upload_all()\n else:\n result = upload_data(upload_type)\n if result != Upload.SUCCESS:\n click.echo(f'Error Code: {result}. Error in upload.')\n return\n click.echo('Upload done!')","def upload_fileobj(Fileobj=None, Bucket=None, Key=None, ExtraArgs=None, Callback=None, Config=None):\n pass","def upload(ctx: click.Context, **kwargs):\n root_commands.cmd_upload(ctx.obj, **kwargs)","def upload(self, file_path, bucket_name, file_name):\n\n self.client.upload_file(file_path, bucket_name, file_name)","def upload_file(self, file_from, file_to):\n dbx = dropbox.Dropbox(self.access_token)\n\n with open(file_from, 'rb') as f:\n file_size = os.path.getsize(file_from)\n CHUNK_SIZE = 4 * 1024 * 1024\n\n if file_size <= CHUNK_SIZE:\n print(dbx.files_upload(f.read(), file_to, mode=dropbox.files.WriteMode.overwrite))\n else:\n upload_session_start_result = dbx.files_upload_session_start(f.read(CHUNK_SIZE))\n cursor = dropbox.files.UploadSessionCursor(session_id=upload_session_start_result.session_id,\n offset=f.tell())\n commit = dropbox.files.CommitInfo(path=file_to, mode=dropbox.files.WriteMode.overwrite)\n\n while f.tell() < file_size:\n if ((file_size - f.tell()) <= CHUNK_SIZE):\n print(dbx.files_upload_session_finish(f.read(CHUNK_SIZE),\n cursor,\n commit))\n else:\n dbx.files_upload_session_append(f.read(CHUNK_SIZE),\n cursor.session_id,\n cursor.offset)\n cursor.offset = f.tell()","def file_upload():\n\n click.secho('*** Uploading image...', fg='green')\n uploaded = _uploaded_file('cover.jpg')\n click.secho(json.dumps(uploaded, indent=2, sort_keys=True), fg='yellow')\n\n click.secho('*** Creating a Picture document for it...', fg='green')\n picture = _make_document('picture', title='cover image', sys_filename=uploaded['path'])\n click.secho(json.dumps(picture, indent=2, sort_keys=True), fg='yellow')\n\n click.secho('*** Attaching it to a Blueray as cover...', fg='green')\n slp = _make_document('movie', title='Silver Linings Playbook')\n blueray = _make_document('blueray', movie_id=slp['_id'], cover_id=picture['_id'])\n click.secho(json.dumps(blueray, indent=2, sort_keys=True), fg='yellow')","def upload_file(self, instance, local_obj, remote_file):\n client = self.connect(instance)\n try:\n sftp = client.open_sftp()\n try:\n self._send_file(sftp, local_obj, remote_file)\n finally:\n sftp.close()\n finally:\n client.close()","def upload(self, filename, **arg):\n\n if filename == None:\n raise UploadException(\"filename OR jpegData must be specified\")\n\n # verify key names\n for a in arg.keys():\n if a != \"api_key\" and a != \"auth_token\" and a != \"folder_id\" and a != 'share':\n sys.stderr.write(\"Box.net api: warning: unknown parameter \\\"%s\\\" sent to Box.net.upload\\n\" % (a))\n\n url = 'http://upload.box.net/api/1.0/upload/%s/%s' % (arg['auth_token'], arg['folder_id'])\n\n\tc = pycurl.Curl()\n\t\n\tc.setopt(c.URL,url.encode('utf-8'))\n\t# fp.close(\n\tc.setopt(c.HTTPPOST,[ (\"file\", filename), \n (\"share\", arg['share']), \n (filename, \n (c.FORM_FILE, filename, \n c.FORM_CONTENTTYPE, get_content_type(filename)))\n ])\n\tc.setopt(c.PROGRESSFUNCTION, progress)\n\tstorage = StringIO()\n\tc.setopt(pycurl.WRITEFUNCTION, storage.write)\n\tc.setopt(c.NOPROGRESS,0)\n\tc.perform()\n\t\n\tloader = '[' + ('=' * (49)) +'>'+ ']'\n\tsys.stdout.write(\"%s %d%%\\r\" % (loader, 100))\n\tsys.stdout.flush()\n\tsys.stdout.write(\"\\n\")\n\tc.close()\n\trspXML=storage.getvalue()\n\t# print 'xml',self.rspXML\n return XMLNode.parseXML(rspXML)","def upload_a_file(self, package, version, file_path):\n cmd_args = [self._push_executable]\n cmd_args += [\"--user\", self._username]\n cmd_args += [\"--api_key\", self._api_key]\n cmd_args += [\"--subject\", self._subject]\n cmd_args += [\"--repo\", self._repo]\n cmd_args += [\"--package\", package]\n cmd_args += [\"--version\", version]\n cmd_args += [\"--file_path\", file_path]\n\n if self._component:\n cmd_args += [\"--component\", self._component]\n if self._distribution:\n cmd_args += [\"--distribution\", self._distribution]\n if self._architecture:\n cmd_args += [\"--architecture\", self._architecture]\n\n cmd_args += [\"--package\", package]\n cmd_args += [\"--version\", version]\n cmd_args += [\"--file_path\", file_path]\n\n try:\n proc = subprocess.Popen(cmd_args,\n stderr=subprocess.PIPE,\n stdout=subprocess.PIPE,\n shell=False)\n (out, err) = proc.communicate()\n if proc.returncode != 0:\n raise RuntimeError(err)\n except subprocess.CalledProcessError as ex:\n raise RuntimeError(\"Failed to upload file {0} due to {1}\".format(file_path, ex))\n\n return True","def upload_file_to_shock(self, file_path, token):\n\n if token is None:\n raise Exception(\"Authentication token required!\")\n\n header = {'Authorization': \"Oauth {0}\".format(token)}\n\n if file_path is None:\n raise Exception(\"No file given for upload to SHOCK!\")\n\n with open(os.path.abspath(file_path), 'rb') as data_file:\n files = {'upload': data_file}\n response = requests.post(\n self.shockURL + '/node', headers=header, files=files,\n stream=True, allow_redirects=True)\n self.check_shock_response(\n response, ('Error trying to upload contig FASTA file {} to Shock: '\n ).format(file_path))\n return response.json()['data']","def handle_request_upload(self, msg):\n\n\t\tdirect_response = not msg.arguments or msg.arguments[0] in ('', '/')\n\t\tresult = []\n\t\tfor file_obj in msg.options:\n\t\t\ttmpfilename, filename, name = file_obj['tmpfile'], file_obj['filename'], file_obj['name']\n\n\t\t\t# limit files to tmpdir\n\t\t\tif not os.path.realpath(tmpfilename).startswith(TEMPUPLOADDIR):\n\t\t\t\traise BadRequest('invalid file: invalid path')\n\n\t\t\t# check if file exists\n\t\t\tif not os.path.isfile(tmpfilename):\n\t\t\t\traise BadRequest('invalid file: file does not exists')\n\n\t\t\t# don't accept files bigger than umc/server/upload/max\n\t\t\tst = os.stat(tmpfilename)\n\t\t\tmax_size = int(ucr.get('umc/server/upload/max', 64)) * 1024\n\t\t\tif st.st_size > max_size:\n\t\t\t\tos.remove(tmpfilename)\n\t\t\t\traise BadRequest('filesize is too large, maximum allowed filesize is %d' % (max_size,))\n\n\t\t\tif direct_response:\n\t\t\t\twith open(tmpfilename) as buf:\n\t\t\t\t\tb64buf = base64.b64encode(buf.read())\n\t\t\t\tresult.append({'filename': filename, 'name': name, 'content': b64buf})\n\n\t\tif direct_response:\n\t\t\tself.finished(msg.id, result)\n\t\telse:\n\t\t\tself.handle_request_command(msg)","def upload(cls, local_file, remote_file='', bucket_name=QINIU_BUCKET_NAME):\n if remote_file == '':\n remote_file = cls.__gen_uuid()\n local_file = cls.__get_abs_path(local_file)\n url = \"/v1/qiniu/upload?key=%s&localFile=%s&token=root-weimiyun-9@usstpwd!\" % (remote_file, local_file)\n try:\n conn = httplib.HTTPConnection(UPLOAD_API_HOST)\n conn.request(method=\"POST\", url=url)\n response = conn.getresponse()\n res = response.read()\n if AUTO_DELETE:\n os.remove(local_file)\n return res, True\n except Exception, e:\n return 'Connection refused', False","def upload_file(url, filename, metadata={}):\n while True:\n files = {'dataFile': open(filename, 'rb')}\n r = requests.post(url, files=files, data=metadata)\n\n # Check if everything went good\n if r.status_code is 200:\n res = r.json()\n\n if res['success'] and md5_checksum(filename) == res['md5']:\n print('[ OK ] File uploaded successfully!')\n return True\n else:\n print('[ WARNING ] Something went werong. Trying again...')\n time.sleep(10)\n else:\n print('[ ERROR ] Connection error. Status code: {}'.format(r.status_code))","def upload(api_token, base_url, upload_file, metadata):\n\n upload_url = f\"{base_url}data_files/api_create?auth_token={api_token}\"\n files = {'file': open(upload_file, 'rb')}\n response = requests.post(upload_url, files=files, data=metadata)\n\n # Print out the outcome of the upload\n if response.status_code == 200:\n print(f'File {upload_file} successfully uploaded to HIEv')\n else:\n print(\n f'ERROR - There was a problem uploading file {upload_file} to HIEv')","def upload(self, name, filepath):\n upload_id = self._client.push(filepath)\n\n endpoint = '/v1/charm/{}/revisions'.format(name)\n response = self._client.post(endpoint, {'upload-id': upload_id})\n status_url = response['status-url']\n logger.debug(\"Upload %s started, got status url %s\", upload_id, status_url)\n\n while True:\n response = self._client.get(status_url)\n logger.debug(\"Status checked: %s\", response)\n\n # as we're asking for a single upload_id, the response will always have only one item\n (revision,) = response['revisions']\n status = revision['status']\n\n if status in UPLOAD_ENDING_STATUSES:\n return Uploaded(\n ok=UPLOAD_ENDING_STATUSES[status],\n status=status, revision=revision['revision'])\n\n # XXX Facundo 2020-06-30: Implement a slight backoff algorithm and fallout after\n # N attempts (which should be big, as per snapcraft experience). Issue: #79.\n time.sleep(POLL_DELAY)","async def upload_file(self):\n logger.debug(\"uploading %s\", self.tgzfile)\n with aiohttp.MultipartWriter(\"form-data\") as mpwriter:\n with open(self.tgzfile, \"rb\") as file_handle:\n part = mpwriter.append(file_handle)\n part.set_content_disposition(\n \"form-data\", name=\"file\", filename=\"inventory.gz\"\n )\n part.headers[aiohttp.hdrs.CONTENT_TYPE] = self.UPLOAD_CONTENT_TYPE\n\n headers = {}\n # TODO : Use mTLS certs not userid/password\n auth = aiohttp.BasicAuth(\n self.config[\"AUTH\"][\"username\"], self.config[\"AUTH\"][\"password\"]\n )\n headers[\"Authorization\"] = auth.encode()\n async with aiohttp.ClientSession(headers=headers) as session:\n async with session.post(\n self.upload_url, ssl=self.ssl_context, data=mpwriter\n ) as response:\n logger.debug(\"Status: %s\", response.status)\n logger.debug(\n \"Content-type: %s\", response.headers[\"Content-Type\"]\n )\n\n return await response.text()","def upload_file(self, file: Union[str, bytes, StringIO, TextIOWrapper, BytesIO], filename: str, directory: str = 'gcodes') -> Dict:\n raise NotImplementedError","def upload(files, token, assume_yes):\r\n # check if token exists\r\n if not os.path.exists(token):\r\n print(\"{} does not exist, please specify a valid token file\".format(token))\r\n else:\r\n # Gather videos specified and vids from folders specified into list\r\n videos = gather_videos(files)\r\n # Now begin upload process\r\n if not videos:\r\n print(\"Nothing to upload\")\r\n # Prompt for confirmation\r\n elif prompt_user(videos, confirmation=assume_yes):\r\n youtube_service = YoutubeService()\r\n # Authorize with OAuth2 token\r\n youtube_service.authorize(token)\r\n for video in videos:\r\n response_code, response = youtube_service.upload_video(video)\r\n handle_response(response_code, response)\r\n # Response was no, so do nothing\r\n else:\r\n print(\"Exiting...\")","def upload_file(command):\n if 'action' not in command or command['action']!=\"UPLOAD\":\n raise ValueError(\"Command not of type UPLOAD\")\n if 'file_pattern' not in command: \n raise ValueError(\"Missing file pattern\")\n path = command['file_pattern'] \n if not os.path.exists(path):\n raise ValueError(\"No valid file for upload found\")\n returner={}\n handler = Layer1(aws_access_key_id = command['access_key'],aws_secret_access_key = command['secret_access_key'],region_name=command['region_name'])\n uploader = ConcurrentUploader(handler,command['vault_name'],part_size=uchunk)\n file_size = os.path.getsize(path)\n if file_size==0:\n raise ValueError(\"File is empty. Nothing to upload.\")\n csum = chunkedmd5(path)\n itime=time.time()\n file_name = os.path.basename(path)\n machine_id = str(command['target']) if client_name == '' else client_name+' ('+str(command['target']) + ')'\n #Construct a meaningful description object for the file\n #The limits are that the description can be no more than 1024 characters in length and must use only ascii characters between 32 and 126 (i.e., 32<=ord(char)<=126)\n dscrip = command['description']+'\\\\n'\n dscrip = dscrip + \"Uploaded at \"+str(itime)+'\\\\n'+ \"Full path \"+str(path)+'\\\\n'+ \"File size \"+str(file_size)+'\\\\n' + \"MD5 \"+str(csum)+'\\\\n' + \"Source machine id \"+machine_id+'\\\\n'\n print \"Uploading file %s\"%file_name\n #Put some validation stuff here...\n #Do the upload\n archive_id = uploader.upload(path,dscrip)\n print \"Completed successfully. Archive ID: %s\"%archive_id\n #Done the upload, send the bastard back\n returner['archive_id'] = archive_id\n returner['description'] = dscrip\n returner['file_name'] = file_name\n returner['true_path'] = path\n returner['file_size'] = file_size\n returner['md5sum'] = csum\n returner['insert_time']=int(itime)\n returner['region_name']=command['region_name']\n returner['vault_name'] = command['vault_name']\n return returner","def upload_file(self, local_absolute_filepath, upload_filename):\n url = self.base_url + \"/oasis/doTaskUploadFileHelper/\"\n\n with open(local_absolute_filepath) as f:\n in_file = f\n response = self.do_request(\n url,\n in_file_dict={upload_filename:in_file}\n )\n return response","def upload_progress(self, cloud_file, size, uploaded):","def upload(connection, server_pub_key, priv_key, max_message_size):\r\n\r\n while True:\r\n # Get file name from user\r\n file_path = input('Which file would you like to send to the server?: ')\r\n\r\n # Verify file exists\r\n if os.path.isfile(file_path) is True:\r\n break\r\n\r\n # File doesn't exist\r\n else:\r\n print('Could not find specified file, please try again', file=sys.stderr)\r\n\r\n try:\r\n # Tell server file is being sent\r\n connection.sendall(rsa.encrypt(b'UPLOAD', server_pub_key))\r\n time.sleep(1)\r\n connection.sendall(rsa.encrypt(str.encode(file_path), server_pub_key))\r\n time.sleep(1)\r\n\r\n # Tell the server the file size of the file attempting to be uploaded\r\n connection.sendall(rsa.encrypt(str(os.path.getsize(file_path)).encode(), server_pub_key))\r\n time.sleep(1)\r\n\r\n # Get requirement from server\r\n data = rsa.decrypt(connection.recv(1024), priv_key)\r\n if data == b'PERMISSION CHECK':\r\n security_level = input('What security level should the file have?: ')\r\n connection.sendall(rsa.encrypt(security_level.encode(), server_pub_key))\r\n\r\n elif data == b'TRAVERSAL':\r\n print('Failed attempting to upload file outside of scope', file=sys.stderr)\r\n return\r\n\r\n elif data == b'SIZE EXCEEDED':\r\n print('Maximum storage exceeded', file=sys.stderr)\r\n return\r\n\r\n else:\r\n print('Unexpected response from server', file=sys.stderr)\r\n return\r\n\r\n # Attempt to upload file to the server\r\n status = rsa.decrypt(connection.recv(1024), priv_key)\r\n if status == b'CONTINUE':\r\n\r\n # Send the file to the server\r\n shared.send_file(connection, server_pub_key, file_path, max_message_size)\r\n\r\n # Get the result from the server\r\n result = rsa.decrypt(connection.recv(1024), priv_key)\r\n\r\n # If success\r\n if result == b'SUCCESS':\r\n print('Successfully added file to the storage system')\r\n\r\n # If failure\r\n elif result == b'FAILURE':\r\n print('Failed to add file to the storage system')\r\n\r\n # Attempt to overwrite file on the server\r\n elif status == b'OVERWRITE':\r\n\r\n # Ask the user if they would like to overwrite the file on the server\r\n while True:\r\n overwrite = input('Would you like to overwrite the file on the server with the same name?\\n'\r\n '1 - Yes\\n'\r\n '2 - No\\n'\r\n 'Choice: ')\r\n\r\n # Overwrite file\r\n if overwrite == '1':\r\n connection.sendall(rsa.encrypt(b'YES', server_pub_key))\r\n break\r\n\r\n # Don't overwrite file\r\n elif overwrite == '2':\r\n connection.sendall((rsa.encrypt(b'NO', server_pub_key)))\r\n return\r\n\r\n # Invalid input\r\n else:\r\n print('Invalid input, please select an available option', file=sys.stderr)\r\n\r\n # Send the file to the server\r\n shared.send_file(connection, server_pub_key, file_path, max_message_size)\r\n\r\n # Get the result from the server\r\n result = rsa.decrypt(connection.recv(1024), priv_key)\r\n\r\n # If success\r\n if result == b'SUCCESS':\r\n print('Successfully added file to the storage system')\r\n\r\n # If failure\r\n elif result == b'FAILURE':\r\n print('Failed to add file to the storage system')\r\n\r\n else:\r\n print('Failed to upload file to the server with desired security level', file=sys.stderr)\r\n\r\n # Catch file not found\r\n except FileNotFoundError:\r\n print(''.join(['\\nCould not find the file ', file_path]), file=sys.stderr)\r\n connection.sendall(rsa.encrypt(b'MISSING', server_pub_key))","def upload_file(self, upload_link: str, file_data: bytes) -> None:\n r = requests.put(\n upload_link,\n data=file_data,\n headers=self._auth_headers\n )\n if r.status_code in self._errors:\n raise ApiResponseException(\n r.status_code, r.json()[\"error\"][\"message\"])","def _upload_file_bytes(self, conn, http_conn, fp, file_length,\r\n total_bytes_uploaded, cb, num_cb):\r\n buf = fp.read(self.BUFFER_SIZE)\r\n if cb:\r\n if num_cb > 2:\r\n cb_count = file_length / self.BUFFER_SIZE / (num_cb-2)\r\n elif num_cb < 0:\r\n cb_count = -1\r\n else:\r\n cb_count = 0\r\n i = 0\r\n cb(total_bytes_uploaded, file_length)\r\n\r\n # Build resumable upload headers for the transfer. Don't send a\r\n # Content-Range header if the file is 0 bytes long, because the\r\n # resumable upload protocol uses an *inclusive* end-range (so, sending\r\n # 'bytes 0-0/1' would actually mean you're sending a 1-byte file).\r\n put_headers = {}\r\n if file_length:\r\n range_header = self._build_content_range_header(\r\n '%d-%d' % (total_bytes_uploaded, file_length - 1),\r\n file_length)\r\n put_headers['Content-Range'] = range_header\r\n # Set Content-Length to the total bytes we'll send with this PUT.\r\n put_headers['Content-Length'] = str(file_length - total_bytes_uploaded)\r\n http_request = AWSAuthConnection.build_base_http_request(\r\n conn, 'PUT', path=self.tracker_uri_path, auth_path=None,\r\n headers=put_headers, host=self.tracker_uri_host)\r\n http_conn.putrequest('PUT', http_request.path)\r\n for k in put_headers:\r\n http_conn.putheader(k, put_headers[k])\r\n http_conn.endheaders()\r\n\r\n # Turn off debug on http connection so upload content isn't included\r\n # in debug stream.\r\n http_conn.set_debuglevel(0)\r\n while buf:\r\n http_conn.send(buf)\r\n total_bytes_uploaded += len(buf)\r\n if cb:\r\n i += 1\r\n if i == cb_count or cb_count == -1:\r\n cb(total_bytes_uploaded, file_length)\r\n i = 0\r\n buf = fp.read(self.BUFFER_SIZE)\r\n if cb:\r\n cb(total_bytes_uploaded, file_length)\r\n if total_bytes_uploaded != file_length:\r\n # Abort (and delete the tracker file) so if the user retries\r\n # they'll start a new resumable upload rather than potentially\r\n # attempting to pick back up later where we left off.\r\n raise ResumableUploadException(\r\n 'File changed during upload: EOF at %d bytes of %d byte file.' %\r\n (total_bytes_uploaded, file_length),\r\n ResumableTransferDisposition.ABORT)\r\n resp = http_conn.getresponse()\r\n body = resp.read()\r\n # Restore http connection debug level.\r\n http_conn.set_debuglevel(conn.debug)\r\n\r\n if resp.status == 200:\r\n return resp.getheader('etag') # Success\r\n # Retry timeout (408) and status 500 and 503 errors after a delay.\r\n elif resp.status in [408, 500, 503]:\r\n disposition = ResumableTransferDisposition.WAIT_BEFORE_RETRY\r\n else:\r\n # Catch all for any other error codes.\r\n disposition = ResumableTransferDisposition.ABORT\r\n raise ResumableUploadException('Got response code %d while attempting '\r\n 'upload (%s)' %\r\n (resp.status, resp.reason), disposition)","def upload(self,filename,remotefolder=None):\n\n if remotefolder == None:\n remotefolder = self.remotefolder\n\n self.status = 0\n\n try:\n self.ftp.cwd(remotefolder)\n\n self.file = open(filename, 'rb')\n\n (head, tail) = os.path.split(filename)\n\n command = \"STOR \" + tail\n\n print('Uploading: ' + tail)\n self.ftp.storbinary(command, self.file)\n print('Upload Completed')\n\n except ftplib.all_errors:\n print('Error Uploading ' + tail)\n self.status = 1\n return self.status\n\n self.file.close()\n\n #back to initial directory in __init__()\n self.ftp.cwd(self.remotefolder)\n\n return self.status","def imgupload(self, accountid=None, upfile=None, cameraid=None, timestamp=None, duration=None, params=None):\n self.uploaddir = os.path.join(self.staticdir, 'uploads')\n print(\"UploadFile: Name: %s, Type: %s \" % (upfile.filename, upfile.content_type))\n fext = str(upfile.content_type).split('/')[1]\n print(\"Extension: %s\" % (fext))\n logging.info('Recieved request: {}/{}'.format(cameraid, timestamp))\n if not os.path.exists(self.uploaddir):\n logging.info('Upload directory does not exist, creating %s' % (self.uploaddir))\n os.makedirs(self.uploaddir)\n\n if upfile is not None:\n tsx = self.epoch()\n ofile = os.path.join(self.uploaddir, \"%s.%s\" % (tsx, fext))\n print(\"Local filename: %s\" % (ofile))\n ofilex = open(ofile, \"wb\")\n shutil.copyfileobj(upfile.file, ofilex)\n logging.info(\"Copied uploaded file as %s\" % (ofilex))\n ofilex.close()\n wwwbase = os.path.basename(self.staticdir)\n out = {'upimg': \"%s.%s\" % (tsx, fext) }\n infodb = self.dbase['infodb']\n\n info = {'upimg': \"{}.{}\".format(tsx, fext),\n 'epoch': tsx,\n 'accountid': accountid,\n 'timestamp': int(timestamp),\n 'duration' : int(duration),\n 'cameraid': cameraid,\n 'params': params}\n\n # Finally insert received object into db\n res = infodb.insert_one(info)\n print(res)\n return json.dumps(out)\n\n else:\n return \"Parameter: \\\"theFile\\\" was not defined\"","def serve_upload(conn, ssn_key, file_name, client_name):\n # get signal to begin upload\n request = aes.decrypt(ssn_key, conn.recv(1024))\n if request != SIG_START:\n conn.sendall(aes.encrypt(ssn_key, SIG_BAD))\n return print(\"Bob: something went wrong with file transfer\")\n response = aes.encrypt(ssn_key, SIG_GOOD)\n conn.sendall(response)\n print(\"Bob: beginning transfer for {}...\".format(file_name))\n\n # get file contents from client\n contents = list()\n completed_upload = False\n response = aes.encrypt(ssn_key, SIG_GOOD)\n while not completed_upload:\n request = aes.decrypt(ssn_key, conn.recv(1024))\n if request == SIG_END:\n completed_upload = True\n print(\"Bob: completed transfer for {}\".format(file_name))\n else:\n contents.append(request)\n conn.sendall(response)\n\n # save file to server folder\n file_path = \"{}/{}\".format(client_name, file_name)\n os.makedirs(os.path.dirname(file_path), exist_ok=True)\n with open(file_path, \"w\") as outputStream:\n outputStream.write(''.join(contents))\n print(\"Bob: file saved in {}\".format(file_path))","def upload_file(file, bucket_path, bucket=S3_BUCKET):\n # Bucket path should be somedir/name_of_file.ext\n try:\n if isinstance(file, str):\n resource.upload_file(file, bucket, bucket_path)\n else:\n resource.upload_fileobj(file, bucket, bucket_path)\n except:\n raise ChildProcessError('Something broke, Cap\\'n')","def upload(self):\n while not self._upload_queue.empty():\n logger.info('%d files left to upload', self._upload_queue.qsize())\n self._sia_condition_waiter.wait_for_available_upload_slot()\n job = self._upload_queue.get()\n if (not self._process_upload_job_async(job)) and (job.failure_count\n < 3):\n self._upload_queue.put(job)\n self._sia_condition_waiter.wait_for_all_uploads_to_complete()\n self._exit_event.set()","def put_file(self, file_name: str, value: BytesIO):\n value.seek(0)\n self.client.upload_fileobj(value, self.bucket, file_name)","def _upload_file_to_container(self, container, object_key, file_path, connection):\r\n data = open(file_path, 'rb')\r\n logger.debug('upload {file_path} to {container} {object_key}'.format(file_path=file_path, container=container,\r\n object_key=object_key))\r\n\r\n result = connection.object_store.upload_object(container=container,\r\n name=object_key,\r\n data=open(file_path, 'r'))\r\n\r\n logger.debug(result)","def upload_file(client, folder_id, file_name):\n\n new_file = client.folder(folder_id).upload(file_name)\n print(f\"File {new_file.name} uploaded to Box with file ID {new_file.id}\")\n return new_file.id","def upload_segment(self, upload_uri, _range, data, filetype):\n content_range = '%d-%d/%d' % (_range, len(data), len(data))\n upload_headers = {'Content-Type': 'video/%s' % filetype,\n 'Content-Length': len(data),\n 'Content-Range': 'bytes: %s' % content_range}\n\n log.info(\"Sending file of size %d\" % len(data))\n log.info(\"Requesting %s\" % upload_uri)\n request_headers = dict(upload_headers.items() + self.standard_headers.items())\n r = HTTPClient().fetch(upload_uri, method=\"PUT\",\n body=data, headers=request_headers)\n log.info(\"Uploaded segment: status code %d\" % r.code)\n if r.code != 200:\n raise ValueError(\"Upload unsuccessful\")","def upload(self, filepath, channels, content=None, filetype=None, filename=None, title=None, initial_comment=None):\r\n try:\r\n result = self.slack.files.upload(\r\n filepath,\r\n content=content,\r\n filetype=filetype,\r\n filename=filename,\r\n title=title,\r\n initial_comment=initial_comment,\r\n channels=channels)\r\n if result.successful:\r\n return result.body\r\n else:\r\n L.warning('Slack Error : %s', result.error)\r\n raise SlackError(result.error)\r\n except Error as e:\r\n L.warning(str(e))\r\n raise SlackError('%s is not exists.' % channels)","def upload(self, file: Path, destination: Path):\n\t\tif not file.is_file():\n\t\t\traise FileNotFoundError(\"File doesn't exists\")\n\n\t\tif self.destination_path_base is None:\n\t\t\tdestination_path = destination\n\t\telse:\n\t\t\tdestination_path = self.destination_path_base / destination\n\n\t\tLOGI(f\"Started uploading of {file.name}\")\n\n\t\tself._upload(file, destination_path)\n\n\t\tLOGI(f\"Finished uploading of {file.name}\")\n\t\treturn True","async def complete_upload_file(\n body_item: Envelope[FileUploadCompletionBody],\n location_id: LocationID,\n file_id: StorageFileID,\n user_id: UserID,\n):","def upload_file(parameter_id, filename):\n recastapi.file_check(filename)\n \n basic_request_response = basic_request(parameter_id)\n basic_request_id = basic_request_response['id']\n \n file_uuid = str(uuid.uuid1())\n\n #get request ID, so deposition can be retrieved\n point_request_url = '{}/{}'.format(\n recastapi.ENDPOINTS['POINT_REQUESTS'],\n parameter_id)\n point_response = recastapi.get(point_request_url)\n\n request_id = point_response['scan_request_id']\n \n request_url = '{}/{}'.format(\n recastapi.ENDPOINTS['REQUESTS'],\n request_id\n )\n request_response = recastapi.get(request_url)\n deposition_id = request_response['zenodo_deposition_id']\n\n payload = {\n 'file_name': file_uuid,\n 'zenodo_file_id': None,\n 'original_file_name': filename,\n 'basic_request_id': basic_request_id,\n }\n \n files = {'file': open(filename, 'rb')}\n url = '{}/'.format(recastapi.ENDPOINTS['FILES'])\n file_response = recastapi.post(\n url, \n data=payload, \n files=files,\n params = {'deposition_id': deposition_id}\n )\n basic_request_response['metadata'] = file_response\n return basic_request_response","def upload(attachment, filename, title=None, message=None, channels=None):\n\n if not settings.SLACK_TOKEN:\n return {'ok': False, 'error': 'config_error'}\n\n client = WebClient(token=settings.SLACK_TOKEN)\n client.timeout = 600\n\n try:\n if channels:\n response = client.files_upload(channels=channels, file=attachment, filename=filename,\n initial_comment=message, title=title)\n else:\n response = client.files_upload(file=attachment, filename=filename, title=title)\n assert response['ok'] is True\n return {'ok': True, 'file': response['file']}\n except SlackApiError as e:\n assert e.response['ok'] is False\n return e.response","def _videoclipupload(request, hash_key):\n # get video\n video_queryset = Video.objects.all().select_related('owner')\n video = get_object_or_404(video_queryset, hash_key=hash_key)\n \n if request.method == 'POST':\n \n # create a form instance and populate it with data from the request\n form = UploadClipForm(request.POST, request.FILES)\n \n # check whether it's valid\n if form.is_valid():\n # process the data in form.cleaned_data as required\n number = form.cleaned_data['formatted_number'].as_e164\n name = form.cleaned_data['name']\n clip = form.cleaned_data['clip']\n \n # get location of script\n script_dir = os.path.dirname(os.path.abspath(__file__))\n script_path = os.path.join(script_dir, \"process.sh\")\n \n # get locations of input and output files\n input_video = clip.temporary_file_path()\n output_video = NamedTemporaryFile(suffix='.mp4')\n output_image = NamedTemporaryFile(suffix='.jpg')\n output_duration = NamedTemporaryFile()\n \n # get the ffmpeg path\n # on production we had to install the 64 bit static ffmpeg build\n ffmpeg_path = ''\n if not settings.DEBUG:\n ffmpeg_path = '/home/nceruchalu/bin/'\n \n # Generate the output files\n subprocess.check_call(\n [script_path, input_video, output_video.name, \n output_image.name, output_duration.name, ffmpeg_path])\n \n # Get clip duration from the results\n clip_duration = map(float, output_duration)[0]\n \n # if we've come this far then all is well, and we can go ahead\n # and create this clip\n user = User.objects.get_user_by_number(number)\n if not user.is_active and name:\n user.full_name = name\n user.save()\n \n # first add user as a video user, because nobody should\n # add to a clip without being a video user\n VideoUsers.objects.add_users_to_video(video, user)\n \n # Try creating the clip with a thumbnail, if that fails try\n # without the thumbnail\n Clip.objects.create(owner=user,\n video=video,\n mp4=File(output_video),\n photo=ImageFile(output_image),\n duration=clip_duration)\n \n # close and delete the tempoary files\n output_video.close()\n output_image.close()\n output_duration.close()\n \n # redirect to the video details URL but add a querystring param\n # indicating that the video should start from the last clip\n video_url = reverse('web-video-detail',kwargs={'hash_key':hash_key})\n redirect_url = \"{video_url}?latest=1\".format(video_url=video_url)\n \n return HttpResponseRedirect(redirect_url)\n \n else:\n # if a GET (or any other method) we'll create a blank form\n form = UploadClipForm()\n \n return render_to_response('video/upload.html',\n {'video':video,\n 'form':form},\n context_instance=RequestContext(request))","def run(args):\n\n drive_uid = str(args[\"drive_uid\"])\n file_uid = str(args[\"file_uid\"])\n chunk_idx = int(args[\"chunk_index\"])\n secret = str(args[\"secret\"])\n data = string_to_bytes(args[\"data\"])\n checksum = str(args[\"checksum\"])\n\n drive = DriveInfo(drive_uid=drive_uid)\n\n drive.upload_chunk(file_uid=file_uid, chunk_index=chunk_idx,\n secret=secret, chunk=data, checksum=checksum)\n\n return True","def upload_start(self, local_path, cloud_file, size):\n\t\telog(\"uploading {1} ({2})\".format(local_path, cloud_file.path, bytes_scaled(size)))","def storage_ipfs_upload_file(self, file_obj):\n # Request: upload a file\n # curl -X POST -H 'Content-Type: application/json' --data '{\"jsonrpc\":\"2.0\",\"method\":\"edgestore.PutFile\",\"params\":[{\"path\": \"theta-edge-store-demos/demos/image/data/smiley_explorer.png\"}],\"id\":1}' http://localhost:19888/rpc\n # Result\n # {\n # \"jsonrpc\": \"2.0\",\n # \"id\": 1,\n # \"result\": {\n # \"key\": \"0xbc0383809da9fb98c5755e3fa4f19f4ebc7e34308ab321246e4bb54e548fad04\",\n # \"relpath\": \"smiley_explorer.png\",\n # \"success\": true\n # }\n # }\n home_dir = expanduser(\"~\")\n file_path = os.path.join(home_dir, \"thetastore\")\n if not os.path.exists(file_path):\n os.makedirs(file_path, exist_ok=True)\n file_path = os.path.join(file_path, str(uuid.uuid4()))\n with open(file_path, \"wb\") as file_handle:\n file_handle.write(file_obj)\n\n request_data = {\"jsonrpc\":\"2.0\",\n \"method\":\"edgestore.PutFile\",\n \"params\":[{\"path\": file_path}],\n \"id\":1}\n res = httpx.post(\n self.ipfs_upload_uri,\n headers={\"Content-Type\": \"application/json\"},\n data=json.dumps(request_data),\n timeout=None,\n )\n content = res.json()\n result = content.get(\"result\", None)\n if result is None:\n return False, \"Failed to upload file(result is none).\"\n else:\n success = result.get(\"success\", False)\n if not success:\n return False, \"Failed to upload file(success if false).\"\n file_cid = result.get(\"key\", None)\n if file_cid is None:\n return False, \"Failed to upload file(key is none).\"\n return True, file_cid","def _upload_file(self, file_name, full_path, quiet, request, resources):\r\n\r\n if not quiet:\r\n print('Starting upload for file ' + file_name)\r\n\r\n content_length = os.path.getsize(full_path)\r\n token = self.dataset_upload_file(full_path, quiet)\r\n if token is None:\r\n if not quiet:\r\n print('Upload unsuccessful: ' + file_name)\r\n return True\r\n if not quiet:\r\n print('Upload successful: ' + file_name + ' (' +\r\n File.get_size(content_length) + ')')\r\n upload_file = DatasetUploadFile()\r\n upload_file.token = token\r\n if resources:\r\n for item in resources:\r\n if file_name == item.get('path'):\r\n upload_file.description = item.get('description')\r\n if 'schema' in item:\r\n fields = self.get_or_default(item['schema'], 'fields',\r\n [])\r\n processed = []\r\n count = 0\r\n for field in fields:\r\n processed.append(self.process_column(field))\r\n processed[count].order = count\r\n count += 1\r\n upload_file.columns = processed\r\n request.files.append(upload_file)\r\n return False","def uploadFile(self, filename):\n\n try:\n data = filename.open(mode='rb').read()\n ##workaround for file without file extensions\n if filename.suffix == '':\n filename = filename.with_suffix('.dcm')\n files = {'file': (str(filename.name), data)}\n except:\n print(\"opening file\", filename, \"failed, aborting\")\n return\n\n res = self._post(self.url + 'upload', files=files)\n return self.getFile(res['file']['selfUrl']), self.getObject(res['relatedObject']['selfUrl'])","def upload_file( processor, user, local_path ):\n operations.publish_work_item(\n operations.create_asset_from_file(\n file_name = local_path,\n owner = user,\n producer = processor,\n child_number = 0,\n asset_class = models.AssetClass.UPLOAD ))","def file_upload(self, bucket_id, file_path, tmp_file_path):\n\n self.__logger.debug('Upload %s in bucket %s', file_path, bucket_id)\n self.__logger.debug('Temp folder %s', tmp_file_path)\n\n bname = os.path.split(file_path)[1] # File name\n\n file_mime_type = 'text/plain'\n\n # Encrypt file\n self.__logger.debug('Encrypting file...')\n\n file_crypto_tools = FileCrypto()\n\n # File name of encrypted file\n file_name_ready_to_shard_upload = '%s.encrypted' % bname\n # Path where to save the encrypted file in temp dir\n file_path_ready = os.path.join(tmp_file_path,\n file_name_ready_to_shard_upload)\n self.__logger.debug('file_path_ready: %s', file_path_ready)\n\n # Begin file encryption\n file_crypto_tools.encrypt_file(\n 'AES',\n file_path,\n file_path_ready,\n self.client.password)\n\n self.fileisdecrypted_str = ''\n\n file_size = os.stat(file_path).st_size\n self.__logger.info('File encrypted')\n\n # Get the PUSH token from Storj Bridge\n self.__logger.debug('Get PUSH Token')\n\n push_token = None\n try:\n push_token = self.client.token_create(bucket_id, 'PUSH')\n except BridgeError as e:\n self.__logger.error(e)\n self.__logger.debug('PUSH token create exception')\n self.__logger.error('File not uploaded')\n return\n\n self.__logger.info('PUSH Token ID %s', push_token.id)\n\n # Get a frame\n self.__logger.debug('Frame')\n frame = None\n\n try:\n frame = self.client.frame_create()\n except BridgeError as e:\n self.__logger.error(e)\n self.__logger.debug('Unhandled exception while creating file \\\nstaging frame')\n self.__logger.error('File not uploaded')\n return\n\n self.__logger.info('frame.id = %s', frame.id)\n\n # Now generate shards\n self.__logger.debug('Sharding started...')\n shards_manager = model.ShardManager(filepath=file_path_ready,\n tmp_path=tmp_file_path)\n self.all_shards_count = len(shards_manager.shards)\n\n self.__logger.debug('Sharding ended...')\n\n self.__logger.info('There are %s shards', self.all_shards_count)\n\n # Calculate timeout\n self._calculate_timeout(shard_size=shards_manager.shards[0].size,\n mbps=1)\n\n # Upload shards\n mp = ThreadPool()\n res = mp.map(lambda n_s: self.upload_shard(\n n_s[1], n_s[0], frame, file_name_ready_to_shard_upload, tmp_file_path),\n enumerate(shards_manager.shards))\n\n self.__logger.debug('===== RESULTS =====')\n self.__logger.debug(res)\n if False in res or None in res:\n self.__logger.error('File not uploaded: shard %s not uploaded' %\n res.index(False))\n self.__logger.error('Exiting with errors')\n exit(1)\n # finish_upload\n self.__logger.debug('Generating HMAC...')\n\n # create file hash\n hash_sha512_hmac_b64 = self._prepare_bucket_entry_hmac(\n shards_manager.shards)\n hash_sha512_hmac = hashlib.sha224(str(\n hash_sha512_hmac_b64['SHA-512'])).hexdigest()\n\n self.__logger.debug('Now upload file')\n data = {\n 'x-token': push_token.id,\n 'x-filesize': str(file_size),\n 'frame': frame.id,\n 'mimetype': file_mime_type,\n 'filename': str(bname) + str(self.fileisdecrypted_str),\n 'hmac': {\n 'type': 'sha512',\n 'value': hash_sha512_hmac\n },\n }\n\n self.__logger.debug('Finishing upload')\n self.__logger.debug('Adding file %s to bucket...', bname)\n\n success = False\n try:\n # Post an upload_file request\n response = self.client._request(\n method='POST',\n path='/buckets/%s/files' % bucket_id,\n headers={\n 'x-token': push_token.id,\n 'x-filesize': str(file_size),\n },\n json=data,\n )\n success = True\n\n except BridgeError as e:\n self.__logger.error(e)\n self.__logger.debug('Unhandled bridge exception')\n\n if success:\n self.__logger.info('File uploaded successfully!')\n\n # Remove temp files\n try:\n # Remove shards\n file_shards = map(lambda i: '%s-%s' % (file_path_ready, i),\n range(1, self.all_shards_count + 1))\n self.__logger.debug('Remove shards %s' % file_shards)\n map(os.remove, file_shards)\n # Remove encrypted file\n self.__logger.debug('Remove encrypted file %s' % file_path_ready)\n os.remove(file_path_ready)\n except OSError as e:\n self.__logger.error(e)","def upload_file(bucket_name, filename, file):\n client = get_client()\n bucket = client.get_bucket(bucket_name)\n blob = bucket.blob(filename)\n blob.upload_from_file(file)","def UploadFile(self, local_file_name, gcs_file_name,\n mimetype='application/octet-stream'):\n resumable = os.stat(local_file_name).st_size > 0\n media = gapi_http.MediaFileUpload(local_file_name,\n mimetype=mimetype,\n resumable=resumable)\n\n # gsutil's code suggests that 404s and 410s are retryable for resumable\n # uploads (see ResumableUploadStartOverException).\n def _ErrorMatcher(error):\n return (self._CommonErrorMatcher(error)\n or (isinstance(error, gapi_errors.HttpError)\n and error.resp.status in (404, 410)))\n\n return self._RunWithRetries(\n lambda: self._UploadWithProgress(media, gcs_file_name),\n _ErrorMatcher)"],"string":"[\n \"def upload_file(self, file_upload_parameters, progress=None):\\n\\n file_upload_parameters._submit_upload_parameters.timeout_in_milliseconds = file_upload_parameters.timeout_in_milliseconds\\n operation = self.submit_upload(file_upload_parameters._submit_upload_parameters)\\n return self.download_upload_result(operation, file_upload_parameters, progress)\",\n \"def upload_file(self, f):\\n return self._telegraph.upload_file(f)\",\n \"def upload(self, filename, file_path):\\n return\",\n \"def upload_file(self, file, max_chunk_size=1024 * 1024, parallelism=10):\\n\\n upload_key = self.internal.media_and_files.GetFileUploadUrl(\\n media_and_files_pb2.RequestGetFileUploadUrl(\\n expected_size=os.path.getsize(file)\\n )\\n ).upload_key\\n\\n with ThreadPoolExecutor(max_workers=parallelism) as executor:\\n result = list(\\n executor.map(\\n lambda x: self.upload_file_chunk(*x),\\n (\\n (part_number, chunk, upload_key) for part_number, chunk in enumerate(\\n read_file_in_chunks(file, max_chunk_size)\\n )\\n )\\n\\n )\\n )\\n\\n if not all(result):\\n return None\\n\\n return self.internal.media_and_files.CommitFileUpload(\\n media_and_files_pb2.RequestCommitFileUpload(\\n upload_key=upload_key,\\n file_name=os.path.basename(file)\\n )\\n ).uploaded_file_location\",\n \"def put_upload(self):\\n # print \\\"starting upload...\\\", self.current_upload['filepath']\\n self.touch()\\n self.log(\\\"STARTING_UPLOAD\\\", level=INFO)\\n try:\\n Backend.put_file(self.fileobj, self.current_upload[\\\"gcs_url\\\"])\\n except exceptions.FilePutError as err:\\n self.handle_put_error(err, self.fileobj)\\n raise\",\n \"def upload_file(self):\\n request = copy.deepcopy(self.request_template)\\n data = json.dumps(request)\\n curr_file = {\\n 'request': data,\\n 'file': open(self.file_path, 'rb')\\n }\\n print(\\\"Sending Upload request of av for file {}\\\".format(self.file_name))\\n try:\\n response = requests.post(url=self.url + \\\"upload\\\", files=curr_file, verify=False)\\n except Exception as E:\\n print(\\\"Upload file failed. file: {} , failure: {}\\\".format(self.file_name, E))\\n raise\\n response_j = response.json()\\n print(\\\"av Upload response status for file {} : {}\\\".format(self.file_name,\\n response_j[\\\"response\\\"][0][\\\"status\\\"][\\\"label\\\"]))\\n return response_j\",\n \"def upload_file(self, filename: str) -> SlackResponse:\\n try:\\n # Send Slack Message\\n response = self.client.files_upload(\\n file=filename,\\n )\\n return response\\n except SlackApiError as e:\\n logging.error(f\\\"Error uploading file: {e}\\\")\",\n \"def upload_file(self, file_name, content):\\n return self.files.upload(file_name, content)\",\n \"def upload_file(\\n self, bucket_id: uplink.Path, filename: uplink.Path, file: uplink.Body\\n ):\\n pass\",\n \"def upload(self, asset, file):\\n uploader = FrameioUploader(asset, file)\\n uploader.upload()\",\n \"def post(self):\\n filename = str(time.time())\\n filepath = os.path.join(\\n os.path.join(current_app.config['UPLOAD_FOLDER'], filename))\\n with open(filepath, 'bw') as uploadfile:\\n chunk_size = 1024\\n while True:\\n chunk = request.stream.read(chunk_size)\\n if len(chunk) == 0:\\n break\\n uploadfile.write(chunk)\\n current_app.logger.info('file %s upload successfully', filename)\\n return {'timestamp': filename}, http.HTTPStatus.CREATED\",\n \"async def _upload(self) -> None:\\n\\n # filename given?\\n filename = str(uuid.uuid4()) if self.filename is None else self.filename\\n\\n # check\\n if self._upload_path is None:\\n raise ValueError(\\\"No upload URL given.\\\")\\n\\n # send data and return image ID\\n async with aiohttp.ClientSession() as session:\\n data = aiohttp.FormData()\\n data.add_field(\\\"file\\\", self._buffer, filename=self.filename)\\n async with session.post(self._upload_path, auth=self._auth, data=data, timeout=self._timeout) as response:\\n if response.status == 401:\\n log.error(\\\"Wrong credentials for uploading file.\\\")\\n raise FileNotFoundError\\n elif response.status != 200:\\n log.error(f\\\"Could not upload file to filecache: {response.status} {response.reason}\\\")\\n raise FileNotFoundError\",\n \"def upload_file(self, file_path, file_name, output_path):\",\n \"async def upload(self, request):\\n\\n userid = await authenticated_userid(request)\\n project = await request.app.context_project(request, userid)\\n\\n payload = await request.post()\\n\\n filename = payload['file'].filename\\n upload_stream = payload['file'].file\\n\\n ext = os.path.splitext(filename)[1]\\n\\n if not re_filename_ext.match(ext):\\n # paranoid check in case a script doesn't protect from code injection\\n raise web.HTTPBadRequest(text='file extension not supported: %s' % filename)\\n\\n camera_id = uuid.uuid1().hex\\n\\n log = request['slog']\\n log.debug('request: camera upload', filename=filename)\\n\\n config = request.app.config\\n\\n tmppath = dump_stream(config['media']['tempdir'], upload_stream)\\n\\n log.debug('file dump', camera_id=camera_id, tmppath=tmppath)\\n\\n await Camera.insert(request,\\n camera_id=camera_id,\\n filename=filename,\\n project_id=project.project_id)\\n\\n await request.app.task_broker.publish('camera_upload', {\\n 'userid': userid,\\n 'project_id': project.project_id,\\n 'camera_id': camera_id,\\n 'tmppath': tmppath,\\n 'filename': filename\\n }, log=log)\\n\\n response_js = {\\n 'camera_file_id': camera_id\\n }\\n\\n return web.json_response(response_js, status=HTTPStatus.CREATED)\",\n \"def upload():\\n global video_file_name\\n\\n if request.method == 'POST' and 'file' in request.files:\\n\\n # clear uploads folder\\n cleanup_uploads()\\n\\n # Video has been uploaded\\n filename = video.save(request.files['file'])\\n video_file_name[0] = filename\\n\\n # Process video on a new thread\\n # threading.Thread(target=process_video, args=[os.path.join('uploads', filename)]).start()\\n # threading.Thread(target=process_audio, args=[os.path.join('uploads', filename)]).start()\\n threading.Thread(target=process_video, args=[os.path.join(basedir, 'static', 'data', 'uploads', filename)]).start()\\n threading.Thread(target=process_audio, args=[os.path.join(basedir, 'static', 'data', 'uploads', filename)]).start()\\n\\n return jsonify({'success': True}), 200, {'ContentType': 'application/json'}\\n\\n elif request.method == 'GET':\\n return render_template('home.html')\",\n \"def upload(self, fullfilename, remotefolder=None):\\n print(\\\"[Remote Server] Uploading %s to %s:%s\\\" %(fullfilename, self.server, self.remotefolder))\\n\\n if not self.status:\\n return 0\\n\\n if remotefolder == None:\\n remotefolder = self.remotefolder\\n\\n if not self.cd(remotefolder):\\n return 0\\n\\n if not self.sendFile(fullfilename):\\n print(\\\"[Remote Server] Error uploading file %s\\\" %fullfilename)\\n return 0\\n\\n print(\\\"[Remote Server] upload finished successfully\\\")\\n\\n return 1\",\n \"def upload(upload_url: str, file_path: str) -> None:\\n with open(file_path, 'r') as data:\\n try:\\n r = requests.put(\\n upload_url,\\n data=data,\\n headers={\\\"Content-Type\\\": \\\"application/octet-stream\\\"},\\n )\\n r.raise_for_status()\\n except requests.exceptions.HTTPError as err:\\n print('Error uploading file')\\n raise err\",\n \"def UploadFile(filename, file_id, content, is_binary, status, is_base):\\r\\n file_too_large = False\\r\\n if is_base:\\r\\n type = \\\"base\\\"\\r\\n else:\\r\\n type = \\\"current\\\"\\r\\n if len(content) > MAX_UPLOAD_SIZE:\\r\\n print (\\\"Not uploading the %s file for %s because it's too large.\\\" %\\r\\n (type, filename))\\r\\n file_too_large = True\\r\\n content = \\\"\\\"\\r\\n checksum = md5(content).hexdigest()\\r\\n if options.verbose > 0 and not file_too_large:\\r\\n print \\\"Uploading %s file for %s\\\" % (type, filename)\\r\\n url = \\\"/%d/upload_content/%d/%d\\\" % (int(issue), int(patchset), file_id)\\r\\n form_fields = [(\\\"filename\\\", filename),\\r\\n (\\\"status\\\", status),\\r\\n (\\\"checksum\\\", checksum),\\r\\n (\\\"is_binary\\\", str(is_binary)),\\r\\n (\\\"is_current\\\", str(not is_base)),\\r\\n ]\\r\\n if file_too_large:\\r\\n form_fields.append((\\\"file_too_large\\\", \\\"1\\\"))\\r\\n if options.email:\\r\\n form_fields.append((\\\"user\\\", options.email))\\r\\n ctype, body = EncodeMultipartFormData(form_fields,\\r\\n [(\\\"data\\\", filename, content)])\\r\\n response_body = rpc_server.Send(url, body,\\r\\n content_type=ctype)\\r\\n if not response_body.startswith(\\\"OK\\\"):\\r\\n StatusUpdate(\\\" --> %s\\\" % response_body)\\r\\n sys.exit(1)\",\n \"def _upload_file(self, file, container):\\r\\n try:\\r\\n cnt = self.get_container(container)\\r\\n obj = cnt.get_object(file.filename)\\r\\n obj.delete()\\r\\n return self._upload_file_to_rackspace(file, container)\\r\\n except pyrax.exceptions.NoSuchObject:\\r\\n return self._upload_file_to_rackspace(file, container)\\r\\n except pyrax.exceptions.UploadFailed:\\r\\n return False\",\n \"def upload_file(Filename=None, Bucket=None, Key=None, ExtraArgs=None, Callback=None, Config=None):\\n pass\",\n \"def UploadFile(filename, file_id, content, is_binary, status, is_base):\\n file_too_large = False\\n if is_base:\\n type = \\\"base\\\"\\n else:\\n type = \\\"current\\\"\\n if len(content) > MAX_UPLOAD_SIZE:\\n print (\\\"Not uploading the %s file for %s because it's too large.\\\" %\\n (type, filename))\\n file_too_large = True\\n content = \\\"\\\"\\n checksum = md5(content).hexdigest()\\n if options.verbose > 0 and not file_too_large:\\n print \\\"Uploading %s file for %s\\\" % (type, filename)\\n url = \\\"/%d/upload_content/%d/%d\\\" % (int(issue), int(patchset), file_id)\\n form_fields = [(\\\"filename\\\", filename),\\n (\\\"status\\\", status),\\n (\\\"checksum\\\", checksum),\\n (\\\"is_binary\\\", str(is_binary)),\\n (\\\"is_current\\\", str(not is_base)),\\n ]\\n if file_too_large:\\n form_fields.append((\\\"file_too_large\\\", \\\"1\\\"))\\n if options.email:\\n form_fields.append((\\\"user\\\", options.email))\\n ctype, body = EncodeMultipartFormData(form_fields,\\n [(\\\"data\\\", filename, content)])\\n response_body = rpc_server.Send(url, body,\\n content_type=ctype)\\n if not response_body.startswith(\\\"OK\\\"):\\n StatusUpdate(\\\" --> %s\\\" % response_body)\\n sys.exit(1)\",\n \"def upload(self, file_path):\\n LOG.debug((\\\"Uploading file to mattermost: %s\\\", file_path))\\n filename = os.path.basename(file_path)\\n post_data = {'channel_id': self.channel_id,\\n 'client_ids': filename}\\n file_data = {'files': (filename, open(file_path, 'rb'))}\\n url = '%s/api/v4/files' % self.server_url\\n response = self._request(self._session.post, url, data=post_data,\\n files=file_data)\\n LOG.debug(\\\"Received: %s - %s\\\", response.status_code, response.text)\\n\\n if response.status_code != 201:\\n if response.status_code == 400:\\n msg = \\\"Type of the uploaded file doesn't match its file \\\" \\\\\\n \\\" extension or uploaded file is an image that \\\" \\\\\\n \\\"exceeds the maximum dimensions\\\"\\n elif response.status_code == 401:\\n msg = \\\"User is not logged in\\\"\\n elif response.status_code == 403:\\n msg = \\\"User does not have permission to upload file to \\\" \\\\\\n \\\"the provided team/channel\\\"\\n elif response.status_code == 413:\\n msg = \\\"Uploaded file is too large\\\"\\n elif response.status_code == 500:\\n msg = \\\"File storage is disabled\\\"\\n else:\\n msg = response\\n raise RuntimeError(\\\"Server unhappy with request, reports: %s\\\"\\n % msg)\\n\\n file_id = response.json()[\\\"file_infos\\\"][0][\\\"id\\\"]\\n return file_id\",\n \"def run(self):\\r\\n m = hashlib.md5\\r\\n self.sock.connect((IP, self.port))\\r\\n #the uploaded file\\r\\n f = open(self.path, 'rb')\\r\\n dat = f.read(1024)\\r\\n print 'uploading...'\\r\\n\\r\\n while dat != '':\\r\\n hashed = m(dat)\\r\\n self.sock.send(hashed.hexdigest())\\r\\n self.data = self.sock.recv(2)\\r\\n\\r\\n if not self.data == 'ok':\\r\\n #somthing went wrong and the thread instantly stops\\r\\n return\\r\\n\\r\\n self.sock.send(dat)\\r\\n self.data = self.sock.recv(2)\\r\\n #in case the data didn't and the hash sent didn't match\\r\\n while self.data == 'sa':\\r\\n #sends again the hash and data\\r\\n hashed = m(dat)\\r\\n self.sock.send(hashed.hexdigest())\\r\\n print hashed.hexdigest()\\r\\n self.data = self.sock.recv(2)\\r\\n print self.data\\r\\n self.sock.send(dat)\\r\\n #print dat\\r\\n self.data = self.sock.recv(2)\\r\\n #can keep sending\\r\\n if self.data == 'kg':\\r\\n self.progress += 1024\\r\\n dat = f.read(1024)\\r\\n else:\\r\\n return\\r\\n\\r\\n if not self.sending:\\r\\n\\r\\n self.sock.send('canceled')\\r\\n self.sock.close()\\r\\n f.close()\\r\\n return\\r\\n\\r\\n\\r\\n f.close()\\r\\n self.sock.send('end-of-upload-d-o-n-e-now-what-?')\\r\\n print 'processing video... this may take a few minutes'\\r\\n self.data = self.sock.recv(1024)\\r\\n if self.data == 'complete':\\r\\n self.sock.close()\\r\\n tasks.complete_upload()\\r\\n else:\\r\\n pass\",\n \"def upload(self, requests, file):\\n # Set the source and dest paths\\n dest_url = self.base_url + '/upload'\\n source_path = os.path.join(self.data_dir, file)\\n\\n # Get the sha256 hash of the file\\n with open(source_path, 'rb') as afile:\\n beforeDigest = hashlib.sha256(afile.read()).hexdigest()\\n\\n print(\\\"Generated a hash of the temp file: \\\" + beforeDigest)\\n\\n # Upload the file and time it\\n with open(source_path, 'rb') as f:\\n startTime = time.time()\\n r = requests.post(dest_url, files={'file': (file, f)}, max_price=5)\\n endTime = time.time()\\n uploadElapsedTime = endTime - startTime\\n\\n print(\\\"Uploaded the file. Elapsed time: \\\" + str(uploadElapsedTime))\\n\\n # Verify the upload was successful\\n if r.json()['success'] is not True:\\n return {'success': False}\\n\\n retVal = {\\n 'success': True,\\n 'time': uploadElapsedTime,\\n 'digest': beforeDigest,\\n 'upload_filename': r.json()['filename']\\n }\\n\\n return retVal\",\n \"def upload_file(file_name, file_id, model, workspace, header_put):\\n filename = \\\"data_files/{}\\\".format(file_name)\\n data_file = open(filename, \\\"r\\\")\\n data = data_file.read()\\n data_file.close()\\n\\n uri = (\\\"https://api.anaplan.com/1/3/workspaces/{}/models/{}/\\\"\\n \\\"files/{}/chunks/0\\\").format(workspace, model, file_id)\\n response = requests.put(uri, headers = header_put, data = data)\\n return response.status_code\",\n \"def UploadFile(self, request_iterator, context):\\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\\n context.set_details('Method not implemented!')\\n raise NotImplementedError('Method not implemented!')\",\n \"def upload_file(self, f):\\n with FilesOpener(f) as files:\\n response = self.session.post(\\n 'https://{}/upload'.format(self.domain),\\n files=files\\n ).json()\\n\\n if isinstance(response, list):\\n error = response[0].get('error')\\n else:\\n error = response.get('error')\\n\\n if error:\\n if isinstance(error, str) and error.startswith('FLOOD_WAIT_'):\\n retry_after = int(error.rsplit('_',1)[-1])\\n raise RetryAfterError(retry_after)\\n else:\\n raise TelegraphException(error)\\n\\n return response\",\n \"def upload_finish(self, cloud_file):\",\n \"def post_channel_file_upload(\\n self,\\n channel_id: str,\\n file_obj: IO,\\n content_type: str,\\n metadata: dict=None,\\n lifetime: str=None,\\n conflict_target: str=None) -> dict:\\n path = '/channels/{}/upload'.format(channel_id)\\n if not metadata:\\n metadata = {}\\n headers = {\\n 'Content-Type': content_type\\n }\\n encoded_metadata = encode_metadata(metadata)\\n headers.update(encoded_metadata)\\n\\n params = {}\\n if lifetime is not None:\\n params['lifetime'] = lifetime\\n if conflict_target is not None:\\n params['conflict_target'] = conflict_target\\n res = self._connection.api_request(\\n method='POST',\\n headers=headers,\\n path=path,\\n params=params,\\n data=file_obj)\\n return decode_file_metadata_if_exist(res)\",\n \"async def put_file(object_name: str, file: File, **kwargs) -> str:\\n # TODO: Do not read file but rather stream content as it comes\\n await file.read()\\n # Get the synchronous file interface from the asynchronous file\\n file_obj = file.file\\n # Store position of cursor (number of bytes read)\\n file_size = file_obj.tell()\\n # Reset cursor at start of file\\n file_obj.seek(0)\\n # Trace file upload with its size\\n logger.debug(f\\\"Uploading file: {object_name} with {file_size} bytes\\\")\\n # Time file upload for debug\\n start = time.time()\\n # Store object on s3 storage\\n client.put_object(\\n bucket_name=DATASETS_BUCKET,\\n object_name=object_name,\\n length=file_size,\\n data=file_obj,\\n )\\n end = time.time()\\n # Log time spent\\n logger.debug(f\\\"Took {end - start} seconds to upload {file_size} bytes\\\")\",\n \"def upload():\\n\\n # TODO: decorator to check token\\n token = request.headers.get(\\\"Authorization\\\")\\n\\n has_text = bool(request.get_json())\\n has_file = request.files and request.files[\\\"file\\\"]\\n if not has_text and not has_file:\\n error = \\\"No text input and no file provided\\\"\\n return jsonify({\\\"success\\\": False, \\\"message\\\": error})\\n\\n filename, error = save_text(request)\\n if error:\\n return jsonify({\\\"success\\\": False, \\\"message\\\": error})\\n\\n job_id = schedule(filename, token)\\n add_user_job(job_id, token)\\n\\n return jsonify({\\\"success\\\": True, \\\"data\\\": {\\\"jobId\\\": job_id}})\",\n \"def node_file_upload(ctx, file, filename):\\n filepath = click.format_filename(file)\\n filename = filename if filename else filepath\\n\\n with open(file, \\\"r\\\") as f:\\n content = f.read()\\n\\n try:\\n ctx.obj['node'].create_file(filename, content=content)\\n except TimeoutError as e:\\n logger.error('Error: %s' % e)\\n exit(1)\",\n \"def upload_file(filename, server, account, projname, language=None,\\n username=None, password=None,\\n append=False, stage=False, date_format=None):\\n stream = transcode_to_stream(filename, date_format)\\n upload_stream(stream_json_lines(stream),\\n server, account, projname, language=language,\\n username=username, password=password,\\n append=append, stage=stage)\",\n \"def upload(filename):\\n client = connect()\\n for _ in range(RETRIES):\\n description = str(time.asctime()).replace(' ', '_')\\n data = client.upload_archive(\\n vaultName=VAULT_NAME,\\n archiveDescription=description,\\n body=open(filename))\\n print(\\\"Success at \\\" + time.asctime() + \\\": ID \\\" + data['archiveId'])\\n return\",\n \"def _upload_file(cluster_connection, girder_client, file, path):\\n\\n r = requests.get(\\n '%s/file/%s/download' % (girder_client.urlBase, file['_id']),\\n headers={'Girder-Token': girder_client.token}, stream=True)\\n check_status(r)\\n cluster_connection.put(r.raw, os.path.join(path, file['name']))\",\n \"def _upload(url, data_file, username, password):\\n url_match = '(http(s)?)\\\\:\\\\/\\\\/localhost'\\n if re.search(url_match, url):\\n print(\\\"Please configure url settings.\\\")\\n exit(1)\\n\\n polarion_request = post(url,\\n data=data_file,\\n auth=auth.HTTPBasicAuth(username,\\n password))\\n status_code = polarion_request.status_code\\n if status_code == codes.ok:\\n return status_code\\n else:\\n print(\\\"Results upload failed with the follow: {}\\\".format(\\n polarion_request.status_code))\\n raise exceptions.RequestException\",\n \"async def upload_file(\\n location_id: LocationID,\\n file_id: StorageFileID,\\n file_size: ByteSize | None,\\n link_type: LinkType = LinkType.PRESIGNED,\\n is_directory: bool = False,\\n):\",\n \"def upload(self):\\n\\n # Try to retrieve the file from the request\\n file = None\\n try:\\n file = request.files[\\\"file\\\"]\\n except KeyError as e:\\n logging.warning(f\\\"File was not found in request: {e}.\\\")\\n flash(\\\"No file given.\\\", \\\"error\\\")\\n return redirect(request.url)\\n except AttributeError as e:\\n logging.warning(f\\\"Error: Request did not contain any files: {e}.\\\")\\n flash(\\\"No file given.\\\", \\\"error\\\")\\n return redirect(request.url)\\n\\n # Check if file was correctly uploaded\\n if not file or len(file.filename) == 0:\\n flash(\\\"No file selected for upload.\\\", \\\"message\\\")\\n return redirect(request.url)\\n\\n \\\"\\\"\\\" Check if file has correct extension. Allowed extensions depend on\\n the connector. To make the code more readable, group connectors\\n with the same allowed file extensions together like this:\\n if connector in ['someconnector', 'someotherconnector']:\\n extensions = [...] \\\"\\\"\\\"\\n\\n if self.connector in [\\\"overtime\\\"]:\\n allowed_extensions = OVERTIME_SUPPORTED_EXTENSIONS\\n else:\\n allowed_extensions = []\\n\\n if not allowed_file(file, allowed_extensions=allowed_extensions):\\n flash(\\\"File extension not allowed.\\\", \\\"warning\\\")\\n return redirect(request.url)\\n\\n \\\"\\\"\\\" File seems uploaded correctly and has correct extension.\\n Generate a new record ID to keep track of the uploaded file.\\n \\\"\\\"\\\"\\n rec_id = generate_temp_record_id()\\n\\n # Save file to disk\\n path = store_temp_file(file, record_id=rec_id)\\n\\n if not path:\\n flash(\\\"Error saving file!\\\", \\\"error\\\")\\n return redirect(request.url)\\n\\n \\\"\\\"\\\" If everything ended successfully, send the user to the\\n confirmation page so he can review his changes \\\"\\\"\\\"\\n\\n return redirect(url_for(f\\\"{self.endpoint}.upload_confirm\\\", rec_id=rec_id))\",\n \"def upload(self, filepath):\\n if self.ver is not None and LooseVersion(str(self.ver)) < LooseVersion('1.4.0'):\\n raise VersionMismatchError('File upload')\\n\\n try:\\n with open(filepath, 'rb') as stream:\\n url = '{0}{1}'.format(self.url, '/uploads.json')\\n response = self.request('post', url, data=stream, headers={'Content-Type': 'application/octet-stream'})\\n except IOError:\\n raise NoFileError()\\n\\n return response['upload']['token']\",\n \"def upload():\\n file = None\\n if 'file' in request.files:\\n file = request.files['file']\\n if file and allowed_file(file.filename):\\n filename = secure_filename(file.filename)\\n file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))\\n return json_response(\\n message=\\\"Upload successful\\\",\\n result=\\\"/v/{}\\\".format(filename)\\n )\\n return json_response(\\n message=\\\"Invalid filename or extension (jpg, png, gif)\\\",\\n status_code=500\\n )\",\n \"def uploadFile(self, filename, name=\\\"Dummy name\\\", type=\\\"DummyType\\\"):\\n\\n with open(filename, 'rb') as f:\\n data = f.read()\\n\\n if (name == \\\"Dummy name\\\"):\\n name = filename\\n\\n data = {'name': name,\\n 'type': type,\\n 'bits': xmlrpclib.Binary(data),\\n 'overwrite': True}\\n\\n try:\\n r = self.server.wp.uploadFile(\\n self.blogid, self.username, self.password, data)\\n except xmlrpclib.Fault as fault:\\n display_XMLRPC_errors(\\\"upload file \\\" + filename, fault)\\n\\n #FIXME: do we really need to split the url ?\\n try:\\n r['url'] = r['url'].split('?')[1]\\n except IndexError:\\n from urlparse import urlparse\\n r['url'] = urlparse(r['url']).path\\n\\n print \\\"uploaded file file =\\\", r['file']\\n print \\\"uploaded file url =\\\", r['url']\\n print \\\"uploaded file type =\\\", r['type']\",\n \"def upload_file(self, file_name, bucket, destination_name):\\n try:\\n not self.client.upload_file(file_name, bucket, destination_name)\\n except Exception as ex:\\n raise ex\",\n \"def upload_video(self, video_file):\\r\\n part = \\\"snippet,status\\\"\\r\\n metadata = self.get_metadata(video_file)\\r\\n body = {\\r\\n \\\"snippet\\\": {\\r\\n \\\"title\\\": metadata['title'],\\r\\n \\\"description\\\": metadata['description'],\\r\\n \\\"tags\\\": metadata['categoryId'],\\r\\n \\\"categoryId\\\": metadata['categoryId']\\r\\n },\\r\\n \\\"status\\\": {\\r\\n \\\"privacyStatus\\\": \\\"public\\\",\\r\\n \\\"license\\\": \\\"youtube\\\", # temporary, see gh#414\\r\\n \\\"embeddable\\\": True,\\r\\n \\\"publicStatsViewable\\\": True\\r\\n }\\r\\n }\\r\\n # This is to fix a bug, the API thinks our .ogg files are audio/ogg\\r\\n mimetype = \\\"video/{}\\\".format(video_file.split(\\\".\\\")[-1])\\r\\n media_body = MediaFileUpload(video_file, chunksize=-1, resumable=True, mimetype=mimetype)\\r\\n insert_request = self.service.videos().insert(part=part, body=body, media_body=media_body)\\r\\n response = None\\r\\n error = None\\r\\n retry = 0\\r\\n sleep_seconds = 5.0\\r\\n while response is None:\\r\\n try:\\r\\n log.info(\\\"Uploading %s\\\" % video_file)\\r\\n (status, response) = insert_request.next_chunk()\\r\\n if 'id' in response:\\r\\n return (Response.SUCCESS, response)\\r\\n else:\\r\\n return (Response.UNEXPECTED_FAILURE, response)\\r\\n except HttpError as e:\\r\\n if e.resp.status in self.RETRIABLE_STATUS_CODES:\\r\\n error = \\\"A retriable HTTP error {} occurred:\\\\n{}\\\".format(e.resp.status, e.content)\\r\\n else:\\r\\n return (Response.UNRETRIABLE_ERROR, {\\\"status\\\": e.resp.status, \\\"content\\\": e.content})\\r\\n except self.RETRIABLE_EXCEPTIONS as e:\\r\\n error = \\\"A retriable error occurred: {}\\\".format(e)\\r\\n except client.AccessTokenRefreshError:\\r\\n return (Response.ACCESS_TOKEN_ERROR, None)\\r\\n if error is not None:\\r\\n log.error(error)\\r\\n retry += 1\\r\\n if retry > self.MAX_RETRIES:\\r\\n return (Response.MAX_RETRIES_REACHED, None)\\r\\n log.info(\\\"Sleeping %s seconds and then retrying...\\\" % sleep_seconds)\\r\\n time.sleep(sleep_seconds)\",\n \"def upload(request):\\n # We pass the 'file_id' in the query string as a GET parameter. If\\n # we read it from the POSTed data, WebOb would read all POSTed\\n # data, which has various features and traps (like setting the\\n # \\\"Content-Length\\\" header to 0) that we do not need since we are\\n # going to read the data ourselves anyway.\\n file_id = request.GET['X-Progress-ID']\\n input_file, file_size, filename = get_file_from_request(request)\\n session = DBSession()\\n u = session.query(Upload).filter_by(id=file_id).one()\\n upload_dir = request.registry.settings['poulda.upload_dir']\\n user_id = authenticated_userid(request)\\n # We use a temporary path to detect unfinished uploads (post\\n # mortem, not in the application itself).\\n path = os.path.join(upload_dir, '_'.join((user_id, file_id)))\\n u.tmp_path = path\\n u.started = int(time.time())\\n u.size = file_size\\n u.state = u'uploading'\\n session.flush()\\n # We need to commit the transaction so that changes to the Upload\\n # object can be seen by the other threads (which will serve the\\n # 'progress' JSON view called by the upload page).\\n transaction.commit()\\n with open(path, 'w') as output:\\n # We must read only 'file_size' bytes from the 'input_file',\\n # not all of it since it also contains the MIME boundary.\\n copy_to_file(input_file, file_size, output)\\n final_path = filename[1 + filename.rfind(os.sep):]\\n final_path = os.path.join(upload_dir, final_path)\\n os.rename(path, final_path)\\n session = DBSession()\\n u = session.query(Upload).filter_by(id=file_id).one()\\n u.state = u'done'\\n u.final_path = unicode(final_path, 'utf-8')\\n return HTTPFound(location='success')\",\n \"def _upload_file_to_rackspace(self, file, container):\\r\\n chksum = pyrax.utils.get_checksum(file)\\r\\n self.cf.upload_file(container,\\r\\n file,\\r\\n obj_name=secure_filename(file.filename),\\r\\n etag=chksum)\\r\\n return True\",\n \"def upload_file(file_directory, file_path, login_request, user_id):\\n\\n data, headers, server_host, server_port = process_request_header(file_directory, file_path, login_request, user_id)\\n\\n request = requests.post(\\\"http://\\\" + server_host + \\\":\\\" + server_port + \\\"/fileOperations/uploadFile\\\", data=data,\\n headers=headers)\\n return request.text\",\n \"def upload(self, dest, overwrite=False):\\n dest = normpath(dest)\\n try:\\n remote = get_remote(dest)\\n except ValueError: # Nothing exists at dest, nothing to worry about.\\n pass\\n else: # Something exists here.\\n if isinstance(remote, RemoteFile) and self.hash() == remote.hash:\\n # Nothing to update.\\n pdbox.info(\\\"%s and %s are identical\\\" % (self.path, remote.uri))\\n return\\n if not overwrite:\\n raise ValueError(\\\"%s exists\\\" % remote.uri)\\n\\n # Uploading can either happen all at once (with a 150 MB limit),\\n # or in chunks. If the file is smaller than the selected chunk size,\\n # then try to upload in one go.\\n chunksize = min(pdbox._args.get(\\\"chunksize\\\", 149.0), 149.0)\\n pdbox.debug(\\\"Chunk size: %.2f MB\\\" % chunksize)\\n if pdbox._args.get(\\\"dryrun\\\"):\\n pdbox.info(\\\"Uploaded %s to %s\\\" % (self.path, dbx_uri(dest)))\\n return None\\n\\n # Set the write mode.\\n if overwrite:\\n mode = dropbox.files.WriteMode.overwrite\\n else:\\n mode = dropbox.files.WriteMode.add\\n\\n chunk = int(chunksize * 1024 * 1024) # Convert B to MB.\\n\\n with open(self.path, \\\"rb\\\") as f:\\n data = f.read()\\n sz = len(data)\\n\\n # TODO: Progress bars.\\n if sz < chunk: # One-shot upload.\\n meta = execute(pdbox.dbx.files_upload, data, dest, mode)\\n else: # Multipart upload.\\n nchunks = math.ceil(sz / chunk)\\n # Initiate the upload with just the first byte.\\n start = execute(pdbox.dbx.files_upload_session_start, f[0])\\n cursor = dropbox.files.UploadSessionCursor(start.session_id, 1)\\n\\n # Now just add each chunk.\\n while sz - cursor.offset > chunk:\\n pdbox.debug(\\n \\\"Uploading chunk %d/%d\\\" % (cursor.offset % chunk, nchunks),\\n )\\n execute(\\n pdbox.dbx.files_upload_session_append_v2,\\n data[cursor.offset:cursor.offset + chunk],\\n cursor,\\n )\\n cursor.offset += chunk\\n\\n # Upload the remaining to finish the transaction.\\n meta = execute(\\n pdbox.dbx.files_upload_session_finish,\\n data[cursor.offset:],\\n dropbox.files.CommitInfo(dest, mode),\\n )\\n\\n pdbox.info(\\\"Uploaded %s to %s\\\" % (self.path, dbx_uri(dest)))\\n return RemoteFile(None, meta=meta)\",\n \"def resumable_upload(insert_request):\\r\\n response = None\\r\\n error = None\\r\\n retry = 0\\r\\n while response is None:\\r\\n try:\\r\\n print(\\\"Uploading file...\\\")\\r\\n status, response = insert_request.next_chunk()\\r\\n if 'id' in response:\\r\\n print(\\\"Video id '%s' was successfully uploaded.\\\" % response['id'])\\r\\n f=open(\\\"uploadedIDs.txt\\\",\\\"a+\\\")\\r\\n f.write(\\\"\\\\n\\\"+response['id'])\\r\\n f.close()\\r\\n else:\\r\\n exit(\\\"The upload failed with an unexpected response: %s\\\" % response)\\r\\n except HttpError:\\r\\n import sys\\r\\n e = sys.exc_info()[1]\\r\\n if e.resp.status in RETRIABLE_STATUS_CODES:\\r\\n error = \\\"A retriable HTTP error %d occurred:\\\\n%s\\\" % (e.resp.status,\\r\\n e.content)\\r\\n else:\\r\\n raise\\r\\n except RETRIABLE_EXCEPTIONS:\\r\\n import sys\\r\\n e = sys.exc_info()[1]\\r\\n error = \\\"A retriable error occurred: %s\\\" % e\\r\\n\\r\\n if error is not None:\\r\\n print(error)\\r\\n retry += 1\\r\\n if retry > MAX_RETRIES:\\r\\n exit(\\\"No longer attempting to retry.\\\")\\r\\n\\r\\n max_sleep = 2 ** retry\\r\\n sleep_seconds = random.random() * max_sleep\\r\\n print(\\\"Sleeping %f seconds and then retrying...\\\" % sleep_seconds)\\r\\n time.sleep(sleep_seconds)\",\n \"def upload_file(self, source, dest):\\n print(f\\\"Uploading {source} to {dest}\\\")\\n with open(source, \\\"rb\\\") as data:\\n self.client.upload_blob(name=dest, data=data)\",\n \"def upload():\\n\\n # Get CAS user roles from session\\n cas_attributes = session.get('CAS_ATTRIBUTES', None)\\n\\n # login_required should mean we're authenticated, but check this\\n if not cas_attributes:\\n abort(403)\\n\\n roles = cas_attributes['cas:authority'].split(',')\\n\\n APP.logger.debug(\\n f'User has CAS roles: {roles} ')\\n\\n # Stop unauthorized users\\n if CONFIG.UPLOAD_ROLE not in roles:\\n abort(403)\\n\\n form = UploadForm()\\n\\n if request.method != 'POST':\\n return render_template('upload.html', form=form, upload_error=False)\\n\\n # User has tried to submit file (POST)\\n # Check if it passes (simple) validation in forms.py\\n if not form.validate_on_submit():\\n return render_template('upload.html', form=form)\\n\\n # Get filename\\n f = form.file.data\\n filename = secure_filename(f.filename)\\n # Add user email & time\\n email = cas_attributes['cas:email']\\n upload_time = dt.now().strftime(\\\"%y%m%d-%H%M%S\\\")\\n ext_filename = email + '_' + upload_time + '_' + filename\\n\\n # Save file\\n try:\\n f.save(os.path.join(CONFIG.UPLOAD_PATH, ext_filename))\\n except Exception as ex:\\n APP.logger.error(\\n f'File {ext_filename} could not be saved due to {ex}')\\n else:\\n APP.logger.info(f'Successfully uploaded file {ext_filename}')\\n\\n # Notify admin\\n msg = Message('New file upload',\\n sender=APP.mail.username,\\n recipients=CONFIG.UPLOAD_EMAIL)\\n msg.body = f\\\"\\\"\\\"\\n Hello SBDI-MOL colleagues,\\n\\n A new file has been uploaded to the ASV portal:\\n\\n Provider email: {email}\\n Upload time: {upload_time}\\n Original filename: {filename}\\n Saved as: {ext_filename}\\n\\n Have a nice day!\\n\\n / Swedish ASV portal\\n \\\"\\\"\\\"\\n try:\\n APP.mail.send(msg)\\n except Exception as ex:\\n APP.logger.error(f\\\"Could not send upload notification due to {ex}\\\")\\n else:\\n APP.logger.info('Successfully sent upload notification.')\\n # Display 'success page' only if upload AND notification worked\\n return render_template('uploaded.html', filename=filename)\\n\\n # Display error msg if EITHER upload OR email failed, so that data\\n # providers get a chance to tell us about uploaded files\\n return render_template('upload.html', form=form, upload_error=True)\",\n \"def upload(self):\\n # TODO: Should CD to the working directory set by the robscript.\\n src = self.state_frame[0]\\n dest = self.state_frame[1]\\n self.send_upload(src, dest, True, None)\\n self.state = STATE_READ_LINE\",\n \"def upload_local_file_to_oh(file_path, file_name, file_metadata, access_token, member_id):\\r\\n try:\\r\\n with open(file_path, 'rb') as fs:\\r\\n upload_stream(fs, file_name, file_metadata, access_token)\\r\\n return True\\r\\n except:\\r\\n print(f'Failed to upload {file_path} to OH for OH member {member_id}')\\r\\n return False\",\n \"def upload(upload_type):\\n env_banner()\\n\\n if upload_type not in Upload.ACCEPTED_TYPES:\\n click.echo('Upload of only certain types are accepted.')\\n click.echo('UPLOAD_TYPE can be [users | players | scores | all]')\\n return\\n\\n upload_data = Upload()\\n if upload_type == 'users':\\n upload_data.file_name = config.USER_FILE_NAME\\n if upload_type == 'all':\\n result = Upload.upload_all()\\n else:\\n result = upload_data(upload_type)\\n if result != Upload.SUCCESS:\\n click.echo(f'Error Code: {result}. Error in upload.')\\n return\\n click.echo('Upload done!')\",\n \"def upload_fileobj(Fileobj=None, Bucket=None, Key=None, ExtraArgs=None, Callback=None, Config=None):\\n pass\",\n \"def upload(ctx: click.Context, **kwargs):\\n root_commands.cmd_upload(ctx.obj, **kwargs)\",\n \"def upload(self, file_path, bucket_name, file_name):\\n\\n self.client.upload_file(file_path, bucket_name, file_name)\",\n \"def upload_file(self, file_from, file_to):\\n dbx = dropbox.Dropbox(self.access_token)\\n\\n with open(file_from, 'rb') as f:\\n file_size = os.path.getsize(file_from)\\n CHUNK_SIZE = 4 * 1024 * 1024\\n\\n if file_size <= CHUNK_SIZE:\\n print(dbx.files_upload(f.read(), file_to, mode=dropbox.files.WriteMode.overwrite))\\n else:\\n upload_session_start_result = dbx.files_upload_session_start(f.read(CHUNK_SIZE))\\n cursor = dropbox.files.UploadSessionCursor(session_id=upload_session_start_result.session_id,\\n offset=f.tell())\\n commit = dropbox.files.CommitInfo(path=file_to, mode=dropbox.files.WriteMode.overwrite)\\n\\n while f.tell() < file_size:\\n if ((file_size - f.tell()) <= CHUNK_SIZE):\\n print(dbx.files_upload_session_finish(f.read(CHUNK_SIZE),\\n cursor,\\n commit))\\n else:\\n dbx.files_upload_session_append(f.read(CHUNK_SIZE),\\n cursor.session_id,\\n cursor.offset)\\n cursor.offset = f.tell()\",\n \"def file_upload():\\n\\n click.secho('*** Uploading image...', fg='green')\\n uploaded = _uploaded_file('cover.jpg')\\n click.secho(json.dumps(uploaded, indent=2, sort_keys=True), fg='yellow')\\n\\n click.secho('*** Creating a Picture document for it...', fg='green')\\n picture = _make_document('picture', title='cover image', sys_filename=uploaded['path'])\\n click.secho(json.dumps(picture, indent=2, sort_keys=True), fg='yellow')\\n\\n click.secho('*** Attaching it to a Blueray as cover...', fg='green')\\n slp = _make_document('movie', title='Silver Linings Playbook')\\n blueray = _make_document('blueray', movie_id=slp['_id'], cover_id=picture['_id'])\\n click.secho(json.dumps(blueray, indent=2, sort_keys=True), fg='yellow')\",\n \"def upload_file(self, instance, local_obj, remote_file):\\n client = self.connect(instance)\\n try:\\n sftp = client.open_sftp()\\n try:\\n self._send_file(sftp, local_obj, remote_file)\\n finally:\\n sftp.close()\\n finally:\\n client.close()\",\n \"def upload(self, filename, **arg):\\n\\n if filename == None:\\n raise UploadException(\\\"filename OR jpegData must be specified\\\")\\n\\n # verify key names\\n for a in arg.keys():\\n if a != \\\"api_key\\\" and a != \\\"auth_token\\\" and a != \\\"folder_id\\\" and a != 'share':\\n sys.stderr.write(\\\"Box.net api: warning: unknown parameter \\\\\\\"%s\\\\\\\" sent to Box.net.upload\\\\n\\\" % (a))\\n\\n url = 'http://upload.box.net/api/1.0/upload/%s/%s' % (arg['auth_token'], arg['folder_id'])\\n\\n\\tc = pycurl.Curl()\\n\\t\\n\\tc.setopt(c.URL,url.encode('utf-8'))\\n\\t# fp.close(\\n\\tc.setopt(c.HTTPPOST,[ (\\\"file\\\", filename), \\n (\\\"share\\\", arg['share']), \\n (filename, \\n (c.FORM_FILE, filename, \\n c.FORM_CONTENTTYPE, get_content_type(filename)))\\n ])\\n\\tc.setopt(c.PROGRESSFUNCTION, progress)\\n\\tstorage = StringIO()\\n\\tc.setopt(pycurl.WRITEFUNCTION, storage.write)\\n\\tc.setopt(c.NOPROGRESS,0)\\n\\tc.perform()\\n\\t\\n\\tloader = '[' + ('=' * (49)) +'>'+ ']'\\n\\tsys.stdout.write(\\\"%s %d%%\\\\r\\\" % (loader, 100))\\n\\tsys.stdout.flush()\\n\\tsys.stdout.write(\\\"\\\\n\\\")\\n\\tc.close()\\n\\trspXML=storage.getvalue()\\n\\t# print 'xml',self.rspXML\\n return XMLNode.parseXML(rspXML)\",\n \"def upload_a_file(self, package, version, file_path):\\n cmd_args = [self._push_executable]\\n cmd_args += [\\\"--user\\\", self._username]\\n cmd_args += [\\\"--api_key\\\", self._api_key]\\n cmd_args += [\\\"--subject\\\", self._subject]\\n cmd_args += [\\\"--repo\\\", self._repo]\\n cmd_args += [\\\"--package\\\", package]\\n cmd_args += [\\\"--version\\\", version]\\n cmd_args += [\\\"--file_path\\\", file_path]\\n\\n if self._component:\\n cmd_args += [\\\"--component\\\", self._component]\\n if self._distribution:\\n cmd_args += [\\\"--distribution\\\", self._distribution]\\n if self._architecture:\\n cmd_args += [\\\"--architecture\\\", self._architecture]\\n\\n cmd_args += [\\\"--package\\\", package]\\n cmd_args += [\\\"--version\\\", version]\\n cmd_args += [\\\"--file_path\\\", file_path]\\n\\n try:\\n proc = subprocess.Popen(cmd_args,\\n stderr=subprocess.PIPE,\\n stdout=subprocess.PIPE,\\n shell=False)\\n (out, err) = proc.communicate()\\n if proc.returncode != 0:\\n raise RuntimeError(err)\\n except subprocess.CalledProcessError as ex:\\n raise RuntimeError(\\\"Failed to upload file {0} due to {1}\\\".format(file_path, ex))\\n\\n return True\",\n \"def upload_file_to_shock(self, file_path, token):\\n\\n if token is None:\\n raise Exception(\\\"Authentication token required!\\\")\\n\\n header = {'Authorization': \\\"Oauth {0}\\\".format(token)}\\n\\n if file_path is None:\\n raise Exception(\\\"No file given for upload to SHOCK!\\\")\\n\\n with open(os.path.abspath(file_path), 'rb') as data_file:\\n files = {'upload': data_file}\\n response = requests.post(\\n self.shockURL + '/node', headers=header, files=files,\\n stream=True, allow_redirects=True)\\n self.check_shock_response(\\n response, ('Error trying to upload contig FASTA file {} to Shock: '\\n ).format(file_path))\\n return response.json()['data']\",\n \"def handle_request_upload(self, msg):\\n\\n\\t\\tdirect_response = not msg.arguments or msg.arguments[0] in ('', '/')\\n\\t\\tresult = []\\n\\t\\tfor file_obj in msg.options:\\n\\t\\t\\ttmpfilename, filename, name = file_obj['tmpfile'], file_obj['filename'], file_obj['name']\\n\\n\\t\\t\\t# limit files to tmpdir\\n\\t\\t\\tif not os.path.realpath(tmpfilename).startswith(TEMPUPLOADDIR):\\n\\t\\t\\t\\traise BadRequest('invalid file: invalid path')\\n\\n\\t\\t\\t# check if file exists\\n\\t\\t\\tif not os.path.isfile(tmpfilename):\\n\\t\\t\\t\\traise BadRequest('invalid file: file does not exists')\\n\\n\\t\\t\\t# don't accept files bigger than umc/server/upload/max\\n\\t\\t\\tst = os.stat(tmpfilename)\\n\\t\\t\\tmax_size = int(ucr.get('umc/server/upload/max', 64)) * 1024\\n\\t\\t\\tif st.st_size > max_size:\\n\\t\\t\\t\\tos.remove(tmpfilename)\\n\\t\\t\\t\\traise BadRequest('filesize is too large, maximum allowed filesize is %d' % (max_size,))\\n\\n\\t\\t\\tif direct_response:\\n\\t\\t\\t\\twith open(tmpfilename) as buf:\\n\\t\\t\\t\\t\\tb64buf = base64.b64encode(buf.read())\\n\\t\\t\\t\\tresult.append({'filename': filename, 'name': name, 'content': b64buf})\\n\\n\\t\\tif direct_response:\\n\\t\\t\\tself.finished(msg.id, result)\\n\\t\\telse:\\n\\t\\t\\tself.handle_request_command(msg)\",\n \"def upload(cls, local_file, remote_file='', bucket_name=QINIU_BUCKET_NAME):\\n if remote_file == '':\\n remote_file = cls.__gen_uuid()\\n local_file = cls.__get_abs_path(local_file)\\n url = \\\"/v1/qiniu/upload?key=%s&localFile=%s&token=root-weimiyun-9@usstpwd!\\\" % (remote_file, local_file)\\n try:\\n conn = httplib.HTTPConnection(UPLOAD_API_HOST)\\n conn.request(method=\\\"POST\\\", url=url)\\n response = conn.getresponse()\\n res = response.read()\\n if AUTO_DELETE:\\n os.remove(local_file)\\n return res, True\\n except Exception, e:\\n return 'Connection refused', False\",\n \"def upload_file(url, filename, metadata={}):\\n while True:\\n files = {'dataFile': open(filename, 'rb')}\\n r = requests.post(url, files=files, data=metadata)\\n\\n # Check if everything went good\\n if r.status_code is 200:\\n res = r.json()\\n\\n if res['success'] and md5_checksum(filename) == res['md5']:\\n print('[ OK ] File uploaded successfully!')\\n return True\\n else:\\n print('[ WARNING ] Something went werong. Trying again...')\\n time.sleep(10)\\n else:\\n print('[ ERROR ] Connection error. Status code: {}'.format(r.status_code))\",\n \"def upload(api_token, base_url, upload_file, metadata):\\n\\n upload_url = f\\\"{base_url}data_files/api_create?auth_token={api_token}\\\"\\n files = {'file': open(upload_file, 'rb')}\\n response = requests.post(upload_url, files=files, data=metadata)\\n\\n # Print out the outcome of the upload\\n if response.status_code == 200:\\n print(f'File {upload_file} successfully uploaded to HIEv')\\n else:\\n print(\\n f'ERROR - There was a problem uploading file {upload_file} to HIEv')\",\n \"def upload(self, name, filepath):\\n upload_id = self._client.push(filepath)\\n\\n endpoint = '/v1/charm/{}/revisions'.format(name)\\n response = self._client.post(endpoint, {'upload-id': upload_id})\\n status_url = response['status-url']\\n logger.debug(\\\"Upload %s started, got status url %s\\\", upload_id, status_url)\\n\\n while True:\\n response = self._client.get(status_url)\\n logger.debug(\\\"Status checked: %s\\\", response)\\n\\n # as we're asking for a single upload_id, the response will always have only one item\\n (revision,) = response['revisions']\\n status = revision['status']\\n\\n if status in UPLOAD_ENDING_STATUSES:\\n return Uploaded(\\n ok=UPLOAD_ENDING_STATUSES[status],\\n status=status, revision=revision['revision'])\\n\\n # XXX Facundo 2020-06-30: Implement a slight backoff algorithm and fallout after\\n # N attempts (which should be big, as per snapcraft experience). Issue: #79.\\n time.sleep(POLL_DELAY)\",\n \"async def upload_file(self):\\n logger.debug(\\\"uploading %s\\\", self.tgzfile)\\n with aiohttp.MultipartWriter(\\\"form-data\\\") as mpwriter:\\n with open(self.tgzfile, \\\"rb\\\") as file_handle:\\n part = mpwriter.append(file_handle)\\n part.set_content_disposition(\\n \\\"form-data\\\", name=\\\"file\\\", filename=\\\"inventory.gz\\\"\\n )\\n part.headers[aiohttp.hdrs.CONTENT_TYPE] = self.UPLOAD_CONTENT_TYPE\\n\\n headers = {}\\n # TODO : Use mTLS certs not userid/password\\n auth = aiohttp.BasicAuth(\\n self.config[\\\"AUTH\\\"][\\\"username\\\"], self.config[\\\"AUTH\\\"][\\\"password\\\"]\\n )\\n headers[\\\"Authorization\\\"] = auth.encode()\\n async with aiohttp.ClientSession(headers=headers) as session:\\n async with session.post(\\n self.upload_url, ssl=self.ssl_context, data=mpwriter\\n ) as response:\\n logger.debug(\\\"Status: %s\\\", response.status)\\n logger.debug(\\n \\\"Content-type: %s\\\", response.headers[\\\"Content-Type\\\"]\\n )\\n\\n return await response.text()\",\n \"def upload_file(self, file: Union[str, bytes, StringIO, TextIOWrapper, BytesIO], filename: str, directory: str = 'gcodes') -> Dict:\\n raise NotImplementedError\",\n \"def upload(files, token, assume_yes):\\r\\n # check if token exists\\r\\n if not os.path.exists(token):\\r\\n print(\\\"{} does not exist, please specify a valid token file\\\".format(token))\\r\\n else:\\r\\n # Gather videos specified and vids from folders specified into list\\r\\n videos = gather_videos(files)\\r\\n # Now begin upload process\\r\\n if not videos:\\r\\n print(\\\"Nothing to upload\\\")\\r\\n # Prompt for confirmation\\r\\n elif prompt_user(videos, confirmation=assume_yes):\\r\\n youtube_service = YoutubeService()\\r\\n # Authorize with OAuth2 token\\r\\n youtube_service.authorize(token)\\r\\n for video in videos:\\r\\n response_code, response = youtube_service.upload_video(video)\\r\\n handle_response(response_code, response)\\r\\n # Response was no, so do nothing\\r\\n else:\\r\\n print(\\\"Exiting...\\\")\",\n \"def upload_file(command):\\n if 'action' not in command or command['action']!=\\\"UPLOAD\\\":\\n raise ValueError(\\\"Command not of type UPLOAD\\\")\\n if 'file_pattern' not in command: \\n raise ValueError(\\\"Missing file pattern\\\")\\n path = command['file_pattern'] \\n if not os.path.exists(path):\\n raise ValueError(\\\"No valid file for upload found\\\")\\n returner={}\\n handler = Layer1(aws_access_key_id = command['access_key'],aws_secret_access_key = command['secret_access_key'],region_name=command['region_name'])\\n uploader = ConcurrentUploader(handler,command['vault_name'],part_size=uchunk)\\n file_size = os.path.getsize(path)\\n if file_size==0:\\n raise ValueError(\\\"File is empty. Nothing to upload.\\\")\\n csum = chunkedmd5(path)\\n itime=time.time()\\n file_name = os.path.basename(path)\\n machine_id = str(command['target']) if client_name == '' else client_name+' ('+str(command['target']) + ')'\\n #Construct a meaningful description object for the file\\n #The limits are that the description can be no more than 1024 characters in length and must use only ascii characters between 32 and 126 (i.e., 32<=ord(char)<=126)\\n dscrip = command['description']+'\\\\\\\\n'\\n dscrip = dscrip + \\\"Uploaded at \\\"+str(itime)+'\\\\\\\\n'+ \\\"Full path \\\"+str(path)+'\\\\\\\\n'+ \\\"File size \\\"+str(file_size)+'\\\\\\\\n' + \\\"MD5 \\\"+str(csum)+'\\\\\\\\n' + \\\"Source machine id \\\"+machine_id+'\\\\\\\\n'\\n print \\\"Uploading file %s\\\"%file_name\\n #Put some validation stuff here...\\n #Do the upload\\n archive_id = uploader.upload(path,dscrip)\\n print \\\"Completed successfully. Archive ID: %s\\\"%archive_id\\n #Done the upload, send the bastard back\\n returner['archive_id'] = archive_id\\n returner['description'] = dscrip\\n returner['file_name'] = file_name\\n returner['true_path'] = path\\n returner['file_size'] = file_size\\n returner['md5sum'] = csum\\n returner['insert_time']=int(itime)\\n returner['region_name']=command['region_name']\\n returner['vault_name'] = command['vault_name']\\n return returner\",\n \"def upload_file(self, local_absolute_filepath, upload_filename):\\n url = self.base_url + \\\"/oasis/doTaskUploadFileHelper/\\\"\\n\\n with open(local_absolute_filepath) as f:\\n in_file = f\\n response = self.do_request(\\n url,\\n in_file_dict={upload_filename:in_file}\\n )\\n return response\",\n \"def upload_progress(self, cloud_file, size, uploaded):\",\n \"def upload(connection, server_pub_key, priv_key, max_message_size):\\r\\n\\r\\n while True:\\r\\n # Get file name from user\\r\\n file_path = input('Which file would you like to send to the server?: ')\\r\\n\\r\\n # Verify file exists\\r\\n if os.path.isfile(file_path) is True:\\r\\n break\\r\\n\\r\\n # File doesn't exist\\r\\n else:\\r\\n print('Could not find specified file, please try again', file=sys.stderr)\\r\\n\\r\\n try:\\r\\n # Tell server file is being sent\\r\\n connection.sendall(rsa.encrypt(b'UPLOAD', server_pub_key))\\r\\n time.sleep(1)\\r\\n connection.sendall(rsa.encrypt(str.encode(file_path), server_pub_key))\\r\\n time.sleep(1)\\r\\n\\r\\n # Tell the server the file size of the file attempting to be uploaded\\r\\n connection.sendall(rsa.encrypt(str(os.path.getsize(file_path)).encode(), server_pub_key))\\r\\n time.sleep(1)\\r\\n\\r\\n # Get requirement from server\\r\\n data = rsa.decrypt(connection.recv(1024), priv_key)\\r\\n if data == b'PERMISSION CHECK':\\r\\n security_level = input('What security level should the file have?: ')\\r\\n connection.sendall(rsa.encrypt(security_level.encode(), server_pub_key))\\r\\n\\r\\n elif data == b'TRAVERSAL':\\r\\n print('Failed attempting to upload file outside of scope', file=sys.stderr)\\r\\n return\\r\\n\\r\\n elif data == b'SIZE EXCEEDED':\\r\\n print('Maximum storage exceeded', file=sys.stderr)\\r\\n return\\r\\n\\r\\n else:\\r\\n print('Unexpected response from server', file=sys.stderr)\\r\\n return\\r\\n\\r\\n # Attempt to upload file to the server\\r\\n status = rsa.decrypt(connection.recv(1024), priv_key)\\r\\n if status == b'CONTINUE':\\r\\n\\r\\n # Send the file to the server\\r\\n shared.send_file(connection, server_pub_key, file_path, max_message_size)\\r\\n\\r\\n # Get the result from the server\\r\\n result = rsa.decrypt(connection.recv(1024), priv_key)\\r\\n\\r\\n # If success\\r\\n if result == b'SUCCESS':\\r\\n print('Successfully added file to the storage system')\\r\\n\\r\\n # If failure\\r\\n elif result == b'FAILURE':\\r\\n print('Failed to add file to the storage system')\\r\\n\\r\\n # Attempt to overwrite file on the server\\r\\n elif status == b'OVERWRITE':\\r\\n\\r\\n # Ask the user if they would like to overwrite the file on the server\\r\\n while True:\\r\\n overwrite = input('Would you like to overwrite the file on the server with the same name?\\\\n'\\r\\n '1 - Yes\\\\n'\\r\\n '2 - No\\\\n'\\r\\n 'Choice: ')\\r\\n\\r\\n # Overwrite file\\r\\n if overwrite == '1':\\r\\n connection.sendall(rsa.encrypt(b'YES', server_pub_key))\\r\\n break\\r\\n\\r\\n # Don't overwrite file\\r\\n elif overwrite == '2':\\r\\n connection.sendall((rsa.encrypt(b'NO', server_pub_key)))\\r\\n return\\r\\n\\r\\n # Invalid input\\r\\n else:\\r\\n print('Invalid input, please select an available option', file=sys.stderr)\\r\\n\\r\\n # Send the file to the server\\r\\n shared.send_file(connection, server_pub_key, file_path, max_message_size)\\r\\n\\r\\n # Get the result from the server\\r\\n result = rsa.decrypt(connection.recv(1024), priv_key)\\r\\n\\r\\n # If success\\r\\n if result == b'SUCCESS':\\r\\n print('Successfully added file to the storage system')\\r\\n\\r\\n # If failure\\r\\n elif result == b'FAILURE':\\r\\n print('Failed to add file to the storage system')\\r\\n\\r\\n else:\\r\\n print('Failed to upload file to the server with desired security level', file=sys.stderr)\\r\\n\\r\\n # Catch file not found\\r\\n except FileNotFoundError:\\r\\n print(''.join(['\\\\nCould not find the file ', file_path]), file=sys.stderr)\\r\\n connection.sendall(rsa.encrypt(b'MISSING', server_pub_key))\",\n \"def upload_file(self, upload_link: str, file_data: bytes) -> None:\\n r = requests.put(\\n upload_link,\\n data=file_data,\\n headers=self._auth_headers\\n )\\n if r.status_code in self._errors:\\n raise ApiResponseException(\\n r.status_code, r.json()[\\\"error\\\"][\\\"message\\\"])\",\n \"def _upload_file_bytes(self, conn, http_conn, fp, file_length,\\r\\n total_bytes_uploaded, cb, num_cb):\\r\\n buf = fp.read(self.BUFFER_SIZE)\\r\\n if cb:\\r\\n if num_cb > 2:\\r\\n cb_count = file_length / self.BUFFER_SIZE / (num_cb-2)\\r\\n elif num_cb < 0:\\r\\n cb_count = -1\\r\\n else:\\r\\n cb_count = 0\\r\\n i = 0\\r\\n cb(total_bytes_uploaded, file_length)\\r\\n\\r\\n # Build resumable upload headers for the transfer. Don't send a\\r\\n # Content-Range header if the file is 0 bytes long, because the\\r\\n # resumable upload protocol uses an *inclusive* end-range (so, sending\\r\\n # 'bytes 0-0/1' would actually mean you're sending a 1-byte file).\\r\\n put_headers = {}\\r\\n if file_length:\\r\\n range_header = self._build_content_range_header(\\r\\n '%d-%d' % (total_bytes_uploaded, file_length - 1),\\r\\n file_length)\\r\\n put_headers['Content-Range'] = range_header\\r\\n # Set Content-Length to the total bytes we'll send with this PUT.\\r\\n put_headers['Content-Length'] = str(file_length - total_bytes_uploaded)\\r\\n http_request = AWSAuthConnection.build_base_http_request(\\r\\n conn, 'PUT', path=self.tracker_uri_path, auth_path=None,\\r\\n headers=put_headers, host=self.tracker_uri_host)\\r\\n http_conn.putrequest('PUT', http_request.path)\\r\\n for k in put_headers:\\r\\n http_conn.putheader(k, put_headers[k])\\r\\n http_conn.endheaders()\\r\\n\\r\\n # Turn off debug on http connection so upload content isn't included\\r\\n # in debug stream.\\r\\n http_conn.set_debuglevel(0)\\r\\n while buf:\\r\\n http_conn.send(buf)\\r\\n total_bytes_uploaded += len(buf)\\r\\n if cb:\\r\\n i += 1\\r\\n if i == cb_count or cb_count == -1:\\r\\n cb(total_bytes_uploaded, file_length)\\r\\n i = 0\\r\\n buf = fp.read(self.BUFFER_SIZE)\\r\\n if cb:\\r\\n cb(total_bytes_uploaded, file_length)\\r\\n if total_bytes_uploaded != file_length:\\r\\n # Abort (and delete the tracker file) so if the user retries\\r\\n # they'll start a new resumable upload rather than potentially\\r\\n # attempting to pick back up later where we left off.\\r\\n raise ResumableUploadException(\\r\\n 'File changed during upload: EOF at %d bytes of %d byte file.' %\\r\\n (total_bytes_uploaded, file_length),\\r\\n ResumableTransferDisposition.ABORT)\\r\\n resp = http_conn.getresponse()\\r\\n body = resp.read()\\r\\n # Restore http connection debug level.\\r\\n http_conn.set_debuglevel(conn.debug)\\r\\n\\r\\n if resp.status == 200:\\r\\n return resp.getheader('etag') # Success\\r\\n # Retry timeout (408) and status 500 and 503 errors after a delay.\\r\\n elif resp.status in [408, 500, 503]:\\r\\n disposition = ResumableTransferDisposition.WAIT_BEFORE_RETRY\\r\\n else:\\r\\n # Catch all for any other error codes.\\r\\n disposition = ResumableTransferDisposition.ABORT\\r\\n raise ResumableUploadException('Got response code %d while attempting '\\r\\n 'upload (%s)' %\\r\\n (resp.status, resp.reason), disposition)\",\n \"def upload(self,filename,remotefolder=None):\\n\\n if remotefolder == None:\\n remotefolder = self.remotefolder\\n\\n self.status = 0\\n\\n try:\\n self.ftp.cwd(remotefolder)\\n\\n self.file = open(filename, 'rb')\\n\\n (head, tail) = os.path.split(filename)\\n\\n command = \\\"STOR \\\" + tail\\n\\n print('Uploading: ' + tail)\\n self.ftp.storbinary(command, self.file)\\n print('Upload Completed')\\n\\n except ftplib.all_errors:\\n print('Error Uploading ' + tail)\\n self.status = 1\\n return self.status\\n\\n self.file.close()\\n\\n #back to initial directory in __init__()\\n self.ftp.cwd(self.remotefolder)\\n\\n return self.status\",\n \"def imgupload(self, accountid=None, upfile=None, cameraid=None, timestamp=None, duration=None, params=None):\\n self.uploaddir = os.path.join(self.staticdir, 'uploads')\\n print(\\\"UploadFile: Name: %s, Type: %s \\\" % (upfile.filename, upfile.content_type))\\n fext = str(upfile.content_type).split('/')[1]\\n print(\\\"Extension: %s\\\" % (fext))\\n logging.info('Recieved request: {}/{}'.format(cameraid, timestamp))\\n if not os.path.exists(self.uploaddir):\\n logging.info('Upload directory does not exist, creating %s' % (self.uploaddir))\\n os.makedirs(self.uploaddir)\\n\\n if upfile is not None:\\n tsx = self.epoch()\\n ofile = os.path.join(self.uploaddir, \\\"%s.%s\\\" % (tsx, fext))\\n print(\\\"Local filename: %s\\\" % (ofile))\\n ofilex = open(ofile, \\\"wb\\\")\\n shutil.copyfileobj(upfile.file, ofilex)\\n logging.info(\\\"Copied uploaded file as %s\\\" % (ofilex))\\n ofilex.close()\\n wwwbase = os.path.basename(self.staticdir)\\n out = {'upimg': \\\"%s.%s\\\" % (tsx, fext) }\\n infodb = self.dbase['infodb']\\n\\n info = {'upimg': \\\"{}.{}\\\".format(tsx, fext),\\n 'epoch': tsx,\\n 'accountid': accountid,\\n 'timestamp': int(timestamp),\\n 'duration' : int(duration),\\n 'cameraid': cameraid,\\n 'params': params}\\n\\n # Finally insert received object into db\\n res = infodb.insert_one(info)\\n print(res)\\n return json.dumps(out)\\n\\n else:\\n return \\\"Parameter: \\\\\\\"theFile\\\\\\\" was not defined\\\"\",\n \"def serve_upload(conn, ssn_key, file_name, client_name):\\n # get signal to begin upload\\n request = aes.decrypt(ssn_key, conn.recv(1024))\\n if request != SIG_START:\\n conn.sendall(aes.encrypt(ssn_key, SIG_BAD))\\n return print(\\\"Bob: something went wrong with file transfer\\\")\\n response = aes.encrypt(ssn_key, SIG_GOOD)\\n conn.sendall(response)\\n print(\\\"Bob: beginning transfer for {}...\\\".format(file_name))\\n\\n # get file contents from client\\n contents = list()\\n completed_upload = False\\n response = aes.encrypt(ssn_key, SIG_GOOD)\\n while not completed_upload:\\n request = aes.decrypt(ssn_key, conn.recv(1024))\\n if request == SIG_END:\\n completed_upload = True\\n print(\\\"Bob: completed transfer for {}\\\".format(file_name))\\n else:\\n contents.append(request)\\n conn.sendall(response)\\n\\n # save file to server folder\\n file_path = \\\"{}/{}\\\".format(client_name, file_name)\\n os.makedirs(os.path.dirname(file_path), exist_ok=True)\\n with open(file_path, \\\"w\\\") as outputStream:\\n outputStream.write(''.join(contents))\\n print(\\\"Bob: file saved in {}\\\".format(file_path))\",\n \"def upload_file(file, bucket_path, bucket=S3_BUCKET):\\n # Bucket path should be somedir/name_of_file.ext\\n try:\\n if isinstance(file, str):\\n resource.upload_file(file, bucket, bucket_path)\\n else:\\n resource.upload_fileobj(file, bucket, bucket_path)\\n except:\\n raise ChildProcessError('Something broke, Cap\\\\'n')\",\n \"def upload(self):\\n while not self._upload_queue.empty():\\n logger.info('%d files left to upload', self._upload_queue.qsize())\\n self._sia_condition_waiter.wait_for_available_upload_slot()\\n job = self._upload_queue.get()\\n if (not self._process_upload_job_async(job)) and (job.failure_count\\n < 3):\\n self._upload_queue.put(job)\\n self._sia_condition_waiter.wait_for_all_uploads_to_complete()\\n self._exit_event.set()\",\n \"def put_file(self, file_name: str, value: BytesIO):\\n value.seek(0)\\n self.client.upload_fileobj(value, self.bucket, file_name)\",\n \"def _upload_file_to_container(self, container, object_key, file_path, connection):\\r\\n data = open(file_path, 'rb')\\r\\n logger.debug('upload {file_path} to {container} {object_key}'.format(file_path=file_path, container=container,\\r\\n object_key=object_key))\\r\\n\\r\\n result = connection.object_store.upload_object(container=container,\\r\\n name=object_key,\\r\\n data=open(file_path, 'r'))\\r\\n\\r\\n logger.debug(result)\",\n \"def upload_file(client, folder_id, file_name):\\n\\n new_file = client.folder(folder_id).upload(file_name)\\n print(f\\\"File {new_file.name} uploaded to Box with file ID {new_file.id}\\\")\\n return new_file.id\",\n \"def upload_segment(self, upload_uri, _range, data, filetype):\\n content_range = '%d-%d/%d' % (_range, len(data), len(data))\\n upload_headers = {'Content-Type': 'video/%s' % filetype,\\n 'Content-Length': len(data),\\n 'Content-Range': 'bytes: %s' % content_range}\\n\\n log.info(\\\"Sending file of size %d\\\" % len(data))\\n log.info(\\\"Requesting %s\\\" % upload_uri)\\n request_headers = dict(upload_headers.items() + self.standard_headers.items())\\n r = HTTPClient().fetch(upload_uri, method=\\\"PUT\\\",\\n body=data, headers=request_headers)\\n log.info(\\\"Uploaded segment: status code %d\\\" % r.code)\\n if r.code != 200:\\n raise ValueError(\\\"Upload unsuccessful\\\")\",\n \"def upload(self, filepath, channels, content=None, filetype=None, filename=None, title=None, initial_comment=None):\\r\\n try:\\r\\n result = self.slack.files.upload(\\r\\n filepath,\\r\\n content=content,\\r\\n filetype=filetype,\\r\\n filename=filename,\\r\\n title=title,\\r\\n initial_comment=initial_comment,\\r\\n channels=channels)\\r\\n if result.successful:\\r\\n return result.body\\r\\n else:\\r\\n L.warning('Slack Error : %s', result.error)\\r\\n raise SlackError(result.error)\\r\\n except Error as e:\\r\\n L.warning(str(e))\\r\\n raise SlackError('%s is not exists.' % channels)\",\n \"def upload(self, file: Path, destination: Path):\\n\\t\\tif not file.is_file():\\n\\t\\t\\traise FileNotFoundError(\\\"File doesn't exists\\\")\\n\\n\\t\\tif self.destination_path_base is None:\\n\\t\\t\\tdestination_path = destination\\n\\t\\telse:\\n\\t\\t\\tdestination_path = self.destination_path_base / destination\\n\\n\\t\\tLOGI(f\\\"Started uploading of {file.name}\\\")\\n\\n\\t\\tself._upload(file, destination_path)\\n\\n\\t\\tLOGI(f\\\"Finished uploading of {file.name}\\\")\\n\\t\\treturn True\",\n \"async def complete_upload_file(\\n body_item: Envelope[FileUploadCompletionBody],\\n location_id: LocationID,\\n file_id: StorageFileID,\\n user_id: UserID,\\n):\",\n \"def upload_file(parameter_id, filename):\\n recastapi.file_check(filename)\\n \\n basic_request_response = basic_request(parameter_id)\\n basic_request_id = basic_request_response['id']\\n \\n file_uuid = str(uuid.uuid1())\\n\\n #get request ID, so deposition can be retrieved\\n point_request_url = '{}/{}'.format(\\n recastapi.ENDPOINTS['POINT_REQUESTS'],\\n parameter_id)\\n point_response = recastapi.get(point_request_url)\\n\\n request_id = point_response['scan_request_id']\\n \\n request_url = '{}/{}'.format(\\n recastapi.ENDPOINTS['REQUESTS'],\\n request_id\\n )\\n request_response = recastapi.get(request_url)\\n deposition_id = request_response['zenodo_deposition_id']\\n\\n payload = {\\n 'file_name': file_uuid,\\n 'zenodo_file_id': None,\\n 'original_file_name': filename,\\n 'basic_request_id': basic_request_id,\\n }\\n \\n files = {'file': open(filename, 'rb')}\\n url = '{}/'.format(recastapi.ENDPOINTS['FILES'])\\n file_response = recastapi.post(\\n url, \\n data=payload, \\n files=files,\\n params = {'deposition_id': deposition_id}\\n )\\n basic_request_response['metadata'] = file_response\\n return basic_request_response\",\n \"def upload(attachment, filename, title=None, message=None, channels=None):\\n\\n if not settings.SLACK_TOKEN:\\n return {'ok': False, 'error': 'config_error'}\\n\\n client = WebClient(token=settings.SLACK_TOKEN)\\n client.timeout = 600\\n\\n try:\\n if channels:\\n response = client.files_upload(channels=channels, file=attachment, filename=filename,\\n initial_comment=message, title=title)\\n else:\\n response = client.files_upload(file=attachment, filename=filename, title=title)\\n assert response['ok'] is True\\n return {'ok': True, 'file': response['file']}\\n except SlackApiError as e:\\n assert e.response['ok'] is False\\n return e.response\",\n \"def _videoclipupload(request, hash_key):\\n # get video\\n video_queryset = Video.objects.all().select_related('owner')\\n video = get_object_or_404(video_queryset, hash_key=hash_key)\\n \\n if request.method == 'POST':\\n \\n # create a form instance and populate it with data from the request\\n form = UploadClipForm(request.POST, request.FILES)\\n \\n # check whether it's valid\\n if form.is_valid():\\n # process the data in form.cleaned_data as required\\n number = form.cleaned_data['formatted_number'].as_e164\\n name = form.cleaned_data['name']\\n clip = form.cleaned_data['clip']\\n \\n # get location of script\\n script_dir = os.path.dirname(os.path.abspath(__file__))\\n script_path = os.path.join(script_dir, \\\"process.sh\\\")\\n \\n # get locations of input and output files\\n input_video = clip.temporary_file_path()\\n output_video = NamedTemporaryFile(suffix='.mp4')\\n output_image = NamedTemporaryFile(suffix='.jpg')\\n output_duration = NamedTemporaryFile()\\n \\n # get the ffmpeg path\\n # on production we had to install the 64 bit static ffmpeg build\\n ffmpeg_path = ''\\n if not settings.DEBUG:\\n ffmpeg_path = '/home/nceruchalu/bin/'\\n \\n # Generate the output files\\n subprocess.check_call(\\n [script_path, input_video, output_video.name, \\n output_image.name, output_duration.name, ffmpeg_path])\\n \\n # Get clip duration from the results\\n clip_duration = map(float, output_duration)[0]\\n \\n # if we've come this far then all is well, and we can go ahead\\n # and create this clip\\n user = User.objects.get_user_by_number(number)\\n if not user.is_active and name:\\n user.full_name = name\\n user.save()\\n \\n # first add user as a video user, because nobody should\\n # add to a clip without being a video user\\n VideoUsers.objects.add_users_to_video(video, user)\\n \\n # Try creating the clip with a thumbnail, if that fails try\\n # without the thumbnail\\n Clip.objects.create(owner=user,\\n video=video,\\n mp4=File(output_video),\\n photo=ImageFile(output_image),\\n duration=clip_duration)\\n \\n # close and delete the tempoary files\\n output_video.close()\\n output_image.close()\\n output_duration.close()\\n \\n # redirect to the video details URL but add a querystring param\\n # indicating that the video should start from the last clip\\n video_url = reverse('web-video-detail',kwargs={'hash_key':hash_key})\\n redirect_url = \\\"{video_url}?latest=1\\\".format(video_url=video_url)\\n \\n return HttpResponseRedirect(redirect_url)\\n \\n else:\\n # if a GET (or any other method) we'll create a blank form\\n form = UploadClipForm()\\n \\n return render_to_response('video/upload.html',\\n {'video':video,\\n 'form':form},\\n context_instance=RequestContext(request))\",\n \"def run(args):\\n\\n drive_uid = str(args[\\\"drive_uid\\\"])\\n file_uid = str(args[\\\"file_uid\\\"])\\n chunk_idx = int(args[\\\"chunk_index\\\"])\\n secret = str(args[\\\"secret\\\"])\\n data = string_to_bytes(args[\\\"data\\\"])\\n checksum = str(args[\\\"checksum\\\"])\\n\\n drive = DriveInfo(drive_uid=drive_uid)\\n\\n drive.upload_chunk(file_uid=file_uid, chunk_index=chunk_idx,\\n secret=secret, chunk=data, checksum=checksum)\\n\\n return True\",\n \"def upload_start(self, local_path, cloud_file, size):\\n\\t\\telog(\\\"uploading {1} ({2})\\\".format(local_path, cloud_file.path, bytes_scaled(size)))\",\n \"def storage_ipfs_upload_file(self, file_obj):\\n # Request: upload a file\\n # curl -X POST -H 'Content-Type: application/json' --data '{\\\"jsonrpc\\\":\\\"2.0\\\",\\\"method\\\":\\\"edgestore.PutFile\\\",\\\"params\\\":[{\\\"path\\\": \\\"theta-edge-store-demos/demos/image/data/smiley_explorer.png\\\"}],\\\"id\\\":1}' http://localhost:19888/rpc\\n # Result\\n # {\\n # \\\"jsonrpc\\\": \\\"2.0\\\",\\n # \\\"id\\\": 1,\\n # \\\"result\\\": {\\n # \\\"key\\\": \\\"0xbc0383809da9fb98c5755e3fa4f19f4ebc7e34308ab321246e4bb54e548fad04\\\",\\n # \\\"relpath\\\": \\\"smiley_explorer.png\\\",\\n # \\\"success\\\": true\\n # }\\n # }\\n home_dir = expanduser(\\\"~\\\")\\n file_path = os.path.join(home_dir, \\\"thetastore\\\")\\n if not os.path.exists(file_path):\\n os.makedirs(file_path, exist_ok=True)\\n file_path = os.path.join(file_path, str(uuid.uuid4()))\\n with open(file_path, \\\"wb\\\") as file_handle:\\n file_handle.write(file_obj)\\n\\n request_data = {\\\"jsonrpc\\\":\\\"2.0\\\",\\n \\\"method\\\":\\\"edgestore.PutFile\\\",\\n \\\"params\\\":[{\\\"path\\\": file_path}],\\n \\\"id\\\":1}\\n res = httpx.post(\\n self.ipfs_upload_uri,\\n headers={\\\"Content-Type\\\": \\\"application/json\\\"},\\n data=json.dumps(request_data),\\n timeout=None,\\n )\\n content = res.json()\\n result = content.get(\\\"result\\\", None)\\n if result is None:\\n return False, \\\"Failed to upload file(result is none).\\\"\\n else:\\n success = result.get(\\\"success\\\", False)\\n if not success:\\n return False, \\\"Failed to upload file(success if false).\\\"\\n file_cid = result.get(\\\"key\\\", None)\\n if file_cid is None:\\n return False, \\\"Failed to upload file(key is none).\\\"\\n return True, file_cid\",\n \"def _upload_file(self, file_name, full_path, quiet, request, resources):\\r\\n\\r\\n if not quiet:\\r\\n print('Starting upload for file ' + file_name)\\r\\n\\r\\n content_length = os.path.getsize(full_path)\\r\\n token = self.dataset_upload_file(full_path, quiet)\\r\\n if token is None:\\r\\n if not quiet:\\r\\n print('Upload unsuccessful: ' + file_name)\\r\\n return True\\r\\n if not quiet:\\r\\n print('Upload successful: ' + file_name + ' (' +\\r\\n File.get_size(content_length) + ')')\\r\\n upload_file = DatasetUploadFile()\\r\\n upload_file.token = token\\r\\n if resources:\\r\\n for item in resources:\\r\\n if file_name == item.get('path'):\\r\\n upload_file.description = item.get('description')\\r\\n if 'schema' in item:\\r\\n fields = self.get_or_default(item['schema'], 'fields',\\r\\n [])\\r\\n processed = []\\r\\n count = 0\\r\\n for field in fields:\\r\\n processed.append(self.process_column(field))\\r\\n processed[count].order = count\\r\\n count += 1\\r\\n upload_file.columns = processed\\r\\n request.files.append(upload_file)\\r\\n return False\",\n \"def uploadFile(self, filename):\\n\\n try:\\n data = filename.open(mode='rb').read()\\n ##workaround for file without file extensions\\n if filename.suffix == '':\\n filename = filename.with_suffix('.dcm')\\n files = {'file': (str(filename.name), data)}\\n except:\\n print(\\\"opening file\\\", filename, \\\"failed, aborting\\\")\\n return\\n\\n res = self._post(self.url + 'upload', files=files)\\n return self.getFile(res['file']['selfUrl']), self.getObject(res['relatedObject']['selfUrl'])\",\n \"def upload_file( processor, user, local_path ):\\n operations.publish_work_item(\\n operations.create_asset_from_file(\\n file_name = local_path,\\n owner = user,\\n producer = processor,\\n child_number = 0,\\n asset_class = models.AssetClass.UPLOAD ))\",\n \"def file_upload(self, bucket_id, file_path, tmp_file_path):\\n\\n self.__logger.debug('Upload %s in bucket %s', file_path, bucket_id)\\n self.__logger.debug('Temp folder %s', tmp_file_path)\\n\\n bname = os.path.split(file_path)[1] # File name\\n\\n file_mime_type = 'text/plain'\\n\\n # Encrypt file\\n self.__logger.debug('Encrypting file...')\\n\\n file_crypto_tools = FileCrypto()\\n\\n # File name of encrypted file\\n file_name_ready_to_shard_upload = '%s.encrypted' % bname\\n # Path where to save the encrypted file in temp dir\\n file_path_ready = os.path.join(tmp_file_path,\\n file_name_ready_to_shard_upload)\\n self.__logger.debug('file_path_ready: %s', file_path_ready)\\n\\n # Begin file encryption\\n file_crypto_tools.encrypt_file(\\n 'AES',\\n file_path,\\n file_path_ready,\\n self.client.password)\\n\\n self.fileisdecrypted_str = ''\\n\\n file_size = os.stat(file_path).st_size\\n self.__logger.info('File encrypted')\\n\\n # Get the PUSH token from Storj Bridge\\n self.__logger.debug('Get PUSH Token')\\n\\n push_token = None\\n try:\\n push_token = self.client.token_create(bucket_id, 'PUSH')\\n except BridgeError as e:\\n self.__logger.error(e)\\n self.__logger.debug('PUSH token create exception')\\n self.__logger.error('File not uploaded')\\n return\\n\\n self.__logger.info('PUSH Token ID %s', push_token.id)\\n\\n # Get a frame\\n self.__logger.debug('Frame')\\n frame = None\\n\\n try:\\n frame = self.client.frame_create()\\n except BridgeError as e:\\n self.__logger.error(e)\\n self.__logger.debug('Unhandled exception while creating file \\\\\\nstaging frame')\\n self.__logger.error('File not uploaded')\\n return\\n\\n self.__logger.info('frame.id = %s', frame.id)\\n\\n # Now generate shards\\n self.__logger.debug('Sharding started...')\\n shards_manager = model.ShardManager(filepath=file_path_ready,\\n tmp_path=tmp_file_path)\\n self.all_shards_count = len(shards_manager.shards)\\n\\n self.__logger.debug('Sharding ended...')\\n\\n self.__logger.info('There are %s shards', self.all_shards_count)\\n\\n # Calculate timeout\\n self._calculate_timeout(shard_size=shards_manager.shards[0].size,\\n mbps=1)\\n\\n # Upload shards\\n mp = ThreadPool()\\n res = mp.map(lambda n_s: self.upload_shard(\\n n_s[1], n_s[0], frame, file_name_ready_to_shard_upload, tmp_file_path),\\n enumerate(shards_manager.shards))\\n\\n self.__logger.debug('===== RESULTS =====')\\n self.__logger.debug(res)\\n if False in res or None in res:\\n self.__logger.error('File not uploaded: shard %s not uploaded' %\\n res.index(False))\\n self.__logger.error('Exiting with errors')\\n exit(1)\\n # finish_upload\\n self.__logger.debug('Generating HMAC...')\\n\\n # create file hash\\n hash_sha512_hmac_b64 = self._prepare_bucket_entry_hmac(\\n shards_manager.shards)\\n hash_sha512_hmac = hashlib.sha224(str(\\n hash_sha512_hmac_b64['SHA-512'])).hexdigest()\\n\\n self.__logger.debug('Now upload file')\\n data = {\\n 'x-token': push_token.id,\\n 'x-filesize': str(file_size),\\n 'frame': frame.id,\\n 'mimetype': file_mime_type,\\n 'filename': str(bname) + str(self.fileisdecrypted_str),\\n 'hmac': {\\n 'type': 'sha512',\\n 'value': hash_sha512_hmac\\n },\\n }\\n\\n self.__logger.debug('Finishing upload')\\n self.__logger.debug('Adding file %s to bucket...', bname)\\n\\n success = False\\n try:\\n # Post an upload_file request\\n response = self.client._request(\\n method='POST',\\n path='/buckets/%s/files' % bucket_id,\\n headers={\\n 'x-token': push_token.id,\\n 'x-filesize': str(file_size),\\n },\\n json=data,\\n )\\n success = True\\n\\n except BridgeError as e:\\n self.__logger.error(e)\\n self.__logger.debug('Unhandled bridge exception')\\n\\n if success:\\n self.__logger.info('File uploaded successfully!')\\n\\n # Remove temp files\\n try:\\n # Remove shards\\n file_shards = map(lambda i: '%s-%s' % (file_path_ready, i),\\n range(1, self.all_shards_count + 1))\\n self.__logger.debug('Remove shards %s' % file_shards)\\n map(os.remove, file_shards)\\n # Remove encrypted file\\n self.__logger.debug('Remove encrypted file %s' % file_path_ready)\\n os.remove(file_path_ready)\\n except OSError as e:\\n self.__logger.error(e)\",\n \"def upload_file(bucket_name, filename, file):\\n client = get_client()\\n bucket = client.get_bucket(bucket_name)\\n blob = bucket.blob(filename)\\n blob.upload_from_file(file)\",\n \"def UploadFile(self, local_file_name, gcs_file_name,\\n mimetype='application/octet-stream'):\\n resumable = os.stat(local_file_name).st_size > 0\\n media = gapi_http.MediaFileUpload(local_file_name,\\n mimetype=mimetype,\\n resumable=resumable)\\n\\n # gsutil's code suggests that 404s and 410s are retryable for resumable\\n # uploads (see ResumableUploadStartOverException).\\n def _ErrorMatcher(error):\\n return (self._CommonErrorMatcher(error)\\n or (isinstance(error, gapi_errors.HttpError)\\n and error.resp.status in (404, 410)))\\n\\n return self._RunWithRetries(\\n lambda: self._UploadWithProgress(media, gcs_file_name),\\n _ErrorMatcher)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6913328","0.64488506","0.6341638","0.6326437","0.63007796","0.62839514","0.62739664","0.62476707","0.6243486","0.6209967","0.6197469","0.6196735","0.6184578","0.6173266","0.6143371","0.6109111","0.6083066","0.60762596","0.6070302","0.60671276","0.60424846","0.60274184","0.6024766","0.6012447","0.601076","0.59952706","0.59899753","0.59785676","0.5919707","0.5919072","0.59066164","0.5897396","0.58968943","0.5895189","0.5891507","0.5889357","0.588897","0.58852196","0.5878649","0.58672756","0.5862237","0.58566386","0.5849042","0.5836735","0.583247","0.58076125","0.5792511","0.5789579","0.5782917","0.5782085","0.5757025","0.575482","0.5753691","0.5752546","0.5746637","0.5742167","0.5731284","0.5728248","0.5714529","0.56974083","0.5694945","0.5692189","0.56907016","0.56877005","0.5679977","0.567","0.56589854","0.5656666","0.5656597","0.5647816","0.5641368","0.56378","0.5623798","0.5623794","0.5619469","0.5618647","0.56185067","0.5608222","0.5607974","0.5606818","0.5590805","0.55825967","0.5581001","0.5580374","0.55739677","0.5558811","0.5557984","0.5552466","0.5548973","0.55356866","0.55355793","0.55348784","0.55242014","0.5520599","0.55148286","0.5514221","0.5511122","0.5503327","0.5481748","0.5477668"],"string":"[\n \"0.6913328\",\n \"0.64488506\",\n \"0.6341638\",\n \"0.6326437\",\n \"0.63007796\",\n \"0.62839514\",\n \"0.62739664\",\n \"0.62476707\",\n \"0.6243486\",\n \"0.6209967\",\n \"0.6197469\",\n \"0.6196735\",\n \"0.6184578\",\n \"0.6173266\",\n \"0.6143371\",\n \"0.6109111\",\n \"0.6083066\",\n \"0.60762596\",\n \"0.6070302\",\n \"0.60671276\",\n \"0.60424846\",\n \"0.60274184\",\n \"0.6024766\",\n \"0.6012447\",\n \"0.601076\",\n \"0.59952706\",\n \"0.59899753\",\n \"0.59785676\",\n \"0.5919707\",\n \"0.5919072\",\n \"0.59066164\",\n \"0.5897396\",\n \"0.58968943\",\n \"0.5895189\",\n \"0.5891507\",\n \"0.5889357\",\n \"0.588897\",\n \"0.58852196\",\n \"0.5878649\",\n \"0.58672756\",\n \"0.5862237\",\n \"0.58566386\",\n \"0.5849042\",\n \"0.5836735\",\n \"0.583247\",\n \"0.58076125\",\n \"0.5792511\",\n \"0.5789579\",\n \"0.5782917\",\n \"0.5782085\",\n \"0.5757025\",\n \"0.575482\",\n \"0.5753691\",\n \"0.5752546\",\n \"0.5746637\",\n \"0.5742167\",\n \"0.5731284\",\n \"0.5728248\",\n \"0.5714529\",\n \"0.56974083\",\n \"0.5694945\",\n \"0.5692189\",\n \"0.56907016\",\n \"0.56877005\",\n \"0.5679977\",\n \"0.567\",\n \"0.56589854\",\n \"0.5656666\",\n \"0.5656597\",\n \"0.5647816\",\n \"0.5641368\",\n \"0.56378\",\n \"0.5623798\",\n \"0.5623794\",\n \"0.5619469\",\n \"0.5618647\",\n \"0.56185067\",\n \"0.5608222\",\n \"0.5607974\",\n \"0.5606818\",\n \"0.5590805\",\n \"0.55825967\",\n \"0.5581001\",\n \"0.5580374\",\n \"0.55739677\",\n \"0.5558811\",\n \"0.5557984\",\n \"0.5552466\",\n \"0.5548973\",\n \"0.55356866\",\n \"0.55355793\",\n \"0.55348784\",\n \"0.55242014\",\n \"0.5520599\",\n \"0.55148286\",\n \"0.5514221\",\n \"0.5511122\",\n \"0.5503327\",\n \"0.5481748\",\n \"0.5477668\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.59804696"},"document_rank":{"kind":"string","value":"27"}}},{"rowIdx":22470,"cells":{"query":{"kind":"string","value":"Open a binary file and return its contents and extension"},"document":{"kind":"string","value":"def read_file(self, filename):\n data = None\n with open(filename, \"rb\") as f:\n data = f.read()\n filetype = filename.split('.')[-1] if '.' in filename.split('/')[-1] else None\n return data, filetype"},"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_binary(fname):\n with open(fname, 'rb+') as f_name:\n data = f_name.read()\n return data","def ReadBinaryFile(name):\n\n try:\n fBinary = open(name, 'rb')\n except:\n return None\n try:\n content = fBinary.read()\n except:\n return None\n finally:\n fBinary.close()\n return content","def open_binary_file(file_name: str, mode: str = 'rb'):\n return open(os.path.join(DATA_FOLDER, file_name), mode=mode)","def ReadFile(fname, binary=True):\n with open(Filename(fname), binary and 'rb' or 'r') as fd:\n data = fd.read()\n #self._out.Info(\"Read file '%s' size %d (%#0x)\" %\n #(fname, len(data), len(data)))\n return data","def open_file(self, filename):\n # UnicodeDecodeError: 'utf-8' codec can't decode byte 0x89 in position 0: invalid start byte\n #\n # https://docs.python.org/3/library/functions.html#open\n # 'r'\topen for reading (default)\n # 't'\ttext mode (default)\n # 'b'\tbinary mode\n #\n # In text mode (the default, or when 't' is included in the mode argument),\n # the contents of the file are returned as str,\n # the bytes having been first decoded using a platform-dependent encoding or using the specified encoding if given.\n with open(filename, 'rb') as f:\n return f.read()","def read_file_bytes(filepath: str):\n with open(filepath, \"rb\") as reader:\n return reader.read()","def _open_file_binary(filename):\n for _byte in open(filename, 'rb').read():\n yield _byte","def get_file(self, filename: str, directory: str = 'gcodes', binary: bool = False) -> str:\n raise NotImplementedError","def load(path, binary=False):\n with open(path, 'rb') as handle:\n tmp = handle.read()\n return tmp if binary else tmp.decode()","def fread(filename):\n with open(filename, 'r') as f:\n return f.read()","def get_file_content(full_path):\n print(full_path)\n bytes = min(32, os.path.getsize(full_path))\n raw = open(full_path, 'rb').read(bytes)\n if '\\\\xff\\\\xfe' in str(raw):\n print(\"utf-16\")\n the_file = open(full_path, encoding=\"utf-16\",\n errors=\"backslashreplace\")\n data = the_file.read()\n else:\n print(\"latin-1\")\n the_file = open(full_path, encoding=\"latin-1\",\n errors=\"backslashreplace\")\n data = the_file.read()\n return data","def load_file(file_path: str, mode: str = \"rb\") -> Union[str, bytes]:\n with open(file_path, mode) as file:\n return file.read()","def read_binary(self):\n with self.open(\"rb\") as f:\n return f.read()","def readfile(fname, mode='rb'):\n f = open(fname, mode)\n raw = f.read()\n f.close()\n return raw","def _Read(filename):\n with open(filename, 'rb') as f:\n return f.read()","def openAndPack(filename):\n inputfile = open(filename, 'rb')\n return inputfile.read()","def getFileContent(self, filePath, mode):\n with open(filePath, mode) as my_file:\n return my_file.read()","def get_binary(self, filepath):\n with open(filepath, \"rb\") as f:\n return b64encode(f.read()).decode('utf-8')","def _file_load(path):\n _, _, file_path = path.split('/', 2)\n with open(file_path, 'r', encoding='utf-8') as file_handle:\n return file_handle.read()","def open(self):\n return File(open(self.get_path()), \"rb\")","def get_file(self, filename):\n log.debug('[%s]: reading: //%s/%s', self.name, self.name, filename)\n try:\n blob = self.repo.head.commit.tree/filename\n return blob.data_stream\n except KeyError as err:\n raise GitError(err)","def read_raw(file_path):\n file = open(file_path, 'rb')\n content = file.read()\n file.close()\n return content","def open(path, override_extension=None):\n extension = override_extension or os.path.splitext(path)[1][1:]\n return read_stream(builtins.open(path, 'rb'), extension)","def _ReadFile(filepath):\n with open(filepath) as f:\n return f.read()","def bz2_file_bytes_reader(path):\n return bz2.open(path, 'rb')","def _read_file(self) -> str:\n with open(self._file_name) as fp:\n return fp.read()","def get_content(self, filename):\n f_id = self.face.FACES.files.find_one({ \"filename\" : filename }, { \"_id\" : 1 })\n return self.face_fs.get(f_id['_id']).read()","def read_file(filename):\n return open(filename).read()","def _get_file(self, path: str) -> Tuple[str, bytes]:\n self._trace(\"fetching: %s\" % path)\n meta, resp = self._connection.files_download(path)\n return (meta.rev, resp.content)","def read_file(dir):\n file = open(dir, 'rb')\n return file.read()","def decode_file(self, filename):\n num_bytes = os.stat(filename)[6]\n data = array.array('B')\n\n with open(filename, 'rb') as f:\n data.fromfile(f, num_bytes)\n\n return self.decode_data(data)","def get_file_data(filename):","def readfile(filename, mode='r'):\n if mode != 'r' and mode != 'rb':\n print(f\"ERROR: incorrect mode : expected 'r' or 'rb' given {mode}\\n\")\n else:\n with open(Path(os.path.expanduser(filename)), mode)as f:\n content = f.read()\n f.close()\n return content","def fileread(self, filename):\n data = None\n f = open(filename, 'r')\n data = f.read()\n f.close()\n try:\n data = data.decode()\n except (UnicodeDecodeError, AttributeError):\n data = data.encode(\"utf-8\")\n\n return data","def get_file(URI):\n return file_fabric.get_class(URI).get_content(URI)","def open_file():\n \n # Open file for binary reading.\n openlocation=fd.askopenfilename()\n with open(openlocation, mode='rb') as file: \n # Read file as bytes held in ints.\n \n fileContent = file.read()\n \n text_store = \"\" # Text that will ultimately be displayed.\n \n for i in fileContent:\n \n b_int = bin(i) # Turn the byte to a string representation of the binary.\n b_int = b_int[2:] # Remove \"0b\" binary marker added by the bin cast. \n \n \n while ((len(b_int)) < 8):\n # As bytes read are truncated to most significant bit, add \n # zeros where needed to make up a byte.\n b_int = \"0\" + b_int\n \n text_store = text_store + b_int\n \n text.delete('1.0', tk.END) # Clear any previous text.\n text.insert(tk.END, text_store) # Add new text.","def openR(fname):\n f=open(fname, 'rb'); a=f.read(3); f.close()\n if a==b'\\xef\\xbb\\xbf': f=open(fname,'r', encoding='utf-8'); f.read(1);\n elif a[:2]==b'\\xff\\xfe' or a[:2]==b'\\xfe\\xff': f = open(fname,'r',encoding='utf16')\n else : f = open(fname,'r')\n return f","def openfile(path:str) -> str:\n with open(file=path, mode='br') as file:\n r_0 = file.readline()\n return str(r_0)","def read_file(filename):\n with open(filename) as fp:\n return fp.read()","def bz2_file_reader(path):\n return bz2.open(path, 'rt')","def _readfile(dirpath, filename):\n try:\n with codecs.open(os.path.join(dirpath, filename), \"r\", \"utf-8\") as f:\n return f.read()\n except IOError:\n return u\"\"","def read_binary(filename):\r\n with open('%s.pickle'%(filename, ), 'rb') as handle:\r\n b = pickle.load(handle)\r\n return b","def readfile(filename):\n with open(filename, encoding=\"utf-8\") as file:\n raw = file.read()\n return raw","def openFile(file):\n file = file.lower()\n if file.endswith('.bz2'):\n return bz2.BZ2File(file)\n elif file.endswith('.gz'):\n return gzip.open(file)\n return open(file)","def file_entry(file_path: str) -> Tuple[str, Any, str]:\n file_name = os.path.split(file_path)[1]\n file_handler = open(file_path, \"rb\")\n content_type = \"application/octet-stream\"\n\n return (file_name, file_handler, content_type)","def read_file(filename):\r\n if Py3:\r\n return open(filename, \"r\", newline='')\r\n return open(filename, \"rb\")","def read_file(filename):\n open_kwargs = {}\n if sys.version_info.major == 3:\n open_kwargs = {'encoding': 'utf-8'}\n\n path = os.path.abspath(os.path.dirname(__file__))\n filepath = os.path.join(path, filename)\n with open(filepath, **open_kwargs) as filecontents:\n return filecontents.read()","def read_as_bytes(filename):\n try:\n with open(filename, \"rb\") as file:\n bytes = array.array(\"B\")\n bytes.frombytes(file.read())\n return bytes\n except FileNotFoundError:\n print(f\"File not found: {filename}\")\n exit()","def GetFile(file_name):\n\n the_file = None\n\n try:\n the_file = open(file_name, 'rb')\n \n except IOError:\n the_file = None\n \n return the_file","def read_file(filename):\n with codecs.open(os.path.join(here, filename), encoding='utf-8') as f:\n content = f.read()\n return content","def get_file_mine(file, size_to_read=(5 * (1024 * 1024))):\n return magic.from_buffer(file.read(size_to_read), mime=True)","def _read_magic(file_handle):\r\n magic = file_handle.read(len(_ZFILE_PREFIX))\r\n # Pickling needs file-handles at the beginning of the file\r\n file_handle.seek(0)\r\n return magic","def read_file(file):\n with open(file, \"r\") as fid:\n return fid.read()","def get_file_contents(filename):\n with open(filename, 'r') as f:\n content = f.read()\n return content","def get_file_content(filename):\n file_contents = ''\n with open(filename) as f:\n file_contents = f.read()\n return file_contents","def get_file_data(file_name):\r\n try:\r\n with open(file_name, 'rb') as input_file:\r\n data = input_file.read()\r\n return data\r\n except Exception as err:\r\n return str(err).encode()","def read(self, filename):\n\t\treturn codecs.open(filename, 'r', 'utf8').read()","def _get_file_content(self):\n with open(self.file_name, mode='rb') as file:\n self.file_content = file.read()","def bbl_file(self, base_file):\n bbl_path = os.path.abspath(os.path.splitext(base_file)[0]) + '.bbl'\n return self.open_encode_safe(bbl_path).readlines()","def return_file(path):\n file_ext = path.split(\".\")[1]\n if file_ext != \"py\":\n with open(path, \"rb\") as file:\n content = file.read()\n if file_ext == \"txt\":\n mime_type = b\"text/plain\"\n elif file_ext == \"html\" or file_ext == \"htm\":\n mime_type = b\"text/html\"\n elif file_ext == \"png\":\n mime_type = b\"image/png\"\n elif file_ext == \"jpg\":\n mime_type = b\"image/jpeg\"\n else:\n content, mime_type = return_py_script(path)\n return content, mime_type","def getFileContent(fn):\n content = None\n if not os.path.exists(fn):\n print(\"Can not open file \" + fn)\n else:\n with open(fn, \"rb\") as f:\n content = f.readlines()\n return content","def read_file(name):\n with open(name, 'r') as my_file:\n return my_file.read().encode('utf-8')","def open_file(path):\n return open(path, encoding='utf-8')","def open_and_read_file(file_path):\n text_data = open(file_path).read()\n # print text_data\n return text_data","def get_file(self, path):\n tarstream = io.BytesIO()\n for chunk in self.container.get_archive(path=path)[0]:\n tarstream.write(chunk)\n tarstream.seek(0)\n with tarfile.open(fileobj=tarstream) as tarfile_:\n for tarinfo in tarfile_.getmembers():\n return tarfile_.extractfile(tarinfo).read().decode()","def ReadFile(self, filename):\r\n file = open(filename, 'rb')\r\n result = \"\"\r\n try:\r\n result = file.read()\r\n finally:\r\n file.close()\r\n return result","def read_file(fname):\n with open(fname, 'r') as fopen:\n fdata = fopen.read()\n return fdata","def getFile(self, file_name: str) -> bytes:\n obj = self.driver.get_object(self.container_name, file_name)\n gen = self.driver.download_object_as_stream(obj)\n file_as_bytes = next(gen)\n return file_as_bytes","def get_file_contents(path):\n try:\n with open(path) as f:\n return f.read()\n except IOError:\n return None","def ReadFile(self, filename):\n file = open(filename, 'rb')\n result = \"\"\n try:\n result = file.read()\n finally:\n file.close()\n return result","def read_metafile(path):\n with codecs.open(path, \"rb\", \"utf-8\") as f:\n return f.read()","def get(self, filename, **kw):\n\n file_path = os.path.join(self.storage_path, filename)\n try:\n file_obj = open(file_path, \"r\")\n except IOError:\n return\n else:\n return file_obj.read()","def get_example_file(filename):\n fullpath = get_example_filepath(filename)\n # Need to ensure that CRCRLF remain intact\n with open(fullpath, \"rb\") as fh:\n data = fh.read().decode(\"utf-8\")\n return data","def test_binary_contents(file, tmp_path):\n name = tmp_path / \"1px.gif\"\n ret = file.managed(name=str(name), contents=BINARY_FILE)\n assert ret.result is True","def file_to_string(path_to_file):\n\t\twith open(path_to_file, 'r') as f:\n\t\t\tcontent = f.read()\n\t\treturn content","def get_file():\n fname = get_var(request, \"fname\")\n return open(fname).read()","def open_file_by_type(file_name, mode=\"r\"):\n file_name = op.expanduser(file_name) # expands '~' to the absolute home dir\n if file_name.endswith(\"bz2\"):\n return bz2.open(file_name, mode)\n elif file_name.endswith(\"gz\"):\n return gzip.open(file_name, mode, encoding=\"utf-8\")\n else:\n return open(file_name, mode, encoding=\"utf-8\")","def ReadFileIntoString(filepath):\n with open(filepath, 'r') as file_handle:\n contents = file_handle.read()\n return contents","def open_file(filename):\n with open(filename, \"rb\") as content:\n return content.read().decode(\"utf-8\", errors=\"ignore\").split(\"\\n\")","def find_file_by_binary(**kwargs):\n return AppServer.service.find_file_by_binary(binary=kwargs['binary'])","def get_file(self, key):\n result = (None, None)\n path = os.path.join(self.directory, self.subdirectory, key)\n if os.path.isfile(path):\n content_type, _ = mimetypes.guess_type(path)\n with open(path, \"rb\") as file:\n result = content_type, file.read()\n return result","def _GetFileContents(filename):\n fpath = os.path.join(PROGRAM_FILES_DIR, filename)\n if os.path.isfile(fpath):\n with open(fpath, 'r') as f:\n content = f.read()\n else:\n content = pkgutil.get_data('gslib', filename)\n fpath = None\n if content is not None:\n if sys.version_info.major > 2 and isinstance(content, bytes):\n content = content.decode('utf-8')\n content = content.strip()\n return (fpath, content)","def txt_loader(fileobj):\n if isinstance(fileobj, bytes):\n data = fileobj.decode('utf-8')\n elif isinstance(fileobj, six.string_types):\n with open(fileobj, 'rb') as f:\n data = f.read().decode('utf-8')\n elif hasattr(fileobj, 'read'):\n data = fileobj.read().decode('utf-8')\n else:\n raise ValueError('fileobj is not a filename or a file object')\n return data","def read_file(file_path):\n with open(file_path, 'r') as infile:\n return infile.read()","def get_textfile_stream(filename, encoding):\n if version == 2:\n stream = io.open(filename, \"r\", encoding=encoding)\n return stream\n else:\n byte_stream = open(filename, \"rb\")\n return byte_stream","def read_file(filename):\n if os.path.isfile(filename):\n with open(filename, 'r') as f:\n return f.read()","def get_content_from_file(path):\n\n\t\tPathUtil.ensure_path_exists(path)\n\t\twith open(path) as file:\n\t\t\tfile_content = file.read()\n\t\treturn file_content","def readfile(path: Union[str, Path]) -> str:\n with open(path) as infile:\n return infile.read()","def SimpleRead(fn):\n content = \"\"\n try:\n content = open(fn).read()\n except :\n print(\"Failed to read file: %s\\n\"%(fn))\n print sys.exc_info()[1]\n\n return content","def read_from_file(path):\n with io.open(path, 'rb') as ios:\n return read(ios)","def file_open(self, path, method='r', content=''):\n\n f = open(path, method)\n if method == 'r' or method ==\"rb\": \n data = f.read()\n f.close()\n return data\n elif method == 'w' or method == 'a' or method == \"wb\":\n f.write(content)\n f.close()","def getFileContent(fileName, encoding = \"UTF-8\"):\n file = io.open(fileName, mode = \"r\", encoding = encoding)\n text = file.read()\n file.close()\n return text","def read(path, encoding=\"utf-8\"):\n with open(path, \"rb\") as f:\n return f.read().decode(encoding)","def get_content(self):\r\n try:\r\n with open(self.path, \"rb\") as f:\r\n raw_data = f.read()\r\n result = chardet.detect(raw_data)\r\n encoding = result['encoding']\r\n if encoding:\r\n with open(self.path, 'r', encoding=encoding) as f:\r\n content = f.readlines()\r\n else:\r\n try:\r\n with open(self.path, 'r', encoding='ansi') as f:\r\n content = f.readlines()\r\n except UnicodeDecodeError:\r\n print('Jakis blad kodowania pliku')\r\n return -1\r\n return content\r\n except FileNotFoundError:\r\n print(f'Wskazany plik {self.path} nie istnieje')\r\n exit()","def read_file(filename):\n with codecs.open(filename, 'r', 'utf8') as f:\n return f.read()","def read_file(filename):\n with codecs.open(filename, 'r', 'utf8') as f:\n return f.read()","def open_and_read_file(file_path):\n\n file = open(file_path)\n file = file.read()\n\n return file","def read_filepath_or_buffer(f, open_flags=\"r\", compression=None):\n f, handles, close = open_filepath_or_buffer(\n f, open_flags=open_flags, compression=compression\n )\n try:\n data = f.read()\n finally:\n if close:\n try:\n f.close()\n except ValueError:\n pass\n return data","def readFile(self, fname):\n res = None\n with open(self.PATH + fname, 'rb') as handle:\n res = pickle.load(handle)\n return res","def load_file(path):\n frmt = path.suffix.replace('.', '', 1)\n if frmt not in SUPPORTED_FORMATS:\n raise RuntimeError(\n 'Unknown file format \"{}\" for file {}. '\n 'Supported formats are: {}.'.format(\n frmt, path,\n ', '.join(sorted(SUPPORTED_FORMATS.keys())),\n )\n )\n\n # Load file\n content = load_content(path.read_text(encoding='utf-8'), frmt)\n return content"],"string":"[\n \"def get_binary(fname):\\n with open(fname, 'rb+') as f_name:\\n data = f_name.read()\\n return data\",\n \"def ReadBinaryFile(name):\\n\\n try:\\n fBinary = open(name, 'rb')\\n except:\\n return None\\n try:\\n content = fBinary.read()\\n except:\\n return None\\n finally:\\n fBinary.close()\\n return content\",\n \"def open_binary_file(file_name: str, mode: str = 'rb'):\\n return open(os.path.join(DATA_FOLDER, file_name), mode=mode)\",\n \"def ReadFile(fname, binary=True):\\n with open(Filename(fname), binary and 'rb' or 'r') as fd:\\n data = fd.read()\\n #self._out.Info(\\\"Read file '%s' size %d (%#0x)\\\" %\\n #(fname, len(data), len(data)))\\n return data\",\n \"def open_file(self, filename):\\n # UnicodeDecodeError: 'utf-8' codec can't decode byte 0x89 in position 0: invalid start byte\\n #\\n # https://docs.python.org/3/library/functions.html#open\\n # 'r'\\topen for reading (default)\\n # 't'\\ttext mode (default)\\n # 'b'\\tbinary mode\\n #\\n # In text mode (the default, or when 't' is included in the mode argument),\\n # the contents of the file are returned as str,\\n # the bytes having been first decoded using a platform-dependent encoding or using the specified encoding if given.\\n with open(filename, 'rb') as f:\\n return f.read()\",\n \"def read_file_bytes(filepath: str):\\n with open(filepath, \\\"rb\\\") as reader:\\n return reader.read()\",\n \"def _open_file_binary(filename):\\n for _byte in open(filename, 'rb').read():\\n yield _byte\",\n \"def get_file(self, filename: str, directory: str = 'gcodes', binary: bool = False) -> str:\\n raise NotImplementedError\",\n \"def load(path, binary=False):\\n with open(path, 'rb') as handle:\\n tmp = handle.read()\\n return tmp if binary else tmp.decode()\",\n \"def fread(filename):\\n with open(filename, 'r') as f:\\n return f.read()\",\n \"def get_file_content(full_path):\\n print(full_path)\\n bytes = min(32, os.path.getsize(full_path))\\n raw = open(full_path, 'rb').read(bytes)\\n if '\\\\\\\\xff\\\\\\\\xfe' in str(raw):\\n print(\\\"utf-16\\\")\\n the_file = open(full_path, encoding=\\\"utf-16\\\",\\n errors=\\\"backslashreplace\\\")\\n data = the_file.read()\\n else:\\n print(\\\"latin-1\\\")\\n the_file = open(full_path, encoding=\\\"latin-1\\\",\\n errors=\\\"backslashreplace\\\")\\n data = the_file.read()\\n return data\",\n \"def load_file(file_path: str, mode: str = \\\"rb\\\") -> Union[str, bytes]:\\n with open(file_path, mode) as file:\\n return file.read()\",\n \"def read_binary(self):\\n with self.open(\\\"rb\\\") as f:\\n return f.read()\",\n \"def readfile(fname, mode='rb'):\\n f = open(fname, mode)\\n raw = f.read()\\n f.close()\\n return raw\",\n \"def _Read(filename):\\n with open(filename, 'rb') as f:\\n return f.read()\",\n \"def openAndPack(filename):\\n inputfile = open(filename, 'rb')\\n return inputfile.read()\",\n \"def getFileContent(self, filePath, mode):\\n with open(filePath, mode) as my_file:\\n return my_file.read()\",\n \"def get_binary(self, filepath):\\n with open(filepath, \\\"rb\\\") as f:\\n return b64encode(f.read()).decode('utf-8')\",\n \"def _file_load(path):\\n _, _, file_path = path.split('/', 2)\\n with open(file_path, 'r', encoding='utf-8') as file_handle:\\n return file_handle.read()\",\n \"def open(self):\\n return File(open(self.get_path()), \\\"rb\\\")\",\n \"def get_file(self, filename):\\n log.debug('[%s]: reading: //%s/%s', self.name, self.name, filename)\\n try:\\n blob = self.repo.head.commit.tree/filename\\n return blob.data_stream\\n except KeyError as err:\\n raise GitError(err)\",\n \"def read_raw(file_path):\\n file = open(file_path, 'rb')\\n content = file.read()\\n file.close()\\n return content\",\n \"def open(path, override_extension=None):\\n extension = override_extension or os.path.splitext(path)[1][1:]\\n return read_stream(builtins.open(path, 'rb'), extension)\",\n \"def _ReadFile(filepath):\\n with open(filepath) as f:\\n return f.read()\",\n \"def bz2_file_bytes_reader(path):\\n return bz2.open(path, 'rb')\",\n \"def _read_file(self) -> str:\\n with open(self._file_name) as fp:\\n return fp.read()\",\n \"def get_content(self, filename):\\n f_id = self.face.FACES.files.find_one({ \\\"filename\\\" : filename }, { \\\"_id\\\" : 1 })\\n return self.face_fs.get(f_id['_id']).read()\",\n \"def read_file(filename):\\n return open(filename).read()\",\n \"def _get_file(self, path: str) -> Tuple[str, bytes]:\\n self._trace(\\\"fetching: %s\\\" % path)\\n meta, resp = self._connection.files_download(path)\\n return (meta.rev, resp.content)\",\n \"def read_file(dir):\\n file = open(dir, 'rb')\\n return file.read()\",\n \"def decode_file(self, filename):\\n num_bytes = os.stat(filename)[6]\\n data = array.array('B')\\n\\n with open(filename, 'rb') as f:\\n data.fromfile(f, num_bytes)\\n\\n return self.decode_data(data)\",\n \"def get_file_data(filename):\",\n \"def readfile(filename, mode='r'):\\n if mode != 'r' and mode != 'rb':\\n print(f\\\"ERROR: incorrect mode : expected 'r' or 'rb' given {mode}\\\\n\\\")\\n else:\\n with open(Path(os.path.expanduser(filename)), mode)as f:\\n content = f.read()\\n f.close()\\n return content\",\n \"def fileread(self, filename):\\n data = None\\n f = open(filename, 'r')\\n data = f.read()\\n f.close()\\n try:\\n data = data.decode()\\n except (UnicodeDecodeError, AttributeError):\\n data = data.encode(\\\"utf-8\\\")\\n\\n return data\",\n \"def get_file(URI):\\n return file_fabric.get_class(URI).get_content(URI)\",\n \"def open_file():\\n \\n # Open file for binary reading.\\n openlocation=fd.askopenfilename()\\n with open(openlocation, mode='rb') as file: \\n # Read file as bytes held in ints.\\n \\n fileContent = file.read()\\n \\n text_store = \\\"\\\" # Text that will ultimately be displayed.\\n \\n for i in fileContent:\\n \\n b_int = bin(i) # Turn the byte to a string representation of the binary.\\n b_int = b_int[2:] # Remove \\\"0b\\\" binary marker added by the bin cast. \\n \\n \\n while ((len(b_int)) < 8):\\n # As bytes read are truncated to most significant bit, add \\n # zeros where needed to make up a byte.\\n b_int = \\\"0\\\" + b_int\\n \\n text_store = text_store + b_int\\n \\n text.delete('1.0', tk.END) # Clear any previous text.\\n text.insert(tk.END, text_store) # Add new text.\",\n \"def openR(fname):\\n f=open(fname, 'rb'); a=f.read(3); f.close()\\n if a==b'\\\\xef\\\\xbb\\\\xbf': f=open(fname,'r', encoding='utf-8'); f.read(1);\\n elif a[:2]==b'\\\\xff\\\\xfe' or a[:2]==b'\\\\xfe\\\\xff': f = open(fname,'r',encoding='utf16')\\n else : f = open(fname,'r')\\n return f\",\n \"def openfile(path:str) -> str:\\n with open(file=path, mode='br') as file:\\n r_0 = file.readline()\\n return str(r_0)\",\n \"def read_file(filename):\\n with open(filename) as fp:\\n return fp.read()\",\n \"def bz2_file_reader(path):\\n return bz2.open(path, 'rt')\",\n \"def _readfile(dirpath, filename):\\n try:\\n with codecs.open(os.path.join(dirpath, filename), \\\"r\\\", \\\"utf-8\\\") as f:\\n return f.read()\\n except IOError:\\n return u\\\"\\\"\",\n \"def read_binary(filename):\\r\\n with open('%s.pickle'%(filename, ), 'rb') as handle:\\r\\n b = pickle.load(handle)\\r\\n return b\",\n \"def readfile(filename):\\n with open(filename, encoding=\\\"utf-8\\\") as file:\\n raw = file.read()\\n return raw\",\n \"def openFile(file):\\n file = file.lower()\\n if file.endswith('.bz2'):\\n return bz2.BZ2File(file)\\n elif file.endswith('.gz'):\\n return gzip.open(file)\\n return open(file)\",\n \"def file_entry(file_path: str) -> Tuple[str, Any, str]:\\n file_name = os.path.split(file_path)[1]\\n file_handler = open(file_path, \\\"rb\\\")\\n content_type = \\\"application/octet-stream\\\"\\n\\n return (file_name, file_handler, content_type)\",\n \"def read_file(filename):\\r\\n if Py3:\\r\\n return open(filename, \\\"r\\\", newline='')\\r\\n return open(filename, \\\"rb\\\")\",\n \"def read_file(filename):\\n open_kwargs = {}\\n if sys.version_info.major == 3:\\n open_kwargs = {'encoding': 'utf-8'}\\n\\n path = os.path.abspath(os.path.dirname(__file__))\\n filepath = os.path.join(path, filename)\\n with open(filepath, **open_kwargs) as filecontents:\\n return filecontents.read()\",\n \"def read_as_bytes(filename):\\n try:\\n with open(filename, \\\"rb\\\") as file:\\n bytes = array.array(\\\"B\\\")\\n bytes.frombytes(file.read())\\n return bytes\\n except FileNotFoundError:\\n print(f\\\"File not found: {filename}\\\")\\n exit()\",\n \"def GetFile(file_name):\\n\\n the_file = None\\n\\n try:\\n the_file = open(file_name, 'rb')\\n \\n except IOError:\\n the_file = None\\n \\n return the_file\",\n \"def read_file(filename):\\n with codecs.open(os.path.join(here, filename), encoding='utf-8') as f:\\n content = f.read()\\n return content\",\n \"def get_file_mine(file, size_to_read=(5 * (1024 * 1024))):\\n return magic.from_buffer(file.read(size_to_read), mime=True)\",\n \"def _read_magic(file_handle):\\r\\n magic = file_handle.read(len(_ZFILE_PREFIX))\\r\\n # Pickling needs file-handles at the beginning of the file\\r\\n file_handle.seek(0)\\r\\n return magic\",\n \"def read_file(file):\\n with open(file, \\\"r\\\") as fid:\\n return fid.read()\",\n \"def get_file_contents(filename):\\n with open(filename, 'r') as f:\\n content = f.read()\\n return content\",\n \"def get_file_content(filename):\\n file_contents = ''\\n with open(filename) as f:\\n file_contents = f.read()\\n return file_contents\",\n \"def get_file_data(file_name):\\r\\n try:\\r\\n with open(file_name, 'rb') as input_file:\\r\\n data = input_file.read()\\r\\n return data\\r\\n except Exception as err:\\r\\n return str(err).encode()\",\n \"def read(self, filename):\\n\\t\\treturn codecs.open(filename, 'r', 'utf8').read()\",\n \"def _get_file_content(self):\\n with open(self.file_name, mode='rb') as file:\\n self.file_content = file.read()\",\n \"def bbl_file(self, base_file):\\n bbl_path = os.path.abspath(os.path.splitext(base_file)[0]) + '.bbl'\\n return self.open_encode_safe(bbl_path).readlines()\",\n \"def return_file(path):\\n file_ext = path.split(\\\".\\\")[1]\\n if file_ext != \\\"py\\\":\\n with open(path, \\\"rb\\\") as file:\\n content = file.read()\\n if file_ext == \\\"txt\\\":\\n mime_type = b\\\"text/plain\\\"\\n elif file_ext == \\\"html\\\" or file_ext == \\\"htm\\\":\\n mime_type = b\\\"text/html\\\"\\n elif file_ext == \\\"png\\\":\\n mime_type = b\\\"image/png\\\"\\n elif file_ext == \\\"jpg\\\":\\n mime_type = b\\\"image/jpeg\\\"\\n else:\\n content, mime_type = return_py_script(path)\\n return content, mime_type\",\n \"def getFileContent(fn):\\n content = None\\n if not os.path.exists(fn):\\n print(\\\"Can not open file \\\" + fn)\\n else:\\n with open(fn, \\\"rb\\\") as f:\\n content = f.readlines()\\n return content\",\n \"def read_file(name):\\n with open(name, 'r') as my_file:\\n return my_file.read().encode('utf-8')\",\n \"def open_file(path):\\n return open(path, encoding='utf-8')\",\n \"def open_and_read_file(file_path):\\n text_data = open(file_path).read()\\n # print text_data\\n return text_data\",\n \"def get_file(self, path):\\n tarstream = io.BytesIO()\\n for chunk in self.container.get_archive(path=path)[0]:\\n tarstream.write(chunk)\\n tarstream.seek(0)\\n with tarfile.open(fileobj=tarstream) as tarfile_:\\n for tarinfo in tarfile_.getmembers():\\n return tarfile_.extractfile(tarinfo).read().decode()\",\n \"def ReadFile(self, filename):\\r\\n file = open(filename, 'rb')\\r\\n result = \\\"\\\"\\r\\n try:\\r\\n result = file.read()\\r\\n finally:\\r\\n file.close()\\r\\n return result\",\n \"def read_file(fname):\\n with open(fname, 'r') as fopen:\\n fdata = fopen.read()\\n return fdata\",\n \"def getFile(self, file_name: str) -> bytes:\\n obj = self.driver.get_object(self.container_name, file_name)\\n gen = self.driver.download_object_as_stream(obj)\\n file_as_bytes = next(gen)\\n return file_as_bytes\",\n \"def get_file_contents(path):\\n try:\\n with open(path) as f:\\n return f.read()\\n except IOError:\\n return None\",\n \"def ReadFile(self, filename):\\n file = open(filename, 'rb')\\n result = \\\"\\\"\\n try:\\n result = file.read()\\n finally:\\n file.close()\\n return result\",\n \"def read_metafile(path):\\n with codecs.open(path, \\\"rb\\\", \\\"utf-8\\\") as f:\\n return f.read()\",\n \"def get(self, filename, **kw):\\n\\n file_path = os.path.join(self.storage_path, filename)\\n try:\\n file_obj = open(file_path, \\\"r\\\")\\n except IOError:\\n return\\n else:\\n return file_obj.read()\",\n \"def get_example_file(filename):\\n fullpath = get_example_filepath(filename)\\n # Need to ensure that CRCRLF remain intact\\n with open(fullpath, \\\"rb\\\") as fh:\\n data = fh.read().decode(\\\"utf-8\\\")\\n return data\",\n \"def test_binary_contents(file, tmp_path):\\n name = tmp_path / \\\"1px.gif\\\"\\n ret = file.managed(name=str(name), contents=BINARY_FILE)\\n assert ret.result is True\",\n \"def file_to_string(path_to_file):\\n\\t\\twith open(path_to_file, 'r') as f:\\n\\t\\t\\tcontent = f.read()\\n\\t\\treturn content\",\n \"def get_file():\\n fname = get_var(request, \\\"fname\\\")\\n return open(fname).read()\",\n \"def open_file_by_type(file_name, mode=\\\"r\\\"):\\n file_name = op.expanduser(file_name) # expands '~' to the absolute home dir\\n if file_name.endswith(\\\"bz2\\\"):\\n return bz2.open(file_name, mode)\\n elif file_name.endswith(\\\"gz\\\"):\\n return gzip.open(file_name, mode, encoding=\\\"utf-8\\\")\\n else:\\n return open(file_name, mode, encoding=\\\"utf-8\\\")\",\n \"def ReadFileIntoString(filepath):\\n with open(filepath, 'r') as file_handle:\\n contents = file_handle.read()\\n return contents\",\n \"def open_file(filename):\\n with open(filename, \\\"rb\\\") as content:\\n return content.read().decode(\\\"utf-8\\\", errors=\\\"ignore\\\").split(\\\"\\\\n\\\")\",\n \"def find_file_by_binary(**kwargs):\\n return AppServer.service.find_file_by_binary(binary=kwargs['binary'])\",\n \"def get_file(self, key):\\n result = (None, None)\\n path = os.path.join(self.directory, self.subdirectory, key)\\n if os.path.isfile(path):\\n content_type, _ = mimetypes.guess_type(path)\\n with open(path, \\\"rb\\\") as file:\\n result = content_type, file.read()\\n return result\",\n \"def _GetFileContents(filename):\\n fpath = os.path.join(PROGRAM_FILES_DIR, filename)\\n if os.path.isfile(fpath):\\n with open(fpath, 'r') as f:\\n content = f.read()\\n else:\\n content = pkgutil.get_data('gslib', filename)\\n fpath = None\\n if content is not None:\\n if sys.version_info.major > 2 and isinstance(content, bytes):\\n content = content.decode('utf-8')\\n content = content.strip()\\n return (fpath, content)\",\n \"def txt_loader(fileobj):\\n if isinstance(fileobj, bytes):\\n data = fileobj.decode('utf-8')\\n elif isinstance(fileobj, six.string_types):\\n with open(fileobj, 'rb') as f:\\n data = f.read().decode('utf-8')\\n elif hasattr(fileobj, 'read'):\\n data = fileobj.read().decode('utf-8')\\n else:\\n raise ValueError('fileobj is not a filename or a file object')\\n return data\",\n \"def read_file(file_path):\\n with open(file_path, 'r') as infile:\\n return infile.read()\",\n \"def get_textfile_stream(filename, encoding):\\n if version == 2:\\n stream = io.open(filename, \\\"r\\\", encoding=encoding)\\n return stream\\n else:\\n byte_stream = open(filename, \\\"rb\\\")\\n return byte_stream\",\n \"def read_file(filename):\\n if os.path.isfile(filename):\\n with open(filename, 'r') as f:\\n return f.read()\",\n \"def get_content_from_file(path):\\n\\n\\t\\tPathUtil.ensure_path_exists(path)\\n\\t\\twith open(path) as file:\\n\\t\\t\\tfile_content = file.read()\\n\\t\\treturn file_content\",\n \"def readfile(path: Union[str, Path]) -> str:\\n with open(path) as infile:\\n return infile.read()\",\n \"def SimpleRead(fn):\\n content = \\\"\\\"\\n try:\\n content = open(fn).read()\\n except :\\n print(\\\"Failed to read file: %s\\\\n\\\"%(fn))\\n print sys.exc_info()[1]\\n\\n return content\",\n \"def read_from_file(path):\\n with io.open(path, 'rb') as ios:\\n return read(ios)\",\n \"def file_open(self, path, method='r', content=''):\\n\\n f = open(path, method)\\n if method == 'r' or method ==\\\"rb\\\": \\n data = f.read()\\n f.close()\\n return data\\n elif method == 'w' or method == 'a' or method == \\\"wb\\\":\\n f.write(content)\\n f.close()\",\n \"def getFileContent(fileName, encoding = \\\"UTF-8\\\"):\\n file = io.open(fileName, mode = \\\"r\\\", encoding = encoding)\\n text = file.read()\\n file.close()\\n return text\",\n \"def read(path, encoding=\\\"utf-8\\\"):\\n with open(path, \\\"rb\\\") as f:\\n return f.read().decode(encoding)\",\n \"def get_content(self):\\r\\n try:\\r\\n with open(self.path, \\\"rb\\\") as f:\\r\\n raw_data = f.read()\\r\\n result = chardet.detect(raw_data)\\r\\n encoding = result['encoding']\\r\\n if encoding:\\r\\n with open(self.path, 'r', encoding=encoding) as f:\\r\\n content = f.readlines()\\r\\n else:\\r\\n try:\\r\\n with open(self.path, 'r', encoding='ansi') as f:\\r\\n content = f.readlines()\\r\\n except UnicodeDecodeError:\\r\\n print('Jakis blad kodowania pliku')\\r\\n return -1\\r\\n return content\\r\\n except FileNotFoundError:\\r\\n print(f'Wskazany plik {self.path} nie istnieje')\\r\\n exit()\",\n \"def read_file(filename):\\n with codecs.open(filename, 'r', 'utf8') as f:\\n return f.read()\",\n \"def read_file(filename):\\n with codecs.open(filename, 'r', 'utf8') as f:\\n return f.read()\",\n \"def open_and_read_file(file_path):\\n\\n file = open(file_path)\\n file = file.read()\\n\\n return file\",\n \"def read_filepath_or_buffer(f, open_flags=\\\"r\\\", compression=None):\\n f, handles, close = open_filepath_or_buffer(\\n f, open_flags=open_flags, compression=compression\\n )\\n try:\\n data = f.read()\\n finally:\\n if close:\\n try:\\n f.close()\\n except ValueError:\\n pass\\n return data\",\n \"def readFile(self, fname):\\n res = None\\n with open(self.PATH + fname, 'rb') as handle:\\n res = pickle.load(handle)\\n return res\",\n \"def load_file(path):\\n frmt = path.suffix.replace('.', '', 1)\\n if frmt not in SUPPORTED_FORMATS:\\n raise RuntimeError(\\n 'Unknown file format \\\"{}\\\" for file {}. '\\n 'Supported formats are: {}.'.format(\\n frmt, path,\\n ', '.join(sorted(SUPPORTED_FORMATS.keys())),\\n )\\n )\\n\\n # Load file\\n content = load_content(path.read_text(encoding='utf-8'), frmt)\\n return content\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.73417205","0.6876805","0.6709726","0.66392875","0.6591508","0.6574621","0.6560591","0.6469011","0.6448845","0.63792706","0.63552374","0.6351509","0.6344689","0.6221042","0.6202726","0.62002987","0.61832154","0.6179081","0.61498123","0.6124398","0.61126554","0.609452","0.6091017","0.6085681","0.60691756","0.6037266","0.60289574","0.6009791","0.59957117","0.5993969","0.5991767","0.59823424","0.5975754","0.5954711","0.594709","0.5926759","0.5882995","0.5878769","0.5878606","0.5866151","0.5860941","0.5850677","0.5839909","0.5838611","0.58347267","0.5832683","0.5830744","0.5821018","0.5816591","0.58099437","0.5808423","0.5804116","0.5798669","0.57978964","0.5797411","0.5793248","0.579224","0.57863516","0.5785052","0.5784697","0.57844","0.57816994","0.5778223","0.57776177","0.5776724","0.5774248","0.57731813","0.57526106","0.5750428","0.57499325","0.57455593","0.5743657","0.5741733","0.5741404","0.57414037","0.57372916","0.5729236","0.5728404","0.5721555","0.5721376","0.57207817","0.57201916","0.5712801","0.5711318","0.57097787","0.5702766","0.5700749","0.56995094","0.5697682","0.5690126","0.56760436","0.5675445","0.5674796","0.5673982","0.5672113","0.5672113","0.5672038","0.5669994","0.56613517","0.56611377"],"string":"[\n \"0.73417205\",\n \"0.6876805\",\n \"0.6709726\",\n \"0.66392875\",\n \"0.6591508\",\n \"0.6574621\",\n \"0.6560591\",\n \"0.6469011\",\n \"0.6448845\",\n \"0.63792706\",\n \"0.63552374\",\n \"0.6351509\",\n \"0.6344689\",\n \"0.6221042\",\n \"0.6202726\",\n \"0.62002987\",\n \"0.61832154\",\n \"0.6179081\",\n \"0.61498123\",\n \"0.6124398\",\n \"0.61126554\",\n \"0.609452\",\n \"0.6091017\",\n \"0.6085681\",\n \"0.60691756\",\n \"0.6037266\",\n \"0.60289574\",\n \"0.6009791\",\n \"0.59957117\",\n \"0.5993969\",\n \"0.5991767\",\n \"0.59823424\",\n \"0.5975754\",\n \"0.5954711\",\n \"0.594709\",\n \"0.5926759\",\n \"0.5882995\",\n \"0.5878769\",\n \"0.5878606\",\n \"0.5866151\",\n \"0.5860941\",\n \"0.5850677\",\n \"0.5839909\",\n \"0.5838611\",\n \"0.58347267\",\n \"0.5832683\",\n \"0.5830744\",\n \"0.5821018\",\n \"0.5816591\",\n \"0.58099437\",\n \"0.5808423\",\n \"0.5804116\",\n \"0.5798669\",\n \"0.57978964\",\n \"0.5797411\",\n \"0.5793248\",\n \"0.579224\",\n \"0.57863516\",\n \"0.5785052\",\n \"0.5784697\",\n \"0.57844\",\n \"0.57816994\",\n \"0.5778223\",\n \"0.57776177\",\n \"0.5776724\",\n \"0.5774248\",\n \"0.57731813\",\n \"0.57526106\",\n \"0.5750428\",\n \"0.57499325\",\n \"0.57455593\",\n \"0.5743657\",\n \"0.5741733\",\n \"0.5741404\",\n \"0.57414037\",\n \"0.57372916\",\n \"0.5729236\",\n \"0.5728404\",\n \"0.5721555\",\n \"0.5721376\",\n \"0.57207817\",\n \"0.57201916\",\n \"0.5712801\",\n \"0.5711318\",\n \"0.57097787\",\n \"0.5702766\",\n \"0.5700749\",\n \"0.56995094\",\n \"0.5697682\",\n \"0.5690126\",\n \"0.56760436\",\n \"0.5675445\",\n \"0.5674796\",\n \"0.5673982\",\n \"0.5672113\",\n \"0.5672113\",\n \"0.5672038\",\n \"0.5669994\",\n \"0.56613517\",\n \"0.56611377\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.62648433"},"document_rank":{"kind":"string","value":"13"}}},{"rowIdx":22471,"cells":{"query":{"kind":"string","value":"Obtain an upload ticket from the API"},"document":{"kind":"string","value":"def get_upload_ticket(self):\n r = HTTPClient().fetch(self.config['apiroot'] + self.ticket_path, method=\"POST\",\n body=urlencode({'type': 'streaming'}), headers = self.standard_headers,\n validate_cert=not self.config['dev'])\n response = json.loads(r.body)\n return response['ticket_id'], response['upload_link_secure'], response['complete_uri']"},"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_service_ticket():\n\n payload = {'username': APIC_EM_USER, 'password': APIC_EM_PASSW}\n url = 'https://' + APIC_EM + '/ticket'\n header = {'content-type': 'application/json'}\n ticket_response = requests.post(url, data=json.dumps(payload), headers=header, verify=False)\n if not ticket_response:\n print('Something went wrong, try again!')\n else:\n ticket_json = ticket_response.json()\n ticket = ticket_json['response']['serviceTicket']\n print('APIC-EM ticket: ', ticket) # print the ticket for reference only, not required\n return ticket","def get_upload(arn=None):\n pass","def ticket(self, ticket_id):\r\n return resources.Ticket(self, ticket_id)","def get_ticket_info(name: str) -> JiraTicket:\n ticket_url = build_rest_url(name)\n res = requests.get(ticket_url, auth=get_jira_auth()).json()\n issue_type = JiraIssueTypeFactory(**res[\"fields\"][\"issuetype\"])\n summary = res[\"fields\"][\"summary\"]\n browse_url = build_browse_url(name)\n\n return JiraTicket(name=name, issue_type=issue_type, summary=summary, url=browse_url)","def get_ticket(self, ticket_id):\n response = self.http_call(\"{0}/tickets/{1}.json\".format(self.uri, ticket_id))\n return json.loads(response.content.decode(sys.stdout.encoding, \"replace\"))","def get_ticket(self, ticket_id):\r\n mask = ('mask[id, title, assignedUser[firstName, lastName],'\r\n 'createDate,lastEditDate,updates[entry],updateCount]')\r\n return self.ticket.getObject(id=ticket_id, mask=mask)","def delete_upload_ticket(self, complete_uri):\n url = self.config['apiroot'] + complete_uri\n log.info(\"Requesting %s\" % url)\n r = HTTPClient().fetch(url, method=\"DELETE\", headers=self.standard_headers,\n validate_cert=not self.config['dev'])\n log.info(\"Upload completed: status code: %d\" % r.code)\n if r.code == 201:\n _id = r.headers['location'].split('/')[-1]\n return _id\n raise ValueError(\"Upload completion unsuccessful\")","def ticket(self, ticket_id):\r\n return tickets.Ticket(self, ticket_id)","def ticket_id(self):\n return self._ticket_id","def prepare_ticket(self, req, ticket, fields, actions):","def create_ticket(self, ticket):\r\n ticket_url = self._zendesk_instance.create_ticket(data=ticket)\r\n return zendesk.get_id_from_url(ticket_url)","def Ticket(ticket):\n try:\n data = ticket_module.verify(ticket)\n name = data['slivers'][0]['name']\n if data != None:\n deliver_ticket(data)\n logger.log('api_calls: Ticket delivered for %s' % name)\n Create(database.db.get(name))\n except Exception, err:\n raise xmlrpclib.Fault(102, 'Ticket error: ' + str(err))","def get_ws_ticket(jwt: str) -> str:\n LOGGER.debug(\"Retrieving ticket...\")\n\n args = {\n \"url\": \"{0}/kafka-ws/v1/ticket\".format(CONFIG['dojot']['url']),\n \"headers\": {\n \"Content-Type\": \"application/json\",\n \"Authorization\": \"Bearer {0}\".format(jwt),\n },\n }\n\n res = DojotAPI.call_api(requests.get, args)\n\n LOGGER.debug(\".. retrieved ticket\")\n return res[\"ticket\"]","def ticket(self, ticket_id):\n return tickets.Ticket(self, ticket_id)","def get(self, **kwargs):\n params = {\"token\": self.token}\n params.update(kwargs)\n return self.api._get(\"uploads/get\", params=params)","def transfer_ticket(self, ticket_id):\n ticket = self.zendesk.get_ticket(ticket_id)\n subject = ticket[\"ticket\"][\"subject\"]\n status = ticket[\"ticket\"][\"status\"]\n description = ticket[\"ticket\"][\"description\"]\n if ticket[\"ticket\"][\"assignee_id\"] is not None:\n assignee_email = self.zendesk.get_assignee_email(ticket[\"ticket\"][\"assignee_id\"])\n try:\n requester = ticket[\"ticket\"][\"via\"][\"source\"][\"from\"][\"address\"]\n except KeyError:\n requester = self.default_requester\n # Terms for the status of a ticket on Samanage differ from those on Zendesk\n # When creating a ticket on Samanage through API only statuses allowed are Closed and New.\n # After the ticket is created status can be changed.\n if status in (\"open\", \"pending\"):\n status = \"New\"\n update_status = \"Assigned\"\n if status in (\"closed\", \"solved\"):\n status = \"Closed\"\n update_status = \"Closed\"\n # We can now make incident on Samanage\n if self.samanage:\n incident = self.samanage.incident(\n subject, requester, self.priority, status, assignee_email, description\n )\n incident_id = json.loads(incident)[\"id\"]\n # Get all comments for a ticket on zendesk\n comments = self.zendesk.get_comments(ticket_id)\n comment_list = []\n for comment in comments[\"comments\"]:\n author = self.zendesk.get_comment_author(comment[\"author_id\"])\n if self.dump:\n comment_list.append({\"author\": author, \"body\": comment[\"body\"]})\n # Transfer comment(s) to Samanage\n if self.samanage:\n self.samanage.comment(\"From:{0}\\n{1}\".format(author, comment[\"body\"]), incident_id)\n # Adding comments to samanage ticket reopens it\n # (re)update the status of the ticket on samanage to specified status\n if self.samanage:\n self.samanage.update_status(update_status, incident_id)\n\n # JSON dump if initalized\n if self.dump:\n with open(\"ticket_dump.json\", \"a\", errors='replace') as dump_file:\n card_details = {ticket_id:{\n \"id\": ticket_id,\n \"subject\": subject,\n \"requester\": requester,\n \"status\": status,\n \"assignee\": assignee_email,\n \"description\": description,\n \"comments\": comment_list}}\n dump_file.write(json.dumps(card_details, ensure_ascii=False,\n sort_keys=True, indent=4))","def get_ticket_by_id(id):\n url = \"https://zccreynosa.zendesk.com/api/v2/tickets/\" + id + \".json\"\n response = requests.get(url, auth=(user, token))\n\n if response.status_code != 200:\n return None\n else:\n json_data = json.loads(response.text)\n return json_data","def get_upload_status(self, upload_id: str, token: str) -> Upload:\n data, _, _ = self.json('get', f'/{upload_id}', token)\n return self._parse_upload_status(data)","def upload_file(self):\n request = copy.deepcopy(self.request_template)\n data = json.dumps(request)\n curr_file = {\n 'request': data,\n 'file': open(self.file_path, 'rb')\n }\n print(\"Sending Upload request of av for file {}\".format(self.file_name))\n try:\n response = requests.post(url=self.url + \"upload\", files=curr_file, verify=False)\n except Exception as E:\n print(\"Upload file failed. file: {} , failure: {}\".format(self.file_name, E))\n raise\n response_j = response.json()\n print(\"av Upload response status for file {} : {}\".format(self.file_name,\n response_j[\"response\"][0][\"status\"][\"label\"]))\n return response_j","def upload(self, filepath):\n if self.ver is not None and LooseVersion(str(self.ver)) < LooseVersion('1.4.0'):\n raise VersionMismatchError('File upload')\n\n try:\n with open(filepath, 'rb') as stream:\n url = '{0}{1}'.format(self.url, '/uploads.json')\n response = self.request('post', url, data=stream, headers={'Content-Type': 'application/octet-stream'})\n except IOError:\n raise NoFileError()\n\n return response['upload']['token']","def issue( self ):\n\n self.state.issueDate = \"%d\" % time.time()\n\n # Encrypt the protocol state.\n aes = AES.new(self.symmTicketKey, mode=AES.MODE_CBC, IV=self.IV)\n state = repr(self.state)\n assert (len(state) % AES.block_size) == 0\n cryptedState = aes.encrypt(state)\n\n # Authenticate ticket name, IV and the encrypted state.\n hmac = HMAC.new(self.hmacTicketKey, self.IV + \\\n cryptedState, digestmod=SHA256).digest()\n\n ticket = self.IV + cryptedState + hmac\n\n log.debug(\"Returning %d-byte ticket.\" % (len(self.IV) +\n len(cryptedState) + len(hmac)))\n\n return ticket","def _get(self, ticket_id):\n ticket_id = int(ticket_id)\n ticket = DB_TICKET_TABLE.get(doc_id=ticket_id)\n if not ticket:\n flask_restful.abort(404, message=f\"Ticket '{ticket_id}' not found!\")\n comments_q = Query()\n comments = DB_COMMENT_TABLE.search((comments_q.ticket_id == ticket_id) &\n (comments_q.type == Comment.TYPE_COMMENT))\n worknotes = DB_COMMENT_TABLE.search((comments_q.ticket_id == ticket_id) &\n (comments_q.type == Comment.TYPE_WORKNOTE))\n res = {\n \"id\" : ticket.doc_id,\n }\n res.update(ticket)\n res['_embedded'] = {\n \"comments\" : self.embed_comment_data_in_result(comments),\n \"worknotes\" : self.embed_comment_data_in_result(worknotes)\n }\n res['_links'] = self.make_links({\n \"self\" : Ticket.get_self_url(ticket.doc_id),\n \"contained_in\" : TicketList.get_self_url(),\n \"customer\" : Customer.get_self_url(res['customer_id']),\n \"user\" : User.get_self_url(res['user_id'])\n })\n return res","def validate_oculus_ticket():\n\n ob = request.get_json()\n try:\n OculusProviderAuthSchema().load(ob)\n except ma.ValidationError as e:\n abort_unauthorized(\"Oculus token property %s is invalid\" % e.field_name)\n\n provider_details = ob['provider_details']\n # Get Oculus authentication config\n oculus_config = get_provider_config('oculus')\n\n if not oculus_config:\n abort(http_client.SERVICE_UNAVAILABLE, description=\"Oculus authentication not configured for current tenant\")\n\n # Call validation and authenticate if ticket is good\n identity_id = run_ticket_validation(\n user_id=provider_details['user_id'],\n access_token=oculus_config['access_token'],\n nonce=provider_details['nonce']\n )\n\n return identity_id","def upload(self) :\n\t\ttry :\n\t\t\treturn self._upload\n\t\texcept Exception as e:\n\t\t\traise e","def support_submit_ticket(self, **kw):\n person_name = \"\"\n if http.request.env.user.name != \"Public user\":\n person_name = http.request.env.user.name\n pic = http.request.env['schedule.person.in.charge'].sudo().search([],order = 'end_date desc',limit=1).pic \n return http.request.render('website_support_indonesia.support_submit_ticket', {'categories': http.request.env['website.support.ticket.categories'].sudo().search([('type_view','=',False)]), 'person_name': person_name, 'email': http.request.env.user.email, 'pic':pic})","def ticket_created(self, ticket):","def verify_ticket(self, ticket):\n raise NotImplementedError()","def get_approved_tickets(ticket):\n with open(\"{}/.ssh/jira.pem\".format(os.environ[\"HOME\"]), \"r\") as key_cert_file:\n key_cert_data = key_cert_file.read()\n oauth_dict = {\n \"access_token\": \"PrxvLiICry8gkXXPHqnorpRHjBCn3V0O\",\n \"access_token_secret\": \"kNX06aseOyJx3GnWmRH1w14UQ4o7puiV\",\n \"consumer_key\": \"ceej-ticket-key\",\n \"key_cert\": key_cert_data\n }\n jira = JIRA({\"server\": \"https://issues.couchbase.com\"}, oauth=oauth_dict)\n links = [link.outwardIssue.key\n for link in jira.issue(ticket).fields.issuelinks\n if hasattr(link, \"outwardIssue\")]\n return links","def test_finalized_no_upload_ticket(self):\n namespace = 'default-gzip'\n request = self.store_request(namespace, pad_string('silence'))\n request.upload_ticket = None\n with self.call_should_fail('400'):\n self.call_api('finalize_gs_upload', self.message_to_dict(request), 200)","def get_auth_token():\n \n form_fields = {\n \"client_id\": client_id,\n \"client_secret\":client_secret,\n \"code\": code,\n \"redirect_uri\": \"http://www.stackprinter.com\"\n }\n form_data = urllib.urlencode(form_fields)\n results = __gae_fetch(url = 'https://stackexchange.com/oauth/access_token',\n method = urlfetch.POST, \n payload = form_data,\n headers={'Content-Type': 'application/x-www-form-urlencoded'})\n response = results.content\n return response","def get_proxy_ticket(self, pgt):\n response = self.session.get(self.get_proxy_url(pgt), verify=self.verify_ssl_certificate)\n if response.status_code == 200:\n from lxml import etree\n root = etree.fromstring(response.content)\n tickets = root.xpath(\n \"//cas:proxyTicket\",\n namespaces={\"cas\": \"http://www.yale.edu/tp/cas\"}\n )\n if len(tickets) == 1:\n return tickets[0].text\n errors = root.xpath(\n \"//cas:authenticationFailure\",\n namespaces={\"cas\": \"http://www.yale.edu/tp/cas\"}\n )\n if len(errors) == 1:\n raise CASError(errors[0].attrib['code'], errors[0].text)\n raise CASError(\"Bad http code %s\" % response.status_code)","def next_upload(self):\n # print \"fetching upload...\"\n try:\n uploads = Backend.next(\n self.account, location=self.location,\n project=self.project) or []\n except (exceptions.BackendDown, exceptions.BackendError):\n raise\n else:\n if uploads:\n # Return the one download in the list\n return uploads[0]","def request_pet_finder_token():\n resp = HTTP_request.post('https://api.petfinder.com/v2/oauth2/token',\n data={\n \"grant_type\": 'client_credentials',\n \"client_id\": PET_FINDER_API_KEY,\n \"client_secret\": PET_FINDER_SECRET\n })\n return resp.json()[\"access_token\"]","def ticket(self) -> \"Ticket\":\n return Ticket(connection=self)","def test_get_upload_job(cidc_api, clean_db, monkeypatch):\n user_id = setup_trial_and_user(cidc_api, monkeypatch)\n user_job, other_job, _ = setup_upload_jobs(cidc_api)\n\n client = cidc_api.test_client()\n\n # Regular CIMAC users aren't allowed to get upload jobs\n res = client.get(f\"upload_jobs/{user_job}\")\n assert res.status_code == 401\n\n make_cimac_biofx_user(user_id, cidc_api)\n\n # 404 for non-existent upload\n res = client.get(f\"upload_jobs/123123\")\n assert res.status_code == 404\n\n # 404 for another user's upload if non-admin\n res = client.get(f\"upload_jobs/{other_job}\")\n assert res.status_code == 404\n\n # 200 for user's upload\n res = client.get(f\"upload_jobs/{user_job}\")\n assert res.status_code == 200\n assert res.json[\"id\"] == user_job\n\n # 200 for another user's upload if admin\n make_admin(user_id, cidc_api)\n res = client.get(f\"upload_jobs/{other_job}\")\n assert res.status_code == 200\n assert res.json[\"id\"] == other_job","def __request(self,endpoint):\n apiRequest = requests.get(\"%s/%s\" % (self.baseurl,endpoint), \n auth=requests.auth.HTTPBasicAuth(self.api_id, self.api_secret))\n try:\n json = apiRequest.json()\n return json\n except JSONDecodeError:\n print(\"Failed to download or failed to parse JSON.\")\n print(apiRequest)\n return None","def ticket_chooser(ticket):\n try:\n # Find the ticket type's button\n ticket_type = browser.find_element_by_xpath(ticket)\n ticket_type.click()\n\n except Exception as e:\n print(\"Ticket type not found\")","def get_uploaded_file(filename):\n client = _get_client()\n key = client.key(_FILE_ENTITY, filename)\n entity = client.get(key)\n if entity:\n return UploadedFile(filename, entity['url'])\n return None","def upload(self, upload_id):\r\n return u.Upload(self, upload_id)","def _ticket(self):\n self._m_ticket += 1\n return(self._m_ticket)","def getUser(self, authenticationToken):\r\n pass","def download(self, id, article_id, ticket_id):\n response = self._connection.session.get(\n self.url + f\"/{ticket_id}/{article_id}/{id}\"\n )\n return self._raise_or_return_json(response)","def _requestSwiftToken(self):\n oauth_access_token = self.accessTokenManager.token\n c, r = http._get(\n self.auth_package.HUBIC_API+'account/credentials/',\n headers={\n 'Authorization': 'Bearer '+oauth_access_token\n }\n )\n result = json.loads(r.read())\n c.close()\n\n if r.status != 200:\n try:\n err =result\n err['code'] = r.status\n except Exception as e:\n err = {}\n\n raise Exception(\"Unable to get swift token, \"\n \"(%s)\"%str(err))\n\n self._endpoint = result['endpoint']\n self._token = result['token']\n self._expire = datetime.strptime( result['expires'][:-6], \"%Y-%m-%dT%H:%M:%S\" ) - timedelta(seconds=10)","def save(self,owner=None):\n\n q = Queue.objects.get(id=int(self.cleaned_data['queue']))\n\n t = Ticket(\n title = self.cleaned_data['title'],\n owner=owner,\n submitter_email = self.cleaned_data['submitter_email'],\n created = datetime.now(),\n status = Ticket.OPEN_STATUS,\n queue = q,\n description = self.cleaned_data['body'],\n priority = self.cleaned_data['priority'],\n account = owner.account\n )\n\n t.save()\n\n f = FollowUp(\n ticket = t,\n title = _('Ticket Opened Via Web'),\n date = datetime.now(),\n public = True,\n comment = self.cleaned_data['body'],\n account = owner.account\n )\n\n f.save()\n\n files = []\n if self.cleaned_data['attachment']:\n import mimetypes\n file = self.cleaned_data['attachment']\n filename = file.name.replace(' ', '_')\n a = Attachment(\n followup=f,\n filename=filename,\n mime_type=mimetypes.guess_type(filename)[0] or 'application/octet-stream',\n size=file.size,\n )\n a.file.save(file.name, file, save=False)\n a.save()\n \n if file.size < getattr(settings, 'MAX_EMAIL_ATTACHMENT_SIZE', 512000):\n # Only files smaller than 512kb (or as defined in \n # settings.MAX_EMAIL_ATTACHMENT_SIZE) are sent via email.\n files.append(a.file.path)\n\n context = {\n 'ticket': t,\n 'queue': q,\n }\n\n messages_sent_to = []\n\n send_templated_mail(\n 'newticket_owner',\n context,\n recipients=t.submitter_email,\n sender=q.from_address,\n fail_silently=True,\n files=files,\n )\n messages_sent_to.append(t.submitter_email)\n\n if q.new_ticket_cc and q.new_ticket_cc not in messages_sent_to:\n send_templated_mail(\n 'newticket_cc',\n context,\n recipients=q.new_ticket_cc,\n sender=q.from_address,\n fail_silently=True,\n files=files,\n )\n messages_sent_to.append(q.new_ticket_cc)\n\n if q.updated_ticket_cc and q.updated_ticket_cc != q.new_ticket_cc and q.updated_ticket_cc not in messages_sent_to:\n send_templated_mail(\n 'newticket_cc',\n context,\n recipients=q.updated_ticket_cc,\n sender=q.from_address,\n fail_silently=True,\n files=files,\n )\n\n return t","def verify_ticket(self, ticket):\n response = self.get_verification_response(ticket)\n return self.verify_response(response)","def upload(request):\n if request.user is None:\n if IS_DEV:\n request.user = users.User(request.POST.get('user', 'test@example.com'))\n else:\n return HttpTextResponse('Login required', status=401)\n # Check against old upload.py usage.\n if request.POST.get('num_parts') > 1:\n return HttpTextResponse('Upload.py is too old, get the latest version.')\n form = UploadForm(request.POST, request.FILES)\n issue = None\n patchset = None\n if form.is_valid():\n issue_id = form.cleaned_data['issue']\n if issue_id:\n action = 'updated'\n issue = models.Issue.get_by_id(issue_id)\n if issue is None:\n form.errors['issue'] = ['No issue exists with that id (%s)' %\n issue_id]\n elif not form.cleaned_data.get('content_upload'):\n form.errors['issue'] = ['Base files upload required for that issue.']\n issue = None\n else:\n if not issue.edit_allowed:\n form.errors['user'] = ['You (%s) don\\'t own this issue (%s)' %\n (request.user, issue_id)]\n issue = None\n elif issue.closed:\n form.errors['issue'] = ['This issue is closed (%s)' % (issue_id)]\n issue = None\n else:\n patchset = _add_patchset_from_form(request, issue, form, 'subject',\n emails_add_only=True)\n if not patchset:\n issue = None\n else:\n action = 'created'\n issue, patchset = _make_new(request, form)\n if issue is None:\n msg = 'Issue creation errors: %s' % repr(form.errors)\n else:\n msg = ('Issue %s. URL: %s' %\n (action,\n request.build_absolute_uri(\n reverse('show_bare_issue_number', args=[issue.key.id()]))))\n if (form.cleaned_data.get('content_upload') or\n form.cleaned_data.get('separate_patches')):\n # Extend the response message: 2nd line is patchset id.\n msg +=\"\\n%d\" % patchset.key.id()\n if form.cleaned_data.get('content_upload'):\n # Extend the response: additional lines are the expected filenames.\n issue.put()\n\n base_hashes = {}\n for file_info in form.cleaned_data.get('base_hashes').split(\"|\"):\n if not file_info:\n break\n checksum, filename = file_info.split(\":\", 1)\n base_hashes[filename] = checksum\n\n logging.info('base_hashes is %r', base_hashes)\n content_entities = []\n new_content_entities = []\n patches = list(patchset.patches)\n logging.info('len(patches) = %r', len(patches))\n\n existing_patches = {}\n patchsets = list(issue.patchsets)\n if len(patchsets) > 1:\n # Only check the last uploaded patchset for speed.\n last_patch_list = patchsets[-2].patches\n patchsets = None # Reduce memory usage.\n for opatch in last_patch_list:\n if opatch.content_key:\n existing_patches[opatch.filename] = opatch\n for patch in patches:\n # Check if the base file is already uploaded in another patchset.\n if (patch.filename in base_hashes and\n patch.filename in existing_patches and\n (base_hashes[patch.filename] ==\n existing_patches[patch.filename].content_key.get().checksum)):\n content_key = existing_patches[patch.filename].content_key\n patch.status = existing_patches[patch.filename].status\n patch.is_binary = existing_patches[patch.filename].is_binary\n patch.content_key = content_key\n\n existing_patches = None # Reduce memory usage.\n\n for patch in patches:\n id_string = patch.key.id()\n if patch.content_key is not None:\n # Base file not needed since we reused a previous upload. Send its\n # patch id in case it's a binary file and the new content needs to\n # be uploaded. We mark this by prepending 'nobase' to the id.\n id_string = \"nobase_\" + str(id_string)\n msg += \"\\n%s %s\" % (id_string, patch.filename)\n\n logging.info('upload response is:\\n %s\\n', msg)\n ndb.put_multi(patches)\n\n return HttpTextResponse(msg)","def test_get_file_not_authd(self):\n rv = self.get('/queue/riker.gif')\n self.assertJSONError(rv, 'TagalleryMissingLoginInformation')\n return","def set_ticket(self, ticket: str) -> None:\n LOG.info(f\"Setting ticket to {ticket}\")\n self.ticket = ticket","def __update_ticket(ticket_id, status, owner_id, comments, notify_requestor, token, user_id, channel, ts):\n\n # Update ticket metadata\n owner = user_profile(owner_id)\n username = ''\n if owner['ok']:\n username = owner['user']['profile'].get('email', '').split('@')[0]\n resp = rt_api.update_ticket(ticket_id, token, status, username)\n if rt_api.permission_error(resp):\n error_message = \"Sorry, it appears you do not have permission to perform this action.\"\n post_ephemeral(channel, error_message, user_id, 'Request Tracker')\n return\n\n # Update ticket in Slack\n current_message = retrieve_message(channel, ts)\n if current_message.get('error', '') == 'not_in_channel':\n join_channel(channel)\n current_message = retrieve_message(channel, ts)\n resp = refresh_ticket_message(channel, current_message['messages'][0])\n if not resp['ok']:\n logger.warning(\"Failed to update ticket in Slack. Please check RT to see if your changes were applied.\")\n\n # Post comments / replies, if applicable\n if comments:\n slack_user = user_profile(user_id)\n display_name = slack_user['user']['profile']['real_name']\n resp = rt_api.ticket_comment(ticket_id, comments + \"\\n\\n- \" + display_name, notify_requestor,\n token=token)\n if rt_api.permission_error(resp):\n error_message = \"Sorry, it appears you do not have permission to perform this action.\"\n post_ephemeral(channel, error_message, user_id, 'Request Tracker')\n return\n\n profile_photo = slack_user['user']['profile']['image_original']\n slack_post(channel, ts, comments, username=display_name, icon_url=profile_photo)","def scan_ticket():\n ticket_num = request.json.get(\"ticketNumber\")\n\n audience = Audience.query.filter(Audience.id == ticket_num).first()\n checkout = Checkout.query.filter(Checkout.audience_id == ticket_num).first()\n if not audience:\n return jsonify({\"code\": \"1\", \"type\": \"ticket not found\"})\n\n if checkout:\n return jsonify({\"code\": \"1\", \"type\": \"ticket already redeemed\", \"name\": audience.name})\n\n checkout_object = Checkout(audience.id)\n add_to_database(checkout_object)\n\n return jsonify({\"code\": \"0\", \"type\": \"checkout successful\", \"name\": audience.name})","def get_data(ticket_info, logger):\n\n # This string will be returned and added as a comment to the SecureChange ticket\n return_str = \"\"\n\n # Log integration starting\n logger.info(\"Running '{}' integration\".format(__name__))\n\n try:\n\n for req in ticket_info['Requests']:\n for src in req['Sources']:\n if src['Private'] and src['Cidr'] == '32':\n return_str = '{}\\n\\n{}\\n\\n{}'.format(return_str, device_query(src['Ip'], fmt='text'), event_query(src['Ip'], fmt='text'))\n for dst in req['Destinations']:\n if dst['Private'] and dst['Cidr'] == '32':\n return_str = '{}\\n\\n{}\\n\\n{}'.format(return_str, device_query(dst['Ip'], fmt='text'), event_query(dst['Ip'], fmt='text'))\n\n\n except Exception as e:\n\n # Log the error and return an empty string\n logger.error(\"Error: {}\".format(e))\n return None\n\n # Log integration completing\n logger.info(\"{} integration completed\".format(__name__))\n\n # Return comment\n return return_str","def api_upload():\n return make_response(file_manager.save_uploaded_file(), 200)","def get_ticket(self, wid, project, nowait=False):\n\n path = os.path.join(self.prjdir, project)\n q = WorkQueue(path)\n if nowait and not q.isquiet():\n # If we don't want to wait and there is Q, we'll just leave\n return\n\n if not q.add(json.dumps(wid.to_h(), sort_keys=True, indent=4)):\n # Marking the wid to be forgotten ensures it's not sent\n # back to BOSS, and the process blocks\n wid.forget = True\n else:\n wid.result = True","def check_upload(box_name, upload_threshold):\n\n try:\n headers = {'Cache-Control': 'no-cache'}\n response = requests.get(API_URL.format(box_name), headers=headers)\n response.raise_for_status()\n except Exception as e:\n print('{} failed getting health from backend: {}'.format(box_name, e))\n return UNKNOWN\n\n try:\n health = response.json()\n except Exception as e:\n print('{} failed decoding health response: {}'.format(box_name, e))\n return UNKNOWN\n\n if 'last_upload' not in health:\n print('{} last_upload not found in health results'.format(box_name))\n return UNKNOWN\n \n last_upload = datetime.now(tzutc()) - parser.parse(health['last_upload'])\n\n if last_upload.total_seconds() > upload_threshold:\n print('{} last uploaded {}'.format(box_name, naturaltime(last_upload)))\n return CRITICAL\n\n print('{} last upload {}'.format(box_name, naturaltime(last_upload)))\n return OK","def upload_file(self, f):\n with FilesOpener(f) as files:\n response = self.session.post(\n 'https://{}/upload'.format(self.domain),\n files=files\n ).json()\n\n if isinstance(response, list):\n error = response[0].get('error')\n else:\n error = response.get('error')\n\n if error:\n if isinstance(error, str) and error.startswith('FLOOD_WAIT_'):\n retry_after = int(error.rsplit('_',1)[-1])\n raise RetryAfterError(retry_after)\n else:\n raise TelegraphException(error)\n\n return response","def raise_jira_ticket(obj,org_id):\n try:\n app_id = obj.get('app_id','') \n vul_name = obj.get('vul_name','')\n cwe = int(obj.get('cwe',0))\n project_key = obj.get('project_key','')\n issuetype = obj.get('issuetype','Bug')\n assignee = obj.get('assignee')\n app_obj = Application.objects.get(pk=app_id)\n if app_id and vul_name:\n vuls = Vulnerability.objects.filter(is_false_positive=False,is_remediated=False,scan__application=app_obj,cwe=cwe,name=vul_name)\n jira_obj = JiraIssueTypes.objects.get(org__id=org_id)\n jira = get_jira_con(jira_obj) \n if jira and vuls.exists(): \n complete_desc = ''\n references = '' \n if app_obj:\n complete_desc += 'Application:\\n{0}\\n\\n'.format(app_obj.name)\n complete_desc += 'Application URL:\\n{0}\\n\\n'.format(app_obj.url)\n if cwe:\n complete_desc += 'CWE :\\n{0}\\n\\n'.format(cwe)\n org_obj = app_obj.org\n if org_obj.orl_config_exists():\n vul_info = get_open_vul_info_from_api(cwe,org_obj)\n complete_desc += 'Description:\\n{0}\\n\\n'.format(vul_info.get('description','')) \n if references:\n complete_desc += 'References:\\n{0}'.format(references) \n data_dict = {\n 'project':{'key':project_key },\n 'issuetype':{'name': issuetype},\n 'priority':{'name': 'Highest'},\n 'summary':vul_name,\n 'description':complete_desc, \n } \n new_issue = jira.create_issue(**data_dict) \n evids = VulnerabilityEvidence.objects.filter(vul__in=vuls) \n attachment = io.StringIO()\n attachment.write('Evidences') \n for evid in evids:\n data = '\\n\\t- {0}\\n\\t\\t- {1}'.format(evid.url,evid.name)\n attachment.write(data) \n jira.add_attachment(issue=new_issue, attachment=attachment, filename='evidences.txt') \n vuls.update(jira_id=str(new_issue),jira_issue_status=str(new_issue.fields.status))\n info_debug_log(event='Raise Jira ticket',status='success')\n if assignee:\n jira.assign_issue(new_issue,assignee)\n info_debug_log(event='Assign Jira ticket to an assignee',status='success')\n except BaseException as e:\n print(\"Error raising JIRA tickets\")\n # general_error_messages.delay(path='raise_jira_ticket function',msg=log_exception(e))\n critical_debug_log(event=e,status='failure')","def upload(api_token, base_url, upload_file, metadata):\n\n upload_url = f\"{base_url}data_files/api_create?auth_token={api_token}\"\n files = {'file': open(upload_file, 'rb')}\n response = requests.post(upload_url, files=files, data=metadata)\n\n # Print out the outcome of the upload\n if response.status_code == 200:\n print(f'File {upload_file} successfully uploaded to HIEv')\n else:\n print(\n f'ERROR - There was a problem uploading file {upload_file} to HIEv')","def AdminTicket(ticket):\n try:\n data, = xmlrpclib.loads(ticket)[0]\n name = data['slivers'][0]['name']\n if data != None:\n deliver_ticket(data)\n logger.log('api_calls: Admin Ticket delivered for %s' % name)\n Create(database.db.get(name))\n except Exception, err:\n raise xmlrpclib.Fault(102, 'Ticket error: ' + str(err))","def _getApiAuthToken(self):\n return settings.EBAY_API_AUTH_TOKEN","def get_oauth_token():\n return session.get('remote_oauth')","def upload(self, upload_request):\n raise NotImplementedError","def request_access_ticket(pat: str, permission_endpoint: str,\n resources: List[dict],\n secure: bool = False) -> str:\n\n headers = {\n 'content-type': \"application/json\",\n 'authorization': \"Bearer \"+pat,\n }\n\n if len(resources) == 1:\n resources = resources[0] # Use a single dict instead of a list for 1 resource\n\n disable_warnings_if_debug(secure)\n response = request(\"POST\", permission_endpoint , json=resources, headers=headers, verify=secure)\n\n if not is_ok(response):\n raise Exception(\"An error occurred while requesting permission for a resource: \"+str(response.status_code)+\":\"+str(response.reason)+\":\"+str(response.text))\n\n try:\n return response.json()[\"ticket\"]\n except Exception as e:\n raise Exception(\"Call to permission endpoint returned unexpected value or error: '\"+response.text+\"'\"+\". Error: \"+str(e))","def test_create_ticket(self):\n with patch('requests.get') as mock:\n mock.return_value.status_code = 200\n pgt = ProxyGrantingTicket.objects.create_ticket('https://www.example.com', 'https://www.example.com/',\n user=self.user, granted_by_pt=self.pt)\n self.assertTrue(re.search(pgt.TICKET_RE, pgt.ticket))\n self.assertTrue(pgt.iou.startswith(pgt.IOU_PREFIX))","def get_multipart_upload(metadata):\n multipart_upload = None\n metadata_mp_id = None\n filename = metadata.path\n if filename.startswith(u\"/\"):\n filename = filename[1:]\n plug.logger.debug(u\"Getting multipart upload of {}\", filename)\n # Retrieve the stored multipart upload ID\n try:\n metadata_mp_id = metadata.extra['mp_id']\n except KeyError: # No multipart upload ID\n # Raise now is faster (doesn't go through all the MP uploads)\n raise DriverError(\"Unable to retrieve multipart upload ID\")\n if metadata_mp_id not in cache:\n # Try to only request multipart uploads of this file\n for mp in S3Conn.list_multipart_uploads(prefix=filename):\n # Go through all the multipart uploads\n # to find the one of this transfer\n if mp.uploadId == metadata_mp_id:\n multipart_upload = mp\n add_to_cache(mp)\n break\n else:\n multipart_upload = cache[metadata_mp_id]\n # At this point it shouldn't be None in any case\n if multipart_upload is None:\n raise DriverError(\"Cannot find upload for file '{}'\"\n .format(filename))\n plug.logger.debug(u\"Found multipart upload of {} - ID {}\",\n filename, multipart_upload.uploadId)\n return multipart_upload","def notify_upload():\n file_type = request.json.get(\"file_type\")\n file_name = request.json.get(\"file_name\")\n file_path = request.json.get(\"file_path\")\n file_size = request.json.get(\"file_size\")\n print(\"File was uploaded:\", file_path, file_name, file_type)\n # Marks the upload as confirmed.\n d = datetime.datetime.utcnow()\n db.upload.update_or_insert(\n ((db.upload.owner == get_user_email()) &\n (db.upload.file_path == file_path)),\n owner=get_user_email(),\n file_path=file_path,\n file_name=file_name,\n file_type=file_type,\n file_date=d,\n file_size=file_size,\n confirmed=True,\n )\n # Returns the file information.\n return dict(\n download_url=gcs_url(GCS_KEYS, file_path, verb='GET'),\n file_date=d,\n )","def get_ticket_detail(ticket_id):\n\n # Get the IX from the ticket\n db_connect = DBConnect().mysql_connect(mysql_user='user5100',\n mysql_password='ePPkLlao')\n db_connect.execute(db_queries.query_ix_codigo(ticket_id))\n ix_code = db_connect.fetchall()\n\n db_connect.execute(db_queries.query_ticket_detail(ticket_id))\n\n json_to_send = FormatJson.format_ticket_detail(\n db_connect.fetchall()[-1],\n ticket_id,\n ix_code[0]['codigo'])\n\n json.dumps(json_to_send)\n db_connect.close()\n return Response(json_to_send, mimetype='application/json;charset=utf-8'),\\\n 200","def initiate_multipart_upload(self):\n request = self.s3.create_request(\"OBJECT_POST\", uri = self.uri, headers = self.headers_baseline, extra = \"?uploads\")\n response = self.s3.send_request(request)\n data = response[\"data\"]\n self.upload_id = getTextFromXml(data, \"UploadId\")\n return self.upload_id","def upload_file(client, folder_id, file_name):\n\n new_file = client.folder(folder_id).upload(file_name)\n print(f\"File {new_file.name} uploaded to Box with file ID {new_file.id}\")\n return new_file.id","def get_file(self, file_id):\n LOG.debug(\"Getting a file from mattermost\")\n url = '%s/api/v4/files/%s' % (self.server_url, file_id)\n LOG.debug(\"Sending: %s\", url)\n response = self._request(self._session.get, url)\n\n if response.status_code != 200:\n raise RuntimeError(\"Server unhappy. (%s)\", response)\n\n return response.content","def getRequest(endpoint):\n\n headers = {\n \"Accept\": \"application/json\",\n \"Authorization\": \"bearer 7TUFM5BG2ikyTr5RQzNvFm3ALKC_7PUzGNcybO91WWny93bEzmmeeze0iXJNSisEno42aN2CKJIQOBr52ZeUeyFABfo1lq0aQDhCMJfsMFmC_l-FRCTeIN1DpqOHvkIgLmq5hlvUBk-q11VzNQZzA8L1QY4JoAGXEnXa-SojXw_2elYfzgpstXxtph9FcStOPX4YX0L1uDNPQAbO7gYLcbiD1d7qIoIA6wK71TO_WoDa10v6z-m1tNmYmhw98odThrSbkxhRJD9ktSAGOX8IgelS6TnkrsUGkzgxZjtGcMu_YnW_391vR7zJWwIPY-8LFc8Ueg\"\n }\n\n response = requests.get(endpoint, headers=headers)\n\n # print(response.json()) # use for testing and debugging\n\n return response.json()","def auth_token(self):","def remote_requestUploadSlot(self):\n # Create a new upload slot by using the transfers register\n slot_id = self.transfers_register.allocate_upload_slot()\n\n # Create the complete ftp address\n return self.transfers_register.address_by_id(slot_id)","def get_file(service, file_id):\n return service.files().get(fileId=file_id).execute()","def test_check_ticket_8(self):\n self.tkt.phage_id = \"\"\n import_genome.check_ticket(\n self.tkt, type_set=self.type_set,\n description_field_set=self.description_field_set,\n eval_mode_set=self.eval_mode_set,\n id_dupe_set=set(), phage_id_dupe_set=set(),\n retain_set=self.retain_set, retrieve_set=self.retrieve_set,\n add_set=self.add_set, parse_set=self.parse_set)\n count = count_status(self.tkt, \"error\")\n self.assertEqual(count, 1)","def parse_ticket(secret, ticket, ip, digest_algo=DEFAULT_DIGEST):\n if isinstance(digest_algo, str):\n # correct specification of digest from hashlib or fail\n digest_algo = getattr(hashlib, digest_algo)\n digest_hexa_size = digest_algo().digest_size * 2\n ticket = ticket.strip('\"')\n digest = ticket[:digest_hexa_size]\n try:\n timestamp = int(ticket[digest_hexa_size:digest_hexa_size + 8], 16)\n except ValueError as e:\n raise BadTicket('Timestamp is not a hex integer: %s' % e)\n try:\n userid, data = ticket[digest_hexa_size + 8:].split('!', 1)\n except ValueError:\n raise BadTicket('userid is not followed by !')\n userid = urllib.parse.unquote(userid)\n if '!' in data:\n tokens, user_data = data.split('!', 1)\n else:\n # @@: Is this the right order?\n tokens = ''\n user_data = data\n\n expected = calculate_digest(ip, timestamp, secret,\n userid, tokens, user_data,\n digest_algo)\n\n if expected != digest:\n raise BadTicket('Digest signature is not correct',\n expected=(expected, digest))\n\n tokens = tokens.split(',')\n\n return (timestamp, userid, tokens, user_data)","def m_apiInstance_AuthenticationStatusUpdate(self, sender, e):\r\n if e.Status.IsSuccess:\r\n # Add code here to begin working with the TT API\r\n # lookup an instrument\r\n self.m_req = ttapi.InstrumentLookupSubscription(self.m_apiInstance.Session, ttapi.Dispatcher.Current, ttapi.ProductKey(ttapi.MarketKey.Cme, ttapi.ProductType.Future, \"YM\"), \"Jun17\")\r\n self.m_req.Update += self.m_req_Update\r\n print(\"Connection Success!\")\r\n self.m_req.Start()\r\n else:\r\n print(\"TT Login failed: {0}\".format(e.Status.StatusMessage))\r\n self.Dispose()","def _get_file_to_upload(self, file_mime_type, file_no):\n tmp_file_handle = NamedTemporaryFile(mode=\"w\", encoding=\"utf-8\", delete=True)\n return (\n {\n \"file_name\": self.filename.format(file_no),\n \"file_handle\": tmp_file_handle,\n \"file_mime_type\": file_mime_type,\n \"file_row_count\": 0,\n },\n tmp_file_handle,\n )","def ticket_range(self):\n response = self.http_call(\"{0}/tickets.json\".format(self.uri))\n return math.ceil(response.json()[\"count\"] / 100) + 1","def uploadResourceFile(url, user, pWd, resourceName, fileName, fullPath):\n print(\"uploading file for resource \" + url + \" resource=\" + \n resourceName + ' user=' + user)\n apiURL = url + '/access/1/catalog/resources/' + resourceName + \"/files\"\n print(\"\\turl=\" + apiURL)\n # header = {\"accept\": \"*/*\", \"Content-Type\" : \"multipart/form-data\"} \n header = {\"accept\": \"*/*\"} \n print(\"\\t\" + str(header))\n params = {\"scannerid\": \"LineageScanner\", \"filename\": fileName, \n \"optionid\": \"File\"}\n print(\"\\t\" + str(params))\n# files = {'file': fullPath}\n mimeType = 'text/csv'\n readMode = 'rt'\n if fileName.endswith(\".zip\"):\n mimeType = 'application/zip'\n readMode = 'rb'\n \n file = { 'file' : (fileName, open(fullPath, readMode), mimeType)}\n print('\\t' + str(file))\n uploadResp = requests.post(apiURL, data=params, files=file, headers=header, \n auth=HTTPBasicAuth(user,pWd))\n print(\"\\tresponse=\" + str(uploadResp.status_code))\n if uploadResp.status_code == 200:\n # valid - return the jsom\n return uploadResp.status_code\n else:\n # not valid\n print(\"\\tupload file failed\")\n print(\"\\t\" + str(uploadResp))\n print(\"\\t\" + str(uploadResp.text))\n return uploadResp.status_code","def get_file_from_tg(file_id):\n get_file_url = BOT_URL + f'/getFile'\n\n response = requests.get(get_file_url, params={'file_id': file_id})\n json_data = json.loads(response.content)\n print(json_data)\n file_path = json_data['result']['file_path'] if json_data['result'] else None\n print(file_path)\n\n file_url = f'https://api.telegram.org/file/bot{TOKEN}/{file_path}'\n print(file_url)\n r = requests.get(file_url, allow_redirects=True)\n\n file_bytes = r.content\n print('got file')\n\n # test purposes\n file_name = f'debug_test_photo.jpg'\n open(file_name, 'wb').write(file_bytes)\n\n print(type(file_bytes))\n username = process_file(file_bytes)\n return username","def get_ticket_id():\n return str(time.time()) + str(uuid.uuid4())","def upload():\n upload_old_file = False\n file = request.files[\"data\"]\n stream_data = file.stream\n \n website_ticket = \"https://tedxruppticket.web.app/\"\n\n df = pd.read_csv(stream_data)\n name = df[\"name\"].values\n\n if \"id\" in df.columns:\n _id = df[\"id\"].values\n _name = df[\"name\"].values\n \n audiences = []\n for i in range(len(_id)):\n audiences.append(Audience(_id[i], _name[i]))\n add_to_database(audiences, multiple=True)\n\n return jsonify({\"data\": \"added\"})\n\n tickets = df[\"Ticket\"].values\n ticket_num = df[\"Ticket\"].values.sum()\n \n _name = []\n for i, n in enumerate(tickets):\n for j in range(n):\n print()\n _name.append(name[i])\n\n _id = [str(uuid4()).upper()[:7] for i in range(ticket_num)]\n\n if \"id\" in df.columns:\n upload_old_file = True\n _id = df[\"id\"].values\n\n audiences = []\n _tickets = []\n for i in range(len(_id)):\n audiences.append(Audience(_id[i], _name[i]))\n _tickets.append(website_ticket + f\"{_id[i]}\")\n add_to_database(audiences, multiple=True)\n\n output_dataframe = pd.DataFrame(data={\n \"id\": _id,\n \"name\": _name,\n \"ticket_url\": _tickets\n })\n\n output_dataframe.to_csv(\"./files/CircleAudiences.csv\")\n data_obj = output_dataframe.set_index(\"id\").to_dict()\n\n return {\"code\": \"0\", \"data\": data_obj}","def test_pre_upload_ok(self):\n namespace = 'default-gzip'\n good_digests = generate_collection(namespace, ['a pony'])\n self.assertEqual(good_digests.namespace.namespace, namespace)\n response = self.call_api(\n 'preupload', self.message_to_dict(good_digests), 200)\n message = response.json.get(u'items', [{}])[0]\n self.assertEqual('', message.get(u'gs_upload_url', ''))\n expected = validate(\n message.get(u'upload_ticket', ''),\n handlers_endpoints_v1.UPLOAD_MESSAGES[0])\n self.assertEqual(\n expected, generate_embedded(namespace, good_digests.items[0]))","def test_check_ticket_9(self):\n self.tkt.type = \"add\"\n import_genome.check_ticket(\n self.tkt, type_set=self.type_set,\n description_field_set=self.description_field_set,\n eval_mode_set=self.eval_mode_set,\n id_dupe_set=set(), phage_id_dupe_set=set(),\n retain_set=self.retain_set, retrieve_set=self.retrieve_set,\n add_set=self.add_set, parse_set=self.parse_set)\n count = count_status(self.tkt, \"error\")\n self.assertEqual(count, 1)","def find_url(ticketID):\n uri = 'https://jira.duraspace.org/browse/' + ticketID\n print uri\n return uri","def upload():\n\n # Get CAS user roles from session\n cas_attributes = session.get('CAS_ATTRIBUTES', None)\n\n # login_required should mean we're authenticated, but check this\n if not cas_attributes:\n abort(403)\n\n roles = cas_attributes['cas:authority'].split(',')\n\n APP.logger.debug(\n f'User has CAS roles: {roles} ')\n\n # Stop unauthorized users\n if CONFIG.UPLOAD_ROLE not in roles:\n abort(403)\n\n form = UploadForm()\n\n if request.method != 'POST':\n return render_template('upload.html', form=form, upload_error=False)\n\n # User has tried to submit file (POST)\n # Check if it passes (simple) validation in forms.py\n if not form.validate_on_submit():\n return render_template('upload.html', form=form)\n\n # Get filename\n f = form.file.data\n filename = secure_filename(f.filename)\n # Add user email & time\n email = cas_attributes['cas:email']\n upload_time = dt.now().strftime(\"%y%m%d-%H%M%S\")\n ext_filename = email + '_' + upload_time + '_' + filename\n\n # Save file\n try:\n f.save(os.path.join(CONFIG.UPLOAD_PATH, ext_filename))\n except Exception as ex:\n APP.logger.error(\n f'File {ext_filename} could not be saved due to {ex}')\n else:\n APP.logger.info(f'Successfully uploaded file {ext_filename}')\n\n # Notify admin\n msg = Message('New file upload',\n sender=APP.mail.username,\n recipients=CONFIG.UPLOAD_EMAIL)\n msg.body = f\"\"\"\n Hello SBDI-MOL colleagues,\n\n A new file has been uploaded to the ASV portal:\n\n Provider email: {email}\n Upload time: {upload_time}\n Original filename: {filename}\n Saved as: {ext_filename}\n\n Have a nice day!\n\n / Swedish ASV portal\n \"\"\"\n try:\n APP.mail.send(msg)\n except Exception as ex:\n APP.logger.error(f\"Could not send upload notification due to {ex}\")\n else:\n APP.logger.info('Successfully sent upload notification.')\n # Display 'success page' only if upload AND notification worked\n return render_template('uploaded.html', filename=filename)\n\n # Display error msg if EITHER upload OR email failed, so that data\n # providers get a chance to tell us about uploaded files\n return render_template('upload.html', form=form, upload_error=True)","def get_box(req):","def _perform_request(cls, url, payload, auth_token):\n\n LOGGER.debug('Performing request to %s', url)\n\n # request headers\n headers = {'X-Auth-Token': auth_token,\n 'Content-type': 'application/json'}\n # perform request and return its result\n try:\n return requests.post(\n url, data=payload, headers=headers,\n timeout=(Config.instance().CEILOMETER_TIMEOUT / 1000.))\n except RequestException as exc:\n LOGGER.error('Ceilometer request error: %s', six.text_type(exc))\n return None","def __create_ticket(user, subject, description, topic):\n\n target = settings.SLACK_TARGET_TFED\n if topic == 'Database':\n target = settings.SLACK_TARGET_TFED_DB\n user_email = user['user']['profile'].get('email', 'lnl-no-reply@wpi.edu')\n display_name = user['user']['profile']['real_name']\n resp = rt_api.create_ticket(topic, user_email, subject, description + \"\\n\\n- \" + display_name)\n ticket_id = resp.get('id', None)\n if ticket_id:\n ticket_info = {\n \"url\": 'https://lnl-rt.wpi.edu/rt/Ticket/Display.html?id=' + ticket_id,\n \"id\": ticket_id,\n \"subject\": subject,\n \"description\": description,\n \"status\": \"New\",\n \"assignee\": None,\n \"reporter\": user['user']['name']\n }\n ticket = views.tfed_ticket(ticket_info)\n slack_post(target, text=description, content=ticket, username='Request Tracker')\n return\n error_message = \"Whoops! It appears something went wrong while attempting to submit your request. \" \\\n \"Please wait a few minutes then try again. If the problem persists, please email \" \\\n \"us directly at tfed@wpi.edu.\"\n post_ephemeral(target, error_message, user['user']['id'], username=\"Request Tracker\")","def test_check_ticket_2(self):\n import_genome.check_ticket(\n self.tkt, type_set=self.type_set,\n description_field_set=self.description_field_set,\n eval_mode_set=self.eval_mode_set,\n id_dupe_set=set([1]), phage_id_dupe_set=set(),\n retain_set=self.retain_set, retrieve_set=self.retrieve_set,\n add_set=self.add_set, parse_set=self.parse_set)\n count = count_status(self.tkt, \"error\")\n self.assertEqual(count, 1)","def test_check_ticket_11(self):\n self.tkt.data_retrieve = {\"invalid\"}\n import_genome.check_ticket(\n self.tkt, type_set=self.type_set,\n description_field_set=self.description_field_set,\n eval_mode_set=self.eval_mode_set,\n id_dupe_set=set(), phage_id_dupe_set=set(),\n retain_set=self.retain_set, retrieve_set=self.retrieve_set,\n add_set=self.add_set, parse_set=self.parse_set)\n count = count_status(self.tkt, \"error\")\n self.assertEqual(count, 1)","def get_ticket_info(self):\n return json.dumps(self.__dict__)","def create_ticket(data):\n firebase_uid = data['session'].split('/')[-1]\n contexts = data['queryResult']['outputContexts']\n for i in contexts:\n if 'visit_data' in i['name']:\n context = i\n break\n\n date = datetime.datetime.now()\n date = date.strftime(\"%d-%m-%Y\")\n\n raw_params = context['parameters']\n ticket_params = {\n \"Agent\": \"None\",\n \"Product Type\": raw_params[\"product_type\"],\n \"Type\": \"House Call\",\n \"Issue Type\": raw_params[\"issue_type\"],\n \"Description\": raw_params[\"description\"],\n \"Model Number\": raw_params[\"model_number\"],\n \"Serial Number\": raw_params[\"serial_number\"],\n \"Status\": \"Open\",\n \"Date\": date,\n \"Time Slot Chosen\": \"0\",\n \"Time Slots\": {\"Slot 1\": {\"Time\": \"0\", \"Date\": \"0\"},\n \"Slot 2\": {\"Time\": \"0\", \"Date\": \"0\"},\n \"Slot 3\": {\"Time\": \"0\", \"Date\": \"0\"}},\n \"Progress\": \"Under Review\",\n \"Free Time\": {\n \"Date\": \"0\",\n \"Time\": \"0\",\n },\n \"Details of Call\": {\n \"Time\": \"0\",\n \"Date\": \"0\"}\n }\n ticket_id = str(uuid.uuid4())[:8]\n db = firebase.database()\n db.child(\n 'user_data').child(\n firebase_uid).child(\n 'Complaints').child(ticket_id).set(ticket_params)\n\n fulfillment_response = {\n \"fulfillmentText\":\n \"You ticket was successfully registered. The reference number is \" + ticket_id +\n \". Based on the availability of our agents, we will give you three time slots to choose from. You can \"\n \"either go to the \\\"Tickets\\\" section of the app and update your preference or do so by talking to me.\"}\n return fulfillment_response","def get_uapi_token(jamf_url, enc_creds, verbosity):\n headers = {\n \"authorization\": \"Basic {}\".format(enc_creds),\n \"content-type\": \"application/json\",\n \"accept\": \"application/json\",\n }\n url = \"{}/uapi/auth/tokens\".format(jamf_url)\n http = requests.Session()\n if verbosity > 2:\n http.hooks[\"response\"] = [logging_hook]\n\n r = http.post(url, headers=headers)\n if verbosity > 2:\n print(r.content)\n if r.status_code == 200:\n obj = json.loads(r.text)\n try:\n token = str(obj[\"token\"])\n print(\"Session token received\")\n return token\n except KeyError:\n print(\"ERROR: No token received\")\n return\n else:\n print(\"ERROR: No token received\")\n return","def post(self):\n\n upload_files = self.get_uploads('file')\n blob_info = upload_files[0]\n self.redirect('/?upload_info=%s' % urllib.quote(blob_info.filename))","def get_files_api():\n return get_api_client(cloudsmith_api.FilesApi)","def upload(self, file_path):\n LOG.debug((\"Uploading file to mattermost: %s\", file_path))\n filename = os.path.basename(file_path)\n post_data = {'channel_id': self.channel_id,\n 'client_ids': filename}\n file_data = {'files': (filename, open(file_path, 'rb'))}\n url = '%s/api/v4/files' % self.server_url\n response = self._request(self._session.post, url, data=post_data,\n files=file_data)\n LOG.debug(\"Received: %s - %s\", response.status_code, response.text)\n\n if response.status_code != 201:\n if response.status_code == 400:\n msg = \"Type of the uploaded file doesn't match its file \" \\\n \" extension or uploaded file is an image that \" \\\n \"exceeds the maximum dimensions\"\n elif response.status_code == 401:\n msg = \"User is not logged in\"\n elif response.status_code == 403:\n msg = \"User does not have permission to upload file to \" \\\n \"the provided team/channel\"\n elif response.status_code == 413:\n msg = \"Uploaded file is too large\"\n elif response.status_code == 500:\n msg = \"File storage is disabled\"\n else:\n msg = response\n raise RuntimeError(\"Server unhappy with request, reports: %s\"\n % msg)\n\n file_id = response.json()[\"file_infos\"][0][\"id\"]\n return file_id","def test_check_ticket_4(self):\n self.tkt.type = \"invalid\"\n import_genome.check_ticket(\n self.tkt, type_set=self.type_set,\n description_field_set=self.description_field_set,\n eval_mode_set=self.eval_mode_set,\n id_dupe_set=set(), phage_id_dupe_set=set(),\n retain_set=self.retain_set, retrieve_set=self.retrieve_set,\n add_set=self.add_set, parse_set=self.parse_set)\n count = count_status(self.tkt, \"error\")\n self.assertEqual(count, 1)","def test_get_single_poll_submission(self):\r\n poll_id = None # Change me!!\r\n poll_session_id = None # Change me!!\r\n id = None # Change me!!\r\n\r\n r = self.client.get_single_poll_submission(id, poll_id, poll_session_id)","def get_fingerprint():\n print('Requesting fingerprint...')\n config = {\n 'host': os.environ.get('ACR_HOST'),\n 'access_key': os.environ.get('ACR_ACCESS_KEY'), \n 'access_secret': os.environ.get('ACR_ACCESS_SECRET'),\n 'timeout': 10\n }\n recognizer = ACRCloudRecognizer(config)\n mp3_path = helpers.get_mp3_output_path()\n start_seconds = 0\n rec_length = helpers.get_recording_length_seconds()\n result = json.loads(recognizer.recognize_by_file(mp3_path, start_seconds, rec_length))\n save_fingerprint_result_to_file(result)\n if int(result['status']['code']) == 0:\n try:\n song = {\n 'name': get_song_name_from_result(result),\n 'id': get_spotify_id_from_result(result),\n 'artist': get_first_artist_from_result(result),\n 'seconds_remaining': get_song_seconds_remaining(result),\n 'percent_remaining': get_song_percent_remaining(result),\n }\n print('Song Found: {}'.format(song['name']))\n return song\n except KeyError:\n pass\n return None"],"string":"[\n \"def get_service_ticket():\\n\\n payload = {'username': APIC_EM_USER, 'password': APIC_EM_PASSW}\\n url = 'https://' + APIC_EM + '/ticket'\\n header = {'content-type': 'application/json'}\\n ticket_response = requests.post(url, data=json.dumps(payload), headers=header, verify=False)\\n if not ticket_response:\\n print('Something went wrong, try again!')\\n else:\\n ticket_json = ticket_response.json()\\n ticket = ticket_json['response']['serviceTicket']\\n print('APIC-EM ticket: ', ticket) # print the ticket for reference only, not required\\n return ticket\",\n \"def get_upload(arn=None):\\n pass\",\n \"def ticket(self, ticket_id):\\r\\n return resources.Ticket(self, ticket_id)\",\n \"def get_ticket_info(name: str) -> JiraTicket:\\n ticket_url = build_rest_url(name)\\n res = requests.get(ticket_url, auth=get_jira_auth()).json()\\n issue_type = JiraIssueTypeFactory(**res[\\\"fields\\\"][\\\"issuetype\\\"])\\n summary = res[\\\"fields\\\"][\\\"summary\\\"]\\n browse_url = build_browse_url(name)\\n\\n return JiraTicket(name=name, issue_type=issue_type, summary=summary, url=browse_url)\",\n \"def get_ticket(self, ticket_id):\\n response = self.http_call(\\\"{0}/tickets/{1}.json\\\".format(self.uri, ticket_id))\\n return json.loads(response.content.decode(sys.stdout.encoding, \\\"replace\\\"))\",\n \"def get_ticket(self, ticket_id):\\r\\n mask = ('mask[id, title, assignedUser[firstName, lastName],'\\r\\n 'createDate,lastEditDate,updates[entry],updateCount]')\\r\\n return self.ticket.getObject(id=ticket_id, mask=mask)\",\n \"def delete_upload_ticket(self, complete_uri):\\n url = self.config['apiroot'] + complete_uri\\n log.info(\\\"Requesting %s\\\" % url)\\n r = HTTPClient().fetch(url, method=\\\"DELETE\\\", headers=self.standard_headers,\\n validate_cert=not self.config['dev'])\\n log.info(\\\"Upload completed: status code: %d\\\" % r.code)\\n if r.code == 201:\\n _id = r.headers['location'].split('/')[-1]\\n return _id\\n raise ValueError(\\\"Upload completion unsuccessful\\\")\",\n \"def ticket(self, ticket_id):\\r\\n return tickets.Ticket(self, ticket_id)\",\n \"def ticket_id(self):\\n return self._ticket_id\",\n \"def prepare_ticket(self, req, ticket, fields, actions):\",\n \"def create_ticket(self, ticket):\\r\\n ticket_url = self._zendesk_instance.create_ticket(data=ticket)\\r\\n return zendesk.get_id_from_url(ticket_url)\",\n \"def Ticket(ticket):\\n try:\\n data = ticket_module.verify(ticket)\\n name = data['slivers'][0]['name']\\n if data != None:\\n deliver_ticket(data)\\n logger.log('api_calls: Ticket delivered for %s' % name)\\n Create(database.db.get(name))\\n except Exception, err:\\n raise xmlrpclib.Fault(102, 'Ticket error: ' + str(err))\",\n \"def get_ws_ticket(jwt: str) -> str:\\n LOGGER.debug(\\\"Retrieving ticket...\\\")\\n\\n args = {\\n \\\"url\\\": \\\"{0}/kafka-ws/v1/ticket\\\".format(CONFIG['dojot']['url']),\\n \\\"headers\\\": {\\n \\\"Content-Type\\\": \\\"application/json\\\",\\n \\\"Authorization\\\": \\\"Bearer {0}\\\".format(jwt),\\n },\\n }\\n\\n res = DojotAPI.call_api(requests.get, args)\\n\\n LOGGER.debug(\\\".. retrieved ticket\\\")\\n return res[\\\"ticket\\\"]\",\n \"def ticket(self, ticket_id):\\n return tickets.Ticket(self, ticket_id)\",\n \"def get(self, **kwargs):\\n params = {\\\"token\\\": self.token}\\n params.update(kwargs)\\n return self.api._get(\\\"uploads/get\\\", params=params)\",\n \"def transfer_ticket(self, ticket_id):\\n ticket = self.zendesk.get_ticket(ticket_id)\\n subject = ticket[\\\"ticket\\\"][\\\"subject\\\"]\\n status = ticket[\\\"ticket\\\"][\\\"status\\\"]\\n description = ticket[\\\"ticket\\\"][\\\"description\\\"]\\n if ticket[\\\"ticket\\\"][\\\"assignee_id\\\"] is not None:\\n assignee_email = self.zendesk.get_assignee_email(ticket[\\\"ticket\\\"][\\\"assignee_id\\\"])\\n try:\\n requester = ticket[\\\"ticket\\\"][\\\"via\\\"][\\\"source\\\"][\\\"from\\\"][\\\"address\\\"]\\n except KeyError:\\n requester = self.default_requester\\n # Terms for the status of a ticket on Samanage differ from those on Zendesk\\n # When creating a ticket on Samanage through API only statuses allowed are Closed and New.\\n # After the ticket is created status can be changed.\\n if status in (\\\"open\\\", \\\"pending\\\"):\\n status = \\\"New\\\"\\n update_status = \\\"Assigned\\\"\\n if status in (\\\"closed\\\", \\\"solved\\\"):\\n status = \\\"Closed\\\"\\n update_status = \\\"Closed\\\"\\n # We can now make incident on Samanage\\n if self.samanage:\\n incident = self.samanage.incident(\\n subject, requester, self.priority, status, assignee_email, description\\n )\\n incident_id = json.loads(incident)[\\\"id\\\"]\\n # Get all comments for a ticket on zendesk\\n comments = self.zendesk.get_comments(ticket_id)\\n comment_list = []\\n for comment in comments[\\\"comments\\\"]:\\n author = self.zendesk.get_comment_author(comment[\\\"author_id\\\"])\\n if self.dump:\\n comment_list.append({\\\"author\\\": author, \\\"body\\\": comment[\\\"body\\\"]})\\n # Transfer comment(s) to Samanage\\n if self.samanage:\\n self.samanage.comment(\\\"From:{0}\\\\n{1}\\\".format(author, comment[\\\"body\\\"]), incident_id)\\n # Adding comments to samanage ticket reopens it\\n # (re)update the status of the ticket on samanage to specified status\\n if self.samanage:\\n self.samanage.update_status(update_status, incident_id)\\n\\n # JSON dump if initalized\\n if self.dump:\\n with open(\\\"ticket_dump.json\\\", \\\"a\\\", errors='replace') as dump_file:\\n card_details = {ticket_id:{\\n \\\"id\\\": ticket_id,\\n \\\"subject\\\": subject,\\n \\\"requester\\\": requester,\\n \\\"status\\\": status,\\n \\\"assignee\\\": assignee_email,\\n \\\"description\\\": description,\\n \\\"comments\\\": comment_list}}\\n dump_file.write(json.dumps(card_details, ensure_ascii=False,\\n sort_keys=True, indent=4))\",\n \"def get_ticket_by_id(id):\\n url = \\\"https://zccreynosa.zendesk.com/api/v2/tickets/\\\" + id + \\\".json\\\"\\n response = requests.get(url, auth=(user, token))\\n\\n if response.status_code != 200:\\n return None\\n else:\\n json_data = json.loads(response.text)\\n return json_data\",\n \"def get_upload_status(self, upload_id: str, token: str) -> Upload:\\n data, _, _ = self.json('get', f'/{upload_id}', token)\\n return self._parse_upload_status(data)\",\n \"def upload_file(self):\\n request = copy.deepcopy(self.request_template)\\n data = json.dumps(request)\\n curr_file = {\\n 'request': data,\\n 'file': open(self.file_path, 'rb')\\n }\\n print(\\\"Sending Upload request of av for file {}\\\".format(self.file_name))\\n try:\\n response = requests.post(url=self.url + \\\"upload\\\", files=curr_file, verify=False)\\n except Exception as E:\\n print(\\\"Upload file failed. file: {} , failure: {}\\\".format(self.file_name, E))\\n raise\\n response_j = response.json()\\n print(\\\"av Upload response status for file {} : {}\\\".format(self.file_name,\\n response_j[\\\"response\\\"][0][\\\"status\\\"][\\\"label\\\"]))\\n return response_j\",\n \"def upload(self, filepath):\\n if self.ver is not None and LooseVersion(str(self.ver)) < LooseVersion('1.4.0'):\\n raise VersionMismatchError('File upload')\\n\\n try:\\n with open(filepath, 'rb') as stream:\\n url = '{0}{1}'.format(self.url, '/uploads.json')\\n response = self.request('post', url, data=stream, headers={'Content-Type': 'application/octet-stream'})\\n except IOError:\\n raise NoFileError()\\n\\n return response['upload']['token']\",\n \"def issue( self ):\\n\\n self.state.issueDate = \\\"%d\\\" % time.time()\\n\\n # Encrypt the protocol state.\\n aes = AES.new(self.symmTicketKey, mode=AES.MODE_CBC, IV=self.IV)\\n state = repr(self.state)\\n assert (len(state) % AES.block_size) == 0\\n cryptedState = aes.encrypt(state)\\n\\n # Authenticate ticket name, IV and the encrypted state.\\n hmac = HMAC.new(self.hmacTicketKey, self.IV + \\\\\\n cryptedState, digestmod=SHA256).digest()\\n\\n ticket = self.IV + cryptedState + hmac\\n\\n log.debug(\\\"Returning %d-byte ticket.\\\" % (len(self.IV) +\\n len(cryptedState) + len(hmac)))\\n\\n return ticket\",\n \"def _get(self, ticket_id):\\n ticket_id = int(ticket_id)\\n ticket = DB_TICKET_TABLE.get(doc_id=ticket_id)\\n if not ticket:\\n flask_restful.abort(404, message=f\\\"Ticket '{ticket_id}' not found!\\\")\\n comments_q = Query()\\n comments = DB_COMMENT_TABLE.search((comments_q.ticket_id == ticket_id) &\\n (comments_q.type == Comment.TYPE_COMMENT))\\n worknotes = DB_COMMENT_TABLE.search((comments_q.ticket_id == ticket_id) &\\n (comments_q.type == Comment.TYPE_WORKNOTE))\\n res = {\\n \\\"id\\\" : ticket.doc_id,\\n }\\n res.update(ticket)\\n res['_embedded'] = {\\n \\\"comments\\\" : self.embed_comment_data_in_result(comments),\\n \\\"worknotes\\\" : self.embed_comment_data_in_result(worknotes)\\n }\\n res['_links'] = self.make_links({\\n \\\"self\\\" : Ticket.get_self_url(ticket.doc_id),\\n \\\"contained_in\\\" : TicketList.get_self_url(),\\n \\\"customer\\\" : Customer.get_self_url(res['customer_id']),\\n \\\"user\\\" : User.get_self_url(res['user_id'])\\n })\\n return res\",\n \"def validate_oculus_ticket():\\n\\n ob = request.get_json()\\n try:\\n OculusProviderAuthSchema().load(ob)\\n except ma.ValidationError as e:\\n abort_unauthorized(\\\"Oculus token property %s is invalid\\\" % e.field_name)\\n\\n provider_details = ob['provider_details']\\n # Get Oculus authentication config\\n oculus_config = get_provider_config('oculus')\\n\\n if not oculus_config:\\n abort(http_client.SERVICE_UNAVAILABLE, description=\\\"Oculus authentication not configured for current tenant\\\")\\n\\n # Call validation and authenticate if ticket is good\\n identity_id = run_ticket_validation(\\n user_id=provider_details['user_id'],\\n access_token=oculus_config['access_token'],\\n nonce=provider_details['nonce']\\n )\\n\\n return identity_id\",\n \"def upload(self) :\\n\\t\\ttry :\\n\\t\\t\\treturn self._upload\\n\\t\\texcept Exception as e:\\n\\t\\t\\traise e\",\n \"def support_submit_ticket(self, **kw):\\n person_name = \\\"\\\"\\n if http.request.env.user.name != \\\"Public user\\\":\\n person_name = http.request.env.user.name\\n pic = http.request.env['schedule.person.in.charge'].sudo().search([],order = 'end_date desc',limit=1).pic \\n return http.request.render('website_support_indonesia.support_submit_ticket', {'categories': http.request.env['website.support.ticket.categories'].sudo().search([('type_view','=',False)]), 'person_name': person_name, 'email': http.request.env.user.email, 'pic':pic})\",\n \"def ticket_created(self, ticket):\",\n \"def verify_ticket(self, ticket):\\n raise NotImplementedError()\",\n \"def get_approved_tickets(ticket):\\n with open(\\\"{}/.ssh/jira.pem\\\".format(os.environ[\\\"HOME\\\"]), \\\"r\\\") as key_cert_file:\\n key_cert_data = key_cert_file.read()\\n oauth_dict = {\\n \\\"access_token\\\": \\\"PrxvLiICry8gkXXPHqnorpRHjBCn3V0O\\\",\\n \\\"access_token_secret\\\": \\\"kNX06aseOyJx3GnWmRH1w14UQ4o7puiV\\\",\\n \\\"consumer_key\\\": \\\"ceej-ticket-key\\\",\\n \\\"key_cert\\\": key_cert_data\\n }\\n jira = JIRA({\\\"server\\\": \\\"https://issues.couchbase.com\\\"}, oauth=oauth_dict)\\n links = [link.outwardIssue.key\\n for link in jira.issue(ticket).fields.issuelinks\\n if hasattr(link, \\\"outwardIssue\\\")]\\n return links\",\n \"def test_finalized_no_upload_ticket(self):\\n namespace = 'default-gzip'\\n request = self.store_request(namespace, pad_string('silence'))\\n request.upload_ticket = None\\n with self.call_should_fail('400'):\\n self.call_api('finalize_gs_upload', self.message_to_dict(request), 200)\",\n \"def get_auth_token():\\n \\n form_fields = {\\n \\\"client_id\\\": client_id,\\n \\\"client_secret\\\":client_secret,\\n \\\"code\\\": code,\\n \\\"redirect_uri\\\": \\\"http://www.stackprinter.com\\\"\\n }\\n form_data = urllib.urlencode(form_fields)\\n results = __gae_fetch(url = 'https://stackexchange.com/oauth/access_token',\\n method = urlfetch.POST, \\n payload = form_data,\\n headers={'Content-Type': 'application/x-www-form-urlencoded'})\\n response = results.content\\n return response\",\n \"def get_proxy_ticket(self, pgt):\\n response = self.session.get(self.get_proxy_url(pgt), verify=self.verify_ssl_certificate)\\n if response.status_code == 200:\\n from lxml import etree\\n root = etree.fromstring(response.content)\\n tickets = root.xpath(\\n \\\"//cas:proxyTicket\\\",\\n namespaces={\\\"cas\\\": \\\"http://www.yale.edu/tp/cas\\\"}\\n )\\n if len(tickets) == 1:\\n return tickets[0].text\\n errors = root.xpath(\\n \\\"//cas:authenticationFailure\\\",\\n namespaces={\\\"cas\\\": \\\"http://www.yale.edu/tp/cas\\\"}\\n )\\n if len(errors) == 1:\\n raise CASError(errors[0].attrib['code'], errors[0].text)\\n raise CASError(\\\"Bad http code %s\\\" % response.status_code)\",\n \"def next_upload(self):\\n # print \\\"fetching upload...\\\"\\n try:\\n uploads = Backend.next(\\n self.account, location=self.location,\\n project=self.project) or []\\n except (exceptions.BackendDown, exceptions.BackendError):\\n raise\\n else:\\n if uploads:\\n # Return the one download in the list\\n return uploads[0]\",\n \"def request_pet_finder_token():\\n resp = HTTP_request.post('https://api.petfinder.com/v2/oauth2/token',\\n data={\\n \\\"grant_type\\\": 'client_credentials',\\n \\\"client_id\\\": PET_FINDER_API_KEY,\\n \\\"client_secret\\\": PET_FINDER_SECRET\\n })\\n return resp.json()[\\\"access_token\\\"]\",\n \"def ticket(self) -> \\\"Ticket\\\":\\n return Ticket(connection=self)\",\n \"def test_get_upload_job(cidc_api, clean_db, monkeypatch):\\n user_id = setup_trial_and_user(cidc_api, monkeypatch)\\n user_job, other_job, _ = setup_upload_jobs(cidc_api)\\n\\n client = cidc_api.test_client()\\n\\n # Regular CIMAC users aren't allowed to get upload jobs\\n res = client.get(f\\\"upload_jobs/{user_job}\\\")\\n assert res.status_code == 401\\n\\n make_cimac_biofx_user(user_id, cidc_api)\\n\\n # 404 for non-existent upload\\n res = client.get(f\\\"upload_jobs/123123\\\")\\n assert res.status_code == 404\\n\\n # 404 for another user's upload if non-admin\\n res = client.get(f\\\"upload_jobs/{other_job}\\\")\\n assert res.status_code == 404\\n\\n # 200 for user's upload\\n res = client.get(f\\\"upload_jobs/{user_job}\\\")\\n assert res.status_code == 200\\n assert res.json[\\\"id\\\"] == user_job\\n\\n # 200 for another user's upload if admin\\n make_admin(user_id, cidc_api)\\n res = client.get(f\\\"upload_jobs/{other_job}\\\")\\n assert res.status_code == 200\\n assert res.json[\\\"id\\\"] == other_job\",\n \"def __request(self,endpoint):\\n apiRequest = requests.get(\\\"%s/%s\\\" % (self.baseurl,endpoint), \\n auth=requests.auth.HTTPBasicAuth(self.api_id, self.api_secret))\\n try:\\n json = apiRequest.json()\\n return json\\n except JSONDecodeError:\\n print(\\\"Failed to download or failed to parse JSON.\\\")\\n print(apiRequest)\\n return None\",\n \"def ticket_chooser(ticket):\\n try:\\n # Find the ticket type's button\\n ticket_type = browser.find_element_by_xpath(ticket)\\n ticket_type.click()\\n\\n except Exception as e:\\n print(\\\"Ticket type not found\\\")\",\n \"def get_uploaded_file(filename):\\n client = _get_client()\\n key = client.key(_FILE_ENTITY, filename)\\n entity = client.get(key)\\n if entity:\\n return UploadedFile(filename, entity['url'])\\n return None\",\n \"def upload(self, upload_id):\\r\\n return u.Upload(self, upload_id)\",\n \"def _ticket(self):\\n self._m_ticket += 1\\n return(self._m_ticket)\",\n \"def getUser(self, authenticationToken):\\r\\n pass\",\n \"def download(self, id, article_id, ticket_id):\\n response = self._connection.session.get(\\n self.url + f\\\"/{ticket_id}/{article_id}/{id}\\\"\\n )\\n return self._raise_or_return_json(response)\",\n \"def _requestSwiftToken(self):\\n oauth_access_token = self.accessTokenManager.token\\n c, r = http._get(\\n self.auth_package.HUBIC_API+'account/credentials/',\\n headers={\\n 'Authorization': 'Bearer '+oauth_access_token\\n }\\n )\\n result = json.loads(r.read())\\n c.close()\\n\\n if r.status != 200:\\n try:\\n err =result\\n err['code'] = r.status\\n except Exception as e:\\n err = {}\\n\\n raise Exception(\\\"Unable to get swift token, \\\"\\n \\\"(%s)\\\"%str(err))\\n\\n self._endpoint = result['endpoint']\\n self._token = result['token']\\n self._expire = datetime.strptime( result['expires'][:-6], \\\"%Y-%m-%dT%H:%M:%S\\\" ) - timedelta(seconds=10)\",\n \"def save(self,owner=None):\\n\\n q = Queue.objects.get(id=int(self.cleaned_data['queue']))\\n\\n t = Ticket(\\n title = self.cleaned_data['title'],\\n owner=owner,\\n submitter_email = self.cleaned_data['submitter_email'],\\n created = datetime.now(),\\n status = Ticket.OPEN_STATUS,\\n queue = q,\\n description = self.cleaned_data['body'],\\n priority = self.cleaned_data['priority'],\\n account = owner.account\\n )\\n\\n t.save()\\n\\n f = FollowUp(\\n ticket = t,\\n title = _('Ticket Opened Via Web'),\\n date = datetime.now(),\\n public = True,\\n comment = self.cleaned_data['body'],\\n account = owner.account\\n )\\n\\n f.save()\\n\\n files = []\\n if self.cleaned_data['attachment']:\\n import mimetypes\\n file = self.cleaned_data['attachment']\\n filename = file.name.replace(' ', '_')\\n a = Attachment(\\n followup=f,\\n filename=filename,\\n mime_type=mimetypes.guess_type(filename)[0] or 'application/octet-stream',\\n size=file.size,\\n )\\n a.file.save(file.name, file, save=False)\\n a.save()\\n \\n if file.size < getattr(settings, 'MAX_EMAIL_ATTACHMENT_SIZE', 512000):\\n # Only files smaller than 512kb (or as defined in \\n # settings.MAX_EMAIL_ATTACHMENT_SIZE) are sent via email.\\n files.append(a.file.path)\\n\\n context = {\\n 'ticket': t,\\n 'queue': q,\\n }\\n\\n messages_sent_to = []\\n\\n send_templated_mail(\\n 'newticket_owner',\\n context,\\n recipients=t.submitter_email,\\n sender=q.from_address,\\n fail_silently=True,\\n files=files,\\n )\\n messages_sent_to.append(t.submitter_email)\\n\\n if q.new_ticket_cc and q.new_ticket_cc not in messages_sent_to:\\n send_templated_mail(\\n 'newticket_cc',\\n context,\\n recipients=q.new_ticket_cc,\\n sender=q.from_address,\\n fail_silently=True,\\n files=files,\\n )\\n messages_sent_to.append(q.new_ticket_cc)\\n\\n if q.updated_ticket_cc and q.updated_ticket_cc != q.new_ticket_cc and q.updated_ticket_cc not in messages_sent_to:\\n send_templated_mail(\\n 'newticket_cc',\\n context,\\n recipients=q.updated_ticket_cc,\\n sender=q.from_address,\\n fail_silently=True,\\n files=files,\\n )\\n\\n return t\",\n \"def verify_ticket(self, ticket):\\n response = self.get_verification_response(ticket)\\n return self.verify_response(response)\",\n \"def upload(request):\\n if request.user is None:\\n if IS_DEV:\\n request.user = users.User(request.POST.get('user', 'test@example.com'))\\n else:\\n return HttpTextResponse('Login required', status=401)\\n # Check against old upload.py usage.\\n if request.POST.get('num_parts') > 1:\\n return HttpTextResponse('Upload.py is too old, get the latest version.')\\n form = UploadForm(request.POST, request.FILES)\\n issue = None\\n patchset = None\\n if form.is_valid():\\n issue_id = form.cleaned_data['issue']\\n if issue_id:\\n action = 'updated'\\n issue = models.Issue.get_by_id(issue_id)\\n if issue is None:\\n form.errors['issue'] = ['No issue exists with that id (%s)' %\\n issue_id]\\n elif not form.cleaned_data.get('content_upload'):\\n form.errors['issue'] = ['Base files upload required for that issue.']\\n issue = None\\n else:\\n if not issue.edit_allowed:\\n form.errors['user'] = ['You (%s) don\\\\'t own this issue (%s)' %\\n (request.user, issue_id)]\\n issue = None\\n elif issue.closed:\\n form.errors['issue'] = ['This issue is closed (%s)' % (issue_id)]\\n issue = None\\n else:\\n patchset = _add_patchset_from_form(request, issue, form, 'subject',\\n emails_add_only=True)\\n if not patchset:\\n issue = None\\n else:\\n action = 'created'\\n issue, patchset = _make_new(request, form)\\n if issue is None:\\n msg = 'Issue creation errors: %s' % repr(form.errors)\\n else:\\n msg = ('Issue %s. URL: %s' %\\n (action,\\n request.build_absolute_uri(\\n reverse('show_bare_issue_number', args=[issue.key.id()]))))\\n if (form.cleaned_data.get('content_upload') or\\n form.cleaned_data.get('separate_patches')):\\n # Extend the response message: 2nd line is patchset id.\\n msg +=\\\"\\\\n%d\\\" % patchset.key.id()\\n if form.cleaned_data.get('content_upload'):\\n # Extend the response: additional lines are the expected filenames.\\n issue.put()\\n\\n base_hashes = {}\\n for file_info in form.cleaned_data.get('base_hashes').split(\\\"|\\\"):\\n if not file_info:\\n break\\n checksum, filename = file_info.split(\\\":\\\", 1)\\n base_hashes[filename] = checksum\\n\\n logging.info('base_hashes is %r', base_hashes)\\n content_entities = []\\n new_content_entities = []\\n patches = list(patchset.patches)\\n logging.info('len(patches) = %r', len(patches))\\n\\n existing_patches = {}\\n patchsets = list(issue.patchsets)\\n if len(patchsets) > 1:\\n # Only check the last uploaded patchset for speed.\\n last_patch_list = patchsets[-2].patches\\n patchsets = None # Reduce memory usage.\\n for opatch in last_patch_list:\\n if opatch.content_key:\\n existing_patches[opatch.filename] = opatch\\n for patch in patches:\\n # Check if the base file is already uploaded in another patchset.\\n if (patch.filename in base_hashes and\\n patch.filename in existing_patches and\\n (base_hashes[patch.filename] ==\\n existing_patches[patch.filename].content_key.get().checksum)):\\n content_key = existing_patches[patch.filename].content_key\\n patch.status = existing_patches[patch.filename].status\\n patch.is_binary = existing_patches[patch.filename].is_binary\\n patch.content_key = content_key\\n\\n existing_patches = None # Reduce memory usage.\\n\\n for patch in patches:\\n id_string = patch.key.id()\\n if patch.content_key is not None:\\n # Base file not needed since we reused a previous upload. Send its\\n # patch id in case it's a binary file and the new content needs to\\n # be uploaded. We mark this by prepending 'nobase' to the id.\\n id_string = \\\"nobase_\\\" + str(id_string)\\n msg += \\\"\\\\n%s %s\\\" % (id_string, patch.filename)\\n\\n logging.info('upload response is:\\\\n %s\\\\n', msg)\\n ndb.put_multi(patches)\\n\\n return HttpTextResponse(msg)\",\n \"def test_get_file_not_authd(self):\\n rv = self.get('/queue/riker.gif')\\n self.assertJSONError(rv, 'TagalleryMissingLoginInformation')\\n return\",\n \"def set_ticket(self, ticket: str) -> None:\\n LOG.info(f\\\"Setting ticket to {ticket}\\\")\\n self.ticket = ticket\",\n \"def __update_ticket(ticket_id, status, owner_id, comments, notify_requestor, token, user_id, channel, ts):\\n\\n # Update ticket metadata\\n owner = user_profile(owner_id)\\n username = ''\\n if owner['ok']:\\n username = owner['user']['profile'].get('email', '').split('@')[0]\\n resp = rt_api.update_ticket(ticket_id, token, status, username)\\n if rt_api.permission_error(resp):\\n error_message = \\\"Sorry, it appears you do not have permission to perform this action.\\\"\\n post_ephemeral(channel, error_message, user_id, 'Request Tracker')\\n return\\n\\n # Update ticket in Slack\\n current_message = retrieve_message(channel, ts)\\n if current_message.get('error', '') == 'not_in_channel':\\n join_channel(channel)\\n current_message = retrieve_message(channel, ts)\\n resp = refresh_ticket_message(channel, current_message['messages'][0])\\n if not resp['ok']:\\n logger.warning(\\\"Failed to update ticket in Slack. Please check RT to see if your changes were applied.\\\")\\n\\n # Post comments / replies, if applicable\\n if comments:\\n slack_user = user_profile(user_id)\\n display_name = slack_user['user']['profile']['real_name']\\n resp = rt_api.ticket_comment(ticket_id, comments + \\\"\\\\n\\\\n- \\\" + display_name, notify_requestor,\\n token=token)\\n if rt_api.permission_error(resp):\\n error_message = \\\"Sorry, it appears you do not have permission to perform this action.\\\"\\n post_ephemeral(channel, error_message, user_id, 'Request Tracker')\\n return\\n\\n profile_photo = slack_user['user']['profile']['image_original']\\n slack_post(channel, ts, comments, username=display_name, icon_url=profile_photo)\",\n \"def scan_ticket():\\n ticket_num = request.json.get(\\\"ticketNumber\\\")\\n\\n audience = Audience.query.filter(Audience.id == ticket_num).first()\\n checkout = Checkout.query.filter(Checkout.audience_id == ticket_num).first()\\n if not audience:\\n return jsonify({\\\"code\\\": \\\"1\\\", \\\"type\\\": \\\"ticket not found\\\"})\\n\\n if checkout:\\n return jsonify({\\\"code\\\": \\\"1\\\", \\\"type\\\": \\\"ticket already redeemed\\\", \\\"name\\\": audience.name})\\n\\n checkout_object = Checkout(audience.id)\\n add_to_database(checkout_object)\\n\\n return jsonify({\\\"code\\\": \\\"0\\\", \\\"type\\\": \\\"checkout successful\\\", \\\"name\\\": audience.name})\",\n \"def get_data(ticket_info, logger):\\n\\n # This string will be returned and added as a comment to the SecureChange ticket\\n return_str = \\\"\\\"\\n\\n # Log integration starting\\n logger.info(\\\"Running '{}' integration\\\".format(__name__))\\n\\n try:\\n\\n for req in ticket_info['Requests']:\\n for src in req['Sources']:\\n if src['Private'] and src['Cidr'] == '32':\\n return_str = '{}\\\\n\\\\n{}\\\\n\\\\n{}'.format(return_str, device_query(src['Ip'], fmt='text'), event_query(src['Ip'], fmt='text'))\\n for dst in req['Destinations']:\\n if dst['Private'] and dst['Cidr'] == '32':\\n return_str = '{}\\\\n\\\\n{}\\\\n\\\\n{}'.format(return_str, device_query(dst['Ip'], fmt='text'), event_query(dst['Ip'], fmt='text'))\\n\\n\\n except Exception as e:\\n\\n # Log the error and return an empty string\\n logger.error(\\\"Error: {}\\\".format(e))\\n return None\\n\\n # Log integration completing\\n logger.info(\\\"{} integration completed\\\".format(__name__))\\n\\n # Return comment\\n return return_str\",\n \"def api_upload():\\n return make_response(file_manager.save_uploaded_file(), 200)\",\n \"def get_ticket(self, wid, project, nowait=False):\\n\\n path = os.path.join(self.prjdir, project)\\n q = WorkQueue(path)\\n if nowait and not q.isquiet():\\n # If we don't want to wait and there is Q, we'll just leave\\n return\\n\\n if not q.add(json.dumps(wid.to_h(), sort_keys=True, indent=4)):\\n # Marking the wid to be forgotten ensures it's not sent\\n # back to BOSS, and the process blocks\\n wid.forget = True\\n else:\\n wid.result = True\",\n \"def check_upload(box_name, upload_threshold):\\n\\n try:\\n headers = {'Cache-Control': 'no-cache'}\\n response = requests.get(API_URL.format(box_name), headers=headers)\\n response.raise_for_status()\\n except Exception as e:\\n print('{} failed getting health from backend: {}'.format(box_name, e))\\n return UNKNOWN\\n\\n try:\\n health = response.json()\\n except Exception as e:\\n print('{} failed decoding health response: {}'.format(box_name, e))\\n return UNKNOWN\\n\\n if 'last_upload' not in health:\\n print('{} last_upload not found in health results'.format(box_name))\\n return UNKNOWN\\n \\n last_upload = datetime.now(tzutc()) - parser.parse(health['last_upload'])\\n\\n if last_upload.total_seconds() > upload_threshold:\\n print('{} last uploaded {}'.format(box_name, naturaltime(last_upload)))\\n return CRITICAL\\n\\n print('{} last upload {}'.format(box_name, naturaltime(last_upload)))\\n return OK\",\n \"def upload_file(self, f):\\n with FilesOpener(f) as files:\\n response = self.session.post(\\n 'https://{}/upload'.format(self.domain),\\n files=files\\n ).json()\\n\\n if isinstance(response, list):\\n error = response[0].get('error')\\n else:\\n error = response.get('error')\\n\\n if error:\\n if isinstance(error, str) and error.startswith('FLOOD_WAIT_'):\\n retry_after = int(error.rsplit('_',1)[-1])\\n raise RetryAfterError(retry_after)\\n else:\\n raise TelegraphException(error)\\n\\n return response\",\n \"def raise_jira_ticket(obj,org_id):\\n try:\\n app_id = obj.get('app_id','') \\n vul_name = obj.get('vul_name','')\\n cwe = int(obj.get('cwe',0))\\n project_key = obj.get('project_key','')\\n issuetype = obj.get('issuetype','Bug')\\n assignee = obj.get('assignee')\\n app_obj = Application.objects.get(pk=app_id)\\n if app_id and vul_name:\\n vuls = Vulnerability.objects.filter(is_false_positive=False,is_remediated=False,scan__application=app_obj,cwe=cwe,name=vul_name)\\n jira_obj = JiraIssueTypes.objects.get(org__id=org_id)\\n jira = get_jira_con(jira_obj) \\n if jira and vuls.exists(): \\n complete_desc = ''\\n references = '' \\n if app_obj:\\n complete_desc += 'Application:\\\\n{0}\\\\n\\\\n'.format(app_obj.name)\\n complete_desc += 'Application URL:\\\\n{0}\\\\n\\\\n'.format(app_obj.url)\\n if cwe:\\n complete_desc += 'CWE :\\\\n{0}\\\\n\\\\n'.format(cwe)\\n org_obj = app_obj.org\\n if org_obj.orl_config_exists():\\n vul_info = get_open_vul_info_from_api(cwe,org_obj)\\n complete_desc += 'Description:\\\\n{0}\\\\n\\\\n'.format(vul_info.get('description','')) \\n if references:\\n complete_desc += 'References:\\\\n{0}'.format(references) \\n data_dict = {\\n 'project':{'key':project_key },\\n 'issuetype':{'name': issuetype},\\n 'priority':{'name': 'Highest'},\\n 'summary':vul_name,\\n 'description':complete_desc, \\n } \\n new_issue = jira.create_issue(**data_dict) \\n evids = VulnerabilityEvidence.objects.filter(vul__in=vuls) \\n attachment = io.StringIO()\\n attachment.write('Evidences') \\n for evid in evids:\\n data = '\\\\n\\\\t- {0}\\\\n\\\\t\\\\t- {1}'.format(evid.url,evid.name)\\n attachment.write(data) \\n jira.add_attachment(issue=new_issue, attachment=attachment, filename='evidences.txt') \\n vuls.update(jira_id=str(new_issue),jira_issue_status=str(new_issue.fields.status))\\n info_debug_log(event='Raise Jira ticket',status='success')\\n if assignee:\\n jira.assign_issue(new_issue,assignee)\\n info_debug_log(event='Assign Jira ticket to an assignee',status='success')\\n except BaseException as e:\\n print(\\\"Error raising JIRA tickets\\\")\\n # general_error_messages.delay(path='raise_jira_ticket function',msg=log_exception(e))\\n critical_debug_log(event=e,status='failure')\",\n \"def upload(api_token, base_url, upload_file, metadata):\\n\\n upload_url = f\\\"{base_url}data_files/api_create?auth_token={api_token}\\\"\\n files = {'file': open(upload_file, 'rb')}\\n response = requests.post(upload_url, files=files, data=metadata)\\n\\n # Print out the outcome of the upload\\n if response.status_code == 200:\\n print(f'File {upload_file} successfully uploaded to HIEv')\\n else:\\n print(\\n f'ERROR - There was a problem uploading file {upload_file} to HIEv')\",\n \"def AdminTicket(ticket):\\n try:\\n data, = xmlrpclib.loads(ticket)[0]\\n name = data['slivers'][0]['name']\\n if data != None:\\n deliver_ticket(data)\\n logger.log('api_calls: Admin Ticket delivered for %s' % name)\\n Create(database.db.get(name))\\n except Exception, err:\\n raise xmlrpclib.Fault(102, 'Ticket error: ' + str(err))\",\n \"def _getApiAuthToken(self):\\n return settings.EBAY_API_AUTH_TOKEN\",\n \"def get_oauth_token():\\n return session.get('remote_oauth')\",\n \"def upload(self, upload_request):\\n raise NotImplementedError\",\n \"def request_access_ticket(pat: str, permission_endpoint: str,\\n resources: List[dict],\\n secure: bool = False) -> str:\\n\\n headers = {\\n 'content-type': \\\"application/json\\\",\\n 'authorization': \\\"Bearer \\\"+pat,\\n }\\n\\n if len(resources) == 1:\\n resources = resources[0] # Use a single dict instead of a list for 1 resource\\n\\n disable_warnings_if_debug(secure)\\n response = request(\\\"POST\\\", permission_endpoint , json=resources, headers=headers, verify=secure)\\n\\n if not is_ok(response):\\n raise Exception(\\\"An error occurred while requesting permission for a resource: \\\"+str(response.status_code)+\\\":\\\"+str(response.reason)+\\\":\\\"+str(response.text))\\n\\n try:\\n return response.json()[\\\"ticket\\\"]\\n except Exception as e:\\n raise Exception(\\\"Call to permission endpoint returned unexpected value or error: '\\\"+response.text+\\\"'\\\"+\\\". Error: \\\"+str(e))\",\n \"def test_create_ticket(self):\\n with patch('requests.get') as mock:\\n mock.return_value.status_code = 200\\n pgt = ProxyGrantingTicket.objects.create_ticket('https://www.example.com', 'https://www.example.com/',\\n user=self.user, granted_by_pt=self.pt)\\n self.assertTrue(re.search(pgt.TICKET_RE, pgt.ticket))\\n self.assertTrue(pgt.iou.startswith(pgt.IOU_PREFIX))\",\n \"def get_multipart_upload(metadata):\\n multipart_upload = None\\n metadata_mp_id = None\\n filename = metadata.path\\n if filename.startswith(u\\\"/\\\"):\\n filename = filename[1:]\\n plug.logger.debug(u\\\"Getting multipart upload of {}\\\", filename)\\n # Retrieve the stored multipart upload ID\\n try:\\n metadata_mp_id = metadata.extra['mp_id']\\n except KeyError: # No multipart upload ID\\n # Raise now is faster (doesn't go through all the MP uploads)\\n raise DriverError(\\\"Unable to retrieve multipart upload ID\\\")\\n if metadata_mp_id not in cache:\\n # Try to only request multipart uploads of this file\\n for mp in S3Conn.list_multipart_uploads(prefix=filename):\\n # Go through all the multipart uploads\\n # to find the one of this transfer\\n if mp.uploadId == metadata_mp_id:\\n multipart_upload = mp\\n add_to_cache(mp)\\n break\\n else:\\n multipart_upload = cache[metadata_mp_id]\\n # At this point it shouldn't be None in any case\\n if multipart_upload is None:\\n raise DriverError(\\\"Cannot find upload for file '{}'\\\"\\n .format(filename))\\n plug.logger.debug(u\\\"Found multipart upload of {} - ID {}\\\",\\n filename, multipart_upload.uploadId)\\n return multipart_upload\",\n \"def notify_upload():\\n file_type = request.json.get(\\\"file_type\\\")\\n file_name = request.json.get(\\\"file_name\\\")\\n file_path = request.json.get(\\\"file_path\\\")\\n file_size = request.json.get(\\\"file_size\\\")\\n print(\\\"File was uploaded:\\\", file_path, file_name, file_type)\\n # Marks the upload as confirmed.\\n d = datetime.datetime.utcnow()\\n db.upload.update_or_insert(\\n ((db.upload.owner == get_user_email()) &\\n (db.upload.file_path == file_path)),\\n owner=get_user_email(),\\n file_path=file_path,\\n file_name=file_name,\\n file_type=file_type,\\n file_date=d,\\n file_size=file_size,\\n confirmed=True,\\n )\\n # Returns the file information.\\n return dict(\\n download_url=gcs_url(GCS_KEYS, file_path, verb='GET'),\\n file_date=d,\\n )\",\n \"def get_ticket_detail(ticket_id):\\n\\n # Get the IX from the ticket\\n db_connect = DBConnect().mysql_connect(mysql_user='user5100',\\n mysql_password='ePPkLlao')\\n db_connect.execute(db_queries.query_ix_codigo(ticket_id))\\n ix_code = db_connect.fetchall()\\n\\n db_connect.execute(db_queries.query_ticket_detail(ticket_id))\\n\\n json_to_send = FormatJson.format_ticket_detail(\\n db_connect.fetchall()[-1],\\n ticket_id,\\n ix_code[0]['codigo'])\\n\\n json.dumps(json_to_send)\\n db_connect.close()\\n return Response(json_to_send, mimetype='application/json;charset=utf-8'),\\\\\\n 200\",\n \"def initiate_multipart_upload(self):\\n request = self.s3.create_request(\\\"OBJECT_POST\\\", uri = self.uri, headers = self.headers_baseline, extra = \\\"?uploads\\\")\\n response = self.s3.send_request(request)\\n data = response[\\\"data\\\"]\\n self.upload_id = getTextFromXml(data, \\\"UploadId\\\")\\n return self.upload_id\",\n \"def upload_file(client, folder_id, file_name):\\n\\n new_file = client.folder(folder_id).upload(file_name)\\n print(f\\\"File {new_file.name} uploaded to Box with file ID {new_file.id}\\\")\\n return new_file.id\",\n \"def get_file(self, file_id):\\n LOG.debug(\\\"Getting a file from mattermost\\\")\\n url = '%s/api/v4/files/%s' % (self.server_url, file_id)\\n LOG.debug(\\\"Sending: %s\\\", url)\\n response = self._request(self._session.get, url)\\n\\n if response.status_code != 200:\\n raise RuntimeError(\\\"Server unhappy. (%s)\\\", response)\\n\\n return response.content\",\n \"def getRequest(endpoint):\\n\\n headers = {\\n \\\"Accept\\\": \\\"application/json\\\",\\n \\\"Authorization\\\": \\\"bearer 7TUFM5BG2ikyTr5RQzNvFm3ALKC_7PUzGNcybO91WWny93bEzmmeeze0iXJNSisEno42aN2CKJIQOBr52ZeUeyFABfo1lq0aQDhCMJfsMFmC_l-FRCTeIN1DpqOHvkIgLmq5hlvUBk-q11VzNQZzA8L1QY4JoAGXEnXa-SojXw_2elYfzgpstXxtph9FcStOPX4YX0L1uDNPQAbO7gYLcbiD1d7qIoIA6wK71TO_WoDa10v6z-m1tNmYmhw98odThrSbkxhRJD9ktSAGOX8IgelS6TnkrsUGkzgxZjtGcMu_YnW_391vR7zJWwIPY-8LFc8Ueg\\\"\\n }\\n\\n response = requests.get(endpoint, headers=headers)\\n\\n # print(response.json()) # use for testing and debugging\\n\\n return response.json()\",\n \"def auth_token(self):\",\n \"def remote_requestUploadSlot(self):\\n # Create a new upload slot by using the transfers register\\n slot_id = self.transfers_register.allocate_upload_slot()\\n\\n # Create the complete ftp address\\n return self.transfers_register.address_by_id(slot_id)\",\n \"def get_file(service, file_id):\\n return service.files().get(fileId=file_id).execute()\",\n \"def test_check_ticket_8(self):\\n self.tkt.phage_id = \\\"\\\"\\n import_genome.check_ticket(\\n self.tkt, type_set=self.type_set,\\n description_field_set=self.description_field_set,\\n eval_mode_set=self.eval_mode_set,\\n id_dupe_set=set(), phage_id_dupe_set=set(),\\n retain_set=self.retain_set, retrieve_set=self.retrieve_set,\\n add_set=self.add_set, parse_set=self.parse_set)\\n count = count_status(self.tkt, \\\"error\\\")\\n self.assertEqual(count, 1)\",\n \"def parse_ticket(secret, ticket, ip, digest_algo=DEFAULT_DIGEST):\\n if isinstance(digest_algo, str):\\n # correct specification of digest from hashlib or fail\\n digest_algo = getattr(hashlib, digest_algo)\\n digest_hexa_size = digest_algo().digest_size * 2\\n ticket = ticket.strip('\\\"')\\n digest = ticket[:digest_hexa_size]\\n try:\\n timestamp = int(ticket[digest_hexa_size:digest_hexa_size + 8], 16)\\n except ValueError as e:\\n raise BadTicket('Timestamp is not a hex integer: %s' % e)\\n try:\\n userid, data = ticket[digest_hexa_size + 8:].split('!', 1)\\n except ValueError:\\n raise BadTicket('userid is not followed by !')\\n userid = urllib.parse.unquote(userid)\\n if '!' in data:\\n tokens, user_data = data.split('!', 1)\\n else:\\n # @@: Is this the right order?\\n tokens = ''\\n user_data = data\\n\\n expected = calculate_digest(ip, timestamp, secret,\\n userid, tokens, user_data,\\n digest_algo)\\n\\n if expected != digest:\\n raise BadTicket('Digest signature is not correct',\\n expected=(expected, digest))\\n\\n tokens = tokens.split(',')\\n\\n return (timestamp, userid, tokens, user_data)\",\n \"def m_apiInstance_AuthenticationStatusUpdate(self, sender, e):\\r\\n if e.Status.IsSuccess:\\r\\n # Add code here to begin working with the TT API\\r\\n # lookup an instrument\\r\\n self.m_req = ttapi.InstrumentLookupSubscription(self.m_apiInstance.Session, ttapi.Dispatcher.Current, ttapi.ProductKey(ttapi.MarketKey.Cme, ttapi.ProductType.Future, \\\"YM\\\"), \\\"Jun17\\\")\\r\\n self.m_req.Update += self.m_req_Update\\r\\n print(\\\"Connection Success!\\\")\\r\\n self.m_req.Start()\\r\\n else:\\r\\n print(\\\"TT Login failed: {0}\\\".format(e.Status.StatusMessage))\\r\\n self.Dispose()\",\n \"def _get_file_to_upload(self, file_mime_type, file_no):\\n tmp_file_handle = NamedTemporaryFile(mode=\\\"w\\\", encoding=\\\"utf-8\\\", delete=True)\\n return (\\n {\\n \\\"file_name\\\": self.filename.format(file_no),\\n \\\"file_handle\\\": tmp_file_handle,\\n \\\"file_mime_type\\\": file_mime_type,\\n \\\"file_row_count\\\": 0,\\n },\\n tmp_file_handle,\\n )\",\n \"def ticket_range(self):\\n response = self.http_call(\\\"{0}/tickets.json\\\".format(self.uri))\\n return math.ceil(response.json()[\\\"count\\\"] / 100) + 1\",\n \"def uploadResourceFile(url, user, pWd, resourceName, fileName, fullPath):\\n print(\\\"uploading file for resource \\\" + url + \\\" resource=\\\" + \\n resourceName + ' user=' + user)\\n apiURL = url + '/access/1/catalog/resources/' + resourceName + \\\"/files\\\"\\n print(\\\"\\\\turl=\\\" + apiURL)\\n # header = {\\\"accept\\\": \\\"*/*\\\", \\\"Content-Type\\\" : \\\"multipart/form-data\\\"} \\n header = {\\\"accept\\\": \\\"*/*\\\"} \\n print(\\\"\\\\t\\\" + str(header))\\n params = {\\\"scannerid\\\": \\\"LineageScanner\\\", \\\"filename\\\": fileName, \\n \\\"optionid\\\": \\\"File\\\"}\\n print(\\\"\\\\t\\\" + str(params))\\n# files = {'file': fullPath}\\n mimeType = 'text/csv'\\n readMode = 'rt'\\n if fileName.endswith(\\\".zip\\\"):\\n mimeType = 'application/zip'\\n readMode = 'rb'\\n \\n file = { 'file' : (fileName, open(fullPath, readMode), mimeType)}\\n print('\\\\t' + str(file))\\n uploadResp = requests.post(apiURL, data=params, files=file, headers=header, \\n auth=HTTPBasicAuth(user,pWd))\\n print(\\\"\\\\tresponse=\\\" + str(uploadResp.status_code))\\n if uploadResp.status_code == 200:\\n # valid - return the jsom\\n return uploadResp.status_code\\n else:\\n # not valid\\n print(\\\"\\\\tupload file failed\\\")\\n print(\\\"\\\\t\\\" + str(uploadResp))\\n print(\\\"\\\\t\\\" + str(uploadResp.text))\\n return uploadResp.status_code\",\n \"def get_file_from_tg(file_id):\\n get_file_url = BOT_URL + f'/getFile'\\n\\n response = requests.get(get_file_url, params={'file_id': file_id})\\n json_data = json.loads(response.content)\\n print(json_data)\\n file_path = json_data['result']['file_path'] if json_data['result'] else None\\n print(file_path)\\n\\n file_url = f'https://api.telegram.org/file/bot{TOKEN}/{file_path}'\\n print(file_url)\\n r = requests.get(file_url, allow_redirects=True)\\n\\n file_bytes = r.content\\n print('got file')\\n\\n # test purposes\\n file_name = f'debug_test_photo.jpg'\\n open(file_name, 'wb').write(file_bytes)\\n\\n print(type(file_bytes))\\n username = process_file(file_bytes)\\n return username\",\n \"def get_ticket_id():\\n return str(time.time()) + str(uuid.uuid4())\",\n \"def upload():\\n upload_old_file = False\\n file = request.files[\\\"data\\\"]\\n stream_data = file.stream\\n \\n website_ticket = \\\"https://tedxruppticket.web.app/\\\"\\n\\n df = pd.read_csv(stream_data)\\n name = df[\\\"name\\\"].values\\n\\n if \\\"id\\\" in df.columns:\\n _id = df[\\\"id\\\"].values\\n _name = df[\\\"name\\\"].values\\n \\n audiences = []\\n for i in range(len(_id)):\\n audiences.append(Audience(_id[i], _name[i]))\\n add_to_database(audiences, multiple=True)\\n\\n return jsonify({\\\"data\\\": \\\"added\\\"})\\n\\n tickets = df[\\\"Ticket\\\"].values\\n ticket_num = df[\\\"Ticket\\\"].values.sum()\\n \\n _name = []\\n for i, n in enumerate(tickets):\\n for j in range(n):\\n print()\\n _name.append(name[i])\\n\\n _id = [str(uuid4()).upper()[:7] for i in range(ticket_num)]\\n\\n if \\\"id\\\" in df.columns:\\n upload_old_file = True\\n _id = df[\\\"id\\\"].values\\n\\n audiences = []\\n _tickets = []\\n for i in range(len(_id)):\\n audiences.append(Audience(_id[i], _name[i]))\\n _tickets.append(website_ticket + f\\\"{_id[i]}\\\")\\n add_to_database(audiences, multiple=True)\\n\\n output_dataframe = pd.DataFrame(data={\\n \\\"id\\\": _id,\\n \\\"name\\\": _name,\\n \\\"ticket_url\\\": _tickets\\n })\\n\\n output_dataframe.to_csv(\\\"./files/CircleAudiences.csv\\\")\\n data_obj = output_dataframe.set_index(\\\"id\\\").to_dict()\\n\\n return {\\\"code\\\": \\\"0\\\", \\\"data\\\": data_obj}\",\n \"def test_pre_upload_ok(self):\\n namespace = 'default-gzip'\\n good_digests = generate_collection(namespace, ['a pony'])\\n self.assertEqual(good_digests.namespace.namespace, namespace)\\n response = self.call_api(\\n 'preupload', self.message_to_dict(good_digests), 200)\\n message = response.json.get(u'items', [{}])[0]\\n self.assertEqual('', message.get(u'gs_upload_url', ''))\\n expected = validate(\\n message.get(u'upload_ticket', ''),\\n handlers_endpoints_v1.UPLOAD_MESSAGES[0])\\n self.assertEqual(\\n expected, generate_embedded(namespace, good_digests.items[0]))\",\n \"def test_check_ticket_9(self):\\n self.tkt.type = \\\"add\\\"\\n import_genome.check_ticket(\\n self.tkt, type_set=self.type_set,\\n description_field_set=self.description_field_set,\\n eval_mode_set=self.eval_mode_set,\\n id_dupe_set=set(), phage_id_dupe_set=set(),\\n retain_set=self.retain_set, retrieve_set=self.retrieve_set,\\n add_set=self.add_set, parse_set=self.parse_set)\\n count = count_status(self.tkt, \\\"error\\\")\\n self.assertEqual(count, 1)\",\n \"def find_url(ticketID):\\n uri = 'https://jira.duraspace.org/browse/' + ticketID\\n print uri\\n return uri\",\n \"def upload():\\n\\n # Get CAS user roles from session\\n cas_attributes = session.get('CAS_ATTRIBUTES', None)\\n\\n # login_required should mean we're authenticated, but check this\\n if not cas_attributes:\\n abort(403)\\n\\n roles = cas_attributes['cas:authority'].split(',')\\n\\n APP.logger.debug(\\n f'User has CAS roles: {roles} ')\\n\\n # Stop unauthorized users\\n if CONFIG.UPLOAD_ROLE not in roles:\\n abort(403)\\n\\n form = UploadForm()\\n\\n if request.method != 'POST':\\n return render_template('upload.html', form=form, upload_error=False)\\n\\n # User has tried to submit file (POST)\\n # Check if it passes (simple) validation in forms.py\\n if not form.validate_on_submit():\\n return render_template('upload.html', form=form)\\n\\n # Get filename\\n f = form.file.data\\n filename = secure_filename(f.filename)\\n # Add user email & time\\n email = cas_attributes['cas:email']\\n upload_time = dt.now().strftime(\\\"%y%m%d-%H%M%S\\\")\\n ext_filename = email + '_' + upload_time + '_' + filename\\n\\n # Save file\\n try:\\n f.save(os.path.join(CONFIG.UPLOAD_PATH, ext_filename))\\n except Exception as ex:\\n APP.logger.error(\\n f'File {ext_filename} could not be saved due to {ex}')\\n else:\\n APP.logger.info(f'Successfully uploaded file {ext_filename}')\\n\\n # Notify admin\\n msg = Message('New file upload',\\n sender=APP.mail.username,\\n recipients=CONFIG.UPLOAD_EMAIL)\\n msg.body = f\\\"\\\"\\\"\\n Hello SBDI-MOL colleagues,\\n\\n A new file has been uploaded to the ASV portal:\\n\\n Provider email: {email}\\n Upload time: {upload_time}\\n Original filename: {filename}\\n Saved as: {ext_filename}\\n\\n Have a nice day!\\n\\n / Swedish ASV portal\\n \\\"\\\"\\\"\\n try:\\n APP.mail.send(msg)\\n except Exception as ex:\\n APP.logger.error(f\\\"Could not send upload notification due to {ex}\\\")\\n else:\\n APP.logger.info('Successfully sent upload notification.')\\n # Display 'success page' only if upload AND notification worked\\n return render_template('uploaded.html', filename=filename)\\n\\n # Display error msg if EITHER upload OR email failed, so that data\\n # providers get a chance to tell us about uploaded files\\n return render_template('upload.html', form=form, upload_error=True)\",\n \"def get_box(req):\",\n \"def _perform_request(cls, url, payload, auth_token):\\n\\n LOGGER.debug('Performing request to %s', url)\\n\\n # request headers\\n headers = {'X-Auth-Token': auth_token,\\n 'Content-type': 'application/json'}\\n # perform request and return its result\\n try:\\n return requests.post(\\n url, data=payload, headers=headers,\\n timeout=(Config.instance().CEILOMETER_TIMEOUT / 1000.))\\n except RequestException as exc:\\n LOGGER.error('Ceilometer request error: %s', six.text_type(exc))\\n return None\",\n \"def __create_ticket(user, subject, description, topic):\\n\\n target = settings.SLACK_TARGET_TFED\\n if topic == 'Database':\\n target = settings.SLACK_TARGET_TFED_DB\\n user_email = user['user']['profile'].get('email', 'lnl-no-reply@wpi.edu')\\n display_name = user['user']['profile']['real_name']\\n resp = rt_api.create_ticket(topic, user_email, subject, description + \\\"\\\\n\\\\n- \\\" + display_name)\\n ticket_id = resp.get('id', None)\\n if ticket_id:\\n ticket_info = {\\n \\\"url\\\": 'https://lnl-rt.wpi.edu/rt/Ticket/Display.html?id=' + ticket_id,\\n \\\"id\\\": ticket_id,\\n \\\"subject\\\": subject,\\n \\\"description\\\": description,\\n \\\"status\\\": \\\"New\\\",\\n \\\"assignee\\\": None,\\n \\\"reporter\\\": user['user']['name']\\n }\\n ticket = views.tfed_ticket(ticket_info)\\n slack_post(target, text=description, content=ticket, username='Request Tracker')\\n return\\n error_message = \\\"Whoops! It appears something went wrong while attempting to submit your request. \\\" \\\\\\n \\\"Please wait a few minutes then try again. If the problem persists, please email \\\" \\\\\\n \\\"us directly at tfed@wpi.edu.\\\"\\n post_ephemeral(target, error_message, user['user']['id'], username=\\\"Request Tracker\\\")\",\n \"def test_check_ticket_2(self):\\n import_genome.check_ticket(\\n self.tkt, type_set=self.type_set,\\n description_field_set=self.description_field_set,\\n eval_mode_set=self.eval_mode_set,\\n id_dupe_set=set([1]), phage_id_dupe_set=set(),\\n retain_set=self.retain_set, retrieve_set=self.retrieve_set,\\n add_set=self.add_set, parse_set=self.parse_set)\\n count = count_status(self.tkt, \\\"error\\\")\\n self.assertEqual(count, 1)\",\n \"def test_check_ticket_11(self):\\n self.tkt.data_retrieve = {\\\"invalid\\\"}\\n import_genome.check_ticket(\\n self.tkt, type_set=self.type_set,\\n description_field_set=self.description_field_set,\\n eval_mode_set=self.eval_mode_set,\\n id_dupe_set=set(), phage_id_dupe_set=set(),\\n retain_set=self.retain_set, retrieve_set=self.retrieve_set,\\n add_set=self.add_set, parse_set=self.parse_set)\\n count = count_status(self.tkt, \\\"error\\\")\\n self.assertEqual(count, 1)\",\n \"def get_ticket_info(self):\\n return json.dumps(self.__dict__)\",\n \"def create_ticket(data):\\n firebase_uid = data['session'].split('/')[-1]\\n contexts = data['queryResult']['outputContexts']\\n for i in contexts:\\n if 'visit_data' in i['name']:\\n context = i\\n break\\n\\n date = datetime.datetime.now()\\n date = date.strftime(\\\"%d-%m-%Y\\\")\\n\\n raw_params = context['parameters']\\n ticket_params = {\\n \\\"Agent\\\": \\\"None\\\",\\n \\\"Product Type\\\": raw_params[\\\"product_type\\\"],\\n \\\"Type\\\": \\\"House Call\\\",\\n \\\"Issue Type\\\": raw_params[\\\"issue_type\\\"],\\n \\\"Description\\\": raw_params[\\\"description\\\"],\\n \\\"Model Number\\\": raw_params[\\\"model_number\\\"],\\n \\\"Serial Number\\\": raw_params[\\\"serial_number\\\"],\\n \\\"Status\\\": \\\"Open\\\",\\n \\\"Date\\\": date,\\n \\\"Time Slot Chosen\\\": \\\"0\\\",\\n \\\"Time Slots\\\": {\\\"Slot 1\\\": {\\\"Time\\\": \\\"0\\\", \\\"Date\\\": \\\"0\\\"},\\n \\\"Slot 2\\\": {\\\"Time\\\": \\\"0\\\", \\\"Date\\\": \\\"0\\\"},\\n \\\"Slot 3\\\": {\\\"Time\\\": \\\"0\\\", \\\"Date\\\": \\\"0\\\"}},\\n \\\"Progress\\\": \\\"Under Review\\\",\\n \\\"Free Time\\\": {\\n \\\"Date\\\": \\\"0\\\",\\n \\\"Time\\\": \\\"0\\\",\\n },\\n \\\"Details of Call\\\": {\\n \\\"Time\\\": \\\"0\\\",\\n \\\"Date\\\": \\\"0\\\"}\\n }\\n ticket_id = str(uuid.uuid4())[:8]\\n db = firebase.database()\\n db.child(\\n 'user_data').child(\\n firebase_uid).child(\\n 'Complaints').child(ticket_id).set(ticket_params)\\n\\n fulfillment_response = {\\n \\\"fulfillmentText\\\":\\n \\\"You ticket was successfully registered. The reference number is \\\" + ticket_id +\\n \\\". Based on the availability of our agents, we will give you three time slots to choose from. You can \\\"\\n \\\"either go to the \\\\\\\"Tickets\\\\\\\" section of the app and update your preference or do so by talking to me.\\\"}\\n return fulfillment_response\",\n \"def get_uapi_token(jamf_url, enc_creds, verbosity):\\n headers = {\\n \\\"authorization\\\": \\\"Basic {}\\\".format(enc_creds),\\n \\\"content-type\\\": \\\"application/json\\\",\\n \\\"accept\\\": \\\"application/json\\\",\\n }\\n url = \\\"{}/uapi/auth/tokens\\\".format(jamf_url)\\n http = requests.Session()\\n if verbosity > 2:\\n http.hooks[\\\"response\\\"] = [logging_hook]\\n\\n r = http.post(url, headers=headers)\\n if verbosity > 2:\\n print(r.content)\\n if r.status_code == 200:\\n obj = json.loads(r.text)\\n try:\\n token = str(obj[\\\"token\\\"])\\n print(\\\"Session token received\\\")\\n return token\\n except KeyError:\\n print(\\\"ERROR: No token received\\\")\\n return\\n else:\\n print(\\\"ERROR: No token received\\\")\\n return\",\n \"def post(self):\\n\\n upload_files = self.get_uploads('file')\\n blob_info = upload_files[0]\\n self.redirect('/?upload_info=%s' % urllib.quote(blob_info.filename))\",\n \"def get_files_api():\\n return get_api_client(cloudsmith_api.FilesApi)\",\n \"def upload(self, file_path):\\n LOG.debug((\\\"Uploading file to mattermost: %s\\\", file_path))\\n filename = os.path.basename(file_path)\\n post_data = {'channel_id': self.channel_id,\\n 'client_ids': filename}\\n file_data = {'files': (filename, open(file_path, 'rb'))}\\n url = '%s/api/v4/files' % self.server_url\\n response = self._request(self._session.post, url, data=post_data,\\n files=file_data)\\n LOG.debug(\\\"Received: %s - %s\\\", response.status_code, response.text)\\n\\n if response.status_code != 201:\\n if response.status_code == 400:\\n msg = \\\"Type of the uploaded file doesn't match its file \\\" \\\\\\n \\\" extension or uploaded file is an image that \\\" \\\\\\n \\\"exceeds the maximum dimensions\\\"\\n elif response.status_code == 401:\\n msg = \\\"User is not logged in\\\"\\n elif response.status_code == 403:\\n msg = \\\"User does not have permission to upload file to \\\" \\\\\\n \\\"the provided team/channel\\\"\\n elif response.status_code == 413:\\n msg = \\\"Uploaded file is too large\\\"\\n elif response.status_code == 500:\\n msg = \\\"File storage is disabled\\\"\\n else:\\n msg = response\\n raise RuntimeError(\\\"Server unhappy with request, reports: %s\\\"\\n % msg)\\n\\n file_id = response.json()[\\\"file_infos\\\"][0][\\\"id\\\"]\\n return file_id\",\n \"def test_check_ticket_4(self):\\n self.tkt.type = \\\"invalid\\\"\\n import_genome.check_ticket(\\n self.tkt, type_set=self.type_set,\\n description_field_set=self.description_field_set,\\n eval_mode_set=self.eval_mode_set,\\n id_dupe_set=set(), phage_id_dupe_set=set(),\\n retain_set=self.retain_set, retrieve_set=self.retrieve_set,\\n add_set=self.add_set, parse_set=self.parse_set)\\n count = count_status(self.tkt, \\\"error\\\")\\n self.assertEqual(count, 1)\",\n \"def test_get_single_poll_submission(self):\\r\\n poll_id = None # Change me!!\\r\\n poll_session_id = None # Change me!!\\r\\n id = None # Change me!!\\r\\n\\r\\n r = self.client.get_single_poll_submission(id, poll_id, poll_session_id)\",\n \"def get_fingerprint():\\n print('Requesting fingerprint...')\\n config = {\\n 'host': os.environ.get('ACR_HOST'),\\n 'access_key': os.environ.get('ACR_ACCESS_KEY'), \\n 'access_secret': os.environ.get('ACR_ACCESS_SECRET'),\\n 'timeout': 10\\n }\\n recognizer = ACRCloudRecognizer(config)\\n mp3_path = helpers.get_mp3_output_path()\\n start_seconds = 0\\n rec_length = helpers.get_recording_length_seconds()\\n result = json.loads(recognizer.recognize_by_file(mp3_path, start_seconds, rec_length))\\n save_fingerprint_result_to_file(result)\\n if int(result['status']['code']) == 0:\\n try:\\n song = {\\n 'name': get_song_name_from_result(result),\\n 'id': get_spotify_id_from_result(result),\\n 'artist': get_first_artist_from_result(result),\\n 'seconds_remaining': get_song_seconds_remaining(result),\\n 'percent_remaining': get_song_percent_remaining(result),\\n }\\n print('Song Found: {}'.format(song['name']))\\n return song\\n except KeyError:\\n pass\\n return None\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.67064804","0.5887511","0.58059216","0.5711891","0.558435","0.55571055","0.55388236","0.5534896","0.55088025","0.54868495","0.54192376","0.531124","0.5295683","0.5222806","0.5221019","0.51976347","0.51389897","0.51316166","0.51182204","0.50823814","0.50782853","0.50688165","0.5063119","0.504373","0.50406444","0.5028732","0.49751207","0.49703068","0.4959613","0.49467874","0.4933064","0.49206287","0.48975542","0.4886903","0.48721722","0.48713174","0.48576492","0.48517206","0.48513892","0.48476306","0.48295015","0.4829435","0.48239768","0.48234975","0.48222098","0.4810322","0.4807912","0.48027158","0.4792805","0.47878164","0.47877228","0.47699764","0.475408","0.47453547","0.4742337","0.4737961","0.47374427","0.47253954","0.47224542","0.4721154","0.47174665","0.47171375","0.47151068","0.4705124","0.46959275","0.46944436","0.46833172","0.46715924","0.46674156","0.46659374","0.46596786","0.46554977","0.46543425","0.4649019","0.46443132","0.4641602","0.4635053","0.4627985","0.46204472","0.46183777","0.46123517","0.46046647","0.46039605","0.46015584","0.45996413","0.4599139","0.45975563","0.45947325","0.45940495","0.4592191","0.45813593","0.4577875","0.45690823","0.45676345","0.45660573","0.4551478","0.45427278","0.45372692","0.45365247","0.45339316"],"string":"[\n \"0.67064804\",\n \"0.5887511\",\n \"0.58059216\",\n \"0.5711891\",\n \"0.558435\",\n \"0.55571055\",\n \"0.55388236\",\n \"0.5534896\",\n \"0.55088025\",\n \"0.54868495\",\n \"0.54192376\",\n \"0.531124\",\n \"0.5295683\",\n \"0.5222806\",\n \"0.5221019\",\n \"0.51976347\",\n \"0.51389897\",\n \"0.51316166\",\n \"0.51182204\",\n \"0.50823814\",\n \"0.50782853\",\n \"0.50688165\",\n \"0.5063119\",\n \"0.504373\",\n \"0.50406444\",\n \"0.5028732\",\n \"0.49751207\",\n \"0.49703068\",\n \"0.4959613\",\n \"0.49467874\",\n \"0.4933064\",\n \"0.49206287\",\n \"0.48975542\",\n \"0.4886903\",\n \"0.48721722\",\n \"0.48713174\",\n \"0.48576492\",\n \"0.48517206\",\n \"0.48513892\",\n \"0.48476306\",\n \"0.48295015\",\n \"0.4829435\",\n \"0.48239768\",\n \"0.48234975\",\n \"0.48222098\",\n \"0.4810322\",\n \"0.4807912\",\n \"0.48027158\",\n \"0.4792805\",\n \"0.47878164\",\n \"0.47877228\",\n \"0.47699764\",\n \"0.475408\",\n \"0.47453547\",\n \"0.4742337\",\n \"0.4737961\",\n \"0.47374427\",\n \"0.47253954\",\n \"0.47224542\",\n \"0.4721154\",\n \"0.47174665\",\n \"0.47171375\",\n \"0.47151068\",\n \"0.4705124\",\n \"0.46959275\",\n \"0.46944436\",\n \"0.46833172\",\n \"0.46715924\",\n \"0.46674156\",\n \"0.46659374\",\n \"0.46596786\",\n \"0.46554977\",\n \"0.46543425\",\n \"0.4649019\",\n \"0.46443132\",\n \"0.4641602\",\n \"0.4635053\",\n \"0.4627985\",\n \"0.46204472\",\n \"0.46183777\",\n \"0.46123517\",\n \"0.46046647\",\n \"0.46039605\",\n \"0.46015584\",\n \"0.45996413\",\n \"0.4599139\",\n \"0.45975563\",\n \"0.45947325\",\n \"0.45940495\",\n \"0.4592191\",\n \"0.45813593\",\n \"0.4577875\",\n \"0.45690823\",\n \"0.45676345\",\n \"0.45660573\",\n \"0.4551478\",\n \"0.45427278\",\n \"0.45372692\",\n \"0.45365247\",\n \"0.45339316\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8335117"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":22472,"cells":{"query":{"kind":"string","value":"Upload a piece of a video file to Vimeo Makes a PUT request to the given URL with the given binary data The _range parameter indicates the first byte to send. The first time you attempt an upload, this will be 0. The next time, it will be the number returned from get_last_uploaded_byte, if that number is less than the total size of the video file in bytes."},"document":{"kind":"string","value":"def upload_segment(self, upload_uri, _range, data, filetype):\n content_range = '%d-%d/%d' % (_range, len(data), len(data))\n upload_headers = {'Content-Type': 'video/%s' % filetype,\n 'Content-Length': len(data),\n 'Content-Range': 'bytes: %s' % content_range}\n\n log.info(\"Sending file of size %d\" % len(data))\n log.info(\"Requesting %s\" % upload_uri)\n request_headers = dict(upload_headers.items() + self.standard_headers.items())\n r = HTTPClient().fetch(upload_uri, method=\"PUT\",\n body=data, headers=request_headers)\n log.info(\"Uploaded segment: status code %d\" % r.code)\n if r.code != 200:\n raise ValueError(\"Upload unsuccessful\")"},"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 upload_range( # type: ignore\n self, data, # type: bytes\n start_range, # type: int\n end_range, # type: int\n validate_content=False, # type: Optional[bool]\n timeout=None, # type: Optional[int]\n encoding='UTF-8',\n **kwargs\n ):\n # type: (...) -> Dict[str, Any]\n if self.require_encryption or (self.key_encryption_key is not None):\n raise ValueError(\"Encryption not supported.\")\n if isinstance(data, six.text_type):\n data = data.encode(encoding)\n\n content_range = 'bytes={0}-{1}'.format(start_range, end_range)\n content_length = end_range - start_range + 1\n try:\n return self._client.file.upload_range( # type: ignore\n range=content_range,\n content_length=content_length,\n optionalbody=data,\n timeout=timeout,\n validate_content=validate_content,\n cls=return_response_headers,\n **kwargs)\n except StorageErrorException as error:\n process_storage_error(error)","def upload_file(self, source, dest):\n print(f\"Uploading {source} to {dest}\")\n with open(source, \"rb\") as data:\n self.client.upload_blob(name=dest, data=data)","def _upload_file_bytes(self, conn, http_conn, fp, file_length,\r\n total_bytes_uploaded, cb, num_cb):\r\n buf = fp.read(self.BUFFER_SIZE)\r\n if cb:\r\n if num_cb > 2:\r\n cb_count = file_length / self.BUFFER_SIZE / (num_cb-2)\r\n elif num_cb < 0:\r\n cb_count = -1\r\n else:\r\n cb_count = 0\r\n i = 0\r\n cb(total_bytes_uploaded, file_length)\r\n\r\n # Build resumable upload headers for the transfer. Don't send a\r\n # Content-Range header if the file is 0 bytes long, because the\r\n # resumable upload protocol uses an *inclusive* end-range (so, sending\r\n # 'bytes 0-0/1' would actually mean you're sending a 1-byte file).\r\n put_headers = {}\r\n if file_length:\r\n range_header = self._build_content_range_header(\r\n '%d-%d' % (total_bytes_uploaded, file_length - 1),\r\n file_length)\r\n put_headers['Content-Range'] = range_header\r\n # Set Content-Length to the total bytes we'll send with this PUT.\r\n put_headers['Content-Length'] = str(file_length - total_bytes_uploaded)\r\n http_request = AWSAuthConnection.build_base_http_request(\r\n conn, 'PUT', path=self.tracker_uri_path, auth_path=None,\r\n headers=put_headers, host=self.tracker_uri_host)\r\n http_conn.putrequest('PUT', http_request.path)\r\n for k in put_headers:\r\n http_conn.putheader(k, put_headers[k])\r\n http_conn.endheaders()\r\n\r\n # Turn off debug on http connection so upload content isn't included\r\n # in debug stream.\r\n http_conn.set_debuglevel(0)\r\n while buf:\r\n http_conn.send(buf)\r\n total_bytes_uploaded += len(buf)\r\n if cb:\r\n i += 1\r\n if i == cb_count or cb_count == -1:\r\n cb(total_bytes_uploaded, file_length)\r\n i = 0\r\n buf = fp.read(self.BUFFER_SIZE)\r\n if cb:\r\n cb(total_bytes_uploaded, file_length)\r\n if total_bytes_uploaded != file_length:\r\n # Abort (and delete the tracker file) so if the user retries\r\n # they'll start a new resumable upload rather than potentially\r\n # attempting to pick back up later where we left off.\r\n raise ResumableUploadException(\r\n 'File changed during upload: EOF at %d bytes of %d byte file.' %\r\n (total_bytes_uploaded, file_length),\r\n ResumableTransferDisposition.ABORT)\r\n resp = http_conn.getresponse()\r\n body = resp.read()\r\n # Restore http connection debug level.\r\n http_conn.set_debuglevel(conn.debug)\r\n\r\n if resp.status == 200:\r\n return resp.getheader('etag') # Success\r\n # Retry timeout (408) and status 500 and 503 errors after a delay.\r\n elif resp.status in [408, 500, 503]:\r\n disposition = ResumableTransferDisposition.WAIT_BEFORE_RETRY\r\n else:\r\n # Catch all for any other error codes.\r\n disposition = ResumableTransferDisposition.ABORT\r\n raise ResumableUploadException('Got response code %d while attempting '\r\n 'upload (%s)' %\r\n (resp.status, resp.reason), disposition)","def put_file(self, file_name: str, value: BytesIO):\n value.seek(0)\n self.client.upload_fileobj(value, self.bucket, file_name)","def upload_part(self, seq, offset, chunk_size, labels, buffer = ''):\n # TODO implement Content-MD5\n debug(\"Uploading part %i of %r (%s bytes)\" % (seq, self.upload_id, chunk_size))\n headers = { \"content-length\": chunk_size }\n query_string = \"?partNumber=%i&uploadId=%s\" % (seq, self.upload_id)\n request = self.s3.create_request(\"OBJECT_PUT\", uri = self.uri, headers = headers, extra = query_string)\n response = self.s3.send_file(request, self.file, labels, buffer, offset = offset, chunk_size = chunk_size)\n self.parts[seq] = response[\"headers\"][\"etag\"]\n return response","def upload(self, dest, overwrite=False):\n dest = normpath(dest)\n try:\n remote = get_remote(dest)\n except ValueError: # Nothing exists at dest, nothing to worry about.\n pass\n else: # Something exists here.\n if isinstance(remote, RemoteFile) and self.hash() == remote.hash:\n # Nothing to update.\n pdbox.info(\"%s and %s are identical\" % (self.path, remote.uri))\n return\n if not overwrite:\n raise ValueError(\"%s exists\" % remote.uri)\n\n # Uploading can either happen all at once (with a 150 MB limit),\n # or in chunks. If the file is smaller than the selected chunk size,\n # then try to upload in one go.\n chunksize = min(pdbox._args.get(\"chunksize\", 149.0), 149.0)\n pdbox.debug(\"Chunk size: %.2f MB\" % chunksize)\n if pdbox._args.get(\"dryrun\"):\n pdbox.info(\"Uploaded %s to %s\" % (self.path, dbx_uri(dest)))\n return None\n\n # Set the write mode.\n if overwrite:\n mode = dropbox.files.WriteMode.overwrite\n else:\n mode = dropbox.files.WriteMode.add\n\n chunk = int(chunksize * 1024 * 1024) # Convert B to MB.\n\n with open(self.path, \"rb\") as f:\n data = f.read()\n sz = len(data)\n\n # TODO: Progress bars.\n if sz < chunk: # One-shot upload.\n meta = execute(pdbox.dbx.files_upload, data, dest, mode)\n else: # Multipart upload.\n nchunks = math.ceil(sz / chunk)\n # Initiate the upload with just the first byte.\n start = execute(pdbox.dbx.files_upload_session_start, f[0])\n cursor = dropbox.files.UploadSessionCursor(start.session_id, 1)\n\n # Now just add each chunk.\n while sz - cursor.offset > chunk:\n pdbox.debug(\n \"Uploading chunk %d/%d\" % (cursor.offset % chunk, nchunks),\n )\n execute(\n pdbox.dbx.files_upload_session_append_v2,\n data[cursor.offset:cursor.offset + chunk],\n cursor,\n )\n cursor.offset += chunk\n\n # Upload the remaining to finish the transaction.\n meta = execute(\n pdbox.dbx.files_upload_session_finish,\n data[cursor.offset:],\n dropbox.files.CommitInfo(dest, mode),\n )\n\n pdbox.info(\"Uploaded %s to %s\" % (self.path, dbx_uri(dest)))\n return RemoteFile(None, meta=meta)","def upload_file(self, upload_link: str, file_data: bytes) -> None:\n r = requests.put(\n upload_link,\n data=file_data,\n headers=self._auth_headers\n )\n if r.status_code in self._errors:\n raise ApiResponseException(\n r.status_code, r.json()[\"error\"][\"message\"])","def upload(upload_url: str, file_path: str) -> None:\n with open(file_path, 'r') as data:\n try:\n r = requests.put(\n upload_url,\n data=data,\n headers={\"Content-Type\": \"application/octet-stream\"},\n )\n r.raise_for_status()\n except requests.exceptions.HTTPError as err:\n print('Error uploading file')\n raise err","def put_raw(key: str, data: bytes, project: str, bucket: str) -> None:\n bucket = bucket_construct(project, bucket)\n blob = google.cloud.storage.blob.Blob(name=key, bucket=bucket)\n\n blob.upload_from_string(data, 'application/octet-stream')\n LOG.info(f'Successfully uploaded file to {key} in {bucket}')","def upload(path, stream, buffer_size=None):\n fs.upload(path, stream, buffer_size)","def upload_file(\n self, data, # type: Any\n length=None, # type: Optional[int]\n metadata=None, # type: Optional[Dict[str, str]]\n content_settings=None, # type: Optional[ContentSettings]\n validate_content=False, # type: bool\n max_connections=1, # type: Optional[int]\n timeout=None, # type: Optional[int]\n encoding='UTF-8', # type: str\n **kwargs # type: Any\n ):\n # type: (...) -> Dict[str, Any]\n if self.require_encryption or (self.key_encryption_key is not None):\n raise ValueError(\"Encryption not supported.\")\n\n if isinstance(data, six.text_type):\n data = data.encode(encoding)\n if length is None:\n length = get_length(data)\n if isinstance(data, bytes):\n data = data[:length]\n\n if isinstance(data, bytes):\n stream = BytesIO(data)\n elif hasattr(data, 'read'):\n stream = data\n elif hasattr(data, '__iter__'):\n stream = IterStreamer(data, encoding=encoding) # type: ignore\n else:\n raise TypeError(\"Unsupported data type: {}\".format(type(data)))\n return upload_file_helper( # type: ignore\n self,\n stream,\n length,\n metadata,\n content_settings,\n validate_content,\n timeout,\n max_connections,\n self._config.data_settings,\n **kwargs)","def put_binary(self, url: str, data: Any, content_type: str = None, token: str = None) -> dict:\n res = requests.put(url,\n data=data,\n headers=self._get_headers(\n token,\n {\"Content-Type\": (content_type or \"application/octet-stream\")}\n ),\n verify=False,\n proxies=self._get_proxies())\n if not res.ok:\n logger.error(http_debug(res))\n elif logger.isEnabledFor(logging.DEBUG):\n logger.debug(http_debug(res))\n return self._parse_json_response(res, token)","def run(self):\r\n m = hashlib.md5\r\n self.sock.connect((IP, self.port))\r\n #the uploaded file\r\n f = open(self.path, 'rb')\r\n dat = f.read(1024)\r\n print 'uploading...'\r\n\r\n while dat != '':\r\n hashed = m(dat)\r\n self.sock.send(hashed.hexdigest())\r\n self.data = self.sock.recv(2)\r\n\r\n if not self.data == 'ok':\r\n #somthing went wrong and the thread instantly stops\r\n return\r\n\r\n self.sock.send(dat)\r\n self.data = self.sock.recv(2)\r\n #in case the data didn't and the hash sent didn't match\r\n while self.data == 'sa':\r\n #sends again the hash and data\r\n hashed = m(dat)\r\n self.sock.send(hashed.hexdigest())\r\n print hashed.hexdigest()\r\n self.data = self.sock.recv(2)\r\n print self.data\r\n self.sock.send(dat)\r\n #print dat\r\n self.data = self.sock.recv(2)\r\n #can keep sending\r\n if self.data == 'kg':\r\n self.progress += 1024\r\n dat = f.read(1024)\r\n else:\r\n return\r\n\r\n if not self.sending:\r\n\r\n self.sock.send('canceled')\r\n self.sock.close()\r\n f.close()\r\n return\r\n\r\n\r\n f.close()\r\n self.sock.send('end-of-upload-d-o-n-e-now-what-?')\r\n print 'processing video... this may take a few minutes'\r\n self.data = self.sock.recv(1024)\r\n if self.data == 'complete':\r\n self.sock.close()\r\n tasks.complete_upload()\r\n else:\r\n pass","def _upload(url, data_file, username, password):\n url_match = '(http(s)?)\\:\\/\\/localhost'\n if re.search(url_match, url):\n print(\"Please configure url settings.\")\n exit(1)\n\n polarion_request = post(url,\n data=data_file,\n auth=auth.HTTPBasicAuth(username,\n password))\n status_code = polarion_request.status_code\n if status_code == codes.ok:\n return status_code\n else:\n print(\"Results upload failed with the follow: {}\".format(\n polarion_request.status_code))\n raise exceptions.RequestException","def add_bytes(self, data, **kwargs):\n\t\tbody, headers = multipart.stream_bytes(data, self.chunk_size)\n\t\treturn self._client.request('/add', decoder='json',\n\t\t data=body, headers=headers, **kwargs)","def run(args):\n\n drive_uid = str(args[\"drive_uid\"])\n file_uid = str(args[\"file_uid\"])\n chunk_idx = int(args[\"chunk_index\"])\n secret = str(args[\"secret\"])\n data = string_to_bytes(args[\"data\"])\n checksum = str(args[\"checksum\"])\n\n drive = DriveInfo(drive_uid=drive_uid)\n\n drive.upload_chunk(file_uid=file_uid, chunk_index=chunk_idx,\n secret=secret, chunk=data, checksum=checksum)\n\n return True","def upload_chunk(self, file_obj, length, offset=0, upload_id=None):\n\n params = dict()\n\n if upload_id:\n params['upload_id'] = upload_id\n params['offset'] = offset\n\n url, ignored_params, headers = self.request(\"/chunked_upload\", params,\n method='PUT', content_server=True)\n\n try:\n reply = self.rest_client.PUT(url, file_obj, headers)\n return reply['offset'], reply['upload_id']\n except ErrorResponse as e:\n raise e","def videoclipupload(request, hash_key):\n # need access to temporary_file_path of uploaded clip files so \n # this view always reads files to disk during uploads\n request.upload_handlers = [TemporaryFileUploadHandler()]\n return _videoclipupload(request, hash_key)","def put(self):\n parser.parse(self.arg_schema_put, request, location='json_or_form')\n if not request.files.get('upload_data'):\n raise FileError(\"Missing upload file.\")\n # Figure out how to validate inputs\n mime_type = request.files.get('upload_data').mimetype\n if mime_type == 'text/csv':\n self.upload_csv_data(request.files.get('upload_data'))\n else:\n raise FileError(\"Bad upload file type received.\")\n return {'status': 200}","def users_video_upload(self):\n email_token = auth.current_user()[0]\n content = request.form\n if not UPLOAD_VIDEO_MANDATORY_FIELDS.issubset(content.keys()) or not \"video\" in request.files:\n self.logger.debug((messages.MISSING_FIELDS_ERROR % (UPLOAD_VIDEO_MANDATORY_FIELDS - set(content.keys()))))\n return messages.ERROR_JSON % (\n messages.MISSING_FIELDS_ERROR % (UPLOAD_VIDEO_MANDATORY_FIELDS - set(content.keys()))), 400\n title = content[\"title\"]\n location = content[\"location\"]\n visible = True if content[\"visible\"] == \"true\" else False\n video = request.files['video'].stream\n description = content[\"description\"] if \"description\" in content else None\n try:\n file_location = self.media_server.upload_video(user_email=email_token,\n title=title, video=video)\n except InvalidVideoFormatError:\n self.logger.debug(messages.INVALID_VIDEO_FORMAT)\n return messages.ERROR_JSON % messages.INVALID_VIDEO_FORMAT, 400\n video_data = VideoData(title=title, location=location, creation_time=datetime.now(),\n file_location=file_location, visible=visible, description=description)\n self.video_database.add_video(user_email=email_token, video_data=video_data)\n response_dict = video_data._asdict()\n response_dict[\"creation_time\"] = response_dict[\"creation_time\"].isoformat()\n return json.dumps(response_dict), 200","def upload(self, path, data, headers={}):\n\n client = AsyncHTTPClient()\n method = 'PUT'\n url = self.generate_url(path)\n url_object = urlparse(url)\n params = {\n 'SignatureMethod': 'AWS4-HMAC-SHA256'\n }\n\n headers.update({\n 'Content-Length': str(len(data)),\n 'Content-Type': self._guess_mimetype(path),\n 'Date': self._rfc822_datetime(),\n 'Host': url_object.hostname,\n 'X-Amz-Content-sha256': hashlib.sha256(data).hexdigest(),\n })\n\n try:\n response = yield client.fetch(\n self.sign_request(\n url_object.hostname,\n url_object.path,\n params,\n headers,\n method,\n data\n ),\n method=method,\n body=data,\n connect_timeout=AWS_S3_CONNECT_TIMEOUT,\n request_timeout=AWS_S3_REQUEST_TIMEOUT,\n headers=headers\n )\n except HTTPError as error:\n log.error(error)\n if error.response:\n log.error(error.response.body)\n raise Return(None)\n\n raise Return(response)","def uploadFile(self, filename, name=\"Dummy name\", type=\"DummyType\"):\n\n with open(filename, 'rb') as f:\n data = f.read()\n\n if (name == \"Dummy name\"):\n name = filename\n\n data = {'name': name,\n 'type': type,\n 'bits': xmlrpclib.Binary(data),\n 'overwrite': True}\n\n try:\n r = self.server.wp.uploadFile(\n self.blogid, self.username, self.password, data)\n except xmlrpclib.Fault as fault:\n display_XMLRPC_errors(\"upload file \" + filename, fault)\n\n #FIXME: do we really need to split the url ?\n try:\n r['url'] = r['url'].split('?')[1]\n except IndexError:\n from urlparse import urlparse\n r['url'] = urlparse(r['url']).path\n\n print \"uploaded file file =\", r['file']\n print \"uploaded file url =\", r['url']\n print \"uploaded file type =\", r['type']","def upload():\n global video_file_name\n\n if request.method == 'POST' and 'file' in request.files:\n\n # clear uploads folder\n cleanup_uploads()\n\n # Video has been uploaded\n filename = video.save(request.files['file'])\n video_file_name[0] = filename\n\n # Process video on a new thread\n # threading.Thread(target=process_video, args=[os.path.join('uploads', filename)]).start()\n # threading.Thread(target=process_audio, args=[os.path.join('uploads', filename)]).start()\n threading.Thread(target=process_video, args=[os.path.join(basedir, 'static', 'data', 'uploads', filename)]).start()\n threading.Thread(target=process_audio, args=[os.path.join(basedir, 'static', 'data', 'uploads', filename)]).start()\n\n return jsonify({'success': True}), 200, {'ContentType': 'application/json'}\n\n elif request.method == 'GET':\n return render_template('home.html')","def _videoclipupload(request, hash_key):\n # get video\n video_queryset = Video.objects.all().select_related('owner')\n video = get_object_or_404(video_queryset, hash_key=hash_key)\n \n if request.method == 'POST':\n \n # create a form instance and populate it with data from the request\n form = UploadClipForm(request.POST, request.FILES)\n \n # check whether it's valid\n if form.is_valid():\n # process the data in form.cleaned_data as required\n number = form.cleaned_data['formatted_number'].as_e164\n name = form.cleaned_data['name']\n clip = form.cleaned_data['clip']\n \n # get location of script\n script_dir = os.path.dirname(os.path.abspath(__file__))\n script_path = os.path.join(script_dir, \"process.sh\")\n \n # get locations of input and output files\n input_video = clip.temporary_file_path()\n output_video = NamedTemporaryFile(suffix='.mp4')\n output_image = NamedTemporaryFile(suffix='.jpg')\n output_duration = NamedTemporaryFile()\n \n # get the ffmpeg path\n # on production we had to install the 64 bit static ffmpeg build\n ffmpeg_path = ''\n if not settings.DEBUG:\n ffmpeg_path = '/home/nceruchalu/bin/'\n \n # Generate the output files\n subprocess.check_call(\n [script_path, input_video, output_video.name, \n output_image.name, output_duration.name, ffmpeg_path])\n \n # Get clip duration from the results\n clip_duration = map(float, output_duration)[0]\n \n # if we've come this far then all is well, and we can go ahead\n # and create this clip\n user = User.objects.get_user_by_number(number)\n if not user.is_active and name:\n user.full_name = name\n user.save()\n \n # first add user as a video user, because nobody should\n # add to a clip without being a video user\n VideoUsers.objects.add_users_to_video(video, user)\n \n # Try creating the clip with a thumbnail, if that fails try\n # without the thumbnail\n Clip.objects.create(owner=user,\n video=video,\n mp4=File(output_video),\n photo=ImageFile(output_image),\n duration=clip_duration)\n \n # close and delete the tempoary files\n output_video.close()\n output_image.close()\n output_duration.close()\n \n # redirect to the video details URL but add a querystring param\n # indicating that the video should start from the last clip\n video_url = reverse('web-video-detail',kwargs={'hash_key':hash_key})\n redirect_url = \"{video_url}?latest=1\".format(video_url=video_url)\n \n return HttpResponseRedirect(redirect_url)\n \n else:\n # if a GET (or any other method) we'll create a blank form\n form = UploadClipForm()\n \n return render_to_response('video/upload.html',\n {'video':video,\n 'form':form},\n context_instance=RequestContext(request))","async def put_file(object_name: str, file: File, **kwargs) -> str:\n # TODO: Do not read file but rather stream content as it comes\n await file.read()\n # Get the synchronous file interface from the asynchronous file\n file_obj = file.file\n # Store position of cursor (number of bytes read)\n file_size = file_obj.tell()\n # Reset cursor at start of file\n file_obj.seek(0)\n # Trace file upload with its size\n logger.debug(f\"Uploading file: {object_name} with {file_size} bytes\")\n # Time file upload for debug\n start = time.time()\n # Store object on s3 storage\n client.put_object(\n bucket_name=DATASETS_BUCKET,\n object_name=object_name,\n length=file_size,\n data=file_obj,\n )\n end = time.time()\n # Log time spent\n logger.debug(f\"Took {end - start} seconds to upload {file_size} bytes\")","def upload_blob(bucket_name, data, destination_blob_name):\n\n storage_client = storage.Client()\n bucket = storage_client.bucket(bucket_name)\n blob = bucket.blob(destination_blob_name)\n\n blob.upload_from_string(data)","def put(self,data):\n\n\t\tself.fd.write(str(data))\n\t\treturn 1","def upload(self, fullfilename, remotefolder=None):\n print(\"[Remote Server] Uploading %s to %s:%s\" %(fullfilename, self.server, self.remotefolder))\n\n if not self.status:\n return 0\n\n if remotefolder == None:\n remotefolder = self.remotefolder\n\n if not self.cd(remotefolder):\n return 0\n\n if not self.sendFile(fullfilename):\n print(\"[Remote Server] Error uploading file %s\" %fullfilename)\n return 0\n\n print(\"[Remote Server] upload finished successfully\")\n\n return 1","def upload_blob(bucket, data_string, destination_blob_name):\n\n blob = bucket.blob(destination_blob_name)\n\n blob.upload_from_string(data_string)\n\n print(\n \"File {} uploaded to {}.\".format(\n destination_blob_name, bucket\n )\n )","def upload_blob(bucket, data_string, destination_blob_name):\n\n blob = bucket.blob(destination_blob_name)\n\n blob.upload_from_string(data_string)\n\n print(\n \"File {} uploaded to {}.\".format(\n destination_blob_name, bucket\n )\n )","def upload(cls, local_file, remote_file='', bucket_name=QINIU_BUCKET_NAME):\n if remote_file == '':\n remote_file = cls.__gen_uuid()\n local_file = cls.__get_abs_path(local_file)\n url = \"/v1/qiniu/upload?key=%s&localFile=%s&token=root-weimiyun-9@usstpwd!\" % (remote_file, local_file)\n try:\n conn = httplib.HTTPConnection(UPLOAD_API_HOST)\n conn.request(method=\"POST\", url=url)\n response = conn.getresponse()\n res = response.read()\n if AUTO_DELETE:\n os.remove(local_file)\n return res, True\n except Exception, e:\n return 'Connection refused', False","def upload_video(self, video, caption=None, upload_id=None, thumbnail=None, options={}):\n return upload_video(self, video, caption, upload_id, thumbnail, options)","def gcloud_upload_file(audio_data, gcloud_bucket_name):\n bucket = gce_storage_client.get_bucket(gcloud_bucket_name)\n remote_filepath = \"%s\" % uuid4()\n\n blob = bucket.blob(remote_filepath)\n\n # Upload the audio\n blob.upload_from_string(audio_data)\n\n url = blob.public_url\n if isinstance(url, six.binary_type):\n url = url.decode('utf-8')\n\n return url","def __call__(self, name, post_check_hook=None):\n def do_upload():\n video_data, filetype = self.read_file(name)\n\n ticket_id, upload_uri, complete_uri = self.get_upload_ticket()\n log.info(\"Ticket ID: %s\" % ticket_id)\n\n _range = 0\n hook_break = False\n while _range < len(video_data) and hook_break != True:\n self.upload_segment(upload_uri, _range, video_data, filetype or 'mp4')\n _range = self.get_last_uploaded_byte(upload_uri)\n # hook is passed the range, breaks retry cycle if it returns True\n if post_check_hook:\n hook_break = post_check_hook(_range)\n\n log.info(\"Upload completed\")\n return self.delete_upload_ticket(complete_uri)\n\n return do_upload()","def upload_file(file_name, file_id, model, workspace, header_put):\n filename = \"data_files/{}\".format(file_name)\n data_file = open(filename, \"r\")\n data = data_file.read()\n data_file.close()\n\n uri = (\"https://api.anaplan.com/1/3/workspaces/{}/models/{}/\"\n \"files/{}/chunks/0\").format(workspace, model, file_id)\n response = requests.put(uri, headers = header_put, data = data)\n return response.status_code","def put_upload(self):\n # print \"starting upload...\", self.current_upload['filepath']\n self.touch()\n self.log(\"STARTING_UPLOAD\", level=INFO)\n try:\n Backend.put_file(self.fileobj, self.current_upload[\"gcs_url\"])\n except exceptions.FilePutError as err:\n self.handle_put_error(err, self.fileobj)\n raise","def upload_file(self, file_upload_parameters, progress=None):\n\n file_upload_parameters._submit_upload_parameters.timeout_in_milliseconds = file_upload_parameters.timeout_in_milliseconds\n operation = self.submit_upload(file_upload_parameters._submit_upload_parameters)\n return self.download_upload_result(operation, file_upload_parameters, progress)","def upload_video(self, video_file):\r\n part = \"snippet,status\"\r\n metadata = self.get_metadata(video_file)\r\n body = {\r\n \"snippet\": {\r\n \"title\": metadata['title'],\r\n \"description\": metadata['description'],\r\n \"tags\": metadata['categoryId'],\r\n \"categoryId\": metadata['categoryId']\r\n },\r\n \"status\": {\r\n \"privacyStatus\": \"public\",\r\n \"license\": \"youtube\", # temporary, see gh#414\r\n \"embeddable\": True,\r\n \"publicStatsViewable\": True\r\n }\r\n }\r\n # This is to fix a bug, the API thinks our .ogg files are audio/ogg\r\n mimetype = \"video/{}\".format(video_file.split(\".\")[-1])\r\n media_body = MediaFileUpload(video_file, chunksize=-1, resumable=True, mimetype=mimetype)\r\n insert_request = self.service.videos().insert(part=part, body=body, media_body=media_body)\r\n response = None\r\n error = None\r\n retry = 0\r\n sleep_seconds = 5.0\r\n while response is None:\r\n try:\r\n log.info(\"Uploading %s\" % video_file)\r\n (status, response) = insert_request.next_chunk()\r\n if 'id' in response:\r\n return (Response.SUCCESS, response)\r\n else:\r\n return (Response.UNEXPECTED_FAILURE, response)\r\n except HttpError as e:\r\n if e.resp.status in self.RETRIABLE_STATUS_CODES:\r\n error = \"A retriable HTTP error {} occurred:\\n{}\".format(e.resp.status, e.content)\r\n else:\r\n return (Response.UNRETRIABLE_ERROR, {\"status\": e.resp.status, \"content\": e.content})\r\n except self.RETRIABLE_EXCEPTIONS as e:\r\n error = \"A retriable error occurred: {}\".format(e)\r\n except client.AccessTokenRefreshError:\r\n return (Response.ACCESS_TOKEN_ERROR, None)\r\n if error is not None:\r\n log.error(error)\r\n retry += 1\r\n if retry > self.MAX_RETRIES:\r\n return (Response.MAX_RETRIES_REACHED, None)\r\n log.info(\"Sleeping %s seconds and then retrying...\" % sleep_seconds)\r\n time.sleep(sleep_seconds)","async def upload(self, request):\n\n userid = await authenticated_userid(request)\n project = await request.app.context_project(request, userid)\n\n payload = await request.post()\n\n filename = payload['file'].filename\n upload_stream = payload['file'].file\n\n ext = os.path.splitext(filename)[1]\n\n if not re_filename_ext.match(ext):\n # paranoid check in case a script doesn't protect from code injection\n raise web.HTTPBadRequest(text='file extension not supported: %s' % filename)\n\n camera_id = uuid.uuid1().hex\n\n log = request['slog']\n log.debug('request: camera upload', filename=filename)\n\n config = request.app.config\n\n tmppath = dump_stream(config['media']['tempdir'], upload_stream)\n\n log.debug('file dump', camera_id=camera_id, tmppath=tmppath)\n\n await Camera.insert(request,\n camera_id=camera_id,\n filename=filename,\n project_id=project.project_id)\n\n await request.app.task_broker.publish('camera_upload', {\n 'userid': userid,\n 'project_id': project.project_id,\n 'camera_id': camera_id,\n 'tmppath': tmppath,\n 'filename': filename\n }, log=log)\n\n response_js = {\n 'camera_file_id': camera_id\n }\n\n return web.json_response(response_js, status=HTTPStatus.CREATED)","def test_get_object_to_file_with_range(self):\n err = None\n try:\n response = self.bos.put_object_from_string(self.BUCKET, self.KEY, \"a\" * 1000)\n except BceServerError as e:\n err = e\n finally:\n self.assertIsNone(err)\n self.check_headers(response)\n\n fp = open(self.FILENAME, \"w\")\n fp.write('a' * 100)\n fp.close()\n\n fp = open(self.FILENAME, \"rb\")\n md5 = utils.get_md5_from_fp(fp)\n fp.close()\n os.remove(self.FILENAME)\n\n err = None\n try:\n response = self.bos.get_object_to_file(self.BUCKET,\n self.KEY,\n self.FILENAME,\n range=(0, 99))\n except BceServerError as e:\n err = e\n finally:\n self.assertIsNone(err)\n self.check_headers(response)\n self.assertEqual(response.metadata.content_range, 'bytes 0-99/1000')\n\n fp = open(self.FILENAME, \"rb\")\n get_md5 = utils.get_md5_from_fp(fp)\n fp.close()\n self.assertEqual(get_md5, md5)","async def _upload(self) -> None:\n\n # filename given?\n filename = str(uuid.uuid4()) if self.filename is None else self.filename\n\n # check\n if self._upload_path is None:\n raise ValueError(\"No upload URL given.\")\n\n # send data and return image ID\n async with aiohttp.ClientSession() as session:\n data = aiohttp.FormData()\n data.add_field(\"file\", self._buffer, filename=self.filename)\n async with session.post(self._upload_path, auth=self._auth, data=data, timeout=self._timeout) as response:\n if response.status == 401:\n log.error(\"Wrong credentials for uploading file.\")\n raise FileNotFoundError\n elif response.status != 200:\n log.error(f\"Could not upload file to filecache: {response.status} {response.reason}\")\n raise FileNotFoundError","def test_upload_video(self):\n with self.client:\n path = '../data/example.mp4'\n path = os.path.join(os.path.dirname(__file__), path)\n with open(os.path.abspath(path), 'rb') as file:\n data = dict(file=(file, 'example.mp4'))\n response = self.client.post('/upload',\n content_type='multipart/form-data',\n data=data)\n file.close()\n self.assertEqual(status.HTTP_200_OK, response.status_code)\n self.assertIn('example.mp4', os.listdir(VIDEOS_SAVE_PATH))\n self.assertIn('example.mp4', [video.filename for video in Video.query.all()])","def touch(cls, upload, location=None, bytes_downloaded=0):\n path = \"uploader/touch/%s\" % upload[\"id\"]\n kwargs = {\"bytes_transferred\": bytes_downloaded, \"location\": location}\n try:\n return Backend.put(path, kwargs, headers=Backend.headers())\n except requests.HTTPError as err:\n if err.response.status_code == 410:\n LOGGER.warning(\"Cannot Touch file %s. Already finished \\\n (not active) (410)\", upload[\"id\"])\n raise err\n except:\n raise","def upload_file(self, file_from, file_to):\n dbx = dropbox.Dropbox(self.access_token)\n\n with open(file_from, 'rb') as f:\n file_size = os.path.getsize(file_from)\n CHUNK_SIZE = 4 * 1024 * 1024\n\n if file_size <= CHUNK_SIZE:\n print(dbx.files_upload(f.read(), file_to, mode=dropbox.files.WriteMode.overwrite))\n else:\n upload_session_start_result = dbx.files_upload_session_start(f.read(CHUNK_SIZE))\n cursor = dropbox.files.UploadSessionCursor(session_id=upload_session_start_result.session_id,\n offset=f.tell())\n commit = dropbox.files.CommitInfo(path=file_to, mode=dropbox.files.WriteMode.overwrite)\n\n while f.tell() < file_size:\n if ((file_size - f.tell()) <= CHUNK_SIZE):\n print(dbx.files_upload_session_finish(f.read(CHUNK_SIZE),\n cursor,\n commit))\n else:\n dbx.files_upload_session_append(f.read(CHUNK_SIZE),\n cursor.session_id,\n cursor.offset)\n cursor.offset = f.tell()","def put(self, data):\n self.io.put(data)","def _put_object(self, sha: str) -> None:\n data = git.encode_object(sha)\n path = self._object_path(sha)\n self._trace(\"writing: %s\" % path)\n retries = 0\n mode = dropbox.files.WriteMode.overwrite\n\n if len(data) <= CHUNK_SIZE:\n while True:\n try:\n self._connection.files_upload(data, path, mode, strict_conflict=True, mute=True)\n except dropbox.exceptions.InternalServerError:\n self._trace(\"internal server error writing %s, retrying\" % sha)\n if retries < MAX_RETRIES:\n retries += 1\n else:\n raise\n else:\n break\n else:\n cursor = dropbox.files.UploadSessionCursor(offset=0)\n done_uploading = False\n\n while not done_uploading:\n try:\n end = cursor.offset + CHUNK_SIZE\n chunk = data[(cursor.offset) : end]\n\n if cursor.offset == 0:\n # upload first chunk\n result = self._connection.files_upload_session_start(chunk)\n cursor.session_id = result.session_id\n elif end < len(data):\n # upload intermediate chunks\n self._connection.files_upload_session_append_v2(chunk, cursor)\n else:\n # upload the last chunk\n commit_info = dropbox.files.CommitInfo(\n path, mode, strict_conflict=True, mute=True\n )\n self._connection.files_upload_session_finish(chunk, cursor, commit_info)\n done_uploading = True\n\n # advance cursor to next chunk\n cursor.offset = end\n\n except dropbox.files.UploadSessionOffsetError as offset_error:\n self._trace(\"offset error writing %s, retrying\" % sha)\n cursor.offset = offset_error.correct_offset\n if retries < MAX_RETRIES:\n retries += 1\n else:\n raise\n except dropbox.exceptions.InternalServerError:\n self._trace(\"internal server error writing %s, retrying\" % sha)\n if retries < MAX_RETRIES:\n retries += 1\n else:\n raise","def upload_file(self, f):\n return self._telegraph.upload_file(f)","def _upload(auth_http, project_id, bucket_name, file_path, object_name, acl):\n with open(file_path, 'rb') as f:\n data = f.read()\n content_type, content_encoding = mimetypes.guess_type(file_path)\n\n headers = {\n 'x-goog-project-id': project_id,\n 'x-goog-api-version': API_VERSION,\n 'x-goog-acl': acl,\n 'Content-Length': '%d' % len(data)\n }\n if content_type: headers['Content-Type'] = content_type\n if content_type: headers['Content-Encoding'] = content_encoding\n\n try:\n response, content = auth_http.request(\n 'http://%s.storage.googleapis.com/%s' % (bucket_name, object_name),\n method='PUT',\n headers=headers,\n body=data)\n except httplib2.ServerNotFoundError, se:\n raise Error(404, 'Server not found.')\n\n if response.status >= 300:\n raise Error(response.status, response.reason)\n\n return content","def upload_file_handle(\n self,\n bucket: str,\n object_name: str,\n src_file_handle: typing.BinaryIO):\n raise NotImplementedError()","def clear_range( # type: ignore\n self, start_range, # type: int\n end_range, # type: int\n timeout=None, # type: Optional[int]\n **kwargs\n ):\n # type: (...) -> Dict[str, Any]\n if self.require_encryption or (self.key_encryption_key is not None):\n raise ValueError(\"Unsupported method for encryption.\")\n\n if start_range is None or start_range % 512 != 0:\n raise ValueError(\"start_range must be an integer that aligns with 512 file size\")\n if end_range is None or end_range % 512 != 511:\n raise ValueError(\"end_range must be an integer that aligns with 512 file size\")\n content_range = 'bytes={0}-{1}'.format(start_range, end_range)\n try:\n return self._client.file.upload_range( # type: ignore\n timeout=timeout,\n cls=return_response_headers,\n content_length=0,\n file_range_write=\"clear\",\n range=content_range,\n **kwargs)\n except StorageErrorException as error:\n process_storage_error(error)","def _upload_chunk(self, final=False):\n out = self.fs.session.post(\n self.location,\n data=self.buffer.getvalue(),\n headers={\"content-type\": \"application/octet-stream\"},\n )\n out.raise_for_status()\n return True","def InsertVideo(self, album_or_uri, video, filename_or_handle, content_type='image/jpeg'):\n try:\n assert(isinstance(video, VideoEntry))\n except AssertionError:\n raise GooglePhotosException({\n 'status': GPHOTOS_INVALID_ARGUMENT,\n 'body': '`video` must be a gdata.photos.VideoEntry instance',\n 'reason': 'Found %s, not PhotoEntry' % type(video)\n })\n try:\n majtype, mintype = content_type.split('/')\n # assert(mintype in SUPPORTED_UPLOAD_TYPES)\n except (ValueError, AssertionError):\n raise GooglePhotosException({\n 'status': GPHOTOS_INVALID_CONTENT_TYPE,\n 'body': 'This is not a valid content type: %s' % content_type,\n 'reason': 'Accepted content types:'\n })\n if isinstance(filename_or_handle, (str, unicode)) and \\\n os.path.exists(filename_or_handle): # it's a file name\n mediasource = gdata.MediaSource()\n mediasource.setFile(filename_or_handle, content_type)\n elif hasattr(filename_or_handle, 'read'):# it's a file-like resource\n if hasattr(filename_or_handle, 'seek'):\n filename_or_handle.seek(0) # rewind pointer to the start of the file\n # gdata.MediaSource needs the content length, so read the whole image\n file_handle = StringIO.StringIO(filename_or_handle.read())\n name = 'image'\n if hasattr(filename_or_handle, 'name'):\n name = filename_or_handle.name\n mediasource = gdata.MediaSource(file_handle, content_type,\n content_length=file_handle.len, file_name=name)\n else: #filename_or_handle is not valid\n raise GooglePhotosException({'status':GPHOTOS_INVALID_ARGUMENT,\n 'body':'`filename_or_handle` must be a path name or a file-like object',\n 'reason':'Found %s, not path name or object with a .read() method' % \\\n type(filename_or_handle)\n })\n\n if isinstance(album_or_uri, (str, unicode)): # it's a uri\n feed_uri = album_or_uri\n elif hasattr(album_or_uri, 'GetFeedLink'): # it's a AlbumFeed object\n feed_uri = album_or_uri.GetFeedLink().href\n\n try:\n return self.Post(video, uri=feed_uri, media_source=mediasource,\n converter=None)\n except gdata.service.RequestError as e:\n raise GooglePhotosException(e.args[0])","def upload_file(local_path, s3_path):\n with open(local_path, 'rb') as binary_data:\n s3.Bucket(bucket_name).put_object(Key=s3_path, Body=binary_data)","def put_file(cls, filegen, signed_url):\n headers = {\"Content-Type\": \"application/octet-stream\"}\n try:\n resp = requests.put(signed_url, headers=headers, data=filegen)\n except Exception as err:\n raise exceptions.FilePutError(err)\n else:\n return resp","def put(self, data):\n\n algo, fp = fingerprint(data)\n self.log.debug(\"PUT [%s:%r]: %d bytes\", algo, fp, len(data))\n\n meta = {\n \"fp_algo\": algo,\n \"lib\": self.__version__,\n }\n\n algo, fp = fingerprint(data)\n\n compression_meta, compressed_data = self.compressor.compress(data)\n meta.update(compression_meta)\n\n self.log.debug(\"PUT [%s:%r]: %d bytes, %d compressed with \\\"%s\\\"\", algo, fp, len(data), len(compressed_data),\n compression_meta[\"compression\"])\n\n jargs = self._make_cas_put_arg(compressed_data, meta)\n\n ret, _ = self.ioctx.execute(fp, \"cas\", \"put\", jargs)\n\n if ret == 0:\n return fp\n else:\n raise CASError(\"PUT failed\")","def put_file(sock, source, destination, bytecount=20):\n if not os.path.isfile(source):\n print(\"[-] file %s does not exist\" % source)\n return False\n with open(source, \"rb\") as fp:\n cur_string = \"\"\n count = 1\n byte = fp.read(1)\n cur_string += \"\\\\x\" + hex(ord(byte))[2:]\n while byte != \"\":\n count += 1\n byte = fp.read(1)\n try:\n cur_string += \"\\\\x\" + hex(ord(byte))[2:]\n except TypeError:\n break\n if count % bytecount == 0:\n redirect = \" >\" if count <= bytecount else \">> \"\n final = \"printf \\\"\" + cur_string + \"\\\"\" + redirect + destination + \"\\n\"\n sock.send(final.encode())\n cur_string = \"\"\n if cur_string != \"\":\n redirect = \" >\" if count <= bytecount else \">> \"\n final = \"printf \\\"\" + cur_string + \"\\\"\" + redirect + destination + \"\\n\"\n sock.send(final.encode())\n\n return True","def put(self, file_path, key=None):\n try:\n key_name = key if key else os.path.basename(file_path)\n size = os.stat(file_path).st_size\n if size < 104857600: #100 mb\n k = Key(self.bucket)\n k.key = key_name\n sent = k.set_contents_from_filename(file_path)\n log.info('Uploading %s to S3 (%s)' % (key_name, self.bucket_name))\n return sent == size\n else:\n log.info('Multipart Uploading %s to S3 (%s)' % (key_name, self.bucket_name))\n mp = self.bucket.initiate_multipart_upload(key_name)\n chunk_size = 52428800\n chunk_count = int(math.ceil(size / float(chunk_size)))\n #Send the file parts, using FileChunkIO to create a file-like object\n # that points to a certain byte range within the original file. We\n # set bytes to never exceed the original file size.\n for i in range(chunk_count):\n offset = chunk_size * i\n bytes = min(chunk_size, size - offset)\n with FileChunkIO(file_path, 'r', offset=offset, bytes=bytes) as fp:\n mp.upload_part_from_file(fp, part_num=i + 1)\n # Finish the upload\n mp.complete_upload()\n return True\n except Exception, e:\n log.error('Failed to upload to S3 (%s)' % (self.bucket_name), exc_info=True)\n return False","def post(self):\n filename = str(time.time())\n filepath = os.path.join(\n os.path.join(current_app.config['UPLOAD_FOLDER'], filename))\n with open(filepath, 'bw') as uploadfile:\n chunk_size = 1024\n while True:\n chunk = request.stream.read(chunk_size)\n if len(chunk) == 0:\n break\n uploadfile.write(chunk)\n current_app.logger.info('file %s upload successfully', filename)\n return {'timestamp': filename}, http.HTTPStatus.CREATED","def put(self, filename, content=None, overwrite=True):\n if content is None:\n content = ''\n\n mode = WriteMode.overwrite if overwrite else WriteMode.add\n\n try:\n self.client.files_upload(content, self.base + filename, mode=mode)\n return True\n except ApiError:\n return False","async def upload_file(\n location_id: LocationID,\n file_id: StorageFileID,\n file_size: ByteSize | None,\n link_type: LinkType = LinkType.PRESIGNED,\n is_directory: bool = False,\n):","def process_download_flv(self, data, meta_file_name, connection_time):\n # content-length in bytes\n self.data_len = float(data.info().get('Content-length', None))\n config_pytomo.LOG.debug('Content-length: %s' % self.data_len)\n #meta_file = open(meta_file_name, 'ab')\n meta_file = open(meta_file_name, 'ab+')\n flv_tags = tags.FLV(meta_file)\n self._total_bytes = 0\n #nb_zero_data = 0\n self.state = INITIAL_BUFFERING_STATE\n #block_size = 1024\n block_size = 1\n start = time.time()\n config_pytomo.LOG.debug('start time: %s' % start)\n while True:\n # Download and write\n before = time.time()\n if (before - start) > self.download_time:\n config_pytomo.LOG.debug('Downloaded video during %i seconds, '\n 'stopping' % (before - start))\n break\n # read in bytes\n data_block = data.read(block_size)\n if not self.time_to_get_first_byte:\n first_byte_time = time.time()\n self.time_to_get_first_byte = first_byte_time - connection_time\n block_size = 1024\n data_block_len = len(data_block)\n if data_block_len == 0:\n config_pytomo.LOG.debug('\\nFinished downloading video')\n break\n write_no_seek(meta_file, data_block)\n self._total_bytes += data_block_len\n self.update_with_tags(flv_tags)\n after = time.time()\n if not self.data_duration:\n try:\n self.data_duration = get_data_duration(meta_file_name)\n except ParseError, mes:\n config_pytomo.LOG.info('data duration not yet found: %s'\n % mes)\n self.current_time = after - start\n time_difference = after - before\n self.update_state(time_difference)\n block_size = self.best_block_size(time_difference, data_block_len)\n instant_thp = (8e-3 * data_block_len / (time_difference)\n if (time_difference) != 0 else None)\n if time_difference > MAX_TH_MIN_UPDATE_TIME:\n self.max_instant_thp = max(self.max_instant_thp, instant_thp)\n meta_file.close()\n return after - start","def data(self, data):\n try:\n self.put_nowait(data)\n except Full:\n pass","def putFile(filename, file = None, localFilename = None):\n if not file and not filename:\n print(\"Please pass a valid file or filename\")\n\n if filename and not file:\n file = open(filename, \"rb\")\n\n print(\"filename: {}, file: {}\".format(filename, file))\n r = requests.put(\"{host}/{filename}\".format(host = host, filename = filename), files = {\"file\": file})\n return (r.ok, r.status_code, r.text)","def finish(cls, upload, location=None, bytes_downloaded=0):\n path = \"uploader/finish/%s\" % upload[\"id\"]\n LOGGER.debug(path)\n payload = {\"bytes_transferred\": bytes_downloaded, \"location\": location}\n try:\n return Backend.put(path, payload, headers=Backend.headers())\n except requests.HTTPError as err:\n if err.response.status_code == 410:\n LOGGER.warning(\"Cannot finish file %s. File not active (410)\",\n upload[\"id\"])\n raise err\n except:\n raise","def put(self):\n\n args = self.put_parser.parse_args(strict=True)\n ecgfile = args.data\n\n # Save the file on disk using a uuid\n filename = \"%s%s.dat\" % (self.file_prefix, uuid())\n ecgfile.save(os.path.join(current_app.config['UPLOAD_FOLDER'], filename))\n\n # Create a new database record for this file\n with open_session() as session:\n ecgdata = ECGData(filename)\n session.add(ecgdata)\n session.flush()\n session.commit()\n\n print(ecgdata.id)\n\n return gen_response({\"id\": ecgdata.id})","def UploadFile(self, request_iterator, context):\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')","def upload_file(self, file_name, content):\n return self.files.upload(file_name, content)","def UploadFile(filename, file_id, content, is_binary, status, is_base):\r\n file_too_large = False\r\n if is_base:\r\n type = \"base\"\r\n else:\r\n type = \"current\"\r\n if len(content) > MAX_UPLOAD_SIZE:\r\n print (\"Not uploading the %s file for %s because it's too large.\" %\r\n (type, filename))\r\n file_too_large = True\r\n content = \"\"\r\n checksum = md5(content).hexdigest()\r\n if options.verbose > 0 and not file_too_large:\r\n print \"Uploading %s file for %s\" % (type, filename)\r\n url = \"/%d/upload_content/%d/%d\" % (int(issue), int(patchset), file_id)\r\n form_fields = [(\"filename\", filename),\r\n (\"status\", status),\r\n (\"checksum\", checksum),\r\n (\"is_binary\", str(is_binary)),\r\n (\"is_current\", str(not is_base)),\r\n ]\r\n if file_too_large:\r\n form_fields.append((\"file_too_large\", \"1\"))\r\n if options.email:\r\n form_fields.append((\"user\", options.email))\r\n ctype, body = EncodeMultipartFormData(form_fields,\r\n [(\"data\", filename, content)])\r\n response_body = rpc_server.Send(url, body,\r\n content_type=ctype)\r\n if not response_body.startswith(\"OK\"):\r\n StatusUpdate(\" --> %s\" % response_body)\r\n sys.exit(1)","def put(self, path, data=\"\", mimetype=None, metadata={}):\n # Deal with missing mimetype\n if not mimetype:\n type_, enc_ = mimetypes.guess_type(path)\n if not type_:\n mimetype = \"application/octet-stream\"\n else:\n if enc_ == \"gzip\" and type_ == \"application/x-tar\":\n mimetype = \"application/x-gtar\"\n elif enc_ == \"gzip\":\n mimetype = \"application/x-gzip\"\n elif enc_ == \"bzip2\" and type_ == \"application/x-tar\":\n mimetype = \"application/x-gtar\"\n elif enc_ == \"bzip2\":\n mimetype = \"application/x-bzip2\"\n else:\n mimetype = type_\n # Deal with varying data type\n if isinstance(data, dict):\n data = json.dumps(data)\n\n if metadata:\n # PUT the data as a CDMI object\n # Create the CDMI Data Object Structure\n d = {\"metadata\": metadata}\n if data:\n d.update(\n {\n \"value\": b64encode(data),\n \"valuetransferencoding\": \"base64\",\n \"mimetype\": mimetype,\n }\n )\n data = json.dumps(d)\n # Add the metadata parameters into the URL\n # p = ''.join([\"metadata:{0};\".format(k)\n # for k\n # in metadata])\n # req_url = req_url + '?' + p\n return self.put_cdmi(path, data)\n else:\n # PUT the data in non-CDMI to avoid unnecessary base64 overhead\n # req_url = self.normalize_cdmi_url(path)\n self.put_http(path, data, mimetype)\n # return self.get_cdmi(os.path.split(path)[0])\n return self.get_cdmi(path)","def UploadFile(filename, file_id, content, is_binary, status, is_base):\n file_too_large = False\n if is_base:\n type = \"base\"\n else:\n type = \"current\"\n if len(content) > MAX_UPLOAD_SIZE:\n print (\"Not uploading the %s file for %s because it's too large.\" %\n (type, filename))\n file_too_large = True\n content = \"\"\n checksum = md5(content).hexdigest()\n if options.verbose > 0 and not file_too_large:\n print \"Uploading %s file for %s\" % (type, filename)\n url = \"/%d/upload_content/%d/%d\" % (int(issue), int(patchset), file_id)\n form_fields = [(\"filename\", filename),\n (\"status\", status),\n (\"checksum\", checksum),\n (\"is_binary\", str(is_binary)),\n (\"is_current\", str(not is_base)),\n ]\n if file_too_large:\n form_fields.append((\"file_too_large\", \"1\"))\n if options.email:\n form_fields.append((\"user\", options.email))\n ctype, body = EncodeMultipartFormData(form_fields,\n [(\"data\", filename, content)])\n response_body = rpc_server.Send(url, body,\n content_type=ctype)\n if not response_body.startswith(\"OK\"):\n StatusUpdate(\" --> %s\" % response_body)\n sys.exit(1)","def upload_file(Filename=None, Bucket=None, Key=None, ExtraArgs=None, Callback=None, Config=None):\n pass","def put(url, data=None, **kwargs):\n\n return request('put', url, data=data, **kwargs)","def upload_local_file_to_oh(file_path, file_name, file_metadata, access_token, member_id):\r\n try:\r\n with open(file_path, 'rb') as fs:\r\n upload_stream(fs, file_name, file_metadata, access_token)\r\n return True\r\n except:\r\n print(f'Failed to upload {file_path} to OH for OH member {member_id}')\r\n return False","def upload(self):\n # TODO: Should CD to the working directory set by the robscript.\n src = self.state_frame[0]\n dest = self.state_frame[1]\n self.send_upload(src, dest, True, None)\n self.state = STATE_READ_LINE","def file_upload():\n\n click.secho('*** Uploading image...', fg='green')\n uploaded = _uploaded_file('cover.jpg')\n click.secho(json.dumps(uploaded, indent=2, sort_keys=True), fg='yellow')\n\n click.secho('*** Creating a Picture document for it...', fg='green')\n picture = _make_document('picture', title='cover image', sys_filename=uploaded['path'])\n click.secho(json.dumps(picture, indent=2, sort_keys=True), fg='yellow')\n\n click.secho('*** Attaching it to a Blueray as cover...', fg='green')\n slp = _make_document('movie', title='Silver Linings Playbook')\n blueray = _make_document('blueray', movie_id=slp['_id'], cover_id=picture['_id'])\n click.secho(json.dumps(blueray, indent=2, sort_keys=True), fg='yellow')","def putFile( self, path, sourceSize = 0 ):\n res = checkArgumentFormat( path )\n if not res['OK']:\n return res\n urls = res['Value']\n successful = {}\n failed = {}\n for dest_url, src_file in urls.items():\n gLogger.debug( \"DIPStorage.putFile: Executing transfer of %s to %s\" % ( src_file, dest_url ) )\n res = self.__putFile( src_file, dest_url )\n if res['OK']:\n successful[dest_url] = res['Value']\n else:\n failed[dest_url] = res['Message']\n resDict = {'Failed':failed, 'Successful':successful}\n return S_OK( resDict )","def multipart_push(self, upload_id, url, part_number, chunk_size, data, md5=None):\n path = self.base_path / url\n assert path.is_file(), f\"{self}: multipart upload file {path} does not exist.\"\n with path.open(\"r+b\") as stream:\n stream.seek((part_number - 1) * chunk_size)\n shutil.copyfileobj(data, stream, 1024 * 1024)\n return dict()","def test_put_object_from_file(self):\n self.get_file(20)\n response = self.bos.put_object_from_file(self.BUCKET, self.KEY, self.FILENAME)\n self.check_headers(response, [\"etag\"])","async def tus_upload_part(request: web.Request) -> web.Response:\n ctx: Context = request.app[\"ctx\"]\n secret = ctx.local_config[\"storage-proxy\"][\"secret\"]\n async with check_params(\n request,\n t.Dict(\n {\n t.Key(\"token\"): tx.JsonWebToken(\n secret=secret, inner_iv=upload_token_data_iv\n ),\n }\n ),\n read_from=CheckParamSource.QUERY,\n ) as params:\n token_data = params[\"token\"]\n async with ctx.get_volume(token_data[\"volume\"]) as volume:\n headers = await prepare_tus_session_headers(request, token_data, volume)\n vfpath = volume.mangle_vfpath(token_data[\"vfid\"])\n upload_temp_path = vfpath / \".upload\" / token_data[\"session\"]\n\n async with AsyncFileWriter(\n target_filename=upload_temp_path,\n access_mode=\"ab\",\n max_chunks=DEFAULT_INFLIGHT_CHUNKS,\n ) as writer:\n while not request.content.at_eof():\n chunk = await request.content.read(DEFAULT_CHUNK_SIZE)\n await writer.write(chunk)\n\n current_size = Path(upload_temp_path).stat().st_size\n if current_size >= int(token_data[\"size\"]):\n target_path = vfpath / token_data[\"relpath\"]\n upload_temp_path.rename(target_path)\n try:\n loop = asyncio.get_running_loop()\n await loop.run_in_executor(\n None, lambda: upload_temp_path.parent.rmdir()\n )\n except OSError:\n pass\n headers[\"Upload-Offset\"] = str(current_size)\n return web.Response(status=204, headers=headers)","def put(self, location, data=None, headers={}):\n return self._communicate(vxg.core.request.PutRequest,\n location, data, headers)","def upload_file(\n self, bucket_id: uplink.Path, filename: uplink.Path, file: uplink.Body\n ):\n pass","def upload(self, filename, file_path):\n return","def get_last_uploaded_byte(self, check_uri):\n upload_check_headers = {'Content-Range': 'bytes */*'}\n request_headers = dict(upload_check_headers.items() + self.standard_headers.items())\n try:\n HTTPClient().fetch(check_uri, method=\"PUT\", body='', headers=request_headers)\n except HTTPError as e:\n log.info(\"Upload check: status code %s\" % e.code)\n if e.code == 308:\n _range = int(e.response.headers['Range'].split('-')[1])\n log.info(\"Last uploaded byte: %d\" % _range)\n return _range\n else: raise\n raise ValueError(\"Upload check unsuccessful\")","def write(self, b):\n if isinstance(b, six.string_types):\n b = bytearray(b, encoding='utf-8')\n\n logger.debug(\"writing %r bytes to %r\", len(b), self._buf)\n\n self._buf.write(b)\n\n if self._buf.tell() >= self._upload_chunk_size:\n self._upload_next_part()\n\n return len(b)","def UploadFile(self, local_file_name, gcs_file_name,\n mimetype='application/octet-stream'):\n resumable = os.stat(local_file_name).st_size > 0\n media = gapi_http.MediaFileUpload(local_file_name,\n mimetype=mimetype,\n resumable=resumable)\n\n # gsutil's code suggests that 404s and 410s are retryable for resumable\n # uploads (see ResumableUploadStartOverException).\n def _ErrorMatcher(error):\n return (self._CommonErrorMatcher(error)\n or (isinstance(error, gapi_errors.HttpError)\n and error.resp.status in (404, 410)))\n\n return self._RunWithRetries(\n lambda: self._UploadWithProgress(media, gcs_file_name),\n _ErrorMatcher)","def upload_blob(source_file_name, destination_blob_name, bucket=default_bucket):\n # TODO: Save time compared to the following code of Blob name check?\n if bucket.get_blob(destination_blob_name):\n timestamp = time.strftime('%Y%m%d-%H%M%S')\n blob_name, seperator, file_ext = destination_blob_name.rpartition('.')\n destination_blob_name = f\"{blob_name}_{timestamp}{seperator}{file_ext}\"\n # Create a new blob for where to upload the file's content.\n blob = bucket.blob(destination_blob_name)\n blob.upload_from_filename(source_file_name) # blob.upload_from_file(source_file)\n blob.make_public() # blob.make_private()\n # app.logger.debug(f\"File {source_file_name} uploaded to {destination_blob_name} . \")\n # TODO: Plan for return value if we wanted blob.make_private()\n return blob.public_url","def put(self, path, request):\n\n content_type = 'application/json'\n\n try:\n data = json_decode(request.body)\n self.fileInterface.set(path, data)\n response = self.fileInterface.get(path)\n status_code = 200\n except FileInterfaceError as e:\n response = {'error': str(e)}\n status_code = 400\n except (TypeError, ValueError) as e:\n response = {'error': 'Failed to decode PUT request body: {}'.format(str(e))}\n status_code = 400\n\n logging.debug(response)\n\n return ApiAdapterResponse(response, content_type=content_type,\n status_code=status_code)","def upload_file_to_file(self, iFn, pathNfn):\n # check if file is exist\n b = os.path.getsize(iFn)\n source_size = os.stat(iFn).st_size\n startt = time.time()\n if b < 1000000000:\n # regular upload (file size is less than 1GB)\n key = self.bucket.new_key(pathNfn)\n fsize = key.set_contents_from_filename(iFn) # TODO: check that the file is in the server, if so, abort it\n key.set_acl('public-read')\n else:\n # multi part upload (file size is larger than 1GB)\n key = self.bucket.new_key(pathNfn)\n mp = self.bucket.initiate_multipart_upload(pathNfn)\n # chunk_size = 52428800\n chunk_size = int(source_size / float(100))\n chunk_count = int(math.ceil(source_size / float(chunk_size)))\n # Send the file parts, using FileChunkIO to create a file-like object\n # that points to a certain byte range within the original file. We\n # set bytes to never exceed the original file size.\n widgets = ['Upload Progress: ', pgb.Percentage(), ' ', pgb.Bar(marker=pgb.RotatingMarker()), ' ', pgb.ETA(), ' '] #, pgb.FileTransferSpeed()]\n pbar = pgb.ProgressBar(widgets=widgets, maxval=100)\n pbar.start()\n for i in range(chunk_count):\n pbar.update(99*i/float(chunk_count) + 1)\n # print \"chunk count\", i, \"out of\", chunk_count\n offset = chunk_size * i\n bytes = min(chunk_size, source_size - offset)\n with FileChunkIO(iFn, 'r', offset=offset, bytes=bytes) as fp:\n mp.upload_part_from_file(fp, part_num=i + 1)\n mp.complete_upload()\n pbar.finish()\n key.set_acl('public-read')\n fsize = source_size\n endt = time.time()\n print iFn,'is uploaded to',pathNfn,'(filesize:',fsize,') (',endt-startt,'sec elapsed )'","def upload(request):\n # We pass the 'file_id' in the query string as a GET parameter. If\n # we read it from the POSTed data, WebOb would read all POSTed\n # data, which has various features and traps (like setting the\n # \"Content-Length\" header to 0) that we do not need since we are\n # going to read the data ourselves anyway.\n file_id = request.GET['X-Progress-ID']\n input_file, file_size, filename = get_file_from_request(request)\n session = DBSession()\n u = session.query(Upload).filter_by(id=file_id).one()\n upload_dir = request.registry.settings['poulda.upload_dir']\n user_id = authenticated_userid(request)\n # We use a temporary path to detect unfinished uploads (post\n # mortem, not in the application itself).\n path = os.path.join(upload_dir, '_'.join((user_id, file_id)))\n u.tmp_path = path\n u.started = int(time.time())\n u.size = file_size\n u.state = u'uploading'\n session.flush()\n # We need to commit the transaction so that changes to the Upload\n # object can be seen by the other threads (which will serve the\n # 'progress' JSON view called by the upload page).\n transaction.commit()\n with open(path, 'w') as output:\n # We must read only 'file_size' bytes from the 'input_file',\n # not all of it since it also contains the MIME boundary.\n copy_to_file(input_file, file_size, output)\n final_path = filename[1 + filename.rfind(os.sep):]\n final_path = os.path.join(upload_dir, final_path)\n os.rename(path, final_path)\n session = DBSession()\n u = session.query(Upload).filter_by(id=file_id).one()\n u.state = u'done'\n u.final_path = unicode(final_path, 'utf-8')\n return HTTPFound(location='success')","def put(self, path, data):\n response = self._create_connection(\"PUT\", path, json.dumps(data))\n return self._handle_response(response)","def upload_blob(bucket_name, source_file_name, destination_blob_name):\n storage_client = storage.Client()\n bucket = storage_client.get_bucket(bucket_name)\n blob = bucket.blob(destination_blob_name)\n\n blob.upload_from_filename(source_file_name)\n\n print('File {} uploaded to {}.'.format(\n Crabbie.queue_file,\n destination_blob_name))","def persist_file(self, path, buf, info, meta=None, headers=None):\n headers = {\n \"Authorization\": \"UPYUN: {}:{}\".format(self.OPERATOR, self.SIGNATURE),\n \"Date\": format_date_time(int(time.time())),\n }\n url = \"http://v0.api.upyun.com:5000/{}/{}{}\".format(\n self.bucket, self.prefix, path)\n\n def upload():\n try:\n res = requests.put(url, headers=headers, data=buf)\n if res.status_code != 200:\n logger.info(\n \"failed to upload file %s to upyun, response code: %s, text:\\n%s\",\n path, res.status_code, res.text)\n else:\n logger.debug(\"uploaded file %s to upyun\", path)\n except Exception:\n logger.warn(\"upload file %s to upyun failed\",\n path, exc_info=True)\n return threads.deferToThread(upload)","def put(self, url, data=None):\r\n response = self.requestHelper.put(url, data=data)\r\n return self.process(response)","def send_file_partial(path):\r\n range_header = request.headers.get('Range', None)\r\n if not range_header: return send_file(path)\r\n \r\n size = os.path.getsize(path) \r\n byte1, byte2 = 0, None\r\n \r\n m = re.search('(\\d+)-(\\d*)', range_header)\r\n g = m.groups()\r\n \r\n if g[0]: byte1 = int(g[0])\r\n if g[1]: byte2 = int(g[1])\r\n\r\n length = size - byte1\r\n if byte2 is not None:\r\n length = byte2 - byte1\r\n \r\n data = None\r\n with open(path, 'rb') as f:\r\n f.seek(byte1)\r\n data = f.read(length)\r\n\r\n rv = Response(data, \r\n 206,\r\n mimetype=mimetypes.guess_type(path)[0], \r\n direct_passthrough=True)\r\n rv.headers.add('Content-Range', 'bytes {0}-{1}/{2}'.format(byte1, byte1 + length - 1, size))\r\n\r\n return rv","def upload_object(object_location: ObjectLocation, stream: io.BytesIO) -> None:\n s3 = boto3.client(\"s3\")\n result = s3.upload_fileobj(stream, object_location.bucket.name, object_location.key)\n log.debug(f\"Result of upload to {object_location}: {result}\")","def put_byte(self, *args) -> \"int\":\n return _ida_fpro.qfile_t_put_byte(self, *args)","def upload_file(bucket, local_file_path, remote_destination_path):\n bucket = get_bucket(bucket)\n k = Key(bucket)\n k.key = remote_destination_path\n k.set_contents_from_filename(local_file_path)","def _put(self, url, data=None):\n url = urljoin(self.base_url, url)\n try:\n r = self._make_request(**dict(\n method='PUT',\n url=url,\n json=data,\n auth=self.auth,\n timeout=self.timeout,\n hooks=self.request_hooks,\n headers=self.request_headers\n ))\n except requests.exceptions.RequestException as e:\n raise e\n else:\n if r.status_code >= 400:\n _raise_response_error(r)\n return r.json()","async def put(src: Union[str, bytes],\n dst: str,\n replace: bool = True,\n public_read: bool = False) -> bool:\n if replace is False and await has(dst) is True:\n return True\n _ = dst.strip('/').split('/')\n bucket = _[0]\n key = '/'.join(_[1:])\n # about multipart upload, see\n # https://skonik.me/uploading-large-file-to-s3-using-aiobotocore/\n if isinstance(src, str) is True:\n file = Path(src).resolve()\n if dst.endswith('/'):\n key = key + '/' + file.name\n try:\n async with _create_client() as client, \\\n aiofiles.open(file, 'rb') as f:\n if file.stat().st_size <= FILE_CHUNK_SIZE: # 5MB small file\n await client.put_object(\n Bucket=bucket,\n Key=key,\n Body=await f.read(),\n ACL='public-read' if public_read else 'private')\n logger.info(f'Put object \"{src}\" to \"{dst}\".')\n return True\n else: # large file upload, using multipart\n src_size = file.stat().st_size\n chunks_count = ceil(src_size / FILE_CHUNK_SIZE)\n create_mp_upload_resp = \\\n await client.create_multipart_upload(\n Bucket=bucket,\n Key=key,\n ACL='public-read' if public_read else 'private')\n upload_id = create_mp_upload_resp['UploadId']\n tasks = []\n\n async def upload_chunk(client, file, upload_id,\n chunk_number, src_size, bucket,\n key):\n offset = chunk_number * FILE_CHUNK_SIZE\n remaining_bytes = src_size - offset\n bytes_to_read = min((FILE_CHUNK_SIZE, remaining_bytes))\n part_number = chunk_number + 1\n\n file.seek(offset)\n data = await file.read(bytes_to_read)\n resp = await client.upload_part(\n Bucket=bucket,\n Key=key,\n UploadId=upload_id,\n Body=data,\n PartNumber=part_number,\n )\n global _part_info\n _part_info['Parts'].append({\n 'PartNumber': part_number,\n 'ETag': resp['ETag']\n })\n\n for chunk_number in range(chunks_count):\n tasks.append(\n upload_chunk(\n client=client,\n file=f,\n upload_id=upload_id,\n chunk_number=chunk_number,\n src_size=src_size,\n bucket=bucket,\n key=key,\n ))\n await asyncio.gather(*tasks)\n\n list_parts_resp = await client.list_parts(\n Bucket=bucket, Key=key, UploadId=upload_id)\n\n part_list = sorted(_part_info['Parts'],\n key=lambda k: k['PartNumber'])\n _part_info['Parts'] = part_list\n\n if len(list_parts_resp['Parts']) == chunks_count:\n await client.complete_multipart_upload(\n Bucket=bucket,\n Key=key,\n UploadId=upload_id,\n MultipartUpload=_part_info)\n logger.info(f'Put object \"{src}\" to \"{dst}\".')\n return True\n else:\n await client.abort_multipart_upload(\n Bucket=bucket,\n Key=key,\n UploadId=upload_id,\n )\n return False\n except (ClientError, FileNotFoundError):\n return False\n elif isinstance(src, bytes) is True:\n if dst.endswith('/'):\n key = key + '/'\n try:\n async with _create_client() as client:\n if (src_size := len(src)) <= FILE_CHUNK_SIZE:\n await client.put_object(\n Bucket=bucket,\n Key=key,\n Body=src,\n ACL='public-read' if public_read else 'private')\n logger.info(f'Put {src_size} bytes to \"{dst}\".')\n return True\n else:\n chunks_count = ceil(src_size / FILE_CHUNK_SIZE)\n\n create_mp_upload_resp = \\\n await client.create_multipart_upload(\n Bucket=bucket,\n Key=key,\n ACL='public-read' if public_read else 'private')\n upload_id = create_mp_upload_resp['UploadId']\n tasks = []\n\n async def upload_chunk(client, file, upload_id,\n chunk_number, bucket, key):\n offset = chunk_number * FILE_CHUNK_SIZE\n part_number = chunk_number + 1\n\n data = file[offset:offset + FILE_CHUNK_SIZE]\n resp = await client.upload_part(\n Bucket=bucket,\n Key=key,\n UploadId=upload_id,\n Body=data,\n PartNumber=part_number,\n )\n global _part_info\n _part_info['Parts'].append({\n 'PartNumber': part_number,\n 'ETag': resp['ETag']\n })\n\n for chunk_number in range(chunks_count):\n tasks.append(\n upload_chunk(\n client=client,\n file=src,\n upload_id=upload_id,\n chunk_number=chunk_number,\n bucket=bucket,\n key=key,\n ))\n await asyncio.gather(*tasks)\n\n list_parts_resp = await client.list_parts(\n Bucket=bucket, Key=key, UploadId=upload_id)\n\n part_list = sorted(_part_info['Parts'],\n key=lambda k: k['PartNumber'])\n _part_info['Parts'] = part_list\n\n if len(list_parts_resp['Parts']) == chunks_count:\n await client.complete_multipart_upload(\n Bucket=bucket,\n Key=key,\n UploadId=upload_id,\n MultipartUpload=_part_info)\n logger.info(f'Put {src_size} bytes to \"{dst}\".')\n return True\n else:\n await client.abort_multipart_upload(\n Bucket=bucket,\n Key=key,\n UploadId=upload_id,\n )\n return False\n except ClientError:\n return False\n finally:\n del src # release memory","def upload(api_token, base_url, upload_file, metadata):\n\n upload_url = f\"{base_url}data_files/api_create?auth_token={api_token}\"\n files = {'file': open(upload_file, 'rb')}\n response = requests.post(upload_url, files=files, data=metadata)\n\n # Print out the outcome of the upload\n if response.status_code == 200:\n print(f'File {upload_file} successfully uploaded to HIEv')\n else:\n print(\n f'ERROR - There was a problem uploading file {upload_file} to HIEv')"],"string":"[\n \"def upload_range( # type: ignore\\n self, data, # type: bytes\\n start_range, # type: int\\n end_range, # type: int\\n validate_content=False, # type: Optional[bool]\\n timeout=None, # type: Optional[int]\\n encoding='UTF-8',\\n **kwargs\\n ):\\n # type: (...) -> Dict[str, Any]\\n if self.require_encryption or (self.key_encryption_key is not None):\\n raise ValueError(\\\"Encryption not supported.\\\")\\n if isinstance(data, six.text_type):\\n data = data.encode(encoding)\\n\\n content_range = 'bytes={0}-{1}'.format(start_range, end_range)\\n content_length = end_range - start_range + 1\\n try:\\n return self._client.file.upload_range( # type: ignore\\n range=content_range,\\n content_length=content_length,\\n optionalbody=data,\\n timeout=timeout,\\n validate_content=validate_content,\\n cls=return_response_headers,\\n **kwargs)\\n except StorageErrorException as error:\\n process_storage_error(error)\",\n \"def upload_file(self, source, dest):\\n print(f\\\"Uploading {source} to {dest}\\\")\\n with open(source, \\\"rb\\\") as data:\\n self.client.upload_blob(name=dest, data=data)\",\n \"def _upload_file_bytes(self, conn, http_conn, fp, file_length,\\r\\n total_bytes_uploaded, cb, num_cb):\\r\\n buf = fp.read(self.BUFFER_SIZE)\\r\\n if cb:\\r\\n if num_cb > 2:\\r\\n cb_count = file_length / self.BUFFER_SIZE / (num_cb-2)\\r\\n elif num_cb < 0:\\r\\n cb_count = -1\\r\\n else:\\r\\n cb_count = 0\\r\\n i = 0\\r\\n cb(total_bytes_uploaded, file_length)\\r\\n\\r\\n # Build resumable upload headers for the transfer. Don't send a\\r\\n # Content-Range header if the file is 0 bytes long, because the\\r\\n # resumable upload protocol uses an *inclusive* end-range (so, sending\\r\\n # 'bytes 0-0/1' would actually mean you're sending a 1-byte file).\\r\\n put_headers = {}\\r\\n if file_length:\\r\\n range_header = self._build_content_range_header(\\r\\n '%d-%d' % (total_bytes_uploaded, file_length - 1),\\r\\n file_length)\\r\\n put_headers['Content-Range'] = range_header\\r\\n # Set Content-Length to the total bytes we'll send with this PUT.\\r\\n put_headers['Content-Length'] = str(file_length - total_bytes_uploaded)\\r\\n http_request = AWSAuthConnection.build_base_http_request(\\r\\n conn, 'PUT', path=self.tracker_uri_path, auth_path=None,\\r\\n headers=put_headers, host=self.tracker_uri_host)\\r\\n http_conn.putrequest('PUT', http_request.path)\\r\\n for k in put_headers:\\r\\n http_conn.putheader(k, put_headers[k])\\r\\n http_conn.endheaders()\\r\\n\\r\\n # Turn off debug on http connection so upload content isn't included\\r\\n # in debug stream.\\r\\n http_conn.set_debuglevel(0)\\r\\n while buf:\\r\\n http_conn.send(buf)\\r\\n total_bytes_uploaded += len(buf)\\r\\n if cb:\\r\\n i += 1\\r\\n if i == cb_count or cb_count == -1:\\r\\n cb(total_bytes_uploaded, file_length)\\r\\n i = 0\\r\\n buf = fp.read(self.BUFFER_SIZE)\\r\\n if cb:\\r\\n cb(total_bytes_uploaded, file_length)\\r\\n if total_bytes_uploaded != file_length:\\r\\n # Abort (and delete the tracker file) so if the user retries\\r\\n # they'll start a new resumable upload rather than potentially\\r\\n # attempting to pick back up later where we left off.\\r\\n raise ResumableUploadException(\\r\\n 'File changed during upload: EOF at %d bytes of %d byte file.' %\\r\\n (total_bytes_uploaded, file_length),\\r\\n ResumableTransferDisposition.ABORT)\\r\\n resp = http_conn.getresponse()\\r\\n body = resp.read()\\r\\n # Restore http connection debug level.\\r\\n http_conn.set_debuglevel(conn.debug)\\r\\n\\r\\n if resp.status == 200:\\r\\n return resp.getheader('etag') # Success\\r\\n # Retry timeout (408) and status 500 and 503 errors after a delay.\\r\\n elif resp.status in [408, 500, 503]:\\r\\n disposition = ResumableTransferDisposition.WAIT_BEFORE_RETRY\\r\\n else:\\r\\n # Catch all for any other error codes.\\r\\n disposition = ResumableTransferDisposition.ABORT\\r\\n raise ResumableUploadException('Got response code %d while attempting '\\r\\n 'upload (%s)' %\\r\\n (resp.status, resp.reason), disposition)\",\n \"def put_file(self, file_name: str, value: BytesIO):\\n value.seek(0)\\n self.client.upload_fileobj(value, self.bucket, file_name)\",\n \"def upload_part(self, seq, offset, chunk_size, labels, buffer = ''):\\n # TODO implement Content-MD5\\n debug(\\\"Uploading part %i of %r (%s bytes)\\\" % (seq, self.upload_id, chunk_size))\\n headers = { \\\"content-length\\\": chunk_size }\\n query_string = \\\"?partNumber=%i&uploadId=%s\\\" % (seq, self.upload_id)\\n request = self.s3.create_request(\\\"OBJECT_PUT\\\", uri = self.uri, headers = headers, extra = query_string)\\n response = self.s3.send_file(request, self.file, labels, buffer, offset = offset, chunk_size = chunk_size)\\n self.parts[seq] = response[\\\"headers\\\"][\\\"etag\\\"]\\n return response\",\n \"def upload(self, dest, overwrite=False):\\n dest = normpath(dest)\\n try:\\n remote = get_remote(dest)\\n except ValueError: # Nothing exists at dest, nothing to worry about.\\n pass\\n else: # Something exists here.\\n if isinstance(remote, RemoteFile) and self.hash() == remote.hash:\\n # Nothing to update.\\n pdbox.info(\\\"%s and %s are identical\\\" % (self.path, remote.uri))\\n return\\n if not overwrite:\\n raise ValueError(\\\"%s exists\\\" % remote.uri)\\n\\n # Uploading can either happen all at once (with a 150 MB limit),\\n # or in chunks. If the file is smaller than the selected chunk size,\\n # then try to upload in one go.\\n chunksize = min(pdbox._args.get(\\\"chunksize\\\", 149.0), 149.0)\\n pdbox.debug(\\\"Chunk size: %.2f MB\\\" % chunksize)\\n if pdbox._args.get(\\\"dryrun\\\"):\\n pdbox.info(\\\"Uploaded %s to %s\\\" % (self.path, dbx_uri(dest)))\\n return None\\n\\n # Set the write mode.\\n if overwrite:\\n mode = dropbox.files.WriteMode.overwrite\\n else:\\n mode = dropbox.files.WriteMode.add\\n\\n chunk = int(chunksize * 1024 * 1024) # Convert B to MB.\\n\\n with open(self.path, \\\"rb\\\") as f:\\n data = f.read()\\n sz = len(data)\\n\\n # TODO: Progress bars.\\n if sz < chunk: # One-shot upload.\\n meta = execute(pdbox.dbx.files_upload, data, dest, mode)\\n else: # Multipart upload.\\n nchunks = math.ceil(sz / chunk)\\n # Initiate the upload with just the first byte.\\n start = execute(pdbox.dbx.files_upload_session_start, f[0])\\n cursor = dropbox.files.UploadSessionCursor(start.session_id, 1)\\n\\n # Now just add each chunk.\\n while sz - cursor.offset > chunk:\\n pdbox.debug(\\n \\\"Uploading chunk %d/%d\\\" % (cursor.offset % chunk, nchunks),\\n )\\n execute(\\n pdbox.dbx.files_upload_session_append_v2,\\n data[cursor.offset:cursor.offset + chunk],\\n cursor,\\n )\\n cursor.offset += chunk\\n\\n # Upload the remaining to finish the transaction.\\n meta = execute(\\n pdbox.dbx.files_upload_session_finish,\\n data[cursor.offset:],\\n dropbox.files.CommitInfo(dest, mode),\\n )\\n\\n pdbox.info(\\\"Uploaded %s to %s\\\" % (self.path, dbx_uri(dest)))\\n return RemoteFile(None, meta=meta)\",\n \"def upload_file(self, upload_link: str, file_data: bytes) -> None:\\n r = requests.put(\\n upload_link,\\n data=file_data,\\n headers=self._auth_headers\\n )\\n if r.status_code in self._errors:\\n raise ApiResponseException(\\n r.status_code, r.json()[\\\"error\\\"][\\\"message\\\"])\",\n \"def upload(upload_url: str, file_path: str) -> None:\\n with open(file_path, 'r') as data:\\n try:\\n r = requests.put(\\n upload_url,\\n data=data,\\n headers={\\\"Content-Type\\\": \\\"application/octet-stream\\\"},\\n )\\n r.raise_for_status()\\n except requests.exceptions.HTTPError as err:\\n print('Error uploading file')\\n raise err\",\n \"def put_raw(key: str, data: bytes, project: str, bucket: str) -> None:\\n bucket = bucket_construct(project, bucket)\\n blob = google.cloud.storage.blob.Blob(name=key, bucket=bucket)\\n\\n blob.upload_from_string(data, 'application/octet-stream')\\n LOG.info(f'Successfully uploaded file to {key} in {bucket}')\",\n \"def upload(path, stream, buffer_size=None):\\n fs.upload(path, stream, buffer_size)\",\n \"def upload_file(\\n self, data, # type: Any\\n length=None, # type: Optional[int]\\n metadata=None, # type: Optional[Dict[str, str]]\\n content_settings=None, # type: Optional[ContentSettings]\\n validate_content=False, # type: bool\\n max_connections=1, # type: Optional[int]\\n timeout=None, # type: Optional[int]\\n encoding='UTF-8', # type: str\\n **kwargs # type: Any\\n ):\\n # type: (...) -> Dict[str, Any]\\n if self.require_encryption or (self.key_encryption_key is not None):\\n raise ValueError(\\\"Encryption not supported.\\\")\\n\\n if isinstance(data, six.text_type):\\n data = data.encode(encoding)\\n if length is None:\\n length = get_length(data)\\n if isinstance(data, bytes):\\n data = data[:length]\\n\\n if isinstance(data, bytes):\\n stream = BytesIO(data)\\n elif hasattr(data, 'read'):\\n stream = data\\n elif hasattr(data, '__iter__'):\\n stream = IterStreamer(data, encoding=encoding) # type: ignore\\n else:\\n raise TypeError(\\\"Unsupported data type: {}\\\".format(type(data)))\\n return upload_file_helper( # type: ignore\\n self,\\n stream,\\n length,\\n metadata,\\n content_settings,\\n validate_content,\\n timeout,\\n max_connections,\\n self._config.data_settings,\\n **kwargs)\",\n \"def put_binary(self, url: str, data: Any, content_type: str = None, token: str = None) -> dict:\\n res = requests.put(url,\\n data=data,\\n headers=self._get_headers(\\n token,\\n {\\\"Content-Type\\\": (content_type or \\\"application/octet-stream\\\")}\\n ),\\n verify=False,\\n proxies=self._get_proxies())\\n if not res.ok:\\n logger.error(http_debug(res))\\n elif logger.isEnabledFor(logging.DEBUG):\\n logger.debug(http_debug(res))\\n return self._parse_json_response(res, token)\",\n \"def run(self):\\r\\n m = hashlib.md5\\r\\n self.sock.connect((IP, self.port))\\r\\n #the uploaded file\\r\\n f = open(self.path, 'rb')\\r\\n dat = f.read(1024)\\r\\n print 'uploading...'\\r\\n\\r\\n while dat != '':\\r\\n hashed = m(dat)\\r\\n self.sock.send(hashed.hexdigest())\\r\\n self.data = self.sock.recv(2)\\r\\n\\r\\n if not self.data == 'ok':\\r\\n #somthing went wrong and the thread instantly stops\\r\\n return\\r\\n\\r\\n self.sock.send(dat)\\r\\n self.data = self.sock.recv(2)\\r\\n #in case the data didn't and the hash sent didn't match\\r\\n while self.data == 'sa':\\r\\n #sends again the hash and data\\r\\n hashed = m(dat)\\r\\n self.sock.send(hashed.hexdigest())\\r\\n print hashed.hexdigest()\\r\\n self.data = self.sock.recv(2)\\r\\n print self.data\\r\\n self.sock.send(dat)\\r\\n #print dat\\r\\n self.data = self.sock.recv(2)\\r\\n #can keep sending\\r\\n if self.data == 'kg':\\r\\n self.progress += 1024\\r\\n dat = f.read(1024)\\r\\n else:\\r\\n return\\r\\n\\r\\n if not self.sending:\\r\\n\\r\\n self.sock.send('canceled')\\r\\n self.sock.close()\\r\\n f.close()\\r\\n return\\r\\n\\r\\n\\r\\n f.close()\\r\\n self.sock.send('end-of-upload-d-o-n-e-now-what-?')\\r\\n print 'processing video... this may take a few minutes'\\r\\n self.data = self.sock.recv(1024)\\r\\n if self.data == 'complete':\\r\\n self.sock.close()\\r\\n tasks.complete_upload()\\r\\n else:\\r\\n pass\",\n \"def _upload(url, data_file, username, password):\\n url_match = '(http(s)?)\\\\:\\\\/\\\\/localhost'\\n if re.search(url_match, url):\\n print(\\\"Please configure url settings.\\\")\\n exit(1)\\n\\n polarion_request = post(url,\\n data=data_file,\\n auth=auth.HTTPBasicAuth(username,\\n password))\\n status_code = polarion_request.status_code\\n if status_code == codes.ok:\\n return status_code\\n else:\\n print(\\\"Results upload failed with the follow: {}\\\".format(\\n polarion_request.status_code))\\n raise exceptions.RequestException\",\n \"def add_bytes(self, data, **kwargs):\\n\\t\\tbody, headers = multipart.stream_bytes(data, self.chunk_size)\\n\\t\\treturn self._client.request('/add', decoder='json',\\n\\t\\t data=body, headers=headers, **kwargs)\",\n \"def run(args):\\n\\n drive_uid = str(args[\\\"drive_uid\\\"])\\n file_uid = str(args[\\\"file_uid\\\"])\\n chunk_idx = int(args[\\\"chunk_index\\\"])\\n secret = str(args[\\\"secret\\\"])\\n data = string_to_bytes(args[\\\"data\\\"])\\n checksum = str(args[\\\"checksum\\\"])\\n\\n drive = DriveInfo(drive_uid=drive_uid)\\n\\n drive.upload_chunk(file_uid=file_uid, chunk_index=chunk_idx,\\n secret=secret, chunk=data, checksum=checksum)\\n\\n return True\",\n \"def upload_chunk(self, file_obj, length, offset=0, upload_id=None):\\n\\n params = dict()\\n\\n if upload_id:\\n params['upload_id'] = upload_id\\n params['offset'] = offset\\n\\n url, ignored_params, headers = self.request(\\\"/chunked_upload\\\", params,\\n method='PUT', content_server=True)\\n\\n try:\\n reply = self.rest_client.PUT(url, file_obj, headers)\\n return reply['offset'], reply['upload_id']\\n except ErrorResponse as e:\\n raise e\",\n \"def videoclipupload(request, hash_key):\\n # need access to temporary_file_path of uploaded clip files so \\n # this view always reads files to disk during uploads\\n request.upload_handlers = [TemporaryFileUploadHandler()]\\n return _videoclipupload(request, hash_key)\",\n \"def put(self):\\n parser.parse(self.arg_schema_put, request, location='json_or_form')\\n if not request.files.get('upload_data'):\\n raise FileError(\\\"Missing upload file.\\\")\\n # Figure out how to validate inputs\\n mime_type = request.files.get('upload_data').mimetype\\n if mime_type == 'text/csv':\\n self.upload_csv_data(request.files.get('upload_data'))\\n else:\\n raise FileError(\\\"Bad upload file type received.\\\")\\n return {'status': 200}\",\n \"def users_video_upload(self):\\n email_token = auth.current_user()[0]\\n content = request.form\\n if not UPLOAD_VIDEO_MANDATORY_FIELDS.issubset(content.keys()) or not \\\"video\\\" in request.files:\\n self.logger.debug((messages.MISSING_FIELDS_ERROR % (UPLOAD_VIDEO_MANDATORY_FIELDS - set(content.keys()))))\\n return messages.ERROR_JSON % (\\n messages.MISSING_FIELDS_ERROR % (UPLOAD_VIDEO_MANDATORY_FIELDS - set(content.keys()))), 400\\n title = content[\\\"title\\\"]\\n location = content[\\\"location\\\"]\\n visible = True if content[\\\"visible\\\"] == \\\"true\\\" else False\\n video = request.files['video'].stream\\n description = content[\\\"description\\\"] if \\\"description\\\" in content else None\\n try:\\n file_location = self.media_server.upload_video(user_email=email_token,\\n title=title, video=video)\\n except InvalidVideoFormatError:\\n self.logger.debug(messages.INVALID_VIDEO_FORMAT)\\n return messages.ERROR_JSON % messages.INVALID_VIDEO_FORMAT, 400\\n video_data = VideoData(title=title, location=location, creation_time=datetime.now(),\\n file_location=file_location, visible=visible, description=description)\\n self.video_database.add_video(user_email=email_token, video_data=video_data)\\n response_dict = video_data._asdict()\\n response_dict[\\\"creation_time\\\"] = response_dict[\\\"creation_time\\\"].isoformat()\\n return json.dumps(response_dict), 200\",\n \"def upload(self, path, data, headers={}):\\n\\n client = AsyncHTTPClient()\\n method = 'PUT'\\n url = self.generate_url(path)\\n url_object = urlparse(url)\\n params = {\\n 'SignatureMethod': 'AWS4-HMAC-SHA256'\\n }\\n\\n headers.update({\\n 'Content-Length': str(len(data)),\\n 'Content-Type': self._guess_mimetype(path),\\n 'Date': self._rfc822_datetime(),\\n 'Host': url_object.hostname,\\n 'X-Amz-Content-sha256': hashlib.sha256(data).hexdigest(),\\n })\\n\\n try:\\n response = yield client.fetch(\\n self.sign_request(\\n url_object.hostname,\\n url_object.path,\\n params,\\n headers,\\n method,\\n data\\n ),\\n method=method,\\n body=data,\\n connect_timeout=AWS_S3_CONNECT_TIMEOUT,\\n request_timeout=AWS_S3_REQUEST_TIMEOUT,\\n headers=headers\\n )\\n except HTTPError as error:\\n log.error(error)\\n if error.response:\\n log.error(error.response.body)\\n raise Return(None)\\n\\n raise Return(response)\",\n \"def uploadFile(self, filename, name=\\\"Dummy name\\\", type=\\\"DummyType\\\"):\\n\\n with open(filename, 'rb') as f:\\n data = f.read()\\n\\n if (name == \\\"Dummy name\\\"):\\n name = filename\\n\\n data = {'name': name,\\n 'type': type,\\n 'bits': xmlrpclib.Binary(data),\\n 'overwrite': True}\\n\\n try:\\n r = self.server.wp.uploadFile(\\n self.blogid, self.username, self.password, data)\\n except xmlrpclib.Fault as fault:\\n display_XMLRPC_errors(\\\"upload file \\\" + filename, fault)\\n\\n #FIXME: do we really need to split the url ?\\n try:\\n r['url'] = r['url'].split('?')[1]\\n except IndexError:\\n from urlparse import urlparse\\n r['url'] = urlparse(r['url']).path\\n\\n print \\\"uploaded file file =\\\", r['file']\\n print \\\"uploaded file url =\\\", r['url']\\n print \\\"uploaded file type =\\\", r['type']\",\n \"def upload():\\n global video_file_name\\n\\n if request.method == 'POST' and 'file' in request.files:\\n\\n # clear uploads folder\\n cleanup_uploads()\\n\\n # Video has been uploaded\\n filename = video.save(request.files['file'])\\n video_file_name[0] = filename\\n\\n # Process video on a new thread\\n # threading.Thread(target=process_video, args=[os.path.join('uploads', filename)]).start()\\n # threading.Thread(target=process_audio, args=[os.path.join('uploads', filename)]).start()\\n threading.Thread(target=process_video, args=[os.path.join(basedir, 'static', 'data', 'uploads', filename)]).start()\\n threading.Thread(target=process_audio, args=[os.path.join(basedir, 'static', 'data', 'uploads', filename)]).start()\\n\\n return jsonify({'success': True}), 200, {'ContentType': 'application/json'}\\n\\n elif request.method == 'GET':\\n return render_template('home.html')\",\n \"def _videoclipupload(request, hash_key):\\n # get video\\n video_queryset = Video.objects.all().select_related('owner')\\n video = get_object_or_404(video_queryset, hash_key=hash_key)\\n \\n if request.method == 'POST':\\n \\n # create a form instance and populate it with data from the request\\n form = UploadClipForm(request.POST, request.FILES)\\n \\n # check whether it's valid\\n if form.is_valid():\\n # process the data in form.cleaned_data as required\\n number = form.cleaned_data['formatted_number'].as_e164\\n name = form.cleaned_data['name']\\n clip = form.cleaned_data['clip']\\n \\n # get location of script\\n script_dir = os.path.dirname(os.path.abspath(__file__))\\n script_path = os.path.join(script_dir, \\\"process.sh\\\")\\n \\n # get locations of input and output files\\n input_video = clip.temporary_file_path()\\n output_video = NamedTemporaryFile(suffix='.mp4')\\n output_image = NamedTemporaryFile(suffix='.jpg')\\n output_duration = NamedTemporaryFile()\\n \\n # get the ffmpeg path\\n # on production we had to install the 64 bit static ffmpeg build\\n ffmpeg_path = ''\\n if not settings.DEBUG:\\n ffmpeg_path = '/home/nceruchalu/bin/'\\n \\n # Generate the output files\\n subprocess.check_call(\\n [script_path, input_video, output_video.name, \\n output_image.name, output_duration.name, ffmpeg_path])\\n \\n # Get clip duration from the results\\n clip_duration = map(float, output_duration)[0]\\n \\n # if we've come this far then all is well, and we can go ahead\\n # and create this clip\\n user = User.objects.get_user_by_number(number)\\n if not user.is_active and name:\\n user.full_name = name\\n user.save()\\n \\n # first add user as a video user, because nobody should\\n # add to a clip without being a video user\\n VideoUsers.objects.add_users_to_video(video, user)\\n \\n # Try creating the clip with a thumbnail, if that fails try\\n # without the thumbnail\\n Clip.objects.create(owner=user,\\n video=video,\\n mp4=File(output_video),\\n photo=ImageFile(output_image),\\n duration=clip_duration)\\n \\n # close and delete the tempoary files\\n output_video.close()\\n output_image.close()\\n output_duration.close()\\n \\n # redirect to the video details URL but add a querystring param\\n # indicating that the video should start from the last clip\\n video_url = reverse('web-video-detail',kwargs={'hash_key':hash_key})\\n redirect_url = \\\"{video_url}?latest=1\\\".format(video_url=video_url)\\n \\n return HttpResponseRedirect(redirect_url)\\n \\n else:\\n # if a GET (or any other method) we'll create a blank form\\n form = UploadClipForm()\\n \\n return render_to_response('video/upload.html',\\n {'video':video,\\n 'form':form},\\n context_instance=RequestContext(request))\",\n \"async def put_file(object_name: str, file: File, **kwargs) -> str:\\n # TODO: Do not read file but rather stream content as it comes\\n await file.read()\\n # Get the synchronous file interface from the asynchronous file\\n file_obj = file.file\\n # Store position of cursor (number of bytes read)\\n file_size = file_obj.tell()\\n # Reset cursor at start of file\\n file_obj.seek(0)\\n # Trace file upload with its size\\n logger.debug(f\\\"Uploading file: {object_name} with {file_size} bytes\\\")\\n # Time file upload for debug\\n start = time.time()\\n # Store object on s3 storage\\n client.put_object(\\n bucket_name=DATASETS_BUCKET,\\n object_name=object_name,\\n length=file_size,\\n data=file_obj,\\n )\\n end = time.time()\\n # Log time spent\\n logger.debug(f\\\"Took {end - start} seconds to upload {file_size} bytes\\\")\",\n \"def upload_blob(bucket_name, data, destination_blob_name):\\n\\n storage_client = storage.Client()\\n bucket = storage_client.bucket(bucket_name)\\n blob = bucket.blob(destination_blob_name)\\n\\n blob.upload_from_string(data)\",\n \"def put(self,data):\\n\\n\\t\\tself.fd.write(str(data))\\n\\t\\treturn 1\",\n \"def upload(self, fullfilename, remotefolder=None):\\n print(\\\"[Remote Server] Uploading %s to %s:%s\\\" %(fullfilename, self.server, self.remotefolder))\\n\\n if not self.status:\\n return 0\\n\\n if remotefolder == None:\\n remotefolder = self.remotefolder\\n\\n if not self.cd(remotefolder):\\n return 0\\n\\n if not self.sendFile(fullfilename):\\n print(\\\"[Remote Server] Error uploading file %s\\\" %fullfilename)\\n return 0\\n\\n print(\\\"[Remote Server] upload finished successfully\\\")\\n\\n return 1\",\n \"def upload_blob(bucket, data_string, destination_blob_name):\\n\\n blob = bucket.blob(destination_blob_name)\\n\\n blob.upload_from_string(data_string)\\n\\n print(\\n \\\"File {} uploaded to {}.\\\".format(\\n destination_blob_name, bucket\\n )\\n )\",\n \"def upload_blob(bucket, data_string, destination_blob_name):\\n\\n blob = bucket.blob(destination_blob_name)\\n\\n blob.upload_from_string(data_string)\\n\\n print(\\n \\\"File {} uploaded to {}.\\\".format(\\n destination_blob_name, bucket\\n )\\n )\",\n \"def upload(cls, local_file, remote_file='', bucket_name=QINIU_BUCKET_NAME):\\n if remote_file == '':\\n remote_file = cls.__gen_uuid()\\n local_file = cls.__get_abs_path(local_file)\\n url = \\\"/v1/qiniu/upload?key=%s&localFile=%s&token=root-weimiyun-9@usstpwd!\\\" % (remote_file, local_file)\\n try:\\n conn = httplib.HTTPConnection(UPLOAD_API_HOST)\\n conn.request(method=\\\"POST\\\", url=url)\\n response = conn.getresponse()\\n res = response.read()\\n if AUTO_DELETE:\\n os.remove(local_file)\\n return res, True\\n except Exception, e:\\n return 'Connection refused', False\",\n \"def upload_video(self, video, caption=None, upload_id=None, thumbnail=None, options={}):\\n return upload_video(self, video, caption, upload_id, thumbnail, options)\",\n \"def gcloud_upload_file(audio_data, gcloud_bucket_name):\\n bucket = gce_storage_client.get_bucket(gcloud_bucket_name)\\n remote_filepath = \\\"%s\\\" % uuid4()\\n\\n blob = bucket.blob(remote_filepath)\\n\\n # Upload the audio\\n blob.upload_from_string(audio_data)\\n\\n url = blob.public_url\\n if isinstance(url, six.binary_type):\\n url = url.decode('utf-8')\\n\\n return url\",\n \"def __call__(self, name, post_check_hook=None):\\n def do_upload():\\n video_data, filetype = self.read_file(name)\\n\\n ticket_id, upload_uri, complete_uri = self.get_upload_ticket()\\n log.info(\\\"Ticket ID: %s\\\" % ticket_id)\\n\\n _range = 0\\n hook_break = False\\n while _range < len(video_data) and hook_break != True:\\n self.upload_segment(upload_uri, _range, video_data, filetype or 'mp4')\\n _range = self.get_last_uploaded_byte(upload_uri)\\n # hook is passed the range, breaks retry cycle if it returns True\\n if post_check_hook:\\n hook_break = post_check_hook(_range)\\n\\n log.info(\\\"Upload completed\\\")\\n return self.delete_upload_ticket(complete_uri)\\n\\n return do_upload()\",\n \"def upload_file(file_name, file_id, model, workspace, header_put):\\n filename = \\\"data_files/{}\\\".format(file_name)\\n data_file = open(filename, \\\"r\\\")\\n data = data_file.read()\\n data_file.close()\\n\\n uri = (\\\"https://api.anaplan.com/1/3/workspaces/{}/models/{}/\\\"\\n \\\"files/{}/chunks/0\\\").format(workspace, model, file_id)\\n response = requests.put(uri, headers = header_put, data = data)\\n return response.status_code\",\n \"def put_upload(self):\\n # print \\\"starting upload...\\\", self.current_upload['filepath']\\n self.touch()\\n self.log(\\\"STARTING_UPLOAD\\\", level=INFO)\\n try:\\n Backend.put_file(self.fileobj, self.current_upload[\\\"gcs_url\\\"])\\n except exceptions.FilePutError as err:\\n self.handle_put_error(err, self.fileobj)\\n raise\",\n \"def upload_file(self, file_upload_parameters, progress=None):\\n\\n file_upload_parameters._submit_upload_parameters.timeout_in_milliseconds = file_upload_parameters.timeout_in_milliseconds\\n operation = self.submit_upload(file_upload_parameters._submit_upload_parameters)\\n return self.download_upload_result(operation, file_upload_parameters, progress)\",\n \"def upload_video(self, video_file):\\r\\n part = \\\"snippet,status\\\"\\r\\n metadata = self.get_metadata(video_file)\\r\\n body = {\\r\\n \\\"snippet\\\": {\\r\\n \\\"title\\\": metadata['title'],\\r\\n \\\"description\\\": metadata['description'],\\r\\n \\\"tags\\\": metadata['categoryId'],\\r\\n \\\"categoryId\\\": metadata['categoryId']\\r\\n },\\r\\n \\\"status\\\": {\\r\\n \\\"privacyStatus\\\": \\\"public\\\",\\r\\n \\\"license\\\": \\\"youtube\\\", # temporary, see gh#414\\r\\n \\\"embeddable\\\": True,\\r\\n \\\"publicStatsViewable\\\": True\\r\\n }\\r\\n }\\r\\n # This is to fix a bug, the API thinks our .ogg files are audio/ogg\\r\\n mimetype = \\\"video/{}\\\".format(video_file.split(\\\".\\\")[-1])\\r\\n media_body = MediaFileUpload(video_file, chunksize=-1, resumable=True, mimetype=mimetype)\\r\\n insert_request = self.service.videos().insert(part=part, body=body, media_body=media_body)\\r\\n response = None\\r\\n error = None\\r\\n retry = 0\\r\\n sleep_seconds = 5.0\\r\\n while response is None:\\r\\n try:\\r\\n log.info(\\\"Uploading %s\\\" % video_file)\\r\\n (status, response) = insert_request.next_chunk()\\r\\n if 'id' in response:\\r\\n return (Response.SUCCESS, response)\\r\\n else:\\r\\n return (Response.UNEXPECTED_FAILURE, response)\\r\\n except HttpError as e:\\r\\n if e.resp.status in self.RETRIABLE_STATUS_CODES:\\r\\n error = \\\"A retriable HTTP error {} occurred:\\\\n{}\\\".format(e.resp.status, e.content)\\r\\n else:\\r\\n return (Response.UNRETRIABLE_ERROR, {\\\"status\\\": e.resp.status, \\\"content\\\": e.content})\\r\\n except self.RETRIABLE_EXCEPTIONS as e:\\r\\n error = \\\"A retriable error occurred: {}\\\".format(e)\\r\\n except client.AccessTokenRefreshError:\\r\\n return (Response.ACCESS_TOKEN_ERROR, None)\\r\\n if error is not None:\\r\\n log.error(error)\\r\\n retry += 1\\r\\n if retry > self.MAX_RETRIES:\\r\\n return (Response.MAX_RETRIES_REACHED, None)\\r\\n log.info(\\\"Sleeping %s seconds and then retrying...\\\" % sleep_seconds)\\r\\n time.sleep(sleep_seconds)\",\n \"async def upload(self, request):\\n\\n userid = await authenticated_userid(request)\\n project = await request.app.context_project(request, userid)\\n\\n payload = await request.post()\\n\\n filename = payload['file'].filename\\n upload_stream = payload['file'].file\\n\\n ext = os.path.splitext(filename)[1]\\n\\n if not re_filename_ext.match(ext):\\n # paranoid check in case a script doesn't protect from code injection\\n raise web.HTTPBadRequest(text='file extension not supported: %s' % filename)\\n\\n camera_id = uuid.uuid1().hex\\n\\n log = request['slog']\\n log.debug('request: camera upload', filename=filename)\\n\\n config = request.app.config\\n\\n tmppath = dump_stream(config['media']['tempdir'], upload_stream)\\n\\n log.debug('file dump', camera_id=camera_id, tmppath=tmppath)\\n\\n await Camera.insert(request,\\n camera_id=camera_id,\\n filename=filename,\\n project_id=project.project_id)\\n\\n await request.app.task_broker.publish('camera_upload', {\\n 'userid': userid,\\n 'project_id': project.project_id,\\n 'camera_id': camera_id,\\n 'tmppath': tmppath,\\n 'filename': filename\\n }, log=log)\\n\\n response_js = {\\n 'camera_file_id': camera_id\\n }\\n\\n return web.json_response(response_js, status=HTTPStatus.CREATED)\",\n \"def test_get_object_to_file_with_range(self):\\n err = None\\n try:\\n response = self.bos.put_object_from_string(self.BUCKET, self.KEY, \\\"a\\\" * 1000)\\n except BceServerError as e:\\n err = e\\n finally:\\n self.assertIsNone(err)\\n self.check_headers(response)\\n\\n fp = open(self.FILENAME, \\\"w\\\")\\n fp.write('a' * 100)\\n fp.close()\\n\\n fp = open(self.FILENAME, \\\"rb\\\")\\n md5 = utils.get_md5_from_fp(fp)\\n fp.close()\\n os.remove(self.FILENAME)\\n\\n err = None\\n try:\\n response = self.bos.get_object_to_file(self.BUCKET,\\n self.KEY,\\n self.FILENAME,\\n range=(0, 99))\\n except BceServerError as e:\\n err = e\\n finally:\\n self.assertIsNone(err)\\n self.check_headers(response)\\n self.assertEqual(response.metadata.content_range, 'bytes 0-99/1000')\\n\\n fp = open(self.FILENAME, \\\"rb\\\")\\n get_md5 = utils.get_md5_from_fp(fp)\\n fp.close()\\n self.assertEqual(get_md5, md5)\",\n \"async def _upload(self) -> None:\\n\\n # filename given?\\n filename = str(uuid.uuid4()) if self.filename is None else self.filename\\n\\n # check\\n if self._upload_path is None:\\n raise ValueError(\\\"No upload URL given.\\\")\\n\\n # send data and return image ID\\n async with aiohttp.ClientSession() as session:\\n data = aiohttp.FormData()\\n data.add_field(\\\"file\\\", self._buffer, filename=self.filename)\\n async with session.post(self._upload_path, auth=self._auth, data=data, timeout=self._timeout) as response:\\n if response.status == 401:\\n log.error(\\\"Wrong credentials for uploading file.\\\")\\n raise FileNotFoundError\\n elif response.status != 200:\\n log.error(f\\\"Could not upload file to filecache: {response.status} {response.reason}\\\")\\n raise FileNotFoundError\",\n \"def test_upload_video(self):\\n with self.client:\\n path = '../data/example.mp4'\\n path = os.path.join(os.path.dirname(__file__), path)\\n with open(os.path.abspath(path), 'rb') as file:\\n data = dict(file=(file, 'example.mp4'))\\n response = self.client.post('/upload',\\n content_type='multipart/form-data',\\n data=data)\\n file.close()\\n self.assertEqual(status.HTTP_200_OK, response.status_code)\\n self.assertIn('example.mp4', os.listdir(VIDEOS_SAVE_PATH))\\n self.assertIn('example.mp4', [video.filename for video in Video.query.all()])\",\n \"def touch(cls, upload, location=None, bytes_downloaded=0):\\n path = \\\"uploader/touch/%s\\\" % upload[\\\"id\\\"]\\n kwargs = {\\\"bytes_transferred\\\": bytes_downloaded, \\\"location\\\": location}\\n try:\\n return Backend.put(path, kwargs, headers=Backend.headers())\\n except requests.HTTPError as err:\\n if err.response.status_code == 410:\\n LOGGER.warning(\\\"Cannot Touch file %s. Already finished \\\\\\n (not active) (410)\\\", upload[\\\"id\\\"])\\n raise err\\n except:\\n raise\",\n \"def upload_file(self, file_from, file_to):\\n dbx = dropbox.Dropbox(self.access_token)\\n\\n with open(file_from, 'rb') as f:\\n file_size = os.path.getsize(file_from)\\n CHUNK_SIZE = 4 * 1024 * 1024\\n\\n if file_size <= CHUNK_SIZE:\\n print(dbx.files_upload(f.read(), file_to, mode=dropbox.files.WriteMode.overwrite))\\n else:\\n upload_session_start_result = dbx.files_upload_session_start(f.read(CHUNK_SIZE))\\n cursor = dropbox.files.UploadSessionCursor(session_id=upload_session_start_result.session_id,\\n offset=f.tell())\\n commit = dropbox.files.CommitInfo(path=file_to, mode=dropbox.files.WriteMode.overwrite)\\n\\n while f.tell() < file_size:\\n if ((file_size - f.tell()) <= CHUNK_SIZE):\\n print(dbx.files_upload_session_finish(f.read(CHUNK_SIZE),\\n cursor,\\n commit))\\n else:\\n dbx.files_upload_session_append(f.read(CHUNK_SIZE),\\n cursor.session_id,\\n cursor.offset)\\n cursor.offset = f.tell()\",\n \"def put(self, data):\\n self.io.put(data)\",\n \"def _put_object(self, sha: str) -> None:\\n data = git.encode_object(sha)\\n path = self._object_path(sha)\\n self._trace(\\\"writing: %s\\\" % path)\\n retries = 0\\n mode = dropbox.files.WriteMode.overwrite\\n\\n if len(data) <= CHUNK_SIZE:\\n while True:\\n try:\\n self._connection.files_upload(data, path, mode, strict_conflict=True, mute=True)\\n except dropbox.exceptions.InternalServerError:\\n self._trace(\\\"internal server error writing %s, retrying\\\" % sha)\\n if retries < MAX_RETRIES:\\n retries += 1\\n else:\\n raise\\n else:\\n break\\n else:\\n cursor = dropbox.files.UploadSessionCursor(offset=0)\\n done_uploading = False\\n\\n while not done_uploading:\\n try:\\n end = cursor.offset + CHUNK_SIZE\\n chunk = data[(cursor.offset) : end]\\n\\n if cursor.offset == 0:\\n # upload first chunk\\n result = self._connection.files_upload_session_start(chunk)\\n cursor.session_id = result.session_id\\n elif end < len(data):\\n # upload intermediate chunks\\n self._connection.files_upload_session_append_v2(chunk, cursor)\\n else:\\n # upload the last chunk\\n commit_info = dropbox.files.CommitInfo(\\n path, mode, strict_conflict=True, mute=True\\n )\\n self._connection.files_upload_session_finish(chunk, cursor, commit_info)\\n done_uploading = True\\n\\n # advance cursor to next chunk\\n cursor.offset = end\\n\\n except dropbox.files.UploadSessionOffsetError as offset_error:\\n self._trace(\\\"offset error writing %s, retrying\\\" % sha)\\n cursor.offset = offset_error.correct_offset\\n if retries < MAX_RETRIES:\\n retries += 1\\n else:\\n raise\\n except dropbox.exceptions.InternalServerError:\\n self._trace(\\\"internal server error writing %s, retrying\\\" % sha)\\n if retries < MAX_RETRIES:\\n retries += 1\\n else:\\n raise\",\n \"def upload_file(self, f):\\n return self._telegraph.upload_file(f)\",\n \"def _upload(auth_http, project_id, bucket_name, file_path, object_name, acl):\\n with open(file_path, 'rb') as f:\\n data = f.read()\\n content_type, content_encoding = mimetypes.guess_type(file_path)\\n\\n headers = {\\n 'x-goog-project-id': project_id,\\n 'x-goog-api-version': API_VERSION,\\n 'x-goog-acl': acl,\\n 'Content-Length': '%d' % len(data)\\n }\\n if content_type: headers['Content-Type'] = content_type\\n if content_type: headers['Content-Encoding'] = content_encoding\\n\\n try:\\n response, content = auth_http.request(\\n 'http://%s.storage.googleapis.com/%s' % (bucket_name, object_name),\\n method='PUT',\\n headers=headers,\\n body=data)\\n except httplib2.ServerNotFoundError, se:\\n raise Error(404, 'Server not found.')\\n\\n if response.status >= 300:\\n raise Error(response.status, response.reason)\\n\\n return content\",\n \"def upload_file_handle(\\n self,\\n bucket: str,\\n object_name: str,\\n src_file_handle: typing.BinaryIO):\\n raise NotImplementedError()\",\n \"def clear_range( # type: ignore\\n self, start_range, # type: int\\n end_range, # type: int\\n timeout=None, # type: Optional[int]\\n **kwargs\\n ):\\n # type: (...) -> Dict[str, Any]\\n if self.require_encryption or (self.key_encryption_key is not None):\\n raise ValueError(\\\"Unsupported method for encryption.\\\")\\n\\n if start_range is None or start_range % 512 != 0:\\n raise ValueError(\\\"start_range must be an integer that aligns with 512 file size\\\")\\n if end_range is None or end_range % 512 != 511:\\n raise ValueError(\\\"end_range must be an integer that aligns with 512 file size\\\")\\n content_range = 'bytes={0}-{1}'.format(start_range, end_range)\\n try:\\n return self._client.file.upload_range( # type: ignore\\n timeout=timeout,\\n cls=return_response_headers,\\n content_length=0,\\n file_range_write=\\\"clear\\\",\\n range=content_range,\\n **kwargs)\\n except StorageErrorException as error:\\n process_storage_error(error)\",\n \"def _upload_chunk(self, final=False):\\n out = self.fs.session.post(\\n self.location,\\n data=self.buffer.getvalue(),\\n headers={\\\"content-type\\\": \\\"application/octet-stream\\\"},\\n )\\n out.raise_for_status()\\n return True\",\n \"def InsertVideo(self, album_or_uri, video, filename_or_handle, content_type='image/jpeg'):\\n try:\\n assert(isinstance(video, VideoEntry))\\n except AssertionError:\\n raise GooglePhotosException({\\n 'status': GPHOTOS_INVALID_ARGUMENT,\\n 'body': '`video` must be a gdata.photos.VideoEntry instance',\\n 'reason': 'Found %s, not PhotoEntry' % type(video)\\n })\\n try:\\n majtype, mintype = content_type.split('/')\\n # assert(mintype in SUPPORTED_UPLOAD_TYPES)\\n except (ValueError, AssertionError):\\n raise GooglePhotosException({\\n 'status': GPHOTOS_INVALID_CONTENT_TYPE,\\n 'body': 'This is not a valid content type: %s' % content_type,\\n 'reason': 'Accepted content types:'\\n })\\n if isinstance(filename_or_handle, (str, unicode)) and \\\\\\n os.path.exists(filename_or_handle): # it's a file name\\n mediasource = gdata.MediaSource()\\n mediasource.setFile(filename_or_handle, content_type)\\n elif hasattr(filename_or_handle, 'read'):# it's a file-like resource\\n if hasattr(filename_or_handle, 'seek'):\\n filename_or_handle.seek(0) # rewind pointer to the start of the file\\n # gdata.MediaSource needs the content length, so read the whole image\\n file_handle = StringIO.StringIO(filename_or_handle.read())\\n name = 'image'\\n if hasattr(filename_or_handle, 'name'):\\n name = filename_or_handle.name\\n mediasource = gdata.MediaSource(file_handle, content_type,\\n content_length=file_handle.len, file_name=name)\\n else: #filename_or_handle is not valid\\n raise GooglePhotosException({'status':GPHOTOS_INVALID_ARGUMENT,\\n 'body':'`filename_or_handle` must be a path name or a file-like object',\\n 'reason':'Found %s, not path name or object with a .read() method' % \\\\\\n type(filename_or_handle)\\n })\\n\\n if isinstance(album_or_uri, (str, unicode)): # it's a uri\\n feed_uri = album_or_uri\\n elif hasattr(album_or_uri, 'GetFeedLink'): # it's a AlbumFeed object\\n feed_uri = album_or_uri.GetFeedLink().href\\n\\n try:\\n return self.Post(video, uri=feed_uri, media_source=mediasource,\\n converter=None)\\n except gdata.service.RequestError as e:\\n raise GooglePhotosException(e.args[0])\",\n \"def upload_file(local_path, s3_path):\\n with open(local_path, 'rb') as binary_data:\\n s3.Bucket(bucket_name).put_object(Key=s3_path, Body=binary_data)\",\n \"def put_file(cls, filegen, signed_url):\\n headers = {\\\"Content-Type\\\": \\\"application/octet-stream\\\"}\\n try:\\n resp = requests.put(signed_url, headers=headers, data=filegen)\\n except Exception as err:\\n raise exceptions.FilePutError(err)\\n else:\\n return resp\",\n \"def put(self, data):\\n\\n algo, fp = fingerprint(data)\\n self.log.debug(\\\"PUT [%s:%r]: %d bytes\\\", algo, fp, len(data))\\n\\n meta = {\\n \\\"fp_algo\\\": algo,\\n \\\"lib\\\": self.__version__,\\n }\\n\\n algo, fp = fingerprint(data)\\n\\n compression_meta, compressed_data = self.compressor.compress(data)\\n meta.update(compression_meta)\\n\\n self.log.debug(\\\"PUT [%s:%r]: %d bytes, %d compressed with \\\\\\\"%s\\\\\\\"\\\", algo, fp, len(data), len(compressed_data),\\n compression_meta[\\\"compression\\\"])\\n\\n jargs = self._make_cas_put_arg(compressed_data, meta)\\n\\n ret, _ = self.ioctx.execute(fp, \\\"cas\\\", \\\"put\\\", jargs)\\n\\n if ret == 0:\\n return fp\\n else:\\n raise CASError(\\\"PUT failed\\\")\",\n \"def put_file(sock, source, destination, bytecount=20):\\n if not os.path.isfile(source):\\n print(\\\"[-] file %s does not exist\\\" % source)\\n return False\\n with open(source, \\\"rb\\\") as fp:\\n cur_string = \\\"\\\"\\n count = 1\\n byte = fp.read(1)\\n cur_string += \\\"\\\\\\\\x\\\" + hex(ord(byte))[2:]\\n while byte != \\\"\\\":\\n count += 1\\n byte = fp.read(1)\\n try:\\n cur_string += \\\"\\\\\\\\x\\\" + hex(ord(byte))[2:]\\n except TypeError:\\n break\\n if count % bytecount == 0:\\n redirect = \\\" >\\\" if count <= bytecount else \\\">> \\\"\\n final = \\\"printf \\\\\\\"\\\" + cur_string + \\\"\\\\\\\"\\\" + redirect + destination + \\\"\\\\n\\\"\\n sock.send(final.encode())\\n cur_string = \\\"\\\"\\n if cur_string != \\\"\\\":\\n redirect = \\\" >\\\" if count <= bytecount else \\\">> \\\"\\n final = \\\"printf \\\\\\\"\\\" + cur_string + \\\"\\\\\\\"\\\" + redirect + destination + \\\"\\\\n\\\"\\n sock.send(final.encode())\\n\\n return True\",\n \"def put(self, file_path, key=None):\\n try:\\n key_name = key if key else os.path.basename(file_path)\\n size = os.stat(file_path).st_size\\n if size < 104857600: #100 mb\\n k = Key(self.bucket)\\n k.key = key_name\\n sent = k.set_contents_from_filename(file_path)\\n log.info('Uploading %s to S3 (%s)' % (key_name, self.bucket_name))\\n return sent == size\\n else:\\n log.info('Multipart Uploading %s to S3 (%s)' % (key_name, self.bucket_name))\\n mp = self.bucket.initiate_multipart_upload(key_name)\\n chunk_size = 52428800\\n chunk_count = int(math.ceil(size / float(chunk_size)))\\n #Send the file parts, using FileChunkIO to create a file-like object\\n # that points to a certain byte range within the original file. We\\n # set bytes to never exceed the original file size.\\n for i in range(chunk_count):\\n offset = chunk_size * i\\n bytes = min(chunk_size, size - offset)\\n with FileChunkIO(file_path, 'r', offset=offset, bytes=bytes) as fp:\\n mp.upload_part_from_file(fp, part_num=i + 1)\\n # Finish the upload\\n mp.complete_upload()\\n return True\\n except Exception, e:\\n log.error('Failed to upload to S3 (%s)' % (self.bucket_name), exc_info=True)\\n return False\",\n \"def post(self):\\n filename = str(time.time())\\n filepath = os.path.join(\\n os.path.join(current_app.config['UPLOAD_FOLDER'], filename))\\n with open(filepath, 'bw') as uploadfile:\\n chunk_size = 1024\\n while True:\\n chunk = request.stream.read(chunk_size)\\n if len(chunk) == 0:\\n break\\n uploadfile.write(chunk)\\n current_app.logger.info('file %s upload successfully', filename)\\n return {'timestamp': filename}, http.HTTPStatus.CREATED\",\n \"def put(self, filename, content=None, overwrite=True):\\n if content is None:\\n content = ''\\n\\n mode = WriteMode.overwrite if overwrite else WriteMode.add\\n\\n try:\\n self.client.files_upload(content, self.base + filename, mode=mode)\\n return True\\n except ApiError:\\n return False\",\n \"async def upload_file(\\n location_id: LocationID,\\n file_id: StorageFileID,\\n file_size: ByteSize | None,\\n link_type: LinkType = LinkType.PRESIGNED,\\n is_directory: bool = False,\\n):\",\n \"def process_download_flv(self, data, meta_file_name, connection_time):\\n # content-length in bytes\\n self.data_len = float(data.info().get('Content-length', None))\\n config_pytomo.LOG.debug('Content-length: %s' % self.data_len)\\n #meta_file = open(meta_file_name, 'ab')\\n meta_file = open(meta_file_name, 'ab+')\\n flv_tags = tags.FLV(meta_file)\\n self._total_bytes = 0\\n #nb_zero_data = 0\\n self.state = INITIAL_BUFFERING_STATE\\n #block_size = 1024\\n block_size = 1\\n start = time.time()\\n config_pytomo.LOG.debug('start time: %s' % start)\\n while True:\\n # Download and write\\n before = time.time()\\n if (before - start) > self.download_time:\\n config_pytomo.LOG.debug('Downloaded video during %i seconds, '\\n 'stopping' % (before - start))\\n break\\n # read in bytes\\n data_block = data.read(block_size)\\n if not self.time_to_get_first_byte:\\n first_byte_time = time.time()\\n self.time_to_get_first_byte = first_byte_time - connection_time\\n block_size = 1024\\n data_block_len = len(data_block)\\n if data_block_len == 0:\\n config_pytomo.LOG.debug('\\\\nFinished downloading video')\\n break\\n write_no_seek(meta_file, data_block)\\n self._total_bytes += data_block_len\\n self.update_with_tags(flv_tags)\\n after = time.time()\\n if not self.data_duration:\\n try:\\n self.data_duration = get_data_duration(meta_file_name)\\n except ParseError, mes:\\n config_pytomo.LOG.info('data duration not yet found: %s'\\n % mes)\\n self.current_time = after - start\\n time_difference = after - before\\n self.update_state(time_difference)\\n block_size = self.best_block_size(time_difference, data_block_len)\\n instant_thp = (8e-3 * data_block_len / (time_difference)\\n if (time_difference) != 0 else None)\\n if time_difference > MAX_TH_MIN_UPDATE_TIME:\\n self.max_instant_thp = max(self.max_instant_thp, instant_thp)\\n meta_file.close()\\n return after - start\",\n \"def data(self, data):\\n try:\\n self.put_nowait(data)\\n except Full:\\n pass\",\n \"def putFile(filename, file = None, localFilename = None):\\n if not file and not filename:\\n print(\\\"Please pass a valid file or filename\\\")\\n\\n if filename and not file:\\n file = open(filename, \\\"rb\\\")\\n\\n print(\\\"filename: {}, file: {}\\\".format(filename, file))\\n r = requests.put(\\\"{host}/{filename}\\\".format(host = host, filename = filename), files = {\\\"file\\\": file})\\n return (r.ok, r.status_code, r.text)\",\n \"def finish(cls, upload, location=None, bytes_downloaded=0):\\n path = \\\"uploader/finish/%s\\\" % upload[\\\"id\\\"]\\n LOGGER.debug(path)\\n payload = {\\\"bytes_transferred\\\": bytes_downloaded, \\\"location\\\": location}\\n try:\\n return Backend.put(path, payload, headers=Backend.headers())\\n except requests.HTTPError as err:\\n if err.response.status_code == 410:\\n LOGGER.warning(\\\"Cannot finish file %s. File not active (410)\\\",\\n upload[\\\"id\\\"])\\n raise err\\n except:\\n raise\",\n \"def put(self):\\n\\n args = self.put_parser.parse_args(strict=True)\\n ecgfile = args.data\\n\\n # Save the file on disk using a uuid\\n filename = \\\"%s%s.dat\\\" % (self.file_prefix, uuid())\\n ecgfile.save(os.path.join(current_app.config['UPLOAD_FOLDER'], filename))\\n\\n # Create a new database record for this file\\n with open_session() as session:\\n ecgdata = ECGData(filename)\\n session.add(ecgdata)\\n session.flush()\\n session.commit()\\n\\n print(ecgdata.id)\\n\\n return gen_response({\\\"id\\\": ecgdata.id})\",\n \"def UploadFile(self, request_iterator, context):\\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\\n context.set_details('Method not implemented!')\\n raise NotImplementedError('Method not implemented!')\",\n \"def upload_file(self, file_name, content):\\n return self.files.upload(file_name, content)\",\n \"def UploadFile(filename, file_id, content, is_binary, status, is_base):\\r\\n file_too_large = False\\r\\n if is_base:\\r\\n type = \\\"base\\\"\\r\\n else:\\r\\n type = \\\"current\\\"\\r\\n if len(content) > MAX_UPLOAD_SIZE:\\r\\n print (\\\"Not uploading the %s file for %s because it's too large.\\\" %\\r\\n (type, filename))\\r\\n file_too_large = True\\r\\n content = \\\"\\\"\\r\\n checksum = md5(content).hexdigest()\\r\\n if options.verbose > 0 and not file_too_large:\\r\\n print \\\"Uploading %s file for %s\\\" % (type, filename)\\r\\n url = \\\"/%d/upload_content/%d/%d\\\" % (int(issue), int(patchset), file_id)\\r\\n form_fields = [(\\\"filename\\\", filename),\\r\\n (\\\"status\\\", status),\\r\\n (\\\"checksum\\\", checksum),\\r\\n (\\\"is_binary\\\", str(is_binary)),\\r\\n (\\\"is_current\\\", str(not is_base)),\\r\\n ]\\r\\n if file_too_large:\\r\\n form_fields.append((\\\"file_too_large\\\", \\\"1\\\"))\\r\\n if options.email:\\r\\n form_fields.append((\\\"user\\\", options.email))\\r\\n ctype, body = EncodeMultipartFormData(form_fields,\\r\\n [(\\\"data\\\", filename, content)])\\r\\n response_body = rpc_server.Send(url, body,\\r\\n content_type=ctype)\\r\\n if not response_body.startswith(\\\"OK\\\"):\\r\\n StatusUpdate(\\\" --> %s\\\" % response_body)\\r\\n sys.exit(1)\",\n \"def put(self, path, data=\\\"\\\", mimetype=None, metadata={}):\\n # Deal with missing mimetype\\n if not mimetype:\\n type_, enc_ = mimetypes.guess_type(path)\\n if not type_:\\n mimetype = \\\"application/octet-stream\\\"\\n else:\\n if enc_ == \\\"gzip\\\" and type_ == \\\"application/x-tar\\\":\\n mimetype = \\\"application/x-gtar\\\"\\n elif enc_ == \\\"gzip\\\":\\n mimetype = \\\"application/x-gzip\\\"\\n elif enc_ == \\\"bzip2\\\" and type_ == \\\"application/x-tar\\\":\\n mimetype = \\\"application/x-gtar\\\"\\n elif enc_ == \\\"bzip2\\\":\\n mimetype = \\\"application/x-bzip2\\\"\\n else:\\n mimetype = type_\\n # Deal with varying data type\\n if isinstance(data, dict):\\n data = json.dumps(data)\\n\\n if metadata:\\n # PUT the data as a CDMI object\\n # Create the CDMI Data Object Structure\\n d = {\\\"metadata\\\": metadata}\\n if data:\\n d.update(\\n {\\n \\\"value\\\": b64encode(data),\\n \\\"valuetransferencoding\\\": \\\"base64\\\",\\n \\\"mimetype\\\": mimetype,\\n }\\n )\\n data = json.dumps(d)\\n # Add the metadata parameters into the URL\\n # p = ''.join([\\\"metadata:{0};\\\".format(k)\\n # for k\\n # in metadata])\\n # req_url = req_url + '?' + p\\n return self.put_cdmi(path, data)\\n else:\\n # PUT the data in non-CDMI to avoid unnecessary base64 overhead\\n # req_url = self.normalize_cdmi_url(path)\\n self.put_http(path, data, mimetype)\\n # return self.get_cdmi(os.path.split(path)[0])\\n return self.get_cdmi(path)\",\n \"def UploadFile(filename, file_id, content, is_binary, status, is_base):\\n file_too_large = False\\n if is_base:\\n type = \\\"base\\\"\\n else:\\n type = \\\"current\\\"\\n if len(content) > MAX_UPLOAD_SIZE:\\n print (\\\"Not uploading the %s file for %s because it's too large.\\\" %\\n (type, filename))\\n file_too_large = True\\n content = \\\"\\\"\\n checksum = md5(content).hexdigest()\\n if options.verbose > 0 and not file_too_large:\\n print \\\"Uploading %s file for %s\\\" % (type, filename)\\n url = \\\"/%d/upload_content/%d/%d\\\" % (int(issue), int(patchset), file_id)\\n form_fields = [(\\\"filename\\\", filename),\\n (\\\"status\\\", status),\\n (\\\"checksum\\\", checksum),\\n (\\\"is_binary\\\", str(is_binary)),\\n (\\\"is_current\\\", str(not is_base)),\\n ]\\n if file_too_large:\\n form_fields.append((\\\"file_too_large\\\", \\\"1\\\"))\\n if options.email:\\n form_fields.append((\\\"user\\\", options.email))\\n ctype, body = EncodeMultipartFormData(form_fields,\\n [(\\\"data\\\", filename, content)])\\n response_body = rpc_server.Send(url, body,\\n content_type=ctype)\\n if not response_body.startswith(\\\"OK\\\"):\\n StatusUpdate(\\\" --> %s\\\" % response_body)\\n sys.exit(1)\",\n \"def upload_file(Filename=None, Bucket=None, Key=None, ExtraArgs=None, Callback=None, Config=None):\\n pass\",\n \"def put(url, data=None, **kwargs):\\n\\n return request('put', url, data=data, **kwargs)\",\n \"def upload_local_file_to_oh(file_path, file_name, file_metadata, access_token, member_id):\\r\\n try:\\r\\n with open(file_path, 'rb') as fs:\\r\\n upload_stream(fs, file_name, file_metadata, access_token)\\r\\n return True\\r\\n except:\\r\\n print(f'Failed to upload {file_path} to OH for OH member {member_id}')\\r\\n return False\",\n \"def upload(self):\\n # TODO: Should CD to the working directory set by the robscript.\\n src = self.state_frame[0]\\n dest = self.state_frame[1]\\n self.send_upload(src, dest, True, None)\\n self.state = STATE_READ_LINE\",\n \"def file_upload():\\n\\n click.secho('*** Uploading image...', fg='green')\\n uploaded = _uploaded_file('cover.jpg')\\n click.secho(json.dumps(uploaded, indent=2, sort_keys=True), fg='yellow')\\n\\n click.secho('*** Creating a Picture document for it...', fg='green')\\n picture = _make_document('picture', title='cover image', sys_filename=uploaded['path'])\\n click.secho(json.dumps(picture, indent=2, sort_keys=True), fg='yellow')\\n\\n click.secho('*** Attaching it to a Blueray as cover...', fg='green')\\n slp = _make_document('movie', title='Silver Linings Playbook')\\n blueray = _make_document('blueray', movie_id=slp['_id'], cover_id=picture['_id'])\\n click.secho(json.dumps(blueray, indent=2, sort_keys=True), fg='yellow')\",\n \"def putFile( self, path, sourceSize = 0 ):\\n res = checkArgumentFormat( path )\\n if not res['OK']:\\n return res\\n urls = res['Value']\\n successful = {}\\n failed = {}\\n for dest_url, src_file in urls.items():\\n gLogger.debug( \\\"DIPStorage.putFile: Executing transfer of %s to %s\\\" % ( src_file, dest_url ) )\\n res = self.__putFile( src_file, dest_url )\\n if res['OK']:\\n successful[dest_url] = res['Value']\\n else:\\n failed[dest_url] = res['Message']\\n resDict = {'Failed':failed, 'Successful':successful}\\n return S_OK( resDict )\",\n \"def multipart_push(self, upload_id, url, part_number, chunk_size, data, md5=None):\\n path = self.base_path / url\\n assert path.is_file(), f\\\"{self}: multipart upload file {path} does not exist.\\\"\\n with path.open(\\\"r+b\\\") as stream:\\n stream.seek((part_number - 1) * chunk_size)\\n shutil.copyfileobj(data, stream, 1024 * 1024)\\n return dict()\",\n \"def test_put_object_from_file(self):\\n self.get_file(20)\\n response = self.bos.put_object_from_file(self.BUCKET, self.KEY, self.FILENAME)\\n self.check_headers(response, [\\\"etag\\\"])\",\n \"async def tus_upload_part(request: web.Request) -> web.Response:\\n ctx: Context = request.app[\\\"ctx\\\"]\\n secret = ctx.local_config[\\\"storage-proxy\\\"][\\\"secret\\\"]\\n async with check_params(\\n request,\\n t.Dict(\\n {\\n t.Key(\\\"token\\\"): tx.JsonWebToken(\\n secret=secret, inner_iv=upload_token_data_iv\\n ),\\n }\\n ),\\n read_from=CheckParamSource.QUERY,\\n ) as params:\\n token_data = params[\\\"token\\\"]\\n async with ctx.get_volume(token_data[\\\"volume\\\"]) as volume:\\n headers = await prepare_tus_session_headers(request, token_data, volume)\\n vfpath = volume.mangle_vfpath(token_data[\\\"vfid\\\"])\\n upload_temp_path = vfpath / \\\".upload\\\" / token_data[\\\"session\\\"]\\n\\n async with AsyncFileWriter(\\n target_filename=upload_temp_path,\\n access_mode=\\\"ab\\\",\\n max_chunks=DEFAULT_INFLIGHT_CHUNKS,\\n ) as writer:\\n while not request.content.at_eof():\\n chunk = await request.content.read(DEFAULT_CHUNK_SIZE)\\n await writer.write(chunk)\\n\\n current_size = Path(upload_temp_path).stat().st_size\\n if current_size >= int(token_data[\\\"size\\\"]):\\n target_path = vfpath / token_data[\\\"relpath\\\"]\\n upload_temp_path.rename(target_path)\\n try:\\n loop = asyncio.get_running_loop()\\n await loop.run_in_executor(\\n None, lambda: upload_temp_path.parent.rmdir()\\n )\\n except OSError:\\n pass\\n headers[\\\"Upload-Offset\\\"] = str(current_size)\\n return web.Response(status=204, headers=headers)\",\n \"def put(self, location, data=None, headers={}):\\n return self._communicate(vxg.core.request.PutRequest,\\n location, data, headers)\",\n \"def upload_file(\\n self, bucket_id: uplink.Path, filename: uplink.Path, file: uplink.Body\\n ):\\n pass\",\n \"def upload(self, filename, file_path):\\n return\",\n \"def get_last_uploaded_byte(self, check_uri):\\n upload_check_headers = {'Content-Range': 'bytes */*'}\\n request_headers = dict(upload_check_headers.items() + self.standard_headers.items())\\n try:\\n HTTPClient().fetch(check_uri, method=\\\"PUT\\\", body='', headers=request_headers)\\n except HTTPError as e:\\n log.info(\\\"Upload check: status code %s\\\" % e.code)\\n if e.code == 308:\\n _range = int(e.response.headers['Range'].split('-')[1])\\n log.info(\\\"Last uploaded byte: %d\\\" % _range)\\n return _range\\n else: raise\\n raise ValueError(\\\"Upload check unsuccessful\\\")\",\n \"def write(self, b):\\n if isinstance(b, six.string_types):\\n b = bytearray(b, encoding='utf-8')\\n\\n logger.debug(\\\"writing %r bytes to %r\\\", len(b), self._buf)\\n\\n self._buf.write(b)\\n\\n if self._buf.tell() >= self._upload_chunk_size:\\n self._upload_next_part()\\n\\n return len(b)\",\n \"def UploadFile(self, local_file_name, gcs_file_name,\\n mimetype='application/octet-stream'):\\n resumable = os.stat(local_file_name).st_size > 0\\n media = gapi_http.MediaFileUpload(local_file_name,\\n mimetype=mimetype,\\n resumable=resumable)\\n\\n # gsutil's code suggests that 404s and 410s are retryable for resumable\\n # uploads (see ResumableUploadStartOverException).\\n def _ErrorMatcher(error):\\n return (self._CommonErrorMatcher(error)\\n or (isinstance(error, gapi_errors.HttpError)\\n and error.resp.status in (404, 410)))\\n\\n return self._RunWithRetries(\\n lambda: self._UploadWithProgress(media, gcs_file_name),\\n _ErrorMatcher)\",\n \"def upload_blob(source_file_name, destination_blob_name, bucket=default_bucket):\\n # TODO: Save time compared to the following code of Blob name check?\\n if bucket.get_blob(destination_blob_name):\\n timestamp = time.strftime('%Y%m%d-%H%M%S')\\n blob_name, seperator, file_ext = destination_blob_name.rpartition('.')\\n destination_blob_name = f\\\"{blob_name}_{timestamp}{seperator}{file_ext}\\\"\\n # Create a new blob for where to upload the file's content.\\n blob = bucket.blob(destination_blob_name)\\n blob.upload_from_filename(source_file_name) # blob.upload_from_file(source_file)\\n blob.make_public() # blob.make_private()\\n # app.logger.debug(f\\\"File {source_file_name} uploaded to {destination_blob_name} . \\\")\\n # TODO: Plan for return value if we wanted blob.make_private()\\n return blob.public_url\",\n \"def put(self, path, request):\\n\\n content_type = 'application/json'\\n\\n try:\\n data = json_decode(request.body)\\n self.fileInterface.set(path, data)\\n response = self.fileInterface.get(path)\\n status_code = 200\\n except FileInterfaceError as e:\\n response = {'error': str(e)}\\n status_code = 400\\n except (TypeError, ValueError) as e:\\n response = {'error': 'Failed to decode PUT request body: {}'.format(str(e))}\\n status_code = 400\\n\\n logging.debug(response)\\n\\n return ApiAdapterResponse(response, content_type=content_type,\\n status_code=status_code)\",\n \"def upload_file_to_file(self, iFn, pathNfn):\\n # check if file is exist\\n b = os.path.getsize(iFn)\\n source_size = os.stat(iFn).st_size\\n startt = time.time()\\n if b < 1000000000:\\n # regular upload (file size is less than 1GB)\\n key = self.bucket.new_key(pathNfn)\\n fsize = key.set_contents_from_filename(iFn) # TODO: check that the file is in the server, if so, abort it\\n key.set_acl('public-read')\\n else:\\n # multi part upload (file size is larger than 1GB)\\n key = self.bucket.new_key(pathNfn)\\n mp = self.bucket.initiate_multipart_upload(pathNfn)\\n # chunk_size = 52428800\\n chunk_size = int(source_size / float(100))\\n chunk_count = int(math.ceil(source_size / float(chunk_size)))\\n # Send the file parts, using FileChunkIO to create a file-like object\\n # that points to a certain byte range within the original file. We\\n # set bytes to never exceed the original file size.\\n widgets = ['Upload Progress: ', pgb.Percentage(), ' ', pgb.Bar(marker=pgb.RotatingMarker()), ' ', pgb.ETA(), ' '] #, pgb.FileTransferSpeed()]\\n pbar = pgb.ProgressBar(widgets=widgets, maxval=100)\\n pbar.start()\\n for i in range(chunk_count):\\n pbar.update(99*i/float(chunk_count) + 1)\\n # print \\\"chunk count\\\", i, \\\"out of\\\", chunk_count\\n offset = chunk_size * i\\n bytes = min(chunk_size, source_size - offset)\\n with FileChunkIO(iFn, 'r', offset=offset, bytes=bytes) as fp:\\n mp.upload_part_from_file(fp, part_num=i + 1)\\n mp.complete_upload()\\n pbar.finish()\\n key.set_acl('public-read')\\n fsize = source_size\\n endt = time.time()\\n print iFn,'is uploaded to',pathNfn,'(filesize:',fsize,') (',endt-startt,'sec elapsed )'\",\n \"def upload(request):\\n # We pass the 'file_id' in the query string as a GET parameter. If\\n # we read it from the POSTed data, WebOb would read all POSTed\\n # data, which has various features and traps (like setting the\\n # \\\"Content-Length\\\" header to 0) that we do not need since we are\\n # going to read the data ourselves anyway.\\n file_id = request.GET['X-Progress-ID']\\n input_file, file_size, filename = get_file_from_request(request)\\n session = DBSession()\\n u = session.query(Upload).filter_by(id=file_id).one()\\n upload_dir = request.registry.settings['poulda.upload_dir']\\n user_id = authenticated_userid(request)\\n # We use a temporary path to detect unfinished uploads (post\\n # mortem, not in the application itself).\\n path = os.path.join(upload_dir, '_'.join((user_id, file_id)))\\n u.tmp_path = path\\n u.started = int(time.time())\\n u.size = file_size\\n u.state = u'uploading'\\n session.flush()\\n # We need to commit the transaction so that changes to the Upload\\n # object can be seen by the other threads (which will serve the\\n # 'progress' JSON view called by the upload page).\\n transaction.commit()\\n with open(path, 'w') as output:\\n # We must read only 'file_size' bytes from the 'input_file',\\n # not all of it since it also contains the MIME boundary.\\n copy_to_file(input_file, file_size, output)\\n final_path = filename[1 + filename.rfind(os.sep):]\\n final_path = os.path.join(upload_dir, final_path)\\n os.rename(path, final_path)\\n session = DBSession()\\n u = session.query(Upload).filter_by(id=file_id).one()\\n u.state = u'done'\\n u.final_path = unicode(final_path, 'utf-8')\\n return HTTPFound(location='success')\",\n \"def put(self, path, data):\\n response = self._create_connection(\\\"PUT\\\", path, json.dumps(data))\\n return self._handle_response(response)\",\n \"def upload_blob(bucket_name, source_file_name, destination_blob_name):\\n storage_client = storage.Client()\\n bucket = storage_client.get_bucket(bucket_name)\\n blob = bucket.blob(destination_blob_name)\\n\\n blob.upload_from_filename(source_file_name)\\n\\n print('File {} uploaded to {}.'.format(\\n Crabbie.queue_file,\\n destination_blob_name))\",\n \"def persist_file(self, path, buf, info, meta=None, headers=None):\\n headers = {\\n \\\"Authorization\\\": \\\"UPYUN: {}:{}\\\".format(self.OPERATOR, self.SIGNATURE),\\n \\\"Date\\\": format_date_time(int(time.time())),\\n }\\n url = \\\"http://v0.api.upyun.com:5000/{}/{}{}\\\".format(\\n self.bucket, self.prefix, path)\\n\\n def upload():\\n try:\\n res = requests.put(url, headers=headers, data=buf)\\n if res.status_code != 200:\\n logger.info(\\n \\\"failed to upload file %s to upyun, response code: %s, text:\\\\n%s\\\",\\n path, res.status_code, res.text)\\n else:\\n logger.debug(\\\"uploaded file %s to upyun\\\", path)\\n except Exception:\\n logger.warn(\\\"upload file %s to upyun failed\\\",\\n path, exc_info=True)\\n return threads.deferToThread(upload)\",\n \"def put(self, url, data=None):\\r\\n response = self.requestHelper.put(url, data=data)\\r\\n return self.process(response)\",\n \"def send_file_partial(path):\\r\\n range_header = request.headers.get('Range', None)\\r\\n if not range_header: return send_file(path)\\r\\n \\r\\n size = os.path.getsize(path) \\r\\n byte1, byte2 = 0, None\\r\\n \\r\\n m = re.search('(\\\\d+)-(\\\\d*)', range_header)\\r\\n g = m.groups()\\r\\n \\r\\n if g[0]: byte1 = int(g[0])\\r\\n if g[1]: byte2 = int(g[1])\\r\\n\\r\\n length = size - byte1\\r\\n if byte2 is not None:\\r\\n length = byte2 - byte1\\r\\n \\r\\n data = None\\r\\n with open(path, 'rb') as f:\\r\\n f.seek(byte1)\\r\\n data = f.read(length)\\r\\n\\r\\n rv = Response(data, \\r\\n 206,\\r\\n mimetype=mimetypes.guess_type(path)[0], \\r\\n direct_passthrough=True)\\r\\n rv.headers.add('Content-Range', 'bytes {0}-{1}/{2}'.format(byte1, byte1 + length - 1, size))\\r\\n\\r\\n return rv\",\n \"def upload_object(object_location: ObjectLocation, stream: io.BytesIO) -> None:\\n s3 = boto3.client(\\\"s3\\\")\\n result = s3.upload_fileobj(stream, object_location.bucket.name, object_location.key)\\n log.debug(f\\\"Result of upload to {object_location}: {result}\\\")\",\n \"def put_byte(self, *args) -> \\\"int\\\":\\n return _ida_fpro.qfile_t_put_byte(self, *args)\",\n \"def upload_file(bucket, local_file_path, remote_destination_path):\\n bucket = get_bucket(bucket)\\n k = Key(bucket)\\n k.key = remote_destination_path\\n k.set_contents_from_filename(local_file_path)\",\n \"def _put(self, url, data=None):\\n url = urljoin(self.base_url, url)\\n try:\\n r = self._make_request(**dict(\\n method='PUT',\\n url=url,\\n json=data,\\n auth=self.auth,\\n timeout=self.timeout,\\n hooks=self.request_hooks,\\n headers=self.request_headers\\n ))\\n except requests.exceptions.RequestException as e:\\n raise e\\n else:\\n if r.status_code >= 400:\\n _raise_response_error(r)\\n return r.json()\",\n \"async def put(src: Union[str, bytes],\\n dst: str,\\n replace: bool = True,\\n public_read: bool = False) -> bool:\\n if replace is False and await has(dst) is True:\\n return True\\n _ = dst.strip('/').split('/')\\n bucket = _[0]\\n key = '/'.join(_[1:])\\n # about multipart upload, see\\n # https://skonik.me/uploading-large-file-to-s3-using-aiobotocore/\\n if isinstance(src, str) is True:\\n file = Path(src).resolve()\\n if dst.endswith('/'):\\n key = key + '/' + file.name\\n try:\\n async with _create_client() as client, \\\\\\n aiofiles.open(file, 'rb') as f:\\n if file.stat().st_size <= FILE_CHUNK_SIZE: # 5MB small file\\n await client.put_object(\\n Bucket=bucket,\\n Key=key,\\n Body=await f.read(),\\n ACL='public-read' if public_read else 'private')\\n logger.info(f'Put object \\\"{src}\\\" to \\\"{dst}\\\".')\\n return True\\n else: # large file upload, using multipart\\n src_size = file.stat().st_size\\n chunks_count = ceil(src_size / FILE_CHUNK_SIZE)\\n create_mp_upload_resp = \\\\\\n await client.create_multipart_upload(\\n Bucket=bucket,\\n Key=key,\\n ACL='public-read' if public_read else 'private')\\n upload_id = create_mp_upload_resp['UploadId']\\n tasks = []\\n\\n async def upload_chunk(client, file, upload_id,\\n chunk_number, src_size, bucket,\\n key):\\n offset = chunk_number * FILE_CHUNK_SIZE\\n remaining_bytes = src_size - offset\\n bytes_to_read = min((FILE_CHUNK_SIZE, remaining_bytes))\\n part_number = chunk_number + 1\\n\\n file.seek(offset)\\n data = await file.read(bytes_to_read)\\n resp = await client.upload_part(\\n Bucket=bucket,\\n Key=key,\\n UploadId=upload_id,\\n Body=data,\\n PartNumber=part_number,\\n )\\n global _part_info\\n _part_info['Parts'].append({\\n 'PartNumber': part_number,\\n 'ETag': resp['ETag']\\n })\\n\\n for chunk_number in range(chunks_count):\\n tasks.append(\\n upload_chunk(\\n client=client,\\n file=f,\\n upload_id=upload_id,\\n chunk_number=chunk_number,\\n src_size=src_size,\\n bucket=bucket,\\n key=key,\\n ))\\n await asyncio.gather(*tasks)\\n\\n list_parts_resp = await client.list_parts(\\n Bucket=bucket, Key=key, UploadId=upload_id)\\n\\n part_list = sorted(_part_info['Parts'],\\n key=lambda k: k['PartNumber'])\\n _part_info['Parts'] = part_list\\n\\n if len(list_parts_resp['Parts']) == chunks_count:\\n await client.complete_multipart_upload(\\n Bucket=bucket,\\n Key=key,\\n UploadId=upload_id,\\n MultipartUpload=_part_info)\\n logger.info(f'Put object \\\"{src}\\\" to \\\"{dst}\\\".')\\n return True\\n else:\\n await client.abort_multipart_upload(\\n Bucket=bucket,\\n Key=key,\\n UploadId=upload_id,\\n )\\n return False\\n except (ClientError, FileNotFoundError):\\n return False\\n elif isinstance(src, bytes) is True:\\n if dst.endswith('/'):\\n key = key + '/'\\n try:\\n async with _create_client() as client:\\n if (src_size := len(src)) <= FILE_CHUNK_SIZE:\\n await client.put_object(\\n Bucket=bucket,\\n Key=key,\\n Body=src,\\n ACL='public-read' if public_read else 'private')\\n logger.info(f'Put {src_size} bytes to \\\"{dst}\\\".')\\n return True\\n else:\\n chunks_count = ceil(src_size / FILE_CHUNK_SIZE)\\n\\n create_mp_upload_resp = \\\\\\n await client.create_multipart_upload(\\n Bucket=bucket,\\n Key=key,\\n ACL='public-read' if public_read else 'private')\\n upload_id = create_mp_upload_resp['UploadId']\\n tasks = []\\n\\n async def upload_chunk(client, file, upload_id,\\n chunk_number, bucket, key):\\n offset = chunk_number * FILE_CHUNK_SIZE\\n part_number = chunk_number + 1\\n\\n data = file[offset:offset + FILE_CHUNK_SIZE]\\n resp = await client.upload_part(\\n Bucket=bucket,\\n Key=key,\\n UploadId=upload_id,\\n Body=data,\\n PartNumber=part_number,\\n )\\n global _part_info\\n _part_info['Parts'].append({\\n 'PartNumber': part_number,\\n 'ETag': resp['ETag']\\n })\\n\\n for chunk_number in range(chunks_count):\\n tasks.append(\\n upload_chunk(\\n client=client,\\n file=src,\\n upload_id=upload_id,\\n chunk_number=chunk_number,\\n bucket=bucket,\\n key=key,\\n ))\\n await asyncio.gather(*tasks)\\n\\n list_parts_resp = await client.list_parts(\\n Bucket=bucket, Key=key, UploadId=upload_id)\\n\\n part_list = sorted(_part_info['Parts'],\\n key=lambda k: k['PartNumber'])\\n _part_info['Parts'] = part_list\\n\\n if len(list_parts_resp['Parts']) == chunks_count:\\n await client.complete_multipart_upload(\\n Bucket=bucket,\\n Key=key,\\n UploadId=upload_id,\\n MultipartUpload=_part_info)\\n logger.info(f'Put {src_size} bytes to \\\"{dst}\\\".')\\n return True\\n else:\\n await client.abort_multipart_upload(\\n Bucket=bucket,\\n Key=key,\\n UploadId=upload_id,\\n )\\n return False\\n except ClientError:\\n return False\\n finally:\\n del src # release memory\",\n \"def upload(api_token, base_url, upload_file, metadata):\\n\\n upload_url = f\\\"{base_url}data_files/api_create?auth_token={api_token}\\\"\\n files = {'file': open(upload_file, 'rb')}\\n response = requests.post(upload_url, files=files, data=metadata)\\n\\n # Print out the outcome of the upload\\n if response.status_code == 200:\\n print(f'File {upload_file} successfully uploaded to HIEv')\\n else:\\n print(\\n f'ERROR - There was a problem uploading file {upload_file} to HIEv')\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6141114","0.5634965","0.56206375","0.550832","0.5482587","0.54187346","0.5404088","0.538972","0.5272799","0.52320516","0.5201052","0.511785","0.51038563","0.5073653","0.5050996","0.49917027","0.49861932","0.49765033","0.49168584","0.49088553","0.48805287","0.48795724","0.4865323","0.48652864","0.48434505","0.4835503","0.48295677","0.48145643","0.48082858","0.48082858","0.48076537","0.47925952","0.47855374","0.47724307","0.47632888","0.47564688","0.47562185","0.47507104","0.4711603","0.4709043","0.4699407","0.46932423","0.46898672","0.4681358","0.46799752","0.46733913","0.46721625","0.4666254","0.46600968","0.46529424","0.4644887","0.46160042","0.4607557","0.46042505","0.4600254","0.4595773","0.45899662","0.4569587","0.45607305","0.45587862","0.45582336","0.45491886","0.4547249","0.4547035","0.4535789","0.452846","0.45254526","0.4524412","0.451567","0.45143226","0.45078823","0.45058796","0.45022157","0.44970867","0.44959876","0.4494689","0.44930628","0.4491836","0.4489929","0.4489264","0.44844365","0.44776186","0.447108","0.44577625","0.44560888","0.44551826","0.4454798","0.44544995","0.44509584","0.44499335","0.4446739","0.44458583","0.44341084","0.4432838","0.44278714","0.4423182","0.44219434","0.44195488","0.44145054","0.44136935"],"string":"[\n \"0.6141114\",\n \"0.5634965\",\n \"0.56206375\",\n \"0.550832\",\n \"0.5482587\",\n \"0.54187346\",\n \"0.5404088\",\n \"0.538972\",\n \"0.5272799\",\n \"0.52320516\",\n \"0.5201052\",\n \"0.511785\",\n \"0.51038563\",\n \"0.5073653\",\n \"0.5050996\",\n \"0.49917027\",\n \"0.49861932\",\n \"0.49765033\",\n \"0.49168584\",\n \"0.49088553\",\n \"0.48805287\",\n \"0.48795724\",\n \"0.4865323\",\n \"0.48652864\",\n \"0.48434505\",\n \"0.4835503\",\n \"0.48295677\",\n \"0.48145643\",\n \"0.48082858\",\n \"0.48082858\",\n \"0.48076537\",\n \"0.47925952\",\n \"0.47855374\",\n \"0.47724307\",\n \"0.47632888\",\n \"0.47564688\",\n \"0.47562185\",\n \"0.47507104\",\n \"0.4711603\",\n \"0.4709043\",\n \"0.4699407\",\n \"0.46932423\",\n \"0.46898672\",\n \"0.4681358\",\n \"0.46799752\",\n \"0.46733913\",\n \"0.46721625\",\n \"0.4666254\",\n \"0.46600968\",\n \"0.46529424\",\n \"0.4644887\",\n \"0.46160042\",\n \"0.4607557\",\n \"0.46042505\",\n \"0.4600254\",\n \"0.4595773\",\n \"0.45899662\",\n \"0.4569587\",\n \"0.45607305\",\n \"0.45587862\",\n \"0.45582336\",\n \"0.45491886\",\n \"0.4547249\",\n \"0.4547035\",\n \"0.4535789\",\n \"0.452846\",\n \"0.45254526\",\n \"0.4524412\",\n \"0.451567\",\n \"0.45143226\",\n \"0.45078823\",\n \"0.45058796\",\n \"0.45022157\",\n \"0.44970867\",\n \"0.44959876\",\n \"0.4494689\",\n \"0.44930628\",\n \"0.4491836\",\n \"0.4489929\",\n \"0.4489264\",\n \"0.44844365\",\n \"0.44776186\",\n \"0.447108\",\n \"0.44577625\",\n \"0.44560888\",\n \"0.44551826\",\n \"0.4454798\",\n \"0.44544995\",\n \"0.44509584\",\n \"0.44499335\",\n \"0.4446739\",\n \"0.44458583\",\n \"0.44341084\",\n \"0.4432838\",\n \"0.44278714\",\n \"0.4423182\",\n \"0.44219434\",\n \"0.44195488\",\n \"0.44145054\",\n \"0.44136935\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7709178"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":22473,"cells":{"query":{"kind":"string","value":"Get the last byte index of the file successfully uploaded Performs a PUT to the given url, which returns a Range header indicating how much of the video file was successfully uploaded. If less than the total file size, this number is used in subsequent calls to upload_segment"},"document":{"kind":"string","value":"def get_last_uploaded_byte(self, check_uri):\n upload_check_headers = {'Content-Range': 'bytes */*'}\n request_headers = dict(upload_check_headers.items() + self.standard_headers.items())\n try:\n HTTPClient().fetch(check_uri, method=\"PUT\", body='', headers=request_headers)\n except HTTPError as e:\n log.info(\"Upload check: status code %s\" % e.code)\n if e.code == 308:\n _range = int(e.response.headers['Range'].split('-')[1])\n log.info(\"Last uploaded byte: %d\" % _range)\n return _range\n else: raise\n raise ValueError(\"Upload check unsuccessful\")"},"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_file_size(url: str):\n header = requests.head(url).headers\n if \"Content-Length\" in header and header[\"Content-Length\"] != 0:\n return int(header[\"Content-Length\"])\n elif \"Location\" in header:\n h = requests.head(header[\"Location\"]).headers\n return int(h.get(\"Content-Length\", 0))\n else:\n return 0","def upload_progress(self, cloud_file, size, uploaded):","def upload_complete(self, path, url, quiet):\r\n file_size = os.path.getsize(path)\r\n try:\r\n with tqdm(total=file_size,\r\n unit='B',\r\n unit_scale=True,\r\n unit_divisor=1024,\r\n disable=quiet) as progress_bar:\r\n with io.open(path, 'rb', buffering=0) as fp:\r\n reader = TqdmBufferedReader(fp, progress_bar)\r\n session = requests.Session()\r\n retries = Retry(total=10, backoff_factor=0.5)\r\n adapter = HTTPAdapter(max_retries=retries)\r\n session.mount('http://', adapter)\r\n session.mount('https://', adapter)\r\n response = session.put(url, data=reader)\r\n except Exception as error:\r\n print(error)\r\n return False\r\n return response.status_code == 200 or response.status_code == 201","def getsize(url):\n \n o = urlparse(url)\n conn = httplib.HTTPConnection(o.netloc)\n conn.request(\"HEAD\", o.path)\n res = conn.getresponse()\n\n if res.status == 301 or res.status == 302:\t# poprawic na kod opisowy\n # print res.reason, \": \", res.getheader('location')\n return getsize(res.getheader('location'))\n\n elif res.status == 200:\n # inne interesujace tagi: etag\n # print res.getheader('content-length')\n return res.getheader('content-length')\n else:\n print \"getsize() UNKNOWN PROBLEM\"\n print res.reason, \": \", res.getheader('location')\n print res.getheaders()\n raise IOError","def upload_segment(self, upload_uri, _range, data, filetype):\n content_range = '%d-%d/%d' % (_range, len(data), len(data))\n upload_headers = {'Content-Type': 'video/%s' % filetype,\n 'Content-Length': len(data),\n 'Content-Range': 'bytes: %s' % content_range}\n\n log.info(\"Sending file of size %d\" % len(data))\n log.info(\"Requesting %s\" % upload_uri)\n request_headers = dict(upload_headers.items() + self.standard_headers.items())\n r = HTTPClient().fetch(upload_uri, method=\"PUT\",\n body=data, headers=request_headers)\n log.info(\"Uploaded segment: status code %d\" % r.code)\n if r.code != 200:\n raise ValueError(\"Upload unsuccessful\")","def get_remote_file_size(url: str = '', httpresponse: object = False) -> int:\n need_to_close = False\n if not httpresponse:\n httpresponse = url_is_alive(url)\n if not httpresponse:\n error_open_mess(url)\n return 0\n need_to_close = True\n\n content_length = httpresponse.getheader('Content-Length')\n if need_to_close:\n httpresponse.close()\n\n return int(content_length) if content_length else 0","def tell(self):\n return self._upload_position","def resumable_upload(insert_request):\r\n response = None\r\n error = None\r\n retry = 0\r\n while response is None:\r\n try:\r\n print(\"Uploading file...\")\r\n status, response = insert_request.next_chunk()\r\n if 'id' in response:\r\n print(\"Video id '%s' was successfully uploaded.\" % response['id'])\r\n f=open(\"uploadedIDs.txt\",\"a+\")\r\n f.write(\"\\n\"+response['id'])\r\n f.close()\r\n else:\r\n exit(\"The upload failed with an unexpected response: %s\" % response)\r\n except HttpError:\r\n import sys\r\n e = sys.exc_info()[1]\r\n if e.resp.status in RETRIABLE_STATUS_CODES:\r\n error = \"A retriable HTTP error %d occurred:\\n%s\" % (e.resp.status,\r\n e.content)\r\n else:\r\n raise\r\n except RETRIABLE_EXCEPTIONS:\r\n import sys\r\n e = sys.exc_info()[1]\r\n error = \"A retriable error occurred: %s\" % e\r\n\r\n if error is not None:\r\n print(error)\r\n retry += 1\r\n if retry > MAX_RETRIES:\r\n exit(\"No longer attempting to retry.\")\r\n\r\n max_sleep = 2 ** retry\r\n sleep_seconds = random.random() * max_sleep\r\n print(\"Sleeping %f seconds and then retrying...\" % sleep_seconds)\r\n time.sleep(sleep_seconds)","def main(url, chunk_size_in_MB, key, bucket):\n\n upload_id = create_multipart_upload(bucket, key)\n parts = download_and_upload(url, upload_id, key, bucket, chunk_size_in_MB)\n location = complete_multipart_upload(key, bucket, upload_id, parts)\n\n return location","def _upload_file_bytes(self, conn, http_conn, fp, file_length,\r\n total_bytes_uploaded, cb, num_cb):\r\n buf = fp.read(self.BUFFER_SIZE)\r\n if cb:\r\n if num_cb > 2:\r\n cb_count = file_length / self.BUFFER_SIZE / (num_cb-2)\r\n elif num_cb < 0:\r\n cb_count = -1\r\n else:\r\n cb_count = 0\r\n i = 0\r\n cb(total_bytes_uploaded, file_length)\r\n\r\n # Build resumable upload headers for the transfer. Don't send a\r\n # Content-Range header if the file is 0 bytes long, because the\r\n # resumable upload protocol uses an *inclusive* end-range (so, sending\r\n # 'bytes 0-0/1' would actually mean you're sending a 1-byte file).\r\n put_headers = {}\r\n if file_length:\r\n range_header = self._build_content_range_header(\r\n '%d-%d' % (total_bytes_uploaded, file_length - 1),\r\n file_length)\r\n put_headers['Content-Range'] = range_header\r\n # Set Content-Length to the total bytes we'll send with this PUT.\r\n put_headers['Content-Length'] = str(file_length - total_bytes_uploaded)\r\n http_request = AWSAuthConnection.build_base_http_request(\r\n conn, 'PUT', path=self.tracker_uri_path, auth_path=None,\r\n headers=put_headers, host=self.tracker_uri_host)\r\n http_conn.putrequest('PUT', http_request.path)\r\n for k in put_headers:\r\n http_conn.putheader(k, put_headers[k])\r\n http_conn.endheaders()\r\n\r\n # Turn off debug on http connection so upload content isn't included\r\n # in debug stream.\r\n http_conn.set_debuglevel(0)\r\n while buf:\r\n http_conn.send(buf)\r\n total_bytes_uploaded += len(buf)\r\n if cb:\r\n i += 1\r\n if i == cb_count or cb_count == -1:\r\n cb(total_bytes_uploaded, file_length)\r\n i = 0\r\n buf = fp.read(self.BUFFER_SIZE)\r\n if cb:\r\n cb(total_bytes_uploaded, file_length)\r\n if total_bytes_uploaded != file_length:\r\n # Abort (and delete the tracker file) so if the user retries\r\n # they'll start a new resumable upload rather than potentially\r\n # attempting to pick back up later where we left off.\r\n raise ResumableUploadException(\r\n 'File changed during upload: EOF at %d bytes of %d byte file.' %\r\n (total_bytes_uploaded, file_length),\r\n ResumableTransferDisposition.ABORT)\r\n resp = http_conn.getresponse()\r\n body = resp.read()\r\n # Restore http connection debug level.\r\n http_conn.set_debuglevel(conn.debug)\r\n\r\n if resp.status == 200:\r\n return resp.getheader('etag') # Success\r\n # Retry timeout (408) and status 500 and 503 errors after a delay.\r\n elif resp.status in [408, 500, 503]:\r\n disposition = ResumableTransferDisposition.WAIT_BEFORE_RETRY\r\n else:\r\n # Catch all for any other error codes.\r\n disposition = ResumableTransferDisposition.ABORT\r\n raise ResumableUploadException('Got response code %d while attempting '\r\n 'upload (%s)' %\r\n (resp.status, resp.reason), disposition)","def finish(cls, upload, location=None, bytes_downloaded=0):\n path = \"uploader/finish/%s\" % upload[\"id\"]\n LOGGER.debug(path)\n payload = {\"bytes_transferred\": bytes_downloaded, \"location\": location}\n try:\n return Backend.put(path, payload, headers=Backend.headers())\n except requests.HTTPError as err:\n if err.response.status_code == 410:\n LOGGER.warning(\"Cannot finish file %s. File not active (410)\",\n upload[\"id\"])\n raise err\n except:\n raise","def download_progress(self, cloud_file, size, downloaded):","def invalid_file_bytes_uploaded(self) -> float:\n return pulumi.get(self, \"invalid_file_bytes_uploaded\")","def get_content_length(self, hdr):\n try:\n return int(hdr.data[\"content-length\"])\n except (ValueError, TypeError):\n return -1","def progress(request):\n file_id = request.GET['X-Progress-ID']\n session = DBSession()\n u = session.query(Upload).filter_by(id=file_id).one()\n data = {'state': u.state}\n if u.state == 'uploading':\n if not os.path.exists(u.tmp_path):\n # The temporary file has not been created yet or it has\n # already been renamed. We return 0 in both case, the\n # front-end code will know what to do.\n received = 0\n else:\n received = os.stat(u.tmp_path).st_size\n data.update({'size': u.size, 'received': received})\n return data","def _upload_chunk(self, final=False):\n out = self.fs.session.post(\n self.location,\n data=self.buffer.getvalue(),\n headers={\"content-type\": \"application/octet-stream\"},\n )\n out.raise_for_status()\n return True","def BytesTransferred(self) -> int:","def content_length(self):\n try:\n value = int(self.environ.get('CONTENT_LENGTH', 0))\n except ValueError:\n return 0\n\n if value >= 0:\n return value\n return 0","def _attempt_resumable_upload(self, key, fp, file_length, headers, cb,\r\n num_cb):\r\n (server_start, server_end) = self.SERVER_HAS_NOTHING\r\n conn = key.bucket.connection\r\n if self.tracker_uri:\r\n # Try to resume existing resumable upload.\r\n try:\r\n (server_start, server_end) = (\r\n self._query_server_pos(conn, file_length))\r\n self.server_has_bytes = server_start\r\n key=key\r\n if conn.debug >= 1:\r\n print 'Resuming transfer.'\r\n except ResumableUploadException, e:\r\n if conn.debug >= 1:\r\n print 'Unable to resume transfer (%s).' % e.message\r\n self._start_new_resumable_upload(key, headers)\r\n else:\r\n self._start_new_resumable_upload(key, headers)\r\n\r\n # upload_start_point allows the code that instantiated the\r\n # ResumableUploadHandler to find out the point from which it started\r\n # uploading (e.g., so it can correctly compute throughput).\r\n if self.upload_start_point is None:\r\n self.upload_start_point = server_end\r\n\r\n if server_end == file_length:\r\n # Boundary condition: complete file was already uploaded (e.g.,\r\n # user interrupted a previous upload attempt after the upload\r\n # completed but before the gsutil tracker file was deleted). Set\r\n # total_bytes_uploaded to server_end so we'll attempt to upload\r\n # no more bytes but will still make final HTTP request and get\r\n # back the response (which contains the etag we need to compare\r\n # at the end).\r\n total_bytes_uploaded = server_end\r\n else:\r\n total_bytes_uploaded = server_end + 1\r\n fp.seek(total_bytes_uploaded)\r\n conn = key.bucket.connection\r\n\r\n # Get a new HTTP connection (vs conn.get_http_connection(), which reuses\r\n # pool connections) because httplib requires a new HTTP connection per\r\n # transaction. (Without this, calling http_conn.getresponse() would get\r\n # \"ResponseNotReady\".)\r\n http_conn = conn.new_http_connection(self.tracker_uri_host,\r\n conn.is_secure)\r\n http_conn.set_debuglevel(conn.debug)\r\n\r\n # Make sure to close http_conn at end so if a local file read\r\n # failure occurs partway through server will terminate current upload\r\n # and can report that progress on next attempt.\r\n try:\r\n return self._upload_file_bytes(conn, http_conn, fp, file_length,\r\n total_bytes_uploaded, cb, num_cb)\r\n except (ResumableUploadException, socket.error):\r\n resp = self._query_server_state(conn, file_length)\r\n if resp.status == 400:\r\n raise ResumableUploadException('Got 400 response from server '\r\n 'state query after failed resumable upload attempt. This '\r\n 'can happen if the file size changed between upload '\r\n 'attempts', ResumableTransferDisposition.ABORT)\r\n else:\r\n raise\r\n finally:\r\n http_conn.close()","def upload_finish(self, cloud_file):","def upload_video(self, video_file):\r\n part = \"snippet,status\"\r\n metadata = self.get_metadata(video_file)\r\n body = {\r\n \"snippet\": {\r\n \"title\": metadata['title'],\r\n \"description\": metadata['description'],\r\n \"tags\": metadata['categoryId'],\r\n \"categoryId\": metadata['categoryId']\r\n },\r\n \"status\": {\r\n \"privacyStatus\": \"public\",\r\n \"license\": \"youtube\", # temporary, see gh#414\r\n \"embeddable\": True,\r\n \"publicStatsViewable\": True\r\n }\r\n }\r\n # This is to fix a bug, the API thinks our .ogg files are audio/ogg\r\n mimetype = \"video/{}\".format(video_file.split(\".\")[-1])\r\n media_body = MediaFileUpload(video_file, chunksize=-1, resumable=True, mimetype=mimetype)\r\n insert_request = self.service.videos().insert(part=part, body=body, media_body=media_body)\r\n response = None\r\n error = None\r\n retry = 0\r\n sleep_seconds = 5.0\r\n while response is None:\r\n try:\r\n log.info(\"Uploading %s\" % video_file)\r\n (status, response) = insert_request.next_chunk()\r\n if 'id' in response:\r\n return (Response.SUCCESS, response)\r\n else:\r\n return (Response.UNEXPECTED_FAILURE, response)\r\n except HttpError as e:\r\n if e.resp.status in self.RETRIABLE_STATUS_CODES:\r\n error = \"A retriable HTTP error {} occurred:\\n{}\".format(e.resp.status, e.content)\r\n else:\r\n return (Response.UNRETRIABLE_ERROR, {\"status\": e.resp.status, \"content\": e.content})\r\n except self.RETRIABLE_EXCEPTIONS as e:\r\n error = \"A retriable error occurred: {}\".format(e)\r\n except client.AccessTokenRefreshError:\r\n return (Response.ACCESS_TOKEN_ERROR, None)\r\n if error is not None:\r\n log.error(error)\r\n retry += 1\r\n if retry > self.MAX_RETRIES:\r\n return (Response.MAX_RETRIES_REACHED, None)\r\n log.info(\"Sleeping %s seconds and then retrying...\" % sleep_seconds)\r\n time.sleep(sleep_seconds)","def handle_put_progress(self, filegen):\n # print \"bytes so-far: \", filegen.bytes_read\n\n if self.maybe_touch():\n self.log(\"UPLOAD_PROGRESS\", level=INFO)\n self.touch()\n Backend.touch(\n self.current_upload,\n bytes_downloaded=filegen.bytes_read,\n location=self.location)","def filesize(self):\n return safeInt(self.tag(\"filesize\"))","def content_length(self):\n # If none, there is nothing we can do, the server didn't have\n # a response.\n return self._expected_size","def media_seek_position(self):\n return self._state.get(\"seek\", None)","def _http_get_position(self) -> float:\n return float(self._http_status()['position'])","def _UploadWithProgressInternal(self, media, gcs_file_name):\n bucket, bucket_path = self._ParseBucketAndPath(gcs_file_name)\n request = self._service.objects().insert(bucket=bucket,\n media_body=media,\n name=bucket_path)\n if media._size == 0: # pylint: disable=protected-access\n return self._RunWithRetries(request.execute, self._CommonErrorMatcher)\n\n response = None\n logged_percent_done = 0\n while response is None:\n status, response = self._RunWithRetries(request.next_chunk,\n self._CommonErrorMatcher)\n if status:\n percent_done = int(status.progress() * 100)\n if percent_done - logged_percent_done >= 5:\n logging.info('Uploading to gs://%s/%s: %d%% complete.',\n bucket,\n bucket_path,\n int(status.progress() * 100))\n logged_percent_done = percent_done\n return response","def get_server_status_code(url):\n # http://stackoverflow.com/questions/1140661\n host, path = urlparse.urlparse(url)[1:3] # elems [1] and [2]\n try:\n conn = httplib.HTTPConnection(host)\n conn.request('HEAD', path)\n return conn.getresponse().status\n except StandardError:\n return None","def get_status_code_for_put_or_post(request_header: str, file: str) -> int:\n ctype = \"Content-Type: text/html\"\n if file == \"/\":\n file = \"/index.html\"\n\n if request_header.find(ctype) == -1:\n return 501\n\n if not os.path.isfile(file[1:]):\n return 201\n\n return 204","def cb(self, complete, total):\n percent = int(complete * 100.0 / total)\n log.info(\"Upload completion: {0}%\".format(percent))","def get_content_length(url, preloading=False):\n prefix = \"preload: \" if preloading else \"\"\n dbg(c.y + prefix + \"getting content-length header\" + c.w)\n response = urlopen(url)\n headers = response.headers\n cl = headers['content-length']\n return int(cl)","def stat_beg_file(self, filename):\n\n self.batchvals['numfiles'] += 1\n self.filevals['filename'] = filename\n self.filevals['start_time'] = time.time()\n\n return -1","def cb(self, complete, total):\n \"\"\"Swift client does not support callbak\"\"\"\n percent = int(complete * 100.0 / total)\n log.info(\"Upload completion: {0}%\".format(percent))","def content_len(self):\n return self.header('CONTENT-LENGTH')","def progress_update(sent, total):\n l.debug(\"%d of %d Mb uploaded to Amazon S3.\", sent / 1000000, total / 1000000)","def _get_file_size(self):\n return self.s3_file.size","def getFileSize( self, path ):\n res = self.__checkArgumentFormat( path )\n if not res['OK']:\n return res\n urls = res['Value']\n successful = {}\n failed = {}\n gLogger.debug( \"DIPStorage.getFileSize: Attempting to obtain size for %s files.\" % len( urls ) )\n res = self.getFileMetadata( urls )\n if not res['OK']:\n return res\n for url, urlDict in res['Value']['Successful'].items():\n if urlDict['Exists']:\n successful[url] = urlDict['Size']\n else:\n failed[url] = 'File does not exist'\n for url, error in res['Value']['Failed'].items():\n failed[url] = error\n resDict = {'Failed':failed, 'Successful':successful}\n return S_OK( resDict )","def upload_chunk(self, file_obj, length, offset=0, upload_id=None):\n\n params = dict()\n\n if upload_id:\n params['upload_id'] = upload_id\n params['offset'] = offset\n\n url, ignored_params, headers = self.request(\"/chunked_upload\", params,\n method='PUT', content_server=True)\n\n try:\n reply = self.rest_client.PUT(url, file_obj, headers)\n return reply['offset'], reply['upload_id']\n except ErrorResponse as e:\n raise e","def _file_exists_in_s3(client, key):\n try:\n obj = client.head_object(Bucket=bucket, Key=key)\n return obj[\"ContentLength\"]\n except ClientError as exc:\n if exc.response[\"Error\"][\"Code\"] != \"404\":\n raise","def http_put_response_hop_limit(self) -> Optional[int]:\n return pulumi.get(self, \"http_put_response_hop_limit\")","def segment_download_size(self):\n # type: () -> int\n return self._segment_download_size","def upload_file_to_file(self, iFn, pathNfn):\n # check if file is exist\n b = os.path.getsize(iFn)\n source_size = os.stat(iFn).st_size\n startt = time.time()\n if b < 1000000000:\n # regular upload (file size is less than 1GB)\n key = self.bucket.new_key(pathNfn)\n fsize = key.set_contents_from_filename(iFn) # TODO: check that the file is in the server, if so, abort it\n key.set_acl('public-read')\n else:\n # multi part upload (file size is larger than 1GB)\n key = self.bucket.new_key(pathNfn)\n mp = self.bucket.initiate_multipart_upload(pathNfn)\n # chunk_size = 52428800\n chunk_size = int(source_size / float(100))\n chunk_count = int(math.ceil(source_size / float(chunk_size)))\n # Send the file parts, using FileChunkIO to create a file-like object\n # that points to a certain byte range within the original file. We\n # set bytes to never exceed the original file size.\n widgets = ['Upload Progress: ', pgb.Percentage(), ' ', pgb.Bar(marker=pgb.RotatingMarker()), ' ', pgb.ETA(), ' '] #, pgb.FileTransferSpeed()]\n pbar = pgb.ProgressBar(widgets=widgets, maxval=100)\n pbar.start()\n for i in range(chunk_count):\n pbar.update(99*i/float(chunk_count) + 1)\n # print \"chunk count\", i, \"out of\", chunk_count\n offset = chunk_size * i\n bytes = min(chunk_size, source_size - offset)\n with FileChunkIO(iFn, 'r', offset=offset, bytes=bytes) as fp:\n mp.upload_part_from_file(fp, part_num=i + 1)\n mp.complete_upload()\n pbar.finish()\n key.set_acl('public-read')\n fsize = source_size\n endt = time.time()\n print iFn,'is uploaded to',pathNfn,'(filesize:',fsize,') (',endt-startt,'sec elapsed )'","def num_frames(self):\n return len(self.video)","def resumeupload():\n resumableChunkNumber = int(request.params['resumableChunkNumber'])\n resumableChunkSize = int(request.params['resumableChunkSize'])\n resumableIdentifier = request.params['resumableIdentifier']\n resumableTotalChunks = int(request.params['resumableTotalChunks'])\n ruModel = get_model('resumableUpload')\n resumableUploadObj = ruModel.get(ruModel.c.guid == resumableIdentifier)\n if request.method == 'GET':\n #----check if the chunk has uploaded\n if resumableUploadObj is not None and int(resumableUploadObj.currentChunk) >= resumableChunkNumber:\n return json({'status':'success'}, status=200)\n else:\n return json({'status':'404'}, status=404)\n else:\n try:\n resumableFilename = request.params['resumableFilename']\n # basePath = os.path.join(application_path(settings.TRANSFER.BASEUPLAOD), request.user.username, resumableIdentifier)\n ufile = request.files.get('file', None)\n if not ufile:\n return json({'status':'err', 'msg':'No file had been upload'}, status=400)\n rf = ResumeFile(fileobj=ufile, identify=resumableIdentifier, fileName=resumableFilename, username=request.user.username,\n totalChunk=resumableTotalChunks, currentChunk=resumableChunkNumber)\n rf.keepChunk(resumableChunkNumber)\n if resumableUploadObj is None:\n ruModel(guid=resumableIdentifier, currentChunk=resumableChunkNumber, totalChunk=resumableTotalChunks, state=0).save()\n else:\n ruModel.filter(ruModel.c.guid == resumableIdentifier).update(currentChunk=resumableChunkNumber)\n return json({'status':'ok'}, status=200)\n except Exception as e:\n return json({'msg':'keep file error'}, status=400)","def get_server_status_code(url):\n host, path = urlparse.urlparse(url)[1:3] # elems [1] and [2]\n print \"host : \", host, \" | path : \", path\n try:\n conn = httplib.HTTPConnection(host)\n conn.request('HEAD', path)\n print \" | status : \" , conn.getresponse().status\n return conn.getresponse().status\n except StandardError as e:\n print \"StandardError : \", e\n return None","def refreshSize(self):\n if self.isLoaded:\n return 0\n else:\n return self.fileInfo.size","def http_put_response_hop_limit(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"http_put_response_hop_limit\")","def http_put_response_hop_limit(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"http_put_response_hop_limit\")","def anon_upload(infile: str):\n if exists(infile):\n URL = upload(infile)\n return URL\n return 5","def filenum_index( # noqa: F811\n index: Optional[int], seq: \"FileSequence\"\n) -> Optional[int]:\n if index is None:\n return None\n if index < 0:\n return index\n else:\n index -= seq.start\n # if the requested index was a positive number, and adjusting for the start\n # number throws it into the negatives, then we know the request is out of\n # range of the sequence, so we need to raise an error here to avoid returning\n # a file from the end of the sequence because of the negative roll over.\n if index < 0:\n raise IndexError(\"Frame Out of range\")\n else:\n return index","async def tus_upload_part(request: web.Request) -> web.Response:\n ctx: Context = request.app[\"ctx\"]\n secret = ctx.local_config[\"storage-proxy\"][\"secret\"]\n async with check_params(\n request,\n t.Dict(\n {\n t.Key(\"token\"): tx.JsonWebToken(\n secret=secret, inner_iv=upload_token_data_iv\n ),\n }\n ),\n read_from=CheckParamSource.QUERY,\n ) as params:\n token_data = params[\"token\"]\n async with ctx.get_volume(token_data[\"volume\"]) as volume:\n headers = await prepare_tus_session_headers(request, token_data, volume)\n vfpath = volume.mangle_vfpath(token_data[\"vfid\"])\n upload_temp_path = vfpath / \".upload\" / token_data[\"session\"]\n\n async with AsyncFileWriter(\n target_filename=upload_temp_path,\n access_mode=\"ab\",\n max_chunks=DEFAULT_INFLIGHT_CHUNKS,\n ) as writer:\n while not request.content.at_eof():\n chunk = await request.content.read(DEFAULT_CHUNK_SIZE)\n await writer.write(chunk)\n\n current_size = Path(upload_temp_path).stat().st_size\n if current_size >= int(token_data[\"size\"]):\n target_path = vfpath / token_data[\"relpath\"]\n upload_temp_path.rename(target_path)\n try:\n loop = asyncio.get_running_loop()\n await loop.run_in_executor(\n None, lambda: upload_temp_path.parent.rmdir()\n )\n except OSError:\n pass\n headers[\"Upload-Offset\"] = str(current_size)\n return web.Response(status=204, headers=headers)","def MarkUploadComplete(self, request, context):\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')","def send_file_partial(path):\r\n range_header = request.headers.get('Range', None)\r\n if not range_header: return send_file(path)\r\n \r\n size = os.path.getsize(path) \r\n byte1, byte2 = 0, None\r\n \r\n m = re.search('(\\d+)-(\\d*)', range_header)\r\n g = m.groups()\r\n \r\n if g[0]: byte1 = int(g[0])\r\n if g[1]: byte2 = int(g[1])\r\n\r\n length = size - byte1\r\n if byte2 is not None:\r\n length = byte2 - byte1\r\n \r\n data = None\r\n with open(path, 'rb') as f:\r\n f.seek(byte1)\r\n data = f.read(length)\r\n\r\n rv = Response(data, \r\n 206,\r\n mimetype=mimetypes.guess_type(path)[0], \r\n direct_passthrough=True)\r\n rv.headers.add('Content-Range', 'bytes {0}-{1}/{2}'.format(byte1, byte1 + length - 1, size))\r\n\r\n return rv","def get_size(self, fileobject):\n # move the cursor to the end of the file\n fileobject.seek(0, 2)\n size = fileobject.tell()\n # move the cursor to the begin of the file\n fileobject.seek(0)\n return size","def get_upload_status(\n self,\n batch_size,\n ):\n\n return self.queue_manager.get_upload_status(\n batch_size, self.sets_finished_while_generating_template\n )","def send_file_partial(path):\n print(\"Start stream response\")\n range_header = request.headers.get('Range', None)\n if not range_header: return send_file(path)\n\n size = os.path.getsize(path)\n byte1, byte2 = 0, None\n\n m = re.search('(\\d+)-(\\d*)', range_header)\n g = m.groups()\n\n if g[0]: byte1 = int(g[0])\n if g[1]: byte2 = int(g[1])\n\n length = size - byte1\n if byte2 is not None:\n length = byte2 - byte1\n\n with open(path, 'rb') as f:\n f.seek(byte1)\n data = f.read(length)\n\n rv = Response(data,\n 206,\n mimetype=mimetypes.guess_type(path)[0],\n direct_passthrough=True)\n rv.headers.add('Content-Range', 'bytes {0}-{1}/{2}'.format(byte1, byte1 + length - 1, size))\n rv.headers.add('Keep-Alive', 'no')\n print(\"Stream done\")\n return rv","def t_getfile(self, link, filename, session):\n\n self.sema.acquire()\n\n filepath = os.path.join(os.getcwd() + '/Downloads/' + str(filename))\n os.makedirs(os.path.dirname(filepath), exist_ok=True)\n\n if not os.path.isfile(filepath):\n self.download_new_file(link, filepath, session)\n else:\n\n current_bytes = os.stat(filepath).st_size\n\n headers = requests.head(link).headers\n\n print(headers)\n if 'content-length' not in headers:\n print(f\"server doesn't support content-length for {link}\")\n self.sema.release()\n return\n\n total_bytes = int(requests.head(link).headers['content-length'])\n\n print(total_bytes)\n\n if current_bytes < total_bytes:\n #\n self.continue_file_download(link, filepath, session, current_bytes, total_bytes)\n print(f\"Current byte < total - remaining {total_bytes - current_bytes}\")\n else:\n print(f\"already done: {filename}\")\n\n self.sema.release()","def get_file_size(input_file):\n old_file_position = input_file.tell()\n input_file.seek(0, os.SEEK_END)\n size = input_file.tell()\n input_file.seek(old_file_position, os.SEEK_SET)\n return size","def upload(self, dest, overwrite=False):\n dest = normpath(dest)\n try:\n remote = get_remote(dest)\n except ValueError: # Nothing exists at dest, nothing to worry about.\n pass\n else: # Something exists here.\n if isinstance(remote, RemoteFile) and self.hash() == remote.hash:\n # Nothing to update.\n pdbox.info(\"%s and %s are identical\" % (self.path, remote.uri))\n return\n if not overwrite:\n raise ValueError(\"%s exists\" % remote.uri)\n\n # Uploading can either happen all at once (with a 150 MB limit),\n # or in chunks. If the file is smaller than the selected chunk size,\n # then try to upload in one go.\n chunksize = min(pdbox._args.get(\"chunksize\", 149.0), 149.0)\n pdbox.debug(\"Chunk size: %.2f MB\" % chunksize)\n if pdbox._args.get(\"dryrun\"):\n pdbox.info(\"Uploaded %s to %s\" % (self.path, dbx_uri(dest)))\n return None\n\n # Set the write mode.\n if overwrite:\n mode = dropbox.files.WriteMode.overwrite\n else:\n mode = dropbox.files.WriteMode.add\n\n chunk = int(chunksize * 1024 * 1024) # Convert B to MB.\n\n with open(self.path, \"rb\") as f:\n data = f.read()\n sz = len(data)\n\n # TODO: Progress bars.\n if sz < chunk: # One-shot upload.\n meta = execute(pdbox.dbx.files_upload, data, dest, mode)\n else: # Multipart upload.\n nchunks = math.ceil(sz / chunk)\n # Initiate the upload with just the first byte.\n start = execute(pdbox.dbx.files_upload_session_start, f[0])\n cursor = dropbox.files.UploadSessionCursor(start.session_id, 1)\n\n # Now just add each chunk.\n while sz - cursor.offset > chunk:\n pdbox.debug(\n \"Uploading chunk %d/%d\" % (cursor.offset % chunk, nchunks),\n )\n execute(\n pdbox.dbx.files_upload_session_append_v2,\n data[cursor.offset:cursor.offset + chunk],\n cursor,\n )\n cursor.offset += chunk\n\n # Upload the remaining to finish the transaction.\n meta = execute(\n pdbox.dbx.files_upload_session_finish,\n data[cursor.offset:],\n dropbox.files.CommitInfo(dest, mode),\n )\n\n pdbox.info(\"Uploaded %s to %s\" % (self.path, dbx_uri(dest)))\n return RemoteFile(None, meta=meta)","def upload_part(self, seq, offset, chunk_size, labels, buffer = ''):\n # TODO implement Content-MD5\n debug(\"Uploading part %i of %r (%s bytes)\" % (seq, self.upload_id, chunk_size))\n headers = { \"content-length\": chunk_size }\n query_string = \"?partNumber=%i&uploadId=%s\" % (seq, self.upload_id)\n request = self.s3.create_request(\"OBJECT_PUT\", uri = self.uri, headers = headers, extra = query_string)\n response = self.s3.send_file(request, self.file, labels, buffer, offset = offset, chunk_size = chunk_size)\n self.parts[seq] = response[\"headers\"][\"etag\"]\n return response","def get_file_path(url, token):\n parsed = urllib_parse.urlsplit(url)\n upload_shards = os.getenv('UPLOAD_SHARDS')\n if upload_shards is None:\n # No media shards, the path is /uploads\n path = '/uploads'\n else:\n # Get metadata for this URL.\n upload_uid = TatorCache().get_upload_uid_cache(parsed.path)\n response = requests.head(f\"{urllib_parse.urljoin('http://nginx-internal-svc', parsed.path)}\",\n allow_redirects=True,\n headers={'Authorization': f'Token {token}',\n 'Upload-Uid': f'{upload_uid}'})\n upstream = response.headers['X-Upstream']\n logger.info(f\"Upstream for URL {url} is {upstream}\")\n hostname = socket.getfqdn(upstream.split(':')[0])\n logger.info(f\"Hostname for URL {url} is {hostname}\")\n path = f'/{hostname.split(\"-\")[1]}'\n path += f'/{parsed.path.split(\"/\")[-1]}'\n logger.info(f\"Path is {path}\")\n return path","def upload(request):\n # We pass the 'file_id' in the query string as a GET parameter. If\n # we read it from the POSTed data, WebOb would read all POSTed\n # data, which has various features and traps (like setting the\n # \"Content-Length\" header to 0) that we do not need since we are\n # going to read the data ourselves anyway.\n file_id = request.GET['X-Progress-ID']\n input_file, file_size, filename = get_file_from_request(request)\n session = DBSession()\n u = session.query(Upload).filter_by(id=file_id).one()\n upload_dir = request.registry.settings['poulda.upload_dir']\n user_id = authenticated_userid(request)\n # We use a temporary path to detect unfinished uploads (post\n # mortem, not in the application itself).\n path = os.path.join(upload_dir, '_'.join((user_id, file_id)))\n u.tmp_path = path\n u.started = int(time.time())\n u.size = file_size\n u.state = u'uploading'\n session.flush()\n # We need to commit the transaction so that changes to the Upload\n # object can be seen by the other threads (which will serve the\n # 'progress' JSON view called by the upload page).\n transaction.commit()\n with open(path, 'w') as output:\n # We must read only 'file_size' bytes from the 'input_file',\n # not all of it since it also contains the MIME boundary.\n copy_to_file(input_file, file_size, output)\n final_path = filename[1 + filename.rfind(os.sep):]\n final_path = os.path.join(upload_dir, final_path)\n os.rename(path, final_path)\n session = DBSession()\n u = session.query(Upload).filter_by(id=file_id).one()\n u.state = u'done'\n u.final_path = unicode(final_path, 'utf-8')\n return HTTPFound(location='success')","def max_offset(self):\n return self.offset + self.filesize - 1","def chunk_update_callback(completed_bytes: int):\n assert type(completed_bytes) == int\n assert completed_bytes > 0","def complete_upload(self):\r\n xml = self.to_xml()\r\n return self.bucket.complete_multipart_upload(self.key_name,\r\n self.id, xml)","def stat_end_file(self, status, nbytes=0, task_id=None):\n\n self.filevals['end_time'] = time.time()\n self.filevals['status'] = status\n\n if nbytes != 0:\n self.filevals['numbytes'] = nbytes\n self.batchvals['totbytes'] += nbytes\n\n if self.transfer_stats_per_file:\n self.print_file_stats()","def upload_file_by_index(request_id, parameter_index, filename):\n recastapi.file_check(filename)\n\n #get parameter id\n parameter_url = '{}?where=scan_request_id==\"{}\"'.format(\n recastapi.ENDPOINTS['POINT_REQUESTS'], request_id)\n parameters = recastapi.get(parameter_url)\n\n if parameter_index > len(parameters['_items'])-1:\n print \"*\"*60\n print \"Parameter index out of bounds\"\n print \"The request has \", len(parameters['_items']), \" parameters\"\n raise RuntimeError\n\n parameter_id = parameter_index['_items'][parameter_index]['id']\n\n return upload_file(parameter_id=parameter_id,\n filename=filename)","def Seek(channel):\n return VBFiles.getFile(Int(channel)).tell() + 1 # VB starts at 1","def fail(cls, upload, location=None, bytes_downloaded=0):\n path = \"uploader/fail/%s\" % upload[\"id\"]\n payload = {\"bytes_transferred\": bytes_downloaded, \"location\": location}\n try:\n return Backend.put(path, payload, headers=Backend.headers())\n except requests.HTTPError as err:\n if err.response.status_code == 410:\n LOGGER.warning(\"Cannot fail file %s. File not active (410)\",\n upload[\"id\"])\n raise err\n except:\n raise","def pos(self):\n return self.file.tell()","def content_length(self):\n if isinstance(self.raw_content, bytes):\n return len(self.raw_content)\n if isinstance(self.raw_content, str):\n return len(self.raw_content.encode('utf-8'))\n pos = self.raw_content.tell()\n try:\n self.raw_content.seek(0, os.SEEK_END)\n return self.raw_content.tell()\n finally:\n self.raw_content.seek(pos)","def upload_chunked(self, chunk_size = 4 * 1024 * 1024):\n\n while self.offset < self.target_length:\n next_chunk_size = min(chunk_size, self.target_length - self.offset)\n if self.last_block == None:\n self.last_block = self.file_obj.read(next_chunk_size)\n\n try:\n (self.offset, self.upload_id) = self.client.upload_chunk(\n StringIO(self.last_block), next_chunk_size, self.offset, self.upload_id)\n self.last_block = None\n except ErrorResponse as e:\n reply = e.body\n if \"offset\" in reply and reply['offset'] != 0:\n if reply['offset'] > self.offset:\n self.last_block = None\n self.offset = reply['offset']","def segment_download_count(self):\n # type: () -> int\n return self._segment_download_count","def getFileCount(self) -> int:\n ...","async def is_completed_upload_file(\n location_id: LocationID, file_id: StorageFileID, future_id: str, user_id: UserID\n):","def download(url, fpath):\n with open(fpath, 'wb') as f:\n try:\n with requests.get(url, stream=True) as r:\n r.raise_for_status()\n f_size = 0\n for chunk in r.iter_content(chunk_size=8192):\n if chunk:\n f.write(chunk)\n f_size += len(chunk)\n f_hash = get_hash_memory_optimized(fpath)\n return f_size, f_hash\n except Exception as e:\n os.remove(fpath)\n raise AdvarchsDownloadException('Could not download file.' + e.message)","def chunkFileUpload(self, fp, chunksize=1024 * 4096):\n parts = int(math.ceil(fp.stat().st_size / float(chunksize)))\n err = False\n maxchunksize = 1024 * 1024 * 100\n if chunksize >= maxchunksize:\n print(\n 'not uploaded: defined chunksize {0} is bigger than the allowed maximum {1}'.format(chunksize, maxchunksize))\n return None\n\n part = 0\n for part, chunk in enumerate(self.chunkedread(fp, chunksize),1):\n logger.info('({2})uploading part {0} of {1}'.format(part, parts, fp.name))\n files = {'file': (str(fp.name), chunk)}\n res = self._post(self.fullUrl('/chunked_upload?chunk={0}').format(part), files=files)\n\n print('finish, uploaded part {0} of {1} '.format(part, parts))\n res = self._post(self.fullUrl('chunked_upload/commit?filename={0}'.format(fp.name)))\n return self.getFile(res['file']['selfUrl']), self.getObject(res['relatedObject']['selfUrl'])\n\n # relObj = res['relatedObject']\n # obj = self.getObject(relObj['selfUrl'])\n # return obj","def _query_server_pos(self, conn, file_length):\r\n resp = self._query_server_state(conn, file_length)\r\n if resp.status == 200:\r\n return (0, file_length) # Completed upload.\r\n if resp.status != 308:\r\n # This means the server didn't have any state for the given\r\n # upload ID, which can happen (for example) if the caller saved\r\n # the tracker URI to a file and then tried to restart the transfer\r\n # after that upload ID has gone stale. In that case we need to\r\n # start a new transfer (and the caller will then save the new\r\n # tracker URI to the tracker file).\r\n raise ResumableUploadException(\r\n 'Got non-308 response (%s) from server state query' %\r\n resp.status, ResumableTransferDisposition.START_OVER)\r\n got_valid_response = False\r\n range_spec = resp.getheader('range')\r\n if range_spec:\r\n # Parse 'bytes=<from>-<to>' range_spec.\r\n m = re.search('bytes=(\\d+)-(\\d+)', range_spec)\r\n if m:\r\n server_start = long(m.group(1))\r\n server_end = long(m.group(2))\r\n got_valid_response = True\r\n else:\r\n # No Range header, which means the server does not yet have\r\n # any bytes. Note that the Range header uses inclusive 'from'\r\n # and 'to' values. Since Range 0-0 would mean that the server\r\n # has byte 0, omitting the Range header is used to indicate that\r\n # the server doesn't have any bytes.\r\n return self.SERVER_HAS_NOTHING\r\n if not got_valid_response:\r\n raise ResumableUploadException(\r\n 'Couldn\\'t parse upload server state query response (%s)' %\r\n str(resp.getheaders()), ResumableTransferDisposition.START_OVER)\r\n if conn.debug >= 1:\r\n print 'Server has: Range: %d - %d.' % (server_start, server_end)\r\n return (server_start, server_end)","def content_len(self, value):\n self.set_header('CONTENT-LENGTH', value)","async def size(self) -> int:","def get_file(self) -> int:\r\n return self.file","def _filelength(self):\r\n with open(self.fileName, 'rb') as f:\r\n f.seek(0, 2) # move to end of file\r\n length = f.tell() # get current position\r\n return length","def content_length(self, value):\r\n self.set_header('CONTENT-LENGTH', str(value))","def get_status_code_for_get(request_header: str, file: str) -> int:\n modified = True\n modified_header = \"If-Modified-Since:\"\n if file == \"/\":\n file = \"/index.html\"\n\n if not os.path.isfile(file[1:]):\n return 404\n\n # check for If_Modified_Since\n begin_mod_date_ind = request_header.find(modified_header)\n\n if begin_mod_date_ind != -1:\n new_line = \"\\r\\n\"\n end_mod_date_ind = request_header[begin_mod_date_ind:].find(new_line) + begin_mod_date_ind\n date_and_time = request_header[begin_mod_date_ind:end_mod_date_ind][len(modified_header):]\n\n if not HttpServer.date_older_than_file_date(date_and_time, file):\n modified = False\n\n if modified is True:\n return 200\n else:\n return 304","def status(self, code, content_length=None):","def get_file_size(self):\n try:\n return os.path.getsize(self.get_full_path())\n except Exception as e:\n raise SystemExit(f\"Could not complete operation: {e}\")","def get_block(self):\n # Read in data in blocks so as to not tie up the CPU.\n BLOCK_SIZE = 1024\n eof = False\n data = ''\n while not eof:\n next_block = self._stream_handle.read(BLOCK_SIZE)\n if next_block:\n data = data + next_block\n gevent.sleep(0)\n else:\n eof = True\n\n if data != '':\n self._chunker.add_chunk(data, self._timestamp)\n self.file_complete = True\n return len(data)\n else: # EOF\n self.file_complete = True\n raise EOFError","def get_file_size(file):\n\tfile.seek(0, os.SEEK_END)\n\treturn file.tell()","def file_size(self):\n return self._fileSize","def _download(self, location, count):\n # build file location to write this chunk in\n filename = self.filename + '_part_%d' % count\n \tfilepath = os.path.join(os.getcwd(), filename)\n\n # build GET request by calculating the next range of bytes to retrieve\n # building the request headers and making the streaming GET request\n gap = (location, location + self.chunk_size - 1)\n headers = {'Range': 'bytes=%d-%d' % gap}\n resp = requests.get(self.url, headers=headers, stream=True)\n\n if self.verbose:\n print 'Requesting %s' % filename\n\n # if request was not HTTP OK then stop proceeding\n if resp.status_code >= 300:\n if self.verbose:\n print 'Invalid response code: %d' % resp.status_code\n return\n\n # write the requested chunk to the temporary file\n with open(filepath, 'wb') as open_file:\n for chunk in resp.iter_content(chunk_size=BYTES):\n if chunk:\n open_file.write(chunk)\n\n if self.verbose:\n print 'Downloaded %s' % filepath","def post(self):\n filename = str(time.time())\n filepath = os.path.join(\n os.path.join(current_app.config['UPLOAD_FOLDER'], filename))\n with open(filepath, 'bw') as uploadfile:\n chunk_size = 1024\n while True:\n chunk = request.stream.read(chunk_size)\n if len(chunk) == 0:\n break\n uploadfile.write(chunk)\n current_app.logger.info('file %s upload successfully', filename)\n return {'timestamp': filename}, http.HTTPStatus.CREATED","def _check_idx(self, url):\n if not url.endswith('.idx'):\n url += '.idx'\n return requests.head(url).ok","def last_transfer_size(self):\n return self._last_transfer_size","def len(self):\n return self._fsize - self._fp.tell()","def upload_file(self):\n request = copy.deepcopy(self.request_template)\n data = json.dumps(request)\n curr_file = {\n 'request': data,\n 'file': open(self.file_path, 'rb')\n }\n print(\"Sending Upload request of av for file {}\".format(self.file_name))\n try:\n response = requests.post(url=self.url + \"upload\", files=curr_file, verify=False)\n except Exception as E:\n print(\"Upload file failed. file: {} , failure: {}\".format(self.file_name, E))\n raise\n response_j = response.json()\n print(\"av Upload response status for file {} : {}\".format(self.file_name,\n response_j[\"response\"][0][\"status\"][\"label\"]))\n return response_j","def next_upload(self):\n # print \"fetching upload...\"\n try:\n uploads = Backend.next(\n self.account, location=self.location,\n project=self.project) or []\n except (exceptions.BackendDown, exceptions.BackendError):\n raise\n else:\n if uploads:\n # Return the one download in the list\n return uploads[0]","def multipart_push_start(self, url, size=None):\n assert size is not None, f\"{self}: size of multipart upload must be specified.\"\n path = self.base_path / url\n assert not path.exists(), f\"Path {url} already exists, aborting upload.\"\n if not path.is_file():\n with path.open(\"wb\") as stream:\n stream.truncate(size)\n return url","def content_length(self) -> Optional[int]:\n content_length = self._headers.get( # type: ignore[attr-defined]\n hdrs.CONTENT_LENGTH\n )\n\n if content_length is not None:\n return int(content_length)\n else:\n return None","def test_get_object_to_file_with_range(self):\n err = None\n try:\n response = self.bos.put_object_from_string(self.BUCKET, self.KEY, \"a\" * 1000)\n except BceServerError as e:\n err = e\n finally:\n self.assertIsNone(err)\n self.check_headers(response)\n\n fp = open(self.FILENAME, \"w\")\n fp.write('a' * 100)\n fp.close()\n\n fp = open(self.FILENAME, \"rb\")\n md5 = utils.get_md5_from_fp(fp)\n fp.close()\n os.remove(self.FILENAME)\n\n err = None\n try:\n response = self.bos.get_object_to_file(self.BUCKET,\n self.KEY,\n self.FILENAME,\n range=(0, 99))\n except BceServerError as e:\n err = e\n finally:\n self.assertIsNone(err)\n self.check_headers(response)\n self.assertEqual(response.metadata.content_range, 'bytes 0-99/1000')\n\n fp = open(self.FILENAME, \"rb\")\n get_md5 = utils.get_md5_from_fp(fp)\n fp.close()\n self.assertEqual(get_md5, md5)","def end_index(self):\n if self.value == self._query.num_pages:\n # Special case for the last page\n # because there can be orphans.\n return self._query.count\n else:\n return self.value * self._query.page_size.value"],"string":"[\n \"def get_file_size(url: str):\\n header = requests.head(url).headers\\n if \\\"Content-Length\\\" in header and header[\\\"Content-Length\\\"] != 0:\\n return int(header[\\\"Content-Length\\\"])\\n elif \\\"Location\\\" in header:\\n h = requests.head(header[\\\"Location\\\"]).headers\\n return int(h.get(\\\"Content-Length\\\", 0))\\n else:\\n return 0\",\n \"def upload_progress(self, cloud_file, size, uploaded):\",\n \"def upload_complete(self, path, url, quiet):\\r\\n file_size = os.path.getsize(path)\\r\\n try:\\r\\n with tqdm(total=file_size,\\r\\n unit='B',\\r\\n unit_scale=True,\\r\\n unit_divisor=1024,\\r\\n disable=quiet) as progress_bar:\\r\\n with io.open(path, 'rb', buffering=0) as fp:\\r\\n reader = TqdmBufferedReader(fp, progress_bar)\\r\\n session = requests.Session()\\r\\n retries = Retry(total=10, backoff_factor=0.5)\\r\\n adapter = HTTPAdapter(max_retries=retries)\\r\\n session.mount('http://', adapter)\\r\\n session.mount('https://', adapter)\\r\\n response = session.put(url, data=reader)\\r\\n except Exception as error:\\r\\n print(error)\\r\\n return False\\r\\n return response.status_code == 200 or response.status_code == 201\",\n \"def getsize(url):\\n \\n o = urlparse(url)\\n conn = httplib.HTTPConnection(o.netloc)\\n conn.request(\\\"HEAD\\\", o.path)\\n res = conn.getresponse()\\n\\n if res.status == 301 or res.status == 302:\\t# poprawic na kod opisowy\\n # print res.reason, \\\": \\\", res.getheader('location')\\n return getsize(res.getheader('location'))\\n\\n elif res.status == 200:\\n # inne interesujace tagi: etag\\n # print res.getheader('content-length')\\n return res.getheader('content-length')\\n else:\\n print \\\"getsize() UNKNOWN PROBLEM\\\"\\n print res.reason, \\\": \\\", res.getheader('location')\\n print res.getheaders()\\n raise IOError\",\n \"def upload_segment(self, upload_uri, _range, data, filetype):\\n content_range = '%d-%d/%d' % (_range, len(data), len(data))\\n upload_headers = {'Content-Type': 'video/%s' % filetype,\\n 'Content-Length': len(data),\\n 'Content-Range': 'bytes: %s' % content_range}\\n\\n log.info(\\\"Sending file of size %d\\\" % len(data))\\n log.info(\\\"Requesting %s\\\" % upload_uri)\\n request_headers = dict(upload_headers.items() + self.standard_headers.items())\\n r = HTTPClient().fetch(upload_uri, method=\\\"PUT\\\",\\n body=data, headers=request_headers)\\n log.info(\\\"Uploaded segment: status code %d\\\" % r.code)\\n if r.code != 200:\\n raise ValueError(\\\"Upload unsuccessful\\\")\",\n \"def get_remote_file_size(url: str = '', httpresponse: object = False) -> int:\\n need_to_close = False\\n if not httpresponse:\\n httpresponse = url_is_alive(url)\\n if not httpresponse:\\n error_open_mess(url)\\n return 0\\n need_to_close = True\\n\\n content_length = httpresponse.getheader('Content-Length')\\n if need_to_close:\\n httpresponse.close()\\n\\n return int(content_length) if content_length else 0\",\n \"def tell(self):\\n return self._upload_position\",\n \"def resumable_upload(insert_request):\\r\\n response = None\\r\\n error = None\\r\\n retry = 0\\r\\n while response is None:\\r\\n try:\\r\\n print(\\\"Uploading file...\\\")\\r\\n status, response = insert_request.next_chunk()\\r\\n if 'id' in response:\\r\\n print(\\\"Video id '%s' was successfully uploaded.\\\" % response['id'])\\r\\n f=open(\\\"uploadedIDs.txt\\\",\\\"a+\\\")\\r\\n f.write(\\\"\\\\n\\\"+response['id'])\\r\\n f.close()\\r\\n else:\\r\\n exit(\\\"The upload failed with an unexpected response: %s\\\" % response)\\r\\n except HttpError:\\r\\n import sys\\r\\n e = sys.exc_info()[1]\\r\\n if e.resp.status in RETRIABLE_STATUS_CODES:\\r\\n error = \\\"A retriable HTTP error %d occurred:\\\\n%s\\\" % (e.resp.status,\\r\\n e.content)\\r\\n else:\\r\\n raise\\r\\n except RETRIABLE_EXCEPTIONS:\\r\\n import sys\\r\\n e = sys.exc_info()[1]\\r\\n error = \\\"A retriable error occurred: %s\\\" % e\\r\\n\\r\\n if error is not None:\\r\\n print(error)\\r\\n retry += 1\\r\\n if retry > MAX_RETRIES:\\r\\n exit(\\\"No longer attempting to retry.\\\")\\r\\n\\r\\n max_sleep = 2 ** retry\\r\\n sleep_seconds = random.random() * max_sleep\\r\\n print(\\\"Sleeping %f seconds and then retrying...\\\" % sleep_seconds)\\r\\n time.sleep(sleep_seconds)\",\n \"def main(url, chunk_size_in_MB, key, bucket):\\n\\n upload_id = create_multipart_upload(bucket, key)\\n parts = download_and_upload(url, upload_id, key, bucket, chunk_size_in_MB)\\n location = complete_multipart_upload(key, bucket, upload_id, parts)\\n\\n return location\",\n \"def _upload_file_bytes(self, conn, http_conn, fp, file_length,\\r\\n total_bytes_uploaded, cb, num_cb):\\r\\n buf = fp.read(self.BUFFER_SIZE)\\r\\n if cb:\\r\\n if num_cb > 2:\\r\\n cb_count = file_length / self.BUFFER_SIZE / (num_cb-2)\\r\\n elif num_cb < 0:\\r\\n cb_count = -1\\r\\n else:\\r\\n cb_count = 0\\r\\n i = 0\\r\\n cb(total_bytes_uploaded, file_length)\\r\\n\\r\\n # Build resumable upload headers for the transfer. Don't send a\\r\\n # Content-Range header if the file is 0 bytes long, because the\\r\\n # resumable upload protocol uses an *inclusive* end-range (so, sending\\r\\n # 'bytes 0-0/1' would actually mean you're sending a 1-byte file).\\r\\n put_headers = {}\\r\\n if file_length:\\r\\n range_header = self._build_content_range_header(\\r\\n '%d-%d' % (total_bytes_uploaded, file_length - 1),\\r\\n file_length)\\r\\n put_headers['Content-Range'] = range_header\\r\\n # Set Content-Length to the total bytes we'll send with this PUT.\\r\\n put_headers['Content-Length'] = str(file_length - total_bytes_uploaded)\\r\\n http_request = AWSAuthConnection.build_base_http_request(\\r\\n conn, 'PUT', path=self.tracker_uri_path, auth_path=None,\\r\\n headers=put_headers, host=self.tracker_uri_host)\\r\\n http_conn.putrequest('PUT', http_request.path)\\r\\n for k in put_headers:\\r\\n http_conn.putheader(k, put_headers[k])\\r\\n http_conn.endheaders()\\r\\n\\r\\n # Turn off debug on http connection so upload content isn't included\\r\\n # in debug stream.\\r\\n http_conn.set_debuglevel(0)\\r\\n while buf:\\r\\n http_conn.send(buf)\\r\\n total_bytes_uploaded += len(buf)\\r\\n if cb:\\r\\n i += 1\\r\\n if i == cb_count or cb_count == -1:\\r\\n cb(total_bytes_uploaded, file_length)\\r\\n i = 0\\r\\n buf = fp.read(self.BUFFER_SIZE)\\r\\n if cb:\\r\\n cb(total_bytes_uploaded, file_length)\\r\\n if total_bytes_uploaded != file_length:\\r\\n # Abort (and delete the tracker file) so if the user retries\\r\\n # they'll start a new resumable upload rather than potentially\\r\\n # attempting to pick back up later where we left off.\\r\\n raise ResumableUploadException(\\r\\n 'File changed during upload: EOF at %d bytes of %d byte file.' %\\r\\n (total_bytes_uploaded, file_length),\\r\\n ResumableTransferDisposition.ABORT)\\r\\n resp = http_conn.getresponse()\\r\\n body = resp.read()\\r\\n # Restore http connection debug level.\\r\\n http_conn.set_debuglevel(conn.debug)\\r\\n\\r\\n if resp.status == 200:\\r\\n return resp.getheader('etag') # Success\\r\\n # Retry timeout (408) and status 500 and 503 errors after a delay.\\r\\n elif resp.status in [408, 500, 503]:\\r\\n disposition = ResumableTransferDisposition.WAIT_BEFORE_RETRY\\r\\n else:\\r\\n # Catch all for any other error codes.\\r\\n disposition = ResumableTransferDisposition.ABORT\\r\\n raise ResumableUploadException('Got response code %d while attempting '\\r\\n 'upload (%s)' %\\r\\n (resp.status, resp.reason), disposition)\",\n \"def finish(cls, upload, location=None, bytes_downloaded=0):\\n path = \\\"uploader/finish/%s\\\" % upload[\\\"id\\\"]\\n LOGGER.debug(path)\\n payload = {\\\"bytes_transferred\\\": bytes_downloaded, \\\"location\\\": location}\\n try:\\n return Backend.put(path, payload, headers=Backend.headers())\\n except requests.HTTPError as err:\\n if err.response.status_code == 410:\\n LOGGER.warning(\\\"Cannot finish file %s. File not active (410)\\\",\\n upload[\\\"id\\\"])\\n raise err\\n except:\\n raise\",\n \"def download_progress(self, cloud_file, size, downloaded):\",\n \"def invalid_file_bytes_uploaded(self) -> float:\\n return pulumi.get(self, \\\"invalid_file_bytes_uploaded\\\")\",\n \"def get_content_length(self, hdr):\\n try:\\n return int(hdr.data[\\\"content-length\\\"])\\n except (ValueError, TypeError):\\n return -1\",\n \"def progress(request):\\n file_id = request.GET['X-Progress-ID']\\n session = DBSession()\\n u = session.query(Upload).filter_by(id=file_id).one()\\n data = {'state': u.state}\\n if u.state == 'uploading':\\n if not os.path.exists(u.tmp_path):\\n # The temporary file has not been created yet or it has\\n # already been renamed. We return 0 in both case, the\\n # front-end code will know what to do.\\n received = 0\\n else:\\n received = os.stat(u.tmp_path).st_size\\n data.update({'size': u.size, 'received': received})\\n return data\",\n \"def _upload_chunk(self, final=False):\\n out = self.fs.session.post(\\n self.location,\\n data=self.buffer.getvalue(),\\n headers={\\\"content-type\\\": \\\"application/octet-stream\\\"},\\n )\\n out.raise_for_status()\\n return True\",\n \"def BytesTransferred(self) -> int:\",\n \"def content_length(self):\\n try:\\n value = int(self.environ.get('CONTENT_LENGTH', 0))\\n except ValueError:\\n return 0\\n\\n if value >= 0:\\n return value\\n return 0\",\n \"def _attempt_resumable_upload(self, key, fp, file_length, headers, cb,\\r\\n num_cb):\\r\\n (server_start, server_end) = self.SERVER_HAS_NOTHING\\r\\n conn = key.bucket.connection\\r\\n if self.tracker_uri:\\r\\n # Try to resume existing resumable upload.\\r\\n try:\\r\\n (server_start, server_end) = (\\r\\n self._query_server_pos(conn, file_length))\\r\\n self.server_has_bytes = server_start\\r\\n key=key\\r\\n if conn.debug >= 1:\\r\\n print 'Resuming transfer.'\\r\\n except ResumableUploadException, e:\\r\\n if conn.debug >= 1:\\r\\n print 'Unable to resume transfer (%s).' % e.message\\r\\n self._start_new_resumable_upload(key, headers)\\r\\n else:\\r\\n self._start_new_resumable_upload(key, headers)\\r\\n\\r\\n # upload_start_point allows the code that instantiated the\\r\\n # ResumableUploadHandler to find out the point from which it started\\r\\n # uploading (e.g., so it can correctly compute throughput).\\r\\n if self.upload_start_point is None:\\r\\n self.upload_start_point = server_end\\r\\n\\r\\n if server_end == file_length:\\r\\n # Boundary condition: complete file was already uploaded (e.g.,\\r\\n # user interrupted a previous upload attempt after the upload\\r\\n # completed but before the gsutil tracker file was deleted). Set\\r\\n # total_bytes_uploaded to server_end so we'll attempt to upload\\r\\n # no more bytes but will still make final HTTP request and get\\r\\n # back the response (which contains the etag we need to compare\\r\\n # at the end).\\r\\n total_bytes_uploaded = server_end\\r\\n else:\\r\\n total_bytes_uploaded = server_end + 1\\r\\n fp.seek(total_bytes_uploaded)\\r\\n conn = key.bucket.connection\\r\\n\\r\\n # Get a new HTTP connection (vs conn.get_http_connection(), which reuses\\r\\n # pool connections) because httplib requires a new HTTP connection per\\r\\n # transaction. (Without this, calling http_conn.getresponse() would get\\r\\n # \\\"ResponseNotReady\\\".)\\r\\n http_conn = conn.new_http_connection(self.tracker_uri_host,\\r\\n conn.is_secure)\\r\\n http_conn.set_debuglevel(conn.debug)\\r\\n\\r\\n # Make sure to close http_conn at end so if a local file read\\r\\n # failure occurs partway through server will terminate current upload\\r\\n # and can report that progress on next attempt.\\r\\n try:\\r\\n return self._upload_file_bytes(conn, http_conn, fp, file_length,\\r\\n total_bytes_uploaded, cb, num_cb)\\r\\n except (ResumableUploadException, socket.error):\\r\\n resp = self._query_server_state(conn, file_length)\\r\\n if resp.status == 400:\\r\\n raise ResumableUploadException('Got 400 response from server '\\r\\n 'state query after failed resumable upload attempt. This '\\r\\n 'can happen if the file size changed between upload '\\r\\n 'attempts', ResumableTransferDisposition.ABORT)\\r\\n else:\\r\\n raise\\r\\n finally:\\r\\n http_conn.close()\",\n \"def upload_finish(self, cloud_file):\",\n \"def upload_video(self, video_file):\\r\\n part = \\\"snippet,status\\\"\\r\\n metadata = self.get_metadata(video_file)\\r\\n body = {\\r\\n \\\"snippet\\\": {\\r\\n \\\"title\\\": metadata['title'],\\r\\n \\\"description\\\": metadata['description'],\\r\\n \\\"tags\\\": metadata['categoryId'],\\r\\n \\\"categoryId\\\": metadata['categoryId']\\r\\n },\\r\\n \\\"status\\\": {\\r\\n \\\"privacyStatus\\\": \\\"public\\\",\\r\\n \\\"license\\\": \\\"youtube\\\", # temporary, see gh#414\\r\\n \\\"embeddable\\\": True,\\r\\n \\\"publicStatsViewable\\\": True\\r\\n }\\r\\n }\\r\\n # This is to fix a bug, the API thinks our .ogg files are audio/ogg\\r\\n mimetype = \\\"video/{}\\\".format(video_file.split(\\\".\\\")[-1])\\r\\n media_body = MediaFileUpload(video_file, chunksize=-1, resumable=True, mimetype=mimetype)\\r\\n insert_request = self.service.videos().insert(part=part, body=body, media_body=media_body)\\r\\n response = None\\r\\n error = None\\r\\n retry = 0\\r\\n sleep_seconds = 5.0\\r\\n while response is None:\\r\\n try:\\r\\n log.info(\\\"Uploading %s\\\" % video_file)\\r\\n (status, response) = insert_request.next_chunk()\\r\\n if 'id' in response:\\r\\n return (Response.SUCCESS, response)\\r\\n else:\\r\\n return (Response.UNEXPECTED_FAILURE, response)\\r\\n except HttpError as e:\\r\\n if e.resp.status in self.RETRIABLE_STATUS_CODES:\\r\\n error = \\\"A retriable HTTP error {} occurred:\\\\n{}\\\".format(e.resp.status, e.content)\\r\\n else:\\r\\n return (Response.UNRETRIABLE_ERROR, {\\\"status\\\": e.resp.status, \\\"content\\\": e.content})\\r\\n except self.RETRIABLE_EXCEPTIONS as e:\\r\\n error = \\\"A retriable error occurred: {}\\\".format(e)\\r\\n except client.AccessTokenRefreshError:\\r\\n return (Response.ACCESS_TOKEN_ERROR, None)\\r\\n if error is not None:\\r\\n log.error(error)\\r\\n retry += 1\\r\\n if retry > self.MAX_RETRIES:\\r\\n return (Response.MAX_RETRIES_REACHED, None)\\r\\n log.info(\\\"Sleeping %s seconds and then retrying...\\\" % sleep_seconds)\\r\\n time.sleep(sleep_seconds)\",\n \"def handle_put_progress(self, filegen):\\n # print \\\"bytes so-far: \\\", filegen.bytes_read\\n\\n if self.maybe_touch():\\n self.log(\\\"UPLOAD_PROGRESS\\\", level=INFO)\\n self.touch()\\n Backend.touch(\\n self.current_upload,\\n bytes_downloaded=filegen.bytes_read,\\n location=self.location)\",\n \"def filesize(self):\\n return safeInt(self.tag(\\\"filesize\\\"))\",\n \"def content_length(self):\\n # If none, there is nothing we can do, the server didn't have\\n # a response.\\n return self._expected_size\",\n \"def media_seek_position(self):\\n return self._state.get(\\\"seek\\\", None)\",\n \"def _http_get_position(self) -> float:\\n return float(self._http_status()['position'])\",\n \"def _UploadWithProgressInternal(self, media, gcs_file_name):\\n bucket, bucket_path = self._ParseBucketAndPath(gcs_file_name)\\n request = self._service.objects().insert(bucket=bucket,\\n media_body=media,\\n name=bucket_path)\\n if media._size == 0: # pylint: disable=protected-access\\n return self._RunWithRetries(request.execute, self._CommonErrorMatcher)\\n\\n response = None\\n logged_percent_done = 0\\n while response is None:\\n status, response = self._RunWithRetries(request.next_chunk,\\n self._CommonErrorMatcher)\\n if status:\\n percent_done = int(status.progress() * 100)\\n if percent_done - logged_percent_done >= 5:\\n logging.info('Uploading to gs://%s/%s: %d%% complete.',\\n bucket,\\n bucket_path,\\n int(status.progress() * 100))\\n logged_percent_done = percent_done\\n return response\",\n \"def get_server_status_code(url):\\n # http://stackoverflow.com/questions/1140661\\n host, path = urlparse.urlparse(url)[1:3] # elems [1] and [2]\\n try:\\n conn = httplib.HTTPConnection(host)\\n conn.request('HEAD', path)\\n return conn.getresponse().status\\n except StandardError:\\n return None\",\n \"def get_status_code_for_put_or_post(request_header: str, file: str) -> int:\\n ctype = \\\"Content-Type: text/html\\\"\\n if file == \\\"/\\\":\\n file = \\\"/index.html\\\"\\n\\n if request_header.find(ctype) == -1:\\n return 501\\n\\n if not os.path.isfile(file[1:]):\\n return 201\\n\\n return 204\",\n \"def cb(self, complete, total):\\n percent = int(complete * 100.0 / total)\\n log.info(\\\"Upload completion: {0}%\\\".format(percent))\",\n \"def get_content_length(url, preloading=False):\\n prefix = \\\"preload: \\\" if preloading else \\\"\\\"\\n dbg(c.y + prefix + \\\"getting content-length header\\\" + c.w)\\n response = urlopen(url)\\n headers = response.headers\\n cl = headers['content-length']\\n return int(cl)\",\n \"def stat_beg_file(self, filename):\\n\\n self.batchvals['numfiles'] += 1\\n self.filevals['filename'] = filename\\n self.filevals['start_time'] = time.time()\\n\\n return -1\",\n \"def cb(self, complete, total):\\n \\\"\\\"\\\"Swift client does not support callbak\\\"\\\"\\\"\\n percent = int(complete * 100.0 / total)\\n log.info(\\\"Upload completion: {0}%\\\".format(percent))\",\n \"def content_len(self):\\n return self.header('CONTENT-LENGTH')\",\n \"def progress_update(sent, total):\\n l.debug(\\\"%d of %d Mb uploaded to Amazon S3.\\\", sent / 1000000, total / 1000000)\",\n \"def _get_file_size(self):\\n return self.s3_file.size\",\n \"def getFileSize( self, path ):\\n res = self.__checkArgumentFormat( path )\\n if not res['OK']:\\n return res\\n urls = res['Value']\\n successful = {}\\n failed = {}\\n gLogger.debug( \\\"DIPStorage.getFileSize: Attempting to obtain size for %s files.\\\" % len( urls ) )\\n res = self.getFileMetadata( urls )\\n if not res['OK']:\\n return res\\n for url, urlDict in res['Value']['Successful'].items():\\n if urlDict['Exists']:\\n successful[url] = urlDict['Size']\\n else:\\n failed[url] = 'File does not exist'\\n for url, error in res['Value']['Failed'].items():\\n failed[url] = error\\n resDict = {'Failed':failed, 'Successful':successful}\\n return S_OK( resDict )\",\n \"def upload_chunk(self, file_obj, length, offset=0, upload_id=None):\\n\\n params = dict()\\n\\n if upload_id:\\n params['upload_id'] = upload_id\\n params['offset'] = offset\\n\\n url, ignored_params, headers = self.request(\\\"/chunked_upload\\\", params,\\n method='PUT', content_server=True)\\n\\n try:\\n reply = self.rest_client.PUT(url, file_obj, headers)\\n return reply['offset'], reply['upload_id']\\n except ErrorResponse as e:\\n raise e\",\n \"def _file_exists_in_s3(client, key):\\n try:\\n obj = client.head_object(Bucket=bucket, Key=key)\\n return obj[\\\"ContentLength\\\"]\\n except ClientError as exc:\\n if exc.response[\\\"Error\\\"][\\\"Code\\\"] != \\\"404\\\":\\n raise\",\n \"def http_put_response_hop_limit(self) -> Optional[int]:\\n return pulumi.get(self, \\\"http_put_response_hop_limit\\\")\",\n \"def segment_download_size(self):\\n # type: () -> int\\n return self._segment_download_size\",\n \"def upload_file_to_file(self, iFn, pathNfn):\\n # check if file is exist\\n b = os.path.getsize(iFn)\\n source_size = os.stat(iFn).st_size\\n startt = time.time()\\n if b < 1000000000:\\n # regular upload (file size is less than 1GB)\\n key = self.bucket.new_key(pathNfn)\\n fsize = key.set_contents_from_filename(iFn) # TODO: check that the file is in the server, if so, abort it\\n key.set_acl('public-read')\\n else:\\n # multi part upload (file size is larger than 1GB)\\n key = self.bucket.new_key(pathNfn)\\n mp = self.bucket.initiate_multipart_upload(pathNfn)\\n # chunk_size = 52428800\\n chunk_size = int(source_size / float(100))\\n chunk_count = int(math.ceil(source_size / float(chunk_size)))\\n # Send the file parts, using FileChunkIO to create a file-like object\\n # that points to a certain byte range within the original file. We\\n # set bytes to never exceed the original file size.\\n widgets = ['Upload Progress: ', pgb.Percentage(), ' ', pgb.Bar(marker=pgb.RotatingMarker()), ' ', pgb.ETA(), ' '] #, pgb.FileTransferSpeed()]\\n pbar = pgb.ProgressBar(widgets=widgets, maxval=100)\\n pbar.start()\\n for i in range(chunk_count):\\n pbar.update(99*i/float(chunk_count) + 1)\\n # print \\\"chunk count\\\", i, \\\"out of\\\", chunk_count\\n offset = chunk_size * i\\n bytes = min(chunk_size, source_size - offset)\\n with FileChunkIO(iFn, 'r', offset=offset, bytes=bytes) as fp:\\n mp.upload_part_from_file(fp, part_num=i + 1)\\n mp.complete_upload()\\n pbar.finish()\\n key.set_acl('public-read')\\n fsize = source_size\\n endt = time.time()\\n print iFn,'is uploaded to',pathNfn,'(filesize:',fsize,') (',endt-startt,'sec elapsed )'\",\n \"def num_frames(self):\\n return len(self.video)\",\n \"def resumeupload():\\n resumableChunkNumber = int(request.params['resumableChunkNumber'])\\n resumableChunkSize = int(request.params['resumableChunkSize'])\\n resumableIdentifier = request.params['resumableIdentifier']\\n resumableTotalChunks = int(request.params['resumableTotalChunks'])\\n ruModel = get_model('resumableUpload')\\n resumableUploadObj = ruModel.get(ruModel.c.guid == resumableIdentifier)\\n if request.method == 'GET':\\n #----check if the chunk has uploaded\\n if resumableUploadObj is not None and int(resumableUploadObj.currentChunk) >= resumableChunkNumber:\\n return json({'status':'success'}, status=200)\\n else:\\n return json({'status':'404'}, status=404)\\n else:\\n try:\\n resumableFilename = request.params['resumableFilename']\\n # basePath = os.path.join(application_path(settings.TRANSFER.BASEUPLAOD), request.user.username, resumableIdentifier)\\n ufile = request.files.get('file', None)\\n if not ufile:\\n return json({'status':'err', 'msg':'No file had been upload'}, status=400)\\n rf = ResumeFile(fileobj=ufile, identify=resumableIdentifier, fileName=resumableFilename, username=request.user.username,\\n totalChunk=resumableTotalChunks, currentChunk=resumableChunkNumber)\\n rf.keepChunk(resumableChunkNumber)\\n if resumableUploadObj is None:\\n ruModel(guid=resumableIdentifier, currentChunk=resumableChunkNumber, totalChunk=resumableTotalChunks, state=0).save()\\n else:\\n ruModel.filter(ruModel.c.guid == resumableIdentifier).update(currentChunk=resumableChunkNumber)\\n return json({'status':'ok'}, status=200)\\n except Exception as e:\\n return json({'msg':'keep file error'}, status=400)\",\n \"def get_server_status_code(url):\\n host, path = urlparse.urlparse(url)[1:3] # elems [1] and [2]\\n print \\\"host : \\\", host, \\\" | path : \\\", path\\n try:\\n conn = httplib.HTTPConnection(host)\\n conn.request('HEAD', path)\\n print \\\" | status : \\\" , conn.getresponse().status\\n return conn.getresponse().status\\n except StandardError as e:\\n print \\\"StandardError : \\\", e\\n return None\",\n \"def refreshSize(self):\\n if self.isLoaded:\\n return 0\\n else:\\n return self.fileInfo.size\",\n \"def http_put_response_hop_limit(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"http_put_response_hop_limit\\\")\",\n \"def http_put_response_hop_limit(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"http_put_response_hop_limit\\\")\",\n \"def anon_upload(infile: str):\\n if exists(infile):\\n URL = upload(infile)\\n return URL\\n return 5\",\n \"def filenum_index( # noqa: F811\\n index: Optional[int], seq: \\\"FileSequence\\\"\\n) -> Optional[int]:\\n if index is None:\\n return None\\n if index < 0:\\n return index\\n else:\\n index -= seq.start\\n # if the requested index was a positive number, and adjusting for the start\\n # number throws it into the negatives, then we know the request is out of\\n # range of the sequence, so we need to raise an error here to avoid returning\\n # a file from the end of the sequence because of the negative roll over.\\n if index < 0:\\n raise IndexError(\\\"Frame Out of range\\\")\\n else:\\n return index\",\n \"async def tus_upload_part(request: web.Request) -> web.Response:\\n ctx: Context = request.app[\\\"ctx\\\"]\\n secret = ctx.local_config[\\\"storage-proxy\\\"][\\\"secret\\\"]\\n async with check_params(\\n request,\\n t.Dict(\\n {\\n t.Key(\\\"token\\\"): tx.JsonWebToken(\\n secret=secret, inner_iv=upload_token_data_iv\\n ),\\n }\\n ),\\n read_from=CheckParamSource.QUERY,\\n ) as params:\\n token_data = params[\\\"token\\\"]\\n async with ctx.get_volume(token_data[\\\"volume\\\"]) as volume:\\n headers = await prepare_tus_session_headers(request, token_data, volume)\\n vfpath = volume.mangle_vfpath(token_data[\\\"vfid\\\"])\\n upload_temp_path = vfpath / \\\".upload\\\" / token_data[\\\"session\\\"]\\n\\n async with AsyncFileWriter(\\n target_filename=upload_temp_path,\\n access_mode=\\\"ab\\\",\\n max_chunks=DEFAULT_INFLIGHT_CHUNKS,\\n ) as writer:\\n while not request.content.at_eof():\\n chunk = await request.content.read(DEFAULT_CHUNK_SIZE)\\n await writer.write(chunk)\\n\\n current_size = Path(upload_temp_path).stat().st_size\\n if current_size >= int(token_data[\\\"size\\\"]):\\n target_path = vfpath / token_data[\\\"relpath\\\"]\\n upload_temp_path.rename(target_path)\\n try:\\n loop = asyncio.get_running_loop()\\n await loop.run_in_executor(\\n None, lambda: upload_temp_path.parent.rmdir()\\n )\\n except OSError:\\n pass\\n headers[\\\"Upload-Offset\\\"] = str(current_size)\\n return web.Response(status=204, headers=headers)\",\n \"def MarkUploadComplete(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 send_file_partial(path):\\r\\n range_header = request.headers.get('Range', None)\\r\\n if not range_header: return send_file(path)\\r\\n \\r\\n size = os.path.getsize(path) \\r\\n byte1, byte2 = 0, None\\r\\n \\r\\n m = re.search('(\\\\d+)-(\\\\d*)', range_header)\\r\\n g = m.groups()\\r\\n \\r\\n if g[0]: byte1 = int(g[0])\\r\\n if g[1]: byte2 = int(g[1])\\r\\n\\r\\n length = size - byte1\\r\\n if byte2 is not None:\\r\\n length = byte2 - byte1\\r\\n \\r\\n data = None\\r\\n with open(path, 'rb') as f:\\r\\n f.seek(byte1)\\r\\n data = f.read(length)\\r\\n\\r\\n rv = Response(data, \\r\\n 206,\\r\\n mimetype=mimetypes.guess_type(path)[0], \\r\\n direct_passthrough=True)\\r\\n rv.headers.add('Content-Range', 'bytes {0}-{1}/{2}'.format(byte1, byte1 + length - 1, size))\\r\\n\\r\\n return rv\",\n \"def get_size(self, fileobject):\\n # move the cursor to the end of the file\\n fileobject.seek(0, 2)\\n size = fileobject.tell()\\n # move the cursor to the begin of the file\\n fileobject.seek(0)\\n return size\",\n \"def get_upload_status(\\n self,\\n batch_size,\\n ):\\n\\n return self.queue_manager.get_upload_status(\\n batch_size, self.sets_finished_while_generating_template\\n )\",\n \"def send_file_partial(path):\\n print(\\\"Start stream response\\\")\\n range_header = request.headers.get('Range', None)\\n if not range_header: return send_file(path)\\n\\n size = os.path.getsize(path)\\n byte1, byte2 = 0, None\\n\\n m = re.search('(\\\\d+)-(\\\\d*)', range_header)\\n g = m.groups()\\n\\n if g[0]: byte1 = int(g[0])\\n if g[1]: byte2 = int(g[1])\\n\\n length = size - byte1\\n if byte2 is not None:\\n length = byte2 - byte1\\n\\n with open(path, 'rb') as f:\\n f.seek(byte1)\\n data = f.read(length)\\n\\n rv = Response(data,\\n 206,\\n mimetype=mimetypes.guess_type(path)[0],\\n direct_passthrough=True)\\n rv.headers.add('Content-Range', 'bytes {0}-{1}/{2}'.format(byte1, byte1 + length - 1, size))\\n rv.headers.add('Keep-Alive', 'no')\\n print(\\\"Stream done\\\")\\n return rv\",\n \"def t_getfile(self, link, filename, session):\\n\\n self.sema.acquire()\\n\\n filepath = os.path.join(os.getcwd() + '/Downloads/' + str(filename))\\n os.makedirs(os.path.dirname(filepath), exist_ok=True)\\n\\n if not os.path.isfile(filepath):\\n self.download_new_file(link, filepath, session)\\n else:\\n\\n current_bytes = os.stat(filepath).st_size\\n\\n headers = requests.head(link).headers\\n\\n print(headers)\\n if 'content-length' not in headers:\\n print(f\\\"server doesn't support content-length for {link}\\\")\\n self.sema.release()\\n return\\n\\n total_bytes = int(requests.head(link).headers['content-length'])\\n\\n print(total_bytes)\\n\\n if current_bytes < total_bytes:\\n #\\n self.continue_file_download(link, filepath, session, current_bytes, total_bytes)\\n print(f\\\"Current byte < total - remaining {total_bytes - current_bytes}\\\")\\n else:\\n print(f\\\"already done: {filename}\\\")\\n\\n self.sema.release()\",\n \"def get_file_size(input_file):\\n old_file_position = input_file.tell()\\n input_file.seek(0, os.SEEK_END)\\n size = input_file.tell()\\n input_file.seek(old_file_position, os.SEEK_SET)\\n return size\",\n \"def upload(self, dest, overwrite=False):\\n dest = normpath(dest)\\n try:\\n remote = get_remote(dest)\\n except ValueError: # Nothing exists at dest, nothing to worry about.\\n pass\\n else: # Something exists here.\\n if isinstance(remote, RemoteFile) and self.hash() == remote.hash:\\n # Nothing to update.\\n pdbox.info(\\\"%s and %s are identical\\\" % (self.path, remote.uri))\\n return\\n if not overwrite:\\n raise ValueError(\\\"%s exists\\\" % remote.uri)\\n\\n # Uploading can either happen all at once (with a 150 MB limit),\\n # or in chunks. If the file is smaller than the selected chunk size,\\n # then try to upload in one go.\\n chunksize = min(pdbox._args.get(\\\"chunksize\\\", 149.0), 149.0)\\n pdbox.debug(\\\"Chunk size: %.2f MB\\\" % chunksize)\\n if pdbox._args.get(\\\"dryrun\\\"):\\n pdbox.info(\\\"Uploaded %s to %s\\\" % (self.path, dbx_uri(dest)))\\n return None\\n\\n # Set the write mode.\\n if overwrite:\\n mode = dropbox.files.WriteMode.overwrite\\n else:\\n mode = dropbox.files.WriteMode.add\\n\\n chunk = int(chunksize * 1024 * 1024) # Convert B to MB.\\n\\n with open(self.path, \\\"rb\\\") as f:\\n data = f.read()\\n sz = len(data)\\n\\n # TODO: Progress bars.\\n if sz < chunk: # One-shot upload.\\n meta = execute(pdbox.dbx.files_upload, data, dest, mode)\\n else: # Multipart upload.\\n nchunks = math.ceil(sz / chunk)\\n # Initiate the upload with just the first byte.\\n start = execute(pdbox.dbx.files_upload_session_start, f[0])\\n cursor = dropbox.files.UploadSessionCursor(start.session_id, 1)\\n\\n # Now just add each chunk.\\n while sz - cursor.offset > chunk:\\n pdbox.debug(\\n \\\"Uploading chunk %d/%d\\\" % (cursor.offset % chunk, nchunks),\\n )\\n execute(\\n pdbox.dbx.files_upload_session_append_v2,\\n data[cursor.offset:cursor.offset + chunk],\\n cursor,\\n )\\n cursor.offset += chunk\\n\\n # Upload the remaining to finish the transaction.\\n meta = execute(\\n pdbox.dbx.files_upload_session_finish,\\n data[cursor.offset:],\\n dropbox.files.CommitInfo(dest, mode),\\n )\\n\\n pdbox.info(\\\"Uploaded %s to %s\\\" % (self.path, dbx_uri(dest)))\\n return RemoteFile(None, meta=meta)\",\n \"def upload_part(self, seq, offset, chunk_size, labels, buffer = ''):\\n # TODO implement Content-MD5\\n debug(\\\"Uploading part %i of %r (%s bytes)\\\" % (seq, self.upload_id, chunk_size))\\n headers = { \\\"content-length\\\": chunk_size }\\n query_string = \\\"?partNumber=%i&uploadId=%s\\\" % (seq, self.upload_id)\\n request = self.s3.create_request(\\\"OBJECT_PUT\\\", uri = self.uri, headers = headers, extra = query_string)\\n response = self.s3.send_file(request, self.file, labels, buffer, offset = offset, chunk_size = chunk_size)\\n self.parts[seq] = response[\\\"headers\\\"][\\\"etag\\\"]\\n return response\",\n \"def get_file_path(url, token):\\n parsed = urllib_parse.urlsplit(url)\\n upload_shards = os.getenv('UPLOAD_SHARDS')\\n if upload_shards is None:\\n # No media shards, the path is /uploads\\n path = '/uploads'\\n else:\\n # Get metadata for this URL.\\n upload_uid = TatorCache().get_upload_uid_cache(parsed.path)\\n response = requests.head(f\\\"{urllib_parse.urljoin('http://nginx-internal-svc', parsed.path)}\\\",\\n allow_redirects=True,\\n headers={'Authorization': f'Token {token}',\\n 'Upload-Uid': f'{upload_uid}'})\\n upstream = response.headers['X-Upstream']\\n logger.info(f\\\"Upstream for URL {url} is {upstream}\\\")\\n hostname = socket.getfqdn(upstream.split(':')[0])\\n logger.info(f\\\"Hostname for URL {url} is {hostname}\\\")\\n path = f'/{hostname.split(\\\"-\\\")[1]}'\\n path += f'/{parsed.path.split(\\\"/\\\")[-1]}'\\n logger.info(f\\\"Path is {path}\\\")\\n return path\",\n \"def upload(request):\\n # We pass the 'file_id' in the query string as a GET parameter. If\\n # we read it from the POSTed data, WebOb would read all POSTed\\n # data, which has various features and traps (like setting the\\n # \\\"Content-Length\\\" header to 0) that we do not need since we are\\n # going to read the data ourselves anyway.\\n file_id = request.GET['X-Progress-ID']\\n input_file, file_size, filename = get_file_from_request(request)\\n session = DBSession()\\n u = session.query(Upload).filter_by(id=file_id).one()\\n upload_dir = request.registry.settings['poulda.upload_dir']\\n user_id = authenticated_userid(request)\\n # We use a temporary path to detect unfinished uploads (post\\n # mortem, not in the application itself).\\n path = os.path.join(upload_dir, '_'.join((user_id, file_id)))\\n u.tmp_path = path\\n u.started = int(time.time())\\n u.size = file_size\\n u.state = u'uploading'\\n session.flush()\\n # We need to commit the transaction so that changes to the Upload\\n # object can be seen by the other threads (which will serve the\\n # 'progress' JSON view called by the upload page).\\n transaction.commit()\\n with open(path, 'w') as output:\\n # We must read only 'file_size' bytes from the 'input_file',\\n # not all of it since it also contains the MIME boundary.\\n copy_to_file(input_file, file_size, output)\\n final_path = filename[1 + filename.rfind(os.sep):]\\n final_path = os.path.join(upload_dir, final_path)\\n os.rename(path, final_path)\\n session = DBSession()\\n u = session.query(Upload).filter_by(id=file_id).one()\\n u.state = u'done'\\n u.final_path = unicode(final_path, 'utf-8')\\n return HTTPFound(location='success')\",\n \"def max_offset(self):\\n return self.offset + self.filesize - 1\",\n \"def chunk_update_callback(completed_bytes: int):\\n assert type(completed_bytes) == int\\n assert completed_bytes > 0\",\n \"def complete_upload(self):\\r\\n xml = self.to_xml()\\r\\n return self.bucket.complete_multipart_upload(self.key_name,\\r\\n self.id, xml)\",\n \"def stat_end_file(self, status, nbytes=0, task_id=None):\\n\\n self.filevals['end_time'] = time.time()\\n self.filevals['status'] = status\\n\\n if nbytes != 0:\\n self.filevals['numbytes'] = nbytes\\n self.batchvals['totbytes'] += nbytes\\n\\n if self.transfer_stats_per_file:\\n self.print_file_stats()\",\n \"def upload_file_by_index(request_id, parameter_index, filename):\\n recastapi.file_check(filename)\\n\\n #get parameter id\\n parameter_url = '{}?where=scan_request_id==\\\"{}\\\"'.format(\\n recastapi.ENDPOINTS['POINT_REQUESTS'], request_id)\\n parameters = recastapi.get(parameter_url)\\n\\n if parameter_index > len(parameters['_items'])-1:\\n print \\\"*\\\"*60\\n print \\\"Parameter index out of bounds\\\"\\n print \\\"The request has \\\", len(parameters['_items']), \\\" parameters\\\"\\n raise RuntimeError\\n\\n parameter_id = parameter_index['_items'][parameter_index]['id']\\n\\n return upload_file(parameter_id=parameter_id,\\n filename=filename)\",\n \"def Seek(channel):\\n return VBFiles.getFile(Int(channel)).tell() + 1 # VB starts at 1\",\n \"def fail(cls, upload, location=None, bytes_downloaded=0):\\n path = \\\"uploader/fail/%s\\\" % upload[\\\"id\\\"]\\n payload = {\\\"bytes_transferred\\\": bytes_downloaded, \\\"location\\\": location}\\n try:\\n return Backend.put(path, payload, headers=Backend.headers())\\n except requests.HTTPError as err:\\n if err.response.status_code == 410:\\n LOGGER.warning(\\\"Cannot fail file %s. File not active (410)\\\",\\n upload[\\\"id\\\"])\\n raise err\\n except:\\n raise\",\n \"def pos(self):\\n return self.file.tell()\",\n \"def content_length(self):\\n if isinstance(self.raw_content, bytes):\\n return len(self.raw_content)\\n if isinstance(self.raw_content, str):\\n return len(self.raw_content.encode('utf-8'))\\n pos = self.raw_content.tell()\\n try:\\n self.raw_content.seek(0, os.SEEK_END)\\n return self.raw_content.tell()\\n finally:\\n self.raw_content.seek(pos)\",\n \"def upload_chunked(self, chunk_size = 4 * 1024 * 1024):\\n\\n while self.offset < self.target_length:\\n next_chunk_size = min(chunk_size, self.target_length - self.offset)\\n if self.last_block == None:\\n self.last_block = self.file_obj.read(next_chunk_size)\\n\\n try:\\n (self.offset, self.upload_id) = self.client.upload_chunk(\\n StringIO(self.last_block), next_chunk_size, self.offset, self.upload_id)\\n self.last_block = None\\n except ErrorResponse as e:\\n reply = e.body\\n if \\\"offset\\\" in reply and reply['offset'] != 0:\\n if reply['offset'] > self.offset:\\n self.last_block = None\\n self.offset = reply['offset']\",\n \"def segment_download_count(self):\\n # type: () -> int\\n return self._segment_download_count\",\n \"def getFileCount(self) -> int:\\n ...\",\n \"async def is_completed_upload_file(\\n location_id: LocationID, file_id: StorageFileID, future_id: str, user_id: UserID\\n):\",\n \"def download(url, fpath):\\n with open(fpath, 'wb') as f:\\n try:\\n with requests.get(url, stream=True) as r:\\n r.raise_for_status()\\n f_size = 0\\n for chunk in r.iter_content(chunk_size=8192):\\n if chunk:\\n f.write(chunk)\\n f_size += len(chunk)\\n f_hash = get_hash_memory_optimized(fpath)\\n return f_size, f_hash\\n except Exception as e:\\n os.remove(fpath)\\n raise AdvarchsDownloadException('Could not download file.' + e.message)\",\n \"def chunkFileUpload(self, fp, chunksize=1024 * 4096):\\n parts = int(math.ceil(fp.stat().st_size / float(chunksize)))\\n err = False\\n maxchunksize = 1024 * 1024 * 100\\n if chunksize >= maxchunksize:\\n print(\\n 'not uploaded: defined chunksize {0} is bigger than the allowed maximum {1}'.format(chunksize, maxchunksize))\\n return None\\n\\n part = 0\\n for part, chunk in enumerate(self.chunkedread(fp, chunksize),1):\\n logger.info('({2})uploading part {0} of {1}'.format(part, parts, fp.name))\\n files = {'file': (str(fp.name), chunk)}\\n res = self._post(self.fullUrl('/chunked_upload?chunk={0}').format(part), files=files)\\n\\n print('finish, uploaded part {0} of {1} '.format(part, parts))\\n res = self._post(self.fullUrl('chunked_upload/commit?filename={0}'.format(fp.name)))\\n return self.getFile(res['file']['selfUrl']), self.getObject(res['relatedObject']['selfUrl'])\\n\\n # relObj = res['relatedObject']\\n # obj = self.getObject(relObj['selfUrl'])\\n # return obj\",\n \"def _query_server_pos(self, conn, file_length):\\r\\n resp = self._query_server_state(conn, file_length)\\r\\n if resp.status == 200:\\r\\n return (0, file_length) # Completed upload.\\r\\n if resp.status != 308:\\r\\n # This means the server didn't have any state for the given\\r\\n # upload ID, which can happen (for example) if the caller saved\\r\\n # the tracker URI to a file and then tried to restart the transfer\\r\\n # after that upload ID has gone stale. In that case we need to\\r\\n # start a new transfer (and the caller will then save the new\\r\\n # tracker URI to the tracker file).\\r\\n raise ResumableUploadException(\\r\\n 'Got non-308 response (%s) from server state query' %\\r\\n resp.status, ResumableTransferDisposition.START_OVER)\\r\\n got_valid_response = False\\r\\n range_spec = resp.getheader('range')\\r\\n if range_spec:\\r\\n # Parse 'bytes=<from>-<to>' range_spec.\\r\\n m = re.search('bytes=(\\\\d+)-(\\\\d+)', range_spec)\\r\\n if m:\\r\\n server_start = long(m.group(1))\\r\\n server_end = long(m.group(2))\\r\\n got_valid_response = True\\r\\n else:\\r\\n # No Range header, which means the server does not yet have\\r\\n # any bytes. Note that the Range header uses inclusive 'from'\\r\\n # and 'to' values. Since Range 0-0 would mean that the server\\r\\n # has byte 0, omitting the Range header is used to indicate that\\r\\n # the server doesn't have any bytes.\\r\\n return self.SERVER_HAS_NOTHING\\r\\n if not got_valid_response:\\r\\n raise ResumableUploadException(\\r\\n 'Couldn\\\\'t parse upload server state query response (%s)' %\\r\\n str(resp.getheaders()), ResumableTransferDisposition.START_OVER)\\r\\n if conn.debug >= 1:\\r\\n print 'Server has: Range: %d - %d.' % (server_start, server_end)\\r\\n return (server_start, server_end)\",\n \"def content_len(self, value):\\n self.set_header('CONTENT-LENGTH', value)\",\n \"async def size(self) -> int:\",\n \"def get_file(self) -> int:\\r\\n return self.file\",\n \"def _filelength(self):\\r\\n with open(self.fileName, 'rb') as f:\\r\\n f.seek(0, 2) # move to end of file\\r\\n length = f.tell() # get current position\\r\\n return length\",\n \"def content_length(self, value):\\r\\n self.set_header('CONTENT-LENGTH', str(value))\",\n \"def get_status_code_for_get(request_header: str, file: str) -> int:\\n modified = True\\n modified_header = \\\"If-Modified-Since:\\\"\\n if file == \\\"/\\\":\\n file = \\\"/index.html\\\"\\n\\n if not os.path.isfile(file[1:]):\\n return 404\\n\\n # check for If_Modified_Since\\n begin_mod_date_ind = request_header.find(modified_header)\\n\\n if begin_mod_date_ind != -1:\\n new_line = \\\"\\\\r\\\\n\\\"\\n end_mod_date_ind = request_header[begin_mod_date_ind:].find(new_line) + begin_mod_date_ind\\n date_and_time = request_header[begin_mod_date_ind:end_mod_date_ind][len(modified_header):]\\n\\n if not HttpServer.date_older_than_file_date(date_and_time, file):\\n modified = False\\n\\n if modified is True:\\n return 200\\n else:\\n return 304\",\n \"def status(self, code, content_length=None):\",\n \"def get_file_size(self):\\n try:\\n return os.path.getsize(self.get_full_path())\\n except Exception as e:\\n raise SystemExit(f\\\"Could not complete operation: {e}\\\")\",\n \"def get_block(self):\\n # Read in data in blocks so as to not tie up the CPU.\\n BLOCK_SIZE = 1024\\n eof = False\\n data = ''\\n while not eof:\\n next_block = self._stream_handle.read(BLOCK_SIZE)\\n if next_block:\\n data = data + next_block\\n gevent.sleep(0)\\n else:\\n eof = True\\n\\n if data != '':\\n self._chunker.add_chunk(data, self._timestamp)\\n self.file_complete = True\\n return len(data)\\n else: # EOF\\n self.file_complete = True\\n raise EOFError\",\n \"def get_file_size(file):\\n\\tfile.seek(0, os.SEEK_END)\\n\\treturn file.tell()\",\n \"def file_size(self):\\n return self._fileSize\",\n \"def _download(self, location, count):\\n # build file location to write this chunk in\\n filename = self.filename + '_part_%d' % count\\n \\tfilepath = os.path.join(os.getcwd(), filename)\\n\\n # build GET request by calculating the next range of bytes to retrieve\\n # building the request headers and making the streaming GET request\\n gap = (location, location + self.chunk_size - 1)\\n headers = {'Range': 'bytes=%d-%d' % gap}\\n resp = requests.get(self.url, headers=headers, stream=True)\\n\\n if self.verbose:\\n print 'Requesting %s' % filename\\n\\n # if request was not HTTP OK then stop proceeding\\n if resp.status_code >= 300:\\n if self.verbose:\\n print 'Invalid response code: %d' % resp.status_code\\n return\\n\\n # write the requested chunk to the temporary file\\n with open(filepath, 'wb') as open_file:\\n for chunk in resp.iter_content(chunk_size=BYTES):\\n if chunk:\\n open_file.write(chunk)\\n\\n if self.verbose:\\n print 'Downloaded %s' % filepath\",\n \"def post(self):\\n filename = str(time.time())\\n filepath = os.path.join(\\n os.path.join(current_app.config['UPLOAD_FOLDER'], filename))\\n with open(filepath, 'bw') as uploadfile:\\n chunk_size = 1024\\n while True:\\n chunk = request.stream.read(chunk_size)\\n if len(chunk) == 0:\\n break\\n uploadfile.write(chunk)\\n current_app.logger.info('file %s upload successfully', filename)\\n return {'timestamp': filename}, http.HTTPStatus.CREATED\",\n \"def _check_idx(self, url):\\n if not url.endswith('.idx'):\\n url += '.idx'\\n return requests.head(url).ok\",\n \"def last_transfer_size(self):\\n return self._last_transfer_size\",\n \"def len(self):\\n return self._fsize - self._fp.tell()\",\n \"def upload_file(self):\\n request = copy.deepcopy(self.request_template)\\n data = json.dumps(request)\\n curr_file = {\\n 'request': data,\\n 'file': open(self.file_path, 'rb')\\n }\\n print(\\\"Sending Upload request of av for file {}\\\".format(self.file_name))\\n try:\\n response = requests.post(url=self.url + \\\"upload\\\", files=curr_file, verify=False)\\n except Exception as E:\\n print(\\\"Upload file failed. file: {} , failure: {}\\\".format(self.file_name, E))\\n raise\\n response_j = response.json()\\n print(\\\"av Upload response status for file {} : {}\\\".format(self.file_name,\\n response_j[\\\"response\\\"][0][\\\"status\\\"][\\\"label\\\"]))\\n return response_j\",\n \"def next_upload(self):\\n # print \\\"fetching upload...\\\"\\n try:\\n uploads = Backend.next(\\n self.account, location=self.location,\\n project=self.project) or []\\n except (exceptions.BackendDown, exceptions.BackendError):\\n raise\\n else:\\n if uploads:\\n # Return the one download in the list\\n return uploads[0]\",\n \"def multipart_push_start(self, url, size=None):\\n assert size is not None, f\\\"{self}: size of multipart upload must be specified.\\\"\\n path = self.base_path / url\\n assert not path.exists(), f\\\"Path {url} already exists, aborting upload.\\\"\\n if not path.is_file():\\n with path.open(\\\"wb\\\") as stream:\\n stream.truncate(size)\\n return url\",\n \"def content_length(self) -> Optional[int]:\\n content_length = self._headers.get( # type: ignore[attr-defined]\\n hdrs.CONTENT_LENGTH\\n )\\n\\n if content_length is not None:\\n return int(content_length)\\n else:\\n return None\",\n \"def test_get_object_to_file_with_range(self):\\n err = None\\n try:\\n response = self.bos.put_object_from_string(self.BUCKET, self.KEY, \\\"a\\\" * 1000)\\n except BceServerError as e:\\n err = e\\n finally:\\n self.assertIsNone(err)\\n self.check_headers(response)\\n\\n fp = open(self.FILENAME, \\\"w\\\")\\n fp.write('a' * 100)\\n fp.close()\\n\\n fp = open(self.FILENAME, \\\"rb\\\")\\n md5 = utils.get_md5_from_fp(fp)\\n fp.close()\\n os.remove(self.FILENAME)\\n\\n err = None\\n try:\\n response = self.bos.get_object_to_file(self.BUCKET,\\n self.KEY,\\n self.FILENAME,\\n range=(0, 99))\\n except BceServerError as e:\\n err = e\\n finally:\\n self.assertIsNone(err)\\n self.check_headers(response)\\n self.assertEqual(response.metadata.content_range, 'bytes 0-99/1000')\\n\\n fp = open(self.FILENAME, \\\"rb\\\")\\n get_md5 = utils.get_md5_from_fp(fp)\\n fp.close()\\n self.assertEqual(get_md5, md5)\",\n \"def end_index(self):\\n if self.value == self._query.num_pages:\\n # Special case for the last page\\n # because there can be orphans.\\n return self._query.count\\n else:\\n return self.value * self._query.page_size.value\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.61535513","0.5919975","0.5807138","0.5694676","0.56919193","0.5665816","0.562974","0.55861944","0.54725754","0.54708993","0.54157346","0.53107536","0.53089446","0.5262854","0.52135384","0.51811814","0.5161932","0.5133586","0.5120558","0.5094605","0.50907135","0.5025104","0.5024852","0.5018597","0.50179166","0.50114286","0.49969366","0.49924368","0.499172","0.49834093","0.4968431","0.4953618","0.49481076","0.4940965","0.49292126","0.4925384","0.49142784","0.4914222","0.49098918","0.48924306","0.4889205","0.48855162","0.48837894","0.48651773","0.4861882","0.48590127","0.48529664","0.48529664","0.48526078","0.48512062","0.4839209","0.4837768","0.48356196","0.48355126","0.48311278","0.48282772","0.48228028","0.48139486","0.48066086","0.48035535","0.48015532","0.47998738","0.47942483","0.47910506","0.47858438","0.47755602","0.476298","0.47553432","0.47495696","0.47398075","0.47392502","0.47354952","0.47202665","0.47170573","0.4713635","0.47119206","0.47062874","0.46983275","0.46947575","0.4694152","0.46900502","0.468934","0.46885332","0.46753475","0.46743926","0.46666148","0.46611598","0.46578026","0.4655889","0.46494588","0.46474126","0.46455732","0.46373287","0.46312556","0.46273255","0.46228832","0.4620164","0.46200693","0.46200663","0.46198052"],"string":"[\n \"0.61535513\",\n \"0.5919975\",\n \"0.5807138\",\n \"0.5694676\",\n \"0.56919193\",\n \"0.5665816\",\n \"0.562974\",\n \"0.55861944\",\n \"0.54725754\",\n \"0.54708993\",\n \"0.54157346\",\n \"0.53107536\",\n \"0.53089446\",\n \"0.5262854\",\n \"0.52135384\",\n \"0.51811814\",\n \"0.5161932\",\n \"0.5133586\",\n \"0.5120558\",\n \"0.5094605\",\n \"0.50907135\",\n \"0.5025104\",\n \"0.5024852\",\n \"0.5018597\",\n \"0.50179166\",\n \"0.50114286\",\n \"0.49969366\",\n \"0.49924368\",\n \"0.499172\",\n \"0.49834093\",\n \"0.4968431\",\n \"0.4953618\",\n \"0.49481076\",\n \"0.4940965\",\n \"0.49292126\",\n \"0.4925384\",\n \"0.49142784\",\n \"0.4914222\",\n \"0.49098918\",\n \"0.48924306\",\n \"0.4889205\",\n \"0.48855162\",\n \"0.48837894\",\n \"0.48651773\",\n \"0.4861882\",\n \"0.48590127\",\n \"0.48529664\",\n \"0.48529664\",\n \"0.48526078\",\n \"0.48512062\",\n \"0.4839209\",\n \"0.4837768\",\n \"0.48356196\",\n \"0.48355126\",\n \"0.48311278\",\n \"0.48282772\",\n \"0.48228028\",\n \"0.48139486\",\n \"0.48066086\",\n \"0.48035535\",\n \"0.48015532\",\n \"0.47998738\",\n \"0.47942483\",\n \"0.47910506\",\n \"0.47858438\",\n \"0.47755602\",\n \"0.476298\",\n \"0.47553432\",\n \"0.47495696\",\n \"0.47398075\",\n \"0.47392502\",\n \"0.47354952\",\n \"0.47202665\",\n \"0.47170573\",\n \"0.4713635\",\n \"0.47119206\",\n \"0.47062874\",\n \"0.46983275\",\n \"0.46947575\",\n \"0.4694152\",\n \"0.46900502\",\n \"0.468934\",\n \"0.46885332\",\n \"0.46753475\",\n \"0.46743926\",\n \"0.46666148\",\n \"0.46611598\",\n \"0.46578026\",\n \"0.4655889\",\n \"0.46494588\",\n \"0.46474126\",\n \"0.46455732\",\n \"0.46373287\",\n \"0.46312556\",\n \"0.46273255\",\n \"0.46228832\",\n \"0.4620164\",\n \"0.46200693\",\n \"0.46200663\",\n \"0.46198052\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6557539"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":22474,"cells":{"query":{"kind":"string","value":"Delete the upload ticket (to be used once get_last_uploaded_byte() == total file size) Makes a DELETE request to the given URI, removing the upload ticket and setting the upload status to \"processing\""},"document":{"kind":"string","value":"def delete_upload_ticket(self, complete_uri):\n url = self.config['apiroot'] + complete_uri\n log.info(\"Requesting %s\" % url)\n r = HTTPClient().fetch(url, method=\"DELETE\", headers=self.standard_headers,\n validate_cert=not self.config['dev'])\n log.info(\"Upload completed: status code: %d\" % r.code)\n if r.code == 201:\n _id = r.headers['location'].split('/')[-1]\n return _id\n raise ValueError(\"Upload completion unsuccessful\")"},"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 delete(self, _uri):\n print(\"Deleting '%s'\"%(_uri))\n response = self.__httpsRequest('DELETE', _uri, '')","def delete(self):\n try:\n flash_message = request.json[\"flash_message\"]\n folder_path = \"{0}/user_uploads/{1}/{2}/\".format(self.__APP_PATH__, current_user.net_id, request.json[\"folder_name\"])\n request_submitted = path.exists(\"{0}request.submitted\".format(folder_path))\n request_processed = path.exists(\"{0}request.processed\".format(folder_path))\n request_returned = path.exists(\"{0}request.returned\".format(folder_path))\n request_voided = path.exists(\"{0}request.voided\".format(folder_path))\n filename = \"{0}{1}\".format(folder_path, request.json.get(\"file\", None))\n\n if request.json['delete_request'] and not request_submitted and not request_processed and not request_returned and not request_voided:\n rmtree(folder_path, True)\n\n if flash_message:\n flash(\"Request deleted successfully.\", \"success\")\n\n return jsonify({\"success\": True, \"type\": \"success\", \"message\": \"Request deleted successfully.\"})\n elif request.json['delete_request']:\n if flash_message:\n flash(\"This request can no longer be deleted.\", \"error\")\n\n return jsonify({\"success\": False, \"type\": \"error\", \"message\": \"This request can no longer be deleted.\"})\n\n if filename and path.exists(filename):\n remove(filename)\n\n return jsonify({\"type\": \"success\", \"message\": \"File successfully deleted.\"})\n except Exception as e:\n print(e)\n pass\n\n return jsonify({\"type\": \"error\", \"message\": \"Invalid request format or origin.\"})","def delete_upload(arn=None):\n pass","def delete_file(self, upload_id: str, file_path: str, token: str) \\\n -> Upload:\n data, _, _ = self.json('delete', f'/{upload_id}/{file_path}', token)\n return self._parse_upload_status(data)","def delete(cls, uri):\n return cls._perform_request(uri, 'DELETE')","def remote_abortUpload(self, upload_id):\n self.transfers_register.deallocate_upload_slot(upload_id)\n\n # Also remove the file if the upload is already completed\n completed = settings.completed_root.child(upload_id)\n if completed.exists():\n completed.remove()","def delete_file(self, filepath):\n self.ftp.delete(filepath)","def do_DELETE(self): # pylint: disable=missing-docstring\n self._set_headers(204)\n (resource, id) = self.parse_url(self.path)\n\n if resource == \"comments\":\n delete_comment(id)\n elif resource == \"posts\":\n delete_post(id)\n elif resource == \"categories\":\n delete_category(id)\n elif resource == \"posttags\":\n remove_tag(id)\n \n self.wfile.write(\"\".encode())","def delete(self):\n request_data = request.get_json(force=True)\n current_path = self.get_current_path()\n file_name = request_data.get('file_name')\n\n if not file_name:\n abort(400, message=\"File name must not be empty!\")\n\n full_path = os.path.join(current_path, file_name)\n\n if not os.path.exists(full_path):\n abort(400, message=\"File was not found in current path!\")\n\n if not os.path.isfile(full_path):\n abort(400, message=\"File name is not a file!\")\n\n if not self.is_allowed(full_path):\n abort(403, message=\"You are not allowed to this path\")\n\n os.remove(full_path)\n\n return {\"message\": \"OK\"}","def delete(self, *route, **req_data):\n # Read the file ID from the request, with safety.\n try:\n file_id = UUID(req_data['file_id']).hex\n except ValueError:\n return Response(status='400 Bad Request')\n\n # Retrieve and delete the file.\n stored_files = StoredFile.collection()\n to_delete = stored_files.first(id=file_id)\n\n log_activity('%s deleted file %s'%(\n context.user.link, to_delete.filename\n ))\n\n stored_files.delete(to_delete)\n get_bucket().delete(to_delete.data_id)\n\n return Response(status='200 OK')","def rm(self, uri):\n path = osaka.utils.get_uri_path(uri)\n try:\n osaka.utils.LOGGER.debug(\"Removing {0} as a file\".format(uri))\n self.webdav.delete(path)\n except Exception as e:\n osaka.utils.LOGGER.debug(\n \"Removing {0} as a directory, file encountered error {1}\".format(uri, e)\n )\n self.webdav.rmdir(path)","def delete(self, filename):\n pass","def _delete(self, uri, headers=None):\n if self.openam_url[-1:] == '/':\n openam_path = self.openam_url + uri\n else:\n openam_path = self.openam_url + \"/\" + uri\n\n try:\n data = requests.delete(openam_path, headers=headers, timeout=self.timeout, verify=self.verify)\n except requests.exceptions.RequestException as e:\n data = {'error': e}\n return data","def delete_file(filename):\n\tprint client.file_delete(filename)","def delete(self,filename):\n\n try:\n self.ftp.delete(filename)\n except:\n print('Error deleting remote file:%s'%filename)\n return 1\n\n return 0","def _cleanup_uploads(self):\n logger.debug(\"Performing blob upload cleanup\")\n\n while True:\n # Find all blob uploads older than the threshold (typically a week) and delete them.\n with UseThenDisconnect(app.config):\n stale_upload = model.get_stale_blob_upload(DELETION_DATE_THRESHOLD)\n if stale_upload is None:\n logger.debug(\"No additional stale blob uploads found\")\n return\n\n # Remove the stale upload from storage.\n logger.debug(\"Removing stale blob upload %s\", stale_upload.uuid)\n assert stale_upload.created <= (datetime.utcnow() - DELETION_DATE_THRESHOLD)\n\n try:\n storage.cancel_chunked_upload(\n [stale_upload.location_name], stale_upload.uuid, stale_upload.storage_metadata\n )\n except Exception as ex:\n logger.debug(\n \"Got error when trying to cancel chunked upload %s: %s\",\n stale_upload.uuid,\n ex.message,\n )\n\n # Delete the stale upload's row.\n with UseThenDisconnect(app.config):\n model.delete_blob_upload(stale_upload)\n\n logger.debug(\"Removed stale blob upload %s\", stale_upload.uuid)","def delete(request):\n wfsxml = request.POST.get('wfsxml', False) # FOR GEOSERVER\n uuid = request.POST.get('uuid', False)\n # MAKE GEOSERVER WFS TRANSACTION\n error = post_to_geoserver(wfsxml, GeoPostBase.wfsURL)\n # ALL GOOD\n if error:\n return server_error(error)\n # IF WFS TRANSACTION ERROR\n else:\n pass\n # Delete photo from bucket\n delete_from_bucket(uuid, GeoPostBase.imageBucket)\n return HttpResponseRedirect(reverse('geopost_home'))","async def delete_file(location_id: LocationID, file_id: StorageFileID, user_id: UserID):","def _delete(self, url):\n return self._request(url, method=\"DELETE\")","def delete(self, filename, threadID):\n self.lock.acquire()\n removed_file = self.files_on_disk.pop(filename)\n\n # remove file from the directory\n if not os.path.exists(\".storage/\" + filename):\n self.lock.release()\n return \"ERROR: NO SUCH FILE\\n\"\n else:\n os.remove(\".storage/\" + filename)\n i = 0\n j = 0\n while(i<self.size and j<removed_file.num_blocks):\n if(self.disk_mem[i]==removed_file.letter):\n self.disk_mem[i] = \".\"\n j+=1\n i += 1\n print(\"[thread %d] Deleted %s file '%c' (deallocated %d blocks)\" %\n (threadID, removed_file.name, removed_file.letter, removed_file.num_blocks))\n self.show(threadID)\n self.lock.release()\n return \"ACK\\n\"","def delete(self, host, file):","def _delete(self, url):\n url = urljoin(self.base_url, url)\n try:\n r = self._make_request(**dict(\n method='DELETE',\n url=url,\n auth=self.auth,\n timeout=self.timeout,\n hooks=self.request_hooks,\n headers=self.request_headers\n ))\n except requests.exceptions.RequestException as e:\n raise e\n else:\n if r.status_code >= 400:\n _raise_response_error(r)\n if r.status_code == 204:\n return\n return r.json()","def do_delete_request(self, uri, headers, timeout_ms):\n return self._do_request('DELETE', uri, headers, None, timeout_ms, None)","def __deleteLock(self, url):\n response = self._adapter.deleteRequest(url, self._baseHeader)\n return response","def delete(self, relative_url, accepted_status_codes):\n return self._send_request(\n relative_url, accepted_status_codes, self._session.delete\n )","def cancel_upload(self):\r\n self.bucket.cancel_multipart_upload(self.key_name, self.id)","def terminate(self):\n logger.debug(\"terminating\")\n self._upload = None\n self._buf.close()","def delete(self, uri, body=None, headers=None, auth=False):\n return self.send_request('DELETE', uri, body, headers, auth)","def delete_file(self, path):\n raise HTTPError(\n 501,\n \"Narrative deletion not implemented here. Deletion is handled elsewhere.\",\n )","def base_delete(url_path):\n response = requests.delete(url=settings.URL_API + url_path)\n return response","def _delete(self, ref: str) -> None:\n self._trace(\"deleting ref %s\" % ref)\n head = self.read_symbolic_ref(\"HEAD\")\n if head and ref == head[1]:\n _write(\"error %s refusing to delete the current branch: %s\" % (ref, head))\n return\n try:\n self._connection.files_delete(self._ref_path(ref))\n except dropbox.exceptions.ApiError as e:\n if not isinstance(e.error, dropbox.files.DeleteError):\n raise\n # someone else might have deleted it first, that's fine\n self._refs.pop(ref, None) # discard\n self._pushed.pop(ref, None) # discard\n _write(\"ok %s\" % ref)","def delete(self, uri, **kwargs):\n return self.session.delete(uri, **kwargs)","def test_delete(client: FlaskClient):\n file = get_example_file(ExampleFileType.Txt)\n response_upload = util.upload_file(client, DEFAULT_USER, file)\n response_delete = util.delete_file(client, DEFAULT_USER, response_upload.json[\"id\"])\n assert response_delete.status == \"204 NO CONTENT\"\n response_download = util.download_file(\n client, DEFAULT_USER, response_upload.json[\"id\"]\n )\n assert response_download.status == \"404 NOT FOUND\"","def _delete_job(self, job):","def delete(self, url):\r\n response = self.requestHelper.delete(url)\r\n return response.status_code == 200","def delete(self, filename):\n raise NotImplementedError","def delete_file(_oid, attachmentId=None):\n md = Metadata.objects.get_or_404(pk=_oid)\n attachment = ''\n test_upload_path_prefix = \"uploadedfiles\"\n test_environment = False\n\n username = _authenticate_user_from_session(request)\n\n if username:\n try:\n try:\n md = Metadata.objects.get(id=_oid)\n \n try:\n # if developing locally we'll also want to remove file\n url = filter(\n lambda a: str(a.id) == attachmentId, md.attachments\n ).pop().url\n if str(os.environ['FLASKCONFIG']) == 'testing' or str(os.environ['FLASKCONFIG']) == 'development':\n test_environment = True\n os.remove(\n os.path.join(\n app.config['UPLOADS_DEFAULT_DEST'],\n test_upload_path_prefix,\n _oid,\n os.path.basename(url)\n )\n )\n else:\n os.remove(\n os.path.join(\n app.config['UPLOADS_DEFAULT_DEST'],\n _oid,\n os.path.basename(url)\n )\n )\n except Exception:\n #Throw exception specific for test or non-test enviroment\n if test_environment:\n file_path = app.config['UPLOADS_DEFAULT_DEST'] + \"/\" + test_upload_path_prefix + \"/\" + _oid + \"/\" + os.path.basename(url)\n else:\n file_path = app.config['UPLOADS_DEFAULT_DEST'] + \"/\" + _oid + \"/\" + os.path.basename(url)\n\n print \"There was a problem deleting the file! Tried to reach path: \" + file_path \n \n # don't need to save after this since we're updating existing\n Metadata.objects(id=_oid).update_one(\n pull__attachments__id=attachmentId\n )\n \n md = Metadata.objects.get(id=_oid)\n \n # we'll just go ahead and not care if it doesn't exist\n except ValueError:\n pass\n\n\n except KeyError:\n try:\n keys = request.json.keys()\n keys_str = ', '.join(keys)\n except Exception as e:\n print \"Error: \" + str(e)\n return Response(\"Server error deleting file...\", status=500)\n\n return jsonify(\n {\n 'message':\n 'Key(s) ' + keys_str + ' not recognized. ' +\n 'Must contain \\'attachment\\''\n },\n status=400\n )\n\n return jsonify(dict(message=attachment + ' successfully (at/de)tached!', record=md))\n \n else:\n return Response('Bad or missing session id.', status=401)","async def delete(self, delete: TPayload) -> None:","def delete_file(file_id):\n url = 'https://slack.com/api/files.delete'\n data = urllib.parse.urlencode(\n (\n (\"token\", ACCESS_TOKEN),\n (\"file\", file_id)\n )\n )\n data = data.encode(\"ascii\")\n headers = {\"Content-Type\": \"application/x-www-form-urlencoded\"}\n request = urllib.request.Request(url, data, headers)\n urllib.request.urlopen(request)","def delete(self, url):\n return self.session.delete(url=self.base_url + url)","def delFile(self, file_name):\n ret = self.rest.delete(self.ngx_server, self.getUrl() + '/' + file_name, {}, [200, 404])\n if ret == False:\n self.logger.error('delete file:%s from tfs failed.' % file_name)\n raise Exception, 'delete file:%s from tfs failed.' % file_name\n self.logger.info('Delete Tfs file: %s' % file_name)\n return True","def delete(url, data=None, **_):\n # Checks input parameters\n assert '/process/%s' % dummy_id in url in url","def fusion_api_delete_storage_pool(self, uri=None, api=None, headers=None):\n return self.pool.delete(uri=uri, api=api, headers=headers)","def test_delete_sync(created_test_helper, setup_teardown_file_operations):\n # get current test params\n test_params = created_test_helper.get_test_data(TESTLOC)\n\n # Upload a PNG file < 1MB in size\n upload_file_response = created_test_helper.upload(\n file_name=test_params[\"test_file\"])\n\n # delete file which was uploaded\n upload_file_id = upload_file_response['file']['id']\n created_test_helper.delete([upload_file_id])\n\n # validate file uploaded was deleted\n created_test_helper.validate_delete(upload_file_id, file_type=\"images\")","def delete(self, url):\n return self.request(url, \"DELETE\")","def delete(self):\n self.call('DELETE', expect=error.NO_CONTENT)","def delete(task_id, pfn):\n\n activated = PoolManager.db.query('UPDATE `standalone_deletion_tasks` SET `status` = \\'active\\' WHERE `id` = %s', task_id)\n if activated == 0:\n # task was cancelled\n return -1, None, None, '', '' \n\n return gfal_exec('unlink', (pfn,), deletion_nonerrors)","def do_DELETE(self,):\n self.http_method = 'DELETE'\n self.response()","def fusion_api_delete_resource(self, uri, api=None, headers=None):\n if api:\n headers = self.fusion_client._set_req_api_version(api=api)\n elif not headers:\n headers = self.fusion_client._headers\n uri = 'https://%s%s' % (self.fusion_client._host, uri)\n return self.fusion_client.delete(uri, headers)","def fusion_api_delete_rack_manager(self, uri, name=None, param='', api=None, headers=None):\n return self.rackmanager.delete(uri=uri, name=name, param=param, api=api, headers=headers)","async def delete(self, request):\n\n userid = await authenticated_userid(request)\n project = await request.app.context_project(request, userid)\n\n await request.post()\n\n camera_id = request.match_info['camera_file_id']\n\n log = request['slog']\n log.debug('request: camera delete', camera_id=camera_id)\n\n camera = await Camera.get(request,\n camera_id=camera_id,\n project_id=project.project_id,\n userid=userid)\n if not camera:\n raise web.HTTPNotFound(text=\"Camera file '%s' not found\" % camera_id)\n\n await request.app.task_broker.publish('camera_delete', {\n 'userid': userid,\n 'project_id': project.project_id,\n 'camera_id': camera_id,\n }, log=log)\n\n await camera.set_status(request, 'DELETING')\n\n return web.HTTPNoContent()","def delete(self, url):\n return self._request('DELETE', url)","def access_gemini_url_delete_method(context, endpoint):\n url = urljoin(context.gemini_api_url, endpoint)\n context.response = requests.delete(url)","def delete_call(url, headers=None):\n response = requests.delete(url, headers=headers)\n\n if response.status_code == 401:\n raise RedirectException(reverse('b2b_control:logout'))\n\n return response","def finalize_pending(self, data):\n if self.request.method.upper() == 'DELETE':\n data.delete()","def handle_delete(uuid):\n location = os.path.join(app.config['UPLOAD_DIRECTORY'], uuid)\n print(uuid)\n print(location)\n shutil.rmtree(location)","def handle_delete(uuid):\n location = os.path.join(app.config['UPLOAD_DIRECTORY'], uuid)\n print(uuid)\n print(location)\n shutil.rmtree(location)","async def abort_upload_file(\n location_id: LocationID, file_id: StorageFileID, user_id: UserID\n):","def delete(self):\n self.request().delete()","def delete_oh_file(access_token, file_name):\r\n try:\r\n deletion_response = delete_file(access_token, file_basename=file_name)\r\n\r\n if deletion_response.status_code == 200:\r\n return True\r\n else:\r\n print(f'An error was encountered trying to delete file {file_name}')\r\n return False\r\n except:\r\n print(f'An error was encountered trying to delete file {file_name}')\r\n return False","def delete(self, path):\n \n # prep\n delete_url = self.url(path)\n\n # request\n response = requests.delete(delete_url, auth=self.auth, headers=API.HEADERS)\n\n # test and return\n self.raise_for_status(response)","def delete_request_journal(self):\n requests.delete(self.requests_url).raise_for_status()","def flow_file_chunk_delete(sender, instance, **kwargs):\n instance.file.delete(False)","def delete_job(self, filename):\n job = Jobs.get(Jobs.filename == filename)\n job.delete_instance()","def test_upload_file_product_then_delete_immediately(self):\n with open('./client_assets/dog.jpg', 'rb') as file:\n coll = json.dumps({\n \"collectionName\": \"Apple %d\" % randint(0, 9),\n \"collectionDesc\": \"Apple desc %s\" % (randint(0, 9))\n })\n response = self.client.post('http://localhost:8000/webapp/api/products/', {\n 'title': 'test post', 'imageSource': file, 'collection': coll}, format='multipart')\n print(response.data)\n postID = response.data['id']\n self.assertEqual(response.status_code, 201)\n with open('./client_assets/index.jpg', 'rb') as file:\n response = self.client.delete(\n 'http://localhost:8000/webapp/api/products/%s/' % postID)\n print(response.data)\n self.assertEqual(response.status_code, 204)","def _delete(self, remote_filename):\n\n file_id = self.get_file_id(remote_filename)\n if file_id is None:\n raise BackendException(\n 'File \"%s\" cannot be deleted: it does not exist' % (\n remote_filename))\n response = self.http_client.put(self.metadata_url + 'trash/' + file_id)\n response.raise_for_status()\n del self.names_to_ids[remote_filename]","def Delete(self, path):\n\n # try to request\n try:\n request = requests.delete(self.config[\"url\"] + \"/weaviate/v1\" + path)\n except urllib.error.HTTPError as error:\n return None\n\n return request.status_code","def test_6d_delete_file(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.deleting_data_test_ready:\n raise unittest.SkipTest(\"Skipped for failed to prepare deleting test.\")\n self.dismiss_dialogs()\n function = js_func[\"delete\"] % GST.gs_file_paths[\"file_to_delete_path\"]\n try:\n self.send_request(function, \"delete_data()\")\n except Exception as e:\n raise DeleteException(e.__str__()) \n try:\n response = self.get_response()\n assert \"Success\" in response\n self.refresh_page()\n except AssertionError:\n raise DeleteException(response)","def test_finalized_no_upload_ticket(self):\n namespace = 'default-gzip'\n request = self.store_request(namespace, pad_string('silence'))\n request.upload_ticket = None\n with self.call_should_fail('400'):\n self.call_api('finalize_gs_upload', self.message_to_dict(request), 200)","def http_delete_and_check_success(url, check_util=None, verify=True, auth=None):\n with requests.Session() as session:\n delete_resp = session.delete(\n url,\n verify=verify,\n stream=False,\n timeout=2.0,\n auth=auth)\n delete_resp.raise_for_status()\n if delete_resp.status_code != HTTPStatus.NO_CONTENT and delete_resp.text:\n delete_resp_json = delete_resp.json()\n else:\n delete_resp_json = None\n\n success, message, return_code = success_message_code(delete_resp, check_util)\n\n rest_return_obj = RestReturn(success=success, message=message, http_status=return_code,\n json_body=delete_resp_json,\n response_object=delete_resp)\n return rest_return_obj","def delete(\n self, uri, resource=None, logon_required=True, renew_session=True):\n try:\n self._urihandler.delete(self._hmc, uri, logon_required)\n except HTTPError as exc:\n new_exc = zhmcclient.HTTPError(exc.response())\n new_exc.__cause__ = None\n raise new_exc # zhmcclient.HTTPError\n except ConnectionError as exc:\n new_exc = zhmcclient.ConnectionError(exc.message, None)\n new_exc.__cause__ = None\n raise new_exc # zhmcclient.ConnectionError","def delete_file(self, name, container):\r\n try:\r\n path = os.path.join(self.upload_folder, container, name)\r\n os.remove(path)\r\n return True\r\n except:\r\n return False","def file_delete(self, path):\n params = {'root': self.session.root, 'path': format_path(path)}\n\n url, params, headers = self.request(\"/fileops/delete\", params)\n\n return self.rest_client.POST(url, params, headers)","def delete(self, path, request):\n response = 'FileInterfaceAdapter: DELETE on path {}'.format(path)\n status_code = 200\n\n logging.debug(response)\n\n return ApiAdapterResponse(response, status_code=status_code)","def delete(self, src):\n\n if self.noop:\n logger.info(\"No-Op Delete: %s.tar\" % self.bucket + src)\n else:\n logger.info(\"Trying to delete %s.tar\" % self.bucket + src)\n self.client.delete(self.bucket + src + \".tar\")","def delete(cls, webhook_endpoint_id):\n return Requester.delete(cls.endpoint + '/' + webhook_endpoint_id)","def _queue_delete(self, queue):\n\n queue.delete()","def delete(self):\n\t\t#self.log.info(\"Deleting file {}\".format(self._filepath))\n\t\tos.remove(self._filepath)","def __delete(self, url, headers=None):\n return self.__req(url, \"DELETE\", headers=headers)","def delete(self):\n if not pdbox._args.get(\"dryrun\"):\n result = execute(pdbox.dbx.files_delete_v2, self.path)\n pdbox.debug(\"Metadata response: %s\" % result.metadata)\n pdbox.info(\"Deleted %s\" % self.uri)","def photo_edit_file_cleanup(sender, **kwargs):\n instance = kwargs.get('instance')\n filename = instance.upload.url[1:]\n if os.path.exists(filename):\n os.remove(filename)","def delete(self):\n with self.locked():\n self.path.delete()","def delete(self):\n with self.locked():\n self.path.delete()","def handle_delf_conf(self, job):\n print(\"User deleted a file.\")\n # Delete the file from all servers\n self.put_job_in_all_queues(job)\n\n list_job_results = self.get_internal_results_from_all_servers()\n\n if len(list_job_results) == 0:\n # We got no responses back, there are probably no servers active\n self.put_external_result(self.generate_failure_job(\"Unsuccessful, no servers responded\"))\n return\n\n # Check all the servers had success\n for result in list_job_results:\n if result.result[\"outcome\"] != \"success\":\n self.put_external_result(\n self.generate_failure_job(\"Unsuccessful, one of the servers did not have success\"))\n return\n print(\"All servers deleted file '{}'.\".format(job.data[\"file_name\"]))\n\n # Tell the client we successfully uploaded to all servers\n response_result = copy.deepcopy(list_job_results[0])\n response_result.processed_by = None\n self.put_external_result(response_result)","def fusion_api_delete_repository(self, uri, api=None, headers=None):\n return self.repository.delete(uri=uri, api=api, headers=headers)","def delete_upload(sender, **kwargs):\n instance = kwargs['instance']\n path_to_delete = '%s/%s.%s' % (instance.path,instance.uuid,instance.ext)\n if not os.path.isdir(path_to_delete):\n os.unlink(path_to_delete)","def delete(self):\r\n request = http.Request('DELETE', self.get_url())\r\n\r\n return request, parsers.parse_empty","def delete(path: str):\n token = get_token()\n headers = {\n \"Authorization\": f\"Bearer {token}\"\n }\n return requests.delete(get_base_url() + path, headers=headers)","def _delete(filename):\n return os.remove(filename)","def finish(cls, upload, location=None, bytes_downloaded=0):\n path = \"uploader/finish/%s\" % upload[\"id\"]\n LOGGER.debug(path)\n payload = {\"bytes_transferred\": bytes_downloaded, \"location\": location}\n try:\n return Backend.put(path, payload, headers=Backend.headers())\n except requests.HTTPError as err:\n if err.response.status_code == 410:\n LOGGER.warning(\"Cannot finish file %s. File not active (410)\",\n upload[\"id\"])\n raise err\n except:\n raise","def delete_file(sender, instance, *args, **kwargs):\n if instance.photo:\n _delete_file(instance.photo.path)","def delete(self):\n\n try:\n remove(self.file)\n except OSError:\n pass","def ftp_DELE(self, path):\r\n try:\r\n print 'try to delete file:'+path\r\n self.run_as_current_user(self.fs.remove, path)\r\n except (OSError, FilesystemError) as err:\r\n why = _strerror(err)\r\n self.respond('550 %s.' % why)\r\n else:\r\n self.respond(\"250 File removed.\")\r\n return path","def delete(self, call, params={}): \n # Build an endpoint using the parameters...\n endpoint = self._calls[call](params)\n url = '{}/{}'.format(str(self), str(endpoint))\n return self.deleter.respond(url)","def post(self):\n args = request.args or request.json\n file_id = args.get('file_id')\n services.file.remove_star(file_id)\n return {\n \"status\": True\n }","def delete_file(self, name):\n del self.files[name]","def DELETE(url, mime_type='text/html'):\n def_app = DefaultHttpRequestHandler()\n return def_app.RequestMap(url, methods=['DELETE'], produces=mime_type)","def _delete(self, path):\n return self._api.delete_status(path, headers={\"Hawkular-Tenant\": self.tenant_id})","def voters_upload_cancel(request, election):\n voter_file_id = request.session.get('voter_file_id', None)\n if voter_file_id:\n vf = VoterFile.objects.get(id = voter_file_id)\n vf.delete()\n del request.session['voter_file_id']\n\n return HttpResponseRedirect(settings.SECURE_URL_HOST + reverse(url_names.election.ELECTION_VIEW, args=[election.uuid]))","def delete(api, url, payload=None, headers=None, auth=_KERBEROS_AUTH,\n proxies=None, retries=_NUM_OF_RETRIES, timeout=None):\n return call(api, url, 'delete', payload=payload,\n headers=headers, auth=auth, proxies=proxies, retries=retries,\n timeout=timeout)"],"string":"[\n \"def delete(self, _uri):\\n print(\\\"Deleting '%s'\\\"%(_uri))\\n response = self.__httpsRequest('DELETE', _uri, '')\",\n \"def delete(self):\\n try:\\n flash_message = request.json[\\\"flash_message\\\"]\\n folder_path = \\\"{0}/user_uploads/{1}/{2}/\\\".format(self.__APP_PATH__, current_user.net_id, request.json[\\\"folder_name\\\"])\\n request_submitted = path.exists(\\\"{0}request.submitted\\\".format(folder_path))\\n request_processed = path.exists(\\\"{0}request.processed\\\".format(folder_path))\\n request_returned = path.exists(\\\"{0}request.returned\\\".format(folder_path))\\n request_voided = path.exists(\\\"{0}request.voided\\\".format(folder_path))\\n filename = \\\"{0}{1}\\\".format(folder_path, request.json.get(\\\"file\\\", None))\\n\\n if request.json['delete_request'] and not request_submitted and not request_processed and not request_returned and not request_voided:\\n rmtree(folder_path, True)\\n\\n if flash_message:\\n flash(\\\"Request deleted successfully.\\\", \\\"success\\\")\\n\\n return jsonify({\\\"success\\\": True, \\\"type\\\": \\\"success\\\", \\\"message\\\": \\\"Request deleted successfully.\\\"})\\n elif request.json['delete_request']:\\n if flash_message:\\n flash(\\\"This request can no longer be deleted.\\\", \\\"error\\\")\\n\\n return jsonify({\\\"success\\\": False, \\\"type\\\": \\\"error\\\", \\\"message\\\": \\\"This request can no longer be deleted.\\\"})\\n\\n if filename and path.exists(filename):\\n remove(filename)\\n\\n return jsonify({\\\"type\\\": \\\"success\\\", \\\"message\\\": \\\"File successfully deleted.\\\"})\\n except Exception as e:\\n print(e)\\n pass\\n\\n return jsonify({\\\"type\\\": \\\"error\\\", \\\"message\\\": \\\"Invalid request format or origin.\\\"})\",\n \"def delete_upload(arn=None):\\n pass\",\n \"def delete_file(self, upload_id: str, file_path: str, token: str) \\\\\\n -> Upload:\\n data, _, _ = self.json('delete', f'/{upload_id}/{file_path}', token)\\n return self._parse_upload_status(data)\",\n \"def delete(cls, uri):\\n return cls._perform_request(uri, 'DELETE')\",\n \"def remote_abortUpload(self, upload_id):\\n self.transfers_register.deallocate_upload_slot(upload_id)\\n\\n # Also remove the file if the upload is already completed\\n completed = settings.completed_root.child(upload_id)\\n if completed.exists():\\n completed.remove()\",\n \"def delete_file(self, filepath):\\n self.ftp.delete(filepath)\",\n \"def do_DELETE(self): # pylint: disable=missing-docstring\\n self._set_headers(204)\\n (resource, id) = self.parse_url(self.path)\\n\\n if resource == \\\"comments\\\":\\n delete_comment(id)\\n elif resource == \\\"posts\\\":\\n delete_post(id)\\n elif resource == \\\"categories\\\":\\n delete_category(id)\\n elif resource == \\\"posttags\\\":\\n remove_tag(id)\\n \\n self.wfile.write(\\\"\\\".encode())\",\n \"def delete(self):\\n request_data = request.get_json(force=True)\\n current_path = self.get_current_path()\\n file_name = request_data.get('file_name')\\n\\n if not file_name:\\n abort(400, message=\\\"File name must not be empty!\\\")\\n\\n full_path = os.path.join(current_path, file_name)\\n\\n if not os.path.exists(full_path):\\n abort(400, message=\\\"File was not found in current path!\\\")\\n\\n if not os.path.isfile(full_path):\\n abort(400, message=\\\"File name is not a file!\\\")\\n\\n if not self.is_allowed(full_path):\\n abort(403, message=\\\"You are not allowed to this path\\\")\\n\\n os.remove(full_path)\\n\\n return {\\\"message\\\": \\\"OK\\\"}\",\n \"def delete(self, *route, **req_data):\\n # Read the file ID from the request, with safety.\\n try:\\n file_id = UUID(req_data['file_id']).hex\\n except ValueError:\\n return Response(status='400 Bad Request')\\n\\n # Retrieve and delete the file.\\n stored_files = StoredFile.collection()\\n to_delete = stored_files.first(id=file_id)\\n\\n log_activity('%s deleted file %s'%(\\n context.user.link, to_delete.filename\\n ))\\n\\n stored_files.delete(to_delete)\\n get_bucket().delete(to_delete.data_id)\\n\\n return Response(status='200 OK')\",\n \"def rm(self, uri):\\n path = osaka.utils.get_uri_path(uri)\\n try:\\n osaka.utils.LOGGER.debug(\\\"Removing {0} as a file\\\".format(uri))\\n self.webdav.delete(path)\\n except Exception as e:\\n osaka.utils.LOGGER.debug(\\n \\\"Removing {0} as a directory, file encountered error {1}\\\".format(uri, e)\\n )\\n self.webdav.rmdir(path)\",\n \"def delete(self, filename):\\n pass\",\n \"def _delete(self, uri, headers=None):\\n if self.openam_url[-1:] == '/':\\n openam_path = self.openam_url + uri\\n else:\\n openam_path = self.openam_url + \\\"/\\\" + uri\\n\\n try:\\n data = requests.delete(openam_path, headers=headers, timeout=self.timeout, verify=self.verify)\\n except requests.exceptions.RequestException as e:\\n data = {'error': e}\\n return data\",\n \"def delete_file(filename):\\n\\tprint client.file_delete(filename)\",\n \"def delete(self,filename):\\n\\n try:\\n self.ftp.delete(filename)\\n except:\\n print('Error deleting remote file:%s'%filename)\\n return 1\\n\\n return 0\",\n \"def _cleanup_uploads(self):\\n logger.debug(\\\"Performing blob upload cleanup\\\")\\n\\n while True:\\n # Find all blob uploads older than the threshold (typically a week) and delete them.\\n with UseThenDisconnect(app.config):\\n stale_upload = model.get_stale_blob_upload(DELETION_DATE_THRESHOLD)\\n if stale_upload is None:\\n logger.debug(\\\"No additional stale blob uploads found\\\")\\n return\\n\\n # Remove the stale upload from storage.\\n logger.debug(\\\"Removing stale blob upload %s\\\", stale_upload.uuid)\\n assert stale_upload.created <= (datetime.utcnow() - DELETION_DATE_THRESHOLD)\\n\\n try:\\n storage.cancel_chunked_upload(\\n [stale_upload.location_name], stale_upload.uuid, stale_upload.storage_metadata\\n )\\n except Exception as ex:\\n logger.debug(\\n \\\"Got error when trying to cancel chunked upload %s: %s\\\",\\n stale_upload.uuid,\\n ex.message,\\n )\\n\\n # Delete the stale upload's row.\\n with UseThenDisconnect(app.config):\\n model.delete_blob_upload(stale_upload)\\n\\n logger.debug(\\\"Removed stale blob upload %s\\\", stale_upload.uuid)\",\n \"def delete(request):\\n wfsxml = request.POST.get('wfsxml', False) # FOR GEOSERVER\\n uuid = request.POST.get('uuid', False)\\n # MAKE GEOSERVER WFS TRANSACTION\\n error = post_to_geoserver(wfsxml, GeoPostBase.wfsURL)\\n # ALL GOOD\\n if error:\\n return server_error(error)\\n # IF WFS TRANSACTION ERROR\\n else:\\n pass\\n # Delete photo from bucket\\n delete_from_bucket(uuid, GeoPostBase.imageBucket)\\n return HttpResponseRedirect(reverse('geopost_home'))\",\n \"async def delete_file(location_id: LocationID, file_id: StorageFileID, user_id: UserID):\",\n \"def _delete(self, url):\\n return self._request(url, method=\\\"DELETE\\\")\",\n \"def delete(self, filename, threadID):\\n self.lock.acquire()\\n removed_file = self.files_on_disk.pop(filename)\\n\\n # remove file from the directory\\n if not os.path.exists(\\\".storage/\\\" + filename):\\n self.lock.release()\\n return \\\"ERROR: NO SUCH FILE\\\\n\\\"\\n else:\\n os.remove(\\\".storage/\\\" + filename)\\n i = 0\\n j = 0\\n while(i<self.size and j<removed_file.num_blocks):\\n if(self.disk_mem[i]==removed_file.letter):\\n self.disk_mem[i] = \\\".\\\"\\n j+=1\\n i += 1\\n print(\\\"[thread %d] Deleted %s file '%c' (deallocated %d blocks)\\\" %\\n (threadID, removed_file.name, removed_file.letter, removed_file.num_blocks))\\n self.show(threadID)\\n self.lock.release()\\n return \\\"ACK\\\\n\\\"\",\n \"def delete(self, host, file):\",\n \"def _delete(self, url):\\n url = urljoin(self.base_url, url)\\n try:\\n r = self._make_request(**dict(\\n method='DELETE',\\n url=url,\\n auth=self.auth,\\n timeout=self.timeout,\\n hooks=self.request_hooks,\\n headers=self.request_headers\\n ))\\n except requests.exceptions.RequestException as e:\\n raise e\\n else:\\n if r.status_code >= 400:\\n _raise_response_error(r)\\n if r.status_code == 204:\\n return\\n return r.json()\",\n \"def do_delete_request(self, uri, headers, timeout_ms):\\n return self._do_request('DELETE', uri, headers, None, timeout_ms, None)\",\n \"def __deleteLock(self, url):\\n response = self._adapter.deleteRequest(url, self._baseHeader)\\n return response\",\n \"def delete(self, relative_url, accepted_status_codes):\\n return self._send_request(\\n relative_url, accepted_status_codes, self._session.delete\\n )\",\n \"def cancel_upload(self):\\r\\n self.bucket.cancel_multipart_upload(self.key_name, self.id)\",\n \"def terminate(self):\\n logger.debug(\\\"terminating\\\")\\n self._upload = None\\n self._buf.close()\",\n \"def delete(self, uri, body=None, headers=None, auth=False):\\n return self.send_request('DELETE', uri, body, headers, auth)\",\n \"def delete_file(self, path):\\n raise HTTPError(\\n 501,\\n \\\"Narrative deletion not implemented here. Deletion is handled elsewhere.\\\",\\n )\",\n \"def base_delete(url_path):\\n response = requests.delete(url=settings.URL_API + url_path)\\n return response\",\n \"def _delete(self, ref: str) -> None:\\n self._trace(\\\"deleting ref %s\\\" % ref)\\n head = self.read_symbolic_ref(\\\"HEAD\\\")\\n if head and ref == head[1]:\\n _write(\\\"error %s refusing to delete the current branch: %s\\\" % (ref, head))\\n return\\n try:\\n self._connection.files_delete(self._ref_path(ref))\\n except dropbox.exceptions.ApiError as e:\\n if not isinstance(e.error, dropbox.files.DeleteError):\\n raise\\n # someone else might have deleted it first, that's fine\\n self._refs.pop(ref, None) # discard\\n self._pushed.pop(ref, None) # discard\\n _write(\\\"ok %s\\\" % ref)\",\n \"def delete(self, uri, **kwargs):\\n return self.session.delete(uri, **kwargs)\",\n \"def test_delete(client: FlaskClient):\\n file = get_example_file(ExampleFileType.Txt)\\n response_upload = util.upload_file(client, DEFAULT_USER, file)\\n response_delete = util.delete_file(client, DEFAULT_USER, response_upload.json[\\\"id\\\"])\\n assert response_delete.status == \\\"204 NO CONTENT\\\"\\n response_download = util.download_file(\\n client, DEFAULT_USER, response_upload.json[\\\"id\\\"]\\n )\\n assert response_download.status == \\\"404 NOT FOUND\\\"\",\n \"def _delete_job(self, job):\",\n \"def delete(self, url):\\r\\n response = self.requestHelper.delete(url)\\r\\n return response.status_code == 200\",\n \"def delete(self, filename):\\n raise NotImplementedError\",\n \"def delete_file(_oid, attachmentId=None):\\n md = Metadata.objects.get_or_404(pk=_oid)\\n attachment = ''\\n test_upload_path_prefix = \\\"uploadedfiles\\\"\\n test_environment = False\\n\\n username = _authenticate_user_from_session(request)\\n\\n if username:\\n try:\\n try:\\n md = Metadata.objects.get(id=_oid)\\n \\n try:\\n # if developing locally we'll also want to remove file\\n url = filter(\\n lambda a: str(a.id) == attachmentId, md.attachments\\n ).pop().url\\n if str(os.environ['FLASKCONFIG']) == 'testing' or str(os.environ['FLASKCONFIG']) == 'development':\\n test_environment = True\\n os.remove(\\n os.path.join(\\n app.config['UPLOADS_DEFAULT_DEST'],\\n test_upload_path_prefix,\\n _oid,\\n os.path.basename(url)\\n )\\n )\\n else:\\n os.remove(\\n os.path.join(\\n app.config['UPLOADS_DEFAULT_DEST'],\\n _oid,\\n os.path.basename(url)\\n )\\n )\\n except Exception:\\n #Throw exception specific for test or non-test enviroment\\n if test_environment:\\n file_path = app.config['UPLOADS_DEFAULT_DEST'] + \\\"/\\\" + test_upload_path_prefix + \\\"/\\\" + _oid + \\\"/\\\" + os.path.basename(url)\\n else:\\n file_path = app.config['UPLOADS_DEFAULT_DEST'] + \\\"/\\\" + _oid + \\\"/\\\" + os.path.basename(url)\\n\\n print \\\"There was a problem deleting the file! Tried to reach path: \\\" + file_path \\n \\n # don't need to save after this since we're updating existing\\n Metadata.objects(id=_oid).update_one(\\n pull__attachments__id=attachmentId\\n )\\n \\n md = Metadata.objects.get(id=_oid)\\n \\n # we'll just go ahead and not care if it doesn't exist\\n except ValueError:\\n pass\\n\\n\\n except KeyError:\\n try:\\n keys = request.json.keys()\\n keys_str = ', '.join(keys)\\n except Exception as e:\\n print \\\"Error: \\\" + str(e)\\n return Response(\\\"Server error deleting file...\\\", status=500)\\n\\n return jsonify(\\n {\\n 'message':\\n 'Key(s) ' + keys_str + ' not recognized. ' +\\n 'Must contain \\\\'attachment\\\\''\\n },\\n status=400\\n )\\n\\n return jsonify(dict(message=attachment + ' successfully (at/de)tached!', record=md))\\n \\n else:\\n return Response('Bad or missing session id.', status=401)\",\n \"async def delete(self, delete: TPayload) -> None:\",\n \"def delete_file(file_id):\\n url = 'https://slack.com/api/files.delete'\\n data = urllib.parse.urlencode(\\n (\\n (\\\"token\\\", ACCESS_TOKEN),\\n (\\\"file\\\", file_id)\\n )\\n )\\n data = data.encode(\\\"ascii\\\")\\n headers = {\\\"Content-Type\\\": \\\"application/x-www-form-urlencoded\\\"}\\n request = urllib.request.Request(url, data, headers)\\n urllib.request.urlopen(request)\",\n \"def delete(self, url):\\n return self.session.delete(url=self.base_url + url)\",\n \"def delFile(self, file_name):\\n ret = self.rest.delete(self.ngx_server, self.getUrl() + '/' + file_name, {}, [200, 404])\\n if ret == False:\\n self.logger.error('delete file:%s from tfs failed.' % file_name)\\n raise Exception, 'delete file:%s from tfs failed.' % file_name\\n self.logger.info('Delete Tfs file: %s' % file_name)\\n return True\",\n \"def delete(url, data=None, **_):\\n # Checks input parameters\\n assert '/process/%s' % dummy_id in url in url\",\n \"def fusion_api_delete_storage_pool(self, uri=None, api=None, headers=None):\\n return self.pool.delete(uri=uri, api=api, headers=headers)\",\n \"def test_delete_sync(created_test_helper, setup_teardown_file_operations):\\n # get current test params\\n test_params = created_test_helper.get_test_data(TESTLOC)\\n\\n # Upload a PNG file < 1MB in size\\n upload_file_response = created_test_helper.upload(\\n file_name=test_params[\\\"test_file\\\"])\\n\\n # delete file which was uploaded\\n upload_file_id = upload_file_response['file']['id']\\n created_test_helper.delete([upload_file_id])\\n\\n # validate file uploaded was deleted\\n created_test_helper.validate_delete(upload_file_id, file_type=\\\"images\\\")\",\n \"def delete(self, url):\\n return self.request(url, \\\"DELETE\\\")\",\n \"def delete(self):\\n self.call('DELETE', expect=error.NO_CONTENT)\",\n \"def delete(task_id, pfn):\\n\\n activated = PoolManager.db.query('UPDATE `standalone_deletion_tasks` SET `status` = \\\\'active\\\\' WHERE `id` = %s', task_id)\\n if activated == 0:\\n # task was cancelled\\n return -1, None, None, '', '' \\n\\n return gfal_exec('unlink', (pfn,), deletion_nonerrors)\",\n \"def do_DELETE(self,):\\n self.http_method = 'DELETE'\\n self.response()\",\n \"def fusion_api_delete_resource(self, uri, api=None, headers=None):\\n if api:\\n headers = self.fusion_client._set_req_api_version(api=api)\\n elif not headers:\\n headers = self.fusion_client._headers\\n uri = 'https://%s%s' % (self.fusion_client._host, uri)\\n return self.fusion_client.delete(uri, headers)\",\n \"def fusion_api_delete_rack_manager(self, uri, name=None, param='', api=None, headers=None):\\n return self.rackmanager.delete(uri=uri, name=name, param=param, api=api, headers=headers)\",\n \"async def delete(self, request):\\n\\n userid = await authenticated_userid(request)\\n project = await request.app.context_project(request, userid)\\n\\n await request.post()\\n\\n camera_id = request.match_info['camera_file_id']\\n\\n log = request['slog']\\n log.debug('request: camera delete', camera_id=camera_id)\\n\\n camera = await Camera.get(request,\\n camera_id=camera_id,\\n project_id=project.project_id,\\n userid=userid)\\n if not camera:\\n raise web.HTTPNotFound(text=\\\"Camera file '%s' not found\\\" % camera_id)\\n\\n await request.app.task_broker.publish('camera_delete', {\\n 'userid': userid,\\n 'project_id': project.project_id,\\n 'camera_id': camera_id,\\n }, log=log)\\n\\n await camera.set_status(request, 'DELETING')\\n\\n return web.HTTPNoContent()\",\n \"def delete(self, url):\\n return self._request('DELETE', url)\",\n \"def access_gemini_url_delete_method(context, endpoint):\\n url = urljoin(context.gemini_api_url, endpoint)\\n context.response = requests.delete(url)\",\n \"def delete_call(url, headers=None):\\n response = requests.delete(url, headers=headers)\\n\\n if response.status_code == 401:\\n raise RedirectException(reverse('b2b_control:logout'))\\n\\n return response\",\n \"def finalize_pending(self, data):\\n if self.request.method.upper() == 'DELETE':\\n data.delete()\",\n \"def handle_delete(uuid):\\n location = os.path.join(app.config['UPLOAD_DIRECTORY'], uuid)\\n print(uuid)\\n print(location)\\n shutil.rmtree(location)\",\n \"def handle_delete(uuid):\\n location = os.path.join(app.config['UPLOAD_DIRECTORY'], uuid)\\n print(uuid)\\n print(location)\\n shutil.rmtree(location)\",\n \"async def abort_upload_file(\\n location_id: LocationID, file_id: StorageFileID, user_id: UserID\\n):\",\n \"def delete(self):\\n self.request().delete()\",\n \"def delete_oh_file(access_token, file_name):\\r\\n try:\\r\\n deletion_response = delete_file(access_token, file_basename=file_name)\\r\\n\\r\\n if deletion_response.status_code == 200:\\r\\n return True\\r\\n else:\\r\\n print(f'An error was encountered trying to delete file {file_name}')\\r\\n return False\\r\\n except:\\r\\n print(f'An error was encountered trying to delete file {file_name}')\\r\\n return False\",\n \"def delete(self, path):\\n \\n # prep\\n delete_url = self.url(path)\\n\\n # request\\n response = requests.delete(delete_url, auth=self.auth, headers=API.HEADERS)\\n\\n # test and return\\n self.raise_for_status(response)\",\n \"def delete_request_journal(self):\\n requests.delete(self.requests_url).raise_for_status()\",\n \"def flow_file_chunk_delete(sender, instance, **kwargs):\\n instance.file.delete(False)\",\n \"def delete_job(self, filename):\\n job = Jobs.get(Jobs.filename == filename)\\n job.delete_instance()\",\n \"def test_upload_file_product_then_delete_immediately(self):\\n with open('./client_assets/dog.jpg', 'rb') as file:\\n coll = json.dumps({\\n \\\"collectionName\\\": \\\"Apple %d\\\" % randint(0, 9),\\n \\\"collectionDesc\\\": \\\"Apple desc %s\\\" % (randint(0, 9))\\n })\\n response = self.client.post('http://localhost:8000/webapp/api/products/', {\\n 'title': 'test post', 'imageSource': file, 'collection': coll}, format='multipart')\\n print(response.data)\\n postID = response.data['id']\\n self.assertEqual(response.status_code, 201)\\n with open('./client_assets/index.jpg', 'rb') as file:\\n response = self.client.delete(\\n 'http://localhost:8000/webapp/api/products/%s/' % postID)\\n print(response.data)\\n self.assertEqual(response.status_code, 204)\",\n \"def _delete(self, remote_filename):\\n\\n file_id = self.get_file_id(remote_filename)\\n if file_id is None:\\n raise BackendException(\\n 'File \\\"%s\\\" cannot be deleted: it does not exist' % (\\n remote_filename))\\n response = self.http_client.put(self.metadata_url + 'trash/' + file_id)\\n response.raise_for_status()\\n del self.names_to_ids[remote_filename]\",\n \"def Delete(self, path):\\n\\n # try to request\\n try:\\n request = requests.delete(self.config[\\\"url\\\"] + \\\"/weaviate/v1\\\" + path)\\n except urllib.error.HTTPError as error:\\n return None\\n\\n return request.status_code\",\n \"def test_6d_delete_file(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.deleting_data_test_ready:\\n raise unittest.SkipTest(\\\"Skipped for failed to prepare deleting test.\\\")\\n self.dismiss_dialogs()\\n function = js_func[\\\"delete\\\"] % GST.gs_file_paths[\\\"file_to_delete_path\\\"]\\n try:\\n self.send_request(function, \\\"delete_data()\\\")\\n except Exception as e:\\n raise DeleteException(e.__str__()) \\n try:\\n response = self.get_response()\\n assert \\\"Success\\\" in response\\n self.refresh_page()\\n except AssertionError:\\n raise DeleteException(response)\",\n \"def test_finalized_no_upload_ticket(self):\\n namespace = 'default-gzip'\\n request = self.store_request(namespace, pad_string('silence'))\\n request.upload_ticket = None\\n with self.call_should_fail('400'):\\n self.call_api('finalize_gs_upload', self.message_to_dict(request), 200)\",\n \"def http_delete_and_check_success(url, check_util=None, verify=True, auth=None):\\n with requests.Session() as session:\\n delete_resp = session.delete(\\n url,\\n verify=verify,\\n stream=False,\\n timeout=2.0,\\n auth=auth)\\n delete_resp.raise_for_status()\\n if delete_resp.status_code != HTTPStatus.NO_CONTENT and delete_resp.text:\\n delete_resp_json = delete_resp.json()\\n else:\\n delete_resp_json = None\\n\\n success, message, return_code = success_message_code(delete_resp, check_util)\\n\\n rest_return_obj = RestReturn(success=success, message=message, http_status=return_code,\\n json_body=delete_resp_json,\\n response_object=delete_resp)\\n return rest_return_obj\",\n \"def delete(\\n self, uri, resource=None, logon_required=True, renew_session=True):\\n try:\\n self._urihandler.delete(self._hmc, uri, logon_required)\\n except HTTPError as exc:\\n new_exc = zhmcclient.HTTPError(exc.response())\\n new_exc.__cause__ = None\\n raise new_exc # zhmcclient.HTTPError\\n except ConnectionError as exc:\\n new_exc = zhmcclient.ConnectionError(exc.message, None)\\n new_exc.__cause__ = None\\n raise new_exc # zhmcclient.ConnectionError\",\n \"def delete_file(self, name, container):\\r\\n try:\\r\\n path = os.path.join(self.upload_folder, container, name)\\r\\n os.remove(path)\\r\\n return True\\r\\n except:\\r\\n return False\",\n \"def file_delete(self, path):\\n params = {'root': self.session.root, 'path': format_path(path)}\\n\\n url, params, headers = self.request(\\\"/fileops/delete\\\", params)\\n\\n return self.rest_client.POST(url, params, headers)\",\n \"def delete(self, path, request):\\n response = 'FileInterfaceAdapter: DELETE on path {}'.format(path)\\n status_code = 200\\n\\n logging.debug(response)\\n\\n return ApiAdapterResponse(response, status_code=status_code)\",\n \"def delete(self, src):\\n\\n if self.noop:\\n logger.info(\\\"No-Op Delete: %s.tar\\\" % self.bucket + src)\\n else:\\n logger.info(\\\"Trying to delete %s.tar\\\" % self.bucket + src)\\n self.client.delete(self.bucket + src + \\\".tar\\\")\",\n \"def delete(cls, webhook_endpoint_id):\\n return Requester.delete(cls.endpoint + '/' + webhook_endpoint_id)\",\n \"def _queue_delete(self, queue):\\n\\n queue.delete()\",\n \"def delete(self):\\n\\t\\t#self.log.info(\\\"Deleting file {}\\\".format(self._filepath))\\n\\t\\tos.remove(self._filepath)\",\n \"def __delete(self, url, headers=None):\\n return self.__req(url, \\\"DELETE\\\", headers=headers)\",\n \"def delete(self):\\n if not pdbox._args.get(\\\"dryrun\\\"):\\n result = execute(pdbox.dbx.files_delete_v2, self.path)\\n pdbox.debug(\\\"Metadata response: %s\\\" % result.metadata)\\n pdbox.info(\\\"Deleted %s\\\" % self.uri)\",\n \"def photo_edit_file_cleanup(sender, **kwargs):\\n instance = kwargs.get('instance')\\n filename = instance.upload.url[1:]\\n if os.path.exists(filename):\\n os.remove(filename)\",\n \"def delete(self):\\n with self.locked():\\n self.path.delete()\",\n \"def delete(self):\\n with self.locked():\\n self.path.delete()\",\n \"def handle_delf_conf(self, job):\\n print(\\\"User deleted a file.\\\")\\n # Delete the file from all servers\\n self.put_job_in_all_queues(job)\\n\\n list_job_results = self.get_internal_results_from_all_servers()\\n\\n if len(list_job_results) == 0:\\n # We got no responses back, there are probably no servers active\\n self.put_external_result(self.generate_failure_job(\\\"Unsuccessful, no servers responded\\\"))\\n return\\n\\n # Check all the servers had success\\n for result in list_job_results:\\n if result.result[\\\"outcome\\\"] != \\\"success\\\":\\n self.put_external_result(\\n self.generate_failure_job(\\\"Unsuccessful, one of the servers did not have success\\\"))\\n return\\n print(\\\"All servers deleted file '{}'.\\\".format(job.data[\\\"file_name\\\"]))\\n\\n # Tell the client we successfully uploaded to all servers\\n response_result = copy.deepcopy(list_job_results[0])\\n response_result.processed_by = None\\n self.put_external_result(response_result)\",\n \"def fusion_api_delete_repository(self, uri, api=None, headers=None):\\n return self.repository.delete(uri=uri, api=api, headers=headers)\",\n \"def delete_upload(sender, **kwargs):\\n instance = kwargs['instance']\\n path_to_delete = '%s/%s.%s' % (instance.path,instance.uuid,instance.ext)\\n if not os.path.isdir(path_to_delete):\\n os.unlink(path_to_delete)\",\n \"def delete(self):\\r\\n request = http.Request('DELETE', self.get_url())\\r\\n\\r\\n return request, parsers.parse_empty\",\n \"def delete(path: str):\\n token = get_token()\\n headers = {\\n \\\"Authorization\\\": f\\\"Bearer {token}\\\"\\n }\\n return requests.delete(get_base_url() + path, headers=headers)\",\n \"def _delete(filename):\\n return os.remove(filename)\",\n \"def finish(cls, upload, location=None, bytes_downloaded=0):\\n path = \\\"uploader/finish/%s\\\" % upload[\\\"id\\\"]\\n LOGGER.debug(path)\\n payload = {\\\"bytes_transferred\\\": bytes_downloaded, \\\"location\\\": location}\\n try:\\n return Backend.put(path, payload, headers=Backend.headers())\\n except requests.HTTPError as err:\\n if err.response.status_code == 410:\\n LOGGER.warning(\\\"Cannot finish file %s. File not active (410)\\\",\\n upload[\\\"id\\\"])\\n raise err\\n except:\\n raise\",\n \"def delete_file(sender, instance, *args, **kwargs):\\n if instance.photo:\\n _delete_file(instance.photo.path)\",\n \"def delete(self):\\n\\n try:\\n remove(self.file)\\n except OSError:\\n pass\",\n \"def ftp_DELE(self, path):\\r\\n try:\\r\\n print 'try to delete file:'+path\\r\\n self.run_as_current_user(self.fs.remove, path)\\r\\n except (OSError, FilesystemError) as err:\\r\\n why = _strerror(err)\\r\\n self.respond('550 %s.' % why)\\r\\n else:\\r\\n self.respond(\\\"250 File removed.\\\")\\r\\n return path\",\n \"def delete(self, call, params={}): \\n # Build an endpoint using the parameters...\\n endpoint = self._calls[call](params)\\n url = '{}/{}'.format(str(self), str(endpoint))\\n return self.deleter.respond(url)\",\n \"def post(self):\\n args = request.args or request.json\\n file_id = args.get('file_id')\\n services.file.remove_star(file_id)\\n return {\\n \\\"status\\\": True\\n }\",\n \"def delete_file(self, name):\\n del self.files[name]\",\n \"def DELETE(url, mime_type='text/html'):\\n def_app = DefaultHttpRequestHandler()\\n return def_app.RequestMap(url, methods=['DELETE'], produces=mime_type)\",\n \"def _delete(self, path):\\n return self._api.delete_status(path, headers={\\\"Hawkular-Tenant\\\": self.tenant_id})\",\n \"def voters_upload_cancel(request, election):\\n voter_file_id = request.session.get('voter_file_id', None)\\n if voter_file_id:\\n vf = VoterFile.objects.get(id = voter_file_id)\\n vf.delete()\\n del request.session['voter_file_id']\\n\\n return HttpResponseRedirect(settings.SECURE_URL_HOST + reverse(url_names.election.ELECTION_VIEW, args=[election.uuid]))\",\n \"def delete(api, url, payload=None, headers=None, auth=_KERBEROS_AUTH,\\n proxies=None, retries=_NUM_OF_RETRIES, timeout=None):\\n return call(api, url, 'delete', payload=payload,\\n headers=headers, auth=auth, proxies=proxies, retries=retries,\\n timeout=timeout)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.67701095","0.64577615","0.6345733","0.61198354","0.6112959","0.6029433","0.59412366","0.5909342","0.5896206","0.5832445","0.5818534","0.57876617","0.5784361","0.5783744","0.5763343","0.57614774","0.5744645","0.57401735","0.5735063","0.57311624","0.572804","0.57015604","0.56957394","0.5661615","0.564834","0.5642531","0.5635026","0.5588969","0.55822235","0.5565004","0.5555379","0.5546792","0.5544384","0.5531706","0.5527474","0.5520602","0.55102485","0.549634","0.54954517","0.5494184","0.5492981","0.54786354","0.5474575","0.5468911","0.546835","0.54674494","0.54657185","0.5461961","0.5461633","0.5451827","0.5442325","0.5439386","0.5407643","0.5405422","0.54029965","0.5394947","0.5394947","0.53894126","0.5384022","0.5367839","0.5366254","0.53641766","0.53613764","0.5358319","0.5355442","0.5337484","0.53373206","0.53310853","0.533106","0.5318785","0.53174144","0.5315138","0.53080404","0.53030765","0.5302771","0.5297552","0.5295347","0.529283","0.5287903","0.5273018","0.52716607","0.5271304","0.5271304","0.52703106","0.52686393","0.5264542","0.5261766","0.52601457","0.5259418","0.5247031","0.5246951","0.52397877","0.5228792","0.522721","0.52267295","0.5226576","0.52240336","0.52220255","0.5219596","0.5214963"],"string":"[\n \"0.67701095\",\n \"0.64577615\",\n \"0.6345733\",\n \"0.61198354\",\n \"0.6112959\",\n \"0.6029433\",\n \"0.59412366\",\n \"0.5909342\",\n \"0.5896206\",\n \"0.5832445\",\n \"0.5818534\",\n \"0.57876617\",\n \"0.5784361\",\n \"0.5783744\",\n \"0.5763343\",\n \"0.57614774\",\n \"0.5744645\",\n \"0.57401735\",\n \"0.5735063\",\n \"0.57311624\",\n \"0.572804\",\n \"0.57015604\",\n \"0.56957394\",\n \"0.5661615\",\n \"0.564834\",\n \"0.5642531\",\n \"0.5635026\",\n \"0.5588969\",\n \"0.55822235\",\n \"0.5565004\",\n \"0.5555379\",\n \"0.5546792\",\n \"0.5544384\",\n \"0.5531706\",\n \"0.5527474\",\n \"0.5520602\",\n \"0.55102485\",\n \"0.549634\",\n \"0.54954517\",\n \"0.5494184\",\n \"0.5492981\",\n \"0.54786354\",\n \"0.5474575\",\n \"0.5468911\",\n \"0.546835\",\n \"0.54674494\",\n \"0.54657185\",\n \"0.5461961\",\n \"0.5461633\",\n \"0.5451827\",\n \"0.5442325\",\n \"0.5439386\",\n \"0.5407643\",\n \"0.5405422\",\n \"0.54029965\",\n \"0.5394947\",\n \"0.5394947\",\n \"0.53894126\",\n \"0.5384022\",\n \"0.5367839\",\n \"0.5366254\",\n \"0.53641766\",\n \"0.53613764\",\n \"0.5358319\",\n \"0.5355442\",\n \"0.5337484\",\n \"0.53373206\",\n \"0.53310853\",\n \"0.533106\",\n \"0.5318785\",\n \"0.53174144\",\n \"0.5315138\",\n \"0.53080404\",\n \"0.53030765\",\n \"0.5302771\",\n \"0.5297552\",\n \"0.5295347\",\n \"0.529283\",\n \"0.5287903\",\n \"0.5273018\",\n \"0.52716607\",\n \"0.5271304\",\n \"0.5271304\",\n \"0.52703106\",\n \"0.52686393\",\n \"0.5264542\",\n \"0.5261766\",\n \"0.52601457\",\n \"0.5259418\",\n \"0.5247031\",\n \"0.5246951\",\n \"0.52397877\",\n \"0.5228792\",\n \"0.522721\",\n \"0.52267295\",\n \"0.5226576\",\n \"0.52240336\",\n \"0.52220255\",\n \"0.5219596\",\n \"0.5214963\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7901534"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":22475,"cells":{"query":{"kind":"string","value":"r\"\"\"Helper method for yielding various names + members of modules."},"document":{"kind":"string","value":"def get_named_members(model, get_members_fn, prefix='', recurse=True):\n memo = set()\n modules = model.named_modules(prefix=prefix) if recurse else [(prefix, model)]\n for i, (module_prefix, module) in enumerate(modules):\n members = get_members_fn(module)\n for k, v in members:\n if v is None or v in memo:\n print(\"WARNING: reused module parameter\")\n continue\n memo.add(v)\n name = module_prefix + ('.' if module_prefix else '') + k\n yield name, v, i"},"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 _named_members(self, get_members_fn, prefix='', recurse=True):\n memo = set()\n modules = self.named_modules(prefix=prefix) if recurse else [(prefix, self)]\n for module_prefix, module in modules:\n members = get_members_fn(module)\n for k, v in members:\n if v is None or v in memo:\n continue\n memo.add(v)\n name = module_prefix + ('.' if module_prefix else '') + k\n # translate name to ori_name\n if name in self.node_name_map:\n name = self.node_name_map[name]\n yield name, v","def __iter__(self) -> Generator[str, None, None]:\n\n yield from self.__dict__[\"members\"]","def modules(self):\n for desc in self._mappings.values():\n if hasattr(desc, 'module'):\n yield desc.module\n else:\n continue","def package_module_iter( *packages ):\n def module_iter( package, module_iter ):\n if DEBUG: print( \"Package {0}\".format(package) )\n for filename in module_iter:\n basename= os.path.split( filename )[-1]\n module, ext = os.path.splitext(basename)\n if module.startswith(\"__\") and module.endswith(\"__\") and ext == \".py\":\n continue\n if DEBUG: print( \" file {0} module {1}\".format(basename, module) )\n yield basename, module\n\n for package in packages:\n yield package, module_iter(package, glob.glob(os.path.join(package,\"*.py\")))","def modules():","def deep_iter_modules(name):\r\n mod = import_dotted_name(name)\r\n yield name\r\n if not hasattr(mod, '__path__'):\r\n return\r\n for _, name, _ in iter_modules(mod.__path__, name + '.'):\r\n for name in deep_iter_modules(name):\r\n yield name","def get_members():","def walk_modules(module, name=\"\", path=()):\n if not name:\n name = module.__class__.__name__\n named_children = list(module.named_children())\n path = path + (name,)\n yield Trace(path, len(named_children) == 0, module)\n # recursively walk into all submodules\n for name, child_module in named_children:\n yield from walk_modules(child_module, name=name, path=path)","def module_members_by_type(module, type_checker, skip_underscored=True):\n\n for name, member in getmembers(module, type_checker):\n if skip_underscored and name.startswith(UNDERSCORE):\n continue\n yield name, member","def __modules_recurse(self, mod=None):\n if mod is None:\n mod = self\n for module in mod.children():\n if isinstance(module, (nn.ModuleList, nn.Sequential, self.custom_layers)):\n yield from self.__modules_recurse(module)\n else:\n yield module","def do_list_modules(self, arg):\n for module in self.reader.module_list.modules:\n if arg:\n name = GetModuleName(self.reader, module).lower()\n if name.find(arg.lower()) >= 0:\n PrintModuleDetails(self.reader, module)\n else:\n PrintModuleDetails(self.reader, module)\n print()","def deep_get_members(name, predicate=None, private=False):\r\n seen = set()\r\n for name in deep_iter_modules(name):\r\n mod = import_dotted_name(name)\r\n name = name.rsplit('.', 1)[-1]\r\n if not private and name.startswith('_'):\r\n continue\r\n for name, value in getmembers(mod, predicate):\r\n if id(value) in seen or not private and name.startswith('_'):\r\n continue\r\n yield value\r\n seen.add(id(value))","def Modules():\n mod = ida_idd.modinfo_t()\n result = ida_dbg.get_first_module(mod)\n while result:\n # Note: can't simply return `mod` here, since callers might\n # collect all modules in a list, and they would all re-use\n # the underlying C++ object.\n yield ida_idaapi.object_t(name=mod.name, size=mod.size, base=mod.base, rebase_to=mod.rebase_to)\n result = ida_dbg.get_next_module(mod)","def all_registered_modules():\n yield from iterchain(modules.values() for modules in Registry.monomers.values())","def visit_Module(self, node):\n for item in node.body:\n self.visit(item)","def visit_Module(self, node):\n for item in node.body:\n self.visit(item)","def morepath_modules(cls: type[morepath.App]) -> 'Iterator[str]':\n for base in cls.__mro__:\n if not issubclass(base, morepath.App):\n continue\n\n if base is morepath.App:\n continue\n\n module = '.'.join(base.__module__.split('.')[:2])\n\n if module.startswith('test'):\n continue\n\n yield module","def list_modules():\n for module_name in listdir(modules_directory):\n if isdir(join(modules_directory, module_name)):\n log.debug('Load module: {0}'.format(module_name))\n yield module_name","def imports():\n for name, val in globals().items():\n if isinstance(val, getattr(types, \"ModuleType\")):\n yield val.__name__","def __iter__(self):\n idx = ffi.new('uint32_t *')\n mod = lib.ly_ctx_get_module_iter(self._ctx, idx)\n while mod:\n yield Module(self, mod)\n mod = lib.ly_ctx_get_module_iter(self._ctx, idx)","def __call__(self):\n for name in self:\n try:\n yield getattr(self, name)\n except AttributeError:\n raise KeyError(name)","def pairs(self) -> Iterator[tuple[str, list[CommandParser]]]:\n for module, cmds in self._registry[\"by_module\"].items():\n yield (module, cmds)","def iter_structures(self):\n idx_start = 0\n start_name = self.name[idx_start]\n for idx_current, name in enumerate(self.name):\n if start_name != name:\n yield start_name, slice(idx_start, idx_current), self[idx_start:idx_current]\n idx_start = idx_current\n start_name = self.name[idx_start]\n yield start_name, slice(idx_start, None), self[idx_start:]","def iter_spider_classes(module):\n ...","def FileIter(func_name):\n \n if func_name == 'convert_pmids_to_pmcs':\n sdir = partial(os.path.join,'Data', 'SearchResults')\n pmc_file = os.path.join('Data', 'PMC-ids.csv')\n files = [x for x in os.listdir(sdir('')) if x.endswith('.res')]\n for f in files:\n yield (sdir(f), pmc_file), sdir(f+'.conv')\n\n elif func_name == 'search_pubmed':\n sdir = partial(os.path.join,'Data', 'SearchResults')\n queryfile = os.path.join('Data', 'QueryList.txt')\n with open(queryfile) as handle:\n for row in csv.DictReader(handle):\n fname = '%s--%s.res' % (GeneralUtils.slugify(row['org']), \n GeneralUtils.slugify(row['search']))\n ofile = sdir(fname)\n yield queryfile, ofile, row['search']\n\n elif func_name == 'download_pmids':\n \n sdir = partial(os.path.join,'Data', 'SearchResults')\n odir = os.path.join('Data', 'RawXML')\n files = [x for x in os.listdir(sdir('')) if x.endswith('.conv')]\n \n for f in files:\n yield sdir(f), sdir(f+'.dl'), odir\n\n elif func_name == 'extract_text':\n \n sdir = partial(os.path.join, 'Data', 'RawXML')\n odir = partial(os.path.join, 'Data', 'SentenceFiles')\n\n files = sorted([x for x in os.listdir(sdir('')) if x.endswith('.xml')])\n for f in files:\n name = f.split('.')[0]\n if f.startswith('PMC'):\n typ = 'pmc'\n else:\n typ = 'pubmed'\n\n yield sdir(f), odir(name+'.sent'), typ\n\n elif func_name == 'get_mutations':\n \n sdir = partial(os.path.join, 'Data', 'SentenceFiles')\n odir = partial(os.path.join, 'Data', 'MutFiles')\n finder = None#mutfinder_gen('regex.txt')\n\n files = sorted([x for x in os.listdir(sdir('')) if x.endswith('.sent')])\n\n for f in files:\n name = f.split('.')[0]\n yield sdir(f), odir(name + '.mut')\n \n elif func_name == 'process_mut_file':\n \n sdir = partial(os.path.join, 'Data', 'MutFiles')\n odir = partial(os.path.join, 'Data', 'ProteinFiles')\n\n files = sorted([x for x in os.listdir(sdir('')) if x.endswith('.mut')])\n\n for f in files:\n name = f.split('.')[0]\n yield sdir(f), (odir(name + '.prot'), odir(name + '.sen'))\n elif func_name == 'mapping_files':\n path = 'Data/Mapping/'\n items = (('ftp://ftp.uniprot.org/pub/databases/uniprot/current_release/knowledgebase/idmapping/idmapping.dat.gz', 'idmapping.dat.sort'),\n ('ftp://ftp.ncbi.nlm.nih.gov/gene/DATA/gene_info.gz', 'gene_info'),\n ('ftp://ftp.ncbi.nlm.nih.gov/pub/pmc/PMC-ids.csv.gz', 'PMC-ids.csv'),\n ('ftp://nlmpubs.nlm.nih.gov/online/mesh/.asciimesh/d2011.bin', 'd2011.bin'))\n for url, ofile in items:\n yield None, os.path.join(path, ofile), url, path","def process_module_list(self, modules):","def getMembers():","def getMembers():","def getMembers():","def getMembers():","def __iter__(self):\n for name, field in self.iterate_over_fields():\n yield name, field","def items(self):\n for name in self.fields:\n yield name, getattr(self, name)","def get_modules(name_only=False):\n\n mods = list()\n\n dtf_db = sqlite3.connect(DTF_DB)\n cur = dtf_db.cursor()\n\n # This just returns the name\n if name_only:\n\n sql = ('SELECT name '\n 'FROM modules ')\n\n for mod in cur.execute(sql):\n mods.append(mod[0])\n\n # This returns a list of items\n else:\n\n sql = ('SELECT name, version, '\n 'about, author '\n 'FROM modules '\n 'ORDER BY name')\n\n cur.execute(sql)\n\n while True:\n\n item = dtf.core.item.Item()\n line = cur.fetchone()\n if line is None:\n break\n\n item.type = dtf.core.item.TYPE_MODULE\n item.name = line[0]\n item.version = line[1]\n item.about = line[2]\n item.author = line[3]\n\n mods.append(item)\n\n return mods","def test_get_members(self):\n pass","def LookupExports(self, names):\n cache = {}\n cache[()] = self\n for name in names:\n if name == '.':\n name = ''\n parts = name.split('.')\n parts, param = tuple(parts[:-1]), parts[-1]\n o = self\n for i in xrange(len(parts), -1, -1):\n before, after = parts[:i], parts[i:]\n o = cache.get(before, None)\n if o is not None:\n break\n assert o is not None\n for i in after:\n before = tuple(list(before) + [i])\n try:\n cache[before] = o = o.Sub(i)\n except KeyError as e:\n # Fill in the full path to the missing element, rather than just\n # its basename (which is often something unhelpful like '1').\n e.args = tuple(['.'.join(tuple([o.basename]) + tuple(e.args))])\n raise\n yield o, param","def iter_submodules(self, *args: Any, **kwargs: Any) -> Iterator[Submodule]:\n return RootModule(self).traverse(*args, **kwargs)","def walk(self):\n for name, child in sorted(self._children.items()):\n if isinstance(child, PackageEntry):\n yield name, child\n else:\n yield from child._walk(f'{name}/')","def _iter_all_modules(\n package: Union[str, types.ModuleType],\n prefix: str = \"\",\n) -> Iterator[str]:\n import os\n import pkgutil\n\n if isinstance(package, str):\n path = package\n else:\n # Type ignored because typeshed doesn't define ModuleType.__path__\n # (only defined on packages).\n package_path = package.__path__ # type: ignore[attr-defined]\n path, prefix = package_path[0], package.__name__ + \".\"\n for _, name, is_package in pkgutil.iter_modules([path]):\n if is_package:\n for m in _iter_all_modules(os.path.join(path, name), prefix=name + \".\"):\n yield prefix + m\n else:\n yield prefix + name","def create_modules(*names):\n source = 'attr = {0!r}'\n created_paths = []\n mapping = {}\n state_manager = None\n uncache_manager = None\n try:\n temp_dir = tempfile.mkdtemp()\n mapping['.root'] = temp_dir\n import_names = set()\n for name in names:\n if not name.endswith('__init__'):\n import_name = name\n else:\n import_name = name[:-len('.__init__')]\n import_names.add(import_name)\n if import_name in sys.modules:\n del sys.modules[import_name]\n name_parts = name.split('.')\n file_path = temp_dir\n for directory in name_parts[:-1]:\n file_path = os.path.join(file_path, directory)\n if not os.path.exists(file_path):\n os.mkdir(file_path)\n created_paths.append(file_path)\n file_path = os.path.join(file_path, name_parts[-1] + '.py')\n with open(file_path, 'w') as file:\n file.write(source.format(name))\n created_paths.append(file_path)\n mapping[name] = file_path\n uncache_manager = util.uncache(*import_names)\n uncache_manager.__enter__()\n state_manager = util.import_state(path=[temp_dir])\n state_manager.__enter__()\n yield mapping\n finally:\n if state_manager is not None:\n state_manager.__exit__(None, None, None)\n if uncache_manager is not None:\n uncache_manager.__exit__(None, None, None)\n support.rmtree(temp_dir)","def __iter__(self):\n for metatag in self.meta.findall(CN('meta:user-defined')):\n yield (metatag.get(CN('meta:name')), metatag.text)","def iterate(cls):\n for name, value in vars(cls).iteritems():\n if name.startswith('__'):\n continue\n yield (name, value)","def LookupAndFixupExports(self, names):\n for o, param in self.LookupExports(names):\n yield o, self._FixExportName(o.obj, param)","def MODULES(self):\n pass","def __iter__(self):\n try:\n groupNames = sorted(self.groups)\n except Exception: # pragma: no cover\n groupNames = self.groups.keys()\n\n for groupName in groupNames:\n yield groupName, self[groupName]","def children_of(self, member_name):\n for relationship in self.relationships[member_name]:\n yield self.members[relationship.child]","def members(self) -> Generator[discord.Member, None, None]:\n for guild in self.guilds:\n for member in guild.members:\n yield member","def __iter__(self):\n\n for help in self.help:\n yield (help[0],) + help[2:]","def _items():\n for writer in writers:\n name = pm.get_manifest(writer.command).display_name\n title = (\n f\"{name} {writer.display_name}\"\n if writer.display_name\n else name\n )\n yield title, writer.filename_extensions","def __iter__(self):\n base = os.path.dirname(__file__)\n f = cached_open(os.path.join(base, self.FILENAME))\n #f = codecs.open(self.FILENAME, 'r', 'utf-8')\n for line in f.split('\\n'):\n go = True\n for pat in self.patterns:\n if not re.findall(pat, line):\n go = False\n break\n if go:\n yield Name(line = line.replace('\\n', ''), gender=self.GENDER)","def modules_in_current_dir(path, module_name):\n yield from modules_from_path(Path(path).parent, module_name)","def parse_modules(self) -> None:\n mods: Dict[str, str] = {}\n matches = self.find_dir(\"LoadModule\")\n iterator = iter(matches)\n # Make sure prev_size != cur_size for do: while: iteration\n prev_size = -1\n\n while len(mods) != prev_size:\n prev_size = len(mods)\n\n for match_name, match_filename in zip(\n iterator, iterator):\n mod_name = self.get_arg(match_name)\n mod_filename = self.get_arg(match_filename)\n if mod_name and mod_filename:\n mods[mod_name] = mod_filename\n mods[os.path.basename(mod_filename)[:-2] + \"c\"] = mod_filename\n else:\n logger.debug(\"Could not read LoadModule directive from Augeas path: %s\",\n match_name[6:])\n self.modules.update(mods)","def __iter__(self):\n for name, _ in getmembers(self.__class__,\n lambda value: isinstance(value, InstrumentedAttribute)):\n yield name","def __next__(self):\n for child in self.children:\n yield child","def _iter_module_files():\n # The list call is necessary on Python 3 in case the module\n # dictionary modifies during iteration.\n for module in list(sys.modules.values()):\n if module is None:\n continue\n filename = getattr(module, \"__file__\", None)\n if filename:\n old = None\n while not os.path.isfile(filename):\n old = filename\n filename = os.path.dirname(filename)\n if filename == old:\n break\n else:\n if filename[-4:] in (\".pyc\", \".pyo\"):\n filename = filename[:-1]\n yield filename","def lib(self):\n for name in dir(self):\n if not name.startswith('__') and type(getattr(self, name)) == tuple:\n yield name, getattr(self, name)[0]","def get_modules(site_path):\n for dirpath, dirnames, filenames in os.walk(site_path):\n module = dirpath[len(site_path) + 1:].split(os.path.sep)\n if module[0].endswith('.egg-info'):\n continue\n if module[0].endswith('.egg') or not module[0]:\n module.pop(0)\n\n for filename in filenames:\n root, ext = os.path.splitext(filename)\n if ext == '.py':\n if not re.match(VALID_PYTHON_FILENAME, root):\n continue\n if root == '__init__':\n yield '.'.join(module)\n else:\n yield '.'.join(module + [root])\n elif ext == '.egg-link':\n with open(os.path.join(dirpath, filename)) as f:\n egglink_path = f.readline().strip()\n linked_module_path = os.path.join(egglink_path, root.replace('-', '_'))\n for modname in get_modules_to_import_from_directory(linked_module_path):\n yield modname\n elif ext == '.egg':\n yield filename.split('-', 1)[0]","def __iter__(self):\n yield self._extent.members()\n yield self._intent.members()","def ListExportsEx(self, name=None, recursive=False):\n if name:\n topobj = self.GetExport(name)\n if (not hasattr(topobj, 'Export') and\n not hasattr(topobj, 'iteritems')):\n # a leaf parameter; it has no sub-exports.\n return\n top = self._Sub(name, topobj)\n else:\n top = self\n if hasattr(top.obj, 'Export'):\n # pylint:disable=protected-access\n it = top._ListExports()\n elif hasattr(top.obj, 'iteritems'):\n # pylint:disable=protected-access\n it = top._ListExportsFromDict()\n else:\n it = [(name, self, None)]\n for fullname, h, subname in it:\n yield fullname, h, subname\n if recursive and fullname.endswith('.'):\n x_it = h.ListExportsEx(subname, recursive=recursive)\n for x_fullname, x_h, x_subname in x_it:\n yield fullname + x_fullname, x_h, x_subname","def enumerate():\n for p in __all__:\n yield (p, scanners[p])","def modules(cls):\n members = inspect.getmembers(cls, lambda a: not (inspect.isroutine(a) and a.__name__ == 'modules'))\n modules = [module for name, module in members if not name.startswith('_')]\n return modules","def __iter__(self):\n return (base.__name__ for base in self.bases)","def process(self, name_and_sequence):\n name, sequence = name_and_sequence\n yield preprocess_utils.get_pacbio_molecule_name(name), sequence","def test_python_name_generator():\n assert list(itertools.islice(get_python_name_generator(\"Some name\"), 3)) == [\n \"test_some_name\",\n \"test_some_name_1\",\n \"test_some_name_2\",\n ]","def generateElementsNamed(list, name):\n for n in list:\n if IElement.providedBy(n) and n.name == name:\n yield n","def names():\n pass","def parse_mod(self, modfile):\n with open(modfile, 'rt') as filehandle:\n for line in filehandle:\n if line.startswith((\"+\", \"-\")):\n enable, name, version, path = self.parse_mod_entry(line)\n yield ModTuple(enable == \"+\", name, version, path, modfile)","def do_modules(self, args):\n logger.debug(\"do_modules() was called\")\n \n parser = CrispyArgumentParser(description=self.do_modules.__doc__, prog=\"modules\")\n \n try:\n pargs = parser.parse_args(shlex.split(args))\n if pargs:\n print \"\\nAvailable modules:\\n===================\"\n for mod in self.srv.get_module_list():\n print \"{:<12} -- {}\".format(mod.__module__, mod.__doc__)\n fprint.success(\"Done.\")\n except MyParserException as e:\n print e","def get_named_parameters(self):\n for name, _ in self.module_to_quantize.named_parameters():\n yield name, getattr(self, name)","def _get_documenter_direct_members(\n documenter: sphinx.ext.autodoc.Documenter\n) -> Iterator[_MemberDocumenterEntry]:\n members_check_module, members = documenter.get_object_members(want_all=True)\n del members_check_module\n if members:\n try:\n # get_object_members does not preserve definition order, but __dict__ does\n # in Python 3.6 and later.\n member_dict = sphinx.util.inspect.safe_getattr(documenter.object,\n '__dict__')\n member_order = {k: i for i, k in enumerate(member_dict.keys())}\n members.sort(key=lambda entry: member_order.get(entry[0], float('inf')))\n except AttributeError:\n pass\n filtered_members = [\n x for x in documenter.filter_members(members, want_all=True)\n if _include_member(*x)\n ]\n for member_name, member_value, is_attr in filtered_members:\n member_documenter = _get_member_documenter(parent=documenter,\n member_name=member_name,\n member_value=member_value,\n is_attr=is_attr)\n if member_documenter is None:\n continue\n full_name = f'{documenter.fullname}.{member_name}'\n entry = _MemberDocumenterEntry(\n cast(sphinx.ext.autodoc.Documenter, member_documenter),\n is_attr,\n member_name,\n full_name=full_name,\n import_name=full_name,\n )\n for transformed_entry in _transform_member(documenter, entry):\n yield from _get_member_overloads(transformed_entry)","def module_info():\n pass","def walk(self):\n for project in [self.get_project(name)\n for name in self.project_names]:\n for sample in [project.get_sample(idx)\n for idx in project.sample_ids]:\n yield (project,sample)","def _members_of(self, full_path, include, exclude, should_include_private):\n def rst_for(doclet):\n renderer = (AutoFunctionRenderer if doclet.get('kind') in ['function', 'typedef']\n else AutoAttributeRenderer)\n # Pass a dummy arg list with no formal param list so\n # _formal_params() won't find an explicit param list in there and\n # override what it finds in the code:\n return renderer(self._directive, self._app, arguments=['dummy']).rst(\n [doclet['name']],\n 'dummy_full_path',\n doclet,\n use_short_name=False)\n\n def doclets_to_include(include):\n \"\"\"Return the doclets that should be included (before excludes and\n access specifiers are taken into account).\n\n This will either be the doclets explicitly listed after the\n ``:members:`` option, in that order; all doclets that are\n members of the class; or listed members with remaining ones\n inserted at the placeholder \"*\".\n\n \"\"\"\n doclets = self._app._sphinxjs_doclets_by_class[tuple(full_path)]\n if not include:\n # Specifying none means listing all.\n return sorted(doclets, key=lambda d: d['name'])\n included_set = set(include)\n\n # If the special name * is included in the list, include\n # all other doclets, in sorted order.\n if '*' in included_set:\n star_index = include.index('*')\n not_included = sorted(d['name'] for d in doclets if d['name'] not in included_set)\n include = include[:star_index] + not_included + include[star_index + 1:]\n included_set.update(not_included)\n\n # Even if there are 2 doclets with the same short name (e.g. a\n # static member and an instance one), keep them both. This\n # prefiltering step should make the below sort less horrible, even\n # though I'm calling index().\n included_doclets = [d for d in doclets if d['name'] in included_set]\n # sort()'s stability should keep same-named doclets in the order\n # JSDoc spits them out in.\n included_doclets.sort(key=lambda d: include.index(d['name']))\n return included_doclets\n\n return '\\n\\n'.join(\n rst_for(doclet) for doclet in doclets_to_include(include)\n if (doclet.get('access', 'public') in ('public', 'protected')\n or (doclet.get('access') == 'private' and should_include_private))\n and doclet['name'] not in exclude)","def getVisitableNodesNamed(self):\n\n return ((\"module\", self.subnode_module),)","def test_generator_method_name(self):\n for i in range(0, 4):\n yield 'try_odd', i","def possible_sources(\n all_modules: Collection[str], identifier: str\n) -> Iterable[tuple[str, str]]:\n if identifier in all_modules:\n yield identifier, \"\"\n return\n\n modulename = identifier\n qualname = None\n while modulename:\n modulename, _, add = modulename.rpartition(\".\")\n qualname = f\"{add}.{qualname}\" if qualname else add\n yield modulename, qualname\n if modulename in all_modules:\n return\n raise ValueError(f\"Invalid identifier: {identifier}\")","def gen_mod(affinities, labels):\n\n for aff in affinities:\n yield modularity.get_modularity(aff, labels).sum()","def _list_modules():\r\n return [\r\n desc.module_class\r\n for desc\r\n in _list_descriptors()\r\n ]","def modules(self):\n return self._modules.keys()","def process_modules(self) -> typing.NoReturn:\n\t\tfor moduleName in self.moduleNameSet:\n\t\t\tdetected_encoding = detect_encoding(moduleName)\n\n\t\t\tprint(f\"Processing {moduleName} ({detected_encoding})\")\n\n\t\t\twith open(moduleName, 'r+', encoding=detected_encoding) as fileStream:\n\t\t\t\t# Store the content of the file\n\t\t\t\tfileContent: str = fileStream.read()\n\t\t\t\t# Sets the file's current position at the offset, the position of the read/write pointer within the file\n\t\t\t\tfileStream.seek(0, 0)\n\t\t\t\t# Truncates the file's size\n\t\t\t\tfileStream.truncate()\n\n\t\t\t\t# Process regex patterns\n\t\t\t\tfor regexDict in regexDictList:\n\t\t\t\t\tfileContent = self.process_function(regexDict, fileContent)\n\n\t\t\t\t# Rewrite the processed content of the file\n\t\t\t\tfileStream.write(fileContent)","def get_docs( mysource , basename ):\n import parser\n ast = parser.suite(mysource)\n return ModuleInfo(ast.totuple(1), basename)","def getSubNames(names):\n i = getIter(names)\n if not i:\n return\n\n try:\n item = i.next()\n while item:\n if isinstance(item, str):\n yield item\n else:\n## elif isinstance(item, tuple) and len(item) == 2:\n yield item[0]\n for c in getSubNames(item[1]):\n yield c\n item = i.next()\n except StopIteration:\n pass","def visit_Module(self, node):\n self.generic_visit(node)\n return self.functions","def test_list_members(self):\n pass","def output_modules(self, modules): \n return set([\n names.add(n)\n for m in modules\n for n in self.graph.successors(m.name)\n ])","def listModules(package):\n logger.debug(\"Listing modules for {0}\".format(package.__name__))\n for importer, modname, ispkg in pkgutil.iter_modules(package.__path__):\n yield (modname, ispkg)","def visit_Module(self, node):\n self.generic_visit(node)\n return self.classes","def semigroup_generators(self):","def generate_from(self, ast: ast_pb2.AST):\n for s in self._generate_headlines():\n yield s\n yield f'PYBIND11_MODULE({self._module_name}, m) {{'\n yield I+('m.doc() = \"CLIF generated pybind11-based module for '\n f'{ast.source}\";')\n for decl in ast.decls:\n if decl.decltype == ast_pb2.Decl.Type.FUNC:\n for s in function.generate_from(decl.func):\n yield s\n yield ''\n yield '}'","def get_children_with_name(self, prefix):\n prefix = prefix.lower()\n for childname, child in self.children.items():\n if childname.lower().startswith(prefix):\n yield child","def list_opts():\n for mod in load_conf_modules():\n mod_opts = mod.list_opts()\n yield mod_opts[0], mod_opts[1]","def names_to_parts(self, pnames, must_exist=True):\n meta = self.mesh.meta\n for pname in pnames:\n part = meta.get_part(pname, must_exist)\n yield part","def pipes(self):\n for name in self._pipes:\n yield name, self._data[name]","def node_names(self):\n\n for node_name in self.nodes.keys():\n\n yield node_name","def harvest(self, *items: Any, base_modules=...) -> List[Tuple[str, Callable]]:\n old_base_modules = self.base_modules\n try:\n if base_modules is not ...:\n self.base_modules = base_modules\n\n if not items:\n items = self.base_modules # type: ignore\n\n for bi in items:\n if isinstance(bi, str):\n bi, name_path = sys.modules[bi], bi\n else:\n name_path = tuple(\n func_name(bi, mod=0, fqdn=0, human=0, partials=1).split(\".\")\n )\n self._harvest(name_path, (bi,))\n\n return self.collected\n finally:\n self.base_modules = old_base_modules","def __iter__(self):\n for p in self.pkg.datastream:\n act = self.action(self.pkgmap, p, p.name)\n if act:\n yield act\n\n # for some reason, some packages may have directories specified\n # in the pkgmap that don't exist in the archive. They need to\n # be found and iterated as well.\n #\n # Some of the blastwave packages also have directories in the\n # archive that don't exist in the package metadata. I don't see\n # a whole lot of point in faking those up.\n for p in self.pkg.manifest:\n if p.pathname.startswith(\"/\"):\n dir = \"root\"\n else:\n dir = \"reloc/\"\n if p.type == \"d\" and \\\n dir + p.pathname not in self.pkg.datastream:\n act = self.action(self.pkgmap, None,\n dir + p.pathname)\n if act:\n yield act\n if p.type in \"ls\":\n act = self.action(self.pkgmap, None,\n dir + p.pathname)\n if act:\n yield act","def get_module_plugins(module, classobj):\n try:\n names = module.__all__\n except AttributeError:\n names = [x for x in vars(module) if not x.startswith('_')]\n for name in names:\n try:\n obj = getattr(module, name)\n except AttributeError:\n continue\n try:\n if issubclass(obj, classobj):\n yield obj\n except TypeError:\n continue","def members(self, items):\n pass","def __iter__(self):\n yield from self.gen","def load_conf_modules():\n for modname in _list_module_names():\n mod = importutils.import_module('monasca_api.conf.' + modname)\n required_funcs = ['register_opts', 'list_opts']\n for func in required_funcs:\n if hasattr(mod, func):\n yield mod","def group_get_members(self,groupname):\n\n if not self.check_prereqs():\n raise StopIteration\n\n db = self.env.get_db_cnx()\n cursor = db.cursor()\n\n query=self.create_query(self.sql_group_get_members_query+\" ORDER BY $username_field$\",{'groupname':groupname,'username_field':self.sql_username_field,'groupname_field':self.sql_groupname_field})\n self.log.debug(\"sqlflexibleauthstore: group_get_members: %s\" % (query,))\n\n cursor.execute(query)\n desc=[i[0] for i in cursor.description]\n for row in cursor:\n dictrow=dict(zip(desc,row))\n yield dictrow[self.sql_username_field]"],"string":"[\n \"def _named_members(self, get_members_fn, prefix='', recurse=True):\\n memo = set()\\n modules = self.named_modules(prefix=prefix) if recurse else [(prefix, self)]\\n for module_prefix, module in modules:\\n members = get_members_fn(module)\\n for k, v in members:\\n if v is None or v in memo:\\n continue\\n memo.add(v)\\n name = module_prefix + ('.' if module_prefix else '') + k\\n # translate name to ori_name\\n if name in self.node_name_map:\\n name = self.node_name_map[name]\\n yield name, v\",\n \"def __iter__(self) -> Generator[str, None, None]:\\n\\n yield from self.__dict__[\\\"members\\\"]\",\n \"def modules(self):\\n for desc in self._mappings.values():\\n if hasattr(desc, 'module'):\\n yield desc.module\\n else:\\n continue\",\n \"def package_module_iter( *packages ):\\n def module_iter( package, module_iter ):\\n if DEBUG: print( \\\"Package {0}\\\".format(package) )\\n for filename in module_iter:\\n basename= os.path.split( filename )[-1]\\n module, ext = os.path.splitext(basename)\\n if module.startswith(\\\"__\\\") and module.endswith(\\\"__\\\") and ext == \\\".py\\\":\\n continue\\n if DEBUG: print( \\\" file {0} module {1}\\\".format(basename, module) )\\n yield basename, module\\n\\n for package in packages:\\n yield package, module_iter(package, glob.glob(os.path.join(package,\\\"*.py\\\")))\",\n \"def modules():\",\n \"def deep_iter_modules(name):\\r\\n mod = import_dotted_name(name)\\r\\n yield name\\r\\n if not hasattr(mod, '__path__'):\\r\\n return\\r\\n for _, name, _ in iter_modules(mod.__path__, name + '.'):\\r\\n for name in deep_iter_modules(name):\\r\\n yield name\",\n \"def get_members():\",\n \"def walk_modules(module, name=\\\"\\\", path=()):\\n if not name:\\n name = module.__class__.__name__\\n named_children = list(module.named_children())\\n path = path + (name,)\\n yield Trace(path, len(named_children) == 0, module)\\n # recursively walk into all submodules\\n for name, child_module in named_children:\\n yield from walk_modules(child_module, name=name, path=path)\",\n \"def module_members_by_type(module, type_checker, skip_underscored=True):\\n\\n for name, member in getmembers(module, type_checker):\\n if skip_underscored and name.startswith(UNDERSCORE):\\n continue\\n yield name, member\",\n \"def __modules_recurse(self, mod=None):\\n if mod is None:\\n mod = self\\n for module in mod.children():\\n if isinstance(module, (nn.ModuleList, nn.Sequential, self.custom_layers)):\\n yield from self.__modules_recurse(module)\\n else:\\n yield module\",\n \"def do_list_modules(self, arg):\\n for module in self.reader.module_list.modules:\\n if arg:\\n name = GetModuleName(self.reader, module).lower()\\n if name.find(arg.lower()) >= 0:\\n PrintModuleDetails(self.reader, module)\\n else:\\n PrintModuleDetails(self.reader, module)\\n print()\",\n \"def deep_get_members(name, predicate=None, private=False):\\r\\n seen = set()\\r\\n for name in deep_iter_modules(name):\\r\\n mod = import_dotted_name(name)\\r\\n name = name.rsplit('.', 1)[-1]\\r\\n if not private and name.startswith('_'):\\r\\n continue\\r\\n for name, value in getmembers(mod, predicate):\\r\\n if id(value) in seen or not private and name.startswith('_'):\\r\\n continue\\r\\n yield value\\r\\n seen.add(id(value))\",\n \"def Modules():\\n mod = ida_idd.modinfo_t()\\n result = ida_dbg.get_first_module(mod)\\n while result:\\n # Note: can't simply return `mod` here, since callers might\\n # collect all modules in a list, and they would all re-use\\n # the underlying C++ object.\\n yield ida_idaapi.object_t(name=mod.name, size=mod.size, base=mod.base, rebase_to=mod.rebase_to)\\n result = ida_dbg.get_next_module(mod)\",\n \"def all_registered_modules():\\n yield from iterchain(modules.values() for modules in Registry.monomers.values())\",\n \"def visit_Module(self, node):\\n for item in node.body:\\n self.visit(item)\",\n \"def visit_Module(self, node):\\n for item in node.body:\\n self.visit(item)\",\n \"def morepath_modules(cls: type[morepath.App]) -> 'Iterator[str]':\\n for base in cls.__mro__:\\n if not issubclass(base, morepath.App):\\n continue\\n\\n if base is morepath.App:\\n continue\\n\\n module = '.'.join(base.__module__.split('.')[:2])\\n\\n if module.startswith('test'):\\n continue\\n\\n yield module\",\n \"def list_modules():\\n for module_name in listdir(modules_directory):\\n if isdir(join(modules_directory, module_name)):\\n log.debug('Load module: {0}'.format(module_name))\\n yield module_name\",\n \"def imports():\\n for name, val in globals().items():\\n if isinstance(val, getattr(types, \\\"ModuleType\\\")):\\n yield val.__name__\",\n \"def __iter__(self):\\n idx = ffi.new('uint32_t *')\\n mod = lib.ly_ctx_get_module_iter(self._ctx, idx)\\n while mod:\\n yield Module(self, mod)\\n mod = lib.ly_ctx_get_module_iter(self._ctx, idx)\",\n \"def __call__(self):\\n for name in self:\\n try:\\n yield getattr(self, name)\\n except AttributeError:\\n raise KeyError(name)\",\n \"def pairs(self) -> Iterator[tuple[str, list[CommandParser]]]:\\n for module, cmds in self._registry[\\\"by_module\\\"].items():\\n yield (module, cmds)\",\n \"def iter_structures(self):\\n idx_start = 0\\n start_name = self.name[idx_start]\\n for idx_current, name in enumerate(self.name):\\n if start_name != name:\\n yield start_name, slice(idx_start, idx_current), self[idx_start:idx_current]\\n idx_start = idx_current\\n start_name = self.name[idx_start]\\n yield start_name, slice(idx_start, None), self[idx_start:]\",\n \"def iter_spider_classes(module):\\n ...\",\n \"def FileIter(func_name):\\n \\n if func_name == 'convert_pmids_to_pmcs':\\n sdir = partial(os.path.join,'Data', 'SearchResults')\\n pmc_file = os.path.join('Data', 'PMC-ids.csv')\\n files = [x for x in os.listdir(sdir('')) if x.endswith('.res')]\\n for f in files:\\n yield (sdir(f), pmc_file), sdir(f+'.conv')\\n\\n elif func_name == 'search_pubmed':\\n sdir = partial(os.path.join,'Data', 'SearchResults')\\n queryfile = os.path.join('Data', 'QueryList.txt')\\n with open(queryfile) as handle:\\n for row in csv.DictReader(handle):\\n fname = '%s--%s.res' % (GeneralUtils.slugify(row['org']), \\n GeneralUtils.slugify(row['search']))\\n ofile = sdir(fname)\\n yield queryfile, ofile, row['search']\\n\\n elif func_name == 'download_pmids':\\n \\n sdir = partial(os.path.join,'Data', 'SearchResults')\\n odir = os.path.join('Data', 'RawXML')\\n files = [x for x in os.listdir(sdir('')) if x.endswith('.conv')]\\n \\n for f in files:\\n yield sdir(f), sdir(f+'.dl'), odir\\n\\n elif func_name == 'extract_text':\\n \\n sdir = partial(os.path.join, 'Data', 'RawXML')\\n odir = partial(os.path.join, 'Data', 'SentenceFiles')\\n\\n files = sorted([x for x in os.listdir(sdir('')) if x.endswith('.xml')])\\n for f in files:\\n name = f.split('.')[0]\\n if f.startswith('PMC'):\\n typ = 'pmc'\\n else:\\n typ = 'pubmed'\\n\\n yield sdir(f), odir(name+'.sent'), typ\\n\\n elif func_name == 'get_mutations':\\n \\n sdir = partial(os.path.join, 'Data', 'SentenceFiles')\\n odir = partial(os.path.join, 'Data', 'MutFiles')\\n finder = None#mutfinder_gen('regex.txt')\\n\\n files = sorted([x for x in os.listdir(sdir('')) if x.endswith('.sent')])\\n\\n for f in files:\\n name = f.split('.')[0]\\n yield sdir(f), odir(name + '.mut')\\n \\n elif func_name == 'process_mut_file':\\n \\n sdir = partial(os.path.join, 'Data', 'MutFiles')\\n odir = partial(os.path.join, 'Data', 'ProteinFiles')\\n\\n files = sorted([x for x in os.listdir(sdir('')) if x.endswith('.mut')])\\n\\n for f in files:\\n name = f.split('.')[0]\\n yield sdir(f), (odir(name + '.prot'), odir(name + '.sen'))\\n elif func_name == 'mapping_files':\\n path = 'Data/Mapping/'\\n items = (('ftp://ftp.uniprot.org/pub/databases/uniprot/current_release/knowledgebase/idmapping/idmapping.dat.gz', 'idmapping.dat.sort'),\\n ('ftp://ftp.ncbi.nlm.nih.gov/gene/DATA/gene_info.gz', 'gene_info'),\\n ('ftp://ftp.ncbi.nlm.nih.gov/pub/pmc/PMC-ids.csv.gz', 'PMC-ids.csv'),\\n ('ftp://nlmpubs.nlm.nih.gov/online/mesh/.asciimesh/d2011.bin', 'd2011.bin'))\\n for url, ofile in items:\\n yield None, os.path.join(path, ofile), url, path\",\n \"def process_module_list(self, modules):\",\n \"def getMembers():\",\n \"def getMembers():\",\n \"def getMembers():\",\n \"def getMembers():\",\n \"def __iter__(self):\\n for name, field in self.iterate_over_fields():\\n yield name, field\",\n \"def items(self):\\n for name in self.fields:\\n yield name, getattr(self, name)\",\n \"def get_modules(name_only=False):\\n\\n mods = list()\\n\\n dtf_db = sqlite3.connect(DTF_DB)\\n cur = dtf_db.cursor()\\n\\n # This just returns the name\\n if name_only:\\n\\n sql = ('SELECT name '\\n 'FROM modules ')\\n\\n for mod in cur.execute(sql):\\n mods.append(mod[0])\\n\\n # This returns a list of items\\n else:\\n\\n sql = ('SELECT name, version, '\\n 'about, author '\\n 'FROM modules '\\n 'ORDER BY name')\\n\\n cur.execute(sql)\\n\\n while True:\\n\\n item = dtf.core.item.Item()\\n line = cur.fetchone()\\n if line is None:\\n break\\n\\n item.type = dtf.core.item.TYPE_MODULE\\n item.name = line[0]\\n item.version = line[1]\\n item.about = line[2]\\n item.author = line[3]\\n\\n mods.append(item)\\n\\n return mods\",\n \"def test_get_members(self):\\n pass\",\n \"def LookupExports(self, names):\\n cache = {}\\n cache[()] = self\\n for name in names:\\n if name == '.':\\n name = ''\\n parts = name.split('.')\\n parts, param = tuple(parts[:-1]), parts[-1]\\n o = self\\n for i in xrange(len(parts), -1, -1):\\n before, after = parts[:i], parts[i:]\\n o = cache.get(before, None)\\n if o is not None:\\n break\\n assert o is not None\\n for i in after:\\n before = tuple(list(before) + [i])\\n try:\\n cache[before] = o = o.Sub(i)\\n except KeyError as e:\\n # Fill in the full path to the missing element, rather than just\\n # its basename (which is often something unhelpful like '1').\\n e.args = tuple(['.'.join(tuple([o.basename]) + tuple(e.args))])\\n raise\\n yield o, param\",\n \"def iter_submodules(self, *args: Any, **kwargs: Any) -> Iterator[Submodule]:\\n return RootModule(self).traverse(*args, **kwargs)\",\n \"def walk(self):\\n for name, child in sorted(self._children.items()):\\n if isinstance(child, PackageEntry):\\n yield name, child\\n else:\\n yield from child._walk(f'{name}/')\",\n \"def _iter_all_modules(\\n package: Union[str, types.ModuleType],\\n prefix: str = \\\"\\\",\\n) -> Iterator[str]:\\n import os\\n import pkgutil\\n\\n if isinstance(package, str):\\n path = package\\n else:\\n # Type ignored because typeshed doesn't define ModuleType.__path__\\n # (only defined on packages).\\n package_path = package.__path__ # type: ignore[attr-defined]\\n path, prefix = package_path[0], package.__name__ + \\\".\\\"\\n for _, name, is_package in pkgutil.iter_modules([path]):\\n if is_package:\\n for m in _iter_all_modules(os.path.join(path, name), prefix=name + \\\".\\\"):\\n yield prefix + m\\n else:\\n yield prefix + name\",\n \"def create_modules(*names):\\n source = 'attr = {0!r}'\\n created_paths = []\\n mapping = {}\\n state_manager = None\\n uncache_manager = None\\n try:\\n temp_dir = tempfile.mkdtemp()\\n mapping['.root'] = temp_dir\\n import_names = set()\\n for name in names:\\n if not name.endswith('__init__'):\\n import_name = name\\n else:\\n import_name = name[:-len('.__init__')]\\n import_names.add(import_name)\\n if import_name in sys.modules:\\n del sys.modules[import_name]\\n name_parts = name.split('.')\\n file_path = temp_dir\\n for directory in name_parts[:-1]:\\n file_path = os.path.join(file_path, directory)\\n if not os.path.exists(file_path):\\n os.mkdir(file_path)\\n created_paths.append(file_path)\\n file_path = os.path.join(file_path, name_parts[-1] + '.py')\\n with open(file_path, 'w') as file:\\n file.write(source.format(name))\\n created_paths.append(file_path)\\n mapping[name] = file_path\\n uncache_manager = util.uncache(*import_names)\\n uncache_manager.__enter__()\\n state_manager = util.import_state(path=[temp_dir])\\n state_manager.__enter__()\\n yield mapping\\n finally:\\n if state_manager is not None:\\n state_manager.__exit__(None, None, None)\\n if uncache_manager is not None:\\n uncache_manager.__exit__(None, None, None)\\n support.rmtree(temp_dir)\",\n \"def __iter__(self):\\n for metatag in self.meta.findall(CN('meta:user-defined')):\\n yield (metatag.get(CN('meta:name')), metatag.text)\",\n \"def iterate(cls):\\n for name, value in vars(cls).iteritems():\\n if name.startswith('__'):\\n continue\\n yield (name, value)\",\n \"def LookupAndFixupExports(self, names):\\n for o, param in self.LookupExports(names):\\n yield o, self._FixExportName(o.obj, param)\",\n \"def MODULES(self):\\n pass\",\n \"def __iter__(self):\\n try:\\n groupNames = sorted(self.groups)\\n except Exception: # pragma: no cover\\n groupNames = self.groups.keys()\\n\\n for groupName in groupNames:\\n yield groupName, self[groupName]\",\n \"def children_of(self, member_name):\\n for relationship in self.relationships[member_name]:\\n yield self.members[relationship.child]\",\n \"def members(self) -> Generator[discord.Member, None, None]:\\n for guild in self.guilds:\\n for member in guild.members:\\n yield member\",\n \"def __iter__(self):\\n\\n for help in self.help:\\n yield (help[0],) + help[2:]\",\n \"def _items():\\n for writer in writers:\\n name = pm.get_manifest(writer.command).display_name\\n title = (\\n f\\\"{name} {writer.display_name}\\\"\\n if writer.display_name\\n else name\\n )\\n yield title, writer.filename_extensions\",\n \"def __iter__(self):\\n base = os.path.dirname(__file__)\\n f = cached_open(os.path.join(base, self.FILENAME))\\n #f = codecs.open(self.FILENAME, 'r', 'utf-8')\\n for line in f.split('\\\\n'):\\n go = True\\n for pat in self.patterns:\\n if not re.findall(pat, line):\\n go = False\\n break\\n if go:\\n yield Name(line = line.replace('\\\\n', ''), gender=self.GENDER)\",\n \"def modules_in_current_dir(path, module_name):\\n yield from modules_from_path(Path(path).parent, module_name)\",\n \"def parse_modules(self) -> None:\\n mods: Dict[str, str] = {}\\n matches = self.find_dir(\\\"LoadModule\\\")\\n iterator = iter(matches)\\n # Make sure prev_size != cur_size for do: while: iteration\\n prev_size = -1\\n\\n while len(mods) != prev_size:\\n prev_size = len(mods)\\n\\n for match_name, match_filename in zip(\\n iterator, iterator):\\n mod_name = self.get_arg(match_name)\\n mod_filename = self.get_arg(match_filename)\\n if mod_name and mod_filename:\\n mods[mod_name] = mod_filename\\n mods[os.path.basename(mod_filename)[:-2] + \\\"c\\\"] = mod_filename\\n else:\\n logger.debug(\\\"Could not read LoadModule directive from Augeas path: %s\\\",\\n match_name[6:])\\n self.modules.update(mods)\",\n \"def __iter__(self):\\n for name, _ in getmembers(self.__class__,\\n lambda value: isinstance(value, InstrumentedAttribute)):\\n yield name\",\n \"def __next__(self):\\n for child in self.children:\\n yield child\",\n \"def _iter_module_files():\\n # The list call is necessary on Python 3 in case the module\\n # dictionary modifies during iteration.\\n for module in list(sys.modules.values()):\\n if module is None:\\n continue\\n filename = getattr(module, \\\"__file__\\\", None)\\n if filename:\\n old = None\\n while not os.path.isfile(filename):\\n old = filename\\n filename = os.path.dirname(filename)\\n if filename == old:\\n break\\n else:\\n if filename[-4:] in (\\\".pyc\\\", \\\".pyo\\\"):\\n filename = filename[:-1]\\n yield filename\",\n \"def lib(self):\\n for name in dir(self):\\n if not name.startswith('__') and type(getattr(self, name)) == tuple:\\n yield name, getattr(self, name)[0]\",\n \"def get_modules(site_path):\\n for dirpath, dirnames, filenames in os.walk(site_path):\\n module = dirpath[len(site_path) + 1:].split(os.path.sep)\\n if module[0].endswith('.egg-info'):\\n continue\\n if module[0].endswith('.egg') or not module[0]:\\n module.pop(0)\\n\\n for filename in filenames:\\n root, ext = os.path.splitext(filename)\\n if ext == '.py':\\n if not re.match(VALID_PYTHON_FILENAME, root):\\n continue\\n if root == '__init__':\\n yield '.'.join(module)\\n else:\\n yield '.'.join(module + [root])\\n elif ext == '.egg-link':\\n with open(os.path.join(dirpath, filename)) as f:\\n egglink_path = f.readline().strip()\\n linked_module_path = os.path.join(egglink_path, root.replace('-', '_'))\\n for modname in get_modules_to_import_from_directory(linked_module_path):\\n yield modname\\n elif ext == '.egg':\\n yield filename.split('-', 1)[0]\",\n \"def __iter__(self):\\n yield self._extent.members()\\n yield self._intent.members()\",\n \"def ListExportsEx(self, name=None, recursive=False):\\n if name:\\n topobj = self.GetExport(name)\\n if (not hasattr(topobj, 'Export') and\\n not hasattr(topobj, 'iteritems')):\\n # a leaf parameter; it has no sub-exports.\\n return\\n top = self._Sub(name, topobj)\\n else:\\n top = self\\n if hasattr(top.obj, 'Export'):\\n # pylint:disable=protected-access\\n it = top._ListExports()\\n elif hasattr(top.obj, 'iteritems'):\\n # pylint:disable=protected-access\\n it = top._ListExportsFromDict()\\n else:\\n it = [(name, self, None)]\\n for fullname, h, subname in it:\\n yield fullname, h, subname\\n if recursive and fullname.endswith('.'):\\n x_it = h.ListExportsEx(subname, recursive=recursive)\\n for x_fullname, x_h, x_subname in x_it:\\n yield fullname + x_fullname, x_h, x_subname\",\n \"def enumerate():\\n for p in __all__:\\n yield (p, scanners[p])\",\n \"def modules(cls):\\n members = inspect.getmembers(cls, lambda a: not (inspect.isroutine(a) and a.__name__ == 'modules'))\\n modules = [module for name, module in members if not name.startswith('_')]\\n return modules\",\n \"def __iter__(self):\\n return (base.__name__ for base in self.bases)\",\n \"def process(self, name_and_sequence):\\n name, sequence = name_and_sequence\\n yield preprocess_utils.get_pacbio_molecule_name(name), sequence\",\n \"def test_python_name_generator():\\n assert list(itertools.islice(get_python_name_generator(\\\"Some name\\\"), 3)) == [\\n \\\"test_some_name\\\",\\n \\\"test_some_name_1\\\",\\n \\\"test_some_name_2\\\",\\n ]\",\n \"def generateElementsNamed(list, name):\\n for n in list:\\n if IElement.providedBy(n) and n.name == name:\\n yield n\",\n \"def names():\\n pass\",\n \"def parse_mod(self, modfile):\\n with open(modfile, 'rt') as filehandle:\\n for line in filehandle:\\n if line.startswith((\\\"+\\\", \\\"-\\\")):\\n enable, name, version, path = self.parse_mod_entry(line)\\n yield ModTuple(enable == \\\"+\\\", name, version, path, modfile)\",\n \"def do_modules(self, args):\\n logger.debug(\\\"do_modules() was called\\\")\\n \\n parser = CrispyArgumentParser(description=self.do_modules.__doc__, prog=\\\"modules\\\")\\n \\n try:\\n pargs = parser.parse_args(shlex.split(args))\\n if pargs:\\n print \\\"\\\\nAvailable modules:\\\\n===================\\\"\\n for mod in self.srv.get_module_list():\\n print \\\"{:<12} -- {}\\\".format(mod.__module__, mod.__doc__)\\n fprint.success(\\\"Done.\\\")\\n except MyParserException as e:\\n print e\",\n \"def get_named_parameters(self):\\n for name, _ in self.module_to_quantize.named_parameters():\\n yield name, getattr(self, name)\",\n \"def _get_documenter_direct_members(\\n documenter: sphinx.ext.autodoc.Documenter\\n) -> Iterator[_MemberDocumenterEntry]:\\n members_check_module, members = documenter.get_object_members(want_all=True)\\n del members_check_module\\n if members:\\n try:\\n # get_object_members does not preserve definition order, but __dict__ does\\n # in Python 3.6 and later.\\n member_dict = sphinx.util.inspect.safe_getattr(documenter.object,\\n '__dict__')\\n member_order = {k: i for i, k in enumerate(member_dict.keys())}\\n members.sort(key=lambda entry: member_order.get(entry[0], float('inf')))\\n except AttributeError:\\n pass\\n filtered_members = [\\n x for x in documenter.filter_members(members, want_all=True)\\n if _include_member(*x)\\n ]\\n for member_name, member_value, is_attr in filtered_members:\\n member_documenter = _get_member_documenter(parent=documenter,\\n member_name=member_name,\\n member_value=member_value,\\n is_attr=is_attr)\\n if member_documenter is None:\\n continue\\n full_name = f'{documenter.fullname}.{member_name}'\\n entry = _MemberDocumenterEntry(\\n cast(sphinx.ext.autodoc.Documenter, member_documenter),\\n is_attr,\\n member_name,\\n full_name=full_name,\\n import_name=full_name,\\n )\\n for transformed_entry in _transform_member(documenter, entry):\\n yield from _get_member_overloads(transformed_entry)\",\n \"def module_info():\\n pass\",\n \"def walk(self):\\n for project in [self.get_project(name)\\n for name in self.project_names]:\\n for sample in [project.get_sample(idx)\\n for idx in project.sample_ids]:\\n yield (project,sample)\",\n \"def _members_of(self, full_path, include, exclude, should_include_private):\\n def rst_for(doclet):\\n renderer = (AutoFunctionRenderer if doclet.get('kind') in ['function', 'typedef']\\n else AutoAttributeRenderer)\\n # Pass a dummy arg list with no formal param list so\\n # _formal_params() won't find an explicit param list in there and\\n # override what it finds in the code:\\n return renderer(self._directive, self._app, arguments=['dummy']).rst(\\n [doclet['name']],\\n 'dummy_full_path',\\n doclet,\\n use_short_name=False)\\n\\n def doclets_to_include(include):\\n \\\"\\\"\\\"Return the doclets that should be included (before excludes and\\n access specifiers are taken into account).\\n\\n This will either be the doclets explicitly listed after the\\n ``:members:`` option, in that order; all doclets that are\\n members of the class; or listed members with remaining ones\\n inserted at the placeholder \\\"*\\\".\\n\\n \\\"\\\"\\\"\\n doclets = self._app._sphinxjs_doclets_by_class[tuple(full_path)]\\n if not include:\\n # Specifying none means listing all.\\n return sorted(doclets, key=lambda d: d['name'])\\n included_set = set(include)\\n\\n # If the special name * is included in the list, include\\n # all other doclets, in sorted order.\\n if '*' in included_set:\\n star_index = include.index('*')\\n not_included = sorted(d['name'] for d in doclets if d['name'] not in included_set)\\n include = include[:star_index] + not_included + include[star_index + 1:]\\n included_set.update(not_included)\\n\\n # Even if there are 2 doclets with the same short name (e.g. a\\n # static member and an instance one), keep them both. This\\n # prefiltering step should make the below sort less horrible, even\\n # though I'm calling index().\\n included_doclets = [d for d in doclets if d['name'] in included_set]\\n # sort()'s stability should keep same-named doclets in the order\\n # JSDoc spits them out in.\\n included_doclets.sort(key=lambda d: include.index(d['name']))\\n return included_doclets\\n\\n return '\\\\n\\\\n'.join(\\n rst_for(doclet) for doclet in doclets_to_include(include)\\n if (doclet.get('access', 'public') in ('public', 'protected')\\n or (doclet.get('access') == 'private' and should_include_private))\\n and doclet['name'] not in exclude)\",\n \"def getVisitableNodesNamed(self):\\n\\n return ((\\\"module\\\", self.subnode_module),)\",\n \"def test_generator_method_name(self):\\n for i in range(0, 4):\\n yield 'try_odd', i\",\n \"def possible_sources(\\n all_modules: Collection[str], identifier: str\\n) -> Iterable[tuple[str, str]]:\\n if identifier in all_modules:\\n yield identifier, \\\"\\\"\\n return\\n\\n modulename = identifier\\n qualname = None\\n while modulename:\\n modulename, _, add = modulename.rpartition(\\\".\\\")\\n qualname = f\\\"{add}.{qualname}\\\" if qualname else add\\n yield modulename, qualname\\n if modulename in all_modules:\\n return\\n raise ValueError(f\\\"Invalid identifier: {identifier}\\\")\",\n \"def gen_mod(affinities, labels):\\n\\n for aff in affinities:\\n yield modularity.get_modularity(aff, labels).sum()\",\n \"def _list_modules():\\r\\n return [\\r\\n desc.module_class\\r\\n for desc\\r\\n in _list_descriptors()\\r\\n ]\",\n \"def modules(self):\\n return self._modules.keys()\",\n \"def process_modules(self) -> typing.NoReturn:\\n\\t\\tfor moduleName in self.moduleNameSet:\\n\\t\\t\\tdetected_encoding = detect_encoding(moduleName)\\n\\n\\t\\t\\tprint(f\\\"Processing {moduleName} ({detected_encoding})\\\")\\n\\n\\t\\t\\twith open(moduleName, 'r+', encoding=detected_encoding) as fileStream:\\n\\t\\t\\t\\t# Store the content of the file\\n\\t\\t\\t\\tfileContent: str = fileStream.read()\\n\\t\\t\\t\\t# Sets the file's current position at the offset, the position of the read/write pointer within the file\\n\\t\\t\\t\\tfileStream.seek(0, 0)\\n\\t\\t\\t\\t# Truncates the file's size\\n\\t\\t\\t\\tfileStream.truncate()\\n\\n\\t\\t\\t\\t# Process regex patterns\\n\\t\\t\\t\\tfor regexDict in regexDictList:\\n\\t\\t\\t\\t\\tfileContent = self.process_function(regexDict, fileContent)\\n\\n\\t\\t\\t\\t# Rewrite the processed content of the file\\n\\t\\t\\t\\tfileStream.write(fileContent)\",\n \"def get_docs( mysource , basename ):\\n import parser\\n ast = parser.suite(mysource)\\n return ModuleInfo(ast.totuple(1), basename)\",\n \"def getSubNames(names):\\n i = getIter(names)\\n if not i:\\n return\\n\\n try:\\n item = i.next()\\n while item:\\n if isinstance(item, str):\\n yield item\\n else:\\n## elif isinstance(item, tuple) and len(item) == 2:\\n yield item[0]\\n for c in getSubNames(item[1]):\\n yield c\\n item = i.next()\\n except StopIteration:\\n pass\",\n \"def visit_Module(self, node):\\n self.generic_visit(node)\\n return self.functions\",\n \"def test_list_members(self):\\n pass\",\n \"def output_modules(self, modules): \\n return set([\\n names.add(n)\\n for m in modules\\n for n in self.graph.successors(m.name)\\n ])\",\n \"def listModules(package):\\n logger.debug(\\\"Listing modules for {0}\\\".format(package.__name__))\\n for importer, modname, ispkg in pkgutil.iter_modules(package.__path__):\\n yield (modname, ispkg)\",\n \"def visit_Module(self, node):\\n self.generic_visit(node)\\n return self.classes\",\n \"def semigroup_generators(self):\",\n \"def generate_from(self, ast: ast_pb2.AST):\\n for s in self._generate_headlines():\\n yield s\\n yield f'PYBIND11_MODULE({self._module_name}, m) {{'\\n yield I+('m.doc() = \\\"CLIF generated pybind11-based module for '\\n f'{ast.source}\\\";')\\n for decl in ast.decls:\\n if decl.decltype == ast_pb2.Decl.Type.FUNC:\\n for s in function.generate_from(decl.func):\\n yield s\\n yield ''\\n yield '}'\",\n \"def get_children_with_name(self, prefix):\\n prefix = prefix.lower()\\n for childname, child in self.children.items():\\n if childname.lower().startswith(prefix):\\n yield child\",\n \"def list_opts():\\n for mod in load_conf_modules():\\n mod_opts = mod.list_opts()\\n yield mod_opts[0], mod_opts[1]\",\n \"def names_to_parts(self, pnames, must_exist=True):\\n meta = self.mesh.meta\\n for pname in pnames:\\n part = meta.get_part(pname, must_exist)\\n yield part\",\n \"def pipes(self):\\n for name in self._pipes:\\n yield name, self._data[name]\",\n \"def node_names(self):\\n\\n for node_name in self.nodes.keys():\\n\\n yield node_name\",\n \"def harvest(self, *items: Any, base_modules=...) -> List[Tuple[str, Callable]]:\\n old_base_modules = self.base_modules\\n try:\\n if base_modules is not ...:\\n self.base_modules = base_modules\\n\\n if not items:\\n items = self.base_modules # type: ignore\\n\\n for bi in items:\\n if isinstance(bi, str):\\n bi, name_path = sys.modules[bi], bi\\n else:\\n name_path = tuple(\\n func_name(bi, mod=0, fqdn=0, human=0, partials=1).split(\\\".\\\")\\n )\\n self._harvest(name_path, (bi,))\\n\\n return self.collected\\n finally:\\n self.base_modules = old_base_modules\",\n \"def __iter__(self):\\n for p in self.pkg.datastream:\\n act = self.action(self.pkgmap, p, p.name)\\n if act:\\n yield act\\n\\n # for some reason, some packages may have directories specified\\n # in the pkgmap that don't exist in the archive. They need to\\n # be found and iterated as well.\\n #\\n # Some of the blastwave packages also have directories in the\\n # archive that don't exist in the package metadata. I don't see\\n # a whole lot of point in faking those up.\\n for p in self.pkg.manifest:\\n if p.pathname.startswith(\\\"/\\\"):\\n dir = \\\"root\\\"\\n else:\\n dir = \\\"reloc/\\\"\\n if p.type == \\\"d\\\" and \\\\\\n dir + p.pathname not in self.pkg.datastream:\\n act = self.action(self.pkgmap, None,\\n dir + p.pathname)\\n if act:\\n yield act\\n if p.type in \\\"ls\\\":\\n act = self.action(self.pkgmap, None,\\n dir + p.pathname)\\n if act:\\n yield act\",\n \"def get_module_plugins(module, classobj):\\n try:\\n names = module.__all__\\n except AttributeError:\\n names = [x for x in vars(module) if not x.startswith('_')]\\n for name in names:\\n try:\\n obj = getattr(module, name)\\n except AttributeError:\\n continue\\n try:\\n if issubclass(obj, classobj):\\n yield obj\\n except TypeError:\\n continue\",\n \"def members(self, items):\\n pass\",\n \"def __iter__(self):\\n yield from self.gen\",\n \"def load_conf_modules():\\n for modname in _list_module_names():\\n mod = importutils.import_module('monasca_api.conf.' + modname)\\n required_funcs = ['register_opts', 'list_opts']\\n for func in required_funcs:\\n if hasattr(mod, func):\\n yield mod\",\n \"def group_get_members(self,groupname):\\n\\n if not self.check_prereqs():\\n raise StopIteration\\n\\n db = self.env.get_db_cnx()\\n cursor = db.cursor()\\n\\n query=self.create_query(self.sql_group_get_members_query+\\\" ORDER BY $username_field$\\\",{'groupname':groupname,'username_field':self.sql_username_field,'groupname_field':self.sql_groupname_field})\\n self.log.debug(\\\"sqlflexibleauthstore: group_get_members: %s\\\" % (query,))\\n\\n cursor.execute(query)\\n desc=[i[0] for i in cursor.description]\\n for row in cursor:\\n dictrow=dict(zip(desc,row))\\n yield dictrow[self.sql_username_field]\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.72332364","0.69934905","0.66442704","0.6641855","0.65818375","0.65763384","0.6490665","0.64709103","0.6409749","0.6361901","0.6215152","0.6214206","0.6196199","0.6162733","0.61518407","0.61518407","0.60533714","0.6052606","0.59672785","0.5960688","0.59477067","0.5928413","0.5913844","0.58394104","0.58236396","0.58023775","0.57802224","0.57802224","0.57802224","0.57802224","0.5772245","0.5750965","0.573479","0.5716767","0.57070434","0.57040143","0.57018405","0.56923264","0.567932","0.56447935","0.5638056","0.56346756","0.5613052","0.5601599","0.55912644","0.5547356","0.5514045","0.5511142","0.549132","0.54822457","0.5460744","0.5449091","0.5442841","0.5438582","0.5436172","0.5434612","0.5432028","0.5405614","0.53960645","0.53939945","0.5389931","0.5386782","0.53860664","0.53766847","0.53709066","0.53371245","0.53345996","0.53313977","0.5331267","0.5331261","0.53300875","0.5324414","0.5314586","0.5299823","0.5299568","0.529929","0.52965117","0.5295951","0.52898437","0.528928","0.52879333","0.5286568","0.5285319","0.5281801","0.52792984","0.5276182","0.52404886","0.5236515","0.52256995","0.5221757","0.5210235","0.5208657","0.5205207","0.52050054","0.51997924","0.51974714","0.5194046","0.5193897","0.51801723","0.5164815"],"string":"[\n \"0.72332364\",\n \"0.69934905\",\n \"0.66442704\",\n \"0.6641855\",\n \"0.65818375\",\n \"0.65763384\",\n \"0.6490665\",\n \"0.64709103\",\n \"0.6409749\",\n \"0.6361901\",\n \"0.6215152\",\n \"0.6214206\",\n \"0.6196199\",\n \"0.6162733\",\n \"0.61518407\",\n \"0.61518407\",\n \"0.60533714\",\n \"0.6052606\",\n \"0.59672785\",\n \"0.5960688\",\n \"0.59477067\",\n \"0.5928413\",\n \"0.5913844\",\n \"0.58394104\",\n \"0.58236396\",\n \"0.58023775\",\n \"0.57802224\",\n \"0.57802224\",\n \"0.57802224\",\n \"0.57802224\",\n \"0.5772245\",\n \"0.5750965\",\n \"0.573479\",\n \"0.5716767\",\n \"0.57070434\",\n \"0.57040143\",\n \"0.57018405\",\n \"0.56923264\",\n \"0.567932\",\n \"0.56447935\",\n \"0.5638056\",\n \"0.56346756\",\n \"0.5613052\",\n \"0.5601599\",\n \"0.55912644\",\n \"0.5547356\",\n \"0.5514045\",\n \"0.5511142\",\n \"0.549132\",\n \"0.54822457\",\n \"0.5460744\",\n \"0.5449091\",\n \"0.5442841\",\n \"0.5438582\",\n \"0.5436172\",\n \"0.5434612\",\n \"0.5432028\",\n \"0.5405614\",\n \"0.53960645\",\n \"0.53939945\",\n \"0.5389931\",\n \"0.5386782\",\n \"0.53860664\",\n \"0.53766847\",\n \"0.53709066\",\n \"0.53371245\",\n \"0.53345996\",\n \"0.53313977\",\n \"0.5331267\",\n \"0.5331261\",\n \"0.53300875\",\n \"0.5324414\",\n \"0.5314586\",\n \"0.5299823\",\n \"0.5299568\",\n \"0.529929\",\n \"0.52965117\",\n \"0.5295951\",\n \"0.52898437\",\n \"0.528928\",\n \"0.52879333\",\n \"0.5286568\",\n \"0.5285319\",\n \"0.5281801\",\n \"0.52792984\",\n \"0.5276182\",\n \"0.52404886\",\n \"0.5236515\",\n \"0.52256995\",\n \"0.5221757\",\n \"0.5210235\",\n \"0.5208657\",\n \"0.5205207\",\n \"0.52050054\",\n \"0.51997924\",\n \"0.51974714\",\n \"0.5194046\",\n \"0.5193897\",\n \"0.51801723\",\n \"0.5164815\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6819973"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":22476,"cells":{"query":{"kind":"string","value":"Init object to store step and trial data."},"document":{"kind":"string","value":"def init_data(stats_list):\n\n data = {stats_name: {} for stats_name in stats_list}\n return data"},"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._trials = [] # private variables _ convention","def __init__( self ):\n self._env = None\n self._steps = None\n\n self._initialize( )","def init(self, **kwargs):\n self._d = {}\n self._th = None\n self._run = True\n self.load()","def __init__(self):\n self.step_list = [steps.Raw()]","def __init__(self, step_name, variable_name, distribution):\n self.step_name = step_name\n self.variable_name = variable_name\n self._distribution = distribution","def trial_prep(self):\n pass","def __init__(self, routine, *args, **kwargs):\n self.kw = kwargs\n Step.__init__(self, routine=routine, *args, **kwargs)\n t1_settings = self.parse_settings(self.get_requested_settings())\n qbcal.T1.__init__(self, dev=self.dev, **t1_settings)","def __init__(self):\n Sampler.__init__(self)\n self._registeredIdentifiers = set() # tracks job identifiers used for this adaptive sampler and its inheritors\n self._prefixToIdentifiers = {} # tracks the mapping of run prefixes to particular identifiers\n self._inputIdentifiers = {} # identifiers for a single realization\n self._targetEvaluation = None # data object with feedback from sample realizations\n self._solutionExport = None # data object for solution printing\n self._requireSolnExport = False # if this object requires a solution export\n # NOTE TargetEvaluations consider all the Step <Output> DataObjects as candidates, so requiring\n # exactly one TargetEvaluation forces only having one <Output> DataObject in AdaptiveSampling\n # MultiRun Steps. For now, we leave it as \"n\".\n self.addAssemblerObject('TargetEvaluation', InputData.Quantity.one_to_infinity) # Place where realization evaluations go","def __init__(self, *args):\n \n self.steps = args","def setUpClass(self):\n self.tmin, self.tmax, self.dt = (0, 10.0, 0.1)\n self.start = { 'x' : 100.0, 'y' : 100.0, 'z' : 100.0,\n 'psi' : 30.0, 'theta' : 0.0, 'phi' : 0.0,\n 'v': 1.0, 'weight' : 100.0, 'fuel' : 100.0}","def __init__(__self__, *,\n multistep_number: int,\n outcome_summary: str,\n run_duration: 'outputs.DurationResponse',\n step_id: str):\n pulumi.set(__self__, \"multistep_number\", multistep_number)\n pulumi.set(__self__, \"outcome_summary\", outcome_summary)\n pulumi.set(__self__, \"run_duration\", run_duration)\n pulumi.set(__self__, \"step_id\", step_id)","def __init__(self, step_id=None, started=None, completed=None, duration_in_millis=None, status=None, stage=None, info_log=None):\n\n self._step_id = None\n self._started = None\n self._completed = None\n self._duration_in_millis = None\n self._status = None\n self._stage = None\n self._info_log = None\n\n self.step_id = step_id\n self.started = started\n if completed is not None:\n self.completed = completed\n if duration_in_millis is not None:\n self.duration_in_millis = duration_in_millis\n self.status = status\n self.stage = stage\n self.info_log = info_log","def experiment_init(self):\n pass","def __init__(self) -> None:\n\n self.train_env = None # Training environment\n self.agent = None # The algorithm used to solve an RL problem is represented by a TF-Agent\n self.replay_buffer = None # The replay buffer keeps track of data collected from the environment\n self.dataset = None # The agent needs access to the replay buffer via an iterable tf.data.Dataset\n self.iterator = None # The iterator of self.dataset","def __init__(self):\n self._logger = logging.getLogger(__name__)\n self.step_name = \"OpenFDA\"","def __init__(self, step_time, step=None):\n self.step_vector = step\n self.step_time = step_time\n self.ref_timer = None","def __init__(self):\n self.data_set = []\n self.finalized_data = LogData()","def __init__(self, train, test, head):\n\n # Save the URL's, in case we want it later\n self.train_url = train\n self.test_url = test\n self.head_url = head\n\n # Grab the data, and store the internal variables\n self._get_data()","def __init__(self):\n\n data_extract=DataExtracter()\n self.data = tuple()","def __init__(self) -> None:\n self.metrics = {}\n self.current = None\n self.run = None","def _init_sample(self):\n self.timestamps = np.zeros(5)\n self.data = np.zeros((5, 12))","def __init__(self, experiment_data):\n self._experiment_data = experiment_data","def initialize(self) -> None:\n self._step = self._start_step\n self._tebd_propagator = compute_tebd_propagator(\n system_chain=self._system_chain,\n time_step=self._parameters.dt/2.0,\n epsrel=self._parameters.epsrel,\n order=self._parameters.order)\n self._results = {}\n self._t_mps = PtTebdBackend(\n gammas=self._initial_augmented_mps.gammas,\n lambdas=self._initial_augmented_mps.lambdas,\n epsrel=self._parameters.epsrel,\n config=self._backend_config)\n self._init_results()\n self._apply_controls(step=self.step, post=False)\n self._append_results()","def __init__(self):\n\n self.start_datetime_wallclock = None;\n self.end_datetime_wallclock = None;\n self.action_count = 0;\n self.reward_cumulative = 0;\n self.mission_xml = None;\n self.mission_type = None;\n self.mission_seed = None;\n self.student_guid = None;\n self.mission_xml_as_expected = None;\n self.is_goal = None;\n self.is_timeout = None;","def __init__(self,\n steps,\n global_settings=None,\n routine=None,\n ):\n super().__init__(steps)\n\n if routine is not None:\n self.routine = routine\n\n if global_settings is not None:\n self.global_settings = global_settings\n else:\n self.global_settings = {}","def __init__(self, path):\n self.path = path \n self.current_location = os.getcwd()\n self.rmsd = dict()\n self._get_rmsd_list()\n self._get_scores_list()\n self._get_running_time()\n self._get_step()","def initialization_step(self):\n # Update where agents are\n self.update_agent_location_vector()\n # update task locations\n self.update_task_location_vector()\n # update deadlines\n self.populate_deadline_vector()\n # update distances to each task and orientation to each task\n self.update_agent_distances_vector()\n self.update_agent_orientation_vector()","def _initialize_data(self):\n self.unique_id = 123\n\n self.gas_valve_open = False\n self.buffer_valve_open = False\n self.pump_valve_open = False\n\n self.operatingmode = 0\n\n self.sample_pressure_high_limit = 100\n self.sample_pressure_low_limit = 10\n self.sample_pressure = 0\n\n self.error = 0\n\n self.buffer_pressure_high = True","def __init__(self, step_time, step_interval=None, n_step=None, ss=None):\n self.ss = ss\n self.n_step = n_step\n self.interval = step_interval\n self.step_time = step_time","def begin(self):\n\n env = self.context.lookup(\"/environment\")\n\n self._test_results_dir = env[\"output_directory\"]\n self._starttime = env[\"starttime\"]\n self._runid = env[\"runid\"]\n\n self._result_filename = os.path.join(self._test_results_dir, \"testrun_results.jsos\")\n self._summary_filename = os.path.join(self._test_results_dir, \"testrun_summary.json\")\n self._import_errors_filename = os.path.join(self._test_results_dir, \"import_errors.jsos\")\n\n return","def __trial__(self, trial, practice):\n from klibs.KLEventQueue import pump\n from klibs.KLUserInterface import show_cursor, hide_cursor\n\n # At start of every trial, before setup_response_collector or trial_prep are run, retrieve\n # the values of the independent variables (factors) for that trial (as generated earlier by\n # TrialFactory) and set them as attributes of the experiment object.\n for iv, value in trial.items():\n setattr(self, iv, value)\n\n pump()\n self.setup_response_collector()\n self.trial_prep()\n tx = None\n try:\n if P.development_mode and (P.dm_trial_show_mouse or (P.eye_tracking and not P.eye_tracker_available)):\n show_cursor()\n self.evm.start_clock()\n if P.eye_tracking and not P.manual_eyelink_recording:\n self.el.start(P.trial_number)\n P.in_trial = True\n self.__log_trial__(self.trial())\n P.in_trial = False\n if P.eye_tracking and not P.manual_eyelink_recording:\n self.el.stop()\n if P.development_mode and (P.dm_trial_show_mouse or (P.eye_tracking and not P.eye_tracker_available)):\n hide_cursor()\n self.evm.stop_clock()\n self.trial_clean_up()\n except TrialException as e:\n self.trial_clean_up()\n self.evm.stop_clock()\n tx = e\n if P.eye_tracking and not P.manual_eyelink_recording:\n # todo: add a warning, here, if the recording hasn't been stopped when under manual control\n self.el.stop()\n if tx:\n raise tx","def __init__(self):\n self.time_limit = None\n self.time_step = None\n self.robot = None\n self.humans = None\n self.global_time = None\n self.human_times = None\n # reward function\n self.discomfort_dist = None\n # simulation configuration\n self.config = None\n self.randomize_attributes = None\n self.square_width = None\n self.circle_radius = None\n self.human_num = None\n # for visualization\n self.states = None\n self.states_traj = None","def _init_trackers(self):\n data = copy.deepcopy(self._data)\n series = ParamTracker.create_series(\n first_date=data.first_date, last_date=data.today, population=data.population)\n tracker = ParamTracker(\n record_df=self._data.records(extras=False), phase_series=series, area=self.area, tau=self.tau)\n self._tracker_dict = {self.MAIN: tracker}","def __init__(self, num_id, name, redone, skipped, tasks):\n self.id = num_id\n self.name = name\n self.redone = redone\n self.skipped = skipped\n self.tasks = tasks\n super().__init__(Elements.STEP)","def __init__(self, report):\n # number of runs per feature to test\n self.runs = report.run_num\n self.histories = report.histories\n # self.features is a list of features that are tested, e.g. the batch sizes [32, 64, 128, 256]\n self.features = _get_features()","def setUp(self):\n self.cmd = BenchResultsProcesser()\n self.exp_keys = ['bench_data', 'time_fig', 'time_str',\n 'mem_fig', 'mem_str']\n # Get the tests folder\n tests_dir = os.path.dirname(os.path.abspath(__file__))\n # Path to the test timing folder\n self.timing_dir = os.path.join(tests_dir, '../support_files/timing')","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 initialize(self):\n self._setup_simulation_from_parameters()\n if \"orrb\" in self.constants.observation_providers:\n self._reset()\n self._goal = self._next_goal()\n self.update_goal_info()\n\n self.observer = self._build_observer()","def __init__(self, step_time, mu=None, sigma=None, n_step=None, ss=None):\n self.ss = ss\n self.n_step = n_step\n self.mu = mu\n self.sigma = sigma\n self.step_time = step_time","def setUp(self):\n self.directory = tempfile.TemporaryDirectory()\n self.dataset = self.dataset_cls(cache_root=self.directory.name)","def __init__(self, session, trial_nr, phase_durations, phase_names,\n parameters, timing, load_next_during_phase,\n verbose, condition='hrf'):\n super().__init__(session, trial_nr, phase_durations, phase_names,\n parameters, timing, verbose, load_next_during_phase)\n self.condition = condition\n self.last_fix_time, self.last_stim_time = 0.0","def __init__(self):\n\n self.current_path = os.getcwd()\n self.data_path = self.current_path + \"/data\"\n\n self.original_files = {}\n self.imitation_files = {}\n self.original_test_files = {}\n self.imitation_test_files = {}\n\n self.training_set = None\n self.original_test_set = None\n self.imitation_test_set = None\n\n self.accuracy = 0.\n self.threshold = 0.\n\n self.get_files()","def experiment_init(self):\n raise NotImplementedError(\"this needs to be implemented!\")","def __init__(self):\n\n #call super class's __init__ method\n super(TRiseSampler, self).__init__(name=\"trise\", observed=False)","def setUpClass(cls):\n super(TestPatientStatsHistory, cls).setUpClass()\n cls.stats_data = {\n \"num_patients_visited\": 1,\n \"num_patients_home_quarantine\": 2,\n \"num_patients_isolation\": 3,\n \"num_patient_referred\": 4,\n }","def __init__(self, train_data, test_data):\n self.train_data = train_data\n self.test_data = test_data\n\n # Emission and transition probability distributions\n self.emission_PD: ConditionalProbDist = None\n self.transition_PD: ConditionalProbDist = None\n self.states = []\n\n self.viterbi = []\n self.backpointer = []","def init_recording(self):\n self.statusBar().showMessage('Initialising...')\n self.streams = resolve_stream('type', 'EEG')\n self.inlet = StreamInlet(self.streams[0])\n self.timeObj = []\n self.sampleObj = []","def setUp(self):\n temperature = np.array([[185.0, 260.65, 273.15, 338.15]], dtype=np.float32)\n self.temperature = set_up_variable_cube(temperature)\n humidity = np.array([[60.0, 70.0, 75.0, 80.0]], dtype=np.float32)\n self.relative_humidity = set_up_variable_cube(\n humidity, name=\"relative_humidity\", units=\"%\"\n )\n pressure = np.array([[1.0e5, 9.9e4, 9.85e4, 9.8e4]], dtype=np.float32)\n self.pressure = set_up_variable_cube(pressure, name=\"air_pressure\", units=\"Pa\")\n self.expected_wbt_data = np.array(\n [[185.0, 259.88306, 271.78006, 333.96066]], dtype=np.float32\n )","def __init__(self, asa_factory: AsaFactory):\n super().__init__(asa_factory) # initialize step_in_progress flag\n self.agent, self.sampler, self.algo = asa_factory()\n self.batch_spec = self.sampler.batch_spec\n self.grad = None\n self.traj_infos = None\n self.opt_info = None","def __init__(self):\n\n #: Dict[str, Any]: Experiment metadata\n self.__experiment_metadata = None\n\n #: List[CurveData]: Processed experiment data set.\n self.__processed_data_set = list()\n\n #: Backend: backend object used for experimentation\n self.__backend = None\n\n # Add expected options to instance variable so that every method can access to.\n for key in self._default_options().__dict__:\n setattr(self, f\"__{key}\", None)\n\n # Add fixed parameters to instance variable so that every method can access to.\n for key in self.__fixed_parameters__:\n setattr(self, f\"__{key}\", None)","def init_storage(self):\n\n # Create the average rotation matrix (first order).\n self.first_frame_order = zeros((INC+1, 3, 3), float64)\n\n # Create the frame order matrix (each element is ensemble averaged and corresponds to a different time step).\n self.second_frame_order = zeros((INC+1, 9, 9), float64)\n\n # Init the rotation matrix.\n self.rot = zeros((3, 3), float64)\n\n # Some data arrays.\n self.full = zeros(INC+1)\n self.count = zeros(INC+1)","def setup(self, step, time, configuration):\n # Set the initial time.\n self.__last_time = time\n self.__initialTime = time\n\n # Allocate space for the spatially resolved information.\n typeList = configuration.types()\n if self.__spatially_resolved:\n self.__spatial_data = numpy.zeros((len(typeList)))\n\n if (self.__resultsPlace is not None):\n for i in range(0, len(typeList)):\n self.__trajRecord.write(str(self.__last_time)+\" \"+str(time)+\" \"+str(i)+\" \"+typeList[i]+\"\\n\")","def __init__(self):\n self.mu = None\n self.type = None\n self.direction = None\n self.eq_points = None\n self.init_conds = None\n self.time = None\n self.trajectory = None\n self.initial_point = None\n self.contour_levels = []","def _init(self) -> None:\n now = time()\n\n # Timestamps for tracking compute durations by task group.\n # Start with length 2 so that we always can compute a valid dt later.\n self.time = [now] * 2\n # The amount of compute since the last timestamp\n self.compute = {}\n # The number of threads at the time\n self.nthreads = [self.scheduler.total_nthreads] * 2","def __init__(self,initial_meter_reading, initial_date):\r\n\t\tself.initial_meter_reading = initial_meter_reading\r\n\t\tself.initial_date = initial_date\r\n\t\tself.total_units_consumed = 0\r\n\t\tself.total_amount_spent = 0","def __init__(self, *args: Any, **kwargs: Any):\n self.run_history: RunHistory = RunHistory()\n self.ensemble_performance_history: List[Dict[str, Any]] = []\n self.trajectory: List[TrajEntry] = []","def __init__(self, data=None, *, task_history=None):\n self._data = data if data is not None else {}\n self._task_history = task_history if task_history is not None else []","def __init__ (self, location, time=0):\n self.stepLocations = {0:(0, 0), 1:(1, 0), 2:(2, 0), 3:(0, 1), 5:(2, 1), 6:(0, 2), 7:(1, 2), 8:(2, 2)}\n self.stepDictionary = {(0, 0): \"back-left\",(1, 0):\"back\",(2, 0):\"back-right\",(0, 1): \"left\",\\\n (2, 1):\"right\",(0, 2):\"front-left\",(1, 2):\"front\", (2, 2):\"front-right\"}\n self.__buildLocation(location)\n self.fileLocs = FileLocations()\n self.time = time","def __init__(self, routine, *args, **kwargs):\n self.kw = kwargs\n Step.__init__(self, routine=routine, *args, **kwargs)\n ramsey_settings = self.parse_settings(self.get_requested_settings())\n qbcal.Ramsey.__init__(self, dev=self.dev, **ramsey_settings)","def __init__(self, base_dir, identifier, **kwargs):\n\n super(NST, self).__init__(**kwargs)\n\n self.base_dir = base_dir\n self.data_id = identifier\n self.data_dir = os.path.join(self.base_dir, self.data_id)\n self.data_type = 'EEG'\n self.data_name = 'NST'\n\n # number of classes in the dataset\n self.n_classes = kwargs.pop('n_classes', 3)\n\n # all NST datasets have the same configuration and parameters\n # length of a trial after trial_sample (in seconds)\n self.trial_len = 4\n # idle period prior to trial start (in seconds)\n self.trial_offset = 4\n # total time of the trial\n self.trial_total = self.trial_offset + self.trial_len+2\n # interval of motor imagery within trial_t (in seconds)\n self.mi_interval = [self.trial_offset, self.trial_offset + self.trial_len]\n\n # additional variables to store data as expected by the ABC\n self.raw_data = None\n self.trials = None\n self.labels = None\n self.sampling_freq = None\n\n # TODO: change the files on disk, don't check in here...\n # the first few sessions had a different file type\n self.f0 = os.path.join(self.data_dir, 'Run1.mat')\n self.f1 = os.path.join(self.data_dir, 'Run2.mat')\n self.f2 = os.path.join(self.data_dir, 'Run3.mat')\n\n # check if files are available\n for f in [self.f0, self.f1, self.f2]:\n if not os.path.isfile(f):\n raise DatasetError(\"NST Dataset ({id}) file '{f}' unavailable\".format(id=self.data_id, f=f))","def __init__(self, step_time, saw_time, step_interval=None, n_step=None, ss=None):\n self.ss = ss\n self.n_step = n_step\n self.interval = step_interval\n self.step_time = step_time\n self.saw_time = saw_time","def __init__(self, *args, **kwargs):\n Step.__init__(self, *args, **kwargs)\n\n # Configure all of the steps\n for key, val in self.step_defs.items():\n cfg = self.steps.get(key)\n if cfg is not None:\n new_step = val.from_config_section(\n cfg, parent=self, name=key,\n config_file=self.config_file)\n else:\n new_step = val(\n key, parent=self, config_file=self.config_file,\n **kwargs.get(key, {}))\n\n setattr(self, key, new_step)","def setup(self, stage=None):\n self.data_train, self.data_val, self.data_test = [None] * 3","def __init__(self):\n\t\tappionScript.AppionScript.__init__(self)\n\t\tself.rundata = {}\n\t\t### extra appionLoop functions:\n\t\tself._addDefaultParams()\n\t\tself.setFunctionResultKeys()\n\t\tself._setRunAndParameters()\n\t\t#self.specialCreateOutputDirs()\n\t\tself._initializeDoneDict()\n\t\tself.result_dirs={}","def __init__(self):\n self._values = {\n 'ver': 2,\n 'name': None,\n 'duration': None,\n 'success': True,\n }\n self._initialize()","def __init__(self, test_case_name):\n Result.__init_output_folder()\n self.__test_result = {} # Store information of a test case\n self.__run = [] # Store information of steps in test case\n self.__test_result[Result.__TEST_CASE] = test_case_name\n self.__test_result[Result.__RESULT] = Status.FAILED\n self.__test_result[Result.__START_TIME] = \\\n str(time.strftime(\"%Y-%m-%d_%H-%M-%S\"))\n self.__json_file_path = \\\n \"{}{}_{}.json\".format(Result.__json_dir,\n self.__test_result[Result.__TEST_CASE],\n self.__test_result[Result.__START_TIME])\n Result.result_of_all_tests.append(self.__json_file_path)","def initialize(self):\n self.data = None\n self.errors = []","def _initialize( self, steps=None ):\n assert isinstance( steps, list ) or ( steps is None )\n\n self._env = Env( 0 )\n self._steps = [ ]\n\n if not steps:\n return\n\n currentLevel = 0\n\n for step in steps:\n if step.level > currentLevel:\n self.beginHypo( )\n currentLevel += 1\n elif step.level < currentLevel:\n self.endHypo( )\n currentLevel -= 1\n\n self.addStep( step.prop, step.rsn )","def __init__(self):\n self.params = {}\n self.counts = 0\n self.failed = 0\n self.path = os.path.join(os.getcwd(), OUTPUT_DIR)\n \n if not os.path.exists(self.path):\n os.makedirs(self.path)","def __init__(self):\n self.statiFile = \"\"\n self.printOrder = []\n self.instCount = 0\n self.initializedVars = {\"GF\":[],\"TF\":[],\"LF\":[]}","def setup_class(cls):\n\n # set the base directory\n cls.basedir = os.path.join(os.path.split(os.path.realpath(__file__))[0], \"base\")\n\n cls.ninj = 50 # number of simulated signals\n cls.maxamp = 5e-23 # maximum amplitude\n cls.freqrange = (10.0, 100.0) # frequency range\n\n # default prior dictionary\n cls.priors = {}\n cls.priors[\"h0\"] = bilby.core.prior.Uniform(\n name=\"h0\", minimum=0.0, maximum=1e-22\n )","def __init__(self, args):\n\n self.directory = args.directory\n \n # generate string with only base data directory\n dirstring = os.path.basename(os.path.normpath(self.directory))\n self.string_directory = \"Data: {}\".format(dirstring) \n\n # get location of this file\n self.filepath = os.path.dirname(os.path.realpath(__file__))\n\n # initialize attributes to hold files and (inferEM, sample) dirs\n self.files = []\n self.betas = []\n self.compare_segs_dict = {}\n self.sample_dirs = {}\n self.prob_dirs = {}\n self.full_dataset = None\n self.full_dataset_tuple = None","def __init__(self, **kwargs): # noqa: E501 # noqa: E501\n self.local_vars_configuration = kwargs.get(\"local_vars_configuration\", Configuration.get_default_copy())\n\n self._unit = None\n self._step = None\n self._samples = None\n self._sample_streams = None\n self._dropped = None\n self._typename = None\n self.discriminator = None\n\n if \"unit\" in kwargs:\n self.unit = kwargs[\"unit\"]\n if \"step\" in kwargs:\n self.step = kwargs[\"step\"]\n self.samples = kwargs.get(\"samples\", None)\n self.sample_streams = kwargs.get(\"sample_streams\", None)\n if \"dropped\" in kwargs:\n self.dropped = kwargs[\"dropped\"]\n if \"typename\" in kwargs:\n self.typename = kwargs[\"typename\"]","def setup(self):\n self.rows = test_helpers.fetch_sample_teradata_rows()\n self.csv_path = 'not/a/real/path'","def __init__(self):\n self.state_locations = None;\n self.state_actions = None;\n self.start_id = None; # The id assigned to the start state\n self.goal_id = None; # The id assigned to the goal state\n self.start_loc = None; # The real word coordinates of the start state\n self.goal_loc = None; # The real word coordinates of the goal state\n self.reward_states_n = None\n self.reward_states = None\n self.reward_sendcommand = None\n self.reward_timeout = None\n self.timeout = None","def _initialize(self,points_data,points_test=None):\n \n if self.type_init=='resp':\n log_assignements = initialize_log_assignements(self.init,self.n_components,points_data,points_test,\n self.covariance_type)\n self._step_M(points_data,log_assignements)\n \n elif self.type_init=='mcw':\n means,cov,log_weights = initialize_mcw(self.init,self.n_components,points_data,points_test,\n self.covariance_type)\n self.means = means\n self.cov = cov\n self.log_weights = log_weights\n \n elif self.type_init=='kmeans':\n self._initialize_cov(points_data)\n \n self._is_initialized = True","def __init__(self):\n\n\t\tself.Helpers = Helpers(\"TassAI\", False)\n\n\t\tself.qs = 16\n\t\tself.context = InferenceContext([self.Helpers.confs[\"iotJumpWay\"][\"MQTT\"][\"TassAI\"][\"runas\"], self.Helpers.confs[\"iotJumpWay\"][\"MQTT\"][\"TassAI\"][\"runas\"], self.Helpers.confs[\"iotJumpWay\"][\"MQTT\"][\"TassAI\"][\"runas\"]], \"\", \"\", \"\")\n\n\t\tself.Helpers.logger.info(\"TassAI Helper Class initialization complete.\")","def __init__(self):\n # load files\n self.init_bin_start_time = load_last_bin_time()\n self.prev_value_cache = load_prev_val_cache()\n self.init_job_vals_dict = load_job_init_vals()\n\n config = load_config()\n self.database_domain = config[ConfigFields.DATABASE_DOMAIN]\n self.metrics = config[ConfigFields.METRICS]\n self.bin_duration = config[ConfigFields.BIN_DURATION]\n\n \"to be overwritten when server time is found in a job\"\n self.current_time = int(time.time())\n self.bin_start_times = None # updated below\n self.final_bin_end_time = None # updated below\n self.update_bin_times() # updates","def __init__(self, iteration=None, detailed_step=None):\n self.iteration = iteration\n self.detailed_step = detailed_step\n self.objective = None\n self.wirelength = None\n self.density = None\n self.density_weight = None\n self.hpwl = None\n self.rmst_wl = None\n self.overflow = None\n self.goverflow = None\n self.route_utilization = None\n self.pin_utilization = None\n self.max_density = None\n self.gmax_density = None\n self.gamma = None\n self.tns = None\n self.wns = None\n self.eval_time = None","def __init__(__self__, *,\n multistep_number: int,\n primary_step: 'outputs.PrimaryStepResponse',\n primary_step_id: str):\n pulumi.set(__self__, \"multistep_number\", multistep_number)\n pulumi.set(__self__, \"primary_step\", primary_step)\n pulumi.set(__self__, \"primary_step_id\", primary_step_id)","def __init__(self, init):\n self.stepforward = int(init)\n self.data = Linkedlist()","def __init__(self, step_runner, properties, environ, universe_view,\n engine_flags=None):\n self._step_runner = step_runner\n self._properties = properties\n self._environ = environ.copy()\n self._universe_view = universe_view\n self._clients = {client.IDENT: client for client in (\n recipe_api.DependencyManagerClient(self),\n recipe_api.PathsClient(),\n recipe_api.PropertiesClient(self),\n recipe_api.SourceManifestClient(self, properties),\n recipe_api.StepClient(self),\n )}\n self._engine_flags = engine_flags\n\n # A stack of ActiveStep objects, holding the most recently executed step at\n # each nest level (objects deeper in the stack have lower nest levels).\n # When we pop from this stack, we close the corresponding step stream.\n self._step_stack = []","def on_init(self,\n steps=None,\n **kwargs):\n super(Steps, self).on_init(**kwargs)\n\n self.steps = []\n if steps:\n for item in steps:\n if isinstance(item, dict):\n self.steps.append(Step(item, len(self.steps)+1))\n else:\n self.steps.append(item)\n\n logging.debug(u\"Adding steps:\")\n for step in self.steps:\n logging.debug(u\"- {}\".format(step.label))\n\n states = ['begin',\n 'running',\n 'completed',\n 'end']\n\n transitions = [\n\n { # engage on first step as soon as we are ready\n 'source': 'begin',\n 'target': 'running',\n 'condition': self.if_ready,\n 'action': self.next_step,\n },\n\n { # wait for the underlying machine of this step to complete\n 'source': 'running',\n 'target': 'completed',\n 'condition': self.step_has_completed,\n },\n\n { # start next step if there is one and if we are ready for it\n 'source': 'completed',\n 'target': 'running',\n 'condition': self.if_next,\n 'action': self.next_step,\n },\n\n { # end this state machine\n 'source': 'completed',\n 'target': 'end',\n 'condition': self.if_end,\n 'action': self.stop,\n },\n\n ]\n\n self.build(states=states,\n transitions=transitions,\n initial='begin')","def __init__(self, measure):\n self.measure = measure # Dictionary of the measurement steps\n self.devices = {} # Dictionary holding all the devices\n self.output_devices = [] # List of devices with output capabilities\n self.daqs = {} # Dictionary that holds for each daq the inputs and outputs.\n self.rotation_stages = [] # If there are rotation stages present, they will show up in this list.\n # This short block is going to become useful in the future, when interfacing with a GUI\n for d in self.measure:\n setattr(self, d, self.measure[d])","def __init__(self, initialState, runningModels, terminalModel):\n self.T = len(runningModels)\n self.initialState = initialState\n self.runningModels = runningModels\n self.runningDatas = [m.createData() for m in runningModels]\n self.terminalModel = terminalModel\n self.terminalData = terminalModel.createData()","def init(self, start_sample, fhat, budget):\n\n self.proposed_points = start_sample\n self.budget = budget\n self.fhat = fhat","def init(self, start_sample, fhat, budget):\n\n self.proposed_points = start_sample\n self.budget = budget\n self.fhat = fhat","def __init__(self, cfg_index, conditions, pars_dir, step_title, use_defaults, input_cfg_json_data):\n self.cfg_index = cfg_index\n self.conditions = conditions\n self.pars_dir = pars_dir\n self.step_title = step_title\n self.use_defaults = use_defaults\n self.input_cfg_json_data = input_cfg_json_data\n if not self.input_cfg_json_data:\n self._get_params()","def __init__(self):\n # Load in the phase assignments as a dataframe so I know what I'm grabbing, helps me keep track of what\n # I'm doing where\n self.l1_code_phase_assignments = pd.read_csv(\"./l1_code_phase_assignments.csv\",\n converters={\"CA_Phase_Select\": ast.literal_eval})\n # set the g1 and g2 feedback taps as members for easy reference\n self.g1_feedback_taps = [3, 10]\n self.g2_feedback_taps = [2, 3, 6, 8, 9, 10]","def __init__(self):\n super().__init__()\n TemplateEngineFactory.register_factory('Jinja2Engine', Jinja2Engine.Factory)\n\n step1 = PrepareAppConfTransfiguration()\n step2 = ConfReaderToContextTransfiguration()\n step3 = ValidateCollectionTags()\n step4 = AttributeChainedTransfiguration('mbean')\n step5 = AttributeChainedTransfiguration('connection')\n\n self.add(step1)\n self.add(step2)\n self.add(step3)\n self.add(step4)\n self.add(step5)","def __init__(\n self,\n path,\n tier,\n embeddings=None,\n preprocessor=None,\n transform=True):\n super().__init__()\n assert os.path.isdir(path)\n assert os.path.exists(os.path.join(path, f'{tier}.csv'))\n assert tier in self.TIERS\n\n self.path = path\n self.tier = tier\n self.data = None\n self.embeddings = embeddings\n self.preprocessor = preprocessor\n self.is_transform = transform\n self._init_dataset()","def __init__(self, *args, **kwargs):\n # Initializing the test & training set\n self._x_train = kwargs['X_train']\n self._y_train = kwargs['Y_train']\n self._x_test = kwargs['X_test']\n self._y_test = kwargs['Y_test']\n\n self.num_iteration = kwargs['num_iteration']\n self.learning_rate = kwargs['learning_rate']","def __init__(self):\n self._results = {}\n self._logs = {}","def setUp(self):\n self.report = dict(title=\"Report 1\", url=\"https://report1\")\n self.data_model = dict(\n metrics=dict(metric_type=dict(name=\"type\")),\n sources=dict(\n quality_time=dict(\n parameters=dict(\n status=dict(\n api_values={\n \"target met (green)\": \"target_met\",\n \"near target met (yellow)\": \"near_target_met\",\n \"target not met (red)\": \"target_not_met\",\n \"technical debt target met (grey)\": \"debt_target_met\",\n \"unknown (white)\": \"unknown\",\n }\n )\n )\n )\n ),\n )","def __init__(self, start=0, step=1):\n self.count, self.step = start, step\n self.lock = threading.Lock()","def __init__(self, store_directory):\n # Read in data\n analysis_script_path = os.path.join(store_directory, 'analysis.yaml')\n if not os.path.isfile(analysis_script_path):\n err_msg = 'Cannot find analysis.yaml script in {}'.format(store_directory)\n raise RuntimeError(err_msg)\n with open(analysis_script_path, 'r') as f:\n analysis = yaml.load(f)\n phases = []\n signs = {}\n ncfiles = {}\n for phase, sign in analysis:\n phases.append(phase)\n signs[phase] = sign\n ncfile_path = os.path.join(store_directory, phase + '.nc')\n ncfiles[phase] = nc.Dataset(ncfile_path, 'r')\n self.phases = phases\n self.signs = signs\n self.ncfiles = ncfiles\n self.nphases = len(phases)\n # Assign flags for other sections along with their global variables\n # General Data\n self._general_run = False\n self.iterations = {}\n # Equilibration\n self._equilibration_run = False\n self.u_ns = {}\n self.nequils = {}\n self.g_ts = {}\n self.Neff_maxs = {}\n # Decorrelation break-down\n self._decorrelation_run = False\n # Mixing Run (state)\n self._mixing_run = False\n # Replica mixing\n self._replica_mixing_run = False\n self._free_energy_run = False","def __init__(self):\n self.machines = {}\n self.configs = {}\n self.systems = {}\n self.jobs = {}\n self.benchmarks = {}\n self.projects = {}","def __init__(self):\n self._profiling_mode = False\n self._total_time_ms = 0.0\n self._traced_records = []\n self._statistical_results = {}","def __init__(self):\n self.__dataset = None","def __init__(self, experiment_dir):\n self.experiment_dir = pathlib.Path(experiment_dir)\n\n parameters_data = parse_parameters(experiment_dir)\n self.__dict__ = {**self.__dict__, **parameters_data}","def setUp(self):\n self.m = m = random.randint(1, 100)\n self.n = n = random.randint(1, 100)\n self.sig = sig = Signature(\"name\", Dim(\"m\"), Dim(\"n\"),\n sData(\"A\", \"ldA * n\"), Ld(\"ldA\", \"m\"),\n dData(\"B\", \"ldB * m\"), Ld(\"ldB\", \"m\"),\n cData(\"C\", \"ldC * n\"), Ld(\"ldC\", \"n\"))\n self.ex = ex = Experiment()\n ex.calls = [sig(m, n, \"X\", None, \"Y\", None, \"Z\", None)]\n ex.infer_lds()\n self.i = Symbol(\"i\")\n self.j = Symbol(\"j\")"],"string":"[\n \"def __init__(self):\\n self._trials = [] # private variables _ convention\",\n \"def __init__( self ):\\n self._env = None\\n self._steps = None\\n\\n self._initialize( )\",\n \"def init(self, **kwargs):\\n self._d = {}\\n self._th = None\\n self._run = True\\n self.load()\",\n \"def __init__(self):\\n self.step_list = [steps.Raw()]\",\n \"def __init__(self, step_name, variable_name, distribution):\\n self.step_name = step_name\\n self.variable_name = variable_name\\n self._distribution = distribution\",\n \"def trial_prep(self):\\n pass\",\n \"def __init__(self, routine, *args, **kwargs):\\n self.kw = kwargs\\n Step.__init__(self, routine=routine, *args, **kwargs)\\n t1_settings = self.parse_settings(self.get_requested_settings())\\n qbcal.T1.__init__(self, dev=self.dev, **t1_settings)\",\n \"def __init__(self):\\n Sampler.__init__(self)\\n self._registeredIdentifiers = set() # tracks job identifiers used for this adaptive sampler and its inheritors\\n self._prefixToIdentifiers = {} # tracks the mapping of run prefixes to particular identifiers\\n self._inputIdentifiers = {} # identifiers for a single realization\\n self._targetEvaluation = None # data object with feedback from sample realizations\\n self._solutionExport = None # data object for solution printing\\n self._requireSolnExport = False # if this object requires a solution export\\n # NOTE TargetEvaluations consider all the Step <Output> DataObjects as candidates, so requiring\\n # exactly one TargetEvaluation forces only having one <Output> DataObject in AdaptiveSampling\\n # MultiRun Steps. For now, we leave it as \\\"n\\\".\\n self.addAssemblerObject('TargetEvaluation', InputData.Quantity.one_to_infinity) # Place where realization evaluations go\",\n \"def __init__(self, *args):\\n \\n self.steps = args\",\n \"def setUpClass(self):\\n self.tmin, self.tmax, self.dt = (0, 10.0, 0.1)\\n self.start = { 'x' : 100.0, 'y' : 100.0, 'z' : 100.0,\\n 'psi' : 30.0, 'theta' : 0.0, 'phi' : 0.0,\\n 'v': 1.0, 'weight' : 100.0, 'fuel' : 100.0}\",\n \"def __init__(__self__, *,\\n multistep_number: int,\\n outcome_summary: str,\\n run_duration: 'outputs.DurationResponse',\\n step_id: str):\\n pulumi.set(__self__, \\\"multistep_number\\\", multistep_number)\\n pulumi.set(__self__, \\\"outcome_summary\\\", outcome_summary)\\n pulumi.set(__self__, \\\"run_duration\\\", run_duration)\\n pulumi.set(__self__, \\\"step_id\\\", step_id)\",\n \"def __init__(self, step_id=None, started=None, completed=None, duration_in_millis=None, status=None, stage=None, info_log=None):\\n\\n self._step_id = None\\n self._started = None\\n self._completed = None\\n self._duration_in_millis = None\\n self._status = None\\n self._stage = None\\n self._info_log = None\\n\\n self.step_id = step_id\\n self.started = started\\n if completed is not None:\\n self.completed = completed\\n if duration_in_millis is not None:\\n self.duration_in_millis = duration_in_millis\\n self.status = status\\n self.stage = stage\\n self.info_log = info_log\",\n \"def experiment_init(self):\\n pass\",\n \"def __init__(self) -> None:\\n\\n self.train_env = None # Training environment\\n self.agent = None # The algorithm used to solve an RL problem is represented by a TF-Agent\\n self.replay_buffer = None # The replay buffer keeps track of data collected from the environment\\n self.dataset = None # The agent needs access to the replay buffer via an iterable tf.data.Dataset\\n self.iterator = None # The iterator of self.dataset\",\n \"def __init__(self):\\n self._logger = logging.getLogger(__name__)\\n self.step_name = \\\"OpenFDA\\\"\",\n \"def __init__(self, step_time, step=None):\\n self.step_vector = step\\n self.step_time = step_time\\n self.ref_timer = None\",\n \"def __init__(self):\\n self.data_set = []\\n self.finalized_data = LogData()\",\n \"def __init__(self, train, test, head):\\n\\n # Save the URL's, in case we want it later\\n self.train_url = train\\n self.test_url = test\\n self.head_url = head\\n\\n # Grab the data, and store the internal variables\\n self._get_data()\",\n \"def __init__(self):\\n\\n data_extract=DataExtracter()\\n self.data = tuple()\",\n \"def __init__(self) -> None:\\n self.metrics = {}\\n self.current = None\\n self.run = None\",\n \"def _init_sample(self):\\n self.timestamps = np.zeros(5)\\n self.data = np.zeros((5, 12))\",\n \"def __init__(self, experiment_data):\\n self._experiment_data = experiment_data\",\n \"def initialize(self) -> None:\\n self._step = self._start_step\\n self._tebd_propagator = compute_tebd_propagator(\\n system_chain=self._system_chain,\\n time_step=self._parameters.dt/2.0,\\n epsrel=self._parameters.epsrel,\\n order=self._parameters.order)\\n self._results = {}\\n self._t_mps = PtTebdBackend(\\n gammas=self._initial_augmented_mps.gammas,\\n lambdas=self._initial_augmented_mps.lambdas,\\n epsrel=self._parameters.epsrel,\\n config=self._backend_config)\\n self._init_results()\\n self._apply_controls(step=self.step, post=False)\\n self._append_results()\",\n \"def __init__(self):\\n\\n self.start_datetime_wallclock = None;\\n self.end_datetime_wallclock = None;\\n self.action_count = 0;\\n self.reward_cumulative = 0;\\n self.mission_xml = None;\\n self.mission_type = None;\\n self.mission_seed = None;\\n self.student_guid = None;\\n self.mission_xml_as_expected = None;\\n self.is_goal = None;\\n self.is_timeout = None;\",\n \"def __init__(self,\\n steps,\\n global_settings=None,\\n routine=None,\\n ):\\n super().__init__(steps)\\n\\n if routine is not None:\\n self.routine = routine\\n\\n if global_settings is not None:\\n self.global_settings = global_settings\\n else:\\n self.global_settings = {}\",\n \"def __init__(self, path):\\n self.path = path \\n self.current_location = os.getcwd()\\n self.rmsd = dict()\\n self._get_rmsd_list()\\n self._get_scores_list()\\n self._get_running_time()\\n self._get_step()\",\n \"def initialization_step(self):\\n # Update where agents are\\n self.update_agent_location_vector()\\n # update task locations\\n self.update_task_location_vector()\\n # update deadlines\\n self.populate_deadline_vector()\\n # update distances to each task and orientation to each task\\n self.update_agent_distances_vector()\\n self.update_agent_orientation_vector()\",\n \"def _initialize_data(self):\\n self.unique_id = 123\\n\\n self.gas_valve_open = False\\n self.buffer_valve_open = False\\n self.pump_valve_open = False\\n\\n self.operatingmode = 0\\n\\n self.sample_pressure_high_limit = 100\\n self.sample_pressure_low_limit = 10\\n self.sample_pressure = 0\\n\\n self.error = 0\\n\\n self.buffer_pressure_high = True\",\n \"def __init__(self, step_time, step_interval=None, n_step=None, ss=None):\\n self.ss = ss\\n self.n_step = n_step\\n self.interval = step_interval\\n self.step_time = step_time\",\n \"def begin(self):\\n\\n env = self.context.lookup(\\\"/environment\\\")\\n\\n self._test_results_dir = env[\\\"output_directory\\\"]\\n self._starttime = env[\\\"starttime\\\"]\\n self._runid = env[\\\"runid\\\"]\\n\\n self._result_filename = os.path.join(self._test_results_dir, \\\"testrun_results.jsos\\\")\\n self._summary_filename = os.path.join(self._test_results_dir, \\\"testrun_summary.json\\\")\\n self._import_errors_filename = os.path.join(self._test_results_dir, \\\"import_errors.jsos\\\")\\n\\n return\",\n \"def __trial__(self, trial, practice):\\n from klibs.KLEventQueue import pump\\n from klibs.KLUserInterface import show_cursor, hide_cursor\\n\\n # At start of every trial, before setup_response_collector or trial_prep are run, retrieve\\n # the values of the independent variables (factors) for that trial (as generated earlier by\\n # TrialFactory) and set them as attributes of the experiment object.\\n for iv, value in trial.items():\\n setattr(self, iv, value)\\n\\n pump()\\n self.setup_response_collector()\\n self.trial_prep()\\n tx = None\\n try:\\n if P.development_mode and (P.dm_trial_show_mouse or (P.eye_tracking and not P.eye_tracker_available)):\\n show_cursor()\\n self.evm.start_clock()\\n if P.eye_tracking and not P.manual_eyelink_recording:\\n self.el.start(P.trial_number)\\n P.in_trial = True\\n self.__log_trial__(self.trial())\\n P.in_trial = False\\n if P.eye_tracking and not P.manual_eyelink_recording:\\n self.el.stop()\\n if P.development_mode and (P.dm_trial_show_mouse or (P.eye_tracking and not P.eye_tracker_available)):\\n hide_cursor()\\n self.evm.stop_clock()\\n self.trial_clean_up()\\n except TrialException as e:\\n self.trial_clean_up()\\n self.evm.stop_clock()\\n tx = e\\n if P.eye_tracking and not P.manual_eyelink_recording:\\n # todo: add a warning, here, if the recording hasn't been stopped when under manual control\\n self.el.stop()\\n if tx:\\n raise tx\",\n \"def __init__(self):\\n self.time_limit = None\\n self.time_step = None\\n self.robot = None\\n self.humans = None\\n self.global_time = None\\n self.human_times = None\\n # reward function\\n self.discomfort_dist = None\\n # simulation configuration\\n self.config = None\\n self.randomize_attributes = None\\n self.square_width = None\\n self.circle_radius = None\\n self.human_num = None\\n # for visualization\\n self.states = None\\n self.states_traj = None\",\n \"def _init_trackers(self):\\n data = copy.deepcopy(self._data)\\n series = ParamTracker.create_series(\\n first_date=data.first_date, last_date=data.today, population=data.population)\\n tracker = ParamTracker(\\n record_df=self._data.records(extras=False), phase_series=series, area=self.area, tau=self.tau)\\n self._tracker_dict = {self.MAIN: tracker}\",\n \"def __init__(self, num_id, name, redone, skipped, tasks):\\n self.id = num_id\\n self.name = name\\n self.redone = redone\\n self.skipped = skipped\\n self.tasks = tasks\\n super().__init__(Elements.STEP)\",\n \"def __init__(self, report):\\n # number of runs per feature to test\\n self.runs = report.run_num\\n self.histories = report.histories\\n # self.features is a list of features that are tested, e.g. the batch sizes [32, 64, 128, 256]\\n self.features = _get_features()\",\n \"def setUp(self):\\n self.cmd = BenchResultsProcesser()\\n self.exp_keys = ['bench_data', 'time_fig', 'time_str',\\n 'mem_fig', 'mem_str']\\n # Get the tests folder\\n tests_dir = os.path.dirname(os.path.abspath(__file__))\\n # Path to the test timing folder\\n self.timing_dir = os.path.join(tests_dir, '../support_files/timing')\",\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 initialize(self):\\n self._setup_simulation_from_parameters()\\n if \\\"orrb\\\" in self.constants.observation_providers:\\n self._reset()\\n self._goal = self._next_goal()\\n self.update_goal_info()\\n\\n self.observer = self._build_observer()\",\n \"def __init__(self, step_time, mu=None, sigma=None, n_step=None, ss=None):\\n self.ss = ss\\n self.n_step = n_step\\n self.mu = mu\\n self.sigma = sigma\\n self.step_time = step_time\",\n \"def setUp(self):\\n self.directory = tempfile.TemporaryDirectory()\\n self.dataset = self.dataset_cls(cache_root=self.directory.name)\",\n \"def __init__(self, session, trial_nr, phase_durations, phase_names,\\n parameters, timing, load_next_during_phase,\\n verbose, condition='hrf'):\\n super().__init__(session, trial_nr, phase_durations, phase_names,\\n parameters, timing, verbose, load_next_during_phase)\\n self.condition = condition\\n self.last_fix_time, self.last_stim_time = 0.0\",\n \"def __init__(self):\\n\\n self.current_path = os.getcwd()\\n self.data_path = self.current_path + \\\"/data\\\"\\n\\n self.original_files = {}\\n self.imitation_files = {}\\n self.original_test_files = {}\\n self.imitation_test_files = {}\\n\\n self.training_set = None\\n self.original_test_set = None\\n self.imitation_test_set = None\\n\\n self.accuracy = 0.\\n self.threshold = 0.\\n\\n self.get_files()\",\n \"def experiment_init(self):\\n raise NotImplementedError(\\\"this needs to be implemented!\\\")\",\n \"def __init__(self):\\n\\n #call super class's __init__ method\\n super(TRiseSampler, self).__init__(name=\\\"trise\\\", observed=False)\",\n \"def setUpClass(cls):\\n super(TestPatientStatsHistory, cls).setUpClass()\\n cls.stats_data = {\\n \\\"num_patients_visited\\\": 1,\\n \\\"num_patients_home_quarantine\\\": 2,\\n \\\"num_patients_isolation\\\": 3,\\n \\\"num_patient_referred\\\": 4,\\n }\",\n \"def __init__(self, train_data, test_data):\\n self.train_data = train_data\\n self.test_data = test_data\\n\\n # Emission and transition probability distributions\\n self.emission_PD: ConditionalProbDist = None\\n self.transition_PD: ConditionalProbDist = None\\n self.states = []\\n\\n self.viterbi = []\\n self.backpointer = []\",\n \"def init_recording(self):\\n self.statusBar().showMessage('Initialising...')\\n self.streams = resolve_stream('type', 'EEG')\\n self.inlet = StreamInlet(self.streams[0])\\n self.timeObj = []\\n self.sampleObj = []\",\n \"def setUp(self):\\n temperature = np.array([[185.0, 260.65, 273.15, 338.15]], dtype=np.float32)\\n self.temperature = set_up_variable_cube(temperature)\\n humidity = np.array([[60.0, 70.0, 75.0, 80.0]], dtype=np.float32)\\n self.relative_humidity = set_up_variable_cube(\\n humidity, name=\\\"relative_humidity\\\", units=\\\"%\\\"\\n )\\n pressure = np.array([[1.0e5, 9.9e4, 9.85e4, 9.8e4]], dtype=np.float32)\\n self.pressure = set_up_variable_cube(pressure, name=\\\"air_pressure\\\", units=\\\"Pa\\\")\\n self.expected_wbt_data = np.array(\\n [[185.0, 259.88306, 271.78006, 333.96066]], dtype=np.float32\\n )\",\n \"def __init__(self, asa_factory: AsaFactory):\\n super().__init__(asa_factory) # initialize step_in_progress flag\\n self.agent, self.sampler, self.algo = asa_factory()\\n self.batch_spec = self.sampler.batch_spec\\n self.grad = None\\n self.traj_infos = None\\n self.opt_info = None\",\n \"def __init__(self):\\n\\n #: Dict[str, Any]: Experiment metadata\\n self.__experiment_metadata = None\\n\\n #: List[CurveData]: Processed experiment data set.\\n self.__processed_data_set = list()\\n\\n #: Backend: backend object used for experimentation\\n self.__backend = None\\n\\n # Add expected options to instance variable so that every method can access to.\\n for key in self._default_options().__dict__:\\n setattr(self, f\\\"__{key}\\\", None)\\n\\n # Add fixed parameters to instance variable so that every method can access to.\\n for key in self.__fixed_parameters__:\\n setattr(self, f\\\"__{key}\\\", None)\",\n \"def init_storage(self):\\n\\n # Create the average rotation matrix (first order).\\n self.first_frame_order = zeros((INC+1, 3, 3), float64)\\n\\n # Create the frame order matrix (each element is ensemble averaged and corresponds to a different time step).\\n self.second_frame_order = zeros((INC+1, 9, 9), float64)\\n\\n # Init the rotation matrix.\\n self.rot = zeros((3, 3), float64)\\n\\n # Some data arrays.\\n self.full = zeros(INC+1)\\n self.count = zeros(INC+1)\",\n \"def setup(self, step, time, configuration):\\n # Set the initial time.\\n self.__last_time = time\\n self.__initialTime = time\\n\\n # Allocate space for the spatially resolved information.\\n typeList = configuration.types()\\n if self.__spatially_resolved:\\n self.__spatial_data = numpy.zeros((len(typeList)))\\n\\n if (self.__resultsPlace is not None):\\n for i in range(0, len(typeList)):\\n self.__trajRecord.write(str(self.__last_time)+\\\" \\\"+str(time)+\\\" \\\"+str(i)+\\\" \\\"+typeList[i]+\\\"\\\\n\\\")\",\n \"def __init__(self):\\n self.mu = None\\n self.type = None\\n self.direction = None\\n self.eq_points = None\\n self.init_conds = None\\n self.time = None\\n self.trajectory = None\\n self.initial_point = None\\n self.contour_levels = []\",\n \"def _init(self) -> None:\\n now = time()\\n\\n # Timestamps for tracking compute durations by task group.\\n # Start with length 2 so that we always can compute a valid dt later.\\n self.time = [now] * 2\\n # The amount of compute since the last timestamp\\n self.compute = {}\\n # The number of threads at the time\\n self.nthreads = [self.scheduler.total_nthreads] * 2\",\n \"def __init__(self,initial_meter_reading, initial_date):\\r\\n\\t\\tself.initial_meter_reading = initial_meter_reading\\r\\n\\t\\tself.initial_date = initial_date\\r\\n\\t\\tself.total_units_consumed = 0\\r\\n\\t\\tself.total_amount_spent = 0\",\n \"def __init__(self, *args: Any, **kwargs: Any):\\n self.run_history: RunHistory = RunHistory()\\n self.ensemble_performance_history: List[Dict[str, Any]] = []\\n self.trajectory: List[TrajEntry] = []\",\n \"def __init__(self, data=None, *, task_history=None):\\n self._data = data if data is not None else {}\\n self._task_history = task_history if task_history is not None else []\",\n \"def __init__ (self, location, time=0):\\n self.stepLocations = {0:(0, 0), 1:(1, 0), 2:(2, 0), 3:(0, 1), 5:(2, 1), 6:(0, 2), 7:(1, 2), 8:(2, 2)}\\n self.stepDictionary = {(0, 0): \\\"back-left\\\",(1, 0):\\\"back\\\",(2, 0):\\\"back-right\\\",(0, 1): \\\"left\\\",\\\\\\n (2, 1):\\\"right\\\",(0, 2):\\\"front-left\\\",(1, 2):\\\"front\\\", (2, 2):\\\"front-right\\\"}\\n self.__buildLocation(location)\\n self.fileLocs = FileLocations()\\n self.time = time\",\n \"def __init__(self, routine, *args, **kwargs):\\n self.kw = kwargs\\n Step.__init__(self, routine=routine, *args, **kwargs)\\n ramsey_settings = self.parse_settings(self.get_requested_settings())\\n qbcal.Ramsey.__init__(self, dev=self.dev, **ramsey_settings)\",\n \"def __init__(self, base_dir, identifier, **kwargs):\\n\\n super(NST, self).__init__(**kwargs)\\n\\n self.base_dir = base_dir\\n self.data_id = identifier\\n self.data_dir = os.path.join(self.base_dir, self.data_id)\\n self.data_type = 'EEG'\\n self.data_name = 'NST'\\n\\n # number of classes in the dataset\\n self.n_classes = kwargs.pop('n_classes', 3)\\n\\n # all NST datasets have the same configuration and parameters\\n # length of a trial after trial_sample (in seconds)\\n self.trial_len = 4\\n # idle period prior to trial start (in seconds)\\n self.trial_offset = 4\\n # total time of the trial\\n self.trial_total = self.trial_offset + self.trial_len+2\\n # interval of motor imagery within trial_t (in seconds)\\n self.mi_interval = [self.trial_offset, self.trial_offset + self.trial_len]\\n\\n # additional variables to store data as expected by the ABC\\n self.raw_data = None\\n self.trials = None\\n self.labels = None\\n self.sampling_freq = None\\n\\n # TODO: change the files on disk, don't check in here...\\n # the first few sessions had a different file type\\n self.f0 = os.path.join(self.data_dir, 'Run1.mat')\\n self.f1 = os.path.join(self.data_dir, 'Run2.mat')\\n self.f2 = os.path.join(self.data_dir, 'Run3.mat')\\n\\n # check if files are available\\n for f in [self.f0, self.f1, self.f2]:\\n if not os.path.isfile(f):\\n raise DatasetError(\\\"NST Dataset ({id}) file '{f}' unavailable\\\".format(id=self.data_id, f=f))\",\n \"def __init__(self, step_time, saw_time, step_interval=None, n_step=None, ss=None):\\n self.ss = ss\\n self.n_step = n_step\\n self.interval = step_interval\\n self.step_time = step_time\\n self.saw_time = saw_time\",\n \"def __init__(self, *args, **kwargs):\\n Step.__init__(self, *args, **kwargs)\\n\\n # Configure all of the steps\\n for key, val in self.step_defs.items():\\n cfg = self.steps.get(key)\\n if cfg is not None:\\n new_step = val.from_config_section(\\n cfg, parent=self, name=key,\\n config_file=self.config_file)\\n else:\\n new_step = val(\\n key, parent=self, config_file=self.config_file,\\n **kwargs.get(key, {}))\\n\\n setattr(self, key, new_step)\",\n \"def setup(self, stage=None):\\n self.data_train, self.data_val, self.data_test = [None] * 3\",\n \"def __init__(self):\\n\\t\\tappionScript.AppionScript.__init__(self)\\n\\t\\tself.rundata = {}\\n\\t\\t### extra appionLoop functions:\\n\\t\\tself._addDefaultParams()\\n\\t\\tself.setFunctionResultKeys()\\n\\t\\tself._setRunAndParameters()\\n\\t\\t#self.specialCreateOutputDirs()\\n\\t\\tself._initializeDoneDict()\\n\\t\\tself.result_dirs={}\",\n \"def __init__(self):\\n self._values = {\\n 'ver': 2,\\n 'name': None,\\n 'duration': None,\\n 'success': True,\\n }\\n self._initialize()\",\n \"def __init__(self, test_case_name):\\n Result.__init_output_folder()\\n self.__test_result = {} # Store information of a test case\\n self.__run = [] # Store information of steps in test case\\n self.__test_result[Result.__TEST_CASE] = test_case_name\\n self.__test_result[Result.__RESULT] = Status.FAILED\\n self.__test_result[Result.__START_TIME] = \\\\\\n str(time.strftime(\\\"%Y-%m-%d_%H-%M-%S\\\"))\\n self.__json_file_path = \\\\\\n \\\"{}{}_{}.json\\\".format(Result.__json_dir,\\n self.__test_result[Result.__TEST_CASE],\\n self.__test_result[Result.__START_TIME])\\n Result.result_of_all_tests.append(self.__json_file_path)\",\n \"def initialize(self):\\n self.data = None\\n self.errors = []\",\n \"def _initialize( self, steps=None ):\\n assert isinstance( steps, list ) or ( steps is None )\\n\\n self._env = Env( 0 )\\n self._steps = [ ]\\n\\n if not steps:\\n return\\n\\n currentLevel = 0\\n\\n for step in steps:\\n if step.level > currentLevel:\\n self.beginHypo( )\\n currentLevel += 1\\n elif step.level < currentLevel:\\n self.endHypo( )\\n currentLevel -= 1\\n\\n self.addStep( step.prop, step.rsn )\",\n \"def __init__(self):\\n self.params = {}\\n self.counts = 0\\n self.failed = 0\\n self.path = os.path.join(os.getcwd(), OUTPUT_DIR)\\n \\n if not os.path.exists(self.path):\\n os.makedirs(self.path)\",\n \"def __init__(self):\\n self.statiFile = \\\"\\\"\\n self.printOrder = []\\n self.instCount = 0\\n self.initializedVars = {\\\"GF\\\":[],\\\"TF\\\":[],\\\"LF\\\":[]}\",\n \"def setup_class(cls):\\n\\n # set the base directory\\n cls.basedir = os.path.join(os.path.split(os.path.realpath(__file__))[0], \\\"base\\\")\\n\\n cls.ninj = 50 # number of simulated signals\\n cls.maxamp = 5e-23 # maximum amplitude\\n cls.freqrange = (10.0, 100.0) # frequency range\\n\\n # default prior dictionary\\n cls.priors = {}\\n cls.priors[\\\"h0\\\"] = bilby.core.prior.Uniform(\\n name=\\\"h0\\\", minimum=0.0, maximum=1e-22\\n )\",\n \"def __init__(self, args):\\n\\n self.directory = args.directory\\n \\n # generate string with only base data directory\\n dirstring = os.path.basename(os.path.normpath(self.directory))\\n self.string_directory = \\\"Data: {}\\\".format(dirstring) \\n\\n # get location of this file\\n self.filepath = os.path.dirname(os.path.realpath(__file__))\\n\\n # initialize attributes to hold files and (inferEM, sample) dirs\\n self.files = []\\n self.betas = []\\n self.compare_segs_dict = {}\\n self.sample_dirs = {}\\n self.prob_dirs = {}\\n self.full_dataset = None\\n self.full_dataset_tuple = None\",\n \"def __init__(self, **kwargs): # noqa: E501 # noqa: E501\\n self.local_vars_configuration = kwargs.get(\\\"local_vars_configuration\\\", Configuration.get_default_copy())\\n\\n self._unit = None\\n self._step = None\\n self._samples = None\\n self._sample_streams = None\\n self._dropped = None\\n self._typename = None\\n self.discriminator = None\\n\\n if \\\"unit\\\" in kwargs:\\n self.unit = kwargs[\\\"unit\\\"]\\n if \\\"step\\\" in kwargs:\\n self.step = kwargs[\\\"step\\\"]\\n self.samples = kwargs.get(\\\"samples\\\", None)\\n self.sample_streams = kwargs.get(\\\"sample_streams\\\", None)\\n if \\\"dropped\\\" in kwargs:\\n self.dropped = kwargs[\\\"dropped\\\"]\\n if \\\"typename\\\" in kwargs:\\n self.typename = kwargs[\\\"typename\\\"]\",\n \"def setup(self):\\n self.rows = test_helpers.fetch_sample_teradata_rows()\\n self.csv_path = 'not/a/real/path'\",\n \"def __init__(self):\\n self.state_locations = None;\\n self.state_actions = None;\\n self.start_id = None; # The id assigned to the start state\\n self.goal_id = None; # The id assigned to the goal state\\n self.start_loc = None; # The real word coordinates of the start state\\n self.goal_loc = None; # The real word coordinates of the goal state\\n self.reward_states_n = None\\n self.reward_states = None\\n self.reward_sendcommand = None\\n self.reward_timeout = None\\n self.timeout = None\",\n \"def _initialize(self,points_data,points_test=None):\\n \\n if self.type_init=='resp':\\n log_assignements = initialize_log_assignements(self.init,self.n_components,points_data,points_test,\\n self.covariance_type)\\n self._step_M(points_data,log_assignements)\\n \\n elif self.type_init=='mcw':\\n means,cov,log_weights = initialize_mcw(self.init,self.n_components,points_data,points_test,\\n self.covariance_type)\\n self.means = means\\n self.cov = cov\\n self.log_weights = log_weights\\n \\n elif self.type_init=='kmeans':\\n self._initialize_cov(points_data)\\n \\n self._is_initialized = True\",\n \"def __init__(self):\\n\\n\\t\\tself.Helpers = Helpers(\\\"TassAI\\\", False)\\n\\n\\t\\tself.qs = 16\\n\\t\\tself.context = InferenceContext([self.Helpers.confs[\\\"iotJumpWay\\\"][\\\"MQTT\\\"][\\\"TassAI\\\"][\\\"runas\\\"], self.Helpers.confs[\\\"iotJumpWay\\\"][\\\"MQTT\\\"][\\\"TassAI\\\"][\\\"runas\\\"], self.Helpers.confs[\\\"iotJumpWay\\\"][\\\"MQTT\\\"][\\\"TassAI\\\"][\\\"runas\\\"]], \\\"\\\", \\\"\\\", \\\"\\\")\\n\\n\\t\\tself.Helpers.logger.info(\\\"TassAI Helper Class initialization complete.\\\")\",\n \"def __init__(self):\\n # load files\\n self.init_bin_start_time = load_last_bin_time()\\n self.prev_value_cache = load_prev_val_cache()\\n self.init_job_vals_dict = load_job_init_vals()\\n\\n config = load_config()\\n self.database_domain = config[ConfigFields.DATABASE_DOMAIN]\\n self.metrics = config[ConfigFields.METRICS]\\n self.bin_duration = config[ConfigFields.BIN_DURATION]\\n\\n \\\"to be overwritten when server time is found in a job\\\"\\n self.current_time = int(time.time())\\n self.bin_start_times = None # updated below\\n self.final_bin_end_time = None # updated below\\n self.update_bin_times() # updates\",\n \"def __init__(self, iteration=None, detailed_step=None):\\n self.iteration = iteration\\n self.detailed_step = detailed_step\\n self.objective = None\\n self.wirelength = None\\n self.density = None\\n self.density_weight = None\\n self.hpwl = None\\n self.rmst_wl = None\\n self.overflow = None\\n self.goverflow = None\\n self.route_utilization = None\\n self.pin_utilization = None\\n self.max_density = None\\n self.gmax_density = None\\n self.gamma = None\\n self.tns = None\\n self.wns = None\\n self.eval_time = None\",\n \"def __init__(__self__, *,\\n multistep_number: int,\\n primary_step: 'outputs.PrimaryStepResponse',\\n primary_step_id: str):\\n pulumi.set(__self__, \\\"multistep_number\\\", multistep_number)\\n pulumi.set(__self__, \\\"primary_step\\\", primary_step)\\n pulumi.set(__self__, \\\"primary_step_id\\\", primary_step_id)\",\n \"def __init__(self, init):\\n self.stepforward = int(init)\\n self.data = Linkedlist()\",\n \"def __init__(self, step_runner, properties, environ, universe_view,\\n engine_flags=None):\\n self._step_runner = step_runner\\n self._properties = properties\\n self._environ = environ.copy()\\n self._universe_view = universe_view\\n self._clients = {client.IDENT: client for client in (\\n recipe_api.DependencyManagerClient(self),\\n recipe_api.PathsClient(),\\n recipe_api.PropertiesClient(self),\\n recipe_api.SourceManifestClient(self, properties),\\n recipe_api.StepClient(self),\\n )}\\n self._engine_flags = engine_flags\\n\\n # A stack of ActiveStep objects, holding the most recently executed step at\\n # each nest level (objects deeper in the stack have lower nest levels).\\n # When we pop from this stack, we close the corresponding step stream.\\n self._step_stack = []\",\n \"def on_init(self,\\n steps=None,\\n **kwargs):\\n super(Steps, self).on_init(**kwargs)\\n\\n self.steps = []\\n if steps:\\n for item in steps:\\n if isinstance(item, dict):\\n self.steps.append(Step(item, len(self.steps)+1))\\n else:\\n self.steps.append(item)\\n\\n logging.debug(u\\\"Adding steps:\\\")\\n for step in self.steps:\\n logging.debug(u\\\"- {}\\\".format(step.label))\\n\\n states = ['begin',\\n 'running',\\n 'completed',\\n 'end']\\n\\n transitions = [\\n\\n { # engage on first step as soon as we are ready\\n 'source': 'begin',\\n 'target': 'running',\\n 'condition': self.if_ready,\\n 'action': self.next_step,\\n },\\n\\n { # wait for the underlying machine of this step to complete\\n 'source': 'running',\\n 'target': 'completed',\\n 'condition': self.step_has_completed,\\n },\\n\\n { # start next step if there is one and if we are ready for it\\n 'source': 'completed',\\n 'target': 'running',\\n 'condition': self.if_next,\\n 'action': self.next_step,\\n },\\n\\n { # end this state machine\\n 'source': 'completed',\\n 'target': 'end',\\n 'condition': self.if_end,\\n 'action': self.stop,\\n },\\n\\n ]\\n\\n self.build(states=states,\\n transitions=transitions,\\n initial='begin')\",\n \"def __init__(self, measure):\\n self.measure = measure # Dictionary of the measurement steps\\n self.devices = {} # Dictionary holding all the devices\\n self.output_devices = [] # List of devices with output capabilities\\n self.daqs = {} # Dictionary that holds for each daq the inputs and outputs.\\n self.rotation_stages = [] # If there are rotation stages present, they will show up in this list.\\n # This short block is going to become useful in the future, when interfacing with a GUI\\n for d in self.measure:\\n setattr(self, d, self.measure[d])\",\n \"def __init__(self, initialState, runningModels, terminalModel):\\n self.T = len(runningModels)\\n self.initialState = initialState\\n self.runningModels = runningModels\\n self.runningDatas = [m.createData() for m in runningModels]\\n self.terminalModel = terminalModel\\n self.terminalData = terminalModel.createData()\",\n \"def init(self, start_sample, fhat, budget):\\n\\n self.proposed_points = start_sample\\n self.budget = budget\\n self.fhat = fhat\",\n \"def init(self, start_sample, fhat, budget):\\n\\n self.proposed_points = start_sample\\n self.budget = budget\\n self.fhat = fhat\",\n \"def __init__(self, cfg_index, conditions, pars_dir, step_title, use_defaults, input_cfg_json_data):\\n self.cfg_index = cfg_index\\n self.conditions = conditions\\n self.pars_dir = pars_dir\\n self.step_title = step_title\\n self.use_defaults = use_defaults\\n self.input_cfg_json_data = input_cfg_json_data\\n if not self.input_cfg_json_data:\\n self._get_params()\",\n \"def __init__(self):\\n # Load in the phase assignments as a dataframe so I know what I'm grabbing, helps me keep track of what\\n # I'm doing where\\n self.l1_code_phase_assignments = pd.read_csv(\\\"./l1_code_phase_assignments.csv\\\",\\n converters={\\\"CA_Phase_Select\\\": ast.literal_eval})\\n # set the g1 and g2 feedback taps as members for easy reference\\n self.g1_feedback_taps = [3, 10]\\n self.g2_feedback_taps = [2, 3, 6, 8, 9, 10]\",\n \"def __init__(self):\\n super().__init__()\\n TemplateEngineFactory.register_factory('Jinja2Engine', Jinja2Engine.Factory)\\n\\n step1 = PrepareAppConfTransfiguration()\\n step2 = ConfReaderToContextTransfiguration()\\n step3 = ValidateCollectionTags()\\n step4 = AttributeChainedTransfiguration('mbean')\\n step5 = AttributeChainedTransfiguration('connection')\\n\\n self.add(step1)\\n self.add(step2)\\n self.add(step3)\\n self.add(step4)\\n self.add(step5)\",\n \"def __init__(\\n self,\\n path,\\n tier,\\n embeddings=None,\\n preprocessor=None,\\n transform=True):\\n super().__init__()\\n assert os.path.isdir(path)\\n assert os.path.exists(os.path.join(path, f'{tier}.csv'))\\n assert tier in self.TIERS\\n\\n self.path = path\\n self.tier = tier\\n self.data = None\\n self.embeddings = embeddings\\n self.preprocessor = preprocessor\\n self.is_transform = transform\\n self._init_dataset()\",\n \"def __init__(self, *args, **kwargs):\\n # Initializing the test & training set\\n self._x_train = kwargs['X_train']\\n self._y_train = kwargs['Y_train']\\n self._x_test = kwargs['X_test']\\n self._y_test = kwargs['Y_test']\\n\\n self.num_iteration = kwargs['num_iteration']\\n self.learning_rate = kwargs['learning_rate']\",\n \"def __init__(self):\\n self._results = {}\\n self._logs = {}\",\n \"def setUp(self):\\n self.report = dict(title=\\\"Report 1\\\", url=\\\"https://report1\\\")\\n self.data_model = dict(\\n metrics=dict(metric_type=dict(name=\\\"type\\\")),\\n sources=dict(\\n quality_time=dict(\\n parameters=dict(\\n status=dict(\\n api_values={\\n \\\"target met (green)\\\": \\\"target_met\\\",\\n \\\"near target met (yellow)\\\": \\\"near_target_met\\\",\\n \\\"target not met (red)\\\": \\\"target_not_met\\\",\\n \\\"technical debt target met (grey)\\\": \\\"debt_target_met\\\",\\n \\\"unknown (white)\\\": \\\"unknown\\\",\\n }\\n )\\n )\\n )\\n ),\\n )\",\n \"def __init__(self, start=0, step=1):\\n self.count, self.step = start, step\\n self.lock = threading.Lock()\",\n \"def __init__(self, store_directory):\\n # Read in data\\n analysis_script_path = os.path.join(store_directory, 'analysis.yaml')\\n if not os.path.isfile(analysis_script_path):\\n err_msg = 'Cannot find analysis.yaml script in {}'.format(store_directory)\\n raise RuntimeError(err_msg)\\n with open(analysis_script_path, 'r') as f:\\n analysis = yaml.load(f)\\n phases = []\\n signs = {}\\n ncfiles = {}\\n for phase, sign in analysis:\\n phases.append(phase)\\n signs[phase] = sign\\n ncfile_path = os.path.join(store_directory, phase + '.nc')\\n ncfiles[phase] = nc.Dataset(ncfile_path, 'r')\\n self.phases = phases\\n self.signs = signs\\n self.ncfiles = ncfiles\\n self.nphases = len(phases)\\n # Assign flags for other sections along with their global variables\\n # General Data\\n self._general_run = False\\n self.iterations = {}\\n # Equilibration\\n self._equilibration_run = False\\n self.u_ns = {}\\n self.nequils = {}\\n self.g_ts = {}\\n self.Neff_maxs = {}\\n # Decorrelation break-down\\n self._decorrelation_run = False\\n # Mixing Run (state)\\n self._mixing_run = False\\n # Replica mixing\\n self._replica_mixing_run = False\\n self._free_energy_run = False\",\n \"def __init__(self):\\n self.machines = {}\\n self.configs = {}\\n self.systems = {}\\n self.jobs = {}\\n self.benchmarks = {}\\n self.projects = {}\",\n \"def __init__(self):\\n self._profiling_mode = False\\n self._total_time_ms = 0.0\\n self._traced_records = []\\n self._statistical_results = {}\",\n \"def __init__(self):\\n self.__dataset = None\",\n \"def __init__(self, experiment_dir):\\n self.experiment_dir = pathlib.Path(experiment_dir)\\n\\n parameters_data = parse_parameters(experiment_dir)\\n self.__dict__ = {**self.__dict__, **parameters_data}\",\n \"def setUp(self):\\n self.m = m = random.randint(1, 100)\\n self.n = n = random.randint(1, 100)\\n self.sig = sig = Signature(\\\"name\\\", Dim(\\\"m\\\"), Dim(\\\"n\\\"),\\n sData(\\\"A\\\", \\\"ldA * n\\\"), Ld(\\\"ldA\\\", \\\"m\\\"),\\n dData(\\\"B\\\", \\\"ldB * m\\\"), Ld(\\\"ldB\\\", \\\"m\\\"),\\n cData(\\\"C\\\", \\\"ldC * n\\\"), Ld(\\\"ldC\\\", \\\"n\\\"))\\n self.ex = ex = Experiment()\\n ex.calls = [sig(m, n, \\\"X\\\", None, \\\"Y\\\", None, \\\"Z\\\", None)]\\n ex.infer_lds()\\n self.i = Symbol(\\\"i\\\")\\n self.j = Symbol(\\\"j\\\")\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.716681","0.70468086","0.6757133","0.6672487","0.66058046","0.6582223","0.653331","0.65125954","0.649741","0.64206564","0.63828874","0.6369628","0.6362635","0.6338107","0.6331931","0.6317953","0.6301544","0.6297562","0.62726104","0.62723035","0.6268866","0.62650734","0.62497956","0.6238481","0.62132776","0.62107575","0.62029135","0.61964625","0.6195898","0.61783457","0.6176611","0.6160938","0.6140194","0.61182576","0.61137706","0.6107904","0.6093393","0.60923445","0.6085975","0.60854036","0.60816175","0.60792017","0.60667783","0.6057927","0.6056549","0.6041297","0.603946","0.60338974","0.60326123","0.602926","0.6028561","0.60183936","0.600567","0.6003902","0.59986675","0.5994246","0.5989575","0.59858876","0.5982472","0.5979893","0.5970099","0.59640026","0.59551686","0.59537536","0.59518397","0.5947304","0.59410375","0.59385514","0.5936955","0.5930239","0.59210265","0.5920025","0.5919266","0.59162307","0.5915938","0.5913457","0.59121054","0.59045655","0.58907527","0.5882523","0.58774996","0.58739644","0.586282","0.5860546","0.5851482","0.5848187","0.5848187","0.58479744","0.5847185","0.5846919","0.5846859","0.58451074","0.5843671","0.5834576","0.5834089","0.5833717","0.583265","0.58321136","0.5829312","0.5825402","0.58252627"],"string":"[\n \"0.716681\",\n \"0.70468086\",\n \"0.6757133\",\n \"0.6672487\",\n \"0.66058046\",\n \"0.6582223\",\n \"0.653331\",\n \"0.65125954\",\n \"0.649741\",\n \"0.64206564\",\n \"0.63828874\",\n \"0.6369628\",\n \"0.6362635\",\n \"0.6338107\",\n \"0.6331931\",\n \"0.6317953\",\n \"0.6301544\",\n \"0.6297562\",\n \"0.62726104\",\n \"0.62723035\",\n \"0.6268866\",\n \"0.62650734\",\n \"0.62497956\",\n \"0.6238481\",\n \"0.62132776\",\n \"0.62107575\",\n \"0.62029135\",\n \"0.61964625\",\n \"0.6195898\",\n \"0.61783457\",\n \"0.6176611\",\n \"0.6160938\",\n \"0.6140194\",\n \"0.61182576\",\n \"0.61137706\",\n \"0.6107904\",\n \"0.6093393\",\n \"0.60923445\",\n \"0.6085975\",\n \"0.60854036\",\n \"0.60816175\",\n \"0.60792017\",\n \"0.60667783\",\n \"0.6057927\",\n \"0.6056549\",\n \"0.6041297\",\n \"0.603946\",\n \"0.60338974\",\n \"0.60326123\",\n \"0.602926\",\n \"0.6028561\",\n \"0.60183936\",\n \"0.600567\",\n \"0.6003902\",\n \"0.59986675\",\n \"0.5994246\",\n \"0.5989575\",\n \"0.59858876\",\n \"0.5982472\",\n \"0.5979893\",\n \"0.5970099\",\n \"0.59640026\",\n \"0.59551686\",\n \"0.59537536\",\n \"0.59518397\",\n \"0.5947304\",\n \"0.59410375\",\n \"0.59385514\",\n \"0.5936955\",\n \"0.5930239\",\n \"0.59210265\",\n \"0.5920025\",\n \"0.5919266\",\n \"0.59162307\",\n \"0.5915938\",\n \"0.5913457\",\n \"0.59121054\",\n \"0.59045655\",\n \"0.58907527\",\n \"0.5882523\",\n \"0.58774996\",\n \"0.58739644\",\n \"0.586282\",\n \"0.5860546\",\n \"0.5851482\",\n \"0.5848187\",\n \"0.5848187\",\n \"0.58479744\",\n \"0.5847185\",\n \"0.5846919\",\n \"0.5846859\",\n \"0.58451074\",\n \"0.5843671\",\n \"0.5834576\",\n \"0.5834089\",\n \"0.5833717\",\n \"0.583265\",\n \"0.58321136\",\n \"0.5829312\",\n \"0.5825402\",\n \"0.58252627\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":22477,"cells":{"query":{"kind":"string","value":"Record trial data and model state after a step during navigation."},"document":{"kind":"string","value":"def get_rec_stats(village, u, s, r, stim, GS, HC, hc_ro, vS, dlS, data,\n co_occ_pars, idx=None):\n\n res = {}\n\n if 'anim_data' in data:\n # Externally observable variables: action, state, reward, x-y position.\n x, y = village.animal_coords()\n res['anim_data'] = {'u': u, 's': s, 'r': r, 'x': x, 'y': y}\n\n if 'stim_data' in data:\n # Stimuli: motor feedback and visual input, both noise-free and noisy.\n res['stim_data'] = stim\n\n if 'gs_state' in data:\n # Instantaneous GS activity.\n res['gs_state'] = GS.P.copy()\n\n if 'hc_state' in data:\n # Instantaneous HC state estimate (full).\n res['hc_state'] = utils.get_copy(HC.s)\n\n if 'hc_ro' in data:\n # Instantaneous HC state estimate (read-out only).\n res['hc_ro'] = hc_ro.copy()\n\n if 'vs_state' in data:\n # Full vS reward estimate across all states.\n res['vs_state'] = vS.r.copy()\n\n if 'dls_state' in data:\n # Full dlS habit matrix across all states and actions.\n res['dls_state'] = dlS.Q.copy()\n\n if 'co_occs' in data:\n # Real location - HC state co-occurance frequency in last n steps.\n co_occ_pars['idx'] = update_co_occ_mat(hc_ro, village.S.index(s),\n **co_occ_pars)\n res['co_occs'] = co_occ_pars['co_occs'].copy()\n\n if 'gs_hc_pos' in data:\n # Mean position of GS - HC connectivity, per HC unit.\n C, xv, yv, circ = HC.GS_HC, GS.xvec, GS.yvec, GS.circular\n res['gs_hc_pos'] = analysis.GS_HC_conn_mean(C, xv, yv, circ)\n\n if 'gs_hc_max' in data:\n # Max value of GS - HC connectivity, per HC unit (proxy of spread).\n # Entropy calculation takes a lot of time --> approximated by max.\n # gs_hc_h = utils.entropy(HC.GS_HC.reshape((len(HC.s_names), -1)).T)\n res['gs_hc_max'] = HC.GS_HC.max(axis=(1, 2))\n\n # Put collected results into data object.\n for k, v in res.items():\n if idx is not None:\n data[k][idx] = v\n else:\n data[k] = v\n\n return"},"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 record(self, step):","def do_step(self) -> None:","def storeState(self):\n\n self.action_history[self.trial] = self.action\n self.ball_history[self.trial] = self.ballcolor","def step(self, model):\n pass","def step(self, model):\n pass","def on_trial_add(self, trial: Trial):\n pass","def step(self):\n self.driver.step()","def save_and_step(self):\n self.last_grads = [param.grad for param in self.agent.model.parameters()]\n self.optimizer.pytorch_step()","def _on_step(self) -> bool:\n\t\t#self.model.get_env().env_method(\"set_model_reference\", self.model.get_parameters())\n\t\tself.env.set_model_reference(self.model.get_parameters())\n\t\tprint(\"current timestep\", self.num_timesteps)\n\t\treturn True","def _post_training(self):\n self._write_state_key()","def step(self):\n\n pass","def report_trial(self):\n pass","def onTrainStepTaken(self, agent):\n pass","def on_step_end(self, step, logs):\n episode = logs['episode']\n self.observations[episode].append(logs['observation'])\n self.rewards[episode].append(logs['reward'])\n self.actions[episode].append(logs['action'])\n self.metrics[episode].append(logs['metrics'])\n self.step += 1","def on_step_end(self, step, logs):\n episode = logs['episode']\n self.observations[episode].append(logs['observation'])\n self.rewards[episode].append(logs['reward'])\n self.actions[episode].append(logs['action'])\n self.metrics[episode].append(logs['metrics'])\n self.step += 1","def step(self):\n #1. Time progresses\n self.time_operator.step()\n \n #2. Form and dissolve relationships\"\n self.relationship_operator.step()\n\n #3. HIV transmission\n self.infection_operator.step()","def step(self):\n self.state_estimator.step()","def on_trial_add(self, trial_runner, trial):\n\n raise NotImplementedError","def trial_completed(self, behavior_data):\r\n # Update elapsed_time\r\n self.elapsed_time = datetime.datetime.now() - self.init_datetime\r\n self.behavior_data = behavior_data\r\n correct = ~np.isnan(\r\n self.behavior_data['States timestamps']['correct'][0][0])\r\n error = ~np.isnan(\r\n self.behavior_data['States timestamps']['error'][0][0])\r\n no_go = ~np.isnan(\r\n self.behavior_data['States timestamps']['no_go'][0][0])\r\n assert correct or error or no_go\r\n # Add trial's response time to the buffer\r\n self.response_time = misc.get_trial_rt(self.behavior_data)\r\n self.response_time_buffer.append(self.response_time)\r\n # Update response buffer -1 for left, 0 for nogo, and 1 for rightward\r\n if (correct and self.position < 0) or (error and self.position > 0):\r\n self.response_side_buffer.append(1)\r\n elif (correct and self.position > 0) or (error and self.position < 0):\r\n self.response_side_buffer.append(-1)\r\n elif no_go:\r\n self.response_side_buffer.append(0)\r\n # Update the trial_correct variable + buffer\r\n self.trial_correct = bool(correct)\r\n self.trial_correct_buffer.append(self.trial_correct)\r\n # Increment the trial correct counter\r\n self.ntrials_correct += self.trial_correct\r\n # Update the water delivered\r\n if self.trial_correct:\r\n self.water_delivered += self.reward_amount\r\n\r\n # SAVE TRIAL DATA\r\n params = self.__dict__.copy()\r\n params.update({'behavior_data': behavior_data})\r\n # Convert to str all non serializable params\r\n params['data_file'] = str(params['data_file'])\r\n params['osc_client'] = 'osc_client_pointer'\r\n params['init_datetime'] = params['init_datetime'].isoformat()\r\n params['elapsed_time'] = str(params['elapsed_time'])\r\n params['position'] = int(params['position'])\r\n # Delete buffered data\r\n params['stim_probability_left_buffer'] = ''\r\n params['position_buffer'] = ''\r\n params['contrast_buffer'] = ''\r\n params['signed_contrast_buffer'] = ''\r\n params['response_time_buffer'] = ''\r\n params['response_side_buffer'] = ''\r\n params['trial_correct_buffer'] = ''\r\n # Dump and save\r\n out = json.dumps(params, cls=ComplexEncoder)\r\n self.data_file.write(out)\r\n self.data_file.write('\\n')\r\n self.data_file.close()\r\n # If more than 42 trials save transfer_me.flag\r\n if self.trial_num == 42:\r\n misc.create_flags(self.data_file_path, self.poop_count)\r\n\r\n return self","def step(self, **kwargs):\n pass","def perform_step(self) -> None:\n pass","def trial(self):\n pass","def step(self): \n self.reset_parameters()\n\n if np.random.uniform(0, 1) < self.model.churn_prob: self.exit_triggered = True \n if self.exit_triggered:\n self.exit()\n else:\n self.register_deposit(self.deposit_intent)\n self.register_contribution(self.contribution_intent)\n self.register_sponsorship(self.sponsor_intent)\n self.register_euro_exchange(self.euro_exchange_intent)\n self.register_teo_exchange(self.teo_exchange_intent)\n self.register_withdraw(self.withdraw_intent)","def after_step():\n raise NotImplementedError","def step_back(\n self):\n if self.backup != None:\n self.data = self.backup","def capture_self(self):\n self.state = 'completed'\n self.save()\n self.safe_post()","def _step(self):\n pass","def __log_trial__(self, trial_data):\n from klibs.KLDatabase import EntryTemplate\n\n trial_template = EntryTemplate('trials')\n trial_template.log(P.id_field_name, P.participant_id)\n for attr in trial_data:\n trial_template.log(attr, trial_data[attr])\n\n return self.database.insert(trial_template)","def step(self, model):\n\n if self.active == 0:\n\n self.activate(model)\n\n elif self.active == 1:\n\n self.move(model)\n\n self.exit_query(model)\n\n self.save(model)\n\n return","def testTrialSaveRestore(self):\n ray.init(num_cpus=3)\n tmpdir = tempfile.mkdtemp()\n\n runner = TrialRunner(local_checkpoint_dir=tmpdir, checkpoint_period=0)\n trials = [\n Trial(\n \"__fake\",\n trial_id=\"trial_terminate\",\n stopping_criterion={\"training_iteration\": 1},\n checkpoint_freq=1)\n ]\n runner.add_trial(trials[0])\n runner.step() # start\n runner.step()\n self.assertEquals(trials[0].status, Trial.TERMINATED)\n\n trials += [\n Trial(\n \"__fake\",\n trial_id=\"trial_fail\",\n stopping_criterion={\"training_iteration\": 3},\n checkpoint_freq=1,\n config={\"mock_error\": True})\n ]\n runner.add_trial(trials[1])\n runner.step()\n runner.step()\n runner.step()\n self.assertEquals(trials[1].status, Trial.ERROR)\n\n trials += [\n Trial(\n \"__fake\",\n trial_id=\"trial_succ\",\n stopping_criterion={\"training_iteration\": 2},\n checkpoint_freq=1)\n ]\n runner.add_trial(trials[2])\n runner.step()\n self.assertEquals(len(runner.trial_executor.get_checkpoints()), 3)\n self.assertEquals(trials[2].status, Trial.RUNNING)\n\n runner2 = TrialRunner(resume=\"LOCAL\", local_checkpoint_dir=tmpdir)\n for tid in [\"trial_terminate\", \"trial_fail\"]:\n original_trial = runner.get_trial(tid)\n restored_trial = runner2.get_trial(tid)\n self.assertEqual(original_trial.status, restored_trial.status)\n\n restored_trial = runner2.get_trial(\"trial_succ\")\n self.assertEqual(Trial.PENDING, restored_trial.status)\n\n runner2.step()\n runner2.step()\n runner2.step()\n self.assertRaises(TuneError, runner2.step)\n shutil.rmtree(tmpdir)","def on_state_transition(self, data, suffix=''):\n self._log(\n \"Recording the following state transition for exploration %s: \"\n \"'%s' to '%s'\" % (\n self.oppiaid, data['oldStateName'], data['newStateName']))","def trial_prep(self):\n pass","def _on_step(self) -> bool:\n # print(\"locals \", self.locals)\n # # what timestep you think\n # print(\"timestep \",CustomCallback.step)\n # # what timestep a2c or ppo2 learn() is on \n # print(\"a2c/ppo2 num timestep\",self.num_timesteps)\n \n # TODO: add flag to save screenshots or not\n subfolder = os.path.join(self.directory, 'screen/')\n filepath = os.path.join(subfolder)\n img_name = '_screenshot_' + str(self.num_timesteps)\n \n if(self.algo == \"A2C\" or self.algo == \"PPO2\"):\n # self.locals['obs'] gives black and white imgs\n obs = self.env.get_images()\n for i in range(self.num_envs):\n mpl.image.imsave(subfolder+\"env_\" + str(i) + img_name + \"_.png\", obs[i])\n elif (self.algo == \"DQN\"):\n self.env.ale.saveScreenPNG(subfolder+\"env_\" + str(0) + img_name + \"_.png\")\n\n step_stats = {self.num_timesteps: {\n 'num_timesteps': self.num_timesteps,\n 'state': self.num_timesteps/self.num_envs,\n }\n }\n # add step to dict\n CustomCallback.main_data_dict.update(step_stats)\n key = self.num_timesteps\n\n # collection of minimum data: action, reward, lives\n if(self.algo == \"DQN\"):\n CustomCallback.main_data_dict[key]['action_env_0'] = self.locals['action']\n CustomCallback.main_data_dict[key]['action_name_env_0'] = self.actions[self.locals['env_action']]\n if(self.game == \"Pong\"):\n CustomCallback.main_data_dict[key]['curr_score_env_0'] = self.locals['episode_rewards'][-1]\n else:\n CustomCallback.main_data_dict[key]['cumulative_life_reward'] = self.locals['episode_rewards'][-1]\n if(self.isLives == True):\n CustomCallback.main_data_dict[CustomCallback.step]['lives'] = self.locals['info']['ale.lives']\n else:\n for i in range(self.num_envs):\n CustomCallback.main_data_dict[key]['action_env_'+str(i)] = self.locals['actions'][i]\n CustomCallback.main_data_dict[key]['action_name_env_'+str(i)] = self.actions[self.locals['actions'][i]]\n CustomCallback.main_data_dict[key]['step_reward_env_'+str(i)] = self.locals['rewards'][i]\n if(self.isLives == True):\n if(CustomCallback.step == 1):\n CustomCallback.main_data_dict[key]['lives_env_'+str(i)] = 3\n if(CustomCallback.step >= 2):\n CustomCallback.main_data_dict[key]['lives_env_'+str(i)] = self.locals['infos'][i]['ale.lives']\n\n if(self.game == \"Pong\" and self.algo != \"DQN\"):\n # extra processing for Pong scores\n self.find_life_game_info_a2c_ppo2_pong()\n\n # at the last step, write data into csv files\n if(CustomCallback.step == (self.num_steps/self.num_envs)):\n self.make_dataframes(self.df_list)\n # save minimal data\n self.df_to_csv(\"df_og.csv\", self.df_list)\n self.df_to_parquet()\n CustomCallback.step = CustomCallback.step + 1\n return True","def saveTeachPath(self,**kwargs):\n try:\n side = kwargs['side']\n except Exception,e:\n rospy.logerr(\"%s\"%str(e))\n self.mm.neglect()\n return\n self.baxter.br.stopRecording(side)\n\n\n # done with learning - go back to previous\n\n self.bl.update_watch_parameters('after')\n self.bl.create_action()\n self.baxter.yes() # head nod confirm\n self.mm.loadPreviousMenu()","def _step(self) -> None:","def step(self, states, actions, rewards, next_states, dones):\n for a in range(self.agents_count):\n # save for each agent\n self.memory.add(states[a], actions[a], rewards[a], next_states[a], dones[a])\n\n # Learn, if enough samples are available in memory\n if len(self.memory) > self.batch_size:\n experiences = self.memory.sample()\n self.learn(experiences)","def train_step(self):\n pass","def steps():\n global recorder\n if recorder is None:\n print \"nothing has been recorded\"\n return None\n result = []\n for r in recorder:\n result += [ r ]\n return result","def SaveProgramState(self, sess=None, global_step=None):\n pass","def getCurrentStep():","def _on_step(self) -> None:\n self._n_calls += 1\n # Account for multiple environments\n # each call to step() corresponds to n_envs transitions\n if self._n_calls % max(self.target_update_interval // self.n_envs, 1) == 0:\n polyak_update(self.q_net.parameters(), self.q_net_target.parameters(), self.tau)\n # Copy running stats, see GH issue #996\n polyak_update(self.batch_norm_stats, self.batch_norm_stats_target, 1.0)\n\n self.exploration_rate = self.exploration_schedule(self._current_progress_remaining)\n self.logger.record(\"rollout/exploration_rate\", self.exploration_rate)","def saveState(self) -> None:\n # TODO: Saves State\n pass","def _save(self, step, model):\n\n raise NotImplementedError()","def remember(self, state, action, reward, next_state, done):\n self.memory.store_transition(state, action, reward, next_state, done)","def on_step_end(self, agent, **kwargs):\n episode = agent.brain.episode\n self.observations[episode].append(kwargs['observation'])\n self.rewards[episode].append(kwargs['reward'])\n self.actions[episode].append(kwargs['action'])\n for k, v in kwargs['metrics'].items():\n self.metrics[episode].setdefault(k, []).append(v)\n self.step += 1","def scenario(self, scenario):\n self.report_scenario_completed()\n self.current_scenario = scenario","def step(self, action):\n obs = self.gym.get_observations()\n all_actions = self.gym.act(obs)\n all_actions.insert(self.gym.training_agent, action)\n state, reward, terminal, info = self.gym.step(all_actions)\n agent_state = self.featurize(state[self.gym.training_agent])\n\n # agent_state_history = self.make_observation(agent_state, self.step)\n agent_reward = reward[self.gym.training_agent]\n\n # self.step += 1\n return agent_state, agent_reward, terminal, info","def step(self):\n self.step_n += 1\n self.step_t += 1\n # TODO: directly calling agent.act will by-pass BaseDeepAgent, which\n # checks and assigns 'sess' arugment. So we manually set sess here. But\n # is there a better way to do this?\n self.action = self.agent.act(\n state=self.state, sess=self.agent.sess\n )\n next_state, vec_reward, done, _ = self.env.step(self.action)\n reward, done = func_compile_exp_agent(self.action, vec_reward, done)\n self.total_reward = reward + self.reward_decay * self.total_reward\n info = self.agent.step(\n state=self.state, action=self.action, reward=reward,\n next_state=next_state, episode_done=done\n )\n self.record(info)\n flag_success = True if done and reward > 0.0 else False\n if self.savedir is not None:\n self.steps_saver.save(self.episode_n, self.step_t, self.state, self.action,\n vec_reward, reward, done, self.total_reward, flag_success)\n self.state = next_state\n if done:\n self.step_t = 0\n return done","def step(self, time_step, is_evaluation=False, add_transition_record=True):\n # Act step: don't act at terminal info states or if its not our turn.\n if (not time_step.last()) and (\n time_step.is_simultaneous_move() or\n self.player_id == time_step.current_player()):\n info_state = time_step.observations[\"info_state\"][self.player_id]\n legal_actions = time_step.observations[\"legal_actions\"][self.player_id]\n # ---------Get Action (Policy)------------#\n epsilon = self._get_epsilon(is_evaluation)\n action, probs = self._action_policy(info_state, legal_actions, epsilon)\n # action, probs = self._epsilon_greedy(info_state, legal_actions, epsilon)\n else:\n action = None\n probs = []\n\n # Don't mess up with the state during evaluation.\n if not is_evaluation:\n if self.prev_timestep:\n self._step_counter += 1\n\n # if self._step_counter % self._learn_every == 0:\n # self._last_loss_value = self.learn()\n\n # if self._step_counter % self._update_target_network_every == 0:\n # self._session.run(self._update_target_network)\n\n if self._prev_timestep and add_transition_record:\n # We may omit record adding here if it's done elsewhere.\n self.add_transition(self._prev_timestep, self._prev_action, time_step)\n\n if time_step.last(): # prepare for the next episode.\n # if self._step_counter > self._replay_buffer_capacity: # count of steps is larger than set length\n # # print(\"In arm_tf_learn\")\n # self._last_loss_value = self.learn()\n # # print(\"player: {}, step_counter: {}, loss: {}\".format(self.player_id, self.step_counter, self._last_loss_value))\n # self._step_counter = 0\n # self._replay_buffer.clear()\n\n self._prev_timestep = None\n self._prev_action = None\n return\n else:\n self._prev_timestep = time_step\n self._prev_action = action\n\n return rl_agent.StepOutput(action=action, probs=probs)","def play_and_record(initial_state, agent, env, exp_replay, n_steps=1):\n s = initial_state\n sum_rewards = 0\n\n # Play the game for n_steps as per instructions above\n <YOUR CODE>\n\n return sum_rewards, s","def step(self, state):","def next_step(self):\n self.proceed()\n self.execute_current()","def record(self, algo, valid_info, test_results=None):\n\n model_path = os.path.join(\n self._working_folder, \"hpo_{}.pt\".format(len(self._results)))\n algo.save_model(model_path)\n test_results_path = ''\n if test_results is not None:\n test_results_path = os.path.join(\n self._working_folder, \"test_results_of_hpo_{}.pt\".format(len(self._results)))\n torch.save({'test_results': test_results}, test_results_path)\n self._results.append((copy.deepcopy(self._cur_config), model_path, valid_info, self._early_stopper.get_cur_step(), test_results_path))","def on_step_clicked(self):\n self.start_threading()\n self.stepping = True\n self.step_event.set()","def initialize_trial(name):\n # initialize a new trial\n trial_id = db.add_unstaged_trial(name)\n\n # generate the response\n resp = redirect('/sorter')\n\n # save the trial cookie\n resp.set_cookie('trial_id', trial_id)\n\n # return the response\n return resp","def saveStateOfThisRun(self):\n with open('stateFile.json', 'w') as statefile:\n json.dump(self.fileTobeUploaded, statefile, indent=4)","def step(self, action):","def step(self):\n value = self.current_event[\"step\"][\"value\"]\n self.current_value.append(value)","def save_state(self):\n pass","def step(\n self,\n actions,\n ) -> Tuple[\"next_state\", \"reward\", \"done\", \"env_info\"]:\n env_info = self.env.step(actions)[self.brain_name]\n next_states = env_info.vector_observations\n rewards = env_info.rewards\n dones = env_info.local_done\n return (next_states, rewards, dones, env_info)","def on_trial_complete(self, trial: Trial, result: Dict[str, Any]):\n pass","def giveReward(self, r): \n assert self.lastobs != None\n assert self.lastaction != None\n\n # store state, action, r, loglh in dataset\n if self.remember:\n self.history.appendLinked(self.lastobs, self.lastaction, r, self.loglh)\n\n self.lastobs = None\n self.lastaction = None","def test_done(self, mock_redirect, mock_save):\n mock_cleaned_data = {\n \"code\": \"tst\",\n \"questionnaire\": self.questionnaire,\n }\n self.view.get_all_cleaned_data = Mock(return_value=mock_cleaned_data)\n self.view.done(self.view.get_form_list)\n self.assertEqual(mock_save.call_count, 1)\n self.assertEqual(mock_redirect.call_count, 1)","def step(self, action):\n state, reward, done, debug_info = self.sample_transition(action)\n self.set_state(state)\n if \"next_state_heuristic\" in debug_info:\n self._current_heuristic = debug_info[\"next_state_heuristic\"]\n return state, reward, done, debug_info","def step():\n \n step = models.Step(action=u\"goto\", target=u\"http://www.joesfunerals.com\")","def step(self, observation: int, reward: float, done: bool) -> int:\n # Save trajectory\n if not done:\n self.traj['s'].append(observation)\n self.traj['r'].append(reward)\n\n # End of episode learning\n if done:\n self._optimize_model()\n\n return 0","def store_outcome(self, step, state, action, action_prob, reward, done):\n state = torch.from_numpy(state).float()\n self.states[step] = state.to(self.training_device)\n self.actions[step] = action.to(self.training_device)\n self.action_probs[step] = action_prob.to(self.training_device)\n self.rewards[step] = torch.from_numpy(np.asarray(np.clip(reward, -1, 1))).float().to(self.training_device)\n with torch.no_grad():\n _, state_values = self.policy.forward(state)\n self.state_values[step] = state_values\n self.dones[step] = torch.Tensor([done]).to(self.training_device)","def Step(self, input_vector, record=False):\n self.current_state = self.activation_function(np.dot(self.input_weights, input_vector) + \\\n np.dot(self.reservoir , self.current_state) )\n if record:\n self.network_history.append(self.current_state)","def step(self):\n self.latent.step()","def step(self, observation: dict) -> dict:\n raise NotImplementedError(\"step\")","def begin_trial(self):\n self._post(endpoint='{}/cm/trial/begin'.format(self.api_version))","def step(self, action_history, observations):\n return self.call(action_history, observations)","def _log_trial(self, is_add: bool):\n try:\n with open(str(self.info.trials_log_file), \"r\") as file:\n trials = util.yaml_load(file.read())\n except FileNotFoundError:\n trials = []\n\n if is_add:\n trials.append(self.trial.to_dict())\n else:\n trials[-1] = self.trial.to_dict()\n\n with open(str(self.info.trials_log_file), \"w\") as file:\n file.write(util.yaml_dump(trials))","def go_to_record_home(self, obj_id):\n url = self.cumulusci.org.lightning_base_url\n url = \"{}/lightning/r/{}/view\".format(url, obj_id)\n self.selenium.go_to(url)\n self.wait_until_loading_is_complete(lex_locators[\"actions\"])","def testStepTrain(self):\n with tf.compat.v1.Session() as sess:\n agent = self._create_test_agent(sess)\n agent.eval_mode = False\n base_observation = np.ones(self.observation_shape + (1,))\n # We mock the replay buffer to verify how the agent interacts with it.\n agent._replay = test_utils.MockReplayBuffer()\n self.evaluate(tf.compat.v1.global_variables_initializer())\n # This will reset state and choose a first action.\n agent.begin_episode(base_observation)\n\n expected_state = self.zero_state\n num_steps = 10\n for step in range(1, num_steps + 1):\n # We make observation a multiple of step for testing purposes (to\n # uniquely identify each observation).\n observation = base_observation * step\n self.assertEqual(agent.step(reward=1, observation=observation), 0)\n stack_pos = step - num_steps - 1\n if stack_pos >= -self.stack_size:\n expected_state[:, :, :, stack_pos] = np.full(\n (1,) + self.observation_shape, step)\n self.assertAllEqual(agent.state, expected_state)\n self.assertAllEqual(\n agent._last_observation,\n np.full(self.observation_shape, num_steps - 1))\n self.assertAllEqual(agent._observation, observation[:, :, 0])\n # We expect one more than num_steps because of the call to begin_episode.\n self.assertEqual(agent.training_steps, num_steps + 1)\n self.assertEqual(agent._replay.add.call_count, num_steps)\n\n agent.end_episode(reward=1)\n self.assertEqual(agent._replay.add.call_count, num_steps + 1)","def step_forward(self):","def on_trial_complete(self, trial_runner, trial, result):\n\n raise NotImplementedError","def step(self, state, actions, rewards, next_state, dones):\n # Save experience in replay memory\n memory.add(state, actions, rewards, next_state, dones)\n\n for num_agent, agent in enumerate(self.agents):\n agent.step(num_agent)","def run_reservoir(self, time_steps, record=False):\n\n for i in range(time_steps):\n self.current_state = self.activation_function(np.dot(self.reservoir, self.current_state))\n\n if record:\n self.network_history.append(self.current_state)","def step(self, step=None):\n pass","def step(self):\n\n percept = self.getPerception(self.agent.capteurs)\n choix = self.agent.getDecision(percept)\n self.__historique.append(((self.table, self.posAgent), choix))\n self.agent.setReward(self.applyChoix(choix))\n self.updateWorld()","def go_to_next_state(self):\n pass","def train_loop_post(self, current_step):\r\n pass","def steps(self, steps):\n\n self._steps = steps","def store(self, state, action, reward, next_state, done):\n self.replay_memory.append((state, action, reward, next_state, done))","def step(a=0):\n\n global simulator, recorder\n if simulator is None:\n print \"Program is not started\"\n return\n __record(pc(), step, a)\n try:\n simulator.step(a)\n except:\n simulation_error()\n exec_hooks(step)\n arrows()","def _on_step(self):\n # self.logger.record(\"current_reward\")\n # self.n_calls is automatically updated because\n # we derive from BaseCallback\n if self.n_calls % self.eval_freq == 0:\n # === YOUR CODE HERE ===#\n # Evaluate the agent:\n # you need to do self.n_eval_episodes loop using self.eval_env\n # hint: you can use self.model.predict(obs, deterministic=True)\n mean_reward, std_reward = evaluate_policy(self.model, self.eval_env, n_eval_episodes=self.n_eval_episodes)\n # Save the latest agent\n self.logger.record(\"eval_mean_reward\", mean_reward)\n self.model.save(self.save_latest)\n # and update self.best_mean_reward\n if mean_reward > self.best_mean_reward:\n self.best_mean_reward = mean_reward\n self.model.save(self.save_path)\n if self.verbose > 0:\n print(\"Saving new best model at {} timesteps\".format(self.n_calls))\n print(\"Saving new best model to {}.zip\".format(self.save_best))\n \n print(\"Best mean reward: {:.2f}\".format(self.best_mean_reward))\n \n\n # ====================== # \n return True","def step(self, states, actions, rewards, next_states, dones):\n # Save experience / reward\n for state, action, reward, next_state, done in zip(states, actions, rewards, next_states, dones):\n self.memory.add(state, action, reward, next_state, done)\n\n # Learn, if enough samples are available in memory\n if len(self.memory) > BATCH_SIZE:\n self.learn(GAMMA)","def _step(self, action):\n \n obs, reward, done, info = self.env.step(action)\n\n \n advice=self.generateAdvice()[1]\n\n obs = {\n \"image\": obs,\n \"advice\": advice\n }\n\n\n \n\n\n\n return obs, reward, done, info","def step(self, action):\n self.move_step(action) # Move.\n r, d = self.check_goal() # Check the reward and done state, and create\n # new environment.\n s_new= self.render_env() # Render the new environment.\n return s_new, r, d","def stepFinished(build, step, results):","def test_form_pass(self):\n resp = self.post_step(\"basics\")\n self.assertWizardResponse(resp, \"config\")\n resp = self.post_step(\"config\", session=list(resp._request.session.items()))\n self.assertIsInstance(resp, HttpResponseRedirect)\n self.assertEqual(resp.status_code, 302)\n self.assertEqual(resp[\"location\"], \"/projects/foobar/\")\n\n proj = Project.objects.get(name=\"foobar\")\n self.assertIsNotNone(proj)","def stepStarted(build, step):","def _step(self):\n title()\n self.runCount = 1\n self.experiment.pause = False\n self._runExperiment()\n self.pause = True","def step(self):\n\n self.agents[random.randint(self.get_agent_count())].step()\n self.steps += 1\n self.time += 1","def save_state(self):\r\n state = {\r\n 'next_student_module_id': self.next_student_module_id,\r\n }\r\n with open(self.STATE_FILE, \"w\") as state_file:\r\n json.dump(state, state_file)\r\n self.say(\"Saved state: {}\".format(json.dumps(state, sort_keys=True)))","def on_trial_result(self, trial_runner, trial, result):\n\n raise NotImplementedError","def process_step(self, request, step, form):\n pass","def record_goal(environ, goal, experiment=None):\n\n if is_bot(environ):\n return\n\n swab = environ['swab.swab']\n if experiment is None:\n try:\n experiments = swab.experiments_by_goal[goal]\n except KeyError:\n raise ValueError(\"Invalid goal: {!r}\".format(goal))\n else:\n experiments = [swab.experiments[experiment]]\n for experiment in experiments:\n if not swab.include(environ, experiment.name):\n continue\n\n variant = show_variant(environ, experiment.name, record=False)\n path = os.path.join(swab.datadir, experiment.name, variant, goal)\n try:\n f = open(path, 'a')\n except IOError:\n makedir(os.path.dirname(path))\n f = open(path, 'a')\n\n try:\n f.write(_logline(_getswabid(environ)))\n finally:\n f.close()","def test_tstep(self):\n model = BDF(debug=None)\n\n sid = 42\n n1 = n2 = 5\n dt1 = dt2 = 0.1\n no1 = no2 = 3\n card = ['TSTEP', sid,\n n1, dt1, no1, None, None, None, None, None,\n n2, dt2, no2]\n model.add_card(card, card[0], comment='tstep comment')\n model.validate()\n tstep = model.tsteps[42]\n tstep.raw_fields()\n tstep.write_card()\n tstep.write_card(size=16)\n\n sid = 43\n N = 5\n DT = 0.1\n NO = 3\n tstep2 = model.add_tstep(sid, N, DT, NO)\n tstep2.raw_fields()\n tstep2.write_card()\n tstep2.write_card(size=16)\n save_load_deck(model)","def step(self, i_episode, states, actions, rewards, next_states, dones):\n #for stepping maddpg\n # index 0 is for agent 0 and index 1 is for agent 1\n full_states = np.reshape(states, newshape=(-1))\n full_next_states = np.reshape(next_states, newshape=(-1))\n \n # Save experience / reward\n self.memory.add(full_states, states, actions, rewards, full_next_states, next_states, dones)\n \n # Learn, if enough samples are available in memory\n if len(self.memory) > BATCH_SIZE and i_episode > self.episodes_before_training:\n for _ in range(NUM_LEARN_STEPS_PER_ENV_STEP): #learn multiple times at every step\n for agent_no in range(self.num_agents):\n samples = self.memory.sample()\n self.learn(samples, agent_no, GAMMA)\n self.soft_update_all()"],"string":"[\n \"def record(self, step):\",\n \"def do_step(self) -> None:\",\n \"def storeState(self):\\n\\n self.action_history[self.trial] = self.action\\n self.ball_history[self.trial] = self.ballcolor\",\n \"def step(self, model):\\n pass\",\n \"def step(self, model):\\n pass\",\n \"def on_trial_add(self, trial: Trial):\\n pass\",\n \"def step(self):\\n self.driver.step()\",\n \"def save_and_step(self):\\n self.last_grads = [param.grad for param in self.agent.model.parameters()]\\n self.optimizer.pytorch_step()\",\n \"def _on_step(self) -> bool:\\n\\t\\t#self.model.get_env().env_method(\\\"set_model_reference\\\", self.model.get_parameters())\\n\\t\\tself.env.set_model_reference(self.model.get_parameters())\\n\\t\\tprint(\\\"current timestep\\\", self.num_timesteps)\\n\\t\\treturn True\",\n \"def _post_training(self):\\n self._write_state_key()\",\n \"def step(self):\\n\\n pass\",\n \"def report_trial(self):\\n pass\",\n \"def onTrainStepTaken(self, agent):\\n pass\",\n \"def on_step_end(self, step, logs):\\n episode = logs['episode']\\n self.observations[episode].append(logs['observation'])\\n self.rewards[episode].append(logs['reward'])\\n self.actions[episode].append(logs['action'])\\n self.metrics[episode].append(logs['metrics'])\\n self.step += 1\",\n \"def on_step_end(self, step, logs):\\n episode = logs['episode']\\n self.observations[episode].append(logs['observation'])\\n self.rewards[episode].append(logs['reward'])\\n self.actions[episode].append(logs['action'])\\n self.metrics[episode].append(logs['metrics'])\\n self.step += 1\",\n \"def step(self):\\n #1. Time progresses\\n self.time_operator.step()\\n \\n #2. Form and dissolve relationships\\\"\\n self.relationship_operator.step()\\n\\n #3. HIV transmission\\n self.infection_operator.step()\",\n \"def step(self):\\n self.state_estimator.step()\",\n \"def on_trial_add(self, trial_runner, trial):\\n\\n raise NotImplementedError\",\n \"def trial_completed(self, behavior_data):\\r\\n # Update elapsed_time\\r\\n self.elapsed_time = datetime.datetime.now() - self.init_datetime\\r\\n self.behavior_data = behavior_data\\r\\n correct = ~np.isnan(\\r\\n self.behavior_data['States timestamps']['correct'][0][0])\\r\\n error = ~np.isnan(\\r\\n self.behavior_data['States timestamps']['error'][0][0])\\r\\n no_go = ~np.isnan(\\r\\n self.behavior_data['States timestamps']['no_go'][0][0])\\r\\n assert correct or error or no_go\\r\\n # Add trial's response time to the buffer\\r\\n self.response_time = misc.get_trial_rt(self.behavior_data)\\r\\n self.response_time_buffer.append(self.response_time)\\r\\n # Update response buffer -1 for left, 0 for nogo, and 1 for rightward\\r\\n if (correct and self.position < 0) or (error and self.position > 0):\\r\\n self.response_side_buffer.append(1)\\r\\n elif (correct and self.position > 0) or (error and self.position < 0):\\r\\n self.response_side_buffer.append(-1)\\r\\n elif no_go:\\r\\n self.response_side_buffer.append(0)\\r\\n # Update the trial_correct variable + buffer\\r\\n self.trial_correct = bool(correct)\\r\\n self.trial_correct_buffer.append(self.trial_correct)\\r\\n # Increment the trial correct counter\\r\\n self.ntrials_correct += self.trial_correct\\r\\n # Update the water delivered\\r\\n if self.trial_correct:\\r\\n self.water_delivered += self.reward_amount\\r\\n\\r\\n # SAVE TRIAL DATA\\r\\n params = self.__dict__.copy()\\r\\n params.update({'behavior_data': behavior_data})\\r\\n # Convert to str all non serializable params\\r\\n params['data_file'] = str(params['data_file'])\\r\\n params['osc_client'] = 'osc_client_pointer'\\r\\n params['init_datetime'] = params['init_datetime'].isoformat()\\r\\n params['elapsed_time'] = str(params['elapsed_time'])\\r\\n params['position'] = int(params['position'])\\r\\n # Delete buffered data\\r\\n params['stim_probability_left_buffer'] = ''\\r\\n params['position_buffer'] = ''\\r\\n params['contrast_buffer'] = ''\\r\\n params['signed_contrast_buffer'] = ''\\r\\n params['response_time_buffer'] = ''\\r\\n params['response_side_buffer'] = ''\\r\\n params['trial_correct_buffer'] = ''\\r\\n # Dump and save\\r\\n out = json.dumps(params, cls=ComplexEncoder)\\r\\n self.data_file.write(out)\\r\\n self.data_file.write('\\\\n')\\r\\n self.data_file.close()\\r\\n # If more than 42 trials save transfer_me.flag\\r\\n if self.trial_num == 42:\\r\\n misc.create_flags(self.data_file_path, self.poop_count)\\r\\n\\r\\n return self\",\n \"def step(self, **kwargs):\\n pass\",\n \"def perform_step(self) -> None:\\n pass\",\n \"def trial(self):\\n pass\",\n \"def step(self): \\n self.reset_parameters()\\n\\n if np.random.uniform(0, 1) < self.model.churn_prob: self.exit_triggered = True \\n if self.exit_triggered:\\n self.exit()\\n else:\\n self.register_deposit(self.deposit_intent)\\n self.register_contribution(self.contribution_intent)\\n self.register_sponsorship(self.sponsor_intent)\\n self.register_euro_exchange(self.euro_exchange_intent)\\n self.register_teo_exchange(self.teo_exchange_intent)\\n self.register_withdraw(self.withdraw_intent)\",\n \"def after_step():\\n raise NotImplementedError\",\n \"def step_back(\\n self):\\n if self.backup != None:\\n self.data = self.backup\",\n \"def capture_self(self):\\n self.state = 'completed'\\n self.save()\\n self.safe_post()\",\n \"def _step(self):\\n pass\",\n \"def __log_trial__(self, trial_data):\\n from klibs.KLDatabase import EntryTemplate\\n\\n trial_template = EntryTemplate('trials')\\n trial_template.log(P.id_field_name, P.participant_id)\\n for attr in trial_data:\\n trial_template.log(attr, trial_data[attr])\\n\\n return self.database.insert(trial_template)\",\n \"def step(self, model):\\n\\n if self.active == 0:\\n\\n self.activate(model)\\n\\n elif self.active == 1:\\n\\n self.move(model)\\n\\n self.exit_query(model)\\n\\n self.save(model)\\n\\n return\",\n \"def testTrialSaveRestore(self):\\n ray.init(num_cpus=3)\\n tmpdir = tempfile.mkdtemp()\\n\\n runner = TrialRunner(local_checkpoint_dir=tmpdir, checkpoint_period=0)\\n trials = [\\n Trial(\\n \\\"__fake\\\",\\n trial_id=\\\"trial_terminate\\\",\\n stopping_criterion={\\\"training_iteration\\\": 1},\\n checkpoint_freq=1)\\n ]\\n runner.add_trial(trials[0])\\n runner.step() # start\\n runner.step()\\n self.assertEquals(trials[0].status, Trial.TERMINATED)\\n\\n trials += [\\n Trial(\\n \\\"__fake\\\",\\n trial_id=\\\"trial_fail\\\",\\n stopping_criterion={\\\"training_iteration\\\": 3},\\n checkpoint_freq=1,\\n config={\\\"mock_error\\\": True})\\n ]\\n runner.add_trial(trials[1])\\n runner.step()\\n runner.step()\\n runner.step()\\n self.assertEquals(trials[1].status, Trial.ERROR)\\n\\n trials += [\\n Trial(\\n \\\"__fake\\\",\\n trial_id=\\\"trial_succ\\\",\\n stopping_criterion={\\\"training_iteration\\\": 2},\\n checkpoint_freq=1)\\n ]\\n runner.add_trial(trials[2])\\n runner.step()\\n self.assertEquals(len(runner.trial_executor.get_checkpoints()), 3)\\n self.assertEquals(trials[2].status, Trial.RUNNING)\\n\\n runner2 = TrialRunner(resume=\\\"LOCAL\\\", local_checkpoint_dir=tmpdir)\\n for tid in [\\\"trial_terminate\\\", \\\"trial_fail\\\"]:\\n original_trial = runner.get_trial(tid)\\n restored_trial = runner2.get_trial(tid)\\n self.assertEqual(original_trial.status, restored_trial.status)\\n\\n restored_trial = runner2.get_trial(\\\"trial_succ\\\")\\n self.assertEqual(Trial.PENDING, restored_trial.status)\\n\\n runner2.step()\\n runner2.step()\\n runner2.step()\\n self.assertRaises(TuneError, runner2.step)\\n shutil.rmtree(tmpdir)\",\n \"def on_state_transition(self, data, suffix=''):\\n self._log(\\n \\\"Recording the following state transition for exploration %s: \\\"\\n \\\"'%s' to '%s'\\\" % (\\n self.oppiaid, data['oldStateName'], data['newStateName']))\",\n \"def trial_prep(self):\\n pass\",\n \"def _on_step(self) -> bool:\\n # print(\\\"locals \\\", self.locals)\\n # # what timestep you think\\n # print(\\\"timestep \\\",CustomCallback.step)\\n # # what timestep a2c or ppo2 learn() is on \\n # print(\\\"a2c/ppo2 num timestep\\\",self.num_timesteps)\\n \\n # TODO: add flag to save screenshots or not\\n subfolder = os.path.join(self.directory, 'screen/')\\n filepath = os.path.join(subfolder)\\n img_name = '_screenshot_' + str(self.num_timesteps)\\n \\n if(self.algo == \\\"A2C\\\" or self.algo == \\\"PPO2\\\"):\\n # self.locals['obs'] gives black and white imgs\\n obs = self.env.get_images()\\n for i in range(self.num_envs):\\n mpl.image.imsave(subfolder+\\\"env_\\\" + str(i) + img_name + \\\"_.png\\\", obs[i])\\n elif (self.algo == \\\"DQN\\\"):\\n self.env.ale.saveScreenPNG(subfolder+\\\"env_\\\" + str(0) + img_name + \\\"_.png\\\")\\n\\n step_stats = {self.num_timesteps: {\\n 'num_timesteps': self.num_timesteps,\\n 'state': self.num_timesteps/self.num_envs,\\n }\\n }\\n # add step to dict\\n CustomCallback.main_data_dict.update(step_stats)\\n key = self.num_timesteps\\n\\n # collection of minimum data: action, reward, lives\\n if(self.algo == \\\"DQN\\\"):\\n CustomCallback.main_data_dict[key]['action_env_0'] = self.locals['action']\\n CustomCallback.main_data_dict[key]['action_name_env_0'] = self.actions[self.locals['env_action']]\\n if(self.game == \\\"Pong\\\"):\\n CustomCallback.main_data_dict[key]['curr_score_env_0'] = self.locals['episode_rewards'][-1]\\n else:\\n CustomCallback.main_data_dict[key]['cumulative_life_reward'] = self.locals['episode_rewards'][-1]\\n if(self.isLives == True):\\n CustomCallback.main_data_dict[CustomCallback.step]['lives'] = self.locals['info']['ale.lives']\\n else:\\n for i in range(self.num_envs):\\n CustomCallback.main_data_dict[key]['action_env_'+str(i)] = self.locals['actions'][i]\\n CustomCallback.main_data_dict[key]['action_name_env_'+str(i)] = self.actions[self.locals['actions'][i]]\\n CustomCallback.main_data_dict[key]['step_reward_env_'+str(i)] = self.locals['rewards'][i]\\n if(self.isLives == True):\\n if(CustomCallback.step == 1):\\n CustomCallback.main_data_dict[key]['lives_env_'+str(i)] = 3\\n if(CustomCallback.step >= 2):\\n CustomCallback.main_data_dict[key]['lives_env_'+str(i)] = self.locals['infos'][i]['ale.lives']\\n\\n if(self.game == \\\"Pong\\\" and self.algo != \\\"DQN\\\"):\\n # extra processing for Pong scores\\n self.find_life_game_info_a2c_ppo2_pong()\\n\\n # at the last step, write data into csv files\\n if(CustomCallback.step == (self.num_steps/self.num_envs)):\\n self.make_dataframes(self.df_list)\\n # save minimal data\\n self.df_to_csv(\\\"df_og.csv\\\", self.df_list)\\n self.df_to_parquet()\\n CustomCallback.step = CustomCallback.step + 1\\n return True\",\n \"def saveTeachPath(self,**kwargs):\\n try:\\n side = kwargs['side']\\n except Exception,e:\\n rospy.logerr(\\\"%s\\\"%str(e))\\n self.mm.neglect()\\n return\\n self.baxter.br.stopRecording(side)\\n\\n\\n # done with learning - go back to previous\\n\\n self.bl.update_watch_parameters('after')\\n self.bl.create_action()\\n self.baxter.yes() # head nod confirm\\n self.mm.loadPreviousMenu()\",\n \"def _step(self) -> None:\",\n \"def step(self, states, actions, rewards, next_states, dones):\\n for a in range(self.agents_count):\\n # save for each agent\\n self.memory.add(states[a], actions[a], rewards[a], next_states[a], dones[a])\\n\\n # Learn, if enough samples are available in memory\\n if len(self.memory) > self.batch_size:\\n experiences = self.memory.sample()\\n self.learn(experiences)\",\n \"def train_step(self):\\n pass\",\n \"def steps():\\n global recorder\\n if recorder is None:\\n print \\\"nothing has been recorded\\\"\\n return None\\n result = []\\n for r in recorder:\\n result += [ r ]\\n return result\",\n \"def SaveProgramState(self, sess=None, global_step=None):\\n pass\",\n \"def getCurrentStep():\",\n \"def _on_step(self) -> None:\\n self._n_calls += 1\\n # Account for multiple environments\\n # each call to step() corresponds to n_envs transitions\\n if self._n_calls % max(self.target_update_interval // self.n_envs, 1) == 0:\\n polyak_update(self.q_net.parameters(), self.q_net_target.parameters(), self.tau)\\n # Copy running stats, see GH issue #996\\n polyak_update(self.batch_norm_stats, self.batch_norm_stats_target, 1.0)\\n\\n self.exploration_rate = self.exploration_schedule(self._current_progress_remaining)\\n self.logger.record(\\\"rollout/exploration_rate\\\", self.exploration_rate)\",\n \"def saveState(self) -> None:\\n # TODO: Saves State\\n pass\",\n \"def _save(self, step, model):\\n\\n raise NotImplementedError()\",\n \"def remember(self, state, action, reward, next_state, done):\\n self.memory.store_transition(state, action, reward, next_state, done)\",\n \"def on_step_end(self, agent, **kwargs):\\n episode = agent.brain.episode\\n self.observations[episode].append(kwargs['observation'])\\n self.rewards[episode].append(kwargs['reward'])\\n self.actions[episode].append(kwargs['action'])\\n for k, v in kwargs['metrics'].items():\\n self.metrics[episode].setdefault(k, []).append(v)\\n self.step += 1\",\n \"def scenario(self, scenario):\\n self.report_scenario_completed()\\n self.current_scenario = scenario\",\n \"def step(self, action):\\n obs = self.gym.get_observations()\\n all_actions = self.gym.act(obs)\\n all_actions.insert(self.gym.training_agent, action)\\n state, reward, terminal, info = self.gym.step(all_actions)\\n agent_state = self.featurize(state[self.gym.training_agent])\\n\\n # agent_state_history = self.make_observation(agent_state, self.step)\\n agent_reward = reward[self.gym.training_agent]\\n\\n # self.step += 1\\n return agent_state, agent_reward, terminal, info\",\n \"def step(self):\\n self.step_n += 1\\n self.step_t += 1\\n # TODO: directly calling agent.act will by-pass BaseDeepAgent, which\\n # checks and assigns 'sess' arugment. So we manually set sess here. But\\n # is there a better way to do this?\\n self.action = self.agent.act(\\n state=self.state, sess=self.agent.sess\\n )\\n next_state, vec_reward, done, _ = self.env.step(self.action)\\n reward, done = func_compile_exp_agent(self.action, vec_reward, done)\\n self.total_reward = reward + self.reward_decay * self.total_reward\\n info = self.agent.step(\\n state=self.state, action=self.action, reward=reward,\\n next_state=next_state, episode_done=done\\n )\\n self.record(info)\\n flag_success = True if done and reward > 0.0 else False\\n if self.savedir is not None:\\n self.steps_saver.save(self.episode_n, self.step_t, self.state, self.action,\\n vec_reward, reward, done, self.total_reward, flag_success)\\n self.state = next_state\\n if done:\\n self.step_t = 0\\n return done\",\n \"def step(self, time_step, is_evaluation=False, add_transition_record=True):\\n # Act step: don't act at terminal info states or if its not our turn.\\n if (not time_step.last()) and (\\n time_step.is_simultaneous_move() or\\n self.player_id == time_step.current_player()):\\n info_state = time_step.observations[\\\"info_state\\\"][self.player_id]\\n legal_actions = time_step.observations[\\\"legal_actions\\\"][self.player_id]\\n # ---------Get Action (Policy)------------#\\n epsilon = self._get_epsilon(is_evaluation)\\n action, probs = self._action_policy(info_state, legal_actions, epsilon)\\n # action, probs = self._epsilon_greedy(info_state, legal_actions, epsilon)\\n else:\\n action = None\\n probs = []\\n\\n # Don't mess up with the state during evaluation.\\n if not is_evaluation:\\n if self.prev_timestep:\\n self._step_counter += 1\\n\\n # if self._step_counter % self._learn_every == 0:\\n # self._last_loss_value = self.learn()\\n\\n # if self._step_counter % self._update_target_network_every == 0:\\n # self._session.run(self._update_target_network)\\n\\n if self._prev_timestep and add_transition_record:\\n # We may omit record adding here if it's done elsewhere.\\n self.add_transition(self._prev_timestep, self._prev_action, time_step)\\n\\n if time_step.last(): # prepare for the next episode.\\n # if self._step_counter > self._replay_buffer_capacity: # count of steps is larger than set length\\n # # print(\\\"In arm_tf_learn\\\")\\n # self._last_loss_value = self.learn()\\n # # print(\\\"player: {}, step_counter: {}, loss: {}\\\".format(self.player_id, self.step_counter, self._last_loss_value))\\n # self._step_counter = 0\\n # self._replay_buffer.clear()\\n\\n self._prev_timestep = None\\n self._prev_action = None\\n return\\n else:\\n self._prev_timestep = time_step\\n self._prev_action = action\\n\\n return rl_agent.StepOutput(action=action, probs=probs)\",\n \"def play_and_record(initial_state, agent, env, exp_replay, n_steps=1):\\n s = initial_state\\n sum_rewards = 0\\n\\n # Play the game for n_steps as per instructions above\\n <YOUR CODE>\\n\\n return sum_rewards, s\",\n \"def step(self, state):\",\n \"def next_step(self):\\n self.proceed()\\n self.execute_current()\",\n \"def record(self, algo, valid_info, test_results=None):\\n\\n model_path = os.path.join(\\n self._working_folder, \\\"hpo_{}.pt\\\".format(len(self._results)))\\n algo.save_model(model_path)\\n test_results_path = ''\\n if test_results is not None:\\n test_results_path = os.path.join(\\n self._working_folder, \\\"test_results_of_hpo_{}.pt\\\".format(len(self._results)))\\n torch.save({'test_results': test_results}, test_results_path)\\n self._results.append((copy.deepcopy(self._cur_config), model_path, valid_info, self._early_stopper.get_cur_step(), test_results_path))\",\n \"def on_step_clicked(self):\\n self.start_threading()\\n self.stepping = True\\n self.step_event.set()\",\n \"def initialize_trial(name):\\n # initialize a new trial\\n trial_id = db.add_unstaged_trial(name)\\n\\n # generate the response\\n resp = redirect('/sorter')\\n\\n # save the trial cookie\\n resp.set_cookie('trial_id', trial_id)\\n\\n # return the response\\n return resp\",\n \"def saveStateOfThisRun(self):\\n with open('stateFile.json', 'w') as statefile:\\n json.dump(self.fileTobeUploaded, statefile, indent=4)\",\n \"def step(self, action):\",\n \"def step(self):\\n value = self.current_event[\\\"step\\\"][\\\"value\\\"]\\n self.current_value.append(value)\",\n \"def save_state(self):\\n pass\",\n \"def step(\\n self,\\n actions,\\n ) -> Tuple[\\\"next_state\\\", \\\"reward\\\", \\\"done\\\", \\\"env_info\\\"]:\\n env_info = self.env.step(actions)[self.brain_name]\\n next_states = env_info.vector_observations\\n rewards = env_info.rewards\\n dones = env_info.local_done\\n return (next_states, rewards, dones, env_info)\",\n \"def on_trial_complete(self, trial: Trial, result: Dict[str, Any]):\\n pass\",\n \"def giveReward(self, r): \\n assert self.lastobs != None\\n assert self.lastaction != None\\n\\n # store state, action, r, loglh in dataset\\n if self.remember:\\n self.history.appendLinked(self.lastobs, self.lastaction, r, self.loglh)\\n\\n self.lastobs = None\\n self.lastaction = None\",\n \"def test_done(self, mock_redirect, mock_save):\\n mock_cleaned_data = {\\n \\\"code\\\": \\\"tst\\\",\\n \\\"questionnaire\\\": self.questionnaire,\\n }\\n self.view.get_all_cleaned_data = Mock(return_value=mock_cleaned_data)\\n self.view.done(self.view.get_form_list)\\n self.assertEqual(mock_save.call_count, 1)\\n self.assertEqual(mock_redirect.call_count, 1)\",\n \"def step(self, action):\\n state, reward, done, debug_info = self.sample_transition(action)\\n self.set_state(state)\\n if \\\"next_state_heuristic\\\" in debug_info:\\n self._current_heuristic = debug_info[\\\"next_state_heuristic\\\"]\\n return state, reward, done, debug_info\",\n \"def step():\\n \\n step = models.Step(action=u\\\"goto\\\", target=u\\\"http://www.joesfunerals.com\\\")\",\n \"def step(self, observation: int, reward: float, done: bool) -> int:\\n # Save trajectory\\n if not done:\\n self.traj['s'].append(observation)\\n self.traj['r'].append(reward)\\n\\n # End of episode learning\\n if done:\\n self._optimize_model()\\n\\n return 0\",\n \"def store_outcome(self, step, state, action, action_prob, reward, done):\\n state = torch.from_numpy(state).float()\\n self.states[step] = state.to(self.training_device)\\n self.actions[step] = action.to(self.training_device)\\n self.action_probs[step] = action_prob.to(self.training_device)\\n self.rewards[step] = torch.from_numpy(np.asarray(np.clip(reward, -1, 1))).float().to(self.training_device)\\n with torch.no_grad():\\n _, state_values = self.policy.forward(state)\\n self.state_values[step] = state_values\\n self.dones[step] = torch.Tensor([done]).to(self.training_device)\",\n \"def Step(self, input_vector, record=False):\\n self.current_state = self.activation_function(np.dot(self.input_weights, input_vector) + \\\\\\n np.dot(self.reservoir , self.current_state) )\\n if record:\\n self.network_history.append(self.current_state)\",\n \"def step(self):\\n self.latent.step()\",\n \"def step(self, observation: dict) -> dict:\\n raise NotImplementedError(\\\"step\\\")\",\n \"def begin_trial(self):\\n self._post(endpoint='{}/cm/trial/begin'.format(self.api_version))\",\n \"def step(self, action_history, observations):\\n return self.call(action_history, observations)\",\n \"def _log_trial(self, is_add: bool):\\n try:\\n with open(str(self.info.trials_log_file), \\\"r\\\") as file:\\n trials = util.yaml_load(file.read())\\n except FileNotFoundError:\\n trials = []\\n\\n if is_add:\\n trials.append(self.trial.to_dict())\\n else:\\n trials[-1] = self.trial.to_dict()\\n\\n with open(str(self.info.trials_log_file), \\\"w\\\") as file:\\n file.write(util.yaml_dump(trials))\",\n \"def go_to_record_home(self, obj_id):\\n url = self.cumulusci.org.lightning_base_url\\n url = \\\"{}/lightning/r/{}/view\\\".format(url, obj_id)\\n self.selenium.go_to(url)\\n self.wait_until_loading_is_complete(lex_locators[\\\"actions\\\"])\",\n \"def testStepTrain(self):\\n with tf.compat.v1.Session() as sess:\\n agent = self._create_test_agent(sess)\\n agent.eval_mode = False\\n base_observation = np.ones(self.observation_shape + (1,))\\n # We mock the replay buffer to verify how the agent interacts with it.\\n agent._replay = test_utils.MockReplayBuffer()\\n self.evaluate(tf.compat.v1.global_variables_initializer())\\n # This will reset state and choose a first action.\\n agent.begin_episode(base_observation)\\n\\n expected_state = self.zero_state\\n num_steps = 10\\n for step in range(1, num_steps + 1):\\n # We make observation a multiple of step for testing purposes (to\\n # uniquely identify each observation).\\n observation = base_observation * step\\n self.assertEqual(agent.step(reward=1, observation=observation), 0)\\n stack_pos = step - num_steps - 1\\n if stack_pos >= -self.stack_size:\\n expected_state[:, :, :, stack_pos] = np.full(\\n (1,) + self.observation_shape, step)\\n self.assertAllEqual(agent.state, expected_state)\\n self.assertAllEqual(\\n agent._last_observation,\\n np.full(self.observation_shape, num_steps - 1))\\n self.assertAllEqual(agent._observation, observation[:, :, 0])\\n # We expect one more than num_steps because of the call to begin_episode.\\n self.assertEqual(agent.training_steps, num_steps + 1)\\n self.assertEqual(agent._replay.add.call_count, num_steps)\\n\\n agent.end_episode(reward=1)\\n self.assertEqual(agent._replay.add.call_count, num_steps + 1)\",\n \"def step_forward(self):\",\n \"def on_trial_complete(self, trial_runner, trial, result):\\n\\n raise NotImplementedError\",\n \"def step(self, state, actions, rewards, next_state, dones):\\n # Save experience in replay memory\\n memory.add(state, actions, rewards, next_state, dones)\\n\\n for num_agent, agent in enumerate(self.agents):\\n agent.step(num_agent)\",\n \"def run_reservoir(self, time_steps, record=False):\\n\\n for i in range(time_steps):\\n self.current_state = self.activation_function(np.dot(self.reservoir, self.current_state))\\n\\n if record:\\n self.network_history.append(self.current_state)\",\n \"def step(self, step=None):\\n pass\",\n \"def step(self):\\n\\n percept = self.getPerception(self.agent.capteurs)\\n choix = self.agent.getDecision(percept)\\n self.__historique.append(((self.table, self.posAgent), choix))\\n self.agent.setReward(self.applyChoix(choix))\\n self.updateWorld()\",\n \"def go_to_next_state(self):\\n pass\",\n \"def train_loop_post(self, current_step):\\r\\n pass\",\n \"def steps(self, steps):\\n\\n self._steps = steps\",\n \"def store(self, state, action, reward, next_state, done):\\n self.replay_memory.append((state, action, reward, next_state, done))\",\n \"def step(a=0):\\n\\n global simulator, recorder\\n if simulator is None:\\n print \\\"Program is not started\\\"\\n return\\n __record(pc(), step, a)\\n try:\\n simulator.step(a)\\n except:\\n simulation_error()\\n exec_hooks(step)\\n arrows()\",\n \"def _on_step(self):\\n # self.logger.record(\\\"current_reward\\\")\\n # self.n_calls is automatically updated because\\n # we derive from BaseCallback\\n if self.n_calls % self.eval_freq == 0:\\n # === YOUR CODE HERE ===#\\n # Evaluate the agent:\\n # you need to do self.n_eval_episodes loop using self.eval_env\\n # hint: you can use self.model.predict(obs, deterministic=True)\\n mean_reward, std_reward = evaluate_policy(self.model, self.eval_env, n_eval_episodes=self.n_eval_episodes)\\n # Save the latest agent\\n self.logger.record(\\\"eval_mean_reward\\\", mean_reward)\\n self.model.save(self.save_latest)\\n # and update self.best_mean_reward\\n if mean_reward > self.best_mean_reward:\\n self.best_mean_reward = mean_reward\\n self.model.save(self.save_path)\\n if self.verbose > 0:\\n print(\\\"Saving new best model at {} timesteps\\\".format(self.n_calls))\\n print(\\\"Saving new best model to {}.zip\\\".format(self.save_best))\\n \\n print(\\\"Best mean reward: {:.2f}\\\".format(self.best_mean_reward))\\n \\n\\n # ====================== # \\n return True\",\n \"def step(self, states, actions, rewards, next_states, dones):\\n # Save experience / reward\\n for state, action, reward, next_state, done in zip(states, actions, rewards, next_states, dones):\\n self.memory.add(state, action, reward, next_state, done)\\n\\n # Learn, if enough samples are available in memory\\n if len(self.memory) > BATCH_SIZE:\\n self.learn(GAMMA)\",\n \"def _step(self, action):\\n \\n obs, reward, done, info = self.env.step(action)\\n\\n \\n advice=self.generateAdvice()[1]\\n\\n obs = {\\n \\\"image\\\": obs,\\n \\\"advice\\\": advice\\n }\\n\\n\\n \\n\\n\\n\\n return obs, reward, done, info\",\n \"def step(self, action):\\n self.move_step(action) # Move.\\n r, d = self.check_goal() # Check the reward and done state, and create\\n # new environment.\\n s_new= self.render_env() # Render the new environment.\\n return s_new, r, d\",\n \"def stepFinished(build, step, results):\",\n \"def test_form_pass(self):\\n resp = self.post_step(\\\"basics\\\")\\n self.assertWizardResponse(resp, \\\"config\\\")\\n resp = self.post_step(\\\"config\\\", session=list(resp._request.session.items()))\\n self.assertIsInstance(resp, HttpResponseRedirect)\\n self.assertEqual(resp.status_code, 302)\\n self.assertEqual(resp[\\\"location\\\"], \\\"/projects/foobar/\\\")\\n\\n proj = Project.objects.get(name=\\\"foobar\\\")\\n self.assertIsNotNone(proj)\",\n \"def stepStarted(build, step):\",\n \"def _step(self):\\n title()\\n self.runCount = 1\\n self.experiment.pause = False\\n self._runExperiment()\\n self.pause = True\",\n \"def step(self):\\n\\n self.agents[random.randint(self.get_agent_count())].step()\\n self.steps += 1\\n self.time += 1\",\n \"def save_state(self):\\r\\n state = {\\r\\n 'next_student_module_id': self.next_student_module_id,\\r\\n }\\r\\n with open(self.STATE_FILE, \\\"w\\\") as state_file:\\r\\n json.dump(state, state_file)\\r\\n self.say(\\\"Saved state: {}\\\".format(json.dumps(state, sort_keys=True)))\",\n \"def on_trial_result(self, trial_runner, trial, result):\\n\\n raise NotImplementedError\",\n \"def process_step(self, request, step, form):\\n pass\",\n \"def record_goal(environ, goal, experiment=None):\\n\\n if is_bot(environ):\\n return\\n\\n swab = environ['swab.swab']\\n if experiment is None:\\n try:\\n experiments = swab.experiments_by_goal[goal]\\n except KeyError:\\n raise ValueError(\\\"Invalid goal: {!r}\\\".format(goal))\\n else:\\n experiments = [swab.experiments[experiment]]\\n for experiment in experiments:\\n if not swab.include(environ, experiment.name):\\n continue\\n\\n variant = show_variant(environ, experiment.name, record=False)\\n path = os.path.join(swab.datadir, experiment.name, variant, goal)\\n try:\\n f = open(path, 'a')\\n except IOError:\\n makedir(os.path.dirname(path))\\n f = open(path, 'a')\\n\\n try:\\n f.write(_logline(_getswabid(environ)))\\n finally:\\n f.close()\",\n \"def test_tstep(self):\\n model = BDF(debug=None)\\n\\n sid = 42\\n n1 = n2 = 5\\n dt1 = dt2 = 0.1\\n no1 = no2 = 3\\n card = ['TSTEP', sid,\\n n1, dt1, no1, None, None, None, None, None,\\n n2, dt2, no2]\\n model.add_card(card, card[0], comment='tstep comment')\\n model.validate()\\n tstep = model.tsteps[42]\\n tstep.raw_fields()\\n tstep.write_card()\\n tstep.write_card(size=16)\\n\\n sid = 43\\n N = 5\\n DT = 0.1\\n NO = 3\\n tstep2 = model.add_tstep(sid, N, DT, NO)\\n tstep2.raw_fields()\\n tstep2.write_card()\\n tstep2.write_card(size=16)\\n save_load_deck(model)\",\n \"def step(self, i_episode, states, actions, rewards, next_states, dones):\\n #for stepping maddpg\\n # index 0 is for agent 0 and index 1 is for agent 1\\n full_states = np.reshape(states, newshape=(-1))\\n full_next_states = np.reshape(next_states, newshape=(-1))\\n \\n # Save experience / reward\\n self.memory.add(full_states, states, actions, rewards, full_next_states, next_states, dones)\\n \\n # Learn, if enough samples are available in memory\\n if len(self.memory) > BATCH_SIZE and i_episode > self.episodes_before_training:\\n for _ in range(NUM_LEARN_STEPS_PER_ENV_STEP): #learn multiple times at every step\\n for agent_no in range(self.num_agents):\\n samples = self.memory.sample()\\n self.learn(samples, agent_no, GAMMA)\\n self.soft_update_all()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6487955","0.6050769","0.5991312","0.5982613","0.5982613","0.5968404","0.591018","0.5843705","0.5831286","0.5749037","0.5696999","0.5677254","0.56736565","0.5658061","0.5658061","0.5654882","0.5654626","0.56524915","0.5651177","0.5645648","0.56064904","0.56004256","0.5590749","0.557699","0.5570788","0.55652905","0.5563377","0.55628276","0.5515313","0.54819685","0.54818237","0.54547423","0.54462695","0.5417452","0.5401813","0.53969973","0.53461224","0.53402424","0.5339361","0.5332079","0.5332016","0.53316116","0.5331064","0.53282297","0.53281844","0.5323818","0.5318816","0.5318734","0.53155255","0.53031784","0.5295379","0.52857643","0.52820724","0.52792567","0.52606034","0.52528065","0.5247655","0.52460307","0.5245281","0.52362","0.5233085","0.5228193","0.52140474","0.52005714","0.51944447","0.51866215","0.51840574","0.51826","0.51754993","0.51634955","0.5163489","0.5155689","0.51526487","0.51511544","0.51498675","0.5149518","0.5142114","0.5141364","0.51406455","0.5135958","0.5134726","0.51168257","0.5113176","0.5111867","0.51043457","0.5103556","0.5098523","0.5087648","0.50819916","0.5079532","0.5077915","0.50756454","0.50753754","0.5071583","0.5071208","0.5068569","0.506831","0.50671124","0.50566506","0.50547534","0.50530076"],"string":"[\n \"0.6487955\",\n \"0.6050769\",\n \"0.5991312\",\n \"0.5982613\",\n \"0.5982613\",\n \"0.5968404\",\n \"0.591018\",\n \"0.5843705\",\n \"0.5831286\",\n \"0.5749037\",\n \"0.5696999\",\n \"0.5677254\",\n \"0.56736565\",\n \"0.5658061\",\n \"0.5658061\",\n \"0.5654882\",\n \"0.5654626\",\n \"0.56524915\",\n \"0.5651177\",\n \"0.5645648\",\n \"0.56064904\",\n \"0.56004256\",\n \"0.5590749\",\n \"0.557699\",\n \"0.5570788\",\n \"0.55652905\",\n \"0.5563377\",\n \"0.55628276\",\n \"0.5515313\",\n \"0.54819685\",\n \"0.54818237\",\n \"0.54547423\",\n \"0.54462695\",\n \"0.5417452\",\n \"0.5401813\",\n \"0.53969973\",\n \"0.53461224\",\n \"0.53402424\",\n \"0.5339361\",\n \"0.5332079\",\n \"0.5332016\",\n \"0.53316116\",\n \"0.5331064\",\n \"0.53282297\",\n \"0.53281844\",\n \"0.5323818\",\n \"0.5318816\",\n \"0.5318734\",\n \"0.53155255\",\n \"0.53031784\",\n \"0.5295379\",\n \"0.52857643\",\n \"0.52820724\",\n \"0.52792567\",\n \"0.52606034\",\n \"0.52528065\",\n \"0.5247655\",\n \"0.52460307\",\n \"0.5245281\",\n \"0.52362\",\n \"0.5233085\",\n \"0.5228193\",\n \"0.52140474\",\n \"0.52005714\",\n \"0.51944447\",\n \"0.51866215\",\n \"0.51840574\",\n \"0.51826\",\n \"0.51754993\",\n \"0.51634955\",\n \"0.5163489\",\n \"0.5155689\",\n \"0.51526487\",\n \"0.51511544\",\n \"0.51498675\",\n \"0.5149518\",\n \"0.5142114\",\n \"0.5141364\",\n \"0.51406455\",\n \"0.5135958\",\n \"0.5134726\",\n \"0.51168257\",\n \"0.5113176\",\n \"0.5111867\",\n \"0.51043457\",\n \"0.5103556\",\n \"0.5098523\",\n \"0.5087648\",\n \"0.50819916\",\n \"0.5079532\",\n \"0.5077915\",\n \"0.50756454\",\n \"0.50753754\",\n \"0.5071583\",\n \"0.5071208\",\n \"0.5068569\",\n \"0.506831\",\n \"0.50671124\",\n \"0.50566506\",\n \"0.50547534\",\n \"0.50530076\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":22478,"cells":{"query":{"kind":"string","value":"Format recordings of GS HC connectivity."},"document":{"kind":"string","value":"def format_GS_HC_rec(res, s_hc, GS_HC):\n\n # GS - HC x-y mean position per HC unit.\n gs_hc_pos = pd.concat({i: pd.DataFrame(kv, index=['x', 'y'], columns=s_hc)\n for i, kv in res['gs_hc_pos'].items()}).unstack()\n\n # GS - HC entropy per HC unit.\n gs_hc_h = pd.Series([utils.entropy(gs_hc.flatten()) for gs_hc in GS_HC],\n index=s_hc)\n\n # GS - HC maximum value per HC unit.\n gs_hc_max = pd.Series([gs_hc.max() for gs_hc in GS_HC], index=s_hc)\n\n return gs_hc_pos, gs_hc_h, gs_hc_max"},"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 coregister_formatted():\r\n\r\n print(\"begin coregister_formatted\")\r\n\r\n # check all records for pairs using the recordBegin time\r\n pair_records()\r\n\r\n # establish the beginning and end of the coregistered records\r\n define_pairedRecords()\r\n\r\n # coregister paired data in a single csv\r\n format_coregister()\r\n\r\n\r\n print(\"completed segment_formatted\")","def format(self, data):","def diagram(self, with_lines=False, with_captions=False):\n out = []\n\n row = 0\n col = 0\n byte_counter = 0\n\n if with_captions:\n out.append(\"1: Version 2: IHL 3: DSCP 4: ECN 5: Total Length\\n\")\n out.append(\"6: Identification 7: Flags 8: Fragment Offset\\n\")\n out.append(\"9: Time To Live 10: Protocol 11: Header Checksum\\n\")\n out.append(\"12: Source IP 13: Destination IP\\n\")\n\n if len(self.payload) > 0:\n out.append(\"14: Payload\\n\")\n\n out.append(\"\\n\")\n\n hb = len(self.header_bytes())\n\n for byte in bytes(self):\n if with_lines:\n # Version, IHL, DSCP, ECN and Total Length\n if col == 0 and row == 0:\n out.append(\" 1 2 3 4 5\\n\")\n out.append(\"┌┴─┐ ┌┴─┐ ┌┴────┐├┐ \")\n out.append(\"┌┴──────────────────┐\\n\")\n\n # Identification, Flags and Fragment Offset\n if col == 0 and row == 1:\n out.append(\" 6 7 8\\n\")\n out.append(\"┌┴──────────────────┐ \")\n out.append(\"┌┴┐┌┴───────────────┐\\n\")\n\n # Time To Live, Protocol and Header Checksum\n if col == 0 and row == 2:\n out.append(\" 9 10 11\\n\")\n out.append(\"┌┴──────┐ ┌┴──────┐ \")\n out.append(\"┌┴──────────────────┐\\n\")\n\n # Source IP\n if col == 0 and row == 3:\n out.append(\" 12\\n\")\n out.append(\"┌┴──────────────────────\")\n out.append(\"────────────────────┐\\n\")\n\n # Destination IP\n if col == 0 and row == 4:\n out.append(\" 13\\n\")\n out.append(\"┌┴──────────────────────\")\n out.append(\"────────────────────┐\\n\")\n\n # Payload\n if byte_counter == hb:\n out.append(\" 14\\n\")\n out.append(\"┌┴──────────────────────\")\n out.append(\"────────────────────┐\\n\")\n\n byte = bin(byte)[2:].rjust(8, \"0\")\n first = byte[:4]\n last = byte[4:]\n\n out.append(first)\n out.append(\" \")\n out.append(last)\n\n if col == 3:\n out.append(\"\\n\")\n col = 0\n row += 1\n else:\n out.append(\" \")\n col += 1\n\n byte_counter += 1\n\n return \"\".join(out).strip()","def format_obd2_record(self, record):\n \n # If it is an OBD2 command, we need to sort out the ecu IDs and reformat multiline messages\n # - an ISO 15765-4 (CAN) reply with or without headers\n # - an old style reply, with or without headers \n # (old style = ISO 9141-2 \"ISO\", ISO 14230-4 \"KWP\", SAE J1850 \"PWM & VPW\")\n \n # the timestamp of when the command was sent\n #ts = '1333808134' # a ctime measurement\n # no timestamps from raw tracefiles\n ts = '0'\n # the command sent\n cmd = str.upper(record[0][0])\n # the results from each responding ECU\n ecuids = {}\n \n \n # This is what we will return\n obd2_record = { \\\n 'timestamp': ts, \\\n 'command': cmd, \\\n 'responses': {} }\n # responses is a dict keyed on ECU id\n # the values are arrays of data bytes\n \n \n # 5 possibilities: can/headers, can/no headers/multiline, can/no headers/singleline, old/headers, old/no headers\n if self.debug > 1 :\n print \"Style:\", self.Style, \" - Headers:\", self.Headers, \" - single/multiline ??\"\n \n if self.Style == 'can':\n if self.Headers == 1:\n # CAN headers format: ECUID, PCIbyte, more bytes...\n # if PCI byte starts with 0, then it is the bytecount of the single frame\n # if PCI byte starts with 1, then it is the first frame of 2 or more, then next byte is the bytecount\n # if PCI byte starts with 2, then it is a continuation frame, second digit of PCI byte is a sequence number\n # frames will never arrive more than 16 frames out of sequence so the single sequence hexdigit is sufficient\n # if message is 16 frames or less, just sort on the seq #\n # if message is longer than 16 frames, ugh... more complicated sort needed\n \n lines = sorted(record[1:])\n # this is effective for up to 16 lines per ECU ID, past that the sort will be silly\n if self.debug > 1 :\n print \"CAN w/H, lines:\"\n pprint.pprint(lines)\n \n linenum = 1\n # first element of each line is the ECU ID\n for line in lines:\n if linenum > 16 :\n print \"Oops. Too many CAN Frames in this message.\"\n break\n linenum += 1\n ecu = line[0]\n if ecu not in ecuids:\n #print \"New ECU\"\n ecuids[ecu] = {}\n ecuids[ecu]['data'] = []\n ecuids[ecu]['count'] = 0\n # PCI byte\n pci1 = line[1][0]\n pci2 = line[1][1]\n \n if pci1 == '0':\n # single line of data\n ecuids[ecu]['count'] = pci2\n for b in line[2:]:\n if len(ecuids[ecu]['data']) < ecuids[ecu]['count']:\n ecuids[ecu]['data'].append(b)\n elif pci1 == '1':\n # first of multiple lines, next byte is the bytecount\n datacount = line[2]\n ecuids[ecu]['count'] = int(datacount, 16)\n for b in line[3:]:\n ecuids[ecu]['data'].append(b)\n elif pci1 == '2':\n # 2nd or later of multiple lines, just data\n for b in line[2:]:\n if len(ecuids[ecu]['data']) < ecuids[ecu]['count']:\n ecuids[ecu]['data'].append(b)\n \n \n \n # CAN / no headers \n elif self.Headers == 0:\n if self.debug > 1 :\n pprint.pprint(record)\n # Since there are no headers, we will assume everything was from '7E8'\n ecu = '7E8'\n ecuids[ecu] = {}\n ecuids[ecu]['count'] = 0\n ecuids[ecu]['data'] = []\n \n # multiline CAN (no headers)\n # byte count on the first line, then each following line has a line num at the front\n if len(record) > 2 and len(record[1]) == 1 and len(record[1][0]) == 3:\n ecuids[ecu]['count'] = int(record[1][0], 16)\n #print \"count:\", ecuids[ecu]['count']\n \n # strip the line numbers & any padding at the end\n #pprint.pprint(record[2:])\n for l in record[2:] :\n if len(l) == 1:\n # this can't handle out-of-order data, but will deal with multi-ecus\n ecu += 'X'\n ecuids[ecu] = {}\n ecuids[ecu]['data'] = []\n ecuids[ecu]['count'] = l[0]\n continue\n \n for d in l[1:] :\n if len(ecuids[ecu]['data']) < ecuids[ecu]['count'] :\n ecuids[ecu]['data'].append(d)\n \n # singleline CAN (no headers)\n else :\n for l in record[1:] :\n if ecu not in ecuids:\n #print \"New ECU\"\n ecuids[ecu] = {}\n ecuids[ecu]['data'] = []\n ecuids[ecu]['count'] = 0\n for d in l :\n ecuids[ecu]['data'].append(d)\n ecuids[ecu]['count'] = len(ecuids[ecu]['data'])\n # fake ECU name for extra data\n ecu += 'X'\n \n \n \n \n # TODO\n elif self.Style == 'old':\n #print \"OLD Style -- ISO/PWM/VPW ..\"\n \n if self.Headers == 1:\n #print \"Headers ON\"\n # trace of this to test is from 2006 acura tsx\n # >0906\n # 48 6B 09 49 06 01 04 A6 FB 5A 0B \n # header:\n # 48 - priority, 6B - receiver, 09 - sender\n #pprint.pprint(record)\n\n lines = sorted(record[1:])\n # TODO - pick out different sender ecus\n \n ecu = lines[0][2]\n print \"ECU:\", ecu\n ecuids[ecu] = {}\n ecuids[ecu]['count'] = 0\n ecuids[ecu]['data'] = []\n\n if len(lines) > 1 :\n print \"OLD style, with Headers, Multiline\"\n # 0: pri, 1: rcvr, 2: sndr, 3: mode, 4: pid, 5: linenum, 6->(n-1): data, lastbyte: checksum\n # add mode & pid once, skip linenums, concat data\n ecuids[ecu]['data'] = lines[0][3:5]\n for l in lines:\n ecuids[ecu]['data'].extend(l[6:-1])\n #print \"data:\",\n #pprint.pprint(ecuids[ecu]['data'])\n elif len(lines) == 1 :\n print \"OLD style, with Headers, Singleline\"\n # some pids (mode 09) will have a message count in byte 5\n # needs to be filtered later\n # 0: pri, 1: rcvr, 2: sndr, 3: mode, 4: pid, 5-(n-1): data, lastbyte: checksum\n # singleline\n ecuids[ecu]['count'] = lines[0][5]\n ecuids[ecu]['data'].extend(lines[0][3:-1])\n else:\n # ERROR !\n #print \"ERROR, data record too short:\"\n #pprint.pprint(record)\n raise self.ErrorIncompleteRecord(\"ERROR - Incomplete Response Record\")\n\n\n\n elif self.Headers == 0:\n print \"Headers OFF\"\n #print \"cmd:\", cmd\n #pprint.pprint(record)\n \n # singleline vs multiline (with line #'s)\n lines = sorted(record[1:])\n \n # Since there are no headers, we will assume everything was from '09'\n ecu = '09'\n ecuids[ecu] = {}\n ecuids[ecu]['count'] = 0\n ecuids[ecu]['data'] = []\n \n #print \"DEBUG ---- LINES:\"\n #pprint.pprint(lines)\n \n if len(lines) > 1 :\n # multiline \n # 0: mode, 1: pid, 2: linenum, 3-n: data\n # add mode & pid once, skip linenums, concat data\n ecuids[ecu]['data'].extend(lines[0][0:2])\n for l in lines:\n ecuids[ecu]['data'].extend(l[3:])\n elif len(lines) == 1 :\n # singleline\n ecuids[ecu]['data'] = lines[0]\n else:\n # ERROR !\n #print \"ERROR, data record too short:\"\n #pprint.pprint(record)\n raise self.ErrorIncompleteRecord(\"ERROR - Incomplete Response Record\")\n\n\n for e in ecuids.iterkeys():\n obd2_record['responses'][e] = ecuids[e]['data']\n #print \"ECU:\", e, \", Data:\",\n #pprint.pprint(ecuids[e]['data'])\n \n \n return obd2_record","def to_line_protocol(self):\n\n tags = self.get_output_tags()\n\n return '{measurement}{tags} {fields}{timestamp}'.format(\n measurement=format_string(self.measurement),\n tags=',' + tags if tags else '',\n fields=self.get_output_fields(),\n timestamp=' {0}'.format(self.timestamp) if self.timestamp else ''\n )","def _format_capability_report(self, data):\n\n\n\n pin_modes = {0: 'Digital_Input', 1: 'Digital_Output',\n 2: 'Analog', 3: 'PWM', 4: 'Servo',\n 5: 'Shift', 6: 'I2C', 7: 'One Wire',\n 8: 'Stepper', 9: 'Encoder'}\n x = 0\n pin = 0\n\n logstring('\\nCapability Report')\n logstring('-----------------\\n')\n while x < len(data):\n # get index of next end marker\n logstring('{} {}{}'.format('Pin', str(pin), ':'))\n while data[x] != 127:\n mode_str = \"\"\n pin_mode = pin_modes.get(data[x])\n mode_str += str(pin_mode)\n x += 1\n bits = data[x]\n logstring('{:>5}{}{} {}'.format(' ', mode_str, ':', bits))\n x += 1\n x += 1\n pin += 1","def _format_capability_report(self, data):\n\n if self.log_output:\n return\n\n else:\n pin_modes = {0: 'Digital_Input', 1: 'Digital_Output',\n 2: 'Analog', 3: 'PWM', 4: 'Servo',\n 5: 'Shift', 6: 'I2C', 7: 'One Wire',\n 8: 'Stepper', 9: 'Encoder'}\n x = 0\n pin = 0\n\n print('\\nCapability Report')\n print('-----------------\\n')\n while x < len(data):\n # get index of next end marker\n print('{} {}{}'.format('Pin', str(pin), ':'))\n while data[x] != 127:\n mode_str = \"\"\n pin_mode = pin_modes.get(data[x])\n mode_str += str(pin_mode)\n x += 1\n bits = data[x]\n print('{:>5}{}{} {}'.format(' ', mode_str, ':', bits))\n x += 1\n x += 1\n pin += 1","def write_record(rec):\n points = str(rec['Points']).replace(\"(\",\"[\").replace(\")\",\"]\")\n l = [ rec['UniqueID'], rec['FAO_USDA'], rec['Soil'], rec['Suborder'],\n rec['maxlat'], rec['maxlon'], rec['minlat'], rec['minlon'], points ]\n strings = map(str, l)\n return \"\\t\".join(strings) + \"\\n\"","def format(self, record):\n\n scrubbed = record[\"message\"]\n # scrubs any messages that match the message pattern\n if isinstance(scrubbed, dict):\n scrubbed = json.dumps(scrubbed)\n for search, replace in self.scrub_patterns.items():\n scrubbed = re.sub(search, replace, scrubbed)\n record[\"extra\"][\"scrubbed\"] = scrubbed\n\n if not record[\"extra\"].get(\"device\") or record[\"extra\"].get(\"device\") is None:\n record[\"extra\"][\"device\"] = \"\"\n else:\n record[\"extra\"][\"device\"] = f\"{record['extra']['device']} - \"\n return self.fmt","def format(self):\n ...","def save(self, fh):\n\t\t#first write head of file\n\t\tfh.write('+INFO:IEEE 802.15.4 ZigBeeSniffer Log.\\r\\n')\n\t\tfh.write('+DATE:' + time.ctime() + '\\r\\n')\n\t\tfor packet in self.packets:\n\t\t\tload = [len(packet.load)] + packet.load\n\t\t\tload = map(str, map(hex, load))\t# int->hex->str\n\t\t\tload = map(lambda x: ('0'+x[2:])[-2:].upper(), load)\t# 0xa -> 0A\n\t\t\tload = ','.join(load)\t\t\t\t\t\t# glue it back\n\t\t\ttimee = ','.join(packet.time)\n\t\t\tfh.write('+FRAM:' + timee + ',' + load + '\\r\\n')\n\t\t\t\n\t\tfor node in self.nodes.nodes:\n\t\t\tpan = node.panId[2:] # cut '0x' from begining\n\t\t\taddr = node.address[2:] # cut '0x' from begining\n\t\t\tline = '+NODE:\"%s\"' % node.name\n\t\t\tline += ',%s,%s' % (pan[2:], pan[:2]) if pan else ',??,??' # PAN-ID\n\t\t\t#now split address into pairs (bytes):\n\t\t\tpairs = map(''.join, zip(iter(addr), iter(addr)))\n\t\t\tfor pair in reversed(pairs): \n\t\t\t\tline += ',%s' % pair\n\t\t\tfor i in range(8 - len(pairs)):\n\t\t\t\tline += ',??'\n\t\t\tline += '\\r\\n'\n\t\t\tfh.write(line)\n\t\t\n\t\tself.changed(False)","def data_format(self):\n for description in SENSOR_TYPES:\n if description.key not in self.conditions:\n continue\n attr_key = description.key\n try:\n if description.primary_key in self.raw_data:\n sensor_value = self.raw_data[description.primary_key][\n description.sensor_key\n ]\n # Format sensor for better readability\n if attr_key == ATTR_NEW_VERSION and sensor_value == \"0.0.0.0\":\n sensor_value = \"Latest\"\n elif attr_key == ATTR_UPTIME:\n sensor_value = round(sensor_value / (3600 * 24), 2)\n elif attr_key == ATTR_LAST_RESTART:\n last_restart = dt_util.now() - timedelta(seconds=sensor_value)\n sensor_value = last_restart.strftime(\"%Y-%m-%d %H:%M:%S\")\n elif attr_key == ATTR_STATUS:\n if sensor_value:\n sensor_value = \"Online\"\n else:\n sensor_value = \"Offline\"\n elif (\n attr_key == ATTR_LOCAL_IP and not self.raw_data[\"wan\"][\"online\"]\n ):\n sensor_value = None\n\n self.data[attr_key] = sensor_value\n except KeyError:\n _LOGGER.error(\n (\n \"Router does not support %s field. \"\n \"Please remove %s from monitored_conditions\"\n ),\n description.sensor_key,\n attr_key,\n )\n self.data[attr_key] = None","def write_data(self, packet):\n # TODO: check frequency instead of just doing one and two - could only be 2\n if not packet.satellites:\n return # No satellites\n t = dt.datetime(1980, 1, 6) + \\\n dt.timedelta(days=7*packet.week, seconds=int(packet.rcvTow),\n microseconds=(packet.rcvTow - int(packet.rcvTow))*10**6) - \\\n dt.timedelta(seconds=packet.leapS)\n epoch = f'> {t.year:4d} {t.month:02d} {t.day:02d} {t.hour:2d} {t.minute:2d} ' \\\n f'{t.second + t.microsecond*10**-6:11.7f} 0{len(packet.satellites):>3d}{\" \":<44}\\n'\n line = ''\n for s in packet.satellites:\n snr0 = s[0].cno\n if s[0].key == '':\n pass\n elif s[0].sigId in [0, 1]:\n line = line + f'{s[0].key:3s}{s[0].prMeas:>14.3f} {snr0:>1d}{s[0].cpMeas:>14.3f} {snr0:>1d}' \\\n f'{s[0].doMeas:>14.3f} {snr0:>1d}{s[0].cno:>14.3f} {snr0:>1d}'\n if len(s) == 2: # If L1 and L2 Frequencies\n snr1 = s[1].cno\n line = line + f'{s[1].prMeas:>14.3f} {snr1:>1d}{s[1].cpMeas:>14.3f} {snr1:>1d}{s[1].doMeas:>14.3f} ' \\\n f'{snr1:>1d}{s[1].cno:>14.3f} {snr1:>1d}\\n'\n\n else:\n line = line + f'{0.0:>14.3f} {0.0:>14.3f} {0.0:>14.3f} {0.0:>14.3f} \\n'\n elif len(s) < 2:\n line = line + f'{0.0:>14.3f} {0.0:>14.3f} {0.0:>14.3f} {0.0:>14.3f} ' \\\n f'{s[0].key:3s}{s[0].prMeas:>14.3f} {snr0:>1d}{s[0].cpMeas:>14.3f} {snr0:>1d}' \\\n f'{s[0].doMeas:>14.3f} {snr0:>1d}{s[0].cno:>14.3f} {snr0:>1d}\\n'\n elif len(s) == 2:\n line = line + f'{s[1].prMeas:>14.3f} {snr1:>1d}{s[1].cpMeas:>14.3f} {snr1:>1d}{s[1].doMeas:>14.3f} ' \\\n f'{snr1:>1d}{s[1].cno:>14.3f} {snr1:>1d}' \\\n f'{s[0].key:3s}{s[0].prMeas:>14.3f} {snr0:>1d}{s[0].cpMeas:>14.3f} {snr0:>1d}' \\\n f'{s[0].doMeas:>14.3f} {snr0:>1d}{s[0].cno:>14.3f} {snr0:>1d}\\n'\n \n try:\n with open(self.fname, 'a') as f:\n f.write(epoch + line)\n except FileNotFoundError:\n print('Bad data directory. Try again.')\n sys.exit(0)","def format_data(self, data):","def header(self):\n\t\tthismsg = \"\\r\\n\"+self.ESC + \"0m \" +self.A220 + self.A220 + self.A220 + self.A220 + self.A220 +\" \" + self.ESC + \"0m\\r\\n\"\n\t\tthismsg += \" \"+self.A220+self.A220+self.A219+self.A219+self.A219+self.A219+self.ESC+\"1;47m\"+self.A176+self.A177+self.A178+self.ESC+\"40m\"+self.A219+self.A219+self.A220+self.A220+self.ESC+\"0m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"1m \"+self.ESC+\"31mS\"+self.ESC+\"0;31mAGA\"+self.ESC+\"37m \"+self.A219+self.A219+self.ESC+\"30;47mo\"+self.ESC+\"37;40m\"+self.A219+self.ESC+\"1;47m\"+self.A176+self.A176+self.A177+self.A177+self.A178+self.ESC+\"40m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\"0m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"1m \"+self.ESC+\"31mO\"+self.ESC+\"0;31mF THE\"+self.ESC+\"37m \"+self.A219+self.A219+self.ESC+\"30;47mO\"+self.ESC+\"37;40m\"+self.A219+self.A219+self.ESC+\"1;47m\"+self.A176+self.A177+self.A177+self.A178+self.ESC+\"40m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\"0m \"+self.A220+self.A220+self.A220+self.ESC+\"1m\"+self.A220+self.A220+self.ESC+\"0m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"1m \"+self.ESC+\"31mR\"+self.ESC+\"0;31mED\"+self.ESC+\"37m \"+self.A219+self.ESC+\"30;47mo\"+self.ESC+\"37;40m\"+self.A219+self.A219+self.ESC+\"1;47m\"+self.A176+self.A176+self.A177+self.A178+self.ESC+\"40m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\"0m \"+self.A223+self.A219+self.ESC+\"1;47m\"+self.A176+self.A219+self.A219+self.A219+self.ESC+\"40m\"+self.A220+self.A220+\" \"+self.ESC+\"0;31m\"+self.A220+self.ESC+\"37m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"31m \"+self.ESC+\"1mD\"+self.ESC+\"0;31mRAGON 0.9.9\"+self.ESC+\"37m \"+self.A223+self.A219+self.A219+self.ESC+\"1;47m\"+self.A176+self.A177+self.A177+self.A178+self.A219+self.ESC+\"40m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A223+self.ESC+\"0m \"+self.A219+self.ESC+\"1;47m\"+self.A219+self.A219+self.ESC+\"40m\"+self.A223+self.A223+self.ESC+\"0;31m\"+self.A220+self.ESC+\"1;41m\"+self.A176+\" \"+self.ESC+\"0;31m\"+self.A220+self.ESC+\"37m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"31m concept\"+self.ESC+\"37m \"+self.A223+self.ESC+\"1;47m\"+self.A176+self.A177+self.A177+self.A178+self.A178+self.ESC+\"40m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A223+self.ESC+\"0m \"+self.ESC+\"1m\"+self.A220+self.ESC+\"0m \"+self.ESC+\"1m\"+self.A220+self.ESC+\"0m \"+self.ESC+\"31m\"+self.A220+self.A220+self.ESC+\"1;41m\"+self.A176+self.A178+\" \"+self.A176+\" \"+self.ESC+\"0;31m\"+self.A220+self.A220+self.ESC+\"37m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"31m Seth Robinson \"+self.ESC+\"37m\"+self.A222+\" \"+self.A223+self.A223+self.ESC+\"1;47m\"+self.A178+self.ESC+\"40m\"+self.A219+self.A219+self.A219+self.A219+self.A223+self.A223+self.ESC+\"0m \"+self.A220+self.ESC+\"1;47m\"+self.A220+self.ESC+\"40m\"+self.A223+\" \"+self.ESC+\"0m\"+self.A220+self.A220+self.ESC+\"1;47m\"+self.A220+self.ESC+\"40m\"+self.A223+self.A223+\" \"+self.ESC+\"0m\"+self.A223+self.A219+self.A220+self.ESC+\"1m\"+self.A220+\" \"+self.ESC+\"0;31m\"+self.A223+self.ESC+\"1;41m\"+self.A176+self.A177+self.A178+\" \"+self.ESC+\"0;31m\"+self.A223+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+\" \"+self.ESC+\"0;31m\"+self.A220+self.ESC+\"37m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"31m by\"+self.ESC+\"0m \"+self.A219+\" \"+self.A220+self.ESC+\"1;47m\"+self.A220+self.ESC+\"40m\"+self.A223+\" \"+self.ESC+\"0m\"+self.A220+self.ESC+\"1;47m\"+self.A220+self.A220+self.A219+self.ESC+\"40m\"+self.A223+self.ESC+\"0m \"+self.A223+self.ESC+\"1;47m\"+self.A176+self.A219+self.ESC+\"40m\"+self.A220+\" \"+self.ESC+\"0;31m\"+self.A223+self.ESC+\"1;41m\"+self.A177+self.A178+\" \"+self.ESC+\"0;37;40m \"+self.ESC+\"1;31;41m\"+self.A176+\" \"+self.A176+\" \"+self.A176+self.ESC+\"0;31m\"+self.A220+self.ESC+\"37m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"1;34m J\"+self.ESC+\"0;34m.\"+self.ESC+\"1mT\"+self.ESC+\"0;34m.\"+self.ESC+\"1mS\"+self.ESC+\"0;34mage\"+self.ESC+\"0m \"+self.A219+self.ESC+\"1;30;47m\"+self.A176+self.ESC+\"0;37;40m\"+self.A221+\" \"+self.A220+self.ESC+\"1;47m\"+self.A177+self.A176+self.ESC+\"40m\"+self.A223+\" \"+self.ESC+\"0m\"+self.A220+self.ESC+\"1;47m\"+self.A220+self.A220+self.A219+self.ESC+\"40m\"+self.A223+self.ESC+\"0m \"+self.A223+self.ESC+\"1;47m\"+self.A177+self.A219+self.A219+self.ESC+\"40m\"+self.A220+\" \"+self.ESC+\"0;31m\"+self.A223+self.ESC+\"1;41m\"+self.A177+self.A176+self.A178+\" \"+self.ESC+\"0;31m\"+self.A220+self.A223+self.ESC+\"1;41m\"+self.A176+self.A178+self.A176+self.A176+self.A177+self.A177+self.ESC+\"0;37;40m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += \" \"+self.A219+self.A219+\" \"+self.ESC+\"1;47m\"+self.A176+self.A177+self.A219+self.ESC+\"40m\"+self.A223+\" \"+self.ESC+\"0m\"+self.A223+self.ESC+\"1m\"+self.A223+self.ESC+\"41m\"+self.A223+self.ESC+\"0;31m\"+self.A220+self.A220+self.A220+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A178+self.A177+self.A176+\" \"+self.A220+self.A220+self.A220+self.A220+self.A223+self.A220+self.ESC+\"1;41m\"+self.A176+self.ESC+\"0;31m\"+self.A220+self.ESC+\"1;41m\"+self.A177+self.ESC+\"0;31m\"+self.A220+self.ESC+\"41m \"+self.ESC+\"37;40m \"+self.ESC+\"31m\"+self.A223+self.ESC+\"1;41m\"+self.A177+self.A178+\" \"+self.A219+self.ESC+\"0;37;40m \"+self.ESC+\"1;31;41m\"+self.A178+self.A178+self.A176+self.A177+self.A177+self.A219+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.A219+self.ESC+\"1;30;47m\"+self.A176+self.ESC+\"0;37;40m \"+self.A219+self.A223+self.ESC+\"1m\"+self.A223+self.ESC+\"0;31m\"+self.A220+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A223+self.A223+self.A223+\" \"+self.A220+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A223+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A223+self.A223+self.A223+\" \"+self.A176+self.A176+\" \"+self.ESC+\"1;41m\"+self.A176+\" \"+self.A178+self.A178+self.A219+self.ESC+\"0;31m\"+self.A220+\" \"+self.A223+self.ESC+\"1;41m\"+self.A177+self.A176+\" \"+self.ESC+\"0;37;40m \"+self.ESC+\"1;31;41m\"+self.A176+self.A178+self.A176+self.A177+self.A177+self.A219+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.A219+self.ESC+\"1;30;47m\"+self.A176+self.ESC+\"0;37;40m \"+self.A219+\" \"+self.ESC+\"31m\"+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A223+\" \"+self.A220+self.A220+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A178+self.A177+self.A176+self.ESC+\"37m \"+self.ESC+\"31m\"+self.A178+self.A177+self.A177+self.A223+\" \"+self.ESC+\"1;41m\"+self.A176+\" \"+self.A177+self.A178+self.A219+\" \"+self.ESC+\"0;31m\"+self.A220+\" \"+self.A223+self.ESC+\"1;41m\"+self.A177+self.A219+self.ESC+\"0;31m\"+self.A220+self.A223+self.ESC+\"1;41m\"+self.A178+\" \"+self.A177+self.A178+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.ESC+\"1;30;47m\"+self.A176+self.ESC+\"0;37;40m\"+self.A219+\" \"+self.A219+\" \"+self.ESC+\"31m\"+self.A220+self.ESC+\"41m \"+self.ESC+\"37;40m \"+self.ESC+\"31m\"+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A178+self.A177+self.A176+\" \"+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A223+self.A223+self.ESC+\"37m \"+self.ESC+\"1;31;41m\"+self.A176+\" \"+self.A178+self.A177+self.A219+self.A219+self.ESC+\"0;37;40m \"+self.ESC+\"1;31;41m\"+self.A176+self.A177+self.A178+self.ESC+\"0;37;40m \"+self.ESC+\"31m\"+self.A220+self.A220+self.A223+self.ESC+\"1;41m\"+self.A178+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.ESC+\"1;30;47m\"+self.A176+self.ESC+\"0;37;40m\"+self.A219+self.ESC+\"1;30;47m\"+self.A176+self.ESC+\"0;37;40m \"+self.ESC+\"31m\"+self.A220+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A219+self.A178+self.A178+self.A177+self.A176+self.A223+\" \"+self.A220+self.A220+\" \"+self.A223+self.ESC+\"37m \"+self.ESC+\"1;31;41m\"+self.A176+\" \"+self.A177+self.A178+self.A219+self.ESC+\"0;37;40m \"+self.ESC+\"1;31;41m\"+self.A177+self.A178+self.A219+self.ESC+\"0;37;40m \"+self.ESC+\"1;31;41m\"+self.A176+self.A178+self.A178+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.A219+self.A219+self.A219+\" \"+self.ESC+\"31m\"+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A221+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A178+self.A223+self.A223+self.A223+\" \"+self.A220+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A223+self.A223+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+\" \"+self.A220+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A178+self.A178+self.A177+self.A223+\" \"+self.ESC+\"1;41m\"+self.A176+\" \"+self.A177+self.A219+self.A219+self.ESC+\"0;37;40m \"+self.ESC+\"1;31;41m\"+self.A176+self.A177+\" \"+self.ESC+\"0;31m\"+self.A220+self.ESC+\"1;41m\"+self.A176+self.A178+self.A178+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.A222+self.ESC+\"1;30;47m\"+self.A176+self.A177+self.ESC+\"0;37;40m\"+self.A221+\" \"+self.ESC+\"31m\"+self.A220+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+\" \"+self.ESC+\"0;31m\"+self.A223+\" \"+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+self.A220+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+self.A220+\" \"+self.A223+self.A223+self.A220+\" \"+self.A223+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A178+self.A178+self.A178+self.ESC+\"37m \"+self.ESC+\"1;31;41m\"+self.A176+\" \"+self.A178+self.A219+self.ESC+\"0;37;40m \"+self.ESC+\"31m\"+self.A223+self.ESC+\"1;41m\"+self.A176+self.A177+self.A178+self.ESC+\"0;37;40m \"+self.ESC+\"1;31;41m\"+self.A176+self.A178+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.A222+self.ESC+\"1;30;47m\"+self.A176+self.A177+self.ESC+\"0;37;40m\"+self.A219+\" \"+self.ESC+\"31m\"+self.A220+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+self.ESC+\"0;31m\"+self.A223+self.A223+\" \"+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+self.A222+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+self.A220+\" \"+self.A223+self.A178+self.ESC+\"37m \"+self.ESC+\"1;31;41m\"+self.A176+self.A177+self.A178+self.A219+self.ESC+\"0;37;40m \"+self.ESC+\"1;31;41m\"+self.A176+self.A177+\" \"+self.A178+self.ESC+\"0;37;40m \"+self.ESC+\"1;31;41m\"+self.A176+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += \" \"+self.A219+self.ESC+\"1;30;47m\"+self.A177+self.ESC+\"0;37;40m\"+self.A219+\" \"+self.ESC+\"31m\"+self.A222+self.A219+self.ESC+\"1;41m\"+self.A176+self.A176+self.ESC+\"0;31m\"+self.A221+\" \"+self.ESC+\"1;5;32m\"+self.A220+self.A220+self.A223+\" \"+self.ESC+\"0;31;41m \"+self.ESC+\"40m\"+self.A178+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A223+self.A223+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+self.A222+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+\" \"+self.A220+self.A220+self.A220+self.A220+self.A223+self.ESC+\"1;41m\"+self.A176+self.A177+self.A178+self.ESC+\"0;31m\"+self.A220+\" \"+self.ESC+\"1;41m\"+self.A176+self.A177+\" \"+self.ESC+\"0;37;40m \"+self.ESC+\"1;31;41m\"+self.A176+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.A219+self.ESC+\"1;30;47m\"+self.A176+self.ESC+\"0;37;40m\"+self.A219+\" \"+self.ESC+\"31m\"+self.A222+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+\" \"+self.ESC+\"0;37;40m \"+self.ESC+\"1;5;32m\"+self.A219+self.A219+self.A223+\" \"+self.ESC+\"0;31m\"+self.A222+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A178+self.A220+self.A222+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+self.A222+self.A219+self.ESC+\"41m \"+self.ESC+\"37;40m \"+self.ESC+\"31;41m \"+self.ESC+\"40m\"+self.A178+self.A223+\" \"+self.A223+self.ESC+\"1;41m\"+self.A176+self.A177+self.A219+self.ESC+\"0;37;40m \"+self.ESC+\"1;31;41m\"+self.A176+self.A177+self.A219+self.A178+self.ESC+\"0;37;40m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += \" \"+self.A222+self.ESC+\"1;30;47m\"+self.A176+self.A177+self.ESC+\"0;37;40m\"+self.A219+\" \"+self.ESC+\"31m\"+self.A222+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A178+self.A176+self.ESC+\"0;37;40m \"+self.ESC+\"1;5;32m\"+self.A223+\" \"+self.ESC+\"0;31m\"+self.A220+self.A220+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A178+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A223+\" \"+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+self.A222+self.A219+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+\" \"+self.A223+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A178+self.A223+\" \"+self.A223+self.ESC+\"1;41m\"+self.A176+self.A219+self.ESC+\"0;37;40m \"+self.ESC+\"31;41m \"+self.A177+self.A219+self.A178+self.ESC+\"37;40m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += \" \"+self.A219+self.ESC+\"1;30;47m\"+self.A176+self.ESC+\"0;37;40m\"+self.A221+\" \"+self.ESC+\"31m\"+self.A222+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A177+self.A176+self.ESC+\"0;31m\"+self.A221+self.A220+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+self.ESC+\"0;31m\"+self.A223+self.A223+\" \"+self.A222+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+self.A222+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+self.A176+self.ESC+\"0;31m\"+self.A220+self.A220+self.ESC+\"37m \"+self.ESC+\"1;31;41m\"+self.A176+self.A177+self.ESC+\"0;37;40m \"+self.ESC+\"31;41m \"+self.A176+self.A177+self.A219+self.A178+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.A219+self.ESC+\"1;30;47m\"+self.A177+self.ESC+\"0;37;40m \"+self.ESC+\"31m\"+self.A220+self.A220+self.A223+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A176+self.A222+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A223+self.ESC+\"37m \"+self.ESC+\"31m\"+self.A220+self.ESC+\"1;41m\"+self.A176+\" \"+self.ESC+\"0;31m\"+self.A220+self.A222+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A177+self.A177+self.A176+\" \"+self.ESC+\"0;37;40m \"+self.ESC+\"31m\"+self.A220+\" \"+self.ESC+\"1;41m\"+self.A176+self.A219+self.ESC+\"0;37;40m \"+self.ESC+\"31;41m \"+self.A177+\" \"+self.A219+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.A219+self.A221+\" \"+self.ESC+\"31;41m \"+self.ESC+\"37;40m \"+self.ESC+\"31;41m \"+self.ESC+\"40m\"+self.A177+self.A177+self.A176+self.A178+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+self.ESC+\"0;37;40m \"+self.ESC+\"31m\"+self.A220+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A177+self.A176+self.A176+\" \"+self.ESC+\"0;31m\"+self.A222+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A178+self.A177+self.A176+self.ESC+\"0;31m\"+self.A220+\" \"+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A178+self.A220+self.A220+\" \"+self.ESC+\"1;41m\"+self.A178+self.ESC+\"0;31m\"+self.A220+\" \"+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A177+self.A219+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.ESC+\"1m\"+self.A220+\" \"+self.ESC+\"0m\"+self.A223+\" \"+self.ESC+\"31;41m \"+self.ESC+\"40m\"+self.A223+\" \"+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A219+self.A178+self.A178+self.A177+self.A176+self.A176+self.A177+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+self.ESC+\"0;37;40m \"+self.ESC+\"31;41m \"+self.A178+self.A177+self.A177+self.A176+\" \"+self.ESC+\"40m\"+self.A220+self.A222+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A178+self.A177+self.A176+self.ESC+\"0;37;40m \"+self.ESC+\"31m\"+self.A223+self.A178+\" \"+self.ESC+\"1;41m\"+self.A177+self.A219+self.ESC+\"0;37;40m \"+self.ESC+\"31;41m \"+self.A176+self.A176+self.A178+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.A223+self.ESC+\"1m\"+self.A219+self.A220+\" \"+self.ESC+\"0;31;41m \"+self.A177+self.A178+self.A176+self.A176+\" \"+self.ESC+\"40m\"+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A219+self.A176+self.A178+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+self.ESC+\"0;37;40m \"+self.ESC+\"31m\"+self.A222+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A219+self.A178+self.A178+self.A177+self.A176+\" \"+self.ESC+\"0;37;40m \"+self.ESC+\"31;41m \"+self.A219+self.A178+self.A177+self.A176+self.ESC+\"37;40m \"+self.ESC+\"31m\"+self.A223+\" \"+self.ESC+\"1;41m\"+self.A176+self.ESC+\"0;31m\"+self.A223+\" \"+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+self.A177+self.A219+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += \" \"+self.A223+self.ESC+\"1;47m\"+self.A223+self.ESC+\"40m\"+self.A219+self.A223+self.ESC+\"0;31m\"+self.A220+self.ESC+\"1;41m\"+self.A178+self.A176+self.A176+\" \"+self.ESC+\"0;31m\"+self.A223+\" \"+self.ESC+\"1;37;47m\"+self.A222+self.ESC+\"40m\"+self.A221+self.A223+self.A220+\" \"+self.ESC+\"0;31m\"+self.A177+\" \"+self.A223+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A221+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+self.ESC+\"0;31m\"+self.A223+\" \"+self.A220+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A219+self.A178+self.A178+self.A177+self.A176+\" \"+self.ESC+\"0;31m\"+self.A220+self.A223+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A219+self.A178+self.A177+self.A176+\" \"+self.ESC+\"0;31m\"+self.A223+\" \"+self.ESC+\"1;41m\"+self.A176+self.ESC+\"0;37;40m \"+self.ESC+\"31;41m \"+self.A176+\" \"+self.A177+self.A219+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.A223+self.ESC+\"31m\"+self.A222+self.ESC+\"1;41m\"+self.A177+self.A176+\" \"+self.ESC+\"0;31m\"+self.A223+\" \"+self.ESC+\"1;37;47m\"+self.A222+self.ESC+\"40m\"+self.A221+\" \"+self.ESC+\"47m\"+self.A222+self.ESC+\"40m\"+self.A221+\" \"+self.ESC+\"0;31m\"+self.A178+self.A177+\" \"+self.A223+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+self.ESC+\"0;37;40m \"+self.ESC+\"33m\"+self.A220+\" \"+self.ESC+\"31m\"+self.A223+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A219+self.A178+self.A177+self.A176+self.A176+\" \"+self.ESC+\"0;31m\"+self.A220+self.A223+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A178+self.A177+self.A176+\" \"+self.ESC+\"0;37;40m \"+self.ESC+\"31;41m \"+self.ESC+\"40m\"+self.A220+self.A223+\" \"+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+self.A176+\" \"+self.A178+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.ESC+\"31;41m \"+self.A176+self.A176+self.ESC+\"37;40m \"+self.A220+self.ESC+\"1m\"+self.A219+self.ESC+\"0m\"+self.A223+self.ESC+\"1m\"+self.A219+self.A221+\" \"+self.A223+\" \"+self.A223+self.A220+\" \"+self.ESC+\"0;31;41m \"+self.ESC+\"40m\"+self.A223+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A223+\" \"+self.ESC+\"33m\"+self.A219+self.A219+\" \"+self.ESC+\"31m\"+self.A223+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A219+self.A178+self.A177+self.A176+\" \"+self.ESC+\"0;37;40m \"+self.ESC+\"31;41m \"+self.A178+self.A177+self.A176+self.ESC+\"40m\"+self.A220+\" \"+self.ESC+\"1;41m\"+self.A176+self.ESC+\"0;37;40m \"+self.ESC+\"31;41m \"+self.A176+self.A177+\" \"+self.A219+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += \" \"+self.A220+self.ESC+\"1m\"+self.A219+\" \"+self.A219+self.A221+\" \"+self.A223+\" \"+self.A220+\" \"+self.A223+self.A220+\" \"+self.ESC+\"0;31m\"+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A223+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A223+\" \"+self.ESC+\"1;33;43m\"+self.A177+self.A176+self.ESC+\"0;33m\"+self.A219+self.A219+\" \"+self.ESC+\"31m\"+self.A223+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A219+self.A178+self.A177+self.A176+\" \"+self.ESC+\"0;31m\"+self.A219+self.A220+self.A222+self.ESC+\"1;41m\"+self.A219+self.A177+self.A176+self.ESC+\"0;31m\"+self.A220+self.ESC+\"1;41m\"+self.A176+self.ESC+\"0;37;40m \"+self.ESC+\"31m\"+self.A220+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+\" \"+self.A178+self.A219+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += \" \"+self.A223+self.ESC+\"1m\"+self.A220+\" \"+self.A223+\" \"+self.A220+self.A223+self.A220+\" \"+self.A223+\" \"+self.A223+self.ESC+\"0;31m\"+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A223+self.A223+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A223+\" \"+self.ESC+\"1;33m\"+self.A223+self.ESC+\"43m\"+self.A219+self.A219+self.ESC+\"40m\"+self.A223+self.ESC+\"0;33m\"+self.A220+self.A219+\" \"+self.ESC+\"31;41m \"+self.ESC+\"1m\"+self.A219+self.A178+self.A177+self.A176+\" \"+self.ESC+\"0;31m\"+self.A222+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A177+self.A177+self.A176+self.ESC+\"0;31m\"+self.A223+\" \"+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+self.A177+self.A178+\" \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.ESC+\"1m\"+self.A220+\" \"+self.ESC+\"0;31m\"+self.A220+self.A220+self.A220+self.A220+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A223+self.ESC+\"37m \"+self.ESC+\"1;33;43m\"+self.A177+self.A176+self.ESC+\"0;33m\"+self.A219+self.A219+self.A220+\" \"+self.ESC+\"31;41m \"+self.ESC+\"1m\"+self.A219+self.A178+self.A177+self.A176+\" \"+self.ESC+\"0;31m\"+self.A220+self.A222+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+self.A176+self.ESC+\"0;37;40m \"+self.ESC+\"31;41m \"+self.A176+self.A177+self.A178+\" \"+self.A219+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.ESC+\"30mÙ\"+self.ESC+\"31m\"+self.A222+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A177+self.A177+self.A176+self.A176+\" \"+self.ESC+\"0;31m\"+self.A223+self.A223+self.ESC+\"37m \"+self.ESC+\"1;33m\"+self.A223+self.ESC+\"43m\"+self.A219+self.ESC+\"0;33m\"+self.A223+self.A223+self.A223+self.A220+\" \"+self.ESC+\"31;41m \"+self.ESC+\"1m\"+self.A219+self.A178+self.A177+self.A176+\" \"+self.ESC+\"0;31m\"+self.A220+self.A222+self.A223+\" \"+self.A220+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A177+self.A176+\" \"+self.A219+\" \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.ESC+\"31m\"+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.ESC+\"37m \"+self.ESC+\"1;33m\"+self.A220+self.A220+self.ESC+\"43m\"+self.A176+self.ESC+\"0;33m\"+self.A219+self.A219+\" \"+self.ESC+\"31;41m \"+self.ESC+\"1m\"+self.A219+self.A178+self.A177+self.A176+\" \"+self.ESC+\"0;37;40m \"+self.ESC+\"31m\"+self.A220+self.ESC+\"1;41m\"+self.A176+self.A177+self.A178+self.A176+self.A176+\" \"+self.A219+self.ESC+\"0;37;40m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += \" \"+self.ESC+\"1;33;43m\"+self.A219+self.A178+self.A177+self.A176+self.ESC+\"0;33m\"+self.A219+\" \"+self.ESC+\"31m\"+self.A223+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A219+self.A178+self.A177+self.A176+\" \"+self.ESC+\"0;31m\"+self.A220+\" \"+self.A223+self.ESC+\"1;41m\"+self.A176+self.A177+self.A177+\" \"+self.ESC+\"0;31m\"+self.A223+self.ESC+\"37m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += \" \"+self.ESC+\"1;33;43m\"+self.A219+self.A178+self.ESC+\"40m\"+self.A223+self.ESC+\"0;33m\"+self.A223+self.A223+self.A220+self.A220+\" \"+self.ESC+\"31;41m \"+self.ESC+\"1m\"+self.A219+self.A178+self.A177+self.A177+self.A176+\" \"+self.ESC+\"0;31m\"+self.A220+\" \"+self.A223+self.ESC+\"1;41m\"+self.A177+self.A177+self.ESC+\"0;37;40m \"+self.ESC+\"31;41m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.ESC+\"1;33m\"+self.A220+self.A220+self.ESC+\"43m\"+self.A219+self.A178+self.A177+self.A176+self.ESC+\"0;33m\"+self.A219+self.A220+\" \"+self.ESC+\"31;41m \"+self.ESC+\"1m\"+self.A219+self.A178+self.A177+self.A177+self.A176+\" \"+self.ESC+\"0;37;40m \"+self.ESC+\"31m\"+self.A223+self.ESC+\"41m \"+self.ESC+\"37;40m \"+self.ESC+\"31;41m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.ESC+\"1;33m\"+self.A220+self.ESC+\"43m\"+self.A219+self.ESC+\"40m\"+self.A223+self.A223+self.A223+self.ESC+\"0;33m\"+self.A223+self.A220+self.A220+self.A220+\" \"+self.ESC+\"31;41m \"+self.ESC+\"1m\"+self.A178+self.A177+self.A176+self.ESC+\"0;31m\"+self.A223+self.A223+self.A223+self.A223+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+\" \"+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+\" \"+self.A223+self.ESC+\"41m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.ESC+\"1;33m\"+self.A220+self.A220+self.ESC+\"43m\"+self.A219+self.A219+self.A178+self.A177+self.A176+self.ESC+\"0;33m\"+self.A219+self.A219+\" \"+self.ESC+\"31m\"+self.A223+self.A223+\" \"+self.A220+self.A220+self.A220+self.A220+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+\" \"+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+\" \"+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+self.A220+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.ESC+\"1;33;43m\"+self.A219+self.A219+self.ESC+\"40m\"+self.A223+self.A223+self.A223+self.ESC+\"0;33m\"+self.A223+self.A223+self.ESC+\"31m\"+self.A220+self.A220+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+self.A178+self.A177+\" \"+self.A176+\" \"+self.ESC+\"0;31m\"+self.A220+\" \"+self.ESC+\"41m \"+self.ESC+\"37;40m \"+self.ESC+\"31m\"+self.A223+self.ESC+\"41m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.ESC+\"1;33m\"+self.A220+self.ESC+\"43m\"+self.A178+self.ESC+\"0;33m\"+self.A220+self.A219+self.A223+\" \"+self.ESC+\"31;41m \"+self.ESC+\"1m\"+self.A176+self.A178+self.A177+self.A177+self.A176+self.A176+self.A176+\" \"+self.ESC+\"0;31m\"+self.A220+\" \"+self.A223+self.ESC+\"41m \"+self.ESC+\"37;40m \"+self.ESC+\"31;41m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.ESC+\"1;33;43m\"+self.A178+self.A177+self.A176+self.ESC+\"0;33m\"+self.A223+self.A223+\" \"+self.ESC+\"31;41m \"+self.ESC+\"1m\"+self.A176+self.A178+self.A178+\" \"+self.A177+\" \"+self.A176+self.A176+\" \"+self.ESC+\"0;37;40m \"+self.ESC+\"31;41m \"+self.ESC+\"37;40m \"+self.ESC+\"31m\"+self.A223+self.ESC+\"41m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.ESC+\"1;33m\"+self.A220+self.A220+self.ESC+\"0;33m\"+self.A220+self.A220+self.A223+\" \"+self.ESC+\"31;41m \"+self.ESC+\"1m\"+self.A176+self.A219+self.A178+self.A178+self.A177+self.A177+self.A176+\" \"+self.A176+\" \"+self.A176+self.A176+\" \"+self.ESC+\"0;31m\"+self.A220+\" \"+self.ESC+\"41m \"+self.ESC+\"37;40m \"+self.ESC+\"31;41m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.ESC+\"0;37m \"+self.ESC+\"1;33m\"+self.A223+self.A223+self.A223+self.ESC+\"0;33m\"+self.A223+\" \"+self.ESC+\"30;41m \"+self.ESC+\"1;31mShatterstar [W/X] \"+self.ESC+\"0;37;40m \"+self.ESC+\"30;41m \"+self.ESC+\"37;40m \"+self.ESC+\"30;41m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.ESC+\"0m\\r\\n\"\n\t\treturn thismsg","def list_all_record(self, args):\n\n for k, v in self.dhcp_client_state.items():\n print(\"mac: %s DHCP state: %s\" % (k.replace('_', ':'), str(v)))\n\n gw_info = UplinkGatewayInfo(store.GatewayInfoMap())\n print(\"Default GW: %s\" % gw_info.get_gw_ip())\n print(\"GW Mac address: %s\" % gw_info.get_gw_mac())","def write_to_vcf(self):\n\n # 1. Generate header info\n date_for_vcf = datetime.now().strftime('%Y%m%d')\n header_info = [\n '##fileformat=VCFv4.2',\n '##fileDate=%s' % date_for_vcf,\n '##source=%s' % self.get_analyser_name(),\n '##reference=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/hg38.fa.gz',\n '##contig=<ID=chr1,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr1.fa.gz>',\n '##contig=<ID=chr2,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr2.fa.gz>',\n '##contig=<ID=chr3,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr3.fa.gz>',\n '##contig=<ID=chr4,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr4.fa.gz>',\n '##contig=<ID=chr5,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr5.fa.gz>',\n '##contig=<ID=chr6,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr6.fa.gz>',\n '##contig=<ID=chr7,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr7.fa.gz>',\n '##contig=<ID=chr8,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr8.fa.gz>',\n '##contig=<ID=chr9,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr9.fa.gz>',\n '##contig=<ID=chr10,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr10.fa.gz>',\n '##contig=<ID=chr11,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr11.fa.gz>',\n '##contig=<ID=chr12,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr12.fa.gz>',\n '##contig=<ID=chr13,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr13.fa.gz>',\n '##contig=<ID=chr14,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr14.fa.gz>',\n '##contig=<ID=chr15,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr15.fa.gz>',\n '##contig=<ID=chr16,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr16.fa.gz>',\n '##contig=<ID=chr17,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr17.fa.gz>',\n '##contig=<ID=chr18,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr18.fa.gz>',\n '##contig=<ID=chr19,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr19.fa.gz>',\n '##contig=<ID=chr20,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr20.fa.gz>',\n '##contig=<ID=chr21,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr21.fa.gz>',\n '##contig=<ID=chr22,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr22.fa.gz>',\n '##contig=<ID=chrM,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chrM.fa.gz>',\n '##contig=<ID=chrX,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chrX.fa.gz>',\n '##contig=<ID=chrY,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chrY.fa.gz>',\n ]\n header_parameters = [\n '##FORMAT=<ID=GT,Number=1,Type=String,Description=\"Genotype\">',\n '##FORMAT=<ID=MTQ,Number=1,Type=String,Description=\"MassArray Typer quality value for SNP call. '\n 'A=Conservative, B=Moderate, C=Aggressive, D=Low Probability, E=User Call, i=Low Intensity. A and B are considered high '\n 'quality scores.\">',\n '##INFO=<ID=PCR,Number=2,Type=String,Description=\"PCR sequences used in assay.\">',\n '##INFO=<ID=AF,Number=A,Type=Float,Description=\"Minor allele frequency from population data.\">',\n '##INFO=<ID=Gene,Number=A,Type=String,Description=\"HGNC Gene Name for gene containing SNP.\">',\n '##INFO=<ID=Build,Number=A,Type=String,Description=\"Genome build used to determine SNP position for assay.\">',\n '##FILTER=<ID=LowCallRate,Description=\"SNP not called in at least 30% of samples in assay.\">',\n ]\n\n # 2. Extract info from XML file\n results = self.get_results()\n snps = self.get_snps()\n pcr_sequences = self.get_pcr_sequences()\n call_rates = self.get_snp_call_rate()\n\n # 3. For each sample, create VCF, add headers, determine genotype of each SNP and write to file.\n for sample, variants in results.items():\n\n with open(os.path.join(self.output, '%s.vcf' % sample), 'w+') as outfile:\n\n header_fields = ['CHROM', 'POS', 'ID', 'REF', 'ALT', 'QUAL', 'FILTER', 'INFO', 'FORMAT', str(sample)]\n\n outfile.write('%s\\n' % '\\n'.join(header_info))\n outfile.write('%s\\n' % '\\n'.join(header_parameters))\n outfile.write('#%s\\n' % '\\t'.join(header_fields))\n\n # for each variant, make a line to add to the file which will\n # then be sorted\n lines_to_write = []\n for snp, info in variants.items():\n\n ref_allele = snps[snp]['ref']\n alt_alleles = snps[snp]['alt']\n alt_list = alt_alleles.split(',')\n\n # Genotype formatting matches VCF v4.0 spec where ./. is no call.\n gt_list = []\n called_genotype = info['genotype']\n if not called_genotype:\n gt_list = ['.', '.']\n elif len(called_genotype) == 1:\n called_genotype += called_genotype\n for allele in list(called_genotype):\n if allele == ref_allele:\n gt_list.append(0)\n else:\n if allele in alt_list:\n idx = alt_list.index(allele)\n gt_list.append(idx + 1)\n else:\n raise ValueError(\n 'Called genotype %s not provided as possible alt in bed file. Sample %s and SNP '\n '%s %s.' % (called_genotype, sample, snp, alt_alleles)\n )\n gt = '/'.join([str(x) for x in gt_list])\n\n # Threshold currently set to 0.3 (70% results have a call).\n snp_call_rate = call_rates[snp]\n if snp_call_rate >= 0.3:\n vcf_filter = 'LowCallRate'\n else:\n vcf_filter = 'PASS'\n\n snp_pcr_seqs = pcr_sequences[snp]\n\n lines_to_write.append(\n '{chr}\\t{pos}\\t{id}\\t{ref}\\t{alt}\\t.\\t{filter}\\tAF={af};PCR={pcr};Gene={gene};Build={build}\\t'\n 'GT:MTQ\\t{gt}:{qual}\\n'.format(\n chr=snps[snp]['chrom'],\n pos=snps[snp]['pos'],\n id=snp,\n ref=ref_allele,\n alt=alt_alleles,\n filter=vcf_filter,\n af=snps[snp]['maf'],\n pcr=','.join(snp_pcr_seqs),\n gene=snps[snp]['gene'],\n build=snps[snp]['genome_build'],\n gt=gt,\n qual=','.join(info['quality'])\n )\n )\n\n sorted_lines_to_write = sorted(\n lines_to_write,\n key=lambda x: (\n # first key for sorting is the int value of chr\n int(x.split('\\t')[0][3:]),\n # second key for sorting is the position of the variant\n int(x.split('\\t')[1])\n )\n )\n\n for line in sorted_lines_to_write:\n outfile.write(line)","def format_report_header(self):","def write_receivers(h):\n file = 'DATA/STATIONS'\n lines = []\n\n # loop over receivers\n for ir in range(h.nr):\n line = ''\n line += 'S%06d' % ir + ' '\n line += 'AA' + ' '\n line += '%11.5e' % h.rx[ir] + ' '\n line += '%11.5e' % h.ry[ir] + ' '\n line += '%3.1f' % 0. + ' '\n line += '%3.1f' % 0. + '\\n'\n lines.extend(line)\n\n # write file\n _writelines(file, lines)","def triage_record(self, record):\n # Filter out any garbage commands/responses\n # Record any changes to the reader state\n # Pass OBD2 records on for formatting\n \n # We need to figure out whether this record is :\n # - line noise / garbage \"?\"\n # - the result of an \"AT\" command \n # - the result of an OBD2 command \n \n # skip over garbage \n if record == [] \\\n or record[0] == [] \\\n or record[0][0] == '' \\\n or record[0][0] == '?' :\n #print \"Garbage record. Skipping.\"\n return []\n\n # handle ELM327 errors\n # \"?\" - unrecognized command\n # \"NO DATA\" - reader timed out waiting for response from vehicle\n # \"BUFFER FULL\" - need to read data from reader faster, ie. increase baud rate on serial connection\n # many more...\n if len(record) > 1 :\n if record[1][0] == '?' \\\n or record[1][0] == 'NO':\n #print \"Garbage record. Skipping.\"\n return []\n\n # record the changes made by AT commands\n cmd = str.upper(record[0][0])\n if cmd[0:2] == 'AT':\n self.interpret_at_cmd(record)\n return []\n \n # remove \"SEARCHING...\" from response\n # example:\n # >0100\n # SEARCHING...\n # 41 00 BE 3E A8 11 \n if len(record) > 1 :\n if record[1][0] == 'SEARCHING...':\n record.pop(1)\n\n # BUFFER FULL - ugh, need to speed up the serial connection\n rl = len(record)\n rec = 0\n while rec < rl:\n if record[rec][0] == 'BUFFER' and record[rec][1] == 'FULL':\n record.pop(rec)\n print \" ERROR - BUFFER FULL - Increase speed of serial connection\"\n #return []\n rec += 1\n # \"BUS BUSY\", \"CAN ERROR\", ???\n\n # if we get a 7F, that means there was an error\n # 10 - general reject\n # 11 - service not supported\n # 12 - subfunction not supported OR invalid format\n # 21 - busy repeat\n # 22 - conditions or sequence not correct \n # 78 - response pending\n if record[1][0] == '7F':\n mode = record[1][1]\n err = record[1][2]\n if err == 10:\n print \"General Error -- Mode:\", mode\n elif err == 11:\n print \"Service Not Supported Error -- Mode:\", mode\n elif err == 12:\n print \"Subfunction Not Supported or Invalid Format Error -- Mode:\", mode\n elif err == 21:\n print \"BUSY, Repeat -- Mode:\", mode\n elif err == 22:\n print \"Conditions or Sequence Not Correct -- Mode:\", mode\n elif err == 78:\n print \"Unknown Error -- Mode:\", mode, \" -- Error code:\", err\n return []\n\n\n # format an OBD 2 command for further processing at a higher layer\n try:\n obd2_record = self.format_obd2_record(record)\n except self.ErrorIncompleteRecord:\n print \"Garbage record. Skipping.\"\n return []\n \n return obd2_record","def format(self, record):\n row = [self.formatTime(record, self.datefmt), record.name, record.levelname]\n keys = filter(self.filterer, record.__dict__)\n extra = [record.__dict__[k] for k in keys]\n\n self.writer.writerow(row + extra + [record.getMessage()])\n data = self.output.getvalue()\n self.output.truncate(0)\n self.output.seek(0)\n return data.strip()","def format(self, record):\n\n\t\t# Use copy.copy - c.f. https://stackoverflow.com/a/7961390\n\t\tcolored_record = copy.copy(record)\n\n\t\tcolor = None\n\t\ttry:\n\t\t\tcolor = record.color\n\t\texcept AttributeError as e:\n\t\t\tpass\n\n\t\tif color is not None:\n\t\t\tif color is None or not color or color == \"none\":\n\t\t\t\tpass\n\t\t\telif color == \"white\":\n\t\t\t\twhite = \"\\033[37m\"\n\t\t\t\tclear = \"\\033[0;0m\"\n\t\t\t\tcolored_record.msg = \"{0:s}{1:s}{2:s}\".format(\n\t\t\t\t\twhite,\n\t\t\t\t\tcolored_record.msg,\n\t\t\t\t\tclear,\n\t\t\t\t)\n\t\t\telse:\n\t\t\t\traise WCMIError(\"error: ConsoleFilter: unrecognized color `{0:s}'.\".format(str(color)))\n\n\t\treturn super().format(colored_record)","def __str__(self) -> str:\n dest_table = sorted(self._destinations)\n res = \"HISTORY_rib:\\n\"\n res += \"Route NH reachable Figure_of_merit Usable flaps Last_time_updated\\n\" \n for route in dest_table:\n for x in self._destinations[route]:\n res += str(x.addr) + \"\\t\" + str(x.nh) + \"\\t\" + str(x.reachable) \\\n + \"\\t\" + str(x.figure_of_merit) + \"\\t\" + str(x.usable) \\\n + \"\\t\" + str(x.flaps) + \"\\t\" + str(x.last_time_updated) + \"\\n\"\n return res","def format_rec_data(tr_data, village, HC, gs_pars, vfeatures, idx_pars=[]):\n\n print('\\nFormatting recorded data...')\n ret_list = []\n\n gs_xvec, gs_yvec = [utils.get_copy(gs_pars[v]) for v in ['xvec', 'yvec']]\n\n if 'anim_data' in tr_data:\n\n anim_data = pd.DataFrame(tr_data['anim_data']).T\n ret_list.append(anim_data)\n\n if 'stim_data' in tr_data:\n\n # Motor input.\n mot_keys = ['umot', 'vmot']\n mot_data = {i: pd.DataFrame({k: d[k] for k in mot_keys}).unstack()\n for i, d in tr_data['stim_data'].items() if d is not None}\n mot_data = pd.DataFrame(mot_data).T\n mot_data.columns.set_levels(['x', 'y'], level=1, inplace=True)\n ret_list.append(mot_data)\n\n # Visual input.\n vis_keys = ['ovis', 'vvis']\n vis_data = {i: pd.DataFrame({k: d[k] for k in vis_keys}).unstack()\n for i, d in tr_data['stim_data'].items() if d is not None}\n vis_data = pd.DataFrame(vis_data).T\n vis_data.columns.set_levels(vfeatures, level=1, inplace=True)\n ret_list.append(vis_data)\n\n if 'gs_state' in tr_data:\n\n gs_state = {k: pd.DataFrame(gs, columns=gs_xvec, index=gs_yvec)\n for k, gs in tr_data['gs_state'].items()}\n gs_state = pd.concat(gs_state)\n gs_state.columns.rename('x', inplace=True)\n gs_state.index.rename('y', level=-1, inplace=True)\n ret_list.append(gs_state)\n\n if 'hc_state' in tr_data:\n\n hc_state = {k: pd.DataFrame(hc, index=HC.s_names)\n for k, hc in tr_data['hc_state'].items()}\n hc_state = pd.concat(hc_state)\n hc_state.index.rename('loc', level=-1, inplace=True)\n hc_state = hc_state[HC.s_types] # reorder columns\n ret_list.append(hc_state)\n\n if 'vs_state' in tr_data:\n\n vs_state = {k: pd.Series(vc, index=HC.s_names)\n for k, vc in tr_data['vs_state'].items()}\n vs_state = pd.concat(vs_state)\n ret_list.append(vs_state)\n\n if 'dls_state' in tr_data:\n\n dls_state = {k: pd.DataFrame(dls, columns=HC.s_names, index=village.U)\n for k, dls in tr_data['dls_state'].items()}\n dls_state = pd.concat(dls_state)\n dls_state.columns.rename('s', inplace=True)\n dls_state.index.rename('u', level=-1, inplace=True)\n ret_list.append(dls_state)\n\n # Set index level names.\n idx_lvl_names = idx_pars + ['step']\n for df in ret_list:\n levels = (None if not isinstance(df.index, pd.core.index.MultiIndex)\n else list(range(len(idx_lvl_names))))\n df.index.set_names(idx_lvl_names, level=levels, inplace=True)\n\n return ret_list","def format(self, record):\n data = dict()\n\n data[\"category\"] = record.name\n data[\"timestamp\"] = datetime.datetime.utcnow()\\\n .replace(tzinfo=utc)\\\n .strftime('%Y-%m-%dT%H:%M:%SZ')\n data[\"level\"] = record.levelname\n data[\"message\"] = record.msg\n data[\"threadName\"] = record.threadName\n data[\"hostname\"] = self.hostname\n \n return data","def output(self):\n\n to_write = []\n\n\n try:\n test_field = self.fields[0]['id']\n except IndexError:\n test_field = None\n\n\n if test_field == None or test_field != 0:\n # missing fields\n return to_write\n\n\n\n to_write += ['#\\n# '+self.name+'\\n#\\n']\n to_write += ['DEF '+\n self.name+' '+\n self.ref+' '+\n '0 '+ # 0\n '1 '+ # off\n self.pin_numbers_visible + ' '+\n self.pin_names_visible + ' '+\n '1 '+\n 'F '+\n self.powerobject + '\\n'\n ]\n\n to_write += ['$FPLIST\\n']\n to_write += ['$ENDFPLIST\\n']\n\n for field in self.fields:\n line = 'F'\n for key in self._F_KEYS:\n line += str(field[key]) + ' '\n to_write += [line.rstrip() + '\\n']\n\n if self.alias != '':\n to_write += ['ALIAS '+self.alias+'\\n']\n\n to_write += ['DRAW\\n']\n\n for draw in self.draws:\n #print \"==================>\",draw.output()\n to_write += [draw.output()]\n\n for connection in self.connections:\n to_write += [connection.output()]\n\n to_write += ['ENDDRAW\\n']\n\n to_write += ['ENDDEF\\n']\n\n\n\n return to_write","def dump(self, packet):\n # TODO\n packet['type'] = \"table\"\n src = packet['src']\n packet['src'] = packet['dst']\n packet['dst'] = src\n\n table_list = []\n\n # TODO fill out table string with routing table\n table_string = \"\"\n # TODO asking for int indexes instead of string for route?\n for ip in self.updates.keys():\n # TODO have to fill ip address of peer\n\n entry = {'network' : self.updates[ip][MESG][NTWK], 'netmask' : self.updates[ip][MESG][NMSK], 'peer' : ip}\n table_list.append(entry)\n packet[MESG] = table_list\n msg = json.dumps(packet)\n #print(json.dumps(packet, sort_keys=True, indent=4))\n\n sock = self.sockets[src]\n sock.sendall(msg.encode())\n return True","def __str__(self):\n lines = []\n # set hsp info line\n statline = []\n # evalue\n evalue = getattr_str(self, \"evalue\", fmt=\"%.2g\")\n statline.append(\"evalue \" + evalue)\n # bitscore\n bitscore = getattr_str(self, \"bitscore\", fmt=\"%.2f\")\n statline.append(\"bitscore \" + bitscore)\n lines.append(\"Quick stats: \" + \"; \".join(statline))\n\n if len(self.fragments) == 1:\n return \"\\n\".join(\n [self._str_hsp_header(), \"\\n\".join(lines), self.fragments[0]._str_aln()]\n )\n else:\n lines.append(\n \" Fragments: %s %s %s %s\" % (\"-\" * 3, \"-\" * 14, \"-\" * 22, \"-\" * 22)\n )\n pattern = \"%16s %14s %22s %22s\"\n lines.append(pattern % (\"#\", \"Span\", \"Query range\", \"Hit range\"))\n lines.append(pattern % (\"-\" * 3, \"-\" * 14, \"-\" * 22, \"-\" * 22))\n for idx, block in enumerate(self.fragments):\n # set hsp line and table\n # alignment span\n aln_span = getattr_str(block, \"aln_span\")\n # query region\n query_start = getattr_str(block, \"query_start\")\n query_end = getattr_str(block, \"query_end\")\n query_range = \"[%s:%s]\" % (query_start, query_end)\n # max column length is 20\n query_range = (\n query_range[:20] + \"~]\" if len(query_range) > 22 else query_range\n )\n # hit region\n hit_start = getattr_str(block, \"hit_start\")\n hit_end = getattr_str(block, \"hit_end\")\n hit_range = \"[%s:%s]\" % (hit_start, hit_end)\n hit_range = hit_range[:20] + \"~]\" if len(hit_range) > 22 else hit_range\n # append the hsp row\n lines.append(pattern % (str(idx), aln_span, query_range, hit_range))\n\n return self._str_hsp_header() + \"\\n\" + \"\\n\".join(lines)","def format_learning(res, s_real, s_hc, GS_HC):\n\n # Format results.\n res = pd.DataFrame(res).T\n res.index.name = 'step'\n\n # Pre-calculate some stats.\n s_real_h = pd.concat({i: pd.Series(kv, index=s_real)\n for i, kv in res['s_real_h'].items()}).unstack()\n s_hc_h = pd.concat({i: pd.Series(kv, index=s_hc)\n for i, kv in res['s_hc_h'].items()}).unstack()\n vc_hc_snr = pd.concat({i: pd.Series(kv, index=s_hc)\n for i, kv in res['vc_hc_snr'].items()}).unstack()\n\n # GS - HC connectivity related stats.\n gs_hc_pos, gs_hc_h, gs_hc_max = format_GS_HC_rec(res, s_hc, GS_HC)\n\n return res, s_real_h, s_hc_h, vc_hc_snr, gs_hc_pos, gs_hc_h, gs_hc_max","def __str__(self):\n return \"{}\\n{}\\n{}\\n{}\".format(self.header,self.sequence,self.line3,self.quality)","def _record(self):\r\n self._plot()\r\n self._csvWriter()\r\n self._logger()","def __str__(self):\n d = {}\n d[\"allocation_id_csv\"] = self.allocation_id_csv\n d[\"bandwidth\"] = self.bandwidth\n d[\"center_frequency\"] = self.center_frequency\n d[\"complex\"] = self.complex\n d[\"decimation\"] = self.decimation\n d[\"enabled\"] = self.enabled\n d[\"gain\"] = self.gain\n d[\"group_id\"] = self.group_id\n d[\"rf_flow_id\"] = self.rf_flow_id\n d[\"sample_rate\"] = self.sample_rate\n d[\"tuner_number\"] = self.tuner_number\n d[\"tuner_type\"] = self.tuner_type\n return str(d)","def xephemFormat(self):\n line = []\n #Field 1: names\n names = [self.getName()]\n identifiers = self.getIdentifiers()\n if identifiers[0] is not None:\n names.append(identifiers[0])\n for i in range(1,4):\n if identifiers[i] is not None:\n names.extend(identifiers[i])\n line.append(\"|\".join(names))\n\n #Field 2: type designation\n objType = self.getType()\n if objType in (\"Galaxy Pair\", \"Galaxy Triplet\", \"Group of galaxies\"):\n line.append(\"f|A\")\n elif objType == \"Globular Cluster\":\n line.append(\"f|C\")\n elif objType == \"Double star\":\n line.append(\"f|D\")\n elif objType in (\"HII Ionized region\", \"Nebula\"):\n line.append(\"f|F\")\n elif objType == \"Galaxy\":\n if self.getHubble().startswith(\"S\"):\n line.append(\"f|G\")\n else:\n line.append(\"f|H\")\n elif objType == \"Dark Nebula\":\n line.append(\"f|K\")\n elif objType in (\"Emission Nebula\", \"Reflection Nebula\"):\n line.append(\"f|N\")\n elif objType in (\"Association of stars\", \"Open Cluster\"):\n line.append(\"f|O\")\n elif objType == \"Planetary Nebula\":\n line.append(\"f|P\")\n elif objType == \"Supernova remnant\":\n line.append(\"f|R\")\n elif objType == \"Star\":\n line.append(\"f|S\")\n elif objType == \"Star cluster + Nebula\":\n line.append(\"f|U\")\n else:\n line.append(\"f\")\n\n #Field 3: Right Ascension\n line.append(self.getRA())\n\n #Field 4: Declination\n line.append(self.getDec())\n\n #Field 5: Magnitude\n #We use the first available magnitude in the sequence b,v,j,h,k\n for mag in self.getMagnitudes():\n if mag is not None:\n line.append(str(mag))\n break\n\n #Field 6: optional Epoch, we let it empty\n line.append(\"\")\n\n #Field 7: Dimensions\n dimensions = []\n #Xephem format wants axes espressed in arcsec, we have arcmin\n for value in (self.getDimensions()[0],self.getDimensions()[1]):\n if value is not None:\n dimensions.append(str(value*60))\n else:\n dimensions.append(\"\")\n if self.getDimensions()[2] is not None:\n dimensions.append(str(value))\n else:\n dimensions.append(\"\")\n line.append(\"|\".join(dimensions))\n\n return \",\".join(line)","def __str__(self):\n s = \"Filename : %s\\n\" % self.fname\n s += \"Data size : %d x %d x %d\\n\" % (self._size[::-1])\n s += \"CCD Chip Size : %d x %d\\n\" % self._chipSize[::-1]\n s += \"File date : %s\\n\" % time.asctime(self._filedate)\n s += \"Exposure Time : %f\\n\" % self.Exposure\n s += \"Num ROI : %d\\n\" % self.NumROI\n s += \"Num ROI Exp : %d\\n\" % self.NumROIExperiment\n s += \"Contoller Ver.: %d\\n\" % self.ControllerVersion\n s += \"Logic Output : %d\\n\" % self.LogicOutput\n #self.AppHiCapLowNoise = self._readInt(4)\n s += \"Timing Mode : %d\\n\" % self.TimingMode\n s += \"Det. Temp : %d\\n\" % self.DetTemperature\n s += \"Det. Type : %d\\n\" % self.DetectorType\n s += \"Trigger Diode : %d\\n\" % self.TriggerDiode\n s += \"Delay Time : %d\\n\" % self.DelayTime\n s += \"Shutter Cont. : %d\\n\" % self.ShutterControl\n s += \"Absorb Live : %d\\n\" % self.AbsorbLive\n s += \"Absorb Mode : %d\\n\" % self.AbsorbMode\n s += \"Virtual Chip : %d\\n\" % self.CanDoVirtualChip\n s += \"Thresh. Min L : %d\\n\" % self.ThresholdMinLive\n s += \"Thresh. Min : %d\\n\" % self.ThresholdMin\n s += \"Thresh. Max L : %d\\n\" % self.ThresholdMaxLive\n s += \"Thresh. Max : %d\\n\" % self.ThresholdMax\n s += \"Geometric Op : %d\\n\" % self.GeometricOps\n s += \"ADC Offset : %d\\n\" % self.ADCOffset\n s += \"ADC Rate : %d\\n\" % self.ADCRate\n s += \"ADC Type : %d\\n\" % self.ADCType\n s += \"ADC Resol. : %d\\n\" % self.ADCRes\n s += \"ADC Bit. Adj. : %d\\n\" % self.ADCBitAdj\n s += \"ADC Gain : %d\\n\" % self.Gain\n \n i = 0\n for roi in self.allROI:\n s += \"ROI %-4d : %-5d %-5d %-5d %-5d %-5d %-5d\\n\" % (i,roi[0], roi[1], roi[2],\n roi[3], roi[4], roi[5])\n i += 1\n \n s += \"\\nComments :\\n\"\n i = 0\n for c in self._comments:\n s += \"%-3d : \" % i\n i += 1\n s += c\n s += \"\\n\"\n return s","def _gto_from_ccdata(self):\n\n gbasis = self.ccdata.gbasis\n lines = []\n\n for no, basis in enumerate(gbasis):\n lines.append(f\"{no + 1:3d} 0\")\n for prims in basis:\n lines.append(f\"{prims[0].lower():s} {len(prims[1]):5d} 1.00\")\n for prim in prims[1]:\n lines.append(f\"{prim[0]:15.9e} {prim[1]:15.9e}\")\n lines.append('')\n lines.append('')\n return lines","def report(self):\n s = \"Conn %s\" % (self.shape,)\n if hasattr(self,'eltype'):\n s += \", eltype=%s\" % self.eltype\n s += '\\n'\n return s + ndarray.__str__(self)","def transmission_summarise():\n\n study_description = self.processDicomField(dcm_info, \"StudyDescription\")\n study_date = self.processDicomField(dcm_info, \"StudyDate\")\n series_description = self.processDicomField(dcm_info, \"SeriesDescription\")\n patient_id = self.processDicomField(dcm_info, \"PatientID\")\n patient_name = self.processDicomField(dcm_info, \"PatientName\")\n protocol_name = self.processDicomField(dcm_info, \"ProtocolName\")\n d_fileInfo = filesInSeries_determine()\n self.dp.qprint('Summary report:')\n self.dp.qprint('PatientID: %s' % patient_id, level = -1)\n self.dp.qprint('PatientName: %s' % patient_name, level = -1)\n self.dp.qprint('StudyDate: %s' % study_date, level = -1)\n self.dp.qprint('StudyDescription: %s' % study_description, level = -1)\n self.dp.qprint('SeriesDescription: %s' % series_description, level = -1)\n self.dp.qprint('ProtocolName: %s' % protocol_name, level = -1)\n if d_fileInfo['status']:\n self.dp.qprint('Number of files in Series: %d' % d_fileInfo['fileCount'], level = -1)\n self.dp.qprint('Directory size (raw): %d' % d_fileInfo['dirSizeRaw'], level = -1)\n self.dp.qprint('Directory size (human): %s' % d_fileInfo['str_dirSize'], level = -1)","def createHeaderRecord(self):\n\n # ascii-character limit for every header record information (in bytes)\n lenVersion = 8\n lenLocalPatientID = 80\n lenLocalRecordingID = 80\n lenStartDate = 8\n lenStartTime = 8\n lennBytesHeader = 8\n lenEDFPlus = 44\n lennDataRecord = 8\n lenDurationDataRecord = 8\n lennSignals = 4\n \n HeaderInfolist = [self.Version, self.LocalPatientID, self.LocalRecordingID, self.StartDate, self.StartTime, self.nBytesHeader, self.EDFPlus,\\\n self.nDataRecord, self.DurationDataRecord, self.nSignals]\n lenHeaderInfo = [lenVersion, lenLocalPatientID, lenLocalRecordingID, lenStartDate, lenStartTime, lennBytesHeader, lenEDFPlus, lennDataRecord,\\\n lenDurationDataRecord, lennSignals]\n\n for i in range(len(HeaderInfolist)):\n maxlen = lenHeaderInfo[i]\n if len(HeaderInfolist[i]) > maxlen:\n # truncates the string if length is greater than limit\n HeaderInfolist[i] = HeaderInfolist[i][:maxlen] \n \n else:\n HeaderInfolist[i] = HeaderInfolist[i].ljust(maxlen)\n \n # converts the list to a string with no separator in between elements\n self.HeaderRecord = ''.join(HeaderInfolist) \n\n # concatenates each BioSignal TechInfo to the Header Record string\n for i in range(len(self.BioSignals[0].TechInfo)):\n for x in range(len(self.BioSignals)):\n self.HeaderRecord = self.HeaderRecord + self.BioSignals[x].TechInfo[i]","def dump(self, prefix = \" - \"):\n\t\tlines = [\n\t\t\t\"\"\n\t\t\t\"Host \",\n\t\t\t\"Light engine: state %d, flags 0x%x\" %\n\t\t\t\t(self.le_state, self.le_flags),\n\t\t\t\"Playback: state %d, flags 0x%x\" %\n\t\t\t\t(self.playback_state, self.playback_flags),\n\t\t\t\"Buffer: %d points\" %\n\t\t\t\t(self.fullness, ),\n\t\t\t\"Playback: %d kpps, %d points played\" %\n\t\t\t\t(self.point_rate, self.point_count),\n\t\t\t\"Source: %d, flags 0x%x\" %\n\t\t\t\t(self.source, self.source_flags)\n\t\t]\n\t\t'''\n\t\tif debug == 2:\n\t\t\tfor l in lines:\n\t\t\t\tprint prefix + l\n\t\t'''","def printCsvHeader(self):\n print \"SOURCE FILENAME, RECORD NUMBER, TIME GEN, \"\\\n \"TIME WRITE, CATEGORY, EVENTID, SOURCE, \"\\\n \"COMPUTER, SID, STRINGS, RAW DATA (HEX), \"\\\n \"DECODED DATA\"","def FormatRecord(self, record, categories):\n catsmodified=False\n res={'id': 0} # zero means create new record\n for i in record.get(\"serials\", []):\n if i['sourcetype']=='egroupware':\n res['id']=i['id']\n break\n res['n_given'],res['n_middle'],res['n_family']=nameparser.getparts(record.get(\"names\", [{}])[0])\n for nf in 'n_given', 'n_middle', 'n_family':\n if res[nf] is None:\n res[nf]=\"\" # set None fields to blank string\n res['fn']=nameparser.formatsimplename(record.get(\"names\", [{}])[0])\n for t,prefix in (\"business\", \"adr_one\"), (\"home\", \"adr_two\"):\n a={}\n adr=record.get(\"addresses\", [])\n for i in adr:\n if i['type']==t:\n for p2,k in (\"_street\", \"street\"), (\"_locality\", \"city\"), (\"_region\", \"state\"), \\\n (\"_postalcode\", \"postalcode\"), (\"_countryname\", \"country\"):\n res[prefix+p2]=i.get(k, \"\")\n if t==\"business\":\n res['org_name']=i.get(\"company\",\"\")\n break\n if \"emails\" in record:\n for t,k in (\"business\", \"email\"), (\"home\", \"email_home\"):\n for i in record[\"emails\"]:\n if i.get(\"type\",None)==t:\n res[k]=i.get(\"email\")\n res[k+\"_type\"]=\"INTERNET\"\n break\n cats={}\n for cat in record.get(\"categories\", []):\n c=cat['category']\n v=categories.get(c, None)\n if v is None:\n catsmodified=True\n for i in xrange(0,-999999,-1):\n if `i` not in cats:\n break\n else:\n i=`v`\n cats[i]=str(c)\n res['cat_id']=cats\n for t,k in (\"home\", \"tel_home\"), (\"cell\", \"tel_cell\"), ('fax','tel_fax'), \\\n ('pager', 'tel_pager'), ('office', 'tel_work'):\n if \"numbers\" in record:\n v=\"\"\n for i in record['numbers']:\n if i['type']==t:\n v=i['number']\n break\n res[k]=phonenumber.format(v)\n if \"memos\" in record:\n memos=record.get(\"memos\", [])\n memos+=[{}]\n res['note']=memos[0].get(\"memo\",\"\")\n if \"urls\" in record:\n urls=record.get(\"urls\", [])\n u=\"\"\n for url in urls:\n if url.get(\"type\", None)==\"business\":\n u=url[\"url\"]\n break\n if len(u)==0:\n urls+=[{'url':\"\"}]\n u=urls[0][\"url\"]\n res['url']=u\n return catsmodified,res","def dumpData(self,out):\n #--Get sizes\n for record in self.records:\n #--Text\n if record.name == 'NAME':\n #--Truncate text?\n if len(self.text) > 511:\n self.text = self.text[:511]\n record.data = self.text\n record.size = len(self.text)\n #--Speaker\n elif record.name == 'ONAM':\n record.data = self.spId+'\\x00'\n record.size = len(self.spId) + 1\n record.getSize()\n record.dump(out)","def _printable(self):\n toPrint = \"Communication header. \"\n toPrint += \"Remote App ID: \" + str(self.remote_app_id) + \" \"\n toPrint += \"Remote Node: \" + str(self.remote_node) + \" \"\n toPrint += \"Remote Port: \" + str(self.remote_port) + \" \"\n\n return toPrint","def make_open_airspace_format(self):\n # Extract coordinates from KML\n for idxline in range(len(self.kml_lines)):\n if '<name>' in self.kml_lines[idxline]:\n self.name = self.kml_lines[idxline].replace('\\t', '').replace('<name>', '').replace('</name>', '').replace('\\n','')\n if not self.name.startswith('TS'):\n self.name = 'TS_' + self.name\n print('Type: %s | Name: %s' % (self.as_type, self.name))\n if '<coordinates>' in self.kml_lines[idxline]:\n self.coordinates_kml = self.kml_lines[idxline + 1].replace('\\t', '').replace('\\n', '')\n break\n # start conversion to airspace format\n \"\"\" AC A\n AN TS_Erzgeb\n AL FL98\n AH FL99\n DP 50:26:22 N 012:17:59 E\n DP 50:25:25 N 012:18:26 E\n DP 50:24:40 N 012:19:01 E\n DP 50:24:06 N 012:19:46 E\"\"\"\n\n # AC A\n self.txt_lines.append('AC %s\\n' % self.as_type)\n # AN TS_Erzgeb\n self.txt_lines.append('AN %s\\n' % self.name)\n # heights\n self.txt_lines.append('AL FL98\\n')\n self.txt_lines.append('AH FL99\\n')\n # coordinates\n for coo_pt in self.coordinates_kml.split(' ')[:-1]:\n # Target format: DP 50:26:22 N 012:17:59 E\n lat_long = coo_pt.split(',')\n # latitude\n latDecAsStr = lat_long[1].split('.')\n #if '.' not in latDecAsStr: # take care of case \"51\" instead of \"51.123456\"\n # latDecAsStr += '.000000'\n lat_degree = abs(int(latDecAsStr[0]))\n #print(f'latDecAsStr {latDecAsStr}')\n if len(latDecAsStr)==1:\n latDecAsStr.append('0')\n lat_secondDec = (float('0.' + latDecAsStr[1])*60) % 1\n lat_minute = round((float('0.' + latDecAsStr[1])*60) - lat_secondDec)\n lat_second = round(lat_secondDec*60)\n cooString = ('DP %02d:%02d:%02d' %(lat_degree,lat_minute,lat_second))\n if latDecAsStr[0].startswith('-'):\n cooString += ' S'\n else:\n cooString += ' N'\n # longitude\n #print(f'converting lat_long {lat_long}')\n # take care of case: no decimal sign included, case \"11\" instead of \"11.123456\"\n if '.' not in lat_long[0]:\n lat_long[0] += '.0'\n lonDecAsStr = lat_long[0].split('.')\n lon_degree = abs(int(lonDecAsStr[0]))\n lon_secondDec = (float('0.' + lonDecAsStr[1]) * 60) % 1\n lon_minute = round((float('0.' + lonDecAsStr[1]) * 60) - lon_secondDec)\n lon_second = round(lon_secondDec * 60)\n cooString += (' %03d:%02d:%02d' % (lon_degree, lon_minute, lon_second))\n if lonDecAsStr[0].startswith('-'):\n cooString += ' W'\n else:\n cooString += ' E'\n cooString += '\\n'\n self.txt_lines.append(cooString)","def format_vcf(self):\n # Create a list of information for a VCF file\n vcf_line = [\n self._contig,\n str(self._position),\n self._snpid,\n self._reference,\n self._alternate,\n '.',\n '.',\n 's'\n ]\n return '\\t'.join(vcf_line) # Join everything together with a tab","def format(self, record):\n extra = {\n \"message\": record.getMessage(),\n \"time\": self.formatTime(record, self.datefmt),\n \"msecs\": record.msecs,\n \"name\": record.name,\n \"level\": record.levelname,\n }\n\n keys = filter(self.filterer, record.__dict__)\n extra.update({k: record.__dict__[k] for k in keys})\n return str(CustomEncoder().encode(extra))","def __str__(self):\n d = {}\n d[\"tuner_number\"] = self.tuner_number\n d[\"output_format\"] = self.output_format\n d[\"output_source\"] = self.output_source\n return str(d)","def asformat(self, format):","def __str__(self):\n tag = []\n for key in self.tags:\n if key == 'label':\n self.type = self.tags[key]\n else:\n try:\n tag.append(\"%s=%0.3f\" % (str(key), self.tags[key]))\n except TypeError:\n tag.append(\"%s=%s\" % (str(key), str(self.tags[key])))\n \n \n tag = \";\".join(tag)\n dat = [self.chrom, self.out_start, self.start, self.in_start, \\\n self.in_end, self.end, self.out_end, self.type, self.size, \\\n tag]\n\n return \"{0}\\t{1}\\t{2}\\t{3}\\t{4}\\t{5}\\t{6}\\t{7}\\t{8}\\t{9}\".format(*dat) \\\n .replace(\"None\", \".\")","def __str__(self):\n if self.filename:\n filename = self.filename\n else:\n filename = 'Unknown'\n if self.endian == '<':\n endian = 'Little Endian'\n else:\n endian = 'Big Endian'\n ret_val = ('FILE: %s\\nRecord Offset: %i byte\\n' +\n 'Header Endianness: %s\\n\\n') % \\\n (filename, self.record_offset, endian)\n ret_val += 'FIXED SECTION OF DATA HEADER\\n'\n for key in self.fixed_header.keys():\n ret_val += '\\t%s: %s\\n' % (key, self.fixed_header[key])\n ret_val += '\\nBLOCKETTES\\n'\n for key in self.blockettes.keys():\n ret_val += '\\t%i:' % key\n if not len(self.blockettes[key]):\n ret_val += '\\tNOT YET IMPLEMENTED\\n'\n for _i, blkt_key in enumerate(self.blockettes[key].keys()):\n if _i == 0:\n tabs = '\\t'\n else:\n tabs = '\\t\\t'\n ret_val += '%s%s: %s\\n' % (tabs, blkt_key,\n self.blockettes[key][blkt_key])\n ret_val += '\\nCALCULATED VALUES\\n'\n ret_val += '\\tCorrected Starttime: %s\\n' % self.corrected_starttime\n return ret_val","def packet_to_str(packet: PacketDescription, simple_diagrams=False, force_show_frames='', show_timestamp=False) \\\n -> PacketDiagramDescription:\n protocol = packet.protocols_str\n note_color = ''\n packet_str = ''\n if 'NGAP' in protocol:\n if nas_req_regex.search(packet.msg_description) is not None:\n note_color = ' {0}'.format(color_nas_req)\n protocol = 'NAS req.'\n else:\n note_color = ' {0}'.format(color_nas_rsp)\n protocol = 'NGAP msg. or NAS rsp.'\n\n # Search NGAP messages\n ngap_matches = ngap_message_type_regex.finditer(packet.msg_description)\n ngap_message_types = [ngap_match.group(1) for ngap_match in ngap_matches if ngap_match is not None]\n if len(ngap_message_types) > 0:\n ngap_seen = set()\n ngap_seen_add = ngap_seen.add\n ngap_message_types = ['NGAP {0}'.format(x) for x in ngap_message_types if\n not (x in ngap_seen or ngap_seen_add(x))]\n\n # Search NAS messages\n nas_matches = nas_message_type_regex.finditer(packet.msg_description)\n nas_message_types = [nas_match.group(1) for nas_match in nas_matches if nas_match is not None]\n if len(nas_message_types) > 0:\n # Remove duplicates: https://stackoverflow.com/questions/480214/how-do-you-remove-duplicates-from-a-list-whilst-preserving-order\n nas_seen = set()\n nas_seen_add = nas_seen.add\n nas_message_types = ['NAS {0}'.format(x) for x in nas_message_types if\n not (x in nas_seen or nas_seen_add(x))]\n\n # Print msg. type\n joint_ngap_nas_msg_types = ngap_message_types + nas_message_types\n if len(joint_ngap_nas_msg_types) > 0:\n protocol = '{0}'.format(',\\\\n'.join(joint_ngap_nas_msg_types))\n\n elif 'HTTP' in protocol:\n # Some customized filtering based on what we have seen\n rsp_match = http_rsp_regex.search(packet.msg_description)\n req_match = http_url_regex.search(packet.msg_description)\n if ('404 page not found' in packet.msg_description) or (rsp_match is not None):\n note_color = ' {0}'.format(color_http2_rsp)\n if rsp_match is not None:\n protocol = '{0} {1} rsp.'.format(protocol, rsp_match.group(1))\n else:\n protocol = protocol + ' 404 rsp.'\n elif req_match is not None:\n note_color = ' {0}'.format(color_http2_req)\n protocol = protocol + ' req.'\n else:\n note_color = ' {0}'.format(color_http2_req)\n protocol = protocol + ' req. or rsp. (no HTTP/2 headers)'\n\n match = list(http_url_regex.finditer(packet.msg_description))\n if len(match) > 0:\n method = ''\n method_match_all = http_method_regex.finditer(packet.msg_description)\n protocols = []\n for idx, method_match in enumerate(method_match_all):\n method = '{0} '.format(method_match.group(1))\n url_split = match[idx].group(1).split('?')\n protocols.append('{0} {1}'.format(method, url_split[0]))\n protocol = '{0}\\\\n'.format(protocol) + '\\\\n'.join(protocols)\n\n elif 'PFCP' in protocol:\n if pfcp_req_regex.search(packet.msg_description) is not None:\n note_color = ' {0}'.format(color_pfcp_req)\n protocol = protocol + ' req.'\n else:\n note_color = ' {0}'.format(color_pfcp_rsp)\n protocol = protocol + ' rsp.'\n\n match = pfcp_message_type_regex.search(packet.msg_description)\n if match is not None:\n protocol = '{0}\\\\n{1}'.format(protocol, match.group(1))\n\n elif 'GTPv2' in protocol:\n if gtpv2_req_regex.search(packet.msg_description) is not None:\n note_color = ' {0}'.format(color_gtpv2_req)\n protocol = protocol + ' req.'\n else:\n note_color = ' {0}'.format(color_gtpv2_rsp)\n protocol = protocol + ' req., rsp. or notification'\n\n match = gtpv2_message_type_regex.search(packet.msg_description)\n if match is not None:\n protocol = '{0}\\\\n{1}'.format(protocol, match.group(1))\n\n elif 'Diameter' in protocol or 'RADIUS' in protocol or \"GTP'\" in protocol:\n note_color = ' {0}'.format(color_diameter_radius_gtpprime)\n protocol = get_diam_description(packet)\n\n if show_timestamp:\n try:\n dt_object = datetime.fromtimestamp(packet.timestamp)\n if dt_object.tzinfo is None:\n tz_str = ''\n else:\n tz_str = ' {0}'.format(dt_object.tzinfo)\n timestamp_hour = ' ({0}:{1}:{2}.{3}{4})'.format(dt_object.hour, dt_object.minute, dt_object.second,\n dt_object.microsecond / 1000, tz_str)\n except:\n timestamp_hour = ''\n protocol = '{0}\\\\n+{1:.3f}s{2}'.format(protocol, packet.timestamp_offsett, timestamp_hour)\n\n frame_number = packet[2]\n packet_str = packet_str + '\"{0}\" -> \"{1}\": {2}, {3}\\n'.format(packet.ip_src, packet.ip_dst, frame_number, protocol)\n packet_str = packet_str + '\\nnote right{0}\\n'.format(note_color)\n\n force_show_frames = [e.strip() for e in force_show_frames.split(',')]\n if simple_diagrams and frame_number not in force_show_frames:\n packet_payload = ''\n else:\n packet_payload = packet.msg_description\n\n if packet_payload != '':\n packet_str = packet_str + '**{0} to {1}**\\n{2}\\n'.format(packet.ip_src, packet.ip_dst, packet_payload)\n else:\n packet_str = packet_str + '**{0} to {1}**\\n'.format(packet.ip_src, packet.ip_dst)\n packet_str = packet_str + 'end note\\n'\n packet_str = packet_str + '\\n'\n return PacketDiagramDescription(packet_str, packet.ip_src, packet.ip_dst, protocol)","def _format_senders_correspondent_53A(self, val):\n senders_correspondent_account = val.get('ACCOUNT')\n senders_correspondent_bic = val.get('BIC')\n if senders_correspondent_bic:\n if senders_correspondent_account:\n val = \"/\" + str(senders_correspondent_account) + \"\\n\" + str(senders_correspondent_bic)\n else:\n val = str(senders_correspondent_bic)\n return val","def __str__(self):\n return_text = \"Time-Triggered Frame information =>\\n\"\n return_text += \" Sender id : \" + str(self.__sender_id) + \"\\n\"\n return_text += \" Receivers ids : \" + str(self.__receivers_id) + \"\\n\"\n return_text += \" Path : \" + str(self.__paths) + \"\\n\"\n return_text += \" End_to_End : \" + str(self.__end_to_end_delay) + \" nanoseconds\\n\"\n return_text += \" Period : \" + str(self.__period) + \" nanoseconds\\n\"\n return_text += \" Starting : \" + str(self.__starting_time) + \" nanoseconds\\n\"\n return_text += \" Deadline : \" + str(self.__deadline) + \" nanoseconds\\n\"\n return_text += \" Size : \" + str(self.__size) + \" bytes\"\n return return_text","def dump(self, packet):\n # packet is already decoded\n msg = {\n SRCE: packet[DEST], \n DEST: packet[SRCE], \n TYPE: TABL,\n MESG: list(map(lambda r: ({ \n NTWK: r[MESG][NTWK],\n NMSK: r[MESG][NMSK],\n PEER: r[SRCE],\n }),\n self.routes))\n }\n self.sockets[packet[SRCE]].send(json.dumps(msg).encode())\n return True","def __str__(self, printODData = False):\n networkStr = \"Link\\tFlow\\tCost\\n\"\n for ij in sorted(self.link, key=lambda ij : self.link[ij].sortKey):\n networkStr += \"%s\\t%f\\t%f\\n\" % (ij, self.link[ij].flow, self.link[ij].cost)\n if printODData == True:\n networkStr += \"\\n\"\n networkStr += \"OD pair\\tDemand\\tLeastCost\\n\"\n for ODpair in self.ODpair:\n networkStr += \"%s\\t%f\\t%f\\n\" % (ODpair, self.ODpair[ODpair].demand, self.ODpair[ODpair].leastCost)\n return networkStr","def display_ack_trans_meas(self, carrier=1):\r\r\n\r\r\n loggerCmw = logging.getLogger(__name__ + ' display_ack_trans_meas')\r\r\n\r\r\n # format should match with formatting in hsdpa_trans_meas class\r\r\n trans_num_str = \"%18s\" % \"Transmission [%]\"\r\r\n sent_str = \"%15s\" % \"Sent\"\r\r\n ack_str = \"%15s\" % \"ACK\"\r\r\n nack_str = \"%15s\" % \"NACK\"\r\r\n dtx_str = \"%15s\" % \"DTX\"\r\r\n\r\r\n print \"\"\r\r\n carrierStr = \"Carrier %s\" %carrier\r\r\n print \"%18s\" %carrierStr\r\r\n title_str = trans_num_str + sent_str + ack_str + nack_str + dtx_str\r\r\n print title_str\r\r\n\r\r\n if carrier == 1:\r\r\n for idx in range(self.NUM_MEAS_PER_TX):\r\r\n print self.trans_meas_1[idx]\r\r\n elif carrier == 2:\r\r\n for idx in range(self.NUM_MEAS_PER_TX):\r\r\n print self.trans_meas_2[idx]\r\r\n else:\r\r\n loggerCmw.error(\"Number of carriers %s is not supported\" %carrier)\r\r\n\r\r\n print \"\" # just add blank line\r\r\n return","def formatRecvdData(data_recvd):\n\n\t##############\tADD YOUR CODE HERE\t##############\n\t\n\tx = PrettyTable()\n\t\t\n\tdict2 = eval(data_recvd)\n\t\t\n\tx.field_names=[\"OPTION_NUMBER\",\"OPTIONS\",\"ADDITIONAL INFO\"]\n\tfor key,value in dict2.items():\n\t\tvalue = str(value)\n\t\tif '{' in value:\n\t\t\t\td = eval(value)\n\t\t\t\tfor i,j in d.items():\n\n\t\t\t\t\tx.add_row([key,i,j])\n\t\t\t\t\n\t\telse:\n\t\t\t\t\n\t\t\tx.add_row([key,value,\"-\"])\n\tprint(x)\n\t\n\n\t##################################################","def format(self, record):\n\n\n if not hasattr(record, 'filename_'):\n record.file_indicator = '-'\n else:\n record.file_indicator = os.path.relpath(record.filename_.strip(),\n self.study_dir)\n record.line_indicator = self.format_aggregated(\n record,\n 'line_number',\n ' line %d:',\n ' lines [%s]:',\n optional=True)\n record.column_indicator = self.format_aggregated(\n record,\n 'column_number',\n ' column %d:',\n ' columns [%s]:',\n optional=True)\n record.cause_indicator = self.format_aggregated(\n record,\n 'cause',\n \"; value encountered: '%s'\",\n \"; values encountered: ['%s']\",\n join_string=\"', '\",\n optional=True)\n\n # format the string based on these fields\n formatted_result = super(LogfileStyleFormatter, self).format(record)\n\n # prepend an empty line if the filename is different than before\n current_filename = getattr(record, 'filename_', '')\n if (self.previous_filename is not None and\n current_filename != self.previous_filename):\n formatted_result = '\\n' + formatted_result\n self.previous_filename = current_filename\n\n return formatted_result","def _format_senders_correspondent_53D(self, val):\n account = val.get('ACCOUNT')\n name = val.get('NAME')\n address = val.get('ADDRESS')\n if name and address:\n name = FSwiftWriterUtils.split_text_and_prefix(name, 35)\n address = FSwiftWriterUtils.split_text_and_prefix(address, 35)\n val = FSwiftWriterUtils.allocate_space_for_name_address_without_constraint(name, address)\n if account:\n val = \"/\" + str(account) + \"\\n\" + str(val)\n return val","def print_aldb_to_log(aldb):\n _LOGGER.info(\"ALDB load status is %s\", aldb.status.name)\n if aldb.status not in [ALDBStatus.LOADED, ALDBStatus.PARTIAL]:\n _LOGGER.warning(\"Device All-Link database not loaded\")\n _LOGGER.warning(\"Use service insteon.load_aldb first\")\n return\n\n _LOGGER.info(\"RecID In Use Mode HWM Group Address Data 1 Data 2 Data 3\")\n _LOGGER.info(\"----- ------ ---- --- ----- -------- ------ ------ ------\")\n for mem_addr in aldb:\n rec = aldb[mem_addr]\n # For now we write this to the log\n # Roadmap is to create a configuration panel\n in_use = \"Y\" if rec.control_flags.is_in_use else \"N\"\n mode = \"C\" if rec.control_flags.is_controller else \"R\"\n hwm = \"Y\" if rec.control_flags.is_high_water_mark else \"N\"\n log_msg = (\n f\" {rec.mem_addr:04x} {in_use:s} {mode:s} {hwm:s} \"\n f\"{rec.group:3d} {rec.address.human:s} {rec.data1:3d} \"\n f\"{rec.data2:3d} {rec.data3:3d}\"\n )\n _LOGGER.info(log_msg)","def _output_vadapter_list_verbose(outf, name, vadapter_list):\n\n if vadapter_list:\n FMT = \"%s\\n%-10s\\n%-20s\\n%-5s\\n%s\\n%s\\n%s\\n%s\\n%s\\n%s\\n%s\\n%s\\n%s\\n%s\\n%s\\n%s\\n%s\\n\"\n# HEADER = (\"id\",\"name\",\"io_module_id\",\"vfabric_id\",\"protocol\",\"init_type\",\"desc\",\"status\",\"mac\"\n# ,\"prmiscuous_mode\",\"silent_listener\",\"vlan\",\"wwnn\",\"wwpn\",\"fc_id\",\"spma\",\"fmpa\") \n\t\n for (n , vadapter) in vadapter_list:\n name = vadapter['NAME']\n id = vadapter['ID']\n protocol = vadapter['PROTOCOL']\n vfabric = vadapter['VFABRIC']\n io_module = vadapter['IO_MODULE_ID']\n desc = vadapter['DESC']\n status = vadapter['STATUS']\n init_type = vadapter['INIT_TYPE']\n running_mode = vadapter['RUNNING_MODE']\n if protocol == 1:\n mac = vadapter['MAC']\n promiscuous = vadapter['PROMISCUOUS']\n silent = vadapter['SILENT']\n vlan = vadapter['VLAN']\n elif protocol == 3:\n wwnn = vadapter['WWNN']\n wwpn = vadapter['WWPN']\n spma = vadapter['SPMA']\n fpma = vadapter['FPMA']\n fc_id = vadapter['FC_ID']\n\n\n if _LIMITED_SHOW:\n outf.write('General Attr:\\nId: %s\\n\\tName: %s\\n\\tIO_Module: %s\\n\\tVfabric: %s\\n\\tProtocol: %s\\n\\tInitization_Type: %s\\n\\tDescription: %s\\n\\tStatus: %-20s\\n\\n' % (id, name, io_module, vfabric, protocol, init_type, desc, status)) \n \n\t\telif _DETAIL_SHOW: \n if protocol == 3:\n outf.write('General Attr:\\nId: %s\\nName: %s\\nIO_Module: %s\\nVfabric: %s\\nProtocol: %s\\nInitization_Type: %s\\nDescription: %s\\nStatus: %-20s\\nRUNNING_MODE :%s\\nFC_Attr:\\n\\tWWNN: %s\\n\\tWWNP: %s\\n\\tFC_ID: %s\\n\\tSPMA: %s\\n\\tFMPA: %s\\n\\n' % (id, name, io_module, vfabric, protocol, init_type, desc, status, running_mode, wwnn, wwpn,fc_id,spma,fpma)) \n \n elif protocol == 1:\n\t\t\t outf.write('General Attr:\\nId: %s\\nName: %s\\nIO_Module: %s\\nVfabric: %s\\nProtocol: %s\\nInitization_Type: %s\\nDescription: %s\\nStatus: %-20s\\nRUNNING_MODE :%s\\nEN_Attr:\\n\\tMAC: %s\\n\\tVLAN: %s\\n\\tPromiscuous: %s\\n\\tSilent Listener: %s\\n\\n' % (id , name, io_module,vfabric, protocol, init_type, desc, status, running_mode, mac,vlan, promiscuous, silent))","def format_coregister():\r\n\r\n study_list = retrieve_ref('study_list')\r\n sensor_list = retrieve_ref('sensor_list')\r\n\r\n for study in study_list:\r\n\r\n df_meta = retrieve_meta(study)\r\n recordNames = list(df_meta['recordName'])\r\n\r\n # name the wearable used for each record\r\n for record in recordNames:\r\n\r\n i = df_meta[ df_meta['recordName']== record].index.values[0]\r\n print('i = ' + str(i))\r\n coregisterBegin = df_meta.loc[i, 'coregisterBegin' ]\r\n coregisterEnd = df_meta.loc[i, 'coregisterEnd' ]\r\n coregisterRecords = df_meta.loc[i, 'coregisterRecords' ]\r\n\r\n for sensor in sensor_list:\r\n\r\n df_coregister = pd.DataFrame()\r\n\r\n if len(coregisterRecords) == len(record):\r\n coregisterRecords = list([coregisterRecords])\r\n\r\n elif len(coregisterRecords) > len(record):\r\n coregisterRecords = coregisterRecords.split(' ')\r\n\r\n print('coregisterRecords = ')\r\n print(coregisterRecords)\r\n\r\n for item in coregisterRecords:\r\n\r\n format_type, segment, recordRef = 'truncate', 'All', item\r\n source = os.path.join('studies', study, 'formatted', format_type, recordRef, segment, sensor + '.csv')\r\n df = pd.read_csv(source)\r\n\r\n assert coregisterEnd > coregisterBegin + 100, 'during coregister format, coregisterBegin >= coregisterEnd'\r\n assert coregisterEnd < max(list(df['timeUnix'])), 'possible error with time'\r\n\r\n print('coregisterEnd = ' + str(coregisterEnd) + ' timeUnixEnd = ' + str(max(list(df['timeUnix']))))\r\n print('timeUnixEnd - coregisterEnd = ' + str((max(list(df['timeUnix'])) - coregisterEnd ) / 60))\r\n print('coregisterEnd - timeUnixBegin = ' + str((coregisterEnd - min(list(df['timeUnix']))) / 60))\r\n\r\n assert coregisterEnd > min(list(df['timeUnix']))\r\n\r\n df = df[df['timeUnix'] > coregisterBegin]\r\n df = df[df['timeUnix'] < coregisterEnd]\r\n\r\n assert len(list(df['timeUnix'])) > 0, 'coregistered df removed'\r\n\r\n recordSplit = item.split('_')\r\n wearableName = recordSplit[1]\r\n\r\n df_coregister['timeUnix'] = list(df['timeUnix'])\r\n df_coregister['timeMinutes'] = list(df['timeMinutes'])\r\n\r\n colName = str(wearableName + '_' + 'measurement')\r\n print('colName = ' + colName)\r\n df_coregister[colName] = list(df['measurement'])\r\n\r\n\r\n path = ['studies', study, 'formatted', 'coregister', record, segment]\r\n path = build_path(path)\r\n file = os.path.join(path, sensor + \".csv\")\r\n df_coregister.to_csv(file)\r\n print('formatted coregister file = ' + str(file))","def printFrame(frame, info):\n global analysis_frame\n if frame.n_contacts > 0:\n if analysis_frame == None:\n analysis_frame = frame\n # print(\"analysis_frame: \", analysis_frame.__dir__(),\"\\n\")\n # #print(\"n_contact: \", analysis_frame.n_contacts.contents.__dir__(),\"\\n\")\n # print(\"contacts: \", analysis_frame.contacts.contents.__dir__(),\"\\n\")\n # print(\"accel_data: \", analysis_frame.accel_data.contents.__dir__(),\"\\n\")\n # # print(\"n_contact: \", analysis_frame.n_contacts.contents,\"\\n\")\n # print(\"contacts: \", analysis_frame.contacts.contents.content_bit_mask.__dir__(),\"\\n\")\n # print(\"accel_data: \", analysis_frame.accel_data.contents.__dir__(),\"\\n\")\n\n #print(\"\\nNum Contacts: \", frame.n_contacts)\n for n in range(frame.n_contacts):\n c = frame.contacts[n]\n #print(\"Contact ID: \", c.id)\n #print(\"Contact x, y position: \", c.x_pos, c.y_pos)\n #print(\"Contact Total Force: \", c.total_force)\n\n # send osc\n client.send_message(\"/this/is/a/different/channel\", [c.id, c.x_pos, c.y_pos, c.total_force])\n client.send_message(\"/this/is/a/channel\", [c.id, c.x_pos, c.y_pos, c.total_force])\n\n if c.state == sensel.CONTACT_START:\n sensel.setLEDBrightness(handle, c.id, 100)\n elif c.state == sensel.CONTACT_END:\n sensel.setLEDBrightness(handle, c.id, 0)","def __str__(self):\n\n string = ''\n string += \"Battery Voltage: \" + \"{0:.2f}\".format(self.getBatteryVoltage()) + '\\n'\n string += \"Data Logging: \" + str(self.getDataLoggingStatus()) + '\\n'\n string += \"Data Filename: \" + self.getFilename() +'\\n'\n\n string += \"Time Since Instrument Reset (s): \" + \"{0:.2f}\".format(self.getResetTime()) + '\\n'\n string += \"Data Age (s): \" + \"{0:.2f}\".format(self.getDataAge()) + '\\n'\n string += '\\n'\n string += \"Interior Temperature 1 (F): \" + \"{0:.2f}\".format(self.getInteriorTemperature1()) + '\\n'\n string += \"Interior Temperature 2 (F): \" + \"{0:.2f}\".format(self.getInteriorTemperature2()) + '\\n'\n string += \"Interior Temperature 3 (F): \" + \"{0:.2f}\".format(self.getInteriorTemperature3()) + '\\n'\n string += \"Exterior Temperature (F): \" + \"{0:.2f}\".format(self.getExteriorTemperature()) + '\\n'\n string += \"Pressure (PSI): \" + \"{0:.2f}\".format(self.getPressure()) + '\\n'\n string += \"Humidity (%): \" + \"{0:.2f}\".format(self.getHumidity()) + '\\n'\n string += '\\n'\n\n string += \"GPS Time: \" + str(self.getGpsTime1()) + '\\n'\n string += \"Latitude: \" + \"{0:.9f}\".format(self.getLatitude1()) + '\\n'\n string += \"Longitude: \" + \"{0:.9f}\".format(self.getLongitude1()) + '\\n'\n string += \"Altitude (ft): \" + \"{0:.2f}\".format(self.getAltitude1()) + '\\n'\n string += \"Speed (MPH): \" + \"{0:.2f}\".format(self.getSpeed()) + '\\n'\n string += '\\n'\n\n string += \"GPS Time: \" + str(self.getGpsTime2()) + '\\n'\n string += \"Latitude: \" + \"{0:.9f}\".format(self.getLatitude2()) + '\\n'\n string += \"Longitude: \" + \"{0:.9f}\".format(self.getLongitude2()) + '\\n'\n string += \"Altitude (ft): \" + \"{0:.2f}\".format(self.getAltitude2()) + '\\n'\n string += '\\n'\n\n ax, ay, az = self.getAcceleration()\n string += \"Acceleration (x, y, z): \"\n string += \"{0:.2f}\".format(ax) + \", \"\n string += \"{0:.2f}\".format(ay) + \", \"\n string += \"{0:.2f}\".format(az) + '\\n'\n\n gx, gy, gz = self.getRates()\n string += \"Rates (x, y, z): \"\n string += \"{0:.2f}\".format(gx) + \", \"\n string += \"{0:.2f}\".format(gy) + \", \"\n string += \"{0:.2f}\".format(gz) + '\\n'\n\n mx, my, mz = self.getMagneticReading()\n string += \"Magnetic Field (x, y, z): \"\n string += \"{0:.2f}\".format(mx) + \", \"\n string += \"{0:.2f}\".format(my) + \", \"\n string += \"{0:.2f}\".format(mz) + '\\n'\n\n roll, pitch, yaw = self.getAttitude()\n string += \"Roll (deg): \" + \"{0:.2f}\".format(roll) + '\\n'\n string += \"Pitch (deg): \" + \"{0:.2f}\".format(pitch) + '\\n'\n string += \"Yaw (deg): \" + \"{0:.2f}\".format(yaw) + '\\n'\n string += '\\n'\n relayStates = self.getRelayStates()\n \n\n string += \"Relay States: \" \n string += (( \"ON \") if relayStates[0] else ( \"OFF \")) \n string += (( \"ON \") if relayStates[1] else ( \"OFF \"))\n string += (( \"ON \") if relayStates[2] else ( \"OFF \"))\n string += (( \"ON \") if relayStates[3] else ( \"OFF \"))\n string += '\\n'\n\n\n return string","def dump(self, prefix = \" - \"):\n\t\tlines = [\n\t\t\t\"MAC: \" + \":\".join(\n\t\t\t\t\"%02x\" % (ord(o), ) for o in self.mac),\n\t\t\t\"HW %d, SW %d\" %\n\t\t\t\t(self.hw_rev, self.sw_rev),\n\t\t\t\"Capabilities: max %d points, %d kpps\" %\n\t\t\t\t(self.buffer_capacity, self.max_point_rate)\n\t\t]\n\t\tfor l in lines:\n\t\t\tprint prefix + l\n\t\tif debug == 1:\n\t\t\tself.status.dump(prefix)","def hsdpa_physical_downlink_settings(self):\r\r\n\r\r\n config_list = []\r\r\n\r\r\n config_list.append (\"\")\r\r\n\r\r\n config_list.append ( \"%-24s %-18s\" % (\"Channel( Carrier 1)\", \"Level\"))\r\r\n config_list.append ( \"%-24s %-18s\" % (\"==================\", \"=====\"))\r\r\n\r\r\n pcpich_level = -10.2\r\r\n self.set_pcpich_code_level(carrier=1, leveldB=pcpich_level)\r\r\n config_list.append ( \"%-24s %-18s\" % (\"P-CPICH\", pcpich_level))\r\r\n\r\r\n psch_level = -15.2\r\r\n ssch_level = psch_level\r\r\n pccpch_level = -12.2\r\r\n self.write('CONFigure:WCDMa:SIGN:DL:LEVel:PSCH %s' %psch_level)\r\r\n self.write('CONFigure:WCDMa:SIGN:DL:LEVel:SSCH %s' %ssch_level)\r\r\n self.write('CONFigure:WCDMa:SIGN:DL:LEVel:PCCPch %s' %pccpch_level)\r\r\n config_list.append ( \"%-24s %-18s\" % (\"P-SCH\", psch_level))\r\r\n config_list.append ( \"%-24s %-18s\" % (\"S-SCH\", ssch_level))\r\r\n config_list.append ( \"%-24s %-18s\" % (\"P-CCPCH\", pccpch_level))\r\r\n\r\r\n\r\r\n # SCCPH power level and channelisation code\r\r\n sccpch_level = -12.2\r\r\n self.set_dl_chan_code_level(dl_chan='SCCPch', code=2, level_dB=sccpch_level)\r\r\n config_list.append ( \"%-24s %-18s\" % (\"S-CCPCH\", sccpch_level))\r\r\n\r\r\n # PICH power level and channelisation code\r\r\n pich_level = -15.2\r\r\n self.set_dl_chan_code_level(dl_chan='PICH', code=2, level_dB=pich_level)\r\r\n config_list.append ( \"%-24s %-18s\" % (\"PICH\", pich_level))\r\r\n\r\r\n # AICH power level and channelisation code\r\r\n aich_level = -15.2\r\r\n self.set_dl_chan_code_level(dl_chan='AICH', code=3, level_dB=aich_level)\r\r\n config_list.append ( \"%-24s %-18s\" % (\"AICH\", aich_level))\r\r\n\r\r\n # DPCH power and channelisation code\r\r\n dpch_level = -18.2\r\r\n self.set_dl_chan_code_level(dl_chan='DPCH', code=3, level_dB=dpch_level)\r\r\n config_list.append ( \"%-24s %-18s\" % (\"DPCH\", dpch_level))\r\r\n\r\r\n # F-DPCH power and channelisation ocde\r\r\n fdpch_level = -18.2\r\r\n self.set_dl_chan_code_level(dl_chan='FDPCh', code=6, level_dB=fdpch_level)\r\r\n config_list.append ( \"%-24s %-18s\" % (\"F-DPCH\", fdpch_level))\r\r\n\r\r\n # DPCH enhanced settings\r\r\n self.configure_enhanced_dl_dpch()\r\r\n\r\r\n\r\r\n # *****************************************************************************\r\r\n # Configure 2 HS-SCCH: level, channelization code, UE ID and dummy UE ID\r\r\n # *****************************************************************************\r\r\n hssch_level_1 = -20.2\r\r\n hssch_level_2 = -20.2\r\r\n self.set_hssch_level(hssch_num=1, carrier=1, leveldB=hssch_level_1)\r\r\n self.set_hssch_level(hssch_num=2, carrier=1, leveldB=hssch_level_2)\r\r\n self.set_hssch_code(hssch_num=1, carrier=1, codeNum=2)\r\r\n self.set_hssch_code(hssch_num=2, carrier=1, codeNum=7)\r\r\n config_list.append ( \"%-24s %-18s\" % (\"HS-SCCH #1\", hssch_level_1))\r\r\n config_list.append ( \"%-24s %-18s\" % (\"HS-SCCH #2\", hssch_level_2))\r\r\n\r\r\n self.set_default_ue_id_hssch(carrier=1)\r\r\n\r\r\n # HS-PDSCH Enhanced Settings\r\r\n self.set_hsdsch_mpo(carrier=1, control=\"AUTO\", pwrOffsetManual=\"\")\r\r\n # unscheduled frame type for HSDPA\r\r\n # possible types are 'DUMMy', 'DTX'\r\r\n self.hsdsch_unsched_frames(carrier=1, usFrameType='DUMMY')\r\r\n\r\r\n # *****************************************************************************\r\r\n # Configure HS-PDSCH: level and first channelization code number\r\r\n # *****************************************************************************\r\r\n\r\r\n hsdsch_level = -1.2\r\r\n self.set_hsdsch_level(carrier=1, leveldB = hsdsch_level)\r\r\n self.set_hsdsch_chanelisation_code(code=1, carrier=1)\r\r\n config_list.append ( \"%-24s %-18s\" % (\"HS-PDSCH\", hsdsch_level))\r\r\n\r\r\n\r\r\n # // *****************************************************************************\r\r\n # Set level and channelization code of E-AGCH, E-HICH and E-RGCH.\r\r\n # *****************************************************************************\r\r\n eagch_level = -20.2\r\r\n ehich_level = -20.2\r\r\n ergch_level = -20.2\r\r\n self.set_dl_chan_code_level(dl_chan='EAGCh', code=3, level_dB=eagch_level)\r\r\n self.set_dl_chan_code_level(dl_chan='EHICh', code=6, level_dB=ehich_level)\r\r\n self.set_dl_chan_code_level(dl_chan='ERGCh', code=6, level_dB=ergch_level)\r\r\n config_list.append ( \"%-24s %-18s\" % (\"E-AGCH\", eagch_level))\r\r\n config_list.append ( \"%-24s %-18s\" % (\"E-HICH\", ehich_level))\r\r\n config_list.append ( \"%-24s %-18s\" % (\"E-RGCH\", ergch_level))\r\r\n\r\r\n config_list.append (\"\")\r\r\n\r\r\n for line in config_list:\r\r\n print line\r\r\n\r\r\n if self.dc_hsdpa:\r\r\n\r\r\n self.hsdpa_physical_downlink_settings_carrier2()","def SERIAL_RTRV_record(self):\n # Assumes records are separated by a '>' prompt.\n # Must be connected & operational\n if self.State == 0:\n # a slightly more informative result might help\n return []\n # max seconds to wait for data\n max_wait = 3\n # seconds to wait before trying again\n try_wait = 0.1\n tries = max_wait / try_wait\n # how much we have waited so far\n waited = 0\n # RECV\n raw_record = []\n # raw_record is a list of non-empty strings, \n # each string is a line of info from the reader\n word = ''\n linebuf = []\n while len(raw_record) < 1 :\n # we need to have something to reply.. \n #print \"chars waiting:\", self.Port.inWaiting()\n #sys.stdout.flush()\n while self.Port.inWaiting() > 0:\n while 1:\n # read 1 char at a time \n # until we get to the '>' prompt\n # \n c = self.Port.read(1)\n # \n if self.RecordTrace == 1:\n self.tf_out.write(c)\n # \n # we are done once we see the prompt\n if c == '>':\n if self.debug > 2 :\n print \"Raw Record: \",\n pprint.pprint(raw_record)\n return raw_record\n # \\r = CR , \\n = LF \n # (serial device uses CR + optionally LF, unix text only uses LF)\n # new array entry but only if there is something to add\n elif c == '\\r' or c == '\\n':\n if word != '':\n linebuf.append(word)\n word = ''\n if linebuf != []:\n raw_record.append(linebuf)\n linebuf = []\n # split line into words\n elif c == ' ':\n if word != '':\n linebuf.append(word)\n word = ''\n # all other chars\n else : \n word = word + c\n \n # wait a bit for the serial line to respond\n if self.debug > 1 :\n print \"NO DATA TO READ!!\"\n if waited < max_wait :\n waited += try_wait\n time.sleep(try_wait)\n else:\n self.recwaiting = 0\n return []","def print(self):\r\n self.print_avec_separateur()","def serdes_dump_state(fpgaid,verbose=1):\n lines=[]\n\n lines.append(\"Group : AEQ\")\n lines.append(\" Logic Channel : %d\" % reconfig_AEQ_get_logical_channel_address(fpgaid,verbose))\n lines.append(\" adapt_done : %d\" % reconfig_AEQ_get_adapt_done(fpgaid,verbose))\n lines.append(\" equalization_results : %d\" % reconfig_AEQ_get_equalization_results(fpgaid,verbose))\n lines.append(\" mode : %d\" % reconfig_AEQ_get_mode(fpgaid,verbose))\n lines.append(\"Group : DFE\")\n lines.append(\" Logic Channel : %d\" % reconfig_DFE_get_logical_channel_address(fpgaid,verbose))\n lines.append(\" adaptation_engine_enable : %d\" % reconfig_DFE_get_adaptation_engine_enable(fpgaid,verbose))\n lines.append(\" power_on : %d\" % reconfig_DFE_get_power_on(fpgaid,verbose))\n lines.append(\" tap_1 : %d\" % reconfig_DFE_get_tap_1(fpgaid,verbose))\n lines.append(\" tap_2 : %d\" % reconfig_DFE_get_tap_2(fpgaid,verbose))\n lines.append(\" tap_2_polarity : %d\" % reconfig_DFE_get_tap_2_polarity(fpgaid,verbose))\n lines.append(\" tap_3 : %d\" % reconfig_DFE_get_tap_3(fpgaid,verbose))\n lines.append(\" tap_3_polarity : %d\" % reconfig_DFE_get_tap_3_polarity(fpgaid,verbose))\n lines.append(\" tap_4 : %d\" % reconfig_DFE_get_tap_4(fpgaid,verbose))\n lines.append(\" tap_4_polarity : %d\" % reconfig_DFE_get_tap_4_polarity(fpgaid,verbose))\n lines.append(\" tap_5 : %d\" % reconfig_DFE_get_tap_5(fpgaid,verbose))\n lines.append(\" tap_5_polarity : %d\" % reconfig_DFE_get_tap_5_polarity(fpgaid,verbose))\n lines.append(\"Group : EyeQ\")\n lines.append(\" Logic Channel : %d\" % reconfig_EyeQ_get_logical_channel_address(fpgaid,verbose))\n lines.append(\" 1D_Eye : %d\" % reconfig_EyeQ_get_1D_Eye(fpgaid,verbose))\n lines.append(\" BERB_Enable : %d\" % reconfig_EyeQ_get_BERB_Enable(fpgaid,verbose))\n lines.append(\" BERB_Snap_Shot_and_Reset : %d\" % reconfig_EyeQ_get_BERB_Snap_Shot_and_Reset(fpgaid,verbose))\n lines.append(\" Bit_Counter31_0 : %d\" % reconfig_EyeQ_get_Bit_Counter31_0(fpgaid,verbose))\n lines.append(\" Bit_Counter63_32 : %d\" % reconfig_EyeQ_get_Bit_Counter63_32(fpgaid,verbose))\n lines.append(\" Counter_Enable : %d\" % reconfig_EyeQ_get_Counter_Enable(fpgaid,verbose))\n lines.append(\" Enable_Eye_Monitor : %d\" % reconfig_EyeQ_get_Enable_Eye_Monitor(fpgaid,verbose))\n lines.append(\" Err_Conter63_32 : %d\" % reconfig_EyeQ_get_Err_Conter63_32(fpgaid,verbose))\n lines.append(\" Err_Counter31_0 : %d\" % reconfig_EyeQ_get_Err_Counter31_0(fpgaid,verbose))\n lines.append(\" Horizontal_phase : %d\" % reconfig_EyeQ_get_Horizontal_phase(fpgaid,verbose))\n lines.append(\" Polarity : %d\" % reconfig_EyeQ_get_Polarity(fpgaid,verbose))\n lines.append(\" Vertical_height : %d\" % reconfig_EyeQ_get_Vertical_height(fpgaid,verbose))\n lines.append(\"Group : PMA\")\n lines.append(\" Logic Channel : %d\" % reconfig_PMA_get_logical_channel_address(fpgaid,verbose))\n lines.append(\" Pre_emphasis_first_post_tap : %d\" % reconfig_PMA_get_Pre_emphasis_first_post_tap(fpgaid,verbose))\n lines.append(\" Pre_emphasis_pre_tap : %d\" % tapformat(reconfig_PMA_get_Pre_emphasis_pre_tap(fpgaid,verbose)))\n lines.append(\" Pre_emphasis_second_post_tap : %d\" % tapformat(reconfig_PMA_get_Pre_emphasis_second_post_tap(fpgaid,verbose)))\n lines.append(\" RX_equalization_control : %d\" % reconfig_PMA_get_RX_equalization_control(fpgaid,verbose))\n lines.append(\" RX_equalization_DC_gain : %d\" % reconfig_PMA_get_RX_equalization_DC_gain(fpgaid,verbose))\n lines.append(\" VOD : %d\" % reconfig_PMA_get_VOD(fpgaid,verbose))\n print \"\\n\".join(lines)\n return lines","def formatHeaderLine(cols, colLabs, indexCols, storeLanes):\n l = []\n if storeLanes is True:\n l.append(\"Lane\")\n if colLabs[1] is not None:\n l.append(\"Sample_ID\")\n if colLabs[2] is not None:\n l.append(\"Sample_Name\")\n if indexCols[0] is not None and len(cols[indexCols[0]]) > 0:\n l.append(\"index\")\n if indexCols[1] is not None and len(cols[indexCols[1]]) > 0:\n l.append(\"index2\")\n if colLabs[3] is not None:\n l.append(\"Sample_Project\")\n return \",\".join(l)","def _gen_report(self):\n print \"------------------------------------------\"\n print \"fio report\"\n print \"------------------------------------------\"\n print \"name\", \" \".join(f for f in FIELDS)\n # print fields\n for name in sorted(self.reports):\n report = self.reports[name]\n #print report\n print name, \" \".join(str(report.get(f)) for f in FIELDS)\n\n print \"*******************************************\"\n # print clats\n index = 0\n for name in sorted(self.reports):\n report = self.reports[name]\n if index == 0:\n print \"clat_percent\", \" \".join(\n str(c[0]) for c in report[\"clats\"])\n print name, \" \".join(str(c[1]) for c in report[\"clats\"])\n index += 1","def dump(self, packet):\n #self.print_table()\n src = packet[\"dst\"]\n dst = packet[\"src\"]\n routes_dump = []\n for route in self.routes:\n for verat in route[\"varats\"]:\n routes_dump.append({\"network\": verat[\"network\"], \"netmask\": verat[\"netmask\"],\n \"peer\": route[\"peer\"]})\n \n a = {\"src\": src, \"dst\": dst, \"type\": \"table\", \"msg\": routes_dump}\n return a","def to_html_table(self):\n td = '<td>'\n nwtd = '<td nowrap=\"true\">'\n ftd = '<td class=\"format\">'\n ctd = '<td class=\"cen\">'\n etd = '</td>'\n \n if self.is_power_onoff():\n out = td + 'Power On/Off' + etd\n else:\n out = nwtd + '<strong>' + self['target'].ljust(20) + '</strong>' + etd\n\n if 'Date' in self:\n out += ctd + self['Date'] + etd\n else:\n out += td + etd\n\n if 'UTstart' in self:\n out += ctd + self['UTstart'] + etd\n else:\n out += td + etd\n\n if 'UTend' in self:\n out += ctd + self['UTend'] + etd\n else:\n out += td + etd\n\n if 'exposure' in self:\n out += ctd + self['exposure'] + etd\n else:\n out += td + etd\n\n if 'sample' in self:\n out += ctd + self['sample'] + etd\n else:\n out += td + etd\n\n if 'nframe' in self:\n out += ctd + self['nframe'] + etd\n else:\n out += td + etd\n \n if self.is_power_onoff():\n out += (td + etd)*3\n else:\n speed = self['speed']\n out += ctd + self['filters'].ljust(11) + etd + ctd + self['x_bin'] + 'x' + self['y_bin'] + etd + ctd + speed + etd \n \n if self.number_windows() > 0:\n out += ctd + self['x1_size'].rjust(4) + 'x' + self['y1_size'].ljust(4) + etd + td + self['x1_start'].ljust(3) + etd + td + self['y1_start'].ljust(4) + etd\n else:\n out += (td + etd)*3\n \n if self.number_windows() > 1:\n out += ctd + self['x2_size'].rjust(4) + 'x' + self['y2_size'].ljust(4) + etd + td + self['x2_start'].ljust(3) + etd + td + self['y2_start'].ljust(4) + etd\n else:\n out += (td + etd)*3\n\n if 'grating' in self:\n out += ctd + self['grating'] + etd\n else:\n out += td + etd\n\n if 'slit_width' in self:\n out += ctd + self['slit_width'] + etd\n else:\n out += td + etd\n\n if 'slit_angle' in self:\n out += ctd + self['slit_angle'] + etd\n else:\n out += td + etd\n \n if 'ID' in self:\n out += ctd + self['ID'] + etd\n else:\n out += td + etd\n\n if 'PI' in self:\n out += ctd + self['PI'] + etd\n else:\n out += td + etd\n \n if 'Comment' in self:\n out += nwtd + self['Comment'] + etd\n else:\n out += td + etd\n\n return out","def writeOut(self):\n # import time\n self.outHeader = self.srcHeader\n for line in self.outHeader:\n self.outFile.write(line + '\\n')\n # now = time.asctime(time.localtime(time.time()))\n # self.outFile.write('%% -- %s -- Written to new alog' % now)\n for time_s in sorted(self.outData):\n for sens in self.outData[time_s]:\n for meas in self.outData[time_s][sens]:\n valu = self.outData[time_s][sens][meas]\n msg_list = [str(time_s), meas, sens, str(valu)]\n line_string = reconstructLine(msg_list)\n self.outFile.write(line_string + '\\n')","def output(self):\n time = \"%04d-%02d-%02d %02d:%02d:%02d\" % (self.year, self.month, \\\n self.date, self.hours, self.minutes, self.seconds)\n return {\n \"id\": self.id,\n \"name\": self.name,\n \"type\": 4,\n \"enabled\": 1,\n \"abstime\": time\n }","def write_pc_cards(bc_file, bc_class):\n bc_file.write('! Output Control\\n')\n oc = bc_class.output_control\n objects = list(oc.param.output_control_option.get_range())\n if oc.output_control_option == objects[0]:\n bc_file.write('OC {}\\n'.format(oc.oc_time_series_id))\n ofs = oc.output_flow_strings\n if not ofs.empty:\n bc_file.write(ofs.to_csv(sep=' ', na_rep='', index=False, header=False,).replace('\\r\\n', '\\n'))\n\n if oc.print_adaptive_mesh:\n bc_file.write('PC ADP\\n')\n if oc.print_numerical_fish_surrogate:\n bc_file.write('PC ELM\\n')\n if oc.screen_output_residual:\n bc_file.write('SOUT RESID\\n')\n if oc.screen_output_all:\n bc_file.write('SOUT ALL\\n')\n if oc.screen_output_mass_error:\n bc_file.write('SOUT MERROR\\n')\n if oc.screen_output_worst_nonlinear_node:\n bc_file.write('SOUT NLNODE\\n')\n if oc.screen_output_worst_linear_node:\n bc_file.write('SOUT LNODE\\n')\n if oc.file_output_wind:\n bc_file.write('FOUT WIND\\n')\n if oc.file_output_wave:\n bc_file.write('FOUT WAVE\\n')\n if oc.file_output_adapted_grid:\n bc_file.write('FOUT ADAPT GRID\\n')\n if oc.file_output_adapted_solution:\n bc_file.write('FOUT ADAPT SW\\n')\n if oc.file_output_adapted_transport:\n bc_file.write('FOUT ADAPT CON\\n')\n if oc.file_output_sediment:\n bc_file.write('FOUT SED\\n')\n\n bc_file.write('\\n') # blank line after Output Control","def to_network_layer(packet):\r\n print(f\"[to_network_layer] packet:{packet}\")","def __str__(self):\n return '<TuebingenMEG: %i samples, %i timepoints, %i channels>' \\\n % (self.nsamples, self.ntimepoints, len(self.channelids))","def _write_coft(parameters):\n # Format\n fmt = block_to_format[\"COFT\"]\n fmt = str2format(fmt[5])\n\n values = [x for x in parameters[\"connection_history\"]]\n out = write_record(values, fmt, multi=True)\n\n return out","def show_history(self, ts=0, count=0, fmt='raw'):\n records = self.station.get_records(since_ts=ts, num_rec=count)\n for i,r in enumerate(records):\n if fmt.lower() == 'raw':\n raw_dump(r['datetime'], r['ptr'], r['raw_data'])\n elif fmt.lower() == 'table':\n table_dump(r['datetime'], r['data'], i==0)\n else:\n print(r['datetime'], r['data'])","def __str__ (self):\n\n return '{:d}: {:s}:{:d} [{:s}]; {:x}, {:.02f}'.format (self.index,\n self.ip, self.port, self.mac, self.flags, self.timeout)","def print_info(self):\n outstr = '================================================= Ambient Noise Cross-correlation Database =================================================\\n'\n outstr += self.__str__()+'\\n'\n outstr += '--------------------------------------------------------------------------------------------------------------------------------------------\\n'\n if 'NoiseXcorr' in self.auxiliary_data.list():\n outstr += 'NoiseXcorr - Cross-correlation seismogram\\n'\n if 'StaInfo' in self.auxiliary_data.list():\n outstr += 'StaInfo - Auxiliary station information\\n'\n if 'DISPbasic1' in self.auxiliary_data.list():\n outstr += 'DISPbasic1 - Basic dispersion curve, no jump correction\\n'\n if 'DISPbasic2' in self.auxiliary_data.list():\n outstr += 'DISPbasic2 - Basic dispersion curve, with jump correction\\n'\n if 'DISPpmf1' in self.auxiliary_data.list():\n outstr += 'DISPpmf1 - PMF dispersion curve, no jump correction\\n'\n if 'DISPpmf2' in self.auxiliary_data.list():\n outstr += 'DISPpmf2 - PMF dispersion curve, with jump correction\\n'\n if 'DISPbasic1interp' in self.auxiliary_data.list():\n outstr += 'DISPbasic1interp - Interpolated DISPbasic1\\n'\n if 'DISPbasic2interp' in self.auxiliary_data.list():\n outstr += 'DISPbasic2interp - Interpolated DISPbasic2\\n'\n if 'DISPpmf1interp' in self.auxiliary_data.list():\n outstr += 'DISPpmf1interp - Interpolated DISPpmf1\\n'\n if 'DISPpmf2interp' in self.auxiliary_data.list():\n outstr += 'DISPpmf2interp - Interpolated DISPpmf2\\n'\n if 'FieldDISPbasic1interp' in self.auxiliary_data.list():\n outstr += 'FieldDISPbasic1interp - Field data of DISPbasic1\\n'\n if 'FieldDISPbasic2interp' in self.auxiliary_data.list():\n outstr += 'FieldDISPbasic2interp - Field data of DISPbasic2\\n'\n if 'FieldDISPpmf1interp' in self.auxiliary_data.list():\n outstr += 'FieldDISPpmf1interp - Field data of DISPpmf1\\n'\n if 'FieldDISPpmf2interp' in self.auxiliary_data.list():\n outstr += 'FieldDISPpmf2interp - Field data of DISPpmf2\\n'\n outstr += '============================================================================================================================================\\n'\n print outstr\n return","def __str__(self):\n table_data = [\n ['', 'C', 'G', 'A', 'T'],\n ['total', str(self.total['C']), str(self.total['G']), str(self.total['A']), str(self.total['T'])],\n ['reverse half strand', str(self.reverse['C']), str(self.reverse['G']), str(self.reverse['A']),\n str(self.reverse['T'])],\n ['forward half strand', str(self.forward['C']), str(self.forward['G']), str(self.forward['A']),\n str(self.forward['T'])]\n ]\n table = AsciiTable(table_data)\n return \"Generation #{}\\n\".format(self.epoch) + table.table","def writeline(self):\n if self.record == 'ATOM' and not self.atom.startswith('H') and len(self.atom) < 4:\n atom = ' {0}'.format(self.atom)\n else:\n atom = self.atom\n aline = (\n \"{0:6s} {1:5d} {2:5s} {3} {4}{5:5d} {6:10.5f}{7:10.5f}\"\n \"{8:10.5f} {9:5s}{10:3d}{11:2d} {12:8.5f} {13} 0\\n\"\n ).format(\n self.record, self.natom, atom, self.res, self.chain, self.nres,\n self.x, self.y, self.z, self.fftype, self.nbond, self.nlonepair,\n self.charge, self.fixed\n )\n cline = \"CONECT{0:6d}{1}\\n\".format(\n self.natom,\n ''.join('{0:6d}'.format(num) for num in self.connections)\n )\n return aline, cline","def repr_fields(self):\n pa = set_blank_if_default(self.pa, 0)\n pb = set_blank_if_default(self.pb, 0)\n\n w1a = set_blank_if_default(self.wa[0], 0.0)\n w2a = set_blank_if_default(self.wa[1], 0.0)\n w3a = set_blank_if_default(self.wa[2], 0.0)\n\n w1b = set_blank_if_default(self.wb[0], 0.0)\n w2b = set_blank_if_default(self.wb[1], 0.0)\n w3b = set_blank_if_default(self.wb[2], 0.0)\n x1, x2, x3 = self.get_x_g0_defaults()\n\n # offt doesn't exist in NX nastran\n offt = set_blank_if_default(self.offt, 'GGG')\n\n list_fields = ['CBAR', self.eid, self.Pid(), self.Ga(), self.Gb(), x1, x2,\n x3, offt, pa, pb, w1a, w2a, w3a, w1b, w2b, w3b]\n return list_fields","def output(self):\n # store tables\n # protocol PK keys table for measurements under a protocol\n # 'line' keys table for line-only section (if enabled)\n # 'all' keys table including everything.\n tables = OrderedDict()\n if self.options.line_section:\n tables[\"line\"] = OrderedDict()\n tables[\"line\"][\"header\"] = self._output_line_header()\n if not self.options.protocol_section:\n tables[\"all\"] = OrderedDict()\n tables[\"all\"][\"header\"] = self._output_header()\n self._do_export(tables)\n return self._build_output(tables)","def info(self):\n self.update_info()\n print('Number of electrodes: ' + str(self.n_elecs))\n print('Recording time in seconds: ' + str(self.dur))\n print('Sample Rate in Hz: '+ str(self.sample_rate))\n print('Number of sessions: ' + str(self.n_sessions))\n print('Date created: ' + str(self.date_created))\n print('Meta data: ' + str(self.meta))","def printinterfacestats(self, outformat):\n\n interfacedata = self.interfacetable\n hostname = self._hostname\n\n ignoreoids = [22, 21, 18, 12]\n\n ifmib = {\n 1:\"ifIndex\",\n 2:\"ifDescr\",\n 3:\"ifType\",\n 4:\"ifMtu\",\n 5:\"ifSpeed\",\n 6:\"ifPhysAddress\",\n 7:\"ifAdminStatus\",\n 8:\"ifOperStatus\",\n 9:\"ifLastChange\",\n 10:\"ifInOctets\",\n 11:\"ifUcastPkts\",\n 12:\"ifInNUcastPkts\",\n 13:\"ifInDiscards\",\n 14:\"ifInErrors\",\n 15:\"ifInUnknownProtos\",\n 16:\"ifOutOctets\",\n 17:\"ifOutUcastPkts\",\n 18:\"ifOutNUcastPkts\",\n 19:\"ifOutDiscards\",\n 20:\"ifOutErrors\",\n 21:\"ifOutQLen\",\n 22:\"ifSpecific\"\n }\n\n # oid value of 8 or 7\n ifstatus = {\n 1: \"up\",\n 2: \"down\",\n 3: \"testing\",\n 4: \"unknown\",\n 5: \"dormant\",\n 6: \"notPresent\",\n 7: \"lowerLayerDown\"\n }\n\n # Print CSV Formated Data\n if outformat == 'csv':\n headerrow = []\n headerrow.append(\"hostname\")\n\n for ifid in sorted(ifmib):\n if ifid not in ignoreoids:\n headerrow.append(ifmib[ifid])\n\n print \",\".join(headerrow)\n\n for ifindex in sorted(interfacedata):\n currentrow = []\n currentrow.append(hostname)\n\n for oid in sorted(interfacedata[ifindex]):\n for skip in ignoreoids:\n if oid == skip:\n exclude = True\n break\n else:\n exclude = False\n if exclude != True:\n if oid == 8 or oid == 7:\n currentrow.append(ifstatus[int(interfacedata[ifindex][oid])])\n else:\n currentrow.append(interfacedata[ifindex][oid])\n print \",\".join(currentrow)\n\n elif outformat == 'table':\n headerrow = []\n headerrow.append(\"hostname\")\n\n for ifid in sorted(ifmib):\n if ifid not in ignoreoids:\n headerrow.append(ifmib[ifid])\n\n thetable = prettytable.PrettyTable(headerrow)\n\n for ifindex in sorted(interfacedata):\n currentrow = [hostname]\n for oid in sorted(interfacedata[ifindex]):\n if oid not in ignoreoids:\n if oid == 8 or oid == 7:\n currentrow.append(ifstatus[int(interfacedata[ifindex][oid])])\n else:\n currentrow.append(interfacedata[ifindex][oid])\n thetable.add_row(currentrow)\n print thetable\n\n elif outformat == 'json':\n\n hostinterfaces = {}\n #hostinterfaces[self._hostname] = {}\n\n for ifindex in sorted(interfacedata):\n #print dict(interfacedata[ifindex].dictrepr().items())\n hostinterfaces[ifindex] = dict(interfacedata[ifindex].dictrepr().items())\n\n #print hostinterfaces\n print json.dumps(hostinterfaces, sort_keys=True, indent=4, \\\n separators=(',', ': '))\n\n else:\n # Print Detailed textual list of interface information\n for ifindex in sorted(interfacedata):\n print \"\\nInterface Number\", ifindex\n for oid in sorted(interfacedata[ifindex]):\n if oid not in ignoreoids:\n print \"{0} ({1}) =\".format(ifmib[oid], oid),\n\n if oid == 8 or oid == 7:\n print ifstatus[int(interfacedata[ifindex][oid])]\n # Need to fid display of MAC Addresses\n #elif oid == 6:\n # print \"{0}\".format(interfacedata[ifindex][oid].hexdigits)\n else:\n print interfacedata[ifindex][oid]\n print \"\\n\",","def print_header(self,opts):\n if COMPANY_LOGO in opts.keys():\n self.imgesprint(opts[COMPANY_LOGO])\n self.esprint(LINE_BREAK)\n\n if COMPANY_NAME in opts.keys():\n self.esprint(opts[COMPANY_NAME])\n self.esprint(LINE_BREAK)\n\n if COMPANY_CONTACT in opts.keys():\n self.esprint(TEL)\n self.esprint(opts[COMPANY_CONTACT])\n self.esprint(LINE_BREAK)\n\n if COMPANY_LOCATION in opts.keys():\n self.esprint(LOC)\n self.esprint(opts[COMPANY_LOCATION])\n self.esprint(LINE_BREAK)\n\n self.n_char(SN_RULER,PAPER_MAX_WIDTH,w=1,h=1)\n self.esprint(LINE_BREAK)\n\n if RECEIPT_NAME in opts.keys():\n RNO = opts[RECEIPT_NUMBER] if RECEIPT_NUMBER in opts.keys() else ''\n RECEIPT = opts[RECEIPT_NAME]+SPACE_CHAR+HASH_NO+RNO\n self.cesprint(RECEIPT)\n char = len(RECEIPT)\n self.cesprint(self.n_char_generate(DO_RULER,char),w=1,h=2)\n self.esprint(LINE_BREAK)\n\n if CUSTOMER_NAME in opts.keys():\n self.lesprint(CUSTOMER,LEFT_PROPERTY_WIDTH)\n self.esprint(COLON_CHAR)\n self.n_char(SPACE_CHAR,LR_ALIGN)\n self.esprint(opts[CUSTOMER_NAME])\n self.esprint(LINE_BREAK)\n\n if CASHIER_NAME in opts.keys():\n self.lesprint(CASHIER,LEFT_PROPERTY_WIDTH)\n self.esprint(COLON_CHAR)\n self.n_char(SPACE_CHAR,LR_ALIGN)\n self.esprint(opts[CASHIER_NAME])\n self.esprint(LINE_BREAK)\n\n if RECEIPT_DATE in opts.keys():\n self.lesprint(DATE,LEFT_PROPERTY_WIDTH)\n self.esprint(COLON_CHAR)\n self.n_char(SPACE_CHAR,LR_ALIGN)\n self.esprint(opts[RECEIPT_DATE])\n self.esprint(LINE_BREAK)\n\n if CURRENCY_NAME in opts.keys():\n self.lesprint(CURRENCY_NAME_TITLE,LEFT_PROPERTY_WIDTH)\n self.esprint(COLON_CHAR)\n self.n_char(SPACE_CHAR,LR_ALIGN)\n self.esprint(opts[CURRENCY_NAME])\n self.esprint(LINE_BREAK)","def show():\n configdb = ConfigDBConnector()\n configdb.connect()\n queue_info = configdb.get_entry('FLEX_COUNTER_TABLE', 'QUEUE')\n port_info = configdb.get_entry('FLEX_COUNTER_TABLE', 'PORT')\n port_drop_info = configdb.get_entry('FLEX_COUNTER_TABLE', PORT_BUFFER_DROP)\n rif_info = configdb.get_entry('FLEX_COUNTER_TABLE', 'RIF')\n queue_wm_info = configdb.get_entry('FLEX_COUNTER_TABLE', 'QUEUE_WATERMARK')\n pg_wm_info = configdb.get_entry('FLEX_COUNTER_TABLE', 'PG_WATERMARK')\n pg_drop_info = configdb.get_entry('FLEX_COUNTER_TABLE', PG_DROP)\n buffer_pool_wm_info = configdb.get_entry('FLEX_COUNTER_TABLE', BUFFER_POOL_WATERMARK)\n acl_info = configdb.get_entry('FLEX_COUNTER_TABLE', ACL)\n tunnel_info = configdb.get_entry('FLEX_COUNTER_TABLE', 'TUNNEL')\n trap_info = configdb.get_entry('FLEX_COUNTER_TABLE', 'FLOW_CNT_TRAP')\n route_info = configdb.get_entry('FLEX_COUNTER_TABLE', 'FLOW_CNT_ROUTE')\n\n header = (\"Type\", \"Interval (in ms)\", \"Status\")\n data = []\n if queue_info:\n data.append([\"QUEUE_STAT\", queue_info.get(\"POLL_INTERVAL\", DEFLT_10_SEC), queue_info.get(\"FLEX_COUNTER_STATUS\", DISABLE)])\n if port_info:\n data.append([\"PORT_STAT\", port_info.get(\"POLL_INTERVAL\", DEFLT_1_SEC), port_info.get(\"FLEX_COUNTER_STATUS\", DISABLE)])\n if port_drop_info:\n data.append([PORT_BUFFER_DROP, port_drop_info.get(\"POLL_INTERVAL\", DEFLT_60_SEC), port_drop_info.get(\"FLEX_COUNTER_STATUS\", DISABLE)])\n if rif_info:\n data.append([\"RIF_STAT\", rif_info.get(\"POLL_INTERVAL\", DEFLT_1_SEC), rif_info.get(\"FLEX_COUNTER_STATUS\", DISABLE)])\n if queue_wm_info:\n data.append([\"QUEUE_WATERMARK_STAT\", queue_wm_info.get(\"POLL_INTERVAL\", DEFLT_60_SEC), queue_wm_info.get(\"FLEX_COUNTER_STATUS\", DISABLE)])\n if pg_wm_info:\n data.append([\"PG_WATERMARK_STAT\", pg_wm_info.get(\"POLL_INTERVAL\", DEFLT_60_SEC), pg_wm_info.get(\"FLEX_COUNTER_STATUS\", DISABLE)])\n if pg_drop_info:\n data.append(['PG_DROP_STAT', pg_drop_info.get(\"POLL_INTERVAL\", DEFLT_10_SEC), pg_drop_info.get(\"FLEX_COUNTER_STATUS\", DISABLE)])\n if buffer_pool_wm_info:\n data.append([\"BUFFER_POOL_WATERMARK_STAT\", buffer_pool_wm_info.get(\"POLL_INTERVAL\", DEFLT_60_SEC), buffer_pool_wm_info.get(\"FLEX_COUNTER_STATUS\", DISABLE)])\n if acl_info:\n data.append([ACL, pg_drop_info.get(\"POLL_INTERVAL\", DEFLT_10_SEC), acl_info.get(\"FLEX_COUNTER_STATUS\", DISABLE)])\n if tunnel_info:\n data.append([\"TUNNEL_STAT\", rif_info.get(\"POLL_INTERVAL\", DEFLT_10_SEC), rif_info.get(\"FLEX_COUNTER_STATUS\", DISABLE)])\n if trap_info:\n data.append([\"FLOW_CNT_TRAP_STAT\", trap_info.get(\"POLL_INTERVAL\", DEFLT_10_SEC), trap_info.get(\"FLEX_COUNTER_STATUS\", DISABLE)])\n if route_info:\n data.append([\"FLOW_CNT_ROUTE_STAT\", route_info.get(\"POLL_INTERVAL\", DEFLT_10_SEC),\n route_info.get(\"FLEX_COUNTER_STATUS\", DISABLE)])\n\n click.echo(tabulate(data, headers=header, tablefmt=\"simple\", missingval=\"\"))","def to_line(self) -> str:\n bytecount = len(self.data)\n nums = bytearray()\n nums.append(bytecount)\n nums.extend(struct.pack(\">H\", self.address))\n nums.append(self.typ)\n nums.extend(self.data)\n crc = sum(nums)\n crc = ((~crc) + 1) & 0xFF\n nums.append(crc)\n line = \":\" + binascii.hexlify(nums).decode(\"ascii\")\n return line","def csv(self):\n return '{:.6f},{},{}'.format(self.sample_time, self.value, self.status)","def parse_traffic(self, text):\n interface = text.splitlines()[1].split('/')[0].strip()\n # pprint(interface)\n raw_entries = []\n for line in text.splitlines():\n if '/s' in line:\n line = line.replace('|', '')\n line = [l.strip() for l in line.split(' ') if len(l) > 0]\n raw_entries.append((line[0], line[-1]))\n\n holder = {}\n entries = []\n\n for date_str, rate in raw_entries:\n date = datetime.datetime.strptime(date_str, self.date_fmt)\n epoch = datetime_to_epoch(date)\n\n rate, unit = rate.split(' ')\n holder['unit'] = unit\n entries.append({'timestamp': epoch, \"rate\": float(rate)})\n holder['measurements'] = entries\n\n return interface, {'data': holder}","def get_data_format(self):\n return {\n \"timestamp\": self.timestamp,\n \"measure\": self.measure,\n \"symbolic\": self.symbolic\n }","def FILE_RTRV_record(self):\n eor = 0\n raw_record = []\n #record = []\n # record is a list of non-empty strings, \n # each string is a line of info from the reader\n word = ''\n linebuf = []\n while len(raw_record) < 1 and self.eof == 0 and eor == 0 :\n # we need to have something to reply.. \n while 1:\n # read 1 char at a time \n # until we get to the '>' prompt\n #\n c = self.tf.read(1)\n #\n #print c,\n #\n if len(c) != 1:\n self.eof = 1\n if self.debug > 2 :\n print \"FILE Raw Record: \",\n pprint.pprint(raw_record)\n return raw_record\n elif c == '>':\n eor = 1\n if self.debug > 2 :\n print \"FILE2 Raw Record: \",\n pprint.pprint(raw_record)\n return raw_record\n # \\r = CR , \\n = LF \n # (serial device uses CR + optionally LF, unix text only uses LF)\n # - new array entry but only if there is something to add \n elif c == '\\r' or c == '\\n':\n if word != '':\n linebuf.append(word)\n word = ''\n if linebuf != []:\n raw_record.append(linebuf)\n linebuf = []\n # split line into words\n elif c == ' ':\n if word != '':\n linebuf.append(word)\n word = ''\n # all other chars\n else : \n word = word + c\n \n time.sleep(0.001)","def format(self, obj):\n pass","def format(self, obj):\n pass","def _write_driver_graph_urls(self):\n try:\n driver_jobid = os.environ['LSB_JOBID']\n except KeyError:\n pass\n else:\n driver_rtm_url = construct_rtm_url(driver_jobid)\n driver_host = socket.gethostname()\n logger.info(f\"Driver LSB_JOBID is: {driver_jobid}\")\n logger.info(f\"Driver host is: {driver_host}\")\n logger.info(f\"Driver RTM graphs: {driver_rtm_url}\")\n\n start_timestamp = get_job_submit_time()\n ganglia_url = construct_ganglia_link(driver_host, start_timestamp)\n\n hostgraph_url_path = 'graph-links.txt'\n with open(hostgraph_url_path, 'a') as f:\n header = f\"=== Client RTM/Ganglia graphs ({socket.gethostname()}) ===\"\n f.write(header + \"\\n\")\n f.write(\"=\"*len(header) + \"\\n\")\n f.write(f\" {driver_rtm_url}\\n\")\n f.write(f\" {ganglia_url}\\n\\n\")","def __str__(self):\n return 'Channel: {{{}}} --> {{{}}} | {} ... '.format(\n ', '.join(sorted([str(i.indi) for i in self.ist])) if self.ist else '-', \n ', '.join(sorted([str(o.indo) for o in self.ost])) if self.ost else '-', \n 'running' if self._running else 'stopped')","def report_csv(self):\n # type: () -> Optional[AnyStr]\n report = self.gen_report()\n if not report:\n return None\n\n none_str = \"unknown\"\n\n def patch_none(to_patch, number_format='2.4f'): # type: (Any, str) -> str\n if to_patch is None:\n return none_str\n if isinstance(to_patch, float):\n s = f\"{{:{number_format}}}\"\n return s.format(to_patch)\n return str(to_patch)\n\n header = \"sample,target_name,target_bbox\"\n for meta_key in self.log_keys:\n header += f\",{str(meta_key)}\"\n if self.top_k:\n for k in range(self.top_k):\n header += f\",detect_{k + 1}_name,detect_{k + 1}_bbox,detect_{k + 1}_conf,detect_{k + 1}_iou\"\n else:\n # unknown count total detections (can be variable)\n header += \",detect_name[N],detect_bbox[N],detect_conf[N],detect_iou[N],(...)[N]\"\n lines = [\"\"] * len(report)\n for i, result in enumerate(report):\n target = result[\"target\"]\n detect = result[\"detect\"]\n if not target:\n entry = f\"{none_str},{none_str},{none_str}\"\n else:\n entry = f\"{target['image_id']},{patch_none(target['class_name'])},{patch_none(target['bbox'])}\"\n for meta_key in self.log_keys:\n entry += f\",{str(target[meta_key])}\"\n for det in detect:\n entry += f\",{det['class_name']},{det['bbox']},{patch_none(det['confidence'])},{patch_none(det['iou'])}\"\n lines[i] = entry\n return \"\\n\".join([header, *lines])"],"string":"[\n \"def coregister_formatted():\\r\\n\\r\\n print(\\\"begin coregister_formatted\\\")\\r\\n\\r\\n # check all records for pairs using the recordBegin time\\r\\n pair_records()\\r\\n\\r\\n # establish the beginning and end of the coregistered records\\r\\n define_pairedRecords()\\r\\n\\r\\n # coregister paired data in a single csv\\r\\n format_coregister()\\r\\n\\r\\n\\r\\n print(\\\"completed segment_formatted\\\")\",\n \"def format(self, data):\",\n \"def diagram(self, with_lines=False, with_captions=False):\\n out = []\\n\\n row = 0\\n col = 0\\n byte_counter = 0\\n\\n if with_captions:\\n out.append(\\\"1: Version 2: IHL 3: DSCP 4: ECN 5: Total Length\\\\n\\\")\\n out.append(\\\"6: Identification 7: Flags 8: Fragment Offset\\\\n\\\")\\n out.append(\\\"9: Time To Live 10: Protocol 11: Header Checksum\\\\n\\\")\\n out.append(\\\"12: Source IP 13: Destination IP\\\\n\\\")\\n\\n if len(self.payload) > 0:\\n out.append(\\\"14: Payload\\\\n\\\")\\n\\n out.append(\\\"\\\\n\\\")\\n\\n hb = len(self.header_bytes())\\n\\n for byte in bytes(self):\\n if with_lines:\\n # Version, IHL, DSCP, ECN and Total Length\\n if col == 0 and row == 0:\\n out.append(\\\" 1 2 3 4 5\\\\n\\\")\\n out.append(\\\"┌┴─┐ ┌┴─┐ ┌┴────┐├┐ \\\")\\n out.append(\\\"┌┴──────────────────┐\\\\n\\\")\\n\\n # Identification, Flags and Fragment Offset\\n if col == 0 and row == 1:\\n out.append(\\\" 6 7 8\\\\n\\\")\\n out.append(\\\"┌┴──────────────────┐ \\\")\\n out.append(\\\"┌┴┐┌┴───────────────┐\\\\n\\\")\\n\\n # Time To Live, Protocol and Header Checksum\\n if col == 0 and row == 2:\\n out.append(\\\" 9 10 11\\\\n\\\")\\n out.append(\\\"┌┴──────┐ ┌┴──────┐ \\\")\\n out.append(\\\"┌┴──────────────────┐\\\\n\\\")\\n\\n # Source IP\\n if col == 0 and row == 3:\\n out.append(\\\" 12\\\\n\\\")\\n out.append(\\\"┌┴──────────────────────\\\")\\n out.append(\\\"────────────────────┐\\\\n\\\")\\n\\n # Destination IP\\n if col == 0 and row == 4:\\n out.append(\\\" 13\\\\n\\\")\\n out.append(\\\"┌┴──────────────────────\\\")\\n out.append(\\\"────────────────────┐\\\\n\\\")\\n\\n # Payload\\n if byte_counter == hb:\\n out.append(\\\" 14\\\\n\\\")\\n out.append(\\\"┌┴──────────────────────\\\")\\n out.append(\\\"────────────────────┐\\\\n\\\")\\n\\n byte = bin(byte)[2:].rjust(8, \\\"0\\\")\\n first = byte[:4]\\n last = byte[4:]\\n\\n out.append(first)\\n out.append(\\\" \\\")\\n out.append(last)\\n\\n if col == 3:\\n out.append(\\\"\\\\n\\\")\\n col = 0\\n row += 1\\n else:\\n out.append(\\\" \\\")\\n col += 1\\n\\n byte_counter += 1\\n\\n return \\\"\\\".join(out).strip()\",\n \"def format_obd2_record(self, record):\\n \\n # If it is an OBD2 command, we need to sort out the ecu IDs and reformat multiline messages\\n # - an ISO 15765-4 (CAN) reply with or without headers\\n # - an old style reply, with or without headers \\n # (old style = ISO 9141-2 \\\"ISO\\\", ISO 14230-4 \\\"KWP\\\", SAE J1850 \\\"PWM & VPW\\\")\\n \\n # the timestamp of when the command was sent\\n #ts = '1333808134' # a ctime measurement\\n # no timestamps from raw tracefiles\\n ts = '0'\\n # the command sent\\n cmd = str.upper(record[0][0])\\n # the results from each responding ECU\\n ecuids = {}\\n \\n \\n # This is what we will return\\n obd2_record = { \\\\\\n 'timestamp': ts, \\\\\\n 'command': cmd, \\\\\\n 'responses': {} }\\n # responses is a dict keyed on ECU id\\n # the values are arrays of data bytes\\n \\n \\n # 5 possibilities: can/headers, can/no headers/multiline, can/no headers/singleline, old/headers, old/no headers\\n if self.debug > 1 :\\n print \\\"Style:\\\", self.Style, \\\" - Headers:\\\", self.Headers, \\\" - single/multiline ??\\\"\\n \\n if self.Style == 'can':\\n if self.Headers == 1:\\n # CAN headers format: ECUID, PCIbyte, more bytes...\\n # if PCI byte starts with 0, then it is the bytecount of the single frame\\n # if PCI byte starts with 1, then it is the first frame of 2 or more, then next byte is the bytecount\\n # if PCI byte starts with 2, then it is a continuation frame, second digit of PCI byte is a sequence number\\n # frames will never arrive more than 16 frames out of sequence so the single sequence hexdigit is sufficient\\n # if message is 16 frames or less, just sort on the seq #\\n # if message is longer than 16 frames, ugh... more complicated sort needed\\n \\n lines = sorted(record[1:])\\n # this is effective for up to 16 lines per ECU ID, past that the sort will be silly\\n if self.debug > 1 :\\n print \\\"CAN w/H, lines:\\\"\\n pprint.pprint(lines)\\n \\n linenum = 1\\n # first element of each line is the ECU ID\\n for line in lines:\\n if linenum > 16 :\\n print \\\"Oops. Too many CAN Frames in this message.\\\"\\n break\\n linenum += 1\\n ecu = line[0]\\n if ecu not in ecuids:\\n #print \\\"New ECU\\\"\\n ecuids[ecu] = {}\\n ecuids[ecu]['data'] = []\\n ecuids[ecu]['count'] = 0\\n # PCI byte\\n pci1 = line[1][0]\\n pci2 = line[1][1]\\n \\n if pci1 == '0':\\n # single line of data\\n ecuids[ecu]['count'] = pci2\\n for b in line[2:]:\\n if len(ecuids[ecu]['data']) < ecuids[ecu]['count']:\\n ecuids[ecu]['data'].append(b)\\n elif pci1 == '1':\\n # first of multiple lines, next byte is the bytecount\\n datacount = line[2]\\n ecuids[ecu]['count'] = int(datacount, 16)\\n for b in line[3:]:\\n ecuids[ecu]['data'].append(b)\\n elif pci1 == '2':\\n # 2nd or later of multiple lines, just data\\n for b in line[2:]:\\n if len(ecuids[ecu]['data']) < ecuids[ecu]['count']:\\n ecuids[ecu]['data'].append(b)\\n \\n \\n \\n # CAN / no headers \\n elif self.Headers == 0:\\n if self.debug > 1 :\\n pprint.pprint(record)\\n # Since there are no headers, we will assume everything was from '7E8'\\n ecu = '7E8'\\n ecuids[ecu] = {}\\n ecuids[ecu]['count'] = 0\\n ecuids[ecu]['data'] = []\\n \\n # multiline CAN (no headers)\\n # byte count on the first line, then each following line has a line num at the front\\n if len(record) > 2 and len(record[1]) == 1 and len(record[1][0]) == 3:\\n ecuids[ecu]['count'] = int(record[1][0], 16)\\n #print \\\"count:\\\", ecuids[ecu]['count']\\n \\n # strip the line numbers & any padding at the end\\n #pprint.pprint(record[2:])\\n for l in record[2:] :\\n if len(l) == 1:\\n # this can't handle out-of-order data, but will deal with multi-ecus\\n ecu += 'X'\\n ecuids[ecu] = {}\\n ecuids[ecu]['data'] = []\\n ecuids[ecu]['count'] = l[0]\\n continue\\n \\n for d in l[1:] :\\n if len(ecuids[ecu]['data']) < ecuids[ecu]['count'] :\\n ecuids[ecu]['data'].append(d)\\n \\n # singleline CAN (no headers)\\n else :\\n for l in record[1:] :\\n if ecu not in ecuids:\\n #print \\\"New ECU\\\"\\n ecuids[ecu] = {}\\n ecuids[ecu]['data'] = []\\n ecuids[ecu]['count'] = 0\\n for d in l :\\n ecuids[ecu]['data'].append(d)\\n ecuids[ecu]['count'] = len(ecuids[ecu]['data'])\\n # fake ECU name for extra data\\n ecu += 'X'\\n \\n \\n \\n \\n # TODO\\n elif self.Style == 'old':\\n #print \\\"OLD Style -- ISO/PWM/VPW ..\\\"\\n \\n if self.Headers == 1:\\n #print \\\"Headers ON\\\"\\n # trace of this to test is from 2006 acura tsx\\n # >0906\\n # 48 6B 09 49 06 01 04 A6 FB 5A 0B \\n # header:\\n # 48 - priority, 6B - receiver, 09 - sender\\n #pprint.pprint(record)\\n\\n lines = sorted(record[1:])\\n # TODO - pick out different sender ecus\\n \\n ecu = lines[0][2]\\n print \\\"ECU:\\\", ecu\\n ecuids[ecu] = {}\\n ecuids[ecu]['count'] = 0\\n ecuids[ecu]['data'] = []\\n\\n if len(lines) > 1 :\\n print \\\"OLD style, with Headers, Multiline\\\"\\n # 0: pri, 1: rcvr, 2: sndr, 3: mode, 4: pid, 5: linenum, 6->(n-1): data, lastbyte: checksum\\n # add mode & pid once, skip linenums, concat data\\n ecuids[ecu]['data'] = lines[0][3:5]\\n for l in lines:\\n ecuids[ecu]['data'].extend(l[6:-1])\\n #print \\\"data:\\\",\\n #pprint.pprint(ecuids[ecu]['data'])\\n elif len(lines) == 1 :\\n print \\\"OLD style, with Headers, Singleline\\\"\\n # some pids (mode 09) will have a message count in byte 5\\n # needs to be filtered later\\n # 0: pri, 1: rcvr, 2: sndr, 3: mode, 4: pid, 5-(n-1): data, lastbyte: checksum\\n # singleline\\n ecuids[ecu]['count'] = lines[0][5]\\n ecuids[ecu]['data'].extend(lines[0][3:-1])\\n else:\\n # ERROR !\\n #print \\\"ERROR, data record too short:\\\"\\n #pprint.pprint(record)\\n raise self.ErrorIncompleteRecord(\\\"ERROR - Incomplete Response Record\\\")\\n\\n\\n\\n elif self.Headers == 0:\\n print \\\"Headers OFF\\\"\\n #print \\\"cmd:\\\", cmd\\n #pprint.pprint(record)\\n \\n # singleline vs multiline (with line #'s)\\n lines = sorted(record[1:])\\n \\n # Since there are no headers, we will assume everything was from '09'\\n ecu = '09'\\n ecuids[ecu] = {}\\n ecuids[ecu]['count'] = 0\\n ecuids[ecu]['data'] = []\\n \\n #print \\\"DEBUG ---- LINES:\\\"\\n #pprint.pprint(lines)\\n \\n if len(lines) > 1 :\\n # multiline \\n # 0: mode, 1: pid, 2: linenum, 3-n: data\\n # add mode & pid once, skip linenums, concat data\\n ecuids[ecu]['data'].extend(lines[0][0:2])\\n for l in lines:\\n ecuids[ecu]['data'].extend(l[3:])\\n elif len(lines) == 1 :\\n # singleline\\n ecuids[ecu]['data'] = lines[0]\\n else:\\n # ERROR !\\n #print \\\"ERROR, data record too short:\\\"\\n #pprint.pprint(record)\\n raise self.ErrorIncompleteRecord(\\\"ERROR - Incomplete Response Record\\\")\\n\\n\\n for e in ecuids.iterkeys():\\n obd2_record['responses'][e] = ecuids[e]['data']\\n #print \\\"ECU:\\\", e, \\\", Data:\\\",\\n #pprint.pprint(ecuids[e]['data'])\\n \\n \\n return obd2_record\",\n \"def to_line_protocol(self):\\n\\n tags = self.get_output_tags()\\n\\n return '{measurement}{tags} {fields}{timestamp}'.format(\\n measurement=format_string(self.measurement),\\n tags=',' + tags if tags else '',\\n fields=self.get_output_fields(),\\n timestamp=' {0}'.format(self.timestamp) if self.timestamp else ''\\n )\",\n \"def _format_capability_report(self, data):\\n\\n\\n\\n pin_modes = {0: 'Digital_Input', 1: 'Digital_Output',\\n 2: 'Analog', 3: 'PWM', 4: 'Servo',\\n 5: 'Shift', 6: 'I2C', 7: 'One Wire',\\n 8: 'Stepper', 9: 'Encoder'}\\n x = 0\\n pin = 0\\n\\n logstring('\\\\nCapability Report')\\n logstring('-----------------\\\\n')\\n while x < len(data):\\n # get index of next end marker\\n logstring('{} {}{}'.format('Pin', str(pin), ':'))\\n while data[x] != 127:\\n mode_str = \\\"\\\"\\n pin_mode = pin_modes.get(data[x])\\n mode_str += str(pin_mode)\\n x += 1\\n bits = data[x]\\n logstring('{:>5}{}{} {}'.format(' ', mode_str, ':', bits))\\n x += 1\\n x += 1\\n pin += 1\",\n \"def _format_capability_report(self, data):\\n\\n if self.log_output:\\n return\\n\\n else:\\n pin_modes = {0: 'Digital_Input', 1: 'Digital_Output',\\n 2: 'Analog', 3: 'PWM', 4: 'Servo',\\n 5: 'Shift', 6: 'I2C', 7: 'One Wire',\\n 8: 'Stepper', 9: 'Encoder'}\\n x = 0\\n pin = 0\\n\\n print('\\\\nCapability Report')\\n print('-----------------\\\\n')\\n while x < len(data):\\n # get index of next end marker\\n print('{} {}{}'.format('Pin', str(pin), ':'))\\n while data[x] != 127:\\n mode_str = \\\"\\\"\\n pin_mode = pin_modes.get(data[x])\\n mode_str += str(pin_mode)\\n x += 1\\n bits = data[x]\\n print('{:>5}{}{} {}'.format(' ', mode_str, ':', bits))\\n x += 1\\n x += 1\\n pin += 1\",\n \"def write_record(rec):\\n points = str(rec['Points']).replace(\\\"(\\\",\\\"[\\\").replace(\\\")\\\",\\\"]\\\")\\n l = [ rec['UniqueID'], rec['FAO_USDA'], rec['Soil'], rec['Suborder'],\\n rec['maxlat'], rec['maxlon'], rec['minlat'], rec['minlon'], points ]\\n strings = map(str, l)\\n return \\\"\\\\t\\\".join(strings) + \\\"\\\\n\\\"\",\n \"def format(self, record):\\n\\n scrubbed = record[\\\"message\\\"]\\n # scrubs any messages that match the message pattern\\n if isinstance(scrubbed, dict):\\n scrubbed = json.dumps(scrubbed)\\n for search, replace in self.scrub_patterns.items():\\n scrubbed = re.sub(search, replace, scrubbed)\\n record[\\\"extra\\\"][\\\"scrubbed\\\"] = scrubbed\\n\\n if not record[\\\"extra\\\"].get(\\\"device\\\") or record[\\\"extra\\\"].get(\\\"device\\\") is None:\\n record[\\\"extra\\\"][\\\"device\\\"] = \\\"\\\"\\n else:\\n record[\\\"extra\\\"][\\\"device\\\"] = f\\\"{record['extra']['device']} - \\\"\\n return self.fmt\",\n \"def format(self):\\n ...\",\n \"def save(self, fh):\\n\\t\\t#first write head of file\\n\\t\\tfh.write('+INFO:IEEE 802.15.4 ZigBeeSniffer Log.\\\\r\\\\n')\\n\\t\\tfh.write('+DATE:' + time.ctime() + '\\\\r\\\\n')\\n\\t\\tfor packet in self.packets:\\n\\t\\t\\tload = [len(packet.load)] + packet.load\\n\\t\\t\\tload = map(str, map(hex, load))\\t# int->hex->str\\n\\t\\t\\tload = map(lambda x: ('0'+x[2:])[-2:].upper(), load)\\t# 0xa -> 0A\\n\\t\\t\\tload = ','.join(load)\\t\\t\\t\\t\\t\\t# glue it back\\n\\t\\t\\ttimee = ','.join(packet.time)\\n\\t\\t\\tfh.write('+FRAM:' + timee + ',' + load + '\\\\r\\\\n')\\n\\t\\t\\t\\n\\t\\tfor node in self.nodes.nodes:\\n\\t\\t\\tpan = node.panId[2:] # cut '0x' from begining\\n\\t\\t\\taddr = node.address[2:] # cut '0x' from begining\\n\\t\\t\\tline = '+NODE:\\\"%s\\\"' % node.name\\n\\t\\t\\tline += ',%s,%s' % (pan[2:], pan[:2]) if pan else ',??,??' # PAN-ID\\n\\t\\t\\t#now split address into pairs (bytes):\\n\\t\\t\\tpairs = map(''.join, zip(iter(addr), iter(addr)))\\n\\t\\t\\tfor pair in reversed(pairs): \\n\\t\\t\\t\\tline += ',%s' % pair\\n\\t\\t\\tfor i in range(8 - len(pairs)):\\n\\t\\t\\t\\tline += ',??'\\n\\t\\t\\tline += '\\\\r\\\\n'\\n\\t\\t\\tfh.write(line)\\n\\t\\t\\n\\t\\tself.changed(False)\",\n \"def data_format(self):\\n for description in SENSOR_TYPES:\\n if description.key not in self.conditions:\\n continue\\n attr_key = description.key\\n try:\\n if description.primary_key in self.raw_data:\\n sensor_value = self.raw_data[description.primary_key][\\n description.sensor_key\\n ]\\n # Format sensor for better readability\\n if attr_key == ATTR_NEW_VERSION and sensor_value == \\\"0.0.0.0\\\":\\n sensor_value = \\\"Latest\\\"\\n elif attr_key == ATTR_UPTIME:\\n sensor_value = round(sensor_value / (3600 * 24), 2)\\n elif attr_key == ATTR_LAST_RESTART:\\n last_restart = dt_util.now() - timedelta(seconds=sensor_value)\\n sensor_value = last_restart.strftime(\\\"%Y-%m-%d %H:%M:%S\\\")\\n elif attr_key == ATTR_STATUS:\\n if sensor_value:\\n sensor_value = \\\"Online\\\"\\n else:\\n sensor_value = \\\"Offline\\\"\\n elif (\\n attr_key == ATTR_LOCAL_IP and not self.raw_data[\\\"wan\\\"][\\\"online\\\"]\\n ):\\n sensor_value = None\\n\\n self.data[attr_key] = sensor_value\\n except KeyError:\\n _LOGGER.error(\\n (\\n \\\"Router does not support %s field. \\\"\\n \\\"Please remove %s from monitored_conditions\\\"\\n ),\\n description.sensor_key,\\n attr_key,\\n )\\n self.data[attr_key] = None\",\n \"def write_data(self, packet):\\n # TODO: check frequency instead of just doing one and two - could only be 2\\n if not packet.satellites:\\n return # No satellites\\n t = dt.datetime(1980, 1, 6) + \\\\\\n dt.timedelta(days=7*packet.week, seconds=int(packet.rcvTow),\\n microseconds=(packet.rcvTow - int(packet.rcvTow))*10**6) - \\\\\\n dt.timedelta(seconds=packet.leapS)\\n epoch = f'> {t.year:4d} {t.month:02d} {t.day:02d} {t.hour:2d} {t.minute:2d} ' \\\\\\n f'{t.second + t.microsecond*10**-6:11.7f} 0{len(packet.satellites):>3d}{\\\" \\\":<44}\\\\n'\\n line = ''\\n for s in packet.satellites:\\n snr0 = s[0].cno\\n if s[0].key == '':\\n pass\\n elif s[0].sigId in [0, 1]:\\n line = line + f'{s[0].key:3s}{s[0].prMeas:>14.3f} {snr0:>1d}{s[0].cpMeas:>14.3f} {snr0:>1d}' \\\\\\n f'{s[0].doMeas:>14.3f} {snr0:>1d}{s[0].cno:>14.3f} {snr0:>1d}'\\n if len(s) == 2: # If L1 and L2 Frequencies\\n snr1 = s[1].cno\\n line = line + f'{s[1].prMeas:>14.3f} {snr1:>1d}{s[1].cpMeas:>14.3f} {snr1:>1d}{s[1].doMeas:>14.3f} ' \\\\\\n f'{snr1:>1d}{s[1].cno:>14.3f} {snr1:>1d}\\\\n'\\n\\n else:\\n line = line + f'{0.0:>14.3f} {0.0:>14.3f} {0.0:>14.3f} {0.0:>14.3f} \\\\n'\\n elif len(s) < 2:\\n line = line + f'{0.0:>14.3f} {0.0:>14.3f} {0.0:>14.3f} {0.0:>14.3f} ' \\\\\\n f'{s[0].key:3s}{s[0].prMeas:>14.3f} {snr0:>1d}{s[0].cpMeas:>14.3f} {snr0:>1d}' \\\\\\n f'{s[0].doMeas:>14.3f} {snr0:>1d}{s[0].cno:>14.3f} {snr0:>1d}\\\\n'\\n elif len(s) == 2:\\n line = line + f'{s[1].prMeas:>14.3f} {snr1:>1d}{s[1].cpMeas:>14.3f} {snr1:>1d}{s[1].doMeas:>14.3f} ' \\\\\\n f'{snr1:>1d}{s[1].cno:>14.3f} {snr1:>1d}' \\\\\\n f'{s[0].key:3s}{s[0].prMeas:>14.3f} {snr0:>1d}{s[0].cpMeas:>14.3f} {snr0:>1d}' \\\\\\n f'{s[0].doMeas:>14.3f} {snr0:>1d}{s[0].cno:>14.3f} {snr0:>1d}\\\\n'\\n \\n try:\\n with open(self.fname, 'a') as f:\\n f.write(epoch + line)\\n except FileNotFoundError:\\n print('Bad data directory. Try again.')\\n sys.exit(0)\",\n \"def format_data(self, data):\",\n \"def header(self):\\n\\t\\tthismsg = \\\"\\\\r\\\\n\\\"+self.ESC + \\\"0m \\\" +self.A220 + self.A220 + self.A220 + self.A220 + self.A220 +\\\" \\\" + self.ESC + \\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += \\\" \\\"+self.A220+self.A220+self.A219+self.A219+self.A219+self.A219+self.ESC+\\\"1;47m\\\"+self.A176+self.A177+self.A178+self.ESC+\\\"40m\\\"+self.A219+self.A219+self.A220+self.A220+self.ESC+\\\"0m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"1m \\\"+self.ESC+\\\"31mS\\\"+self.ESC+\\\"0;31mAGA\\\"+self.ESC+\\\"37m \\\"+self.A219+self.A219+self.ESC+\\\"30;47mo\\\"+self.ESC+\\\"37;40m\\\"+self.A219+self.ESC+\\\"1;47m\\\"+self.A176+self.A176+self.A177+self.A177+self.A178+self.ESC+\\\"40m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\\\"0m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"1m \\\"+self.ESC+\\\"31mO\\\"+self.ESC+\\\"0;31mF THE\\\"+self.ESC+\\\"37m \\\"+self.A219+self.A219+self.ESC+\\\"30;47mO\\\"+self.ESC+\\\"37;40m\\\"+self.A219+self.A219+self.ESC+\\\"1;47m\\\"+self.A176+self.A177+self.A177+self.A178+self.ESC+\\\"40m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\\\"0m \\\"+self.A220+self.A220+self.A220+self.ESC+\\\"1m\\\"+self.A220+self.A220+self.ESC+\\\"0m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"1m \\\"+self.ESC+\\\"31mR\\\"+self.ESC+\\\"0;31mED\\\"+self.ESC+\\\"37m \\\"+self.A219+self.ESC+\\\"30;47mo\\\"+self.ESC+\\\"37;40m\\\"+self.A219+self.A219+self.ESC+\\\"1;47m\\\"+self.A176+self.A176+self.A177+self.A178+self.ESC+\\\"40m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\\\"0m \\\"+self.A223+self.A219+self.ESC+\\\"1;47m\\\"+self.A176+self.A219+self.A219+self.A219+self.ESC+\\\"40m\\\"+self.A220+self.A220+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+self.ESC+\\\"37m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"31m \\\"+self.ESC+\\\"1mD\\\"+self.ESC+\\\"0;31mRAGON 0.9.9\\\"+self.ESC+\\\"37m \\\"+self.A223+self.A219+self.A219+self.ESC+\\\"1;47m\\\"+self.A176+self.A177+self.A177+self.A178+self.A219+self.ESC+\\\"40m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A223+self.ESC+\\\"0m \\\"+self.A219+self.ESC+\\\"1;47m\\\"+self.A219+self.A219+self.ESC+\\\"40m\\\"+self.A223+self.A223+self.ESC+\\\"0;31m\\\"+self.A220+self.ESC+\\\"1;41m\\\"+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+self.ESC+\\\"37m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"31m concept\\\"+self.ESC+\\\"37m \\\"+self.A223+self.ESC+\\\"1;47m\\\"+self.A176+self.A177+self.A177+self.A178+self.A178+self.ESC+\\\"40m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A223+self.ESC+\\\"0m \\\"+self.ESC+\\\"1m\\\"+self.A220+self.ESC+\\\"0m \\\"+self.ESC+\\\"1m\\\"+self.A220+self.ESC+\\\"0m \\\"+self.ESC+\\\"31m\\\"+self.A220+self.A220+self.ESC+\\\"1;41m\\\"+self.A176+self.A178+\\\" \\\"+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+self.A220+self.ESC+\\\"37m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"31m Seth Robinson \\\"+self.ESC+\\\"37m\\\"+self.A222+\\\" \\\"+self.A223+self.A223+self.ESC+\\\"1;47m\\\"+self.A178+self.ESC+\\\"40m\\\"+self.A219+self.A219+self.A219+self.A219+self.A223+self.A223+self.ESC+\\\"0m \\\"+self.A220+self.ESC+\\\"1;47m\\\"+self.A220+self.ESC+\\\"40m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"0m\\\"+self.A220+self.A220+self.ESC+\\\"1;47m\\\"+self.A220+self.ESC+\\\"40m\\\"+self.A223+self.A223+\\\" \\\"+self.ESC+\\\"0m\\\"+self.A223+self.A219+self.A220+self.ESC+\\\"1m\\\"+self.A220+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A223+self.ESC+\\\"1;41m\\\"+self.A176+self.A177+self.A178+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+self.ESC+\\\"37m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"31m by\\\"+self.ESC+\\\"0m \\\"+self.A219+\\\" \\\"+self.A220+self.ESC+\\\"1;47m\\\"+self.A220+self.ESC+\\\"40m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"0m\\\"+self.A220+self.ESC+\\\"1;47m\\\"+self.A220+self.A220+self.A219+self.ESC+\\\"40m\\\"+self.A223+self.ESC+\\\"0m \\\"+self.A223+self.ESC+\\\"1;47m\\\"+self.A176+self.A219+self.ESC+\\\"40m\\\"+self.A220+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A223+self.ESC+\\\"1;41m\\\"+self.A177+self.A178+\\\" \\\"+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A176+\\\" \\\"+self.A176+\\\" \\\"+self.A176+self.ESC+\\\"0;31m\\\"+self.A220+self.ESC+\\\"37m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"1;34m J\\\"+self.ESC+\\\"0;34m.\\\"+self.ESC+\\\"1mT\\\"+self.ESC+\\\"0;34m.\\\"+self.ESC+\\\"1mS\\\"+self.ESC+\\\"0;34mage\\\"+self.ESC+\\\"0m \\\"+self.A219+self.ESC+\\\"1;30;47m\\\"+self.A176+self.ESC+\\\"0;37;40m\\\"+self.A221+\\\" \\\"+self.A220+self.ESC+\\\"1;47m\\\"+self.A177+self.A176+self.ESC+\\\"40m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"0m\\\"+self.A220+self.ESC+\\\"1;47m\\\"+self.A220+self.A220+self.A219+self.ESC+\\\"40m\\\"+self.A223+self.ESC+\\\"0m \\\"+self.A223+self.ESC+\\\"1;47m\\\"+self.A177+self.A219+self.A219+self.ESC+\\\"40m\\\"+self.A220+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A223+self.ESC+\\\"1;41m\\\"+self.A177+self.A176+self.A178+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+self.A223+self.ESC+\\\"1;41m\\\"+self.A176+self.A178+self.A176+self.A176+self.A177+self.A177+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += \\\" \\\"+self.A219+self.A219+\\\" \\\"+self.ESC+\\\"1;47m\\\"+self.A176+self.A177+self.A219+self.ESC+\\\"40m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"0m\\\"+self.A223+self.ESC+\\\"1m\\\"+self.A223+self.ESC+\\\"41m\\\"+self.A223+self.ESC+\\\"0;31m\\\"+self.A220+self.A220+self.A220+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A178+self.A177+self.A176+\\\" \\\"+self.A220+self.A220+self.A220+self.A220+self.A223+self.A220+self.ESC+\\\"1;41m\\\"+self.A176+self.ESC+\\\"0;31m\\\"+self.A220+self.ESC+\\\"1;41m\\\"+self.A177+self.ESC+\\\"0;31m\\\"+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"37;40m \\\"+self.ESC+\\\"31m\\\"+self.A223+self.ESC+\\\"1;41m\\\"+self.A177+self.A178+\\\" \\\"+self.A219+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A178+self.A178+self.A176+self.A177+self.A177+self.A219+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.A219+self.ESC+\\\"1;30;47m\\\"+self.A176+self.ESC+\\\"0;37;40m \\\"+self.A219+self.A223+self.ESC+\\\"1m\\\"+self.A223+self.ESC+\\\"0;31m\\\"+self.A220+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A223+self.A223+self.A223+\\\" \\\"+self.A220+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A223+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A223+self.A223+self.A223+\\\" \\\"+self.A176+self.A176+\\\" \\\"+self.ESC+\\\"1;41m\\\"+self.A176+\\\" \\\"+self.A178+self.A178+self.A219+self.ESC+\\\"0;31m\\\"+self.A220+\\\" \\\"+self.A223+self.ESC+\\\"1;41m\\\"+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A176+self.A178+self.A176+self.A177+self.A177+self.A219+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.A219+self.ESC+\\\"1;30;47m\\\"+self.A176+self.ESC+\\\"0;37;40m \\\"+self.A219+\\\" \\\"+self.ESC+\\\"31m\\\"+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A223+\\\" \\\"+self.A220+self.A220+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A178+self.A177+self.A176+self.ESC+\\\"37m \\\"+self.ESC+\\\"31m\\\"+self.A178+self.A177+self.A177+self.A223+\\\" \\\"+self.ESC+\\\"1;41m\\\"+self.A176+\\\" \\\"+self.A177+self.A178+self.A219+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+\\\" \\\"+self.A223+self.ESC+\\\"1;41m\\\"+self.A177+self.A219+self.ESC+\\\"0;31m\\\"+self.A220+self.A223+self.ESC+\\\"1;41m\\\"+self.A178+\\\" \\\"+self.A177+self.A178+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.ESC+\\\"1;30;47m\\\"+self.A176+self.ESC+\\\"0;37;40m\\\"+self.A219+\\\" \\\"+self.A219+\\\" \\\"+self.ESC+\\\"31m\\\"+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"37;40m \\\"+self.ESC+\\\"31m\\\"+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A178+self.A177+self.A176+\\\" \\\"+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A223+self.A223+self.ESC+\\\"37m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A176+\\\" \\\"+self.A178+self.A177+self.A219+self.A219+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A176+self.A177+self.A178+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31m\\\"+self.A220+self.A220+self.A223+self.ESC+\\\"1;41m\\\"+self.A178+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.ESC+\\\"1;30;47m\\\"+self.A176+self.ESC+\\\"0;37;40m\\\"+self.A219+self.ESC+\\\"1;30;47m\\\"+self.A176+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31m\\\"+self.A220+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A219+self.A178+self.A178+self.A177+self.A176+self.A223+\\\" \\\"+self.A220+self.A220+\\\" \\\"+self.A223+self.ESC+\\\"37m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A176+\\\" \\\"+self.A177+self.A178+self.A219+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A177+self.A178+self.A219+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A176+self.A178+self.A178+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.A219+self.A219+self.A219+\\\" \\\"+self.ESC+\\\"31m\\\"+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A221+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A178+self.A223+self.A223+self.A223+\\\" \\\"+self.A220+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A223+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+\\\" \\\"+self.A220+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A178+self.A178+self.A177+self.A223+\\\" \\\"+self.ESC+\\\"1;41m\\\"+self.A176+\\\" \\\"+self.A177+self.A219+self.A219+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A176+self.A177+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+self.ESC+\\\"1;41m\\\"+self.A176+self.A178+self.A178+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.A222+self.ESC+\\\"1;30;47m\\\"+self.A176+self.A177+self.ESC+\\\"0;37;40m\\\"+self.A221+\\\" \\\"+self.ESC+\\\"31m\\\"+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+self.A220+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+self.A220+\\\" \\\"+self.A223+self.A223+self.A220+\\\" \\\"+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A178+self.A178+self.A178+self.ESC+\\\"37m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A176+\\\" \\\"+self.A178+self.A219+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31m\\\"+self.A223+self.ESC+\\\"1;41m\\\"+self.A176+self.A177+self.A178+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A176+self.A178+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.A222+self.ESC+\\\"1;30;47m\\\"+self.A176+self.A177+self.ESC+\\\"0;37;40m\\\"+self.A219+\\\" \\\"+self.ESC+\\\"31m\\\"+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+self.ESC+\\\"0;31m\\\"+self.A223+self.A223+\\\" \\\"+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+self.A220+\\\" \\\"+self.A223+self.A178+self.ESC+\\\"37m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A176+self.A177+self.A178+self.A219+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A176+self.A177+\\\" \\\"+self.A178+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A176+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += \\\" \\\"+self.A219+self.ESC+\\\"1;30;47m\\\"+self.A177+self.ESC+\\\"0;37;40m\\\"+self.A219+\\\" \\\"+self.ESC+\\\"31m\\\"+self.A222+self.A219+self.ESC+\\\"1;41m\\\"+self.A176+self.A176+self.ESC+\\\"0;31m\\\"+self.A221+\\\" \\\"+self.ESC+\\\"1;5;32m\\\"+self.A220+self.A220+self.A223+\\\" \\\"+self.ESC+\\\"0;31;41m \\\"+self.ESC+\\\"40m\\\"+self.A178+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A223+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+\\\" \\\"+self.A220+self.A220+self.A220+self.A220+self.A223+self.ESC+\\\"1;41m\\\"+self.A176+self.A177+self.A178+self.ESC+\\\"0;31m\\\"+self.A220+\\\" \\\"+self.ESC+\\\"1;41m\\\"+self.A176+self.A177+\\\" \\\"+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A176+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.A219+self.ESC+\\\"1;30;47m\\\"+self.A176+self.ESC+\\\"0;37;40m\\\"+self.A219+\\\" \\\"+self.ESC+\\\"31m\\\"+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+\\\" \\\"+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"1;5;32m\\\"+self.A219+self.A219+self.A223+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A178+self.A220+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+self.A222+self.A219+self.ESC+\\\"41m \\\"+self.ESC+\\\"37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"40m\\\"+self.A178+self.A223+\\\" \\\"+self.A223+self.ESC+\\\"1;41m\\\"+self.A176+self.A177+self.A219+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A176+self.A177+self.A219+self.A178+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += \\\" \\\"+self.A222+self.ESC+\\\"1;30;47m\\\"+self.A176+self.A177+self.ESC+\\\"0;37;40m\\\"+self.A219+\\\" \\\"+self.ESC+\\\"31m\\\"+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A178+self.A176+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"1;5;32m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+self.A220+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A178+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+self.A222+self.A219+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+\\\" \\\"+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A178+self.A223+\\\" \\\"+self.A223+self.ESC+\\\"1;41m\\\"+self.A176+self.A219+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.A177+self.A219+self.A178+self.ESC+\\\"37;40m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += \\\" \\\"+self.A219+self.ESC+\\\"1;30;47m\\\"+self.A176+self.ESC+\\\"0;37;40m\\\"+self.A221+\\\" \\\"+self.ESC+\\\"31m\\\"+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A177+self.A176+self.ESC+\\\"0;31m\\\"+self.A221+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+self.ESC+\\\"0;31m\\\"+self.A223+self.A223+\\\" \\\"+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+self.A176+self.ESC+\\\"0;31m\\\"+self.A220+self.A220+self.ESC+\\\"37m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A176+self.A177+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.A176+self.A177+self.A219+self.A178+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.A219+self.ESC+\\\"1;30;47m\\\"+self.A177+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31m\\\"+self.A220+self.A220+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A176+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A223+self.ESC+\\\"37m \\\"+self.ESC+\\\"31m\\\"+self.A220+self.ESC+\\\"1;41m\\\"+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A177+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31m\\\"+self.A220+\\\" \\\"+self.ESC+\\\"1;41m\\\"+self.A176+self.A219+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.A177+\\\" \\\"+self.A219+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.A219+self.A221+\\\" \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"40m\\\"+self.A177+self.A177+self.A176+self.A178+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31m\\\"+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A177+self.A176+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A178+self.A177+self.A176+self.ESC+\\\"0;31m\\\"+self.A220+\\\" \\\"+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A178+self.A220+self.A220+\\\" \\\"+self.ESC+\\\"1;41m\\\"+self.A178+self.ESC+\\\"0;31m\\\"+self.A220+\\\" \\\"+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A177+self.A219+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.ESC+\\\"1m\\\"+self.A220+\\\" \\\"+self.ESC+\\\"0m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"40m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A219+self.A178+self.A178+self.A177+self.A176+self.A176+self.A177+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.A178+self.A177+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"40m\\\"+self.A220+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A178+self.A177+self.A176+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31m\\\"+self.A223+self.A178+\\\" \\\"+self.ESC+\\\"1;41m\\\"+self.A177+self.A219+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.A176+self.A176+self.A178+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.A223+self.ESC+\\\"1m\\\"+self.A219+self.A220+\\\" \\\"+self.ESC+\\\"0;31;41m \\\"+self.A177+self.A178+self.A176+self.A176+\\\" \\\"+self.ESC+\\\"40m\\\"+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A219+self.A176+self.A178+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31m\\\"+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A219+self.A178+self.A178+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.A219+self.A178+self.A177+self.A176+self.ESC+\\\"37;40m \\\"+self.ESC+\\\"31m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"1;41m\\\"+self.A176+self.ESC+\\\"0;31m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+self.A177+self.A219+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += \\\" \\\"+self.A223+self.ESC+\\\"1;47m\\\"+self.A223+self.ESC+\\\"40m\\\"+self.A219+self.A223+self.ESC+\\\"0;31m\\\"+self.A220+self.ESC+\\\"1;41m\\\"+self.A178+self.A176+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"1;37;47m\\\"+self.A222+self.ESC+\\\"40m\\\"+self.A221+self.A223+self.A220+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A177+\\\" \\\"+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A221+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+self.ESC+\\\"0;31m\\\"+self.A223+\\\" \\\"+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A219+self.A178+self.A178+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A219+self.A178+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"1;41m\\\"+self.A176+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.A176+\\\" \\\"+self.A177+self.A219+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.A223+self.ESC+\\\"31m\\\"+self.A222+self.ESC+\\\"1;41m\\\"+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"1;37;47m\\\"+self.A222+self.ESC+\\\"40m\\\"+self.A221+\\\" \\\"+self.ESC+\\\"47m\\\"+self.A222+self.ESC+\\\"40m\\\"+self.A221+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A178+self.A177+\\\" \\\"+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"33m\\\"+self.A220+\\\" \\\"+self.ESC+\\\"31m\\\"+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A219+self.A178+self.A177+self.A176+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A178+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"40m\\\"+self.A220+self.A223+\\\" \\\"+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+self.A176+\\\" \\\"+self.A178+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.ESC+\\\"31;41m \\\"+self.A176+self.A176+self.ESC+\\\"37;40m \\\"+self.A220+self.ESC+\\\"1m\\\"+self.A219+self.ESC+\\\"0m\\\"+self.A223+self.ESC+\\\"1m\\\"+self.A219+self.A221+\\\" \\\"+self.A223+\\\" \\\"+self.A223+self.A220+\\\" \\\"+self.ESC+\\\"0;31;41m \\\"+self.ESC+\\\"40m\\\"+self.A223+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"33m\\\"+self.A219+self.A219+\\\" \\\"+self.ESC+\\\"31m\\\"+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A219+self.A178+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.A178+self.A177+self.A176+self.ESC+\\\"40m\\\"+self.A220+\\\" \\\"+self.ESC+\\\"1;41m\\\"+self.A176+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.A176+self.A177+\\\" \\\"+self.A219+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += \\\" \\\"+self.A220+self.ESC+\\\"1m\\\"+self.A219+\\\" \\\"+self.A219+self.A221+\\\" \\\"+self.A223+\\\" \\\"+self.A220+\\\" \\\"+self.A223+self.A220+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A223+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"1;33;43m\\\"+self.A177+self.A176+self.ESC+\\\"0;33m\\\"+self.A219+self.A219+\\\" \\\"+self.ESC+\\\"31m\\\"+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A219+self.A178+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A219+self.A220+self.A222+self.ESC+\\\"1;41m\\\"+self.A219+self.A177+self.A176+self.ESC+\\\"0;31m\\\"+self.A220+self.ESC+\\\"1;41m\\\"+self.A176+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31m\\\"+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+\\\" \\\"+self.A178+self.A219+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += \\\" \\\"+self.A223+self.ESC+\\\"1m\\\"+self.A220+\\\" \\\"+self.A223+\\\" \\\"+self.A220+self.A223+self.A220+\\\" \\\"+self.A223+\\\" \\\"+self.A223+self.ESC+\\\"0;31m\\\"+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A223+self.A223+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"1;33m\\\"+self.A223+self.ESC+\\\"43m\\\"+self.A219+self.A219+self.ESC+\\\"40m\\\"+self.A223+self.ESC+\\\"0;33m\\\"+self.A220+self.A219+\\\" \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"1m\\\"+self.A219+self.A178+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A177+self.A177+self.A176+self.ESC+\\\"0;31m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+self.A177+self.A178+\\\" \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.ESC+\\\"1m\\\"+self.A220+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+self.A220+self.A220+self.A220+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A223+self.ESC+\\\"37m \\\"+self.ESC+\\\"1;33;43m\\\"+self.A177+self.A176+self.ESC+\\\"0;33m\\\"+self.A219+self.A219+self.A220+\\\" \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"1m\\\"+self.A219+self.A178+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+self.A176+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.A176+self.A177+self.A178+\\\" \\\"+self.A219+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.ESC+\\\"30mÙ\\\"+self.ESC+\\\"31m\\\"+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A177+self.A177+self.A176+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A223+self.A223+self.ESC+\\\"37m \\\"+self.ESC+\\\"1;33m\\\"+self.A223+self.ESC+\\\"43m\\\"+self.A219+self.ESC+\\\"0;33m\\\"+self.A223+self.A223+self.A223+self.A220+\\\" \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"1m\\\"+self.A219+self.A178+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+self.A222+self.A223+\\\" \\\"+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A177+self.A176+\\\" \\\"+self.A219+\\\" \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.ESC+\\\"31m\\\"+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.ESC+\\\"37m \\\"+self.ESC+\\\"1;33m\\\"+self.A220+self.A220+self.ESC+\\\"43m\\\"+self.A176+self.ESC+\\\"0;33m\\\"+self.A219+self.A219+\\\" \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"1m\\\"+self.A219+self.A178+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31m\\\"+self.A220+self.ESC+\\\"1;41m\\\"+self.A176+self.A177+self.A178+self.A176+self.A176+\\\" \\\"+self.A219+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += \\\" \\\"+self.ESC+\\\"1;33;43m\\\"+self.A219+self.A178+self.A177+self.A176+self.ESC+\\\"0;33m\\\"+self.A219+\\\" \\\"+self.ESC+\\\"31m\\\"+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A219+self.A178+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+\\\" \\\"+self.A223+self.ESC+\\\"1;41m\\\"+self.A176+self.A177+self.A177+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A223+self.ESC+\\\"37m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += \\\" \\\"+self.ESC+\\\"1;33;43m\\\"+self.A219+self.A178+self.ESC+\\\"40m\\\"+self.A223+self.ESC+\\\"0;33m\\\"+self.A223+self.A223+self.A220+self.A220+\\\" \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"1m\\\"+self.A219+self.A178+self.A177+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+\\\" \\\"+self.A223+self.ESC+\\\"1;41m\\\"+self.A177+self.A177+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.ESC+\\\"1;33m\\\"+self.A220+self.A220+self.ESC+\\\"43m\\\"+self.A219+self.A178+self.A177+self.A176+self.ESC+\\\"0;33m\\\"+self.A219+self.A220+\\\" \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"1m\\\"+self.A219+self.A178+self.A177+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31m\\\"+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.ESC+\\\"1;33m\\\"+self.A220+self.ESC+\\\"43m\\\"+self.A219+self.ESC+\\\"40m\\\"+self.A223+self.A223+self.A223+self.ESC+\\\"0;33m\\\"+self.A223+self.A220+self.A220+self.A220+\\\" \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"1m\\\"+self.A178+self.A177+self.A176+self.ESC+\\\"0;31m\\\"+self.A223+self.A223+self.A223+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+\\\" \\\"+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+\\\" \\\"+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.ESC+\\\"1;33m\\\"+self.A220+self.A220+self.ESC+\\\"43m\\\"+self.A219+self.A219+self.A178+self.A177+self.A176+self.ESC+\\\"0;33m\\\"+self.A219+self.A219+\\\" \\\"+self.ESC+\\\"31m\\\"+self.A223+self.A223+\\\" \\\"+self.A220+self.A220+self.A220+self.A220+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+\\\" \\\"+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+\\\" \\\"+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+self.A220+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.ESC+\\\"1;33;43m\\\"+self.A219+self.A219+self.ESC+\\\"40m\\\"+self.A223+self.A223+self.A223+self.ESC+\\\"0;33m\\\"+self.A223+self.A223+self.ESC+\\\"31m\\\"+self.A220+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+self.A178+self.A177+\\\" \\\"+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+\\\" \\\"+self.ESC+\\\"41m \\\"+self.ESC+\\\"37;40m \\\"+self.ESC+\\\"31m\\\"+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.ESC+\\\"1;33m\\\"+self.A220+self.ESC+\\\"43m\\\"+self.A178+self.ESC+\\\"0;33m\\\"+self.A220+self.A219+self.A223+\\\" \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"1m\\\"+self.A176+self.A178+self.A177+self.A177+self.A176+self.A176+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+\\\" \\\"+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.ESC+\\\"1;33;43m\\\"+self.A178+self.A177+self.A176+self.ESC+\\\"0;33m\\\"+self.A223+self.A223+\\\" \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"1m\\\"+self.A176+self.A178+self.A178+\\\" \\\"+self.A177+\\\" \\\"+self.A176+self.A176+\\\" \\\"+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"37;40m \\\"+self.ESC+\\\"31m\\\"+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.ESC+\\\"1;33m\\\"+self.A220+self.A220+self.ESC+\\\"0;33m\\\"+self.A220+self.A220+self.A223+\\\" \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"1m\\\"+self.A176+self.A219+self.A178+self.A178+self.A177+self.A177+self.A176+\\\" \\\"+self.A176+\\\" \\\"+self.A176+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+\\\" \\\"+self.ESC+\\\"41m \\\"+self.ESC+\\\"37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.ESC+\\\"0;37m \\\"+self.ESC+\\\"1;33m\\\"+self.A223+self.A223+self.A223+self.ESC+\\\"0;33m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"30;41m \\\"+self.ESC+\\\"1;31mShatterstar [W/X] \\\"+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"30;41m \\\"+self.ESC+\\\"37;40m \\\"+self.ESC+\\\"30;41m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\treturn thismsg\",\n \"def list_all_record(self, args):\\n\\n for k, v in self.dhcp_client_state.items():\\n print(\\\"mac: %s DHCP state: %s\\\" % (k.replace('_', ':'), str(v)))\\n\\n gw_info = UplinkGatewayInfo(store.GatewayInfoMap())\\n print(\\\"Default GW: %s\\\" % gw_info.get_gw_ip())\\n print(\\\"GW Mac address: %s\\\" % gw_info.get_gw_mac())\",\n \"def write_to_vcf(self):\\n\\n # 1. Generate header info\\n date_for_vcf = datetime.now().strftime('%Y%m%d')\\n header_info = [\\n '##fileformat=VCFv4.2',\\n '##fileDate=%s' % date_for_vcf,\\n '##source=%s' % self.get_analyser_name(),\\n '##reference=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/hg38.fa.gz',\\n '##contig=<ID=chr1,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr1.fa.gz>',\\n '##contig=<ID=chr2,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr2.fa.gz>',\\n '##contig=<ID=chr3,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr3.fa.gz>',\\n '##contig=<ID=chr4,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr4.fa.gz>',\\n '##contig=<ID=chr5,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr5.fa.gz>',\\n '##contig=<ID=chr6,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr6.fa.gz>',\\n '##contig=<ID=chr7,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr7.fa.gz>',\\n '##contig=<ID=chr8,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr8.fa.gz>',\\n '##contig=<ID=chr9,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr9.fa.gz>',\\n '##contig=<ID=chr10,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr10.fa.gz>',\\n '##contig=<ID=chr11,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr11.fa.gz>',\\n '##contig=<ID=chr12,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr12.fa.gz>',\\n '##contig=<ID=chr13,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr13.fa.gz>',\\n '##contig=<ID=chr14,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr14.fa.gz>',\\n '##contig=<ID=chr15,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr15.fa.gz>',\\n '##contig=<ID=chr16,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr16.fa.gz>',\\n '##contig=<ID=chr17,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr17.fa.gz>',\\n '##contig=<ID=chr18,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr18.fa.gz>',\\n '##contig=<ID=chr19,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr19.fa.gz>',\\n '##contig=<ID=chr20,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr20.fa.gz>',\\n '##contig=<ID=chr21,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr21.fa.gz>',\\n '##contig=<ID=chr22,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr22.fa.gz>',\\n '##contig=<ID=chrM,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chrM.fa.gz>',\\n '##contig=<ID=chrX,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chrX.fa.gz>',\\n '##contig=<ID=chrY,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chrY.fa.gz>',\\n ]\\n header_parameters = [\\n '##FORMAT=<ID=GT,Number=1,Type=String,Description=\\\"Genotype\\\">',\\n '##FORMAT=<ID=MTQ,Number=1,Type=String,Description=\\\"MassArray Typer quality value for SNP call. '\\n 'A=Conservative, B=Moderate, C=Aggressive, D=Low Probability, E=User Call, i=Low Intensity. A and B are considered high '\\n 'quality scores.\\\">',\\n '##INFO=<ID=PCR,Number=2,Type=String,Description=\\\"PCR sequences used in assay.\\\">',\\n '##INFO=<ID=AF,Number=A,Type=Float,Description=\\\"Minor allele frequency from population data.\\\">',\\n '##INFO=<ID=Gene,Number=A,Type=String,Description=\\\"HGNC Gene Name for gene containing SNP.\\\">',\\n '##INFO=<ID=Build,Number=A,Type=String,Description=\\\"Genome build used to determine SNP position for assay.\\\">',\\n '##FILTER=<ID=LowCallRate,Description=\\\"SNP not called in at least 30% of samples in assay.\\\">',\\n ]\\n\\n # 2. Extract info from XML file\\n results = self.get_results()\\n snps = self.get_snps()\\n pcr_sequences = self.get_pcr_sequences()\\n call_rates = self.get_snp_call_rate()\\n\\n # 3. For each sample, create VCF, add headers, determine genotype of each SNP and write to file.\\n for sample, variants in results.items():\\n\\n with open(os.path.join(self.output, '%s.vcf' % sample), 'w+') as outfile:\\n\\n header_fields = ['CHROM', 'POS', 'ID', 'REF', 'ALT', 'QUAL', 'FILTER', 'INFO', 'FORMAT', str(sample)]\\n\\n outfile.write('%s\\\\n' % '\\\\n'.join(header_info))\\n outfile.write('%s\\\\n' % '\\\\n'.join(header_parameters))\\n outfile.write('#%s\\\\n' % '\\\\t'.join(header_fields))\\n\\n # for each variant, make a line to add to the file which will\\n # then be sorted\\n lines_to_write = []\\n for snp, info in variants.items():\\n\\n ref_allele = snps[snp]['ref']\\n alt_alleles = snps[snp]['alt']\\n alt_list = alt_alleles.split(',')\\n\\n # Genotype formatting matches VCF v4.0 spec where ./. is no call.\\n gt_list = []\\n called_genotype = info['genotype']\\n if not called_genotype:\\n gt_list = ['.', '.']\\n elif len(called_genotype) == 1:\\n called_genotype += called_genotype\\n for allele in list(called_genotype):\\n if allele == ref_allele:\\n gt_list.append(0)\\n else:\\n if allele in alt_list:\\n idx = alt_list.index(allele)\\n gt_list.append(idx + 1)\\n else:\\n raise ValueError(\\n 'Called genotype %s not provided as possible alt in bed file. Sample %s and SNP '\\n '%s %s.' % (called_genotype, sample, snp, alt_alleles)\\n )\\n gt = '/'.join([str(x) for x in gt_list])\\n\\n # Threshold currently set to 0.3 (70% results have a call).\\n snp_call_rate = call_rates[snp]\\n if snp_call_rate >= 0.3:\\n vcf_filter = 'LowCallRate'\\n else:\\n vcf_filter = 'PASS'\\n\\n snp_pcr_seqs = pcr_sequences[snp]\\n\\n lines_to_write.append(\\n '{chr}\\\\t{pos}\\\\t{id}\\\\t{ref}\\\\t{alt}\\\\t.\\\\t{filter}\\\\tAF={af};PCR={pcr};Gene={gene};Build={build}\\\\t'\\n 'GT:MTQ\\\\t{gt}:{qual}\\\\n'.format(\\n chr=snps[snp]['chrom'],\\n pos=snps[snp]['pos'],\\n id=snp,\\n ref=ref_allele,\\n alt=alt_alleles,\\n filter=vcf_filter,\\n af=snps[snp]['maf'],\\n pcr=','.join(snp_pcr_seqs),\\n gene=snps[snp]['gene'],\\n build=snps[snp]['genome_build'],\\n gt=gt,\\n qual=','.join(info['quality'])\\n )\\n )\\n\\n sorted_lines_to_write = sorted(\\n lines_to_write,\\n key=lambda x: (\\n # first key for sorting is the int value of chr\\n int(x.split('\\\\t')[0][3:]),\\n # second key for sorting is the position of the variant\\n int(x.split('\\\\t')[1])\\n )\\n )\\n\\n for line in sorted_lines_to_write:\\n outfile.write(line)\",\n \"def format_report_header(self):\",\n \"def write_receivers(h):\\n file = 'DATA/STATIONS'\\n lines = []\\n\\n # loop over receivers\\n for ir in range(h.nr):\\n line = ''\\n line += 'S%06d' % ir + ' '\\n line += 'AA' + ' '\\n line += '%11.5e' % h.rx[ir] + ' '\\n line += '%11.5e' % h.ry[ir] + ' '\\n line += '%3.1f' % 0. + ' '\\n line += '%3.1f' % 0. + '\\\\n'\\n lines.extend(line)\\n\\n # write file\\n _writelines(file, lines)\",\n \"def triage_record(self, record):\\n # Filter out any garbage commands/responses\\n # Record any changes to the reader state\\n # Pass OBD2 records on for formatting\\n \\n # We need to figure out whether this record is :\\n # - line noise / garbage \\\"?\\\"\\n # - the result of an \\\"AT\\\" command \\n # - the result of an OBD2 command \\n \\n # skip over garbage \\n if record == [] \\\\\\n or record[0] == [] \\\\\\n or record[0][0] == '' \\\\\\n or record[0][0] == '?' :\\n #print \\\"Garbage record. Skipping.\\\"\\n return []\\n\\n # handle ELM327 errors\\n # \\\"?\\\" - unrecognized command\\n # \\\"NO DATA\\\" - reader timed out waiting for response from vehicle\\n # \\\"BUFFER FULL\\\" - need to read data from reader faster, ie. increase baud rate on serial connection\\n # many more...\\n if len(record) > 1 :\\n if record[1][0] == '?' \\\\\\n or record[1][0] == 'NO':\\n #print \\\"Garbage record. Skipping.\\\"\\n return []\\n\\n # record the changes made by AT commands\\n cmd = str.upper(record[0][0])\\n if cmd[0:2] == 'AT':\\n self.interpret_at_cmd(record)\\n return []\\n \\n # remove \\\"SEARCHING...\\\" from response\\n # example:\\n # >0100\\n # SEARCHING...\\n # 41 00 BE 3E A8 11 \\n if len(record) > 1 :\\n if record[1][0] == 'SEARCHING...':\\n record.pop(1)\\n\\n # BUFFER FULL - ugh, need to speed up the serial connection\\n rl = len(record)\\n rec = 0\\n while rec < rl:\\n if record[rec][0] == 'BUFFER' and record[rec][1] == 'FULL':\\n record.pop(rec)\\n print \\\" ERROR - BUFFER FULL - Increase speed of serial connection\\\"\\n #return []\\n rec += 1\\n # \\\"BUS BUSY\\\", \\\"CAN ERROR\\\", ???\\n\\n # if we get a 7F, that means there was an error\\n # 10 - general reject\\n # 11 - service not supported\\n # 12 - subfunction not supported OR invalid format\\n # 21 - busy repeat\\n # 22 - conditions or sequence not correct \\n # 78 - response pending\\n if record[1][0] == '7F':\\n mode = record[1][1]\\n err = record[1][2]\\n if err == 10:\\n print \\\"General Error -- Mode:\\\", mode\\n elif err == 11:\\n print \\\"Service Not Supported Error -- Mode:\\\", mode\\n elif err == 12:\\n print \\\"Subfunction Not Supported or Invalid Format Error -- Mode:\\\", mode\\n elif err == 21:\\n print \\\"BUSY, Repeat -- Mode:\\\", mode\\n elif err == 22:\\n print \\\"Conditions or Sequence Not Correct -- Mode:\\\", mode\\n elif err == 78:\\n print \\\"Unknown Error -- Mode:\\\", mode, \\\" -- Error code:\\\", err\\n return []\\n\\n\\n # format an OBD 2 command for further processing at a higher layer\\n try:\\n obd2_record = self.format_obd2_record(record)\\n except self.ErrorIncompleteRecord:\\n print \\\"Garbage record. Skipping.\\\"\\n return []\\n \\n return obd2_record\",\n \"def format(self, record):\\n row = [self.formatTime(record, self.datefmt), record.name, record.levelname]\\n keys = filter(self.filterer, record.__dict__)\\n extra = [record.__dict__[k] for k in keys]\\n\\n self.writer.writerow(row + extra + [record.getMessage()])\\n data = self.output.getvalue()\\n self.output.truncate(0)\\n self.output.seek(0)\\n return data.strip()\",\n \"def format(self, record):\\n\\n\\t\\t# Use copy.copy - c.f. https://stackoverflow.com/a/7961390\\n\\t\\tcolored_record = copy.copy(record)\\n\\n\\t\\tcolor = None\\n\\t\\ttry:\\n\\t\\t\\tcolor = record.color\\n\\t\\texcept AttributeError as e:\\n\\t\\t\\tpass\\n\\n\\t\\tif color is not None:\\n\\t\\t\\tif color is None or not color or color == \\\"none\\\":\\n\\t\\t\\t\\tpass\\n\\t\\t\\telif color == \\\"white\\\":\\n\\t\\t\\t\\twhite = \\\"\\\\033[37m\\\"\\n\\t\\t\\t\\tclear = \\\"\\\\033[0;0m\\\"\\n\\t\\t\\t\\tcolored_record.msg = \\\"{0:s}{1:s}{2:s}\\\".format(\\n\\t\\t\\t\\t\\twhite,\\n\\t\\t\\t\\t\\tcolored_record.msg,\\n\\t\\t\\t\\t\\tclear,\\n\\t\\t\\t\\t)\\n\\t\\t\\telse:\\n\\t\\t\\t\\traise WCMIError(\\\"error: ConsoleFilter: unrecognized color `{0:s}'.\\\".format(str(color)))\\n\\n\\t\\treturn super().format(colored_record)\",\n \"def __str__(self) -> str:\\n dest_table = sorted(self._destinations)\\n res = \\\"HISTORY_rib:\\\\n\\\"\\n res += \\\"Route NH reachable Figure_of_merit Usable flaps Last_time_updated\\\\n\\\" \\n for route in dest_table:\\n for x in self._destinations[route]:\\n res += str(x.addr) + \\\"\\\\t\\\" + str(x.nh) + \\\"\\\\t\\\" + str(x.reachable) \\\\\\n + \\\"\\\\t\\\" + str(x.figure_of_merit) + \\\"\\\\t\\\" + str(x.usable) \\\\\\n + \\\"\\\\t\\\" + str(x.flaps) + \\\"\\\\t\\\" + str(x.last_time_updated) + \\\"\\\\n\\\"\\n return res\",\n \"def format_rec_data(tr_data, village, HC, gs_pars, vfeatures, idx_pars=[]):\\n\\n print('\\\\nFormatting recorded data...')\\n ret_list = []\\n\\n gs_xvec, gs_yvec = [utils.get_copy(gs_pars[v]) for v in ['xvec', 'yvec']]\\n\\n if 'anim_data' in tr_data:\\n\\n anim_data = pd.DataFrame(tr_data['anim_data']).T\\n ret_list.append(anim_data)\\n\\n if 'stim_data' in tr_data:\\n\\n # Motor input.\\n mot_keys = ['umot', 'vmot']\\n mot_data = {i: pd.DataFrame({k: d[k] for k in mot_keys}).unstack()\\n for i, d in tr_data['stim_data'].items() if d is not None}\\n mot_data = pd.DataFrame(mot_data).T\\n mot_data.columns.set_levels(['x', 'y'], level=1, inplace=True)\\n ret_list.append(mot_data)\\n\\n # Visual input.\\n vis_keys = ['ovis', 'vvis']\\n vis_data = {i: pd.DataFrame({k: d[k] for k in vis_keys}).unstack()\\n for i, d in tr_data['stim_data'].items() if d is not None}\\n vis_data = pd.DataFrame(vis_data).T\\n vis_data.columns.set_levels(vfeatures, level=1, inplace=True)\\n ret_list.append(vis_data)\\n\\n if 'gs_state' in tr_data:\\n\\n gs_state = {k: pd.DataFrame(gs, columns=gs_xvec, index=gs_yvec)\\n for k, gs in tr_data['gs_state'].items()}\\n gs_state = pd.concat(gs_state)\\n gs_state.columns.rename('x', inplace=True)\\n gs_state.index.rename('y', level=-1, inplace=True)\\n ret_list.append(gs_state)\\n\\n if 'hc_state' in tr_data:\\n\\n hc_state = {k: pd.DataFrame(hc, index=HC.s_names)\\n for k, hc in tr_data['hc_state'].items()}\\n hc_state = pd.concat(hc_state)\\n hc_state.index.rename('loc', level=-1, inplace=True)\\n hc_state = hc_state[HC.s_types] # reorder columns\\n ret_list.append(hc_state)\\n\\n if 'vs_state' in tr_data:\\n\\n vs_state = {k: pd.Series(vc, index=HC.s_names)\\n for k, vc in tr_data['vs_state'].items()}\\n vs_state = pd.concat(vs_state)\\n ret_list.append(vs_state)\\n\\n if 'dls_state' in tr_data:\\n\\n dls_state = {k: pd.DataFrame(dls, columns=HC.s_names, index=village.U)\\n for k, dls in tr_data['dls_state'].items()}\\n dls_state = pd.concat(dls_state)\\n dls_state.columns.rename('s', inplace=True)\\n dls_state.index.rename('u', level=-1, inplace=True)\\n ret_list.append(dls_state)\\n\\n # Set index level names.\\n idx_lvl_names = idx_pars + ['step']\\n for df in ret_list:\\n levels = (None if not isinstance(df.index, pd.core.index.MultiIndex)\\n else list(range(len(idx_lvl_names))))\\n df.index.set_names(idx_lvl_names, level=levels, inplace=True)\\n\\n return ret_list\",\n \"def format(self, record):\\n data = dict()\\n\\n data[\\\"category\\\"] = record.name\\n data[\\\"timestamp\\\"] = datetime.datetime.utcnow()\\\\\\n .replace(tzinfo=utc)\\\\\\n .strftime('%Y-%m-%dT%H:%M:%SZ')\\n data[\\\"level\\\"] = record.levelname\\n data[\\\"message\\\"] = record.msg\\n data[\\\"threadName\\\"] = record.threadName\\n data[\\\"hostname\\\"] = self.hostname\\n \\n return data\",\n \"def output(self):\\n\\n to_write = []\\n\\n\\n try:\\n test_field = self.fields[0]['id']\\n except IndexError:\\n test_field = None\\n\\n\\n if test_field == None or test_field != 0:\\n # missing fields\\n return to_write\\n\\n\\n\\n to_write += ['#\\\\n# '+self.name+'\\\\n#\\\\n']\\n to_write += ['DEF '+\\n self.name+' '+\\n self.ref+' '+\\n '0 '+ # 0\\n '1 '+ # off\\n self.pin_numbers_visible + ' '+\\n self.pin_names_visible + ' '+\\n '1 '+\\n 'F '+\\n self.powerobject + '\\\\n'\\n ]\\n\\n to_write += ['$FPLIST\\\\n']\\n to_write += ['$ENDFPLIST\\\\n']\\n\\n for field in self.fields:\\n line = 'F'\\n for key in self._F_KEYS:\\n line += str(field[key]) + ' '\\n to_write += [line.rstrip() + '\\\\n']\\n\\n if self.alias != '':\\n to_write += ['ALIAS '+self.alias+'\\\\n']\\n\\n to_write += ['DRAW\\\\n']\\n\\n for draw in self.draws:\\n #print \\\"==================>\\\",draw.output()\\n to_write += [draw.output()]\\n\\n for connection in self.connections:\\n to_write += [connection.output()]\\n\\n to_write += ['ENDDRAW\\\\n']\\n\\n to_write += ['ENDDEF\\\\n']\\n\\n\\n\\n return to_write\",\n \"def dump(self, packet):\\n # TODO\\n packet['type'] = \\\"table\\\"\\n src = packet['src']\\n packet['src'] = packet['dst']\\n packet['dst'] = src\\n\\n table_list = []\\n\\n # TODO fill out table string with routing table\\n table_string = \\\"\\\"\\n # TODO asking for int indexes instead of string for route?\\n for ip in self.updates.keys():\\n # TODO have to fill ip address of peer\\n\\n entry = {'network' : self.updates[ip][MESG][NTWK], 'netmask' : self.updates[ip][MESG][NMSK], 'peer' : ip}\\n table_list.append(entry)\\n packet[MESG] = table_list\\n msg = json.dumps(packet)\\n #print(json.dumps(packet, sort_keys=True, indent=4))\\n\\n sock = self.sockets[src]\\n sock.sendall(msg.encode())\\n return True\",\n \"def __str__(self):\\n lines = []\\n # set hsp info line\\n statline = []\\n # evalue\\n evalue = getattr_str(self, \\\"evalue\\\", fmt=\\\"%.2g\\\")\\n statline.append(\\\"evalue \\\" + evalue)\\n # bitscore\\n bitscore = getattr_str(self, \\\"bitscore\\\", fmt=\\\"%.2f\\\")\\n statline.append(\\\"bitscore \\\" + bitscore)\\n lines.append(\\\"Quick stats: \\\" + \\\"; \\\".join(statline))\\n\\n if len(self.fragments) == 1:\\n return \\\"\\\\n\\\".join(\\n [self._str_hsp_header(), \\\"\\\\n\\\".join(lines), self.fragments[0]._str_aln()]\\n )\\n else:\\n lines.append(\\n \\\" Fragments: %s %s %s %s\\\" % (\\\"-\\\" * 3, \\\"-\\\" * 14, \\\"-\\\" * 22, \\\"-\\\" * 22)\\n )\\n pattern = \\\"%16s %14s %22s %22s\\\"\\n lines.append(pattern % (\\\"#\\\", \\\"Span\\\", \\\"Query range\\\", \\\"Hit range\\\"))\\n lines.append(pattern % (\\\"-\\\" * 3, \\\"-\\\" * 14, \\\"-\\\" * 22, \\\"-\\\" * 22))\\n for idx, block in enumerate(self.fragments):\\n # set hsp line and table\\n # alignment span\\n aln_span = getattr_str(block, \\\"aln_span\\\")\\n # query region\\n query_start = getattr_str(block, \\\"query_start\\\")\\n query_end = getattr_str(block, \\\"query_end\\\")\\n query_range = \\\"[%s:%s]\\\" % (query_start, query_end)\\n # max column length is 20\\n query_range = (\\n query_range[:20] + \\\"~]\\\" if len(query_range) > 22 else query_range\\n )\\n # hit region\\n hit_start = getattr_str(block, \\\"hit_start\\\")\\n hit_end = getattr_str(block, \\\"hit_end\\\")\\n hit_range = \\\"[%s:%s]\\\" % (hit_start, hit_end)\\n hit_range = hit_range[:20] + \\\"~]\\\" if len(hit_range) > 22 else hit_range\\n # append the hsp row\\n lines.append(pattern % (str(idx), aln_span, query_range, hit_range))\\n\\n return self._str_hsp_header() + \\\"\\\\n\\\" + \\\"\\\\n\\\".join(lines)\",\n \"def format_learning(res, s_real, s_hc, GS_HC):\\n\\n # Format results.\\n res = pd.DataFrame(res).T\\n res.index.name = 'step'\\n\\n # Pre-calculate some stats.\\n s_real_h = pd.concat({i: pd.Series(kv, index=s_real)\\n for i, kv in res['s_real_h'].items()}).unstack()\\n s_hc_h = pd.concat({i: pd.Series(kv, index=s_hc)\\n for i, kv in res['s_hc_h'].items()}).unstack()\\n vc_hc_snr = pd.concat({i: pd.Series(kv, index=s_hc)\\n for i, kv in res['vc_hc_snr'].items()}).unstack()\\n\\n # GS - HC connectivity related stats.\\n gs_hc_pos, gs_hc_h, gs_hc_max = format_GS_HC_rec(res, s_hc, GS_HC)\\n\\n return res, s_real_h, s_hc_h, vc_hc_snr, gs_hc_pos, gs_hc_h, gs_hc_max\",\n \"def __str__(self):\\n return \\\"{}\\\\n{}\\\\n{}\\\\n{}\\\".format(self.header,self.sequence,self.line3,self.quality)\",\n \"def _record(self):\\r\\n self._plot()\\r\\n self._csvWriter()\\r\\n self._logger()\",\n \"def __str__(self):\\n d = {}\\n d[\\\"allocation_id_csv\\\"] = self.allocation_id_csv\\n d[\\\"bandwidth\\\"] = self.bandwidth\\n d[\\\"center_frequency\\\"] = self.center_frequency\\n d[\\\"complex\\\"] = self.complex\\n d[\\\"decimation\\\"] = self.decimation\\n d[\\\"enabled\\\"] = self.enabled\\n d[\\\"gain\\\"] = self.gain\\n d[\\\"group_id\\\"] = self.group_id\\n d[\\\"rf_flow_id\\\"] = self.rf_flow_id\\n d[\\\"sample_rate\\\"] = self.sample_rate\\n d[\\\"tuner_number\\\"] = self.tuner_number\\n d[\\\"tuner_type\\\"] = self.tuner_type\\n return str(d)\",\n \"def xephemFormat(self):\\n line = []\\n #Field 1: names\\n names = [self.getName()]\\n identifiers = self.getIdentifiers()\\n if identifiers[0] is not None:\\n names.append(identifiers[0])\\n for i in range(1,4):\\n if identifiers[i] is not None:\\n names.extend(identifiers[i])\\n line.append(\\\"|\\\".join(names))\\n\\n #Field 2: type designation\\n objType = self.getType()\\n if objType in (\\\"Galaxy Pair\\\", \\\"Galaxy Triplet\\\", \\\"Group of galaxies\\\"):\\n line.append(\\\"f|A\\\")\\n elif objType == \\\"Globular Cluster\\\":\\n line.append(\\\"f|C\\\")\\n elif objType == \\\"Double star\\\":\\n line.append(\\\"f|D\\\")\\n elif objType in (\\\"HII Ionized region\\\", \\\"Nebula\\\"):\\n line.append(\\\"f|F\\\")\\n elif objType == \\\"Galaxy\\\":\\n if self.getHubble().startswith(\\\"S\\\"):\\n line.append(\\\"f|G\\\")\\n else:\\n line.append(\\\"f|H\\\")\\n elif objType == \\\"Dark Nebula\\\":\\n line.append(\\\"f|K\\\")\\n elif objType in (\\\"Emission Nebula\\\", \\\"Reflection Nebula\\\"):\\n line.append(\\\"f|N\\\")\\n elif objType in (\\\"Association of stars\\\", \\\"Open Cluster\\\"):\\n line.append(\\\"f|O\\\")\\n elif objType == \\\"Planetary Nebula\\\":\\n line.append(\\\"f|P\\\")\\n elif objType == \\\"Supernova remnant\\\":\\n line.append(\\\"f|R\\\")\\n elif objType == \\\"Star\\\":\\n line.append(\\\"f|S\\\")\\n elif objType == \\\"Star cluster + Nebula\\\":\\n line.append(\\\"f|U\\\")\\n else:\\n line.append(\\\"f\\\")\\n\\n #Field 3: Right Ascension\\n line.append(self.getRA())\\n\\n #Field 4: Declination\\n line.append(self.getDec())\\n\\n #Field 5: Magnitude\\n #We use the first available magnitude in the sequence b,v,j,h,k\\n for mag in self.getMagnitudes():\\n if mag is not None:\\n line.append(str(mag))\\n break\\n\\n #Field 6: optional Epoch, we let it empty\\n line.append(\\\"\\\")\\n\\n #Field 7: Dimensions\\n dimensions = []\\n #Xephem format wants axes espressed in arcsec, we have arcmin\\n for value in (self.getDimensions()[0],self.getDimensions()[1]):\\n if value is not None:\\n dimensions.append(str(value*60))\\n else:\\n dimensions.append(\\\"\\\")\\n if self.getDimensions()[2] is not None:\\n dimensions.append(str(value))\\n else:\\n dimensions.append(\\\"\\\")\\n line.append(\\\"|\\\".join(dimensions))\\n\\n return \\\",\\\".join(line)\",\n \"def __str__(self):\\n s = \\\"Filename : %s\\\\n\\\" % self.fname\\n s += \\\"Data size : %d x %d x %d\\\\n\\\" % (self._size[::-1])\\n s += \\\"CCD Chip Size : %d x %d\\\\n\\\" % self._chipSize[::-1]\\n s += \\\"File date : %s\\\\n\\\" % time.asctime(self._filedate)\\n s += \\\"Exposure Time : %f\\\\n\\\" % self.Exposure\\n s += \\\"Num ROI : %d\\\\n\\\" % self.NumROI\\n s += \\\"Num ROI Exp : %d\\\\n\\\" % self.NumROIExperiment\\n s += \\\"Contoller Ver.: %d\\\\n\\\" % self.ControllerVersion\\n s += \\\"Logic Output : %d\\\\n\\\" % self.LogicOutput\\n #self.AppHiCapLowNoise = self._readInt(4)\\n s += \\\"Timing Mode : %d\\\\n\\\" % self.TimingMode\\n s += \\\"Det. Temp : %d\\\\n\\\" % self.DetTemperature\\n s += \\\"Det. Type : %d\\\\n\\\" % self.DetectorType\\n s += \\\"Trigger Diode : %d\\\\n\\\" % self.TriggerDiode\\n s += \\\"Delay Time : %d\\\\n\\\" % self.DelayTime\\n s += \\\"Shutter Cont. : %d\\\\n\\\" % self.ShutterControl\\n s += \\\"Absorb Live : %d\\\\n\\\" % self.AbsorbLive\\n s += \\\"Absorb Mode : %d\\\\n\\\" % self.AbsorbMode\\n s += \\\"Virtual Chip : %d\\\\n\\\" % self.CanDoVirtualChip\\n s += \\\"Thresh. Min L : %d\\\\n\\\" % self.ThresholdMinLive\\n s += \\\"Thresh. Min : %d\\\\n\\\" % self.ThresholdMin\\n s += \\\"Thresh. Max L : %d\\\\n\\\" % self.ThresholdMaxLive\\n s += \\\"Thresh. Max : %d\\\\n\\\" % self.ThresholdMax\\n s += \\\"Geometric Op : %d\\\\n\\\" % self.GeometricOps\\n s += \\\"ADC Offset : %d\\\\n\\\" % self.ADCOffset\\n s += \\\"ADC Rate : %d\\\\n\\\" % self.ADCRate\\n s += \\\"ADC Type : %d\\\\n\\\" % self.ADCType\\n s += \\\"ADC Resol. : %d\\\\n\\\" % self.ADCRes\\n s += \\\"ADC Bit. Adj. : %d\\\\n\\\" % self.ADCBitAdj\\n s += \\\"ADC Gain : %d\\\\n\\\" % self.Gain\\n \\n i = 0\\n for roi in self.allROI:\\n s += \\\"ROI %-4d : %-5d %-5d %-5d %-5d %-5d %-5d\\\\n\\\" % (i,roi[0], roi[1], roi[2],\\n roi[3], roi[4], roi[5])\\n i += 1\\n \\n s += \\\"\\\\nComments :\\\\n\\\"\\n i = 0\\n for c in self._comments:\\n s += \\\"%-3d : \\\" % i\\n i += 1\\n s += c\\n s += \\\"\\\\n\\\"\\n return s\",\n \"def _gto_from_ccdata(self):\\n\\n gbasis = self.ccdata.gbasis\\n lines = []\\n\\n for no, basis in enumerate(gbasis):\\n lines.append(f\\\"{no + 1:3d} 0\\\")\\n for prims in basis:\\n lines.append(f\\\"{prims[0].lower():s} {len(prims[1]):5d} 1.00\\\")\\n for prim in prims[1]:\\n lines.append(f\\\"{prim[0]:15.9e} {prim[1]:15.9e}\\\")\\n lines.append('')\\n lines.append('')\\n return lines\",\n \"def report(self):\\n s = \\\"Conn %s\\\" % (self.shape,)\\n if hasattr(self,'eltype'):\\n s += \\\", eltype=%s\\\" % self.eltype\\n s += '\\\\n'\\n return s + ndarray.__str__(self)\",\n \"def transmission_summarise():\\n\\n study_description = self.processDicomField(dcm_info, \\\"StudyDescription\\\")\\n study_date = self.processDicomField(dcm_info, \\\"StudyDate\\\")\\n series_description = self.processDicomField(dcm_info, \\\"SeriesDescription\\\")\\n patient_id = self.processDicomField(dcm_info, \\\"PatientID\\\")\\n patient_name = self.processDicomField(dcm_info, \\\"PatientName\\\")\\n protocol_name = self.processDicomField(dcm_info, \\\"ProtocolName\\\")\\n d_fileInfo = filesInSeries_determine()\\n self.dp.qprint('Summary report:')\\n self.dp.qprint('PatientID: %s' % patient_id, level = -1)\\n self.dp.qprint('PatientName: %s' % patient_name, level = -1)\\n self.dp.qprint('StudyDate: %s' % study_date, level = -1)\\n self.dp.qprint('StudyDescription: %s' % study_description, level = -1)\\n self.dp.qprint('SeriesDescription: %s' % series_description, level = -1)\\n self.dp.qprint('ProtocolName: %s' % protocol_name, level = -1)\\n if d_fileInfo['status']:\\n self.dp.qprint('Number of files in Series: %d' % d_fileInfo['fileCount'], level = -1)\\n self.dp.qprint('Directory size (raw): %d' % d_fileInfo['dirSizeRaw'], level = -1)\\n self.dp.qprint('Directory size (human): %s' % d_fileInfo['str_dirSize'], level = -1)\",\n \"def createHeaderRecord(self):\\n\\n # ascii-character limit for every header record information (in bytes)\\n lenVersion = 8\\n lenLocalPatientID = 80\\n lenLocalRecordingID = 80\\n lenStartDate = 8\\n lenStartTime = 8\\n lennBytesHeader = 8\\n lenEDFPlus = 44\\n lennDataRecord = 8\\n lenDurationDataRecord = 8\\n lennSignals = 4\\n \\n HeaderInfolist = [self.Version, self.LocalPatientID, self.LocalRecordingID, self.StartDate, self.StartTime, self.nBytesHeader, self.EDFPlus,\\\\\\n self.nDataRecord, self.DurationDataRecord, self.nSignals]\\n lenHeaderInfo = [lenVersion, lenLocalPatientID, lenLocalRecordingID, lenStartDate, lenStartTime, lennBytesHeader, lenEDFPlus, lennDataRecord,\\\\\\n lenDurationDataRecord, lennSignals]\\n\\n for i in range(len(HeaderInfolist)):\\n maxlen = lenHeaderInfo[i]\\n if len(HeaderInfolist[i]) > maxlen:\\n # truncates the string if length is greater than limit\\n HeaderInfolist[i] = HeaderInfolist[i][:maxlen] \\n \\n else:\\n HeaderInfolist[i] = HeaderInfolist[i].ljust(maxlen)\\n \\n # converts the list to a string with no separator in between elements\\n self.HeaderRecord = ''.join(HeaderInfolist) \\n\\n # concatenates each BioSignal TechInfo to the Header Record string\\n for i in range(len(self.BioSignals[0].TechInfo)):\\n for x in range(len(self.BioSignals)):\\n self.HeaderRecord = self.HeaderRecord + self.BioSignals[x].TechInfo[i]\",\n \"def dump(self, prefix = \\\" - \\\"):\\n\\t\\tlines = [\\n\\t\\t\\t\\\"\\\"\\n\\t\\t\\t\\\"Host \\\",\\n\\t\\t\\t\\\"Light engine: state %d, flags 0x%x\\\" %\\n\\t\\t\\t\\t(self.le_state, self.le_flags),\\n\\t\\t\\t\\\"Playback: state %d, flags 0x%x\\\" %\\n\\t\\t\\t\\t(self.playback_state, self.playback_flags),\\n\\t\\t\\t\\\"Buffer: %d points\\\" %\\n\\t\\t\\t\\t(self.fullness, ),\\n\\t\\t\\t\\\"Playback: %d kpps, %d points played\\\" %\\n\\t\\t\\t\\t(self.point_rate, self.point_count),\\n\\t\\t\\t\\\"Source: %d, flags 0x%x\\\" %\\n\\t\\t\\t\\t(self.source, self.source_flags)\\n\\t\\t]\\n\\t\\t'''\\n\\t\\tif debug == 2:\\n\\t\\t\\tfor l in lines:\\n\\t\\t\\t\\tprint prefix + l\\n\\t\\t'''\",\n \"def printCsvHeader(self):\\n print \\\"SOURCE FILENAME, RECORD NUMBER, TIME GEN, \\\"\\\\\\n \\\"TIME WRITE, CATEGORY, EVENTID, SOURCE, \\\"\\\\\\n \\\"COMPUTER, SID, STRINGS, RAW DATA (HEX), \\\"\\\\\\n \\\"DECODED DATA\\\"\",\n \"def FormatRecord(self, record, categories):\\n catsmodified=False\\n res={'id': 0} # zero means create new record\\n for i in record.get(\\\"serials\\\", []):\\n if i['sourcetype']=='egroupware':\\n res['id']=i['id']\\n break\\n res['n_given'],res['n_middle'],res['n_family']=nameparser.getparts(record.get(\\\"names\\\", [{}])[0])\\n for nf in 'n_given', 'n_middle', 'n_family':\\n if res[nf] is None:\\n res[nf]=\\\"\\\" # set None fields to blank string\\n res['fn']=nameparser.formatsimplename(record.get(\\\"names\\\", [{}])[0])\\n for t,prefix in (\\\"business\\\", \\\"adr_one\\\"), (\\\"home\\\", \\\"adr_two\\\"):\\n a={}\\n adr=record.get(\\\"addresses\\\", [])\\n for i in adr:\\n if i['type']==t:\\n for p2,k in (\\\"_street\\\", \\\"street\\\"), (\\\"_locality\\\", \\\"city\\\"), (\\\"_region\\\", \\\"state\\\"), \\\\\\n (\\\"_postalcode\\\", \\\"postalcode\\\"), (\\\"_countryname\\\", \\\"country\\\"):\\n res[prefix+p2]=i.get(k, \\\"\\\")\\n if t==\\\"business\\\":\\n res['org_name']=i.get(\\\"company\\\",\\\"\\\")\\n break\\n if \\\"emails\\\" in record:\\n for t,k in (\\\"business\\\", \\\"email\\\"), (\\\"home\\\", \\\"email_home\\\"):\\n for i in record[\\\"emails\\\"]:\\n if i.get(\\\"type\\\",None)==t:\\n res[k]=i.get(\\\"email\\\")\\n res[k+\\\"_type\\\"]=\\\"INTERNET\\\"\\n break\\n cats={}\\n for cat in record.get(\\\"categories\\\", []):\\n c=cat['category']\\n v=categories.get(c, None)\\n if v is None:\\n catsmodified=True\\n for i in xrange(0,-999999,-1):\\n if `i` not in cats:\\n break\\n else:\\n i=`v`\\n cats[i]=str(c)\\n res['cat_id']=cats\\n for t,k in (\\\"home\\\", \\\"tel_home\\\"), (\\\"cell\\\", \\\"tel_cell\\\"), ('fax','tel_fax'), \\\\\\n ('pager', 'tel_pager'), ('office', 'tel_work'):\\n if \\\"numbers\\\" in record:\\n v=\\\"\\\"\\n for i in record['numbers']:\\n if i['type']==t:\\n v=i['number']\\n break\\n res[k]=phonenumber.format(v)\\n if \\\"memos\\\" in record:\\n memos=record.get(\\\"memos\\\", [])\\n memos+=[{}]\\n res['note']=memos[0].get(\\\"memo\\\",\\\"\\\")\\n if \\\"urls\\\" in record:\\n urls=record.get(\\\"urls\\\", [])\\n u=\\\"\\\"\\n for url in urls:\\n if url.get(\\\"type\\\", None)==\\\"business\\\":\\n u=url[\\\"url\\\"]\\n break\\n if len(u)==0:\\n urls+=[{'url':\\\"\\\"}]\\n u=urls[0][\\\"url\\\"]\\n res['url']=u\\n return catsmodified,res\",\n \"def dumpData(self,out):\\n #--Get sizes\\n for record in self.records:\\n #--Text\\n if record.name == 'NAME':\\n #--Truncate text?\\n if len(self.text) > 511:\\n self.text = self.text[:511]\\n record.data = self.text\\n record.size = len(self.text)\\n #--Speaker\\n elif record.name == 'ONAM':\\n record.data = self.spId+'\\\\x00'\\n record.size = len(self.spId) + 1\\n record.getSize()\\n record.dump(out)\",\n \"def _printable(self):\\n toPrint = \\\"Communication header. \\\"\\n toPrint += \\\"Remote App ID: \\\" + str(self.remote_app_id) + \\\" \\\"\\n toPrint += \\\"Remote Node: \\\" + str(self.remote_node) + \\\" \\\"\\n toPrint += \\\"Remote Port: \\\" + str(self.remote_port) + \\\" \\\"\\n\\n return toPrint\",\n \"def make_open_airspace_format(self):\\n # Extract coordinates from KML\\n for idxline in range(len(self.kml_lines)):\\n if '<name>' in self.kml_lines[idxline]:\\n self.name = self.kml_lines[idxline].replace('\\\\t', '').replace('<name>', '').replace('</name>', '').replace('\\\\n','')\\n if not self.name.startswith('TS'):\\n self.name = 'TS_' + self.name\\n print('Type: %s | Name: %s' % (self.as_type, self.name))\\n if '<coordinates>' in self.kml_lines[idxline]:\\n self.coordinates_kml = self.kml_lines[idxline + 1].replace('\\\\t', '').replace('\\\\n', '')\\n break\\n # start conversion to airspace format\\n \\\"\\\"\\\" AC A\\n AN TS_Erzgeb\\n AL FL98\\n AH FL99\\n DP 50:26:22 N 012:17:59 E\\n DP 50:25:25 N 012:18:26 E\\n DP 50:24:40 N 012:19:01 E\\n DP 50:24:06 N 012:19:46 E\\\"\\\"\\\"\\n\\n # AC A\\n self.txt_lines.append('AC %s\\\\n' % self.as_type)\\n # AN TS_Erzgeb\\n self.txt_lines.append('AN %s\\\\n' % self.name)\\n # heights\\n self.txt_lines.append('AL FL98\\\\n')\\n self.txt_lines.append('AH FL99\\\\n')\\n # coordinates\\n for coo_pt in self.coordinates_kml.split(' ')[:-1]:\\n # Target format: DP 50:26:22 N 012:17:59 E\\n lat_long = coo_pt.split(',')\\n # latitude\\n latDecAsStr = lat_long[1].split('.')\\n #if '.' not in latDecAsStr: # take care of case \\\"51\\\" instead of \\\"51.123456\\\"\\n # latDecAsStr += '.000000'\\n lat_degree = abs(int(latDecAsStr[0]))\\n #print(f'latDecAsStr {latDecAsStr}')\\n if len(latDecAsStr)==1:\\n latDecAsStr.append('0')\\n lat_secondDec = (float('0.' + latDecAsStr[1])*60) % 1\\n lat_minute = round((float('0.' + latDecAsStr[1])*60) - lat_secondDec)\\n lat_second = round(lat_secondDec*60)\\n cooString = ('DP %02d:%02d:%02d' %(lat_degree,lat_minute,lat_second))\\n if latDecAsStr[0].startswith('-'):\\n cooString += ' S'\\n else:\\n cooString += ' N'\\n # longitude\\n #print(f'converting lat_long {lat_long}')\\n # take care of case: no decimal sign included, case \\\"11\\\" instead of \\\"11.123456\\\"\\n if '.' not in lat_long[0]:\\n lat_long[0] += '.0'\\n lonDecAsStr = lat_long[0].split('.')\\n lon_degree = abs(int(lonDecAsStr[0]))\\n lon_secondDec = (float('0.' + lonDecAsStr[1]) * 60) % 1\\n lon_minute = round((float('0.' + lonDecAsStr[1]) * 60) - lon_secondDec)\\n lon_second = round(lon_secondDec * 60)\\n cooString += (' %03d:%02d:%02d' % (lon_degree, lon_minute, lon_second))\\n if lonDecAsStr[0].startswith('-'):\\n cooString += ' W'\\n else:\\n cooString += ' E'\\n cooString += '\\\\n'\\n self.txt_lines.append(cooString)\",\n \"def format_vcf(self):\\n # Create a list of information for a VCF file\\n vcf_line = [\\n self._contig,\\n str(self._position),\\n self._snpid,\\n self._reference,\\n self._alternate,\\n '.',\\n '.',\\n 's'\\n ]\\n return '\\\\t'.join(vcf_line) # Join everything together with a tab\",\n \"def format(self, record):\\n extra = {\\n \\\"message\\\": record.getMessage(),\\n \\\"time\\\": self.formatTime(record, self.datefmt),\\n \\\"msecs\\\": record.msecs,\\n \\\"name\\\": record.name,\\n \\\"level\\\": record.levelname,\\n }\\n\\n keys = filter(self.filterer, record.__dict__)\\n extra.update({k: record.__dict__[k] for k in keys})\\n return str(CustomEncoder().encode(extra))\",\n \"def __str__(self):\\n d = {}\\n d[\\\"tuner_number\\\"] = self.tuner_number\\n d[\\\"output_format\\\"] = self.output_format\\n d[\\\"output_source\\\"] = self.output_source\\n return str(d)\",\n \"def asformat(self, format):\",\n \"def __str__(self):\\n tag = []\\n for key in self.tags:\\n if key == 'label':\\n self.type = self.tags[key]\\n else:\\n try:\\n tag.append(\\\"%s=%0.3f\\\" % (str(key), self.tags[key]))\\n except TypeError:\\n tag.append(\\\"%s=%s\\\" % (str(key), str(self.tags[key])))\\n \\n \\n tag = \\\";\\\".join(tag)\\n dat = [self.chrom, self.out_start, self.start, self.in_start, \\\\\\n self.in_end, self.end, self.out_end, self.type, self.size, \\\\\\n tag]\\n\\n return \\\"{0}\\\\t{1}\\\\t{2}\\\\t{3}\\\\t{4}\\\\t{5}\\\\t{6}\\\\t{7}\\\\t{8}\\\\t{9}\\\".format(*dat) \\\\\\n .replace(\\\"None\\\", \\\".\\\")\",\n \"def __str__(self):\\n if self.filename:\\n filename = self.filename\\n else:\\n filename = 'Unknown'\\n if self.endian == '<':\\n endian = 'Little Endian'\\n else:\\n endian = 'Big Endian'\\n ret_val = ('FILE: %s\\\\nRecord Offset: %i byte\\\\n' +\\n 'Header Endianness: %s\\\\n\\\\n') % \\\\\\n (filename, self.record_offset, endian)\\n ret_val += 'FIXED SECTION OF DATA HEADER\\\\n'\\n for key in self.fixed_header.keys():\\n ret_val += '\\\\t%s: %s\\\\n' % (key, self.fixed_header[key])\\n ret_val += '\\\\nBLOCKETTES\\\\n'\\n for key in self.blockettes.keys():\\n ret_val += '\\\\t%i:' % key\\n if not len(self.blockettes[key]):\\n ret_val += '\\\\tNOT YET IMPLEMENTED\\\\n'\\n for _i, blkt_key in enumerate(self.blockettes[key].keys()):\\n if _i == 0:\\n tabs = '\\\\t'\\n else:\\n tabs = '\\\\t\\\\t'\\n ret_val += '%s%s: %s\\\\n' % (tabs, blkt_key,\\n self.blockettes[key][blkt_key])\\n ret_val += '\\\\nCALCULATED VALUES\\\\n'\\n ret_val += '\\\\tCorrected Starttime: %s\\\\n' % self.corrected_starttime\\n return ret_val\",\n \"def packet_to_str(packet: PacketDescription, simple_diagrams=False, force_show_frames='', show_timestamp=False) \\\\\\n -> PacketDiagramDescription:\\n protocol = packet.protocols_str\\n note_color = ''\\n packet_str = ''\\n if 'NGAP' in protocol:\\n if nas_req_regex.search(packet.msg_description) is not None:\\n note_color = ' {0}'.format(color_nas_req)\\n protocol = 'NAS req.'\\n else:\\n note_color = ' {0}'.format(color_nas_rsp)\\n protocol = 'NGAP msg. or NAS rsp.'\\n\\n # Search NGAP messages\\n ngap_matches = ngap_message_type_regex.finditer(packet.msg_description)\\n ngap_message_types = [ngap_match.group(1) for ngap_match in ngap_matches if ngap_match is not None]\\n if len(ngap_message_types) > 0:\\n ngap_seen = set()\\n ngap_seen_add = ngap_seen.add\\n ngap_message_types = ['NGAP {0}'.format(x) for x in ngap_message_types if\\n not (x in ngap_seen or ngap_seen_add(x))]\\n\\n # Search NAS messages\\n nas_matches = nas_message_type_regex.finditer(packet.msg_description)\\n nas_message_types = [nas_match.group(1) for nas_match in nas_matches if nas_match is not None]\\n if len(nas_message_types) > 0:\\n # Remove duplicates: https://stackoverflow.com/questions/480214/how-do-you-remove-duplicates-from-a-list-whilst-preserving-order\\n nas_seen = set()\\n nas_seen_add = nas_seen.add\\n nas_message_types = ['NAS {0}'.format(x) for x in nas_message_types if\\n not (x in nas_seen or nas_seen_add(x))]\\n\\n # Print msg. type\\n joint_ngap_nas_msg_types = ngap_message_types + nas_message_types\\n if len(joint_ngap_nas_msg_types) > 0:\\n protocol = '{0}'.format(',\\\\\\\\n'.join(joint_ngap_nas_msg_types))\\n\\n elif 'HTTP' in protocol:\\n # Some customized filtering based on what we have seen\\n rsp_match = http_rsp_regex.search(packet.msg_description)\\n req_match = http_url_regex.search(packet.msg_description)\\n if ('404 page not found' in packet.msg_description) or (rsp_match is not None):\\n note_color = ' {0}'.format(color_http2_rsp)\\n if rsp_match is not None:\\n protocol = '{0} {1} rsp.'.format(protocol, rsp_match.group(1))\\n else:\\n protocol = protocol + ' 404 rsp.'\\n elif req_match is not None:\\n note_color = ' {0}'.format(color_http2_req)\\n protocol = protocol + ' req.'\\n else:\\n note_color = ' {0}'.format(color_http2_req)\\n protocol = protocol + ' req. or rsp. (no HTTP/2 headers)'\\n\\n match = list(http_url_regex.finditer(packet.msg_description))\\n if len(match) > 0:\\n method = ''\\n method_match_all = http_method_regex.finditer(packet.msg_description)\\n protocols = []\\n for idx, method_match in enumerate(method_match_all):\\n method = '{0} '.format(method_match.group(1))\\n url_split = match[idx].group(1).split('?')\\n protocols.append('{0} {1}'.format(method, url_split[0]))\\n protocol = '{0}\\\\\\\\n'.format(protocol) + '\\\\\\\\n'.join(protocols)\\n\\n elif 'PFCP' in protocol:\\n if pfcp_req_regex.search(packet.msg_description) is not None:\\n note_color = ' {0}'.format(color_pfcp_req)\\n protocol = protocol + ' req.'\\n else:\\n note_color = ' {0}'.format(color_pfcp_rsp)\\n protocol = protocol + ' rsp.'\\n\\n match = pfcp_message_type_regex.search(packet.msg_description)\\n if match is not None:\\n protocol = '{0}\\\\\\\\n{1}'.format(protocol, match.group(1))\\n\\n elif 'GTPv2' in protocol:\\n if gtpv2_req_regex.search(packet.msg_description) is not None:\\n note_color = ' {0}'.format(color_gtpv2_req)\\n protocol = protocol + ' req.'\\n else:\\n note_color = ' {0}'.format(color_gtpv2_rsp)\\n protocol = protocol + ' req., rsp. or notification'\\n\\n match = gtpv2_message_type_regex.search(packet.msg_description)\\n if match is not None:\\n protocol = '{0}\\\\\\\\n{1}'.format(protocol, match.group(1))\\n\\n elif 'Diameter' in protocol or 'RADIUS' in protocol or \\\"GTP'\\\" in protocol:\\n note_color = ' {0}'.format(color_diameter_radius_gtpprime)\\n protocol = get_diam_description(packet)\\n\\n if show_timestamp:\\n try:\\n dt_object = datetime.fromtimestamp(packet.timestamp)\\n if dt_object.tzinfo is None:\\n tz_str = ''\\n else:\\n tz_str = ' {0}'.format(dt_object.tzinfo)\\n timestamp_hour = ' ({0}:{1}:{2}.{3}{4})'.format(dt_object.hour, dt_object.minute, dt_object.second,\\n dt_object.microsecond / 1000, tz_str)\\n except:\\n timestamp_hour = ''\\n protocol = '{0}\\\\\\\\n+{1:.3f}s{2}'.format(protocol, packet.timestamp_offsett, timestamp_hour)\\n\\n frame_number = packet[2]\\n packet_str = packet_str + '\\\"{0}\\\" -> \\\"{1}\\\": {2}, {3}\\\\n'.format(packet.ip_src, packet.ip_dst, frame_number, protocol)\\n packet_str = packet_str + '\\\\nnote right{0}\\\\n'.format(note_color)\\n\\n force_show_frames = [e.strip() for e in force_show_frames.split(',')]\\n if simple_diagrams and frame_number not in force_show_frames:\\n packet_payload = ''\\n else:\\n packet_payload = packet.msg_description\\n\\n if packet_payload != '':\\n packet_str = packet_str + '**{0} to {1}**\\\\n{2}\\\\n'.format(packet.ip_src, packet.ip_dst, packet_payload)\\n else:\\n packet_str = packet_str + '**{0} to {1}**\\\\n'.format(packet.ip_src, packet.ip_dst)\\n packet_str = packet_str + 'end note\\\\n'\\n packet_str = packet_str + '\\\\n'\\n return PacketDiagramDescription(packet_str, packet.ip_src, packet.ip_dst, protocol)\",\n \"def _format_senders_correspondent_53A(self, val):\\n senders_correspondent_account = val.get('ACCOUNT')\\n senders_correspondent_bic = val.get('BIC')\\n if senders_correspondent_bic:\\n if senders_correspondent_account:\\n val = \\\"/\\\" + str(senders_correspondent_account) + \\\"\\\\n\\\" + str(senders_correspondent_bic)\\n else:\\n val = str(senders_correspondent_bic)\\n return val\",\n \"def __str__(self):\\n return_text = \\\"Time-Triggered Frame information =>\\\\n\\\"\\n return_text += \\\" Sender id : \\\" + str(self.__sender_id) + \\\"\\\\n\\\"\\n return_text += \\\" Receivers ids : \\\" + str(self.__receivers_id) + \\\"\\\\n\\\"\\n return_text += \\\" Path : \\\" + str(self.__paths) + \\\"\\\\n\\\"\\n return_text += \\\" End_to_End : \\\" + str(self.__end_to_end_delay) + \\\" nanoseconds\\\\n\\\"\\n return_text += \\\" Period : \\\" + str(self.__period) + \\\" nanoseconds\\\\n\\\"\\n return_text += \\\" Starting : \\\" + str(self.__starting_time) + \\\" nanoseconds\\\\n\\\"\\n return_text += \\\" Deadline : \\\" + str(self.__deadline) + \\\" nanoseconds\\\\n\\\"\\n return_text += \\\" Size : \\\" + str(self.__size) + \\\" bytes\\\"\\n return return_text\",\n \"def dump(self, packet):\\n # packet is already decoded\\n msg = {\\n SRCE: packet[DEST], \\n DEST: packet[SRCE], \\n TYPE: TABL,\\n MESG: list(map(lambda r: ({ \\n NTWK: r[MESG][NTWK],\\n NMSK: r[MESG][NMSK],\\n PEER: r[SRCE],\\n }),\\n self.routes))\\n }\\n self.sockets[packet[SRCE]].send(json.dumps(msg).encode())\\n return True\",\n \"def __str__(self, printODData = False):\\n networkStr = \\\"Link\\\\tFlow\\\\tCost\\\\n\\\"\\n for ij in sorted(self.link, key=lambda ij : self.link[ij].sortKey):\\n networkStr += \\\"%s\\\\t%f\\\\t%f\\\\n\\\" % (ij, self.link[ij].flow, self.link[ij].cost)\\n if printODData == True:\\n networkStr += \\\"\\\\n\\\"\\n networkStr += \\\"OD pair\\\\tDemand\\\\tLeastCost\\\\n\\\"\\n for ODpair in self.ODpair:\\n networkStr += \\\"%s\\\\t%f\\\\t%f\\\\n\\\" % (ODpair, self.ODpair[ODpair].demand, self.ODpair[ODpair].leastCost)\\n return networkStr\",\n \"def display_ack_trans_meas(self, carrier=1):\\r\\r\\n\\r\\r\\n loggerCmw = logging.getLogger(__name__ + ' display_ack_trans_meas')\\r\\r\\n\\r\\r\\n # format should match with formatting in hsdpa_trans_meas class\\r\\r\\n trans_num_str = \\\"%18s\\\" % \\\"Transmission [%]\\\"\\r\\r\\n sent_str = \\\"%15s\\\" % \\\"Sent\\\"\\r\\r\\n ack_str = \\\"%15s\\\" % \\\"ACK\\\"\\r\\r\\n nack_str = \\\"%15s\\\" % \\\"NACK\\\"\\r\\r\\n dtx_str = \\\"%15s\\\" % \\\"DTX\\\"\\r\\r\\n\\r\\r\\n print \\\"\\\"\\r\\r\\n carrierStr = \\\"Carrier %s\\\" %carrier\\r\\r\\n print \\\"%18s\\\" %carrierStr\\r\\r\\n title_str = trans_num_str + sent_str + ack_str + nack_str + dtx_str\\r\\r\\n print title_str\\r\\r\\n\\r\\r\\n if carrier == 1:\\r\\r\\n for idx in range(self.NUM_MEAS_PER_TX):\\r\\r\\n print self.trans_meas_1[idx]\\r\\r\\n elif carrier == 2:\\r\\r\\n for idx in range(self.NUM_MEAS_PER_TX):\\r\\r\\n print self.trans_meas_2[idx]\\r\\r\\n else:\\r\\r\\n loggerCmw.error(\\\"Number of carriers %s is not supported\\\" %carrier)\\r\\r\\n\\r\\r\\n print \\\"\\\" # just add blank line\\r\\r\\n return\",\n \"def formatRecvdData(data_recvd):\\n\\n\\t##############\\tADD YOUR CODE HERE\\t##############\\n\\t\\n\\tx = PrettyTable()\\n\\t\\t\\n\\tdict2 = eval(data_recvd)\\n\\t\\t\\n\\tx.field_names=[\\\"OPTION_NUMBER\\\",\\\"OPTIONS\\\",\\\"ADDITIONAL INFO\\\"]\\n\\tfor key,value in dict2.items():\\n\\t\\tvalue = str(value)\\n\\t\\tif '{' in value:\\n\\t\\t\\t\\td = eval(value)\\n\\t\\t\\t\\tfor i,j in d.items():\\n\\n\\t\\t\\t\\t\\tx.add_row([key,i,j])\\n\\t\\t\\t\\t\\n\\t\\telse:\\n\\t\\t\\t\\t\\n\\t\\t\\tx.add_row([key,value,\\\"-\\\"])\\n\\tprint(x)\\n\\t\\n\\n\\t##################################################\",\n \"def format(self, record):\\n\\n\\n if not hasattr(record, 'filename_'):\\n record.file_indicator = '-'\\n else:\\n record.file_indicator = os.path.relpath(record.filename_.strip(),\\n self.study_dir)\\n record.line_indicator = self.format_aggregated(\\n record,\\n 'line_number',\\n ' line %d:',\\n ' lines [%s]:',\\n optional=True)\\n record.column_indicator = self.format_aggregated(\\n record,\\n 'column_number',\\n ' column %d:',\\n ' columns [%s]:',\\n optional=True)\\n record.cause_indicator = self.format_aggregated(\\n record,\\n 'cause',\\n \\\"; value encountered: '%s'\\\",\\n \\\"; values encountered: ['%s']\\\",\\n join_string=\\\"', '\\\",\\n optional=True)\\n\\n # format the string based on these fields\\n formatted_result = super(LogfileStyleFormatter, self).format(record)\\n\\n # prepend an empty line if the filename is different than before\\n current_filename = getattr(record, 'filename_', '')\\n if (self.previous_filename is not None and\\n current_filename != self.previous_filename):\\n formatted_result = '\\\\n' + formatted_result\\n self.previous_filename = current_filename\\n\\n return formatted_result\",\n \"def _format_senders_correspondent_53D(self, val):\\n account = val.get('ACCOUNT')\\n name = val.get('NAME')\\n address = val.get('ADDRESS')\\n if name and address:\\n name = FSwiftWriterUtils.split_text_and_prefix(name, 35)\\n address = FSwiftWriterUtils.split_text_and_prefix(address, 35)\\n val = FSwiftWriterUtils.allocate_space_for_name_address_without_constraint(name, address)\\n if account:\\n val = \\\"/\\\" + str(account) + \\\"\\\\n\\\" + str(val)\\n return val\",\n \"def print_aldb_to_log(aldb):\\n _LOGGER.info(\\\"ALDB load status is %s\\\", aldb.status.name)\\n if aldb.status not in [ALDBStatus.LOADED, ALDBStatus.PARTIAL]:\\n _LOGGER.warning(\\\"Device All-Link database not loaded\\\")\\n _LOGGER.warning(\\\"Use service insteon.load_aldb first\\\")\\n return\\n\\n _LOGGER.info(\\\"RecID In Use Mode HWM Group Address Data 1 Data 2 Data 3\\\")\\n _LOGGER.info(\\\"----- ------ ---- --- ----- -------- ------ ------ ------\\\")\\n for mem_addr in aldb:\\n rec = aldb[mem_addr]\\n # For now we write this to the log\\n # Roadmap is to create a configuration panel\\n in_use = \\\"Y\\\" if rec.control_flags.is_in_use else \\\"N\\\"\\n mode = \\\"C\\\" if rec.control_flags.is_controller else \\\"R\\\"\\n hwm = \\\"Y\\\" if rec.control_flags.is_high_water_mark else \\\"N\\\"\\n log_msg = (\\n f\\\" {rec.mem_addr:04x} {in_use:s} {mode:s} {hwm:s} \\\"\\n f\\\"{rec.group:3d} {rec.address.human:s} {rec.data1:3d} \\\"\\n f\\\"{rec.data2:3d} {rec.data3:3d}\\\"\\n )\\n _LOGGER.info(log_msg)\",\n \"def _output_vadapter_list_verbose(outf, name, vadapter_list):\\n\\n if vadapter_list:\\n FMT = \\\"%s\\\\n%-10s\\\\n%-20s\\\\n%-5s\\\\n%s\\\\n%s\\\\n%s\\\\n%s\\\\n%s\\\\n%s\\\\n%s\\\\n%s\\\\n%s\\\\n%s\\\\n%s\\\\n%s\\\\n%s\\\\n\\\"\\n# HEADER = (\\\"id\\\",\\\"name\\\",\\\"io_module_id\\\",\\\"vfabric_id\\\",\\\"protocol\\\",\\\"init_type\\\",\\\"desc\\\",\\\"status\\\",\\\"mac\\\"\\n# ,\\\"prmiscuous_mode\\\",\\\"silent_listener\\\",\\\"vlan\\\",\\\"wwnn\\\",\\\"wwpn\\\",\\\"fc_id\\\",\\\"spma\\\",\\\"fmpa\\\") \\n\\t\\n for (n , vadapter) in vadapter_list:\\n name = vadapter['NAME']\\n id = vadapter['ID']\\n protocol = vadapter['PROTOCOL']\\n vfabric = vadapter['VFABRIC']\\n io_module = vadapter['IO_MODULE_ID']\\n desc = vadapter['DESC']\\n status = vadapter['STATUS']\\n init_type = vadapter['INIT_TYPE']\\n running_mode = vadapter['RUNNING_MODE']\\n if protocol == 1:\\n mac = vadapter['MAC']\\n promiscuous = vadapter['PROMISCUOUS']\\n silent = vadapter['SILENT']\\n vlan = vadapter['VLAN']\\n elif protocol == 3:\\n wwnn = vadapter['WWNN']\\n wwpn = vadapter['WWPN']\\n spma = vadapter['SPMA']\\n fpma = vadapter['FPMA']\\n fc_id = vadapter['FC_ID']\\n\\n\\n if _LIMITED_SHOW:\\n outf.write('General Attr:\\\\nId: %s\\\\n\\\\tName: %s\\\\n\\\\tIO_Module: %s\\\\n\\\\tVfabric: %s\\\\n\\\\tProtocol: %s\\\\n\\\\tInitization_Type: %s\\\\n\\\\tDescription: %s\\\\n\\\\tStatus: %-20s\\\\n\\\\n' % (id, name, io_module, vfabric, protocol, init_type, desc, status)) \\n \\n\\t\\telif _DETAIL_SHOW: \\n if protocol == 3:\\n outf.write('General Attr:\\\\nId: %s\\\\nName: %s\\\\nIO_Module: %s\\\\nVfabric: %s\\\\nProtocol: %s\\\\nInitization_Type: %s\\\\nDescription: %s\\\\nStatus: %-20s\\\\nRUNNING_MODE :%s\\\\nFC_Attr:\\\\n\\\\tWWNN: %s\\\\n\\\\tWWNP: %s\\\\n\\\\tFC_ID: %s\\\\n\\\\tSPMA: %s\\\\n\\\\tFMPA: %s\\\\n\\\\n' % (id, name, io_module, vfabric, protocol, init_type, desc, status, running_mode, wwnn, wwpn,fc_id,spma,fpma)) \\n \\n elif protocol == 1:\\n\\t\\t\\t outf.write('General Attr:\\\\nId: %s\\\\nName: %s\\\\nIO_Module: %s\\\\nVfabric: %s\\\\nProtocol: %s\\\\nInitization_Type: %s\\\\nDescription: %s\\\\nStatus: %-20s\\\\nRUNNING_MODE :%s\\\\nEN_Attr:\\\\n\\\\tMAC: %s\\\\n\\\\tVLAN: %s\\\\n\\\\tPromiscuous: %s\\\\n\\\\tSilent Listener: %s\\\\n\\\\n' % (id , name, io_module,vfabric, protocol, init_type, desc, status, running_mode, mac,vlan, promiscuous, silent))\",\n \"def format_coregister():\\r\\n\\r\\n study_list = retrieve_ref('study_list')\\r\\n sensor_list = retrieve_ref('sensor_list')\\r\\n\\r\\n for study in study_list:\\r\\n\\r\\n df_meta = retrieve_meta(study)\\r\\n recordNames = list(df_meta['recordName'])\\r\\n\\r\\n # name the wearable used for each record\\r\\n for record in recordNames:\\r\\n\\r\\n i = df_meta[ df_meta['recordName']== record].index.values[0]\\r\\n print('i = ' + str(i))\\r\\n coregisterBegin = df_meta.loc[i, 'coregisterBegin' ]\\r\\n coregisterEnd = df_meta.loc[i, 'coregisterEnd' ]\\r\\n coregisterRecords = df_meta.loc[i, 'coregisterRecords' ]\\r\\n\\r\\n for sensor in sensor_list:\\r\\n\\r\\n df_coregister = pd.DataFrame()\\r\\n\\r\\n if len(coregisterRecords) == len(record):\\r\\n coregisterRecords = list([coregisterRecords])\\r\\n\\r\\n elif len(coregisterRecords) > len(record):\\r\\n coregisterRecords = coregisterRecords.split(' ')\\r\\n\\r\\n print('coregisterRecords = ')\\r\\n print(coregisterRecords)\\r\\n\\r\\n for item in coregisterRecords:\\r\\n\\r\\n format_type, segment, recordRef = 'truncate', 'All', item\\r\\n source = os.path.join('studies', study, 'formatted', format_type, recordRef, segment, sensor + '.csv')\\r\\n df = pd.read_csv(source)\\r\\n\\r\\n assert coregisterEnd > coregisterBegin + 100, 'during coregister format, coregisterBegin >= coregisterEnd'\\r\\n assert coregisterEnd < max(list(df['timeUnix'])), 'possible error with time'\\r\\n\\r\\n print('coregisterEnd = ' + str(coregisterEnd) + ' timeUnixEnd = ' + str(max(list(df['timeUnix']))))\\r\\n print('timeUnixEnd - coregisterEnd = ' + str((max(list(df['timeUnix'])) - coregisterEnd ) / 60))\\r\\n print('coregisterEnd - timeUnixBegin = ' + str((coregisterEnd - min(list(df['timeUnix']))) / 60))\\r\\n\\r\\n assert coregisterEnd > min(list(df['timeUnix']))\\r\\n\\r\\n df = df[df['timeUnix'] > coregisterBegin]\\r\\n df = df[df['timeUnix'] < coregisterEnd]\\r\\n\\r\\n assert len(list(df['timeUnix'])) > 0, 'coregistered df removed'\\r\\n\\r\\n recordSplit = item.split('_')\\r\\n wearableName = recordSplit[1]\\r\\n\\r\\n df_coregister['timeUnix'] = list(df['timeUnix'])\\r\\n df_coregister['timeMinutes'] = list(df['timeMinutes'])\\r\\n\\r\\n colName = str(wearableName + '_' + 'measurement')\\r\\n print('colName = ' + colName)\\r\\n df_coregister[colName] = list(df['measurement'])\\r\\n\\r\\n\\r\\n path = ['studies', study, 'formatted', 'coregister', record, segment]\\r\\n path = build_path(path)\\r\\n file = os.path.join(path, sensor + \\\".csv\\\")\\r\\n df_coregister.to_csv(file)\\r\\n print('formatted coregister file = ' + str(file))\",\n \"def printFrame(frame, info):\\n global analysis_frame\\n if frame.n_contacts > 0:\\n if analysis_frame == None:\\n analysis_frame = frame\\n # print(\\\"analysis_frame: \\\", analysis_frame.__dir__(),\\\"\\\\n\\\")\\n # #print(\\\"n_contact: \\\", analysis_frame.n_contacts.contents.__dir__(),\\\"\\\\n\\\")\\n # print(\\\"contacts: \\\", analysis_frame.contacts.contents.__dir__(),\\\"\\\\n\\\")\\n # print(\\\"accel_data: \\\", analysis_frame.accel_data.contents.__dir__(),\\\"\\\\n\\\")\\n # # print(\\\"n_contact: \\\", analysis_frame.n_contacts.contents,\\\"\\\\n\\\")\\n # print(\\\"contacts: \\\", analysis_frame.contacts.contents.content_bit_mask.__dir__(),\\\"\\\\n\\\")\\n # print(\\\"accel_data: \\\", analysis_frame.accel_data.contents.__dir__(),\\\"\\\\n\\\")\\n\\n #print(\\\"\\\\nNum Contacts: \\\", frame.n_contacts)\\n for n in range(frame.n_contacts):\\n c = frame.contacts[n]\\n #print(\\\"Contact ID: \\\", c.id)\\n #print(\\\"Contact x, y position: \\\", c.x_pos, c.y_pos)\\n #print(\\\"Contact Total Force: \\\", c.total_force)\\n\\n # send osc\\n client.send_message(\\\"/this/is/a/different/channel\\\", [c.id, c.x_pos, c.y_pos, c.total_force])\\n client.send_message(\\\"/this/is/a/channel\\\", [c.id, c.x_pos, c.y_pos, c.total_force])\\n\\n if c.state == sensel.CONTACT_START:\\n sensel.setLEDBrightness(handle, c.id, 100)\\n elif c.state == sensel.CONTACT_END:\\n sensel.setLEDBrightness(handle, c.id, 0)\",\n \"def __str__(self):\\n\\n string = ''\\n string += \\\"Battery Voltage: \\\" + \\\"{0:.2f}\\\".format(self.getBatteryVoltage()) + '\\\\n'\\n string += \\\"Data Logging: \\\" + str(self.getDataLoggingStatus()) + '\\\\n'\\n string += \\\"Data Filename: \\\" + self.getFilename() +'\\\\n'\\n\\n string += \\\"Time Since Instrument Reset (s): \\\" + \\\"{0:.2f}\\\".format(self.getResetTime()) + '\\\\n'\\n string += \\\"Data Age (s): \\\" + \\\"{0:.2f}\\\".format(self.getDataAge()) + '\\\\n'\\n string += '\\\\n'\\n string += \\\"Interior Temperature 1 (F): \\\" + \\\"{0:.2f}\\\".format(self.getInteriorTemperature1()) + '\\\\n'\\n string += \\\"Interior Temperature 2 (F): \\\" + \\\"{0:.2f}\\\".format(self.getInteriorTemperature2()) + '\\\\n'\\n string += \\\"Interior Temperature 3 (F): \\\" + \\\"{0:.2f}\\\".format(self.getInteriorTemperature3()) + '\\\\n'\\n string += \\\"Exterior Temperature (F): \\\" + \\\"{0:.2f}\\\".format(self.getExteriorTemperature()) + '\\\\n'\\n string += \\\"Pressure (PSI): \\\" + \\\"{0:.2f}\\\".format(self.getPressure()) + '\\\\n'\\n string += \\\"Humidity (%): \\\" + \\\"{0:.2f}\\\".format(self.getHumidity()) + '\\\\n'\\n string += '\\\\n'\\n\\n string += \\\"GPS Time: \\\" + str(self.getGpsTime1()) + '\\\\n'\\n string += \\\"Latitude: \\\" + \\\"{0:.9f}\\\".format(self.getLatitude1()) + '\\\\n'\\n string += \\\"Longitude: \\\" + \\\"{0:.9f}\\\".format(self.getLongitude1()) + '\\\\n'\\n string += \\\"Altitude (ft): \\\" + \\\"{0:.2f}\\\".format(self.getAltitude1()) + '\\\\n'\\n string += \\\"Speed (MPH): \\\" + \\\"{0:.2f}\\\".format(self.getSpeed()) + '\\\\n'\\n string += '\\\\n'\\n\\n string += \\\"GPS Time: \\\" + str(self.getGpsTime2()) + '\\\\n'\\n string += \\\"Latitude: \\\" + \\\"{0:.9f}\\\".format(self.getLatitude2()) + '\\\\n'\\n string += \\\"Longitude: \\\" + \\\"{0:.9f}\\\".format(self.getLongitude2()) + '\\\\n'\\n string += \\\"Altitude (ft): \\\" + \\\"{0:.2f}\\\".format(self.getAltitude2()) + '\\\\n'\\n string += '\\\\n'\\n\\n ax, ay, az = self.getAcceleration()\\n string += \\\"Acceleration (x, y, z): \\\"\\n string += \\\"{0:.2f}\\\".format(ax) + \\\", \\\"\\n string += \\\"{0:.2f}\\\".format(ay) + \\\", \\\"\\n string += \\\"{0:.2f}\\\".format(az) + '\\\\n'\\n\\n gx, gy, gz = self.getRates()\\n string += \\\"Rates (x, y, z): \\\"\\n string += \\\"{0:.2f}\\\".format(gx) + \\\", \\\"\\n string += \\\"{0:.2f}\\\".format(gy) + \\\", \\\"\\n string += \\\"{0:.2f}\\\".format(gz) + '\\\\n'\\n\\n mx, my, mz = self.getMagneticReading()\\n string += \\\"Magnetic Field (x, y, z): \\\"\\n string += \\\"{0:.2f}\\\".format(mx) + \\\", \\\"\\n string += \\\"{0:.2f}\\\".format(my) + \\\", \\\"\\n string += \\\"{0:.2f}\\\".format(mz) + '\\\\n'\\n\\n roll, pitch, yaw = self.getAttitude()\\n string += \\\"Roll (deg): \\\" + \\\"{0:.2f}\\\".format(roll) + '\\\\n'\\n string += \\\"Pitch (deg): \\\" + \\\"{0:.2f}\\\".format(pitch) + '\\\\n'\\n string += \\\"Yaw (deg): \\\" + \\\"{0:.2f}\\\".format(yaw) + '\\\\n'\\n string += '\\\\n'\\n relayStates = self.getRelayStates()\\n \\n\\n string += \\\"Relay States: \\\" \\n string += (( \\\"ON \\\") if relayStates[0] else ( \\\"OFF \\\")) \\n string += (( \\\"ON \\\") if relayStates[1] else ( \\\"OFF \\\"))\\n string += (( \\\"ON \\\") if relayStates[2] else ( \\\"OFF \\\"))\\n string += (( \\\"ON \\\") if relayStates[3] else ( \\\"OFF \\\"))\\n string += '\\\\n'\\n\\n\\n return string\",\n \"def dump(self, prefix = \\\" - \\\"):\\n\\t\\tlines = [\\n\\t\\t\\t\\\"MAC: \\\" + \\\":\\\".join(\\n\\t\\t\\t\\t\\\"%02x\\\" % (ord(o), ) for o in self.mac),\\n\\t\\t\\t\\\"HW %d, SW %d\\\" %\\n\\t\\t\\t\\t(self.hw_rev, self.sw_rev),\\n\\t\\t\\t\\\"Capabilities: max %d points, %d kpps\\\" %\\n\\t\\t\\t\\t(self.buffer_capacity, self.max_point_rate)\\n\\t\\t]\\n\\t\\tfor l in lines:\\n\\t\\t\\tprint prefix + l\\n\\t\\tif debug == 1:\\n\\t\\t\\tself.status.dump(prefix)\",\n \"def hsdpa_physical_downlink_settings(self):\\r\\r\\n\\r\\r\\n config_list = []\\r\\r\\n\\r\\r\\n config_list.append (\\\"\\\")\\r\\r\\n\\r\\r\\n config_list.append ( \\\"%-24s %-18s\\\" % (\\\"Channel( Carrier 1)\\\", \\\"Level\\\"))\\r\\r\\n config_list.append ( \\\"%-24s %-18s\\\" % (\\\"==================\\\", \\\"=====\\\"))\\r\\r\\n\\r\\r\\n pcpich_level = -10.2\\r\\r\\n self.set_pcpich_code_level(carrier=1, leveldB=pcpich_level)\\r\\r\\n config_list.append ( \\\"%-24s %-18s\\\" % (\\\"P-CPICH\\\", pcpich_level))\\r\\r\\n\\r\\r\\n psch_level = -15.2\\r\\r\\n ssch_level = psch_level\\r\\r\\n pccpch_level = -12.2\\r\\r\\n self.write('CONFigure:WCDMa:SIGN:DL:LEVel:PSCH %s' %psch_level)\\r\\r\\n self.write('CONFigure:WCDMa:SIGN:DL:LEVel:SSCH %s' %ssch_level)\\r\\r\\n self.write('CONFigure:WCDMa:SIGN:DL:LEVel:PCCPch %s' %pccpch_level)\\r\\r\\n config_list.append ( \\\"%-24s %-18s\\\" % (\\\"P-SCH\\\", psch_level))\\r\\r\\n config_list.append ( \\\"%-24s %-18s\\\" % (\\\"S-SCH\\\", ssch_level))\\r\\r\\n config_list.append ( \\\"%-24s %-18s\\\" % (\\\"P-CCPCH\\\", pccpch_level))\\r\\r\\n\\r\\r\\n\\r\\r\\n # SCCPH power level and channelisation code\\r\\r\\n sccpch_level = -12.2\\r\\r\\n self.set_dl_chan_code_level(dl_chan='SCCPch', code=2, level_dB=sccpch_level)\\r\\r\\n config_list.append ( \\\"%-24s %-18s\\\" % (\\\"S-CCPCH\\\", sccpch_level))\\r\\r\\n\\r\\r\\n # PICH power level and channelisation code\\r\\r\\n pich_level = -15.2\\r\\r\\n self.set_dl_chan_code_level(dl_chan='PICH', code=2, level_dB=pich_level)\\r\\r\\n config_list.append ( \\\"%-24s %-18s\\\" % (\\\"PICH\\\", pich_level))\\r\\r\\n\\r\\r\\n # AICH power level and channelisation code\\r\\r\\n aich_level = -15.2\\r\\r\\n self.set_dl_chan_code_level(dl_chan='AICH', code=3, level_dB=aich_level)\\r\\r\\n config_list.append ( \\\"%-24s %-18s\\\" % (\\\"AICH\\\", aich_level))\\r\\r\\n\\r\\r\\n # DPCH power and channelisation code\\r\\r\\n dpch_level = -18.2\\r\\r\\n self.set_dl_chan_code_level(dl_chan='DPCH', code=3, level_dB=dpch_level)\\r\\r\\n config_list.append ( \\\"%-24s %-18s\\\" % (\\\"DPCH\\\", dpch_level))\\r\\r\\n\\r\\r\\n # F-DPCH power and channelisation ocde\\r\\r\\n fdpch_level = -18.2\\r\\r\\n self.set_dl_chan_code_level(dl_chan='FDPCh', code=6, level_dB=fdpch_level)\\r\\r\\n config_list.append ( \\\"%-24s %-18s\\\" % (\\\"F-DPCH\\\", fdpch_level))\\r\\r\\n\\r\\r\\n # DPCH enhanced settings\\r\\r\\n self.configure_enhanced_dl_dpch()\\r\\r\\n\\r\\r\\n\\r\\r\\n # *****************************************************************************\\r\\r\\n # Configure 2 HS-SCCH: level, channelization code, UE ID and dummy UE ID\\r\\r\\n # *****************************************************************************\\r\\r\\n hssch_level_1 = -20.2\\r\\r\\n hssch_level_2 = -20.2\\r\\r\\n self.set_hssch_level(hssch_num=1, carrier=1, leveldB=hssch_level_1)\\r\\r\\n self.set_hssch_level(hssch_num=2, carrier=1, leveldB=hssch_level_2)\\r\\r\\n self.set_hssch_code(hssch_num=1, carrier=1, codeNum=2)\\r\\r\\n self.set_hssch_code(hssch_num=2, carrier=1, codeNum=7)\\r\\r\\n config_list.append ( \\\"%-24s %-18s\\\" % (\\\"HS-SCCH #1\\\", hssch_level_1))\\r\\r\\n config_list.append ( \\\"%-24s %-18s\\\" % (\\\"HS-SCCH #2\\\", hssch_level_2))\\r\\r\\n\\r\\r\\n self.set_default_ue_id_hssch(carrier=1)\\r\\r\\n\\r\\r\\n # HS-PDSCH Enhanced Settings\\r\\r\\n self.set_hsdsch_mpo(carrier=1, control=\\\"AUTO\\\", pwrOffsetManual=\\\"\\\")\\r\\r\\n # unscheduled frame type for HSDPA\\r\\r\\n # possible types are 'DUMMy', 'DTX'\\r\\r\\n self.hsdsch_unsched_frames(carrier=1, usFrameType='DUMMY')\\r\\r\\n\\r\\r\\n # *****************************************************************************\\r\\r\\n # Configure HS-PDSCH: level and first channelization code number\\r\\r\\n # *****************************************************************************\\r\\r\\n\\r\\r\\n hsdsch_level = -1.2\\r\\r\\n self.set_hsdsch_level(carrier=1, leveldB = hsdsch_level)\\r\\r\\n self.set_hsdsch_chanelisation_code(code=1, carrier=1)\\r\\r\\n config_list.append ( \\\"%-24s %-18s\\\" % (\\\"HS-PDSCH\\\", hsdsch_level))\\r\\r\\n\\r\\r\\n\\r\\r\\n # // *****************************************************************************\\r\\r\\n # Set level and channelization code of E-AGCH, E-HICH and E-RGCH.\\r\\r\\n # *****************************************************************************\\r\\r\\n eagch_level = -20.2\\r\\r\\n ehich_level = -20.2\\r\\r\\n ergch_level = -20.2\\r\\r\\n self.set_dl_chan_code_level(dl_chan='EAGCh', code=3, level_dB=eagch_level)\\r\\r\\n self.set_dl_chan_code_level(dl_chan='EHICh', code=6, level_dB=ehich_level)\\r\\r\\n self.set_dl_chan_code_level(dl_chan='ERGCh', code=6, level_dB=ergch_level)\\r\\r\\n config_list.append ( \\\"%-24s %-18s\\\" % (\\\"E-AGCH\\\", eagch_level))\\r\\r\\n config_list.append ( \\\"%-24s %-18s\\\" % (\\\"E-HICH\\\", ehich_level))\\r\\r\\n config_list.append ( \\\"%-24s %-18s\\\" % (\\\"E-RGCH\\\", ergch_level))\\r\\r\\n\\r\\r\\n config_list.append (\\\"\\\")\\r\\r\\n\\r\\r\\n for line in config_list:\\r\\r\\n print line\\r\\r\\n\\r\\r\\n if self.dc_hsdpa:\\r\\r\\n\\r\\r\\n self.hsdpa_physical_downlink_settings_carrier2()\",\n \"def SERIAL_RTRV_record(self):\\n # Assumes records are separated by a '>' prompt.\\n # Must be connected & operational\\n if self.State == 0:\\n # a slightly more informative result might help\\n return []\\n # max seconds to wait for data\\n max_wait = 3\\n # seconds to wait before trying again\\n try_wait = 0.1\\n tries = max_wait / try_wait\\n # how much we have waited so far\\n waited = 0\\n # RECV\\n raw_record = []\\n # raw_record is a list of non-empty strings, \\n # each string is a line of info from the reader\\n word = ''\\n linebuf = []\\n while len(raw_record) < 1 :\\n # we need to have something to reply.. \\n #print \\\"chars waiting:\\\", self.Port.inWaiting()\\n #sys.stdout.flush()\\n while self.Port.inWaiting() > 0:\\n while 1:\\n # read 1 char at a time \\n # until we get to the '>' prompt\\n # \\n c = self.Port.read(1)\\n # \\n if self.RecordTrace == 1:\\n self.tf_out.write(c)\\n # \\n # we are done once we see the prompt\\n if c == '>':\\n if self.debug > 2 :\\n print \\\"Raw Record: \\\",\\n pprint.pprint(raw_record)\\n return raw_record\\n # \\\\r = CR , \\\\n = LF \\n # (serial device uses CR + optionally LF, unix text only uses LF)\\n # new array entry but only if there is something to add\\n elif c == '\\\\r' or c == '\\\\n':\\n if word != '':\\n linebuf.append(word)\\n word = ''\\n if linebuf != []:\\n raw_record.append(linebuf)\\n linebuf = []\\n # split line into words\\n elif c == ' ':\\n if word != '':\\n linebuf.append(word)\\n word = ''\\n # all other chars\\n else : \\n word = word + c\\n \\n # wait a bit for the serial line to respond\\n if self.debug > 1 :\\n print \\\"NO DATA TO READ!!\\\"\\n if waited < max_wait :\\n waited += try_wait\\n time.sleep(try_wait)\\n else:\\n self.recwaiting = 0\\n return []\",\n \"def print(self):\\r\\n self.print_avec_separateur()\",\n \"def serdes_dump_state(fpgaid,verbose=1):\\n lines=[]\\n\\n lines.append(\\\"Group : AEQ\\\")\\n lines.append(\\\" Logic Channel : %d\\\" % reconfig_AEQ_get_logical_channel_address(fpgaid,verbose))\\n lines.append(\\\" adapt_done : %d\\\" % reconfig_AEQ_get_adapt_done(fpgaid,verbose))\\n lines.append(\\\" equalization_results : %d\\\" % reconfig_AEQ_get_equalization_results(fpgaid,verbose))\\n lines.append(\\\" mode : %d\\\" % reconfig_AEQ_get_mode(fpgaid,verbose))\\n lines.append(\\\"Group : DFE\\\")\\n lines.append(\\\" Logic Channel : %d\\\" % reconfig_DFE_get_logical_channel_address(fpgaid,verbose))\\n lines.append(\\\" adaptation_engine_enable : %d\\\" % reconfig_DFE_get_adaptation_engine_enable(fpgaid,verbose))\\n lines.append(\\\" power_on : %d\\\" % reconfig_DFE_get_power_on(fpgaid,verbose))\\n lines.append(\\\" tap_1 : %d\\\" % reconfig_DFE_get_tap_1(fpgaid,verbose))\\n lines.append(\\\" tap_2 : %d\\\" % reconfig_DFE_get_tap_2(fpgaid,verbose))\\n lines.append(\\\" tap_2_polarity : %d\\\" % reconfig_DFE_get_tap_2_polarity(fpgaid,verbose))\\n lines.append(\\\" tap_3 : %d\\\" % reconfig_DFE_get_tap_3(fpgaid,verbose))\\n lines.append(\\\" tap_3_polarity : %d\\\" % reconfig_DFE_get_tap_3_polarity(fpgaid,verbose))\\n lines.append(\\\" tap_4 : %d\\\" % reconfig_DFE_get_tap_4(fpgaid,verbose))\\n lines.append(\\\" tap_4_polarity : %d\\\" % reconfig_DFE_get_tap_4_polarity(fpgaid,verbose))\\n lines.append(\\\" tap_5 : %d\\\" % reconfig_DFE_get_tap_5(fpgaid,verbose))\\n lines.append(\\\" tap_5_polarity : %d\\\" % reconfig_DFE_get_tap_5_polarity(fpgaid,verbose))\\n lines.append(\\\"Group : EyeQ\\\")\\n lines.append(\\\" Logic Channel : %d\\\" % reconfig_EyeQ_get_logical_channel_address(fpgaid,verbose))\\n lines.append(\\\" 1D_Eye : %d\\\" % reconfig_EyeQ_get_1D_Eye(fpgaid,verbose))\\n lines.append(\\\" BERB_Enable : %d\\\" % reconfig_EyeQ_get_BERB_Enable(fpgaid,verbose))\\n lines.append(\\\" BERB_Snap_Shot_and_Reset : %d\\\" % reconfig_EyeQ_get_BERB_Snap_Shot_and_Reset(fpgaid,verbose))\\n lines.append(\\\" Bit_Counter31_0 : %d\\\" % reconfig_EyeQ_get_Bit_Counter31_0(fpgaid,verbose))\\n lines.append(\\\" Bit_Counter63_32 : %d\\\" % reconfig_EyeQ_get_Bit_Counter63_32(fpgaid,verbose))\\n lines.append(\\\" Counter_Enable : %d\\\" % reconfig_EyeQ_get_Counter_Enable(fpgaid,verbose))\\n lines.append(\\\" Enable_Eye_Monitor : %d\\\" % reconfig_EyeQ_get_Enable_Eye_Monitor(fpgaid,verbose))\\n lines.append(\\\" Err_Conter63_32 : %d\\\" % reconfig_EyeQ_get_Err_Conter63_32(fpgaid,verbose))\\n lines.append(\\\" Err_Counter31_0 : %d\\\" % reconfig_EyeQ_get_Err_Counter31_0(fpgaid,verbose))\\n lines.append(\\\" Horizontal_phase : %d\\\" % reconfig_EyeQ_get_Horizontal_phase(fpgaid,verbose))\\n lines.append(\\\" Polarity : %d\\\" % reconfig_EyeQ_get_Polarity(fpgaid,verbose))\\n lines.append(\\\" Vertical_height : %d\\\" % reconfig_EyeQ_get_Vertical_height(fpgaid,verbose))\\n lines.append(\\\"Group : PMA\\\")\\n lines.append(\\\" Logic Channel : %d\\\" % reconfig_PMA_get_logical_channel_address(fpgaid,verbose))\\n lines.append(\\\" Pre_emphasis_first_post_tap : %d\\\" % reconfig_PMA_get_Pre_emphasis_first_post_tap(fpgaid,verbose))\\n lines.append(\\\" Pre_emphasis_pre_tap : %d\\\" % tapformat(reconfig_PMA_get_Pre_emphasis_pre_tap(fpgaid,verbose)))\\n lines.append(\\\" Pre_emphasis_second_post_tap : %d\\\" % tapformat(reconfig_PMA_get_Pre_emphasis_second_post_tap(fpgaid,verbose)))\\n lines.append(\\\" RX_equalization_control : %d\\\" % reconfig_PMA_get_RX_equalization_control(fpgaid,verbose))\\n lines.append(\\\" RX_equalization_DC_gain : %d\\\" % reconfig_PMA_get_RX_equalization_DC_gain(fpgaid,verbose))\\n lines.append(\\\" VOD : %d\\\" % reconfig_PMA_get_VOD(fpgaid,verbose))\\n print \\\"\\\\n\\\".join(lines)\\n return lines\",\n \"def formatHeaderLine(cols, colLabs, indexCols, storeLanes):\\n l = []\\n if storeLanes is True:\\n l.append(\\\"Lane\\\")\\n if colLabs[1] is not None:\\n l.append(\\\"Sample_ID\\\")\\n if colLabs[2] is not None:\\n l.append(\\\"Sample_Name\\\")\\n if indexCols[0] is not None and len(cols[indexCols[0]]) > 0:\\n l.append(\\\"index\\\")\\n if indexCols[1] is not None and len(cols[indexCols[1]]) > 0:\\n l.append(\\\"index2\\\")\\n if colLabs[3] is not None:\\n l.append(\\\"Sample_Project\\\")\\n return \\\",\\\".join(l)\",\n \"def _gen_report(self):\\n print \\\"------------------------------------------\\\"\\n print \\\"fio report\\\"\\n print \\\"------------------------------------------\\\"\\n print \\\"name\\\", \\\" \\\".join(f for f in FIELDS)\\n # print fields\\n for name in sorted(self.reports):\\n report = self.reports[name]\\n #print report\\n print name, \\\" \\\".join(str(report.get(f)) for f in FIELDS)\\n\\n print \\\"*******************************************\\\"\\n # print clats\\n index = 0\\n for name in sorted(self.reports):\\n report = self.reports[name]\\n if index == 0:\\n print \\\"clat_percent\\\", \\\" \\\".join(\\n str(c[0]) for c in report[\\\"clats\\\"])\\n print name, \\\" \\\".join(str(c[1]) for c in report[\\\"clats\\\"])\\n index += 1\",\n \"def dump(self, packet):\\n #self.print_table()\\n src = packet[\\\"dst\\\"]\\n dst = packet[\\\"src\\\"]\\n routes_dump = []\\n for route in self.routes:\\n for verat in route[\\\"varats\\\"]:\\n routes_dump.append({\\\"network\\\": verat[\\\"network\\\"], \\\"netmask\\\": verat[\\\"netmask\\\"],\\n \\\"peer\\\": route[\\\"peer\\\"]})\\n \\n a = {\\\"src\\\": src, \\\"dst\\\": dst, \\\"type\\\": \\\"table\\\", \\\"msg\\\": routes_dump}\\n return a\",\n \"def to_html_table(self):\\n td = '<td>'\\n nwtd = '<td nowrap=\\\"true\\\">'\\n ftd = '<td class=\\\"format\\\">'\\n ctd = '<td class=\\\"cen\\\">'\\n etd = '</td>'\\n \\n if self.is_power_onoff():\\n out = td + 'Power On/Off' + etd\\n else:\\n out = nwtd + '<strong>' + self['target'].ljust(20) + '</strong>' + etd\\n\\n if 'Date' in self:\\n out += ctd + self['Date'] + etd\\n else:\\n out += td + etd\\n\\n if 'UTstart' in self:\\n out += ctd + self['UTstart'] + etd\\n else:\\n out += td + etd\\n\\n if 'UTend' in self:\\n out += ctd + self['UTend'] + etd\\n else:\\n out += td + etd\\n\\n if 'exposure' in self:\\n out += ctd + self['exposure'] + etd\\n else:\\n out += td + etd\\n\\n if 'sample' in self:\\n out += ctd + self['sample'] + etd\\n else:\\n out += td + etd\\n\\n if 'nframe' in self:\\n out += ctd + self['nframe'] + etd\\n else:\\n out += td + etd\\n \\n if self.is_power_onoff():\\n out += (td + etd)*3\\n else:\\n speed = self['speed']\\n out += ctd + self['filters'].ljust(11) + etd + ctd + self['x_bin'] + 'x' + self['y_bin'] + etd + ctd + speed + etd \\n \\n if self.number_windows() > 0:\\n out += ctd + self['x1_size'].rjust(4) + 'x' + self['y1_size'].ljust(4) + etd + td + self['x1_start'].ljust(3) + etd + td + self['y1_start'].ljust(4) + etd\\n else:\\n out += (td + etd)*3\\n \\n if self.number_windows() > 1:\\n out += ctd + self['x2_size'].rjust(4) + 'x' + self['y2_size'].ljust(4) + etd + td + self['x2_start'].ljust(3) + etd + td + self['y2_start'].ljust(4) + etd\\n else:\\n out += (td + etd)*3\\n\\n if 'grating' in self:\\n out += ctd + self['grating'] + etd\\n else:\\n out += td + etd\\n\\n if 'slit_width' in self:\\n out += ctd + self['slit_width'] + etd\\n else:\\n out += td + etd\\n\\n if 'slit_angle' in self:\\n out += ctd + self['slit_angle'] + etd\\n else:\\n out += td + etd\\n \\n if 'ID' in self:\\n out += ctd + self['ID'] + etd\\n else:\\n out += td + etd\\n\\n if 'PI' in self:\\n out += ctd + self['PI'] + etd\\n else:\\n out += td + etd\\n \\n if 'Comment' in self:\\n out += nwtd + self['Comment'] + etd\\n else:\\n out += td + etd\\n\\n return out\",\n \"def writeOut(self):\\n # import time\\n self.outHeader = self.srcHeader\\n for line in self.outHeader:\\n self.outFile.write(line + '\\\\n')\\n # now = time.asctime(time.localtime(time.time()))\\n # self.outFile.write('%% -- %s -- Written to new alog' % now)\\n for time_s in sorted(self.outData):\\n for sens in self.outData[time_s]:\\n for meas in self.outData[time_s][sens]:\\n valu = self.outData[time_s][sens][meas]\\n msg_list = [str(time_s), meas, sens, str(valu)]\\n line_string = reconstructLine(msg_list)\\n self.outFile.write(line_string + '\\\\n')\",\n \"def output(self):\\n time = \\\"%04d-%02d-%02d %02d:%02d:%02d\\\" % (self.year, self.month, \\\\\\n self.date, self.hours, self.minutes, self.seconds)\\n return {\\n \\\"id\\\": self.id,\\n \\\"name\\\": self.name,\\n \\\"type\\\": 4,\\n \\\"enabled\\\": 1,\\n \\\"abstime\\\": time\\n }\",\n \"def write_pc_cards(bc_file, bc_class):\\n bc_file.write('! Output Control\\\\n')\\n oc = bc_class.output_control\\n objects = list(oc.param.output_control_option.get_range())\\n if oc.output_control_option == objects[0]:\\n bc_file.write('OC {}\\\\n'.format(oc.oc_time_series_id))\\n ofs = oc.output_flow_strings\\n if not ofs.empty:\\n bc_file.write(ofs.to_csv(sep=' ', na_rep='', index=False, header=False,).replace('\\\\r\\\\n', '\\\\n'))\\n\\n if oc.print_adaptive_mesh:\\n bc_file.write('PC ADP\\\\n')\\n if oc.print_numerical_fish_surrogate:\\n bc_file.write('PC ELM\\\\n')\\n if oc.screen_output_residual:\\n bc_file.write('SOUT RESID\\\\n')\\n if oc.screen_output_all:\\n bc_file.write('SOUT ALL\\\\n')\\n if oc.screen_output_mass_error:\\n bc_file.write('SOUT MERROR\\\\n')\\n if oc.screen_output_worst_nonlinear_node:\\n bc_file.write('SOUT NLNODE\\\\n')\\n if oc.screen_output_worst_linear_node:\\n bc_file.write('SOUT LNODE\\\\n')\\n if oc.file_output_wind:\\n bc_file.write('FOUT WIND\\\\n')\\n if oc.file_output_wave:\\n bc_file.write('FOUT WAVE\\\\n')\\n if oc.file_output_adapted_grid:\\n bc_file.write('FOUT ADAPT GRID\\\\n')\\n if oc.file_output_adapted_solution:\\n bc_file.write('FOUT ADAPT SW\\\\n')\\n if oc.file_output_adapted_transport:\\n bc_file.write('FOUT ADAPT CON\\\\n')\\n if oc.file_output_sediment:\\n bc_file.write('FOUT SED\\\\n')\\n\\n bc_file.write('\\\\n') # blank line after Output Control\",\n \"def to_network_layer(packet):\\r\\n print(f\\\"[to_network_layer] packet:{packet}\\\")\",\n \"def __str__(self):\\n return '<TuebingenMEG: %i samples, %i timepoints, %i channels>' \\\\\\n % (self.nsamples, self.ntimepoints, len(self.channelids))\",\n \"def _write_coft(parameters):\\n # Format\\n fmt = block_to_format[\\\"COFT\\\"]\\n fmt = str2format(fmt[5])\\n\\n values = [x for x in parameters[\\\"connection_history\\\"]]\\n out = write_record(values, fmt, multi=True)\\n\\n return out\",\n \"def show_history(self, ts=0, count=0, fmt='raw'):\\n records = self.station.get_records(since_ts=ts, num_rec=count)\\n for i,r in enumerate(records):\\n if fmt.lower() == 'raw':\\n raw_dump(r['datetime'], r['ptr'], r['raw_data'])\\n elif fmt.lower() == 'table':\\n table_dump(r['datetime'], r['data'], i==0)\\n else:\\n print(r['datetime'], r['data'])\",\n \"def __str__ (self):\\n\\n return '{:d}: {:s}:{:d} [{:s}]; {:x}, {:.02f}'.format (self.index,\\n self.ip, self.port, self.mac, self.flags, self.timeout)\",\n \"def print_info(self):\\n outstr = '================================================= Ambient Noise Cross-correlation Database =================================================\\\\n'\\n outstr += self.__str__()+'\\\\n'\\n outstr += '--------------------------------------------------------------------------------------------------------------------------------------------\\\\n'\\n if 'NoiseXcorr' in self.auxiliary_data.list():\\n outstr += 'NoiseXcorr - Cross-correlation seismogram\\\\n'\\n if 'StaInfo' in self.auxiliary_data.list():\\n outstr += 'StaInfo - Auxiliary station information\\\\n'\\n if 'DISPbasic1' in self.auxiliary_data.list():\\n outstr += 'DISPbasic1 - Basic dispersion curve, no jump correction\\\\n'\\n if 'DISPbasic2' in self.auxiliary_data.list():\\n outstr += 'DISPbasic2 - Basic dispersion curve, with jump correction\\\\n'\\n if 'DISPpmf1' in self.auxiliary_data.list():\\n outstr += 'DISPpmf1 - PMF dispersion curve, no jump correction\\\\n'\\n if 'DISPpmf2' in self.auxiliary_data.list():\\n outstr += 'DISPpmf2 - PMF dispersion curve, with jump correction\\\\n'\\n if 'DISPbasic1interp' in self.auxiliary_data.list():\\n outstr += 'DISPbasic1interp - Interpolated DISPbasic1\\\\n'\\n if 'DISPbasic2interp' in self.auxiliary_data.list():\\n outstr += 'DISPbasic2interp - Interpolated DISPbasic2\\\\n'\\n if 'DISPpmf1interp' in self.auxiliary_data.list():\\n outstr += 'DISPpmf1interp - Interpolated DISPpmf1\\\\n'\\n if 'DISPpmf2interp' in self.auxiliary_data.list():\\n outstr += 'DISPpmf2interp - Interpolated DISPpmf2\\\\n'\\n if 'FieldDISPbasic1interp' in self.auxiliary_data.list():\\n outstr += 'FieldDISPbasic1interp - Field data of DISPbasic1\\\\n'\\n if 'FieldDISPbasic2interp' in self.auxiliary_data.list():\\n outstr += 'FieldDISPbasic2interp - Field data of DISPbasic2\\\\n'\\n if 'FieldDISPpmf1interp' in self.auxiliary_data.list():\\n outstr += 'FieldDISPpmf1interp - Field data of DISPpmf1\\\\n'\\n if 'FieldDISPpmf2interp' in self.auxiliary_data.list():\\n outstr += 'FieldDISPpmf2interp - Field data of DISPpmf2\\\\n'\\n outstr += '============================================================================================================================================\\\\n'\\n print outstr\\n return\",\n \"def __str__(self):\\n table_data = [\\n ['', 'C', 'G', 'A', 'T'],\\n ['total', str(self.total['C']), str(self.total['G']), str(self.total['A']), str(self.total['T'])],\\n ['reverse half strand', str(self.reverse['C']), str(self.reverse['G']), str(self.reverse['A']),\\n str(self.reverse['T'])],\\n ['forward half strand', str(self.forward['C']), str(self.forward['G']), str(self.forward['A']),\\n str(self.forward['T'])]\\n ]\\n table = AsciiTable(table_data)\\n return \\\"Generation #{}\\\\n\\\".format(self.epoch) + table.table\",\n \"def writeline(self):\\n if self.record == 'ATOM' and not self.atom.startswith('H') and len(self.atom) < 4:\\n atom = ' {0}'.format(self.atom)\\n else:\\n atom = self.atom\\n aline = (\\n \\\"{0:6s} {1:5d} {2:5s} {3} {4}{5:5d} {6:10.5f}{7:10.5f}\\\"\\n \\\"{8:10.5f} {9:5s}{10:3d}{11:2d} {12:8.5f} {13} 0\\\\n\\\"\\n ).format(\\n self.record, self.natom, atom, self.res, self.chain, self.nres,\\n self.x, self.y, self.z, self.fftype, self.nbond, self.nlonepair,\\n self.charge, self.fixed\\n )\\n cline = \\\"CONECT{0:6d}{1}\\\\n\\\".format(\\n self.natom,\\n ''.join('{0:6d}'.format(num) for num in self.connections)\\n )\\n return aline, cline\",\n \"def repr_fields(self):\\n pa = set_blank_if_default(self.pa, 0)\\n pb = set_blank_if_default(self.pb, 0)\\n\\n w1a = set_blank_if_default(self.wa[0], 0.0)\\n w2a = set_blank_if_default(self.wa[1], 0.0)\\n w3a = set_blank_if_default(self.wa[2], 0.0)\\n\\n w1b = set_blank_if_default(self.wb[0], 0.0)\\n w2b = set_blank_if_default(self.wb[1], 0.0)\\n w3b = set_blank_if_default(self.wb[2], 0.0)\\n x1, x2, x3 = self.get_x_g0_defaults()\\n\\n # offt doesn't exist in NX nastran\\n offt = set_blank_if_default(self.offt, 'GGG')\\n\\n list_fields = ['CBAR', self.eid, self.Pid(), self.Ga(), self.Gb(), x1, x2,\\n x3, offt, pa, pb, w1a, w2a, w3a, w1b, w2b, w3b]\\n return list_fields\",\n \"def output(self):\\n # store tables\\n # protocol PK keys table for measurements under a protocol\\n # 'line' keys table for line-only section (if enabled)\\n # 'all' keys table including everything.\\n tables = OrderedDict()\\n if self.options.line_section:\\n tables[\\\"line\\\"] = OrderedDict()\\n tables[\\\"line\\\"][\\\"header\\\"] = self._output_line_header()\\n if not self.options.protocol_section:\\n tables[\\\"all\\\"] = OrderedDict()\\n tables[\\\"all\\\"][\\\"header\\\"] = self._output_header()\\n self._do_export(tables)\\n return self._build_output(tables)\",\n \"def info(self):\\n self.update_info()\\n print('Number of electrodes: ' + str(self.n_elecs))\\n print('Recording time in seconds: ' + str(self.dur))\\n print('Sample Rate in Hz: '+ str(self.sample_rate))\\n print('Number of sessions: ' + str(self.n_sessions))\\n print('Date created: ' + str(self.date_created))\\n print('Meta data: ' + str(self.meta))\",\n \"def printinterfacestats(self, outformat):\\n\\n interfacedata = self.interfacetable\\n hostname = self._hostname\\n\\n ignoreoids = [22, 21, 18, 12]\\n\\n ifmib = {\\n 1:\\\"ifIndex\\\",\\n 2:\\\"ifDescr\\\",\\n 3:\\\"ifType\\\",\\n 4:\\\"ifMtu\\\",\\n 5:\\\"ifSpeed\\\",\\n 6:\\\"ifPhysAddress\\\",\\n 7:\\\"ifAdminStatus\\\",\\n 8:\\\"ifOperStatus\\\",\\n 9:\\\"ifLastChange\\\",\\n 10:\\\"ifInOctets\\\",\\n 11:\\\"ifUcastPkts\\\",\\n 12:\\\"ifInNUcastPkts\\\",\\n 13:\\\"ifInDiscards\\\",\\n 14:\\\"ifInErrors\\\",\\n 15:\\\"ifInUnknownProtos\\\",\\n 16:\\\"ifOutOctets\\\",\\n 17:\\\"ifOutUcastPkts\\\",\\n 18:\\\"ifOutNUcastPkts\\\",\\n 19:\\\"ifOutDiscards\\\",\\n 20:\\\"ifOutErrors\\\",\\n 21:\\\"ifOutQLen\\\",\\n 22:\\\"ifSpecific\\\"\\n }\\n\\n # oid value of 8 or 7\\n ifstatus = {\\n 1: \\\"up\\\",\\n 2: \\\"down\\\",\\n 3: \\\"testing\\\",\\n 4: \\\"unknown\\\",\\n 5: \\\"dormant\\\",\\n 6: \\\"notPresent\\\",\\n 7: \\\"lowerLayerDown\\\"\\n }\\n\\n # Print CSV Formated Data\\n if outformat == 'csv':\\n headerrow = []\\n headerrow.append(\\\"hostname\\\")\\n\\n for ifid in sorted(ifmib):\\n if ifid not in ignoreoids:\\n headerrow.append(ifmib[ifid])\\n\\n print \\\",\\\".join(headerrow)\\n\\n for ifindex in sorted(interfacedata):\\n currentrow = []\\n currentrow.append(hostname)\\n\\n for oid in sorted(interfacedata[ifindex]):\\n for skip in ignoreoids:\\n if oid == skip:\\n exclude = True\\n break\\n else:\\n exclude = False\\n if exclude != True:\\n if oid == 8 or oid == 7:\\n currentrow.append(ifstatus[int(interfacedata[ifindex][oid])])\\n else:\\n currentrow.append(interfacedata[ifindex][oid])\\n print \\\",\\\".join(currentrow)\\n\\n elif outformat == 'table':\\n headerrow = []\\n headerrow.append(\\\"hostname\\\")\\n\\n for ifid in sorted(ifmib):\\n if ifid not in ignoreoids:\\n headerrow.append(ifmib[ifid])\\n\\n thetable = prettytable.PrettyTable(headerrow)\\n\\n for ifindex in sorted(interfacedata):\\n currentrow = [hostname]\\n for oid in sorted(interfacedata[ifindex]):\\n if oid not in ignoreoids:\\n if oid == 8 or oid == 7:\\n currentrow.append(ifstatus[int(interfacedata[ifindex][oid])])\\n else:\\n currentrow.append(interfacedata[ifindex][oid])\\n thetable.add_row(currentrow)\\n print thetable\\n\\n elif outformat == 'json':\\n\\n hostinterfaces = {}\\n #hostinterfaces[self._hostname] = {}\\n\\n for ifindex in sorted(interfacedata):\\n #print dict(interfacedata[ifindex].dictrepr().items())\\n hostinterfaces[ifindex] = dict(interfacedata[ifindex].dictrepr().items())\\n\\n #print hostinterfaces\\n print json.dumps(hostinterfaces, sort_keys=True, indent=4, \\\\\\n separators=(',', ': '))\\n\\n else:\\n # Print Detailed textual list of interface information\\n for ifindex in sorted(interfacedata):\\n print \\\"\\\\nInterface Number\\\", ifindex\\n for oid in sorted(interfacedata[ifindex]):\\n if oid not in ignoreoids:\\n print \\\"{0} ({1}) =\\\".format(ifmib[oid], oid),\\n\\n if oid == 8 or oid == 7:\\n print ifstatus[int(interfacedata[ifindex][oid])]\\n # Need to fid display of MAC Addresses\\n #elif oid == 6:\\n # print \\\"{0}\\\".format(interfacedata[ifindex][oid].hexdigits)\\n else:\\n print interfacedata[ifindex][oid]\\n print \\\"\\\\n\\\",\",\n \"def print_header(self,opts):\\n if COMPANY_LOGO in opts.keys():\\n self.imgesprint(opts[COMPANY_LOGO])\\n self.esprint(LINE_BREAK)\\n\\n if COMPANY_NAME in opts.keys():\\n self.esprint(opts[COMPANY_NAME])\\n self.esprint(LINE_BREAK)\\n\\n if COMPANY_CONTACT in opts.keys():\\n self.esprint(TEL)\\n self.esprint(opts[COMPANY_CONTACT])\\n self.esprint(LINE_BREAK)\\n\\n if COMPANY_LOCATION in opts.keys():\\n self.esprint(LOC)\\n self.esprint(opts[COMPANY_LOCATION])\\n self.esprint(LINE_BREAK)\\n\\n self.n_char(SN_RULER,PAPER_MAX_WIDTH,w=1,h=1)\\n self.esprint(LINE_BREAK)\\n\\n if RECEIPT_NAME in opts.keys():\\n RNO = opts[RECEIPT_NUMBER] if RECEIPT_NUMBER in opts.keys() else ''\\n RECEIPT = opts[RECEIPT_NAME]+SPACE_CHAR+HASH_NO+RNO\\n self.cesprint(RECEIPT)\\n char = len(RECEIPT)\\n self.cesprint(self.n_char_generate(DO_RULER,char),w=1,h=2)\\n self.esprint(LINE_BREAK)\\n\\n if CUSTOMER_NAME in opts.keys():\\n self.lesprint(CUSTOMER,LEFT_PROPERTY_WIDTH)\\n self.esprint(COLON_CHAR)\\n self.n_char(SPACE_CHAR,LR_ALIGN)\\n self.esprint(opts[CUSTOMER_NAME])\\n self.esprint(LINE_BREAK)\\n\\n if CASHIER_NAME in opts.keys():\\n self.lesprint(CASHIER,LEFT_PROPERTY_WIDTH)\\n self.esprint(COLON_CHAR)\\n self.n_char(SPACE_CHAR,LR_ALIGN)\\n self.esprint(opts[CASHIER_NAME])\\n self.esprint(LINE_BREAK)\\n\\n if RECEIPT_DATE in opts.keys():\\n self.lesprint(DATE,LEFT_PROPERTY_WIDTH)\\n self.esprint(COLON_CHAR)\\n self.n_char(SPACE_CHAR,LR_ALIGN)\\n self.esprint(opts[RECEIPT_DATE])\\n self.esprint(LINE_BREAK)\\n\\n if CURRENCY_NAME in opts.keys():\\n self.lesprint(CURRENCY_NAME_TITLE,LEFT_PROPERTY_WIDTH)\\n self.esprint(COLON_CHAR)\\n self.n_char(SPACE_CHAR,LR_ALIGN)\\n self.esprint(opts[CURRENCY_NAME])\\n self.esprint(LINE_BREAK)\",\n \"def show():\\n configdb = ConfigDBConnector()\\n configdb.connect()\\n queue_info = configdb.get_entry('FLEX_COUNTER_TABLE', 'QUEUE')\\n port_info = configdb.get_entry('FLEX_COUNTER_TABLE', 'PORT')\\n port_drop_info = configdb.get_entry('FLEX_COUNTER_TABLE', PORT_BUFFER_DROP)\\n rif_info = configdb.get_entry('FLEX_COUNTER_TABLE', 'RIF')\\n queue_wm_info = configdb.get_entry('FLEX_COUNTER_TABLE', 'QUEUE_WATERMARK')\\n pg_wm_info = configdb.get_entry('FLEX_COUNTER_TABLE', 'PG_WATERMARK')\\n pg_drop_info = configdb.get_entry('FLEX_COUNTER_TABLE', PG_DROP)\\n buffer_pool_wm_info = configdb.get_entry('FLEX_COUNTER_TABLE', BUFFER_POOL_WATERMARK)\\n acl_info = configdb.get_entry('FLEX_COUNTER_TABLE', ACL)\\n tunnel_info = configdb.get_entry('FLEX_COUNTER_TABLE', 'TUNNEL')\\n trap_info = configdb.get_entry('FLEX_COUNTER_TABLE', 'FLOW_CNT_TRAP')\\n route_info = configdb.get_entry('FLEX_COUNTER_TABLE', 'FLOW_CNT_ROUTE')\\n\\n header = (\\\"Type\\\", \\\"Interval (in ms)\\\", \\\"Status\\\")\\n data = []\\n if queue_info:\\n data.append([\\\"QUEUE_STAT\\\", queue_info.get(\\\"POLL_INTERVAL\\\", DEFLT_10_SEC), queue_info.get(\\\"FLEX_COUNTER_STATUS\\\", DISABLE)])\\n if port_info:\\n data.append([\\\"PORT_STAT\\\", port_info.get(\\\"POLL_INTERVAL\\\", DEFLT_1_SEC), port_info.get(\\\"FLEX_COUNTER_STATUS\\\", DISABLE)])\\n if port_drop_info:\\n data.append([PORT_BUFFER_DROP, port_drop_info.get(\\\"POLL_INTERVAL\\\", DEFLT_60_SEC), port_drop_info.get(\\\"FLEX_COUNTER_STATUS\\\", DISABLE)])\\n if rif_info:\\n data.append([\\\"RIF_STAT\\\", rif_info.get(\\\"POLL_INTERVAL\\\", DEFLT_1_SEC), rif_info.get(\\\"FLEX_COUNTER_STATUS\\\", DISABLE)])\\n if queue_wm_info:\\n data.append([\\\"QUEUE_WATERMARK_STAT\\\", queue_wm_info.get(\\\"POLL_INTERVAL\\\", DEFLT_60_SEC), queue_wm_info.get(\\\"FLEX_COUNTER_STATUS\\\", DISABLE)])\\n if pg_wm_info:\\n data.append([\\\"PG_WATERMARK_STAT\\\", pg_wm_info.get(\\\"POLL_INTERVAL\\\", DEFLT_60_SEC), pg_wm_info.get(\\\"FLEX_COUNTER_STATUS\\\", DISABLE)])\\n if pg_drop_info:\\n data.append(['PG_DROP_STAT', pg_drop_info.get(\\\"POLL_INTERVAL\\\", DEFLT_10_SEC), pg_drop_info.get(\\\"FLEX_COUNTER_STATUS\\\", DISABLE)])\\n if buffer_pool_wm_info:\\n data.append([\\\"BUFFER_POOL_WATERMARK_STAT\\\", buffer_pool_wm_info.get(\\\"POLL_INTERVAL\\\", DEFLT_60_SEC), buffer_pool_wm_info.get(\\\"FLEX_COUNTER_STATUS\\\", DISABLE)])\\n if acl_info:\\n data.append([ACL, pg_drop_info.get(\\\"POLL_INTERVAL\\\", DEFLT_10_SEC), acl_info.get(\\\"FLEX_COUNTER_STATUS\\\", DISABLE)])\\n if tunnel_info:\\n data.append([\\\"TUNNEL_STAT\\\", rif_info.get(\\\"POLL_INTERVAL\\\", DEFLT_10_SEC), rif_info.get(\\\"FLEX_COUNTER_STATUS\\\", DISABLE)])\\n if trap_info:\\n data.append([\\\"FLOW_CNT_TRAP_STAT\\\", trap_info.get(\\\"POLL_INTERVAL\\\", DEFLT_10_SEC), trap_info.get(\\\"FLEX_COUNTER_STATUS\\\", DISABLE)])\\n if route_info:\\n data.append([\\\"FLOW_CNT_ROUTE_STAT\\\", route_info.get(\\\"POLL_INTERVAL\\\", DEFLT_10_SEC),\\n route_info.get(\\\"FLEX_COUNTER_STATUS\\\", DISABLE)])\\n\\n click.echo(tabulate(data, headers=header, tablefmt=\\\"simple\\\", missingval=\\\"\\\"))\",\n \"def to_line(self) -> str:\\n bytecount = len(self.data)\\n nums = bytearray()\\n nums.append(bytecount)\\n nums.extend(struct.pack(\\\">H\\\", self.address))\\n nums.append(self.typ)\\n nums.extend(self.data)\\n crc = sum(nums)\\n crc = ((~crc) + 1) & 0xFF\\n nums.append(crc)\\n line = \\\":\\\" + binascii.hexlify(nums).decode(\\\"ascii\\\")\\n return line\",\n \"def csv(self):\\n return '{:.6f},{},{}'.format(self.sample_time, self.value, self.status)\",\n \"def parse_traffic(self, text):\\n interface = text.splitlines()[1].split('/')[0].strip()\\n # pprint(interface)\\n raw_entries = []\\n for line in text.splitlines():\\n if '/s' in line:\\n line = line.replace('|', '')\\n line = [l.strip() for l in line.split(' ') if len(l) > 0]\\n raw_entries.append((line[0], line[-1]))\\n\\n holder = {}\\n entries = []\\n\\n for date_str, rate in raw_entries:\\n date = datetime.datetime.strptime(date_str, self.date_fmt)\\n epoch = datetime_to_epoch(date)\\n\\n rate, unit = rate.split(' ')\\n holder['unit'] = unit\\n entries.append({'timestamp': epoch, \\\"rate\\\": float(rate)})\\n holder['measurements'] = entries\\n\\n return interface, {'data': holder}\",\n \"def get_data_format(self):\\n return {\\n \\\"timestamp\\\": self.timestamp,\\n \\\"measure\\\": self.measure,\\n \\\"symbolic\\\": self.symbolic\\n }\",\n \"def FILE_RTRV_record(self):\\n eor = 0\\n raw_record = []\\n #record = []\\n # record is a list of non-empty strings, \\n # each string is a line of info from the reader\\n word = ''\\n linebuf = []\\n while len(raw_record) < 1 and self.eof == 0 and eor == 0 :\\n # we need to have something to reply.. \\n while 1:\\n # read 1 char at a time \\n # until we get to the '>' prompt\\n #\\n c = self.tf.read(1)\\n #\\n #print c,\\n #\\n if len(c) != 1:\\n self.eof = 1\\n if self.debug > 2 :\\n print \\\"FILE Raw Record: \\\",\\n pprint.pprint(raw_record)\\n return raw_record\\n elif c == '>':\\n eor = 1\\n if self.debug > 2 :\\n print \\\"FILE2 Raw Record: \\\",\\n pprint.pprint(raw_record)\\n return raw_record\\n # \\\\r = CR , \\\\n = LF \\n # (serial device uses CR + optionally LF, unix text only uses LF)\\n # - new array entry but only if there is something to add \\n elif c == '\\\\r' or c == '\\\\n':\\n if word != '':\\n linebuf.append(word)\\n word = ''\\n if linebuf != []:\\n raw_record.append(linebuf)\\n linebuf = []\\n # split line into words\\n elif c == ' ':\\n if word != '':\\n linebuf.append(word)\\n word = ''\\n # all other chars\\n else : \\n word = word + c\\n \\n time.sleep(0.001)\",\n \"def format(self, obj):\\n pass\",\n \"def format(self, obj):\\n pass\",\n \"def _write_driver_graph_urls(self):\\n try:\\n driver_jobid = os.environ['LSB_JOBID']\\n except KeyError:\\n pass\\n else:\\n driver_rtm_url = construct_rtm_url(driver_jobid)\\n driver_host = socket.gethostname()\\n logger.info(f\\\"Driver LSB_JOBID is: {driver_jobid}\\\")\\n logger.info(f\\\"Driver host is: {driver_host}\\\")\\n logger.info(f\\\"Driver RTM graphs: {driver_rtm_url}\\\")\\n\\n start_timestamp = get_job_submit_time()\\n ganglia_url = construct_ganglia_link(driver_host, start_timestamp)\\n\\n hostgraph_url_path = 'graph-links.txt'\\n with open(hostgraph_url_path, 'a') as f:\\n header = f\\\"=== Client RTM/Ganglia graphs ({socket.gethostname()}) ===\\\"\\n f.write(header + \\\"\\\\n\\\")\\n f.write(\\\"=\\\"*len(header) + \\\"\\\\n\\\")\\n f.write(f\\\" {driver_rtm_url}\\\\n\\\")\\n f.write(f\\\" {ganglia_url}\\\\n\\\\n\\\")\",\n \"def __str__(self):\\n return 'Channel: {{{}}} --> {{{}}} | {} ... '.format(\\n ', '.join(sorted([str(i.indi) for i in self.ist])) if self.ist else '-', \\n ', '.join(sorted([str(o.indo) for o in self.ost])) if self.ost else '-', \\n 'running' if self._running else 'stopped')\",\n \"def report_csv(self):\\n # type: () -> Optional[AnyStr]\\n report = self.gen_report()\\n if not report:\\n return None\\n\\n none_str = \\\"unknown\\\"\\n\\n def patch_none(to_patch, number_format='2.4f'): # type: (Any, str) -> str\\n if to_patch is None:\\n return none_str\\n if isinstance(to_patch, float):\\n s = f\\\"{{:{number_format}}}\\\"\\n return s.format(to_patch)\\n return str(to_patch)\\n\\n header = \\\"sample,target_name,target_bbox\\\"\\n for meta_key in self.log_keys:\\n header += f\\\",{str(meta_key)}\\\"\\n if self.top_k:\\n for k in range(self.top_k):\\n header += f\\\",detect_{k + 1}_name,detect_{k + 1}_bbox,detect_{k + 1}_conf,detect_{k + 1}_iou\\\"\\n else:\\n # unknown count total detections (can be variable)\\n header += \\\",detect_name[N],detect_bbox[N],detect_conf[N],detect_iou[N],(...)[N]\\\"\\n lines = [\\\"\\\"] * len(report)\\n for i, result in enumerate(report):\\n target = result[\\\"target\\\"]\\n detect = result[\\\"detect\\\"]\\n if not target:\\n entry = f\\\"{none_str},{none_str},{none_str}\\\"\\n else:\\n entry = f\\\"{target['image_id']},{patch_none(target['class_name'])},{patch_none(target['bbox'])}\\\"\\n for meta_key in self.log_keys:\\n entry += f\\\",{str(target[meta_key])}\\\"\\n for det in detect:\\n entry += f\\\",{det['class_name']},{det['bbox']},{patch_none(det['confidence'])},{patch_none(det['iou'])}\\\"\\n lines[i] = entry\\n return \\\"\\\\n\\\".join([header, *lines])\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5985085","0.5772294","0.5695159","0.5633481","0.5580155","0.553478","0.5474018","0.546638","0.54650944","0.54626495","0.54481137","0.53700316","0.5360647","0.5341959","0.5339633","0.5279129","0.527552","0.5239246","0.5197716","0.5166865","0.51560336","0.5154918","0.51252484","0.5121424","0.51133144","0.51092774","0.51064867","0.5080403","0.5070038","0.50508964","0.50355846","0.5028622","0.5009787","0.49975726","0.49959728","0.4987606","0.49820498","0.49634007","0.49625117","0.49588323","0.49570966","0.49570382","0.4949815","0.49489665","0.4945538","0.4922794","0.49194342","0.49172258","0.49157637","0.4914732","0.49122986","0.49062347","0.49057776","0.49029553","0.48999268","0.48966944","0.48924914","0.4892134","0.48682097","0.48630401","0.4846435","0.4844993","0.48429754","0.48393717","0.48325455","0.48303515","0.4829013","0.48261872","0.4821593","0.48202005","0.48198435","0.48163894","0.48111475","0.48096496","0.4804743","0.48004296","0.47971493","0.4796806","0.47929296","0.47925246","0.4788924","0.4788842","0.47886193","0.4782434","0.47793993","0.4773503","0.47703153","0.4768165","0.4750675","0.47502837","0.47482222","0.4747207","0.4746979","0.47424036","0.4740753","0.47406137","0.47406137","0.47403878","0.4740381","0.47397172"],"string":"[\n \"0.5985085\",\n \"0.5772294\",\n \"0.5695159\",\n \"0.5633481\",\n \"0.5580155\",\n \"0.553478\",\n \"0.5474018\",\n \"0.546638\",\n \"0.54650944\",\n \"0.54626495\",\n \"0.54481137\",\n \"0.53700316\",\n \"0.5360647\",\n \"0.5341959\",\n \"0.5339633\",\n \"0.5279129\",\n \"0.527552\",\n \"0.5239246\",\n \"0.5197716\",\n \"0.5166865\",\n \"0.51560336\",\n \"0.5154918\",\n \"0.51252484\",\n \"0.5121424\",\n \"0.51133144\",\n \"0.51092774\",\n \"0.51064867\",\n \"0.5080403\",\n \"0.5070038\",\n \"0.50508964\",\n \"0.50355846\",\n \"0.5028622\",\n \"0.5009787\",\n \"0.49975726\",\n \"0.49959728\",\n \"0.4987606\",\n \"0.49820498\",\n \"0.49634007\",\n \"0.49625117\",\n \"0.49588323\",\n \"0.49570966\",\n \"0.49570382\",\n \"0.4949815\",\n \"0.49489665\",\n \"0.4945538\",\n \"0.4922794\",\n \"0.49194342\",\n \"0.49172258\",\n \"0.49157637\",\n \"0.4914732\",\n \"0.49122986\",\n \"0.49062347\",\n \"0.49057776\",\n \"0.49029553\",\n \"0.48999268\",\n \"0.48966944\",\n \"0.48924914\",\n \"0.4892134\",\n \"0.48682097\",\n \"0.48630401\",\n \"0.4846435\",\n \"0.4844993\",\n \"0.48429754\",\n \"0.48393717\",\n \"0.48325455\",\n \"0.48303515\",\n \"0.4829013\",\n \"0.48261872\",\n \"0.4821593\",\n \"0.48202005\",\n \"0.48198435\",\n \"0.48163894\",\n \"0.48111475\",\n \"0.48096496\",\n \"0.4804743\",\n \"0.48004296\",\n \"0.47971493\",\n \"0.4796806\",\n \"0.47929296\",\n \"0.47925246\",\n \"0.4788924\",\n \"0.4788842\",\n \"0.47886193\",\n \"0.4782434\",\n \"0.47793993\",\n \"0.4773503\",\n \"0.47703153\",\n \"0.4768165\",\n \"0.4750675\",\n \"0.47502837\",\n \"0.47482222\",\n \"0.4747207\",\n \"0.4746979\",\n \"0.47424036\",\n \"0.4740753\",\n \"0.47406137\",\n \"0.47406137\",\n \"0.47403878\",\n \"0.4740381\",\n \"0.47397172\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.49148738"},"document_rank":{"kind":"string","value":"49"}}},{"rowIdx":22479,"cells":{"query":{"kind":"string","value":"Process and format recorded learning results."},"document":{"kind":"string","value":"def format_learning(res, s_real, s_hc, GS_HC):\n\n # Format results.\n res = pd.DataFrame(res).T\n res.index.name = 'step'\n\n # Pre-calculate some stats.\n s_real_h = pd.concat({i: pd.Series(kv, index=s_real)\n for i, kv in res['s_real_h'].items()}).unstack()\n s_hc_h = pd.concat({i: pd.Series(kv, index=s_hc)\n for i, kv in res['s_hc_h'].items()}).unstack()\n vc_hc_snr = pd.concat({i: pd.Series(kv, index=s_hc)\n for i, kv in res['vc_hc_snr'].items()}).unstack()\n\n # GS - HC connectivity related stats.\n gs_hc_pos, gs_hc_h, gs_hc_max = format_GS_HC_rec(res, s_hc, GS_HC)\n\n return res, s_real_h, s_hc_h, vc_hc_snr, gs_hc_pos, gs_hc_h, gs_hc_max"},"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 process_results(self, episode, eval):\n if episode % 10 == 9:\n ave = np.mean(self.scores[episode - 9:episode])\n print('Episodes: {}, AveScores: {}, Alpha: {}, Steps: {}'.format(\n episode + 1, ave, self.alpha.item(), self.step_count))\n if eval:\n if episode % 100 == 99:\n s1 = './' + self.game_name + '/'\n np.save(s1 + 'scores_eval{}.npy'.format(episode + 1), self.scores)\n print('Evaluation results saved!')\n else:\n if episode % 200 == 199:\n self.save_episode_models(episode)\n self.plot_array(episode)\n print('Model salved!')\n print('Total {} frames!'.format(self.frames_count))","def results_to_file(samples, filename, during_analysis):\r\n\r\n results = []\r\n\r\n if hasattr(samples, \"log_evidence\"):\r\n if samples.log_evidence is not None:\r\n\r\n value = \"{:.8f}\".format(samples.log_evidence)\r\n results += [\r\n frm.add_whitespace(str0=\"Bayesian Evidence \", str1=value, whitespace=90)\r\n ]\r\n results += [\"\\n\"]\r\n\r\n value = \"{:.8f}\".format(max(samples.log_likelihoods))\r\n results += [\r\n frm.add_whitespace(str0=\"Maximum Likelihood \", str1=value, whitespace=90)\r\n ]\r\n results += [\"\\n\\n\"]\r\n\r\n results += [\"Maximum Log Likelihood Model:\\n\\n\"]\r\n\r\n formatter = frm.TextFormatter()\r\n\r\n for i, prior_path in enumerate(samples.model.unique_prior_paths):\r\n formatter.add(\r\n (prior_path, format_str().format(samples.max_log_likelihood_vector[i]))\r\n )\r\n results += [formatter.text + \"\\n\"]\r\n\r\n if hasattr(samples, \"pdf_converged\"):\r\n\r\n if samples.pdf_converged:\r\n\r\n results += samples_text.summary(samples=samples, sigma=3.0, indent=4, line_length=90)\r\n results += [\"\\n\"]\r\n results += samples_text.summary(samples=samples, sigma=1.0, indent=4, line_length=90)\r\n\r\n else:\r\n\r\n results += [\r\n \"\\n WARNING: The samples have not converged enough to compute a PDF and model errors. \\n \"\r\n \"The model below over estimates errors. \\n\\n\"\r\n ]\r\n results += samples_text.summary(samples=samples, sigma=1.0, indent=4, line_length=90)\r\n\r\n results += [\"\\n\\ninstances\\n\"]\r\n\r\n formatter = frm.TextFormatter()\r\n\r\n for t in samples.model.path_float_tuples:\r\n formatter.add(t)\r\n\r\n results += [\"\\n\" + formatter.text]\r\n\r\n frm.output_list_of_strings_to_file(file=filename, list_of_strings=results)","def record_results(results, scenario_name, output_file=\"ValidationResults.csv\"):\n import pandas as pd\n if os.path.isfile(output_file):\n table = pd.read_csv(output_file, index_col=0)\n else:\n table = pd.DataFrame({})\n metrics = {r[2]:r[0] for r in results}\n index = scenario_name\n metrics = pd.DataFrame(metrics, index=[index])\n table = metrics.combine_first(table)\n table = table[metrics.keys()]\n table.to_csv(output_file)","def writeResults(results, best_params, best_score, modelType, penalty, scoreType,\\\n transformedData, scores=None):\n\n strScores = \"\"\n\n if modelType=='NonLinear':\n for model in results:\n print(model)\n strScores += \"{:.4} {} {} {}\\n\".format(model[0]['C'], model[0]['gamma'], model[1], np.std(model[2]))\n elif modelType=='ElasticNet':\n for model in results:\n print(model)\n strScores += \"{:.4} {} {} {}\\n\".format(model[0]['alpha'], model[0]['l1_ratio'], model[1], np.std(model[2]))\n\n elif modelType=='Pipe':\n for model in results:\n print(model)\n if 'classif__C' in model[0].keys():\n strScores += \"{} {:.4} {} {}\\n\".format(model[0]['csp__n_components'], model[0]['classif__C'], model[1], np.std(model[2]))\n else:\n strScores += \"{} {:.4} {} {}\\n\".format(model[0]['csp__n_components'], model[0]['classif__alpha'], model[1], np.std(model[2]))\n\n elif modelType=='Ridge':\n for model in results:\n print(model)\n strScores += \"{:.4} {} {}\\n\".format(model[0]['alpha'], model[1], np.std(model[2]))\n\n \n else: #Linear, C is the only parameter\n for model in results:\n print(model)\n strScores += \"{:.4} {} {}\\n\".format(model[0]['C'], model[1], np.std(model[2]))\n \n\n strScores += \"Best Params : {} Score CrossVal : {} \\n\".format(best_params, best_score)\n\n if scores:\n strScores += \"{}\\n{}\\n\".format(str(scores['cMatrixTrain']),\\\n str(scores['cMatrixTest']))\n\n strScores += \"Accuracy Train : {} Accuracy Test : {} \\n\".format(scores['accTrain'], scores['accTest'])\n strScores += \"F1 Train : {} F1 Test : {} \\n\".format(scores['f1Train'],\\\n scores['f1Test'])\n strScores += \"Roc_Auc Train : {} Roc_Auc Test : {} \\n\".format(scores['rocTrain'],scores['rocTest'])\n else:\n print(\"No Test file\")\n strScores += \"\\nNo Test file\\n=========\\n\"\n \n f = open(\"{}{}HyperSelection{}{}{}.txt\".format(RESULTS_PATH, penalty, modelType.title(), scoreType.title(), transformedData.title()), 'w')\n f.write(strScores)\n f.close()","def parse_results(results_file, measure_text):\r\n \r\n #Read in results file\r\n with open(results_file) as f_in:\r\n results_text = f_in.readlines()\r\n \r\n #Set-up output data frame\r\n results_df = pd.DataFrame(columns=('effect', 'time_window', 'electrodes'))\r\n \r\n r = -1 #data frame row index\r\n \r\n #Parse lines and read relevant results into data frame\r\n for i in range(len(results_text)):\r\n \r\n line = results_text[i]\r\n \r\n #Starting to parse a new simulation\r\n if 'SIMULATION SUMMARY' in line:\r\n r += 1\r\n \r\n #Parse basic simulation parameters\r\n elif 'Simulated experiments' in line:\r\n (field, value) = line.split('\\t')\r\n results_df.loc[r, 'n_experiments'] = int(value)\r\n \r\n elif 'Permutations' in line:\r\n (field, value) = line.split('\\t')\r\n results_df.loc[r, 'n_permutations'] = int(value)\r\n \r\n elif 'Sample size' in line:\r\n (field, value) = line.split('\\t')\r\n results_df.loc[r, 'n_subjects'] = int(value)\r\n \r\n elif 'Effect:' in line:\r\n (field, value) = line.split('\\t')\r\n results_df.loc[r, 'effect'] = os.path.basename(value).strip()\r\n \r\n elif 'Time window' in line:\r\n (field, value) = line.split('\\t')\r\n results_df.loc[r, 'time_window'] = value.strip()\r\n \r\n elif 'Electrodes:' in line:\r\n (field, value) = line.split('\\t')\r\n results_df.loc[r, 'electrodes'] = value.strip()\r\n \r\n elif 'Trials =' in line:\r\n (field, value) = line.split('\\t')\r\n results_df.loc[r, 'n_trials'] = int(value.split()[0])\r\n \r\n elif 'MEAN WINDOW' in line:\r\n (field, value) = results_text[i+1].split('\\t')\r\n results_df.loc[r, 'mean_amp'] = float(value)\r\n \r\n #Find correction method for current point in file\r\n elif 'UNCORRECTED' in line:\r\n method = 'uncorrected'\r\n elif 'SIDAK' in line:\r\n method = 'sidak'\r\n elif 'FMAX' in line:\r\n method = 'Fmax'\r\n elif 'CLUSTER 0.05' in line:\r\n method = 'cluster_05'\r\n elif 'CLUSTER 0.01' in line:\r\n method = 'cluster_01'\r\n elif 'BH' in line:\r\n method = 'BH'\r\n elif 'BY' in line:\r\n method = 'BY'\r\n elif 'BKY' in line:\r\n method = 'BKY'\r\n \r\n #Get the measure of interst and associate with method (from above)\r\n elif measure_text in line:\r\n (field, value) = line.split('\\t')\r\n results_df.loc[r, method] = float(value)\r\n \r\n return results_df","def prepareAccumulatedMetrics(self):\n displayDF = analyzeMetricsDF(self.resultList)\n displayDF.to_csv(\"data/results.csv\")","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 _process_results(self, timestamp, results):\n\n topic_value = self.create_topic_values(results)\n\n _log.debug('Processing Results!')\n if mode:\n _log.debug(\"ACTUATE ON DEVICE.\")\n actuator_error = False\n if make_reservations and results.devices:\n results, actuator_error = self.actuator_request(results)\n if not actuator_error:\n self.actuator_set(topic_value)\n if make_reservations and results.devices and not actuator_error:\n self.actuator_cancel()\n\n for value in results.log_messages:\n _log.debug(\"LOG: {}\".format(value))\n for key, value in results.table_output.items():\n _log.debug(\"TABLE: {}->{}\".format(key, value))\n if output_file_prefix is not None:\n results = self.create_file_output(results)\n if command_output_file is not None:\n self.create_command_file_output(timestamp, topic_value)\n # if len(results.table_output.keys()):\n # results = self.publish_analysis_results(results)\n return results","def log_records(self, records, **kwargs):\n queries = records[\"features\"][: self.test_dataset.n_queries]\n docs = records[\"features\"][self.test_dataset.n_queries :]\n query_names = records[\"audio_names\"][: self.test_dataset.n_queries]\n doc_names = records[\"audio_names\"][self.test_dataset.n_queries :]\n results = defaultdict(list)\n scores = []\n\n # Calculate matching scores\n with ProcessPoolExecutor(self.datarc[\"num_workers\"]) as executor:\n futures = []\n\n for query, query_name in zip(queries, query_names):\n query_name = query_name.replace(\".wav\", \"\")\n for doc, doc_name in zip(docs, doc_names):\n doc_name = doc_name.replace(\".wav\", \"\")\n futures.append(\n executor.submit(match, query, doc, query_name, doc_name)\n )\n\n for future in tqdm(\n as_completed(futures), total=len(futures), ncols=0, desc=\"DTW\"\n ):\n query_name, doc_name, score = future.result()\n results[query_name].append((doc_name, score))\n scores.append(score)\n\n # Determine score threshold\n scores = sorted(scores)\n score_thresh = scores[int(0.99 * len(scores))]\n score_min = scores[0]\n\n # Build XML tree\n root = etree.Element(\n \"stdlist\",\n termlist_filename=\"benchmark.stdlist.xml\",\n indexing_time=\"1.00\",\n language=\"english\",\n index_size=\"1\",\n system_id=\"benchmark\",\n )\n for query_name, doc_scores in results.items():\n term_list = etree.SubElement(\n root,\n \"detected_termlist\",\n termid=query_name,\n term_search_time=\"1.0\",\n oov_term_count=\"1\",\n )\n for doc_name, score in doc_scores:\n etree.SubElement(\n term_list,\n \"term\",\n file=doc_name,\n channel=\"1\",\n tbeg=\"0.000\",\n dur=\"0.00\",\n score=f\"{score - score_min:.4f}\",\n decision=\"YES\" if score > score_thresh else \"NO\",\n )\n\n # Output XML\n tree = etree.ElementTree(root)\n tree.write(\n str(self.expdir / \"benchmark.stdlist.xml\"),\n encoding=\"UTF-8\",\n pretty_print=True,\n )","def after_evaluation(self, epoch, **kw):\n if self.loss_txt is not None:\n self.current_row[MetricName(\"timestamp\")] = f\"{datetime.now():%H:%M:%S}\"\n\n # output log file\n with open(self.loss_txt, \"a\") as f:\n # remove columns where no value was found on the first epoch (could be != 1 if training was resumed)\n if epoch == self.first_epoch:\n for k in list(self.headers.keys()):\n if k not in self.current_row:\n del self.headers[k]\n\n # make headers on epoch 1\n if epoch == 1:\n # write header\n f.write(\"\\t\".join(self.headers.values()) + \"\\n\")\n\n for col in self.headers:\n assert col in self.current_row, str(col) + \" \" + str(self.current_row.keys())\n\n assert all(col in self.current_row for col in self.headers)\n\n f.write(\"\\t\".join([str(self.current_row[col]) for col in self.headers]) + \"\\n\")\n\n self.current_row = {}","def process_sceneset_results(self, training_results, validation_results,\n tmp_dir):\n pass","def format_det_result():\n test_results = load(args.test_result)\n annos = load(args.anno_path)\n test_videos = annos['test_videos'][0]\n resolutions = annos['resolution']\n frm_dets = []\n for pred in track(test_results, description='formating...'):\n video_key = pred['video_id'].split('.mp4')[0]\n frm_num = pred['timestamp']\n bboxes = pred['pred_instances']['bboxes']\n cls_scores = pred['pred_instances']['scores']\n for bbox, cls_score in zip(bboxes, cls_scores):\n video_idx = test_videos.index(video_key)\n pred_label = np.argmax(cls_score)\n score = cls_score[pred_label]\n h, w = resolutions[video_key]\n bbox *= np.array([w, h, w, h])\n instance_result = np.array(\n [video_idx, frm_num, pred_label, score, *bbox])\n frm_dets.append(instance_result)\n frm_dets = np.array(frm_dets)\n video_tubes = link_tubes(annos, frm_dets, K=1)\n dump(frm_dets, args.frm_out_path)\n dump(video_tubes, args.tube_out_path)","def log_validation_results(engine):\n validation_evaluator.run(self.val_dl)\n metrics: Dict[str, float] = validation_evaluator.state.metrics\n avg_accuracy: float = metrics['accuracy']\n avg_bce: float = metrics['bce']\n pbar.log_message(\n f'Validation Results - Epoch: {engine.state.epoch} Avg accuracy: {avg_accuracy:.4f} Avg loss: {avg_bce:.4f}')\n pbar.n = pbar.last_print_n = 0","def handleRecord(inputData):\n # Send it to Numenta detector and get back the results\n result = model.run(inputData)\n\n # Retrieve the anomaly score and write it to a file\n rawScore = result.inferences[\"anomalyScore\"]\n\n if useLikelihood:\n # Compute log(anomaly likelihood)\n anomalyScore = anomalyLikelihood.anomalyProbability(\n inputData[\"value\"], rawScore, inputData[\"timestamp\"])\n logScore = anomalyLikelihood.computeLogLikelihood(anomalyScore)\n return (logScore, rawScore)\n\n return (rawScore, rawScore)","def do_analyse(args):\n\n pd.set_option('display.max_columns', None)\n pd.set_option('display.width', None)\n\n score = 'score'\n\n # Read in the results, and add a boolean target column.\n df = pd.read_csv(args.results, index_col=0)\n df['target'] = df['verify_speaker'] == df['enrol_speaker']\n\n # Calculate ideal 0.01% threshold over the multi-session data.\n nontarget_df = df.loc[df['target'] == False].sort_values(score, ascending=False)\n nontarget_count = nontarget_df[score].count()\n th_calc = nontarget_df.iloc[int(nontarget_count * (1 / 10000))][score]\n\n # Now filter the data so that we only consider mono-session enrolment and verification.\n df = df.loc[df['verify_room'] == df['enrol_room']]\n target_df = df.loc[df['target'] == True].sort_values(score, ascending=False)\n nontarget_df = df.loc[df['target'] == False].sort_values(score, ascending=False)\n target_count = target_df[score].count()\n nontarget_count = nontarget_df[score].count()\n\n # Calculate FA/FR for the user-defined threshold.\n th_user = args.th_user\n fr_user = target_df.loc[target_df[score] < th_user][score].count()\n fa_user = nontarget_df.loc[nontarget_df[score] > th_user][score].count()\n frr_user = fr_user / target_count\n far_user = fa_user / nontarget_count\n label_user = 'User Threshold: th {:.4f}, FR {} ({:.3f}%), FA {} ({:.3f}%)'.format(th_user, fr_user, frr_user * 100,\n fa_user, far_user * 100)\n\n # Calculate the FA/FR for the ideal threshold calculated from the multi-session data.\n fr_calc = target_df.loc[target_df[score] < th_calc][score].count()\n fa_calc = nontarget_df.loc[nontarget_df[score] > th_calc][score].count()\n frr_calc = fr_calc / target_count\n far_calc = fa_calc / nontarget_count\n label_calc = 'Calc Threshold: th {:.4f}, FR {} ({:.3f}%), FA {} ({:.3f}%)'.format(th_calc, fr_calc, frr_calc * 100,\n fa_calc, far_calc * 100)\n\n # Print the stats.\n print('\\nTarget Stats:')\n print(target_df[score].describe())\n print('\\nNon-Target Stats:')\n print(nontarget_df[score].describe())\n print('\\nThresholds:')\n print(label_user)\n print(label_calc)\n\n # Paint the graphs.\n paint_graph(score, 'verify_room', df, th_user, label_user, th_calc, label_calc)\n paint_graph(score, 'enrol_room', df, th_user, label_user, th_calc, label_calc)\n paint_graph(score, 'verify_speaker', df, th_user, label_user, th_calc, label_calc)\n paint_graph(score, 'enrol_speaker', df, th_user, label_user, th_calc, label_calc)","def run(self):\n\n self.preprocess()\n self.restore_ratings()\n self.prepare_UI()\n self.loop_through_units()\n self.cleanup()\n\n print('\\nAll Done - results are available in:\\n\\t{}'.format(self.out_dir))","def analyze_data(self):\n\n self.truth = self.analyze_folder(\"Truth\")\n self.truth.to_csv(self.folder + \"/truth.csv\")\n self.false = self.analyze_folder(\"False\")\n self.flase.to_csv(self.folder + \"/false.csv\")","def log_results(self, filename=None):\n\n self.ad_log['train_auc'] = self.diag['train']['auc'][-1]\n self.ad_log['train_accuracy'] = self.diag['train']['acc'][-1]\n self.ad_log['train_time'] = self.train_time\n\n self.ad_log['test_auc'] = self.diag['test']['auc'][-1]\n self.ad_log['test_accuracy'] = self.diag['test']['acc'][-1]\n self.ad_log['test_time'] = self.test_time\n\n self.ad_log.save_to_file(filename=filename)","def postprocess_result(self):\n output_file = self.analyzer_result_file\n LOG.debug_analyzer(self.analyzer_stdout)\n tidy_stdout = self.analyzer_stdout.splitlines()\n generate_plist_from_tidy_result(output_file, tidy_stdout)\n\n if self.report_hash_type == 'context-free':\n report.use_context_free_hashes(output_file)","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 analyse_results(self, \n target_dir, \n param_file = \"TRAINED_PARAMS_END.model\",\n w_norm_file = \"W_NORMS.dat\",\n num_to_test = 100,\n get_means_from = {\n 'NOISY' :'RECONST_NOISY_ERRORS.dat',\n 'MISSING':'RECONST_MISSING_ERRORS.dat'\n },\n given_inputs = []):\n \n path_to_json = os.path.join(target_dir, \"PARAMETERS.json\")\n \n with open(path_to_json, 'r') as json_file:\n \n param_dict = json.load(json_file)\n \n self.num_hidden = param_dict['GLOBAL']['num_hidden']\n \n num_runs = param_dict['GLOBAL']['num_runs']\n \n path_to_mix_params = os.path.join(target_dir, \"MIX_PARAMS.dat\")\n \n reg_dict = {}\n \n for f_name in param_dict.keys():\n \n if f_name != \"GLOBAL\":\n reg_dict[param_dict[f_name]['algorithm']] =\\\n param_dict[f_name]['regressor']\n \n self.mixture = False\n \n E_gaps = {}\n \n recon_errors = {}\n \n p_tilda_data = {}\n \n end_w_norms = {}\n \n for field in get_means_from.keys():\n \n recon_errors[field] = {}\n \n if os.path.exists(path_to_mix_params):\n \n self.mixture = True\n \n self.mix_params = np.loadtxt(path_to_mix_params)\n \n for run_ind in range(num_runs):\n \n sub_f1 = \"run%s\"%run_ind\n \n sub_dir = os.path.join(target_dir, sub_f1)\n \n if os.path.isdir(sub_dir): \n print(\"Processing %s\"%sub_dir)\n tr_file = \"TRAIN_IMAGES.dat\"\n check_input_file = os.path.join(sub_dir, tr_file)\n \n if os.path.exists(check_input_file):\n inputs = np.loadtxt(check_input_file)\n # to make fair comparison, number of is samples\n # is set to the number of test inputs\n self.num_samples = inputs.shape[0]\n else:\n \n if isinstance(given_inputs, np.ndarray):\n N = given_inputs.shape[0]\n inds = self.np_rand_gen.choice(N, \n num_to_test,\n replace = False)\n \n inputs = given_inputs[inds,:]\n \n self.num_samples = num_to_test\n \n else:\n print(\"Error: %s does not contain file %s\"\n %(sub_dir2, tr_file) +\" and given_inputs is []\")\n print(\"Script execution will terminate\") \n sys.exit()\n \n self.batch_size = inputs.shape[0]\n \n if self.mixture:\n self.set_mixture_means(inputs = inputs)\n \n is_samples, _ = self.is_sampler()\n \n for sub_f2 in os.listdir(sub_dir):\n \n sub_dir2 = os.path.join(sub_dir, sub_f2)\n \n if os.path.isdir(sub_dir2):\n \n if \"_\" in sub_f2:\n spl_str = sub_f2.split(\"_\")\n if len(spl_str) > 3:\n algorithm = spl_str[0]+\"_\"+spl_str[1]\n else:\n algorithm = spl_str[0]\n \n else:\n algorithm = sub_f2\n \n field_name =algorithm \n \n if reg_dict[algorithm] != None:\n if reg_dict[algorithm] in sub_f2:\n if \"_\" in reg_dict[algorithm]:\n reg_name_s = reg_dict[algorithm].split(\"_\")\n \n reg_name_s = reg_name_s[0][0].upper() +\\\n reg_name_s[1][0].upper()\n field_name +=\" %s\"%reg_name_s\n else:\n field_name +=\" %s\"%reg_dict[algorithm]\n \n get_val = sub_f2\n get_val = get_val.split(reg_dict[algorithm])[1]\n \n field_name +=\" %s\"%get_val\n \n if field_name not in E_gaps.keys():\n \n E_gaps[field_name] = []\n \n if field_name not in p_tilda_data.keys():\n \n p_tilda_data[field_name] = []\n \n if field_name not in end_w_norms.keys():\n \n end_w_norms[field_name] = []\n \n par_path = os.path.join(sub_dir2, param_file)\n \n w_norms_path = os.path.join(sub_dir2, w_norm_file)\n \n w_norms = np.loadtxt(w_norms_path)\n \n end_w_norms[field_name].append(w_norms[-1])\n \n if os.path.exists(par_path):\n \n css_diff, p_tilda_vals =\\\n self.compare_css_terms(x_inputs = inputs,\n x_samples = is_samples,\n full_path = par_path)\n \n E_gaps[field_name].append(css_diff)\n \n mean_val = np.mean(p_tilda_vals[0:self.batch_size])\n \n p_tilda_data[field_name].append(mean_val)\n \n else:\n \n print(\"Error: %s does not exist\"%par_path)\n sys.exit()\n \n for f_exp in get_means_from.keys():\n \n file_name = get_means_from[f_exp]\n \n check_err_file = os.path.join(sub_dir2, file_name)\n \n errors = np.loadtxt(check_err_file)\n \n mean_val = np.mean(errors)\n \n if field_name not in recon_errors[f_exp].keys():\n \n recon_errors[f_exp][field_name] = []\n \n recon_errors[f_exp][field_name].append(mean_val)\n \n return E_gaps, recon_errors, p_tilda_data, end_w_norms","def log(self, report, epoch):\n train_return_values = np.asarray([trajectory['reward'].sum()\n for trajectory in report['training_trajectories']])\n trajectories_infos = [trajectory['info'] for trajectory in report.pop('training_trajectories')]\n sum_costs = np.asarray([sum(list(map(lambda info: info.get('cost', 0.0), trajectory)))\n for trajectory in trajectories_infos])\n report.update(dict(\n training_rl_objective=train_return_values.mean(),\n sum_rewards_stddev=train_return_values.std(),\n mean_sum_costs=sum_costs.mean()\n ))\n training_step = report.pop('total_training_steps')\n for key, value in report.items():\n self.training_logger.log_scalar(value, key, training_step)\n self.training_logger.flush()","def analyse(self):\n pass","def _process_results(self):\n self.portfolio.create_backtest_result_dataframe()\n stats = self._show_stats()\n return stats","def run(self):\r\n history = self.extracter.load_user_history()\r\n self.plot_history(history)\r\n \r\n pp_history = self.analyser.preprocess_history(history)\r\n part_worths, attribute_importance, relative_importance = self.analyser.conjoint_analysis(pp_history)\r\n self.plot_analysis(part_worths, relative_importance)\r\n \r\n return history, pp_history, part_worths, relative_importance","def record(self, algo, valid_info, test_results=None):\n\n model_path = os.path.join(\n self._working_folder, \"hpo_{}.pt\".format(len(self._results)))\n algo.save_model(model_path)\n test_results_path = ''\n if test_results is not None:\n test_results_path = os.path.join(\n self._working_folder, \"test_results_of_hpo_{}.pt\".format(len(self._results)))\n torch.save({'test_results': test_results}, test_results_path)\n self._results.append((copy.deepcopy(self._cur_config), model_path, valid_info, self._early_stopper.get_cur_step(), test_results_path))","def compute_metrics(self, results: list) -> dict:\n dump(results, self.out_file_path)\n print_log(\n f'Results has been saved to {self.out_file_path}.',\n logger='current')\n return {}","def _record(self):\r\n self._plot()\r\n self._csvWriter()\r\n self._logger()","def analyze_predictions(submission_model_path, csv_result):\n all_data_info = pd.read_csv(path.join(DATA_PATH, 'all_data_info.csv'))\n submission_model = pd.read_csv(submission_model_path)\n\n genre_label = get_genre_labels(True)\n null_genre = 0\n null_label = 0\n exact_labels = []\n errors = 0\n progress = progressbar.ProgressBar(\n maxval=len(submission_model),\n widgets=[progressbar.Bar('=', '[', ']'), ' ',\n progressbar.Percentage()])\n progress.start()\n for i, row in submission_model.iterrows():\n progress.update(i + 1)\n genre = all_data_info[all_data_info['new_filename'] == row['img']][\n 'genre'].dropna()\n # some paintings don't have a genre. Checking it\n if len(genre) < 1:\n null_genre += 1\n exact_labels.append(row['label'])\n continue\n if genre.values[0] not in genre_label:\n # No label. It's strange, but let's go on...\n null_label += 1\n exact_labels.append(row['label'])\n continue\n exact_labels.append(int(genre_label[genre.values[0]]['label']))\n if row['label'] != exact_labels[-1]:\n errors += 1\n progress.finish()\n\n submission_model['exact_label'] = exact_labels\n submission_model.to_csv(csv_result, index=False)\n\n # plot_confusion_matrix(submission_model)\n\n print('Genre is null: ' + str(null_genre))\n print('Label is null: ' + str(null_label))\n print('Errors: ' + str(errors) + ' (out of ' + str(len(submission_model)) +\n ')')\n print('Accuracy: ' + str(1 - (errors / float(len(submission_model)))))","def process_for_baseline(dat):\n records = []\n count = 0\n for record in dat:\n # Context\n context = record['context'].replace(\"''\", '\" ').replace(\"``\", '\" ') # Replace non-standard quotation marks\n context_tokens = text2tokens(context)\n spans = convert_idx(context, context_tokens) # (token_start_char_idx, token_end_char_idx)\n \n # Question\n ques = record['question'].replace(\"''\", '\" ').replace(\"``\", '\" ') \n ques_tokens = text2tokens(ques)\n \n # Answers\n y1, y2 = charIdx_to_tokenIdx(spans, record['answers']['text'], record['answers']['answer_start']) \n\n res_dict = {\"context_tokens\": context_tokens,\n \"ques_tokens\": ques_tokens,\n \"answer\": record['answers']['text'], \n \"y1s\": y1, \n \"y2s\": y2,\n \"id\": count,\n 'pubId': record['PubID'],\n \"group\": record['group']}\n records.append(res_dict)\n count += 1 \n \n return records","def get_average_scores(self):\n models = self.eval_parameters['average_experiment']['models']\n metrics = self.eval_parameters['average_experiment']['metrics_list']\n metrics_keys = list(metrics.keys())\n print(r\"\\begin{table}[]\")\n print(\"\\centering\")\n print(r\"\\tiny\")\n print(\"\\caption{Average results over 85 topics. Each row represents a different run (top 10 runs of each model). Each column represents a different assessments aggregation.}\")\n print(\"\\label{tab:average_results}\")\n print(r\"\\begin{tabular}{@{}\"+''.join(['l']*(len(metrics_keys)+1))+\"@{}}\")\n print(\"runid\", '&'.join(metric.replace('_','\\_')for metric in metrics_keys),sep='&')\n print(r\"\\\\ \\midrule\")\n for model in models:\n runs = self.get_list_files(self.eval_parameters['average_experiment']['runs_folder'] + model+\"/\")\n for file in runs:\n val = []\n for metric_id in metrics_keys:\n out1 = subprocess.check_output(\n ['../trec_eval-master/trec_eval', '-m', metrics[metric_id]['metric'],\n metrics[metric_id]['qrels'], file])\n val += [str(out1.rstrip().split()[2]).replace('b\\'', '').replace('\\'', '')]\n print(file.replace(self.eval_parameters['average_experiment']['runs_folder'],'').replace('_','\\_'),'&', '&'.join(val),r\"\\\\\")\n print(r\"\\bottomrule\")\n print(r\"\\end{tabular}\")\n print(r\"\\end{table}\")","def process_recognition(result, filename, output_directory, lexical):\n if result.reason == speechsdk.ResultReason.RecognizedSpeech:\n if lexical:\n text = f\"{format(result.text)}\\t{json.loads(result.json)['NBest'][0]['Lexical']}\"\n else:\n text = f\"{format(result.text)}\"\n logging.info(f\"[INFO] - Recognition successful: {filename} -> {result.text}\")\n elif result.reason == speechsdk.ResultReason.NoMatch:\n logging.warning(filename + \"\\t\" + f\"No speech could be recognized: {result.no_match_details}\")\n text = \"\"\n elif result.reason == speechsdk.ResultReason.Canceled:\n cancellation_details = result.cancellation_details\n logging.error(filename+\"\\t\"+ f\"Speech Recognition canceled: {cancellation_details.reason}\")\n if cancellation_details.reason == speechsdk.CancellationReason.Error:\n logging.error(f\"Error details: {cancellation_details.error_details}\")\n text = \"\"\n return text","def process(self):\n self.extract()\n self.transform()\n self.load()","def convert_format( # pylint: disable=arguments-differ\n self, results: List[np.ndarray], out_dir: str # type: ignore\n ) -> None:\n assert isinstance(results, list), \"results must be a list\"\n assert len(results) == len(\n self\n ), f\"Length of res and dset not equal: {len(results)} != {len(self)}\"\n if not os.path.exists(out_dir):\n os.makedirs(out_dir)\n\n scores_list, segms_list, colors_list = [], [], []\n det_frames, seg_frames = [], []\n img_names = []\n ann_id = 0\n\n for img_idx in range(len(self)):\n index = 0\n img_name = self.data_infos[img_idx][\"file_name\"]\n img_names.append(img_name)\n det_frame = Frame(name=img_name, labels=[])\n det_frames.append(det_frame)\n seg_frame = Frame(name=img_name, labels=[])\n seg_frames.append(seg_frame)\n scores, segms, colors = [], [], []\n\n det_results, seg_results = results[img_idx]\n for cat_idx, [cur_det, cur_seg] in enumerate(\n zip(det_results, seg_results)\n ):\n for bbox, segm in zip(cur_det, cur_seg):\n ann_id += 1\n index += 1\n score = bbox[-1]\n\n det_label = Label(\n id=str(ann_id),\n score=score,\n box2d=bbox_to_box2d(self.xyxy2xywh(bbox)),\n category=self.CLASSES[cat_idx],\n )\n det_frame.labels.append(det_label) # type: ignore\n\n seg_label = Label(id=str(ann_id), index=index, score=score)\n seg_frame.labels.append(seg_label) # type: ignore\n\n scores.append(score)\n segms.append(segm)\n colors.append([cat_idx + 1, 0, index >> 8, index & 255])\n\n scores_list.append(scores)\n segms_list.append(segms)\n colors_list.append(colors)\n\n det_out_path = osp.join(out_dir, \"det.json\")\n save(det_out_path, det_frames)\n\n seg_out_path = osp.join(out_dir, \"score.json\")\n seg_frame_dicts = [seg_frame.dict() for seg_frame in seg_frames]\n with open(seg_out_path, \"w\", encoding=\"utf-8\") as fp:\n json.dump(seg_frame_dicts, fp, indent=2)\n\n bitmask_dir = osp.join(out_dir, \"bitmasks\")\n mask_merge_parallel(\n bitmask_dir,\n img_names,\n scores_list,\n segms_list,\n colors_list,\n nproc=4,\n )","def record_metrics(metrics, args):\n with open('interpretation_metrics/model_metrics_{}'.format(args.file_num), 'a') as f:\n f.write(\"META DATA\\n\")\n f.write(\"---------\\n\")\n f.write(\"Model Name: {}\\n\".format(args.model_name))\n f.write(\"Attack Target: {}\\n\".format(args.attack_target))\n f.write(\"Gradient Model File: {}\\n\".format(args.gradient_model_file))\n f.write(\"Predictive Model File: {}\\n\".format(args.predictive_model_file))\n f.write(\"Cuda: {}\\n\".format(args.cuda))\n\n f.write(\"\\nSIMPLE GRADIENT COMBINED MODEL METRICS\\n\")\n f.write(\"----------------------------------------\\n\")\n for key, val in metrics['simple_gradient_combined'].items():\n f.write(\"{}: {:.3f}\\n\".format(key, val))\n\n f.write(\"\\nSIMPLE GRADIENT BASELINE MODEL METRICS\\n\")\n f.write(\"----------------------------------------\\n\")\n for key, val in metrics['simple_gradient_baseline'].items():\n f.write(\"{}: {:.3f}\\n\".format(key, val))\n\n f.write(\"\\nSMOOTH GRADIENT COMBINED MODEL METRICS\\n\")\n f.write(\"----------------------------------------\\n\")\n for key, val in metrics['smooth_gradient_combined'].items():\n f.write(\"{}: {:.3f}\\n\".format(key, val))\n\n f.write(\"\\nSMOOTH GRADIENT BASELINE MODEL METRICS\\n\")\n f.write(\"----------------------------------------\\n\")\n for key, val in metrics['smooth_gradient_baseline'].items():\n f.write(\"{}: {:.3f}\\n\".format(key, val))\n\n f.write(\"\\nINTEGRATED GRADIENT COMBINED MODEL METRICS\\n\")\n f.write(\"--------------------------------------------\\n\")\n for key, val in metrics['integr_gradient_combined'].items():\n f.write(\"{}: {:.3f}\\n\".format(key, val))\n\n f.write(\"\\nINTEGRATED GRADIENT BASELINE MODEL METRICS\\n\")\n f.write(\"--------------------------------------------\\n\")\n for key, val in metrics['integr_gradient_baseline'].items():\n f.write(\"{}: {:.3f}\\n\".format(key, val))","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 run_analyses(y_predict_train, y_train, y_predict, y_test):\n # calculate metrics\n _, training_error = output_error(y_predict_train, y_train)\n (precision, recall, f1, _), testing_error = output_error(y_predict, y_test)\n \n # print out metrics\n print 'Average Precision:', np.average(precision)\n print 'Average Recall:', np.average(recall)\n print 'Average F1:', np.average(f1)\n print 'Training Error:', training_error\n print 'Testing Error:', testing_error","def log_training(self, batch, total_batches, result):\n metrics = [\"loss\", \"accuracy\"]\n for metric in metrics:\n if metric not in self.logs:\n self.logs[metric] = []\n self.logs[metric].append(result[metric])\n if batch % self.log_frequency == 0 or batch + 1 == total_batches:\n print(\"Batch {} / {} = {:.2f} %\".format(batch, total_batches, 100 * batch / total_batches))\n print(\"{:20}: {}\".format(\"Global step\", result[\"global_step\"]))\n print(\"{:20}: {:.4e}\".format(\"Learning rate\", result[\"learning_rate\"]))\n for metric in metrics:\n metric_logs = self.logs[metric]\n average = sum(metric_logs) / len(metric_logs)\n print(\"{:20}: {:.4}\".format(\"Training \" + metric, average))\n self.logs[metric] = []\n val_metrics = self.evaluate(self.batches_valid)\n for k, v in val_metrics.items():\n print(\"{:20}: {:.4}\".format(\"Validation \" + k, v))","def parseResults(result):\n # Split the results based on newline characters\n results_cut = result.text.split('\\n')[12:-49]\n # Initialize lists of the values to be parsed from results_cut \n visit_id = []\n name = []\n ra_hour = []\n ra_min = []\n ra_sec = []\n dec_deg = []\n dec_min = []\n dec_sec = []\n v_mag = []\n ra_motion = []\n dec_motion = []\n # Iterate through results_cut and append them to the respective lists\n for line in results_cut:\n visit_id.append(int(line[6:12]))\n name.append(line[12:36])\n ra_hour.append(int(line[38:40]))\n ra_min.append(int(line[41:43]))\n ra_sec.append(float(line[44:48]))\n dec_deg.append(int(line[49:52]))\n dec_min.append(int(line[53:55]))\n dec_sec.append(int(line[56:58]))\n try:\n v_mag.append(float(line[60:64]))\n except ValueError:\n # If there is no reported v_mag for the object, return -99\n v_mag.append(-99.0)\n ra_motion.append('%s%i' % (line[84], int(line[82:84])))\n dec_motion.append('%s%i' % (line[91], int(line[89:91])))\n # Initialize the pandas dataframe to be returned\n results_df = pd.DataFrame(np.array([visit_id, name, ra_hour, ra_min, ra_sec, \n dec_deg, dec_min, dec_sec, v_mag, \n ra_motion, dec_motion]).T, \n columns=['visit_id', 'name', 'ra_hour', 'ra_min', 'ra_sec', \n 'dec_deg', 'dec_min', 'dec_sec', 'v_mag', \n 'ra_motion', 'dec_motion'])\n # Add the lists to the dataframe\n results_df['visit_id'] = pd.to_numeric(results_df['visit_id'])\n results_df['ra_hour'] = pd.to_numeric(results_df['ra_hour'])\n results_df['ra_min'] = pd.to_numeric(results_df['ra_min'])\n results_df['ra_sec'] = pd.to_numeric(results_df['ra_sec'])\n results_df['dec_deg'] = pd.to_numeric(results_df['dec_deg'])\n results_df['dec_min'] = pd.to_numeric(results_df['dec_min'])\n results_df['dec_sec'] = pd.to_numeric(results_df['dec_sec'])\n results_df['v_mag'] = pd.to_numeric(results_df['v_mag'])\n results_df['ra_motion'] = pd.to_numeric(results_df['ra_motion'])\n results_df['dec_motion'] = pd.to_numeric(results_df['dec_motion'])\n \n return results_df","def write_results(self, results):\n predictions = open('hmm_results.csv', 'w')\n predictions.write(\"Type,Prediction\")\n for type in results:\n if type == 'O':\n continue\n predictions.write(\"\\n\" + str(type) + \",\")\n for interval in results[type]:\n predictions.write(str(interval) + \" \")\n predictions.close()","def process(self, results):\n raise NotImplementedError","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 summarize(self):\n self.smalltalk += \"\\n Data IDs in this bundle: \\n\"\n self._files = {}\n inv_dict = {}\n # sort IDs to make sure pdfs are printed in same oder as they were\n # taken\n for k, v in self.stage_summaries.items():\n for qc_id in flatten_list(v):\n inv_dict[qc_id] = k\n sorted_ids = list(flatten_list(self.stage_summaries.values()))\n sorted_ids.sort(key=int)\n # for stage, value in self.stage_summaries.items():\n for qc_run_id in sorted_ids:\n # stage = inv_dict[qc_run_id]\n # if stage[0:7] == 'failed_':\n # stage = stage[7:]\n # try:\n # s = self.comments[qc_run_id]\n # except KeyError:\n # s = ''\n # self.comments[qc_run_id] = 'Classified as poor result.\\n' + s\n ds = Dataset(qc_run_id, self.db_name)\n device_name = ds.device_name\n f_folder = os.path.join(self.db_folder, \"tuning_results\", device_name)\n # for qc_run_id in flatten_list(value):\n self.smalltalk += str(qc_run_id) + \", \"\n\n # filename = stage + '_fit_ds'\n # filename += str(qc_run_id) + '.png'\n filename = os.path.join(f_folder, str(ds.ds.guid) + \".png\")\n\n self._files[str(qc_run_id)] = filename","def Analyze(self, data):\n self._AddResult()","def parse_log(path_to_log):\n\n re_correct_line = re.compile('^\\w+\\d+')\n re_iteration = re.compile('Iteration (\\d+)')\n # alexnet\n #re_output_loss = re.compile('output #\\d+: loss = ([\\.\\d]+)')\n #re_output_acc = re.compile('output #\\d+: accuracy = ([\\.\\d]+)')\n\n #googlenet\n re_output_loss = re.compile('output #\\d+: loss3\\/loss3 = ([\\.\\d]+)')\n re_output_acc = re.compile('output #\\d+: loss3\\/top-1 = ([\\.\\d]+)')\n\n re_lr = re.compile('lr = ([\\.\\d]+)')\n\n # Pick out lines of interest\n iteration = -1\n test_accuracy = -1\n learning_rate = float('NaN')\n acc = float('NaN')\n train_dict_list = []\n test_dict_list = []\n train_dict_names = ('NumIters', 'Loss', 'Accuracy', 'LearningRate', 'Seconds')\n test_dict_names = ('NumIters', 'Loss', 'Accuracy')\n\n is_test_group = False\n\n logfile_year = extract_seconds.get_log_created_year(path_to_log)\n with open(path_to_log) as f:\n start_time = extract_seconds.get_start_time(f, logfile_year)\n for line in f:\n if not re_correct_line.match(line):\n continue\n iteration_match = re_iteration.search(line)\n if iteration_match:\n iteration = int(iteration_match.group(1))\n if iteration == -1:\n # Only look for other stuff if we've found the first iteration\n continue\n\n time = extract_seconds.extract_datetime_from_line(line, logfile_year)\n seconds = (time - start_time).total_seconds()\n\n lr_match = re_lr.search(line)\n if lr_match:\n learning_rate = float(lr_match.group(1))\n output_acc_match = re_output_acc.search(line)\n if output_acc_match:\n acc = float(output_acc_match.group(1))\n\n output_loss_match = re_output_loss.search(line)\n if output_loss_match:\n if get_line_type(line) == 'test':\n test_loss = float(output_loss_match.group(1))\n test_dict_list.append({'NumIters': iteration,\n 'Loss': test_loss,\n 'Accuracy': acc})\n else:\n train_loss = float(output_loss_match.group(1))\n train_dict_list.append({'NumIters': iteration,\n 'Loss': train_loss,\n 'Accuracy': acc,\n 'LearningRate': learning_rate,\n 'Seconds': seconds})\n\n df_train = pd.DataFrame(columns=train_dict_names)\n df_test = pd.DataFrame(columns=test_dict_names)\n\n for col in train_dict_names:\n df_train[col] = [d[col] for d in train_dict_list]\n for col in test_dict_names:\n df_test[col] = [d[col] for d in test_dict_list]\n\n return df_train, df_test","def test_make_results_verbose1(self):\n\t\ttest = sentiment.LibraryRun(self.text3, self.lib)\n\t\ttest.do_run()\n\t\ttest.make_results_verbose()\n\t\tobj_ut = test.results_verbose\n\t\tself.assertEqual(obj_ut, [['100', 'not good', 2, -1, 0],\n\t\t\t['100', 'not very good', 4, -1, 0]])","def _evaluate(self, record_video=False):\n assert record_video == False\n assert len(self._runners.keys()) == 1\n num_eval = self._config.num_eval\n rollouts = {}\n info_history = Info()\n\n for k, runner in self._runners.items():\n logger.warn(\"Run %d evaluations for %s\", num_eval, k)\n for i in range(num_eval):\n logger.warn(\"Evaluate run %d\", i + 1)\n rollout, info, frames = runner.run_episode(is_train=False)\n\n rollouts[k] = rollout\n logger.info(\n \"rollout: %s\", {k: v for k, v in info.items() if not \"qpos\" in k},\n )\n\n info_history.add(info)\n\n avg_stats = {}\n for k, v in info_history.items():\n if isinstance(v, list) and not isinstance(v[0], wandb.Video):\n avg_stats[k] = np.mean(v)\n\n std_stats = {}\n for k, v in info_history.items():\n if isinstance(v, list) and not isinstance(v[0], wandb.Video):\n std_stats[k] = np.std(v)\n\n logger.info(\n \"Evaluation Average stats: %s\", {k: v for k, v in avg_stats.items()},\n )\n logger.info(\n \"Evaluation Standard Deviation stats: %s\",\n {k: v for k, v in std_stats.items()},\n )\n\n return rollouts, info_history","def consolidate_results(path='./Data'):\n model_files = [load(os.path.join(path, f)) \n for f in os.listdir(path) if os.path.isfile(os.path.join(path, f)) and f.startswith('model_')]\n df_final = pd.DataFrame(columns=['model_name','train_accuracy','test_accuracy',\n 'macro_avg_precision','macro_avg_recall',\n 'macro_avg_f1-score','weighted_avg_precision',\n 'weighted_avg_recall','weighted_avg_f1-score'])\n for model_file in model_files:\n results = model_file['model_results']\n class_report = classification_report(results.category, results.pred, output_dict=True)\n df_final = df_final.append({'model_name':model_file['model_name'],\n 'train_accuracy':'{0:.2f}'.format(model_file['model_CV'].best_score_),\n 'test_accuracy':'{0:.2f}'.format(class_report['accuracy']),\n 'macro_avg_precision':class_report['macro avg']['precision'],\n 'macro_avg_recall':class_report['macro avg']['recall'],\n 'macro_avg_f1-score':class_report['macro avg']['f1-score'],\n 'weighted_avg_precision':class_report['weighted avg']['precision'],\n 'weighted_avg_recall':class_report['weighted avg']['recall'],\n 'weighted_avg_f1-score':class_report['weighted avg']['f1-score']\n },ignore_index=True)\n return(df_final)","def report_csv(self):\n # type: () -> Optional[AnyStr]\n if isinstance(self.report_count, int) and self.report_count <= 0:\n return None\n if self.score is None or self.true is None:\n return None\n pack = list(zip(*[(*pack, )\n for packs in zip(self.score, self.true, *self.meta.values())\n for pack in zip(*packs)]))\n logdata = {key: np.stack(val, axis=0) for key, val in zip([\"pred\", \"target\", *self.meta.keys()], pack)}\n assert all([len(val) == len(logdata[\"target\"]) for val in logdata.values()]), \"messed up unpacking\"\n header = \"target_name,target_score\"\n for k in range(self.top_k):\n header += f\",pred_{k + 1}_name,pred_{k + 1}_score\"\n for meta_key in self.log_keys:\n header += f\",{str(meta_key)}\"\n lines = []\n for sample_idx in range(len(logdata[\"target\"])):\n gt_label_idx = int(logdata[\"target\"][sample_idx])\n pred_scores = logdata[\"pred\"][sample_idx]\n sorted_score_idxs = np.argsort(pred_scores)[::-1]\n sorted_scores = pred_scores[sorted_score_idxs]\n if self.conf_threshold is None or gt_label_idx is None or \\\n pred_scores[gt_label_idx] >= self.conf_threshold:\n if gt_label_idx is not None:\n entry = f\"{self.class_names[gt_label_idx]},{pred_scores[gt_label_idx]:2.4f}\"\n else:\n entry = f\"<unknown>,{0.0:2.4f}\"\n for k in range(self.top_k):\n entry += f\",{self.class_names[sorted_score_idxs[k]]},{sorted_scores[k]:2.4f}\"\n for meta_key in self.log_keys:\n entry += f\",{str(logdata[meta_key][sample_idx])}\"\n lines.append(entry)\n if isinstance(self.report_count, int) and len(lines) >= self.report_count:\n break\n return \"\\n\".join([header, *lines])","def metrics(logger, model, X_train, y_train, X_test, y_test):\n\n results = dict()\n y_preds = model.predict(X_test)\n results['Train Accuracy'] = model.score(X_train, y_train)\n results['Test Accuracy'] = accuracy_score(y_test, y_preds)\n results['Precision'] = precision_score(y_test, y_preds)\n results['Recall'] = recall_score(y_test, y_preds)\n\n metric_cols = data_config['params']['metrics_cols']\n res_df = pd.DataFrame(results.items(), columns=metric_cols)\n\n metrics_path = data_config['outputs']['logreg_metrics']\n res_df.to_csv(metrics_path, index=False)\n print(f'Metrics saved to {metrics_path}')\n\n return","def evaluate(self, eval_batches, result_dir=None, result_prefix=None, save_full_info=False):\n pred_answers, ref_answers = [], []\n total_loss, total_num = 0, 0\n for b_itx, batch in enumerate(eval_batches):\n feed_dict = {self.p: batch['passage_token_ids'],\n self.q: batch['question_token_ids'],\n self.p_char: batch['passage_char_ids'],\n self.q_char: batch['question_char_ids'],\n self.p_length: batch['passage_length'],\n self.q_length: batch['question_length'],\n self.start_label: batch['start_id'],\n self.end_label: batch['end_id'],\n self.dropout_keep_prob: 1.0}\n start_probs, end_probs, loss = self.sess.run([self.start_probs,\n self.end_probs, self.loss], feed_dict)\n\n total_loss += loss * len(batch['raw_data'])\n total_num += len(batch['raw_data'])\n\n padded_p_len = len(batch['passage_token_ids'][0])\n for sample, start_prob, end_prob in zip(batch['raw_data'], start_probs, end_probs):\n\n best_answer = self.find_best_answer(sample, start_prob, end_prob, padded_p_len)\n if save_full_info:\n sample['pred_answers'] = [best_answer]\n pred_answers.append(sample)\n else:\n pred_answers.append({'question_id': sample['question_id'],\n 'question_type': sample['question_type'],\n 'answers': [best_answer],\n 'entity_answers': [[]],\n 'yesno_answers': []})\n if 'answers' in sample:\n ref_answers.append({'question_id': sample['question_id'],\n 'question_type': sample['question_type'],\n 'answers': sample['answers'],\n 'entity_answers': [[]],\n 'yesno_answers': []})\n\n if result_dir is not None and result_prefix is not None:\n result_file = os.path.join(result_dir, result_prefix + '.json')\n with open(result_file, 'w') as fout:\n for pred_answer in pred_answers:\n fout.write(json.dumps(pred_answer, ensure_ascii=False) + '\\n')\n\n self.logger.info('Saving {} results to {}'.format(result_prefix, result_file))\n\n # this average loss is invalid on test set, since we don't have true start_id and end_id\n ave_loss = 1.0 * total_loss / total_num\n # compute the bleu and rouge scores if reference answers is provided\n if len(ref_answers) > 0:\n pred_dict, ref_dict = {}, {}\n for pred, ref in zip(pred_answers, ref_answers):\n question_id = ref['question_id']\n if len(ref['answers']) > 0:\n pred_dict[question_id] = normalize(pred['answers'])\n ref_dict[question_id] = normalize(ref['answers'])\n bleu_rouge = compute_bleu_rouge(pred_dict, ref_dict)\n else:\n bleu_rouge = None\n return ave_loss, bleu_rouge","def save_analysis_results(self):\r\n\r\n logger.debug('Saving analysis results')\r\n\r\n segments_nr = len(self.tracked_faces)\r\n self.anal_results[c.SEGMENTS_NR_KEY] = segments_nr\r\n\r\n people_clusters_nr = len(self.recognized_faces)\r\n self.anal_results[c.PEOPLE_CLUSTERS_NR_KEY] = people_clusters_nr\r\n\r\n # Count relevant tags\r\n relevant_people_nr = 0\r\n for person_dict in self.recognized_faces:\r\n\r\n tag = person_dict[c.ASSIGNED_TAG_KEY]\r\n\r\n if tag != c.UNDEFINED_TAG:\r\n relevant_people_nr += 1\r\n\r\n self.anal_results[c.RELEVANT_PEOPLE_NR_KEY] = relevant_people_nr","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 output_predictions(pipeline):\n ##### Write code here #######\n X_train, y_train_true = load_data_file(TRAIN_FILE)\n X_dev, y_dev_true = load_data_file(DEV_FILE)\n X_test, y_test_true = load_data_file(TEST_FILE)\n\n #train pipeline with dev and train file\n pipeline.fit(X=X_train, y=y_train_true)\n pipeline.fit(X=X_dev, y=y_dev_true)\n\n y_pred_test = pipeline.predict(X=X_test)\n\n df = pd.DataFrame(y_pred_test)\n with open('predictions.tsv', 'w'):\n df.to_csv('predictions.tsv', sep='\\t', index=False, header=False)\n ##### End of your work ######","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 analyze(collector):\n mod_list = [\"Oxidation\", \"Deamidated\", \"Methyl\", \"Acetyl\", \"Phospho\"]\n fieldnames = (\n [\"approach\", \"count_type\", \"validation_engine\", \"unmodified\", \"multimodified\"]\n + mod_list\n + [\"total\"]\n )\n\n csv_writer = csv.DictWriter(open(\"ungrouped_results.csv\", \"w\"), fieldnames)\n csv_writer.writeheader()\n uc = ursgal.UController()\n uc.params[\"validation_score_field\"] = \"PEP\"\n uc.params[\"bigger_scores_better\"] = False\n\n # Count the number of identified peptides and PSMs for the different modifications\n # Spectra with multiple PSMs are sanitized, i.e. only the PSM with best PEP score is counted\n # and only if the best hit has a PEP that is at least two orders of\n # magnitude smaller than the others\n for validation_engine, result_file in collector.items():\n counter_dict = {\"psm\": ddict(set), \"pep\": ddict(set)}\n grouped_psms = uc._group_psms(\n result_file, validation_score_field=\"PEP\", bigger_scores_better=False\n )\n for spec_title, grouped_psm_list in grouped_psms.items():\n best_score, best_line_dict = grouped_psm_list[0]\n if len(grouped_psm_list) > 1:\n second_best_score, second_best_line_dict = grouped_psm_list[1]\n best_peptide_and_mod = (\n best_line_dict[\"Sequence\"] + best_line_dict[\"Modifications\"]\n )\n second_best_peptide_and_mod = (\n second_best_line_dict[\"Sequence\"]\n + second_best_line_dict[\"Modifications\"]\n )\n\n if best_peptide_and_mod == second_best_peptide_and_mod:\n line_dict = best_line_dict\n elif best_line_dict[\"Sequence\"] == second_best_line_dict[\"Sequence\"]:\n if best_score == second_best_score:\n line_dict = best_line_dict\n else:\n if (-1 * math.log10(best_score)) - (\n -1 * math.log10(second_best_score)\n ) >= 2:\n line_dict = best_line_dict\n else:\n continue\n else:\n if (-1 * math.log10(best_score)) - (\n -1 * math.log10(second_best_score)\n ) >= 2:\n line_dict = best_line_dict\n else:\n continue\n else:\n line_dict = best_line_dict\n\n count = 0\n for mod in mod_list:\n if mod in line_dict[\"Modifications\"]:\n count += 1\n key_2_add = \"\"\n if count == 0:\n key_2_add = \"unmodified\"\n elif count >= 2:\n key_2_add = \"multimodified\"\n elif count == 1:\n for mod in mod_list:\n if mod in line_dict[\"Modifications\"]:\n key_2_add = mod\n break\n # for peptide identification comparison\n counter_dict[\"pep\"][key_2_add].add(\n line_dict[\"Sequence\"] + line_dict[\"Modifications\"]\n )\n # for PSM comparison\n counter_dict[\"psm\"][key_2_add].add(\n line_dict[\"Spectrum Title\"]\n + line_dict[\"Sequence\"]\n + line_dict[\"Modifications\"]\n )\n for counter_key, count_dict in counter_dict.items():\n dict_2_write = {\n \"approach\": \"ungrouped\",\n \"count_type\": counter_key,\n \"validation_engine\": validation_engine,\n }\n total_number = 0\n for key, obj_set in count_dict.items():\n dict_2_write[key] = len(obj_set)\n total_number += len(obj_set)\n dict_2_write[\"total\"] = total_number\n csv_writer.writerow(dict_2_write)\n return","def parse_results_file(filename):\n\tfile = open(filename, 'r')\n\tpretext=[line for line in file.readlines() if line.strip()]\n\tfile.close()\n\n\ttext = []\n\tprocessed = []\n\tlanguages = 'NONE'\n\tID = 'NONE'\n\t\n\tmoreheader = raw_input('Extra header labels from question field (e.g.: item,condition,factor1,factor2): ')\n\tstim_type = raw_input('What type are your stims? (i.e. AcceptabilityJudgment): ')\n\toutput_loc = raw_input('Where would you like to put your parsed file? (enter filename path): ')\n\t\n\t#takes out comments\n\tfor line in pretext:\n\t\tif re.match('#', line):\n\t\t\tcontinue\n\t\telse:\n\t\t\ttext.append(line)\n\n\tfirst = 1;\n\n\tfor line in range(len(text)):\n\t\t#get their info\n\t\tif re.search('Form', text[line]):\n\t\t\tif re.search('number', text[line]):\n\t\t\t\tID = re.split('number,', text[line])[1].strip()\n\t\t\telif re.search('age', text[line]):\n\t\t\t\tlanguages = re.split('age,', text[line])[1].strip()\n\n\t\t#looks for the main stimulus type, as entered earlier\t\t\n\t\tif re.search(stim_type, text[line]):\n\t\t\tif first:\n\t\t\t\t#print 'first'\n\t\t\t\tprocessed.append(str(ID+ ','+languages+','+text[line]))\n\t\t\t\tfirst=0\n\t\t\telse:\n\t\t\t\ttoAmend = processed.pop()\n\t\t\t\t#print str('toAmend: ' + toAmend)\n\t\t\t\ttoAdd=''\n\t\t\t\tsplits = re.split('NULL,', text[line])\n\t\t\t\tfor thing in splits[1:]:\n\t\t\t\t\tif thing is not '':\n\t\t\t\t\t\ttoAdd = str(toAdd + ',' + thing.strip(','))\n\t\t\t\t#print str('toAdd: ' + toAdd)\n\t\t\t\tprocessed.append(str(toAmend.strip()+ toAdd))\n\t\t\t\tfirst = 1\n\n\t\t#if the line is a question line, there's more to append\n\t\tif re.search('Question', text[line]):\n\t\t\ttoAmend = processed.pop()\n\t\t\tpart = re.split('\\$', text[line])[1]\n\t\t\tpart.strip('$')\n\t\t\tparts = part.split('%2C')\n\t\t\tprocessed.append(str(toAmend.strip()+ ','+ string.join(parts, ',')+'\\n'))\n\t\t\t\n\toutput = open(output_loc, 'w')\n\n\theader = 'ID,Languages,Time sent,MD5 Hash of IP Address,Controller,Item Number,Element Number,Type,Group,Stimulus,Answer,RT,'\n\n\toutput.write(str(header+moreheader+'\\n'))\n\n\t#put it all into a text file\n\tfor line in processed:\n\t\toutput.write(line)\n\toutput.close()","def log_AD_results(xp_path, learner):\n\n log_file = \"{}/log.txt\".format(xp_path)\n log = open(log_file, \"a\")\n\n log.write(\"Results\\n\\n\")\n\n log.write(\"Train AUC: {} %\\n\".format(round(learner.diag['train']['auc'][-1]*100, 4)))\n log.write(\"Train accuracy: {} %\\n\".format(round(learner.diag['train']['acc'][-1], 4)))\n log.write(\"Train time: {}\\n\\n\".format(round(learner.train_time, 4)))\n\n log.write(\"Val AUC: {} %\\n\".format(round(learner.diag['val']['auc'][-1] * 100, 4)))\n log.write(\"Val accuracy: {} %\\n\\n\".format(round(learner.diag['val']['acc'][-1], 4)))\n\n log.write(\"Test AUC: {} %\\n\".format(round(learner.diag['test']['auc'][-1]*100, 4)))\n log.write(\"Test accuracy: {} %\\n\".format(round(learner.diag['test']['acc'][-1], 4)))\n log.write(\"Test time: {}\\n\".format(round(learner.test_time, 4)))\n\n log.write(\"\\n\\n\")\n log.close()","def _publish_results(self):\n\n doc = Document()\n date = get_stamp()\n\n labels = ExperimentTemplateBase.parameters_to_string(self._topology_parameters_list)\n\n title = 'Mutual Information labels vs ' + self._experiment_name\n self.plot_save(title,\n self._mutual_info,\n self._baseline_mutual_info,\n 'Norm. mutual information',\n labels, date, self._docs_folder, doc)\n\n title = 'Weak classifier accuracy labels vs ' + self._experiment_name\n self.plot_save(title,\n self._classifier_accuracy,\n self._baseline_classifier_accuracy,\n 'Classifier accuracy',\n labels, date, self._docs_folder, doc) #, smoothing_size=3)\n\n title = 'average delta'\n f = plot_multiple_runs(\n self._different_steps[0], # here the X axes are identical\n self._average_delta,\n title=title,\n ylabel='log(delta)',\n xlabel='steps',\n labels=labels\n )\n add_fig_to_doc(f, path.join(self._docs_folder, title), doc)\n\n title = 'average boosting duration'\n f = plot_multiple_runs(\n self._different_steps[0],\n self._average_boosting_dur,\n title=title,\n ylabel='duration',\n xlabel='steps',\n labels=labels\n )\n add_fig_to_doc(f, path.join(self._docs_folder, title), doc)\n\n doc.write_file(path.join(self._docs_folder, to_safe_name(self._complete_name() + date + \".html\")))\n\n print('done')","def process_iteration_result(self, result, context):\n self.infile = os.path.join(context.output_directory, 'trace.txt')\n if os.path.isfile(self.infile):\n self.logger.debug('Running result_processor \"dvfs\"')\n self.outfile = os.path.join(settings.output_directory, 'dvfs.csv')\n self.flush_parse_initialize()\n self.calculate()\n self.percentage()\n self.generate_csv(context)\n self.logger.debug('Completed result_processor \"dvfs\"')\n else:\n self.logger.debug('trace.txt not found.')","def result():\n\n long_description = (\n \"Processing the results from a computed deflex dump file. The \"\n \"following functions are available:\\n\\n\"\n \"* calculate_key_values\\n\"\n \"* something\\n\"\n \"\\nSee the documentation for more details.\"\n )\n parser = argparse.ArgumentParser(\n prog=\"deflex-results\",\n description=long_description,\n formatter_class=argparse.RawDescriptionHelpFormatter,\n )\n parser.add_argument(\n \"--version\", action=\"version\", version=f\"deflex {deflex.__version__}\"\n )\n parser.add_argument(\n \"function\", type=str, help=\"Post-processing function to use.\"\n )\n parser.add_argument(\"in_path\", type=str, help=\"Input file or directory.\")\n parser.add_argument(\"out_path\", type=str, help=\"Output file or directory.\")\n parser.add_argument(\n \"--filetype\",\n dest=\"filetype\",\n const=None,\n default=None,\n nargs=\"?\",\n help=(\n \"The file_type of the output file xlsx or csv. By default the \"\n \"suffix of the output file is used, if possible.\"\n ),\n )\n args = parser.parse_args()\n\n deflex.use_logging()\n\n if args.function == \"calculate_key_values\":\n results = deflex.restore_results(args.in_path)\n df = deflex.calculate_key_values(results)\n elif args.function == \"something\":\n df = pd.DataFrame()\n else:\n msg = (\n f\"The function {args.function}() is not available with \"\n f\"'deflex_results'.\"\n )\n raise NotImplementedError(msg)\n\n if args.filetype is None:\n args.filetype = args.out_path.split(\".\")[-1]\n\n if args.filetype == \"xlsx\":\n df.to_excel(args.out_path)\n elif args.filetype == \"csv\":\n logging.info(f\"Results stored in {args.out_path}\")\n df.to_csv(args.out_path)\n else:\n raise ValueError(f\"File type '{args.filetype}' not valid.\")","def dataStats(self):\n print (\"Performing statistical analysis of the data\")\n # stuff to do","def analyzeResults(self):\n results = [self.analyzeClusterPerformance(c) for c in self.clusterLabels]\n rDF = pd.DataFrame(results)\n self.resultList.append(rDF)","def generate_report(path_to_results: str, save_path: str, format: str) -> None:\n path_to_results = (\n path_to_results if path_to_results[-1] != \"/\" else path_to_results[:-1]\n )\n files = glob.glob(f\"{path_to_results}/*\")\n\n if len(files) == 0:\n raise Exception(f\"There were no result files in the path {path_to_results}\")\n\n raw_results = []\n for result_file in files:\n with open(result_file, \"rb\") as handle:\n raw_results.append(pickle.load(handle))\n\n results_dict = []\n for raw in raw_results:\n runtime = np.mean([i[\"runtime\"] for i in raw[\"results\"]])\n memory = np.mean(\n [\n i[\"memory_stats\"][\"max_memory\"]\n for i in raw[\"results\"]\n if i[\"memory_stats\"] is not None\n ]\n )\n result = {\n \"layer\": raw[\"layer\"],\n \"batch_size\": raw[\"batch_size\"],\n \"num_runs\": raw[\"num_runs\"],\n \"num_repeats\": raw[\"num_repeats\"],\n \"forward_only\": raw[\"forward_only\"],\n \"gsm_mode\": raw[\"gsm_mode\"],\n \"runtime\": runtime,\n \"memory\": memory,\n }\n results_dict.append(result)\n\n results = pd.DataFrame(results_dict)\n\n results[\"gsm_mode\"][results[\"layer\"].str.startswith(\"dp\")] = (\n \"dp_\" + results[\"gsm_mode\"]\n )\n results[\"layer\"] = results[\"layer\"].str.replace(\"dp\", \"\")\n\n pivot = results.pivot_table(\n index=[\"batch_size\", \"num_runs\", \"num_repeats\", \"forward_only\", \"layer\"],\n columns=[\"gsm_mode\"],\n values=[\"runtime\"],\n )\n\n def add_ratio(df, metric, variant):\n if variant not in df.columns.get_level_values(\"gsm_mode\"):\n for ametric in df.columns.get_level_values(0):\n df[(ametric, variant)] = np.nan\n\n df[(metric, f\"{variant}/baseline\")] = (\n df.loc[:, (metric, variant)] / df.loc[:, (metric, \"baseline\")]\n )\n\n if \"baseline\" in results[\"gsm_mode\"].tolist():\n for m in set(results[\"gsm_mode\"].tolist()) - {\"baseline\"}:\n add_ratio(pivot, \"runtime\", m)\n pivot.columns = pivot.columns.set_names(\"value\", level=1)\n\n output = pivot.sort_index(axis=1).sort_values(\n [\"batch_size\", \"num_runs\", \"num_repeats\", \"forward_only\"]\n )\n if format == \"csv\":\n output.to_csv(save_path)\n else:\n output.to_pickle(save_path)","def after_batch(self):\n if self.trainer._mode == 'train':\n with open(os.path.join(self.root_path, 'loss.txt'), 'a+') as fout:\n fout.write(str(self.trainer._epoch) + '\\t' +\n str(self.trainer._loss.detach().cpu().item()) + '\\n')\n\n if self.trainer._mode == 'test' and (self.f is not None):\n for index in range(len(self.trainer._ids)):\n one_input = self.get_one(self.trainer._input, index)\n one_output = self.get_one(self.trainer._output, index)\n\n res = self.f(one_input, one_output)\n id = self.trainer._ids[index]\n\n self.show(res, id)","def collect(self,\n epoch,\n test_loss,\n test_accuracy,\n training_loss,\n training_accuracy):\n print(\n '\\n------------------ Neural Network Testing Result ------------------\\n',\n ' - epoch {0} \\n'.format(epoch),\n ' - test loss {0:1.9f}\\n'.format(test_loss),\n ' - test accuracy {0:1.3f} %\\n'.format(test_accuracy),\n ' - training loss {0:1.9f}\\n'.format(training_loss),\n ' - training accuracy {0:1.3f} %\\n'.format(training_accuracy))\n\n self.test_accuracies.append(test_accuracy / 100.0)\n self.training_accuracies.append(training_accuracy / 100.0)\n self.test_losses.append(test_loss)\n self.training_losses.append(training_loss)\n if self.epochs < epoch:\n self.epochs = epoch","def dwn_all_saved_results(request):\n \n sources = []\n for i in Source.objects.filter(user=request.user):\n sources.append((i.source_id, i.datetime_extracted.strftime('%d/%m/%Y %H:%M'), i.source))\n \n data = []\n for s, timee, s_name in sources:\n objs = ExtractedRelation.objects.filter(source=s)\n for i in objs:\n data.append((i.sentence, i.head, i.tail, i.pred_relation, i.sentiment, i.conf, timee, s_name, i.rel_id, os.path.basename(i.ckpt)))\n \n df = pd.DataFrame(data, columns=['Sentence', 'Head', 'Tail', 'Predicted Relation', 'Predicted Sentiment', 'Confidence', 'Extraction Time', 'Source', 'rel_id', 'Checkpoint'])\n df.to_csv(\"temp/all_analysis_results.csv\", index=False)\n \n return FileResponse(open('temp/all_analysis_results.csv','rb'))","def _extract_results(self) -> None:\n metric_name = self.metric.name\n for inference_name in ['train', 'test', 'opt']:\n # TODO: Extract information from self.search_results\n data = getattr(self.search_results, f'{inference_name}_metric_dict')[metric_name]\n if all([d is None for d in data]):\n if inference_name not in OPTIONAL_INFERENCE_CHOICES:\n raise ValueError(f\"Expected {metric_name} score for {inference_name} set\"\n f\" to not be None, but got {data}\")\n else:\n continue\n self.data[f'single::{inference_name}::{metric_name}'] = np.array(data)\n\n if self.ensemble_results.empty() or inference_name == 'opt':\n continue\n\n data = getattr(self.ensemble_results, f'{inference_name}_scores')\n if all([d is None for d in data]):\n if inference_name not in OPTIONAL_INFERENCE_CHOICES:\n raise ValueError(f\"Expected {metric_name} score for {inference_name} set\"\n f\" to not be None, but got {data}\")\n else:\n continue\n self.data[f'ensemble::{inference_name}::{metric_name}'] = np.array(data)","def evaluate(self):\n\n rew_rl = []\n for ckpt_path in self._config.eval_ckpt_paths:\n self._load_ckpt(ckpt_path, self._config.ckpt_num)\n\n logger.info(\n \"Run %d evaluations for %d environments\",\n self._config.num_eval,\n len(self._envs),\n )\n rollouts, info = self._evaluate(record_video=self._config.record_video)\n\n info_stat = info.get_stat()\n\n rew_rl.append(np.mean(info[\"rew_rl\"]))\n\n logger.info(\"All Eval Rew Values: %s\", rew_rl)\n logger.info(\"Eval Rew RL Average: %f, Std: %f\", np.mean(rew_rl), np.std(rew_rl))\n\n os.makedirs(\"result\", exist_ok=True)\n with h5py.File(\"result/{}.hdf5\".format(self._config.run_name), \"w\") as hf:\n for k, v in info.items():\n hf.create_dataset(k, data=info[k])\n with open(\"result/{}.txt\".format(self._config.run_name), \"w\") as f:\n for k, v in info_stat.items():\n f.write(\"{}\\t{:.03f} $\\\\pm$ {:.03f}\\n\".format(k, v[0], v[1]))","def showResult():\n logging.info('I am in showResult......')\n filename = \"../txt/%s_testall_%d_%d.txt\" % (const.DATASET_NAME,const.TOPIC_NUM,const.TOP_N)\n x = range(1,const.TOP_N,1)\n result = [[[] for i in range(5)] for i in range(const.METHOD_SIZE)]\n #read result from file to result\n if os.path.exists(filename):\n print '%s is existing......' % filename \n rFile = open(filename,\"r\")\n lines = rFile.readlines()\n for line in lines:\n line = line.rstrip('\\n')\n items = line.split(\"INFO:\")\n line = items[1]\n items = line.split(\":\")\n ids = items[0]\n values = items[1]\n idItems = ids.split(\">\")\n mid = int(idItems[0])\n topN = int(idItems[1])\n valueItems = values.split()\n result[mid][0].append(float(valueItems[0]))\n result[mid][1].append(float(valueItems[1]))\n result[mid][2].append(float(valueItems[2]))\n result[mid][3].append(float(valueItems[3]))\n result[mid][4].append(float(valueItems[4]))\n rFile.close()\n else:\n rFile = open(filename,\"w\")\n rFile.close()\n #if some method is not in file, recreate it\n for mid in range(const.METHOD_SIZE):\n if len(result[mid][0]) == 0:\n recalls,precisions,f1s,maes,rmses = getErrorOfRecMethod(mid)\n result[mid][0] = recalls\n result[mid][1] = precisions\n result[mid][2] = f1s\n result[mid][3] = maes\n result[mid][4] = rmses\n\n #plt img of comparing with pure method\n for index in range(5):\n plt.figure(index)\n indexName = util.getIndexName(index)\n mids = [const.ARIMA,const.SIMILAR,const.AVG]\n markerIndex = 0\n for mid in mids:\n if index == 1 or index == 2:\n plt.plot(x[10:],result[mid][index][10:],const.marker[markerIndex],label=util.getMethodName(mid))\n else:\n plt.plot(x,result[mid][index],const.marker[markerIndex],label=util.getMethodName(mid))\n markerIndex += 1\n plt.title(\"%s of Different Recommend Algorithms(Pure)\" % indexName)\n plt.xlabel(\"Number of recommendations\")\n plt.ylabel(indexName)\n plt.legend()\n plt.xlim(1,160)\n plt.savefig(\"../img/pure_%s_%s_%d_%d.png\" % (const.DATASET_NAME,indexName,const.TOPIC_NUM,const.TOP_N))\n #plt.show()\n\n #plt img of comparing with hybrid method\n for index in range(5):\n plt.figure(index+5)\n indexName = util.getIndexName(index)\n mids = [const.ARIMA,const.ARIMA_SIMILAR,const.ARIMA_AVG,const.ALL_HYBRID]\n markerIndex = 0\n for mid in mids:\n if index == 1 or index == 2:\n plt.plot(x[10:],result[mid][index][10:],const.marker[markerIndex],label=util.getMethodName(mid))\n else:\n plt.plot(x,result[mid][index],const.marker[markerIndex],label=util.getMethodName(mid))\n markerIndex += 1\n plt.title(\"%s of Different Recommend Algorithms(Hybrid)\" % indexName)\n plt.xlabel(\"Number of recommendations\")\n plt.ylabel(indexName)\n plt.legend()\n plt.xlim(1,160)\n plt.savefig(\"../img/hybrid_%s_%s_%d_%d.png\" % (const.DATASET_NAME,indexName,const.TOPIC_NUM,const.TOP_N))\n #plt.show()\n\n\n #plt img of comparing with sequential method\n for index in range(5):\n plt.figure(index+10)\n indexName = util.getIndexName(index)\n mids = [const.ARIMA,const.KNN,const.PATTERN,const.MARKOV,const.MARKOV_3]\n markerIndex = 0\n for mid in mids:\n if index == 1 or index == 2:\n plt.plot(x[10:],result[mid][index][10:],const.marker[markerIndex],label=util.getMethodName(mid))\n else:\n plt.plot(x,result[mid][index],const.marker[markerIndex],label=util.getMethodName(mid))\n markerIndex += 1\n plt.title(\"%s of Different Recommend Methods\" % indexName)\n plt.xlabel(\"Number of recommendations\")\n plt.ylabel(indexName)\n plt.legend()\n plt.xlim(1,160)\n plt.savefig(\"../img/seq_%s_%s_%d_%d.png\" % (const.DATASET_NAME,indexName,const.TOPIC_NUM,const.TOP_N))\n #plt.show()\n \n plt.figure(30)\n plt.plot(x,result[const.ARIMA_SIMILAR][3],'k-.',label=util.getMethodName(const.ARIMA_SIMILAR)) \n plt.plot(x,result[const.ARIMA_AVG][3],'k+',label=util.getMethodName(const.ARIMA_AVG)) \n plt.plot(x,result[const.ALL_HYBRID][3],'k',label=util.getMethodName(const.ALL_HYBRID)) \n plt.title(\"MAE of Hybrid Music Recommendation Methods\")\n plt.xlabel(\"Number of recommendations\")\n plt.ylabel(\"MAE\")\n plt.legend()\n plt.xlim(1,160)\n plt.savefig(\"../img/local_global_%s_%s_%d_%d.png\" % (const.DATASET_NAME,\"MAE\",const.TOPIC_NUM,const.TOP_N))\n\n plt.figure(31)\n plt.plot(x,result[const.ARIMA_SIMILAR][4],'k-.',label=util.getMethodName(const.ARIMA_SIMILAR)) \n plt.plot(x,result[const.ARIMA_AVG][4],'k+',label=util.getMethodName(const.ARIMA_AVG)) \n plt.plot(x,result[const.ALL_HYBRID][4],'k',label=util.getMethodName(const.ALL_HYBRID)) \n plt.title(\"RMSE of Hybrid Music Recommendation Methods\")\n plt.xlabel(\"Number of recommendations\")\n plt.ylabel(\"RMSE\")\n plt.legend()\n plt.xlim(1,160)\n plt.savefig(\"../img/local_global_%s_%s_%d_%d.png\" % (const.DATASET_NAME,\"RMSE\",const.TOPIC_NUM,const.TOP_N))\n\n plt.figure(19)\n improvement = []\n for i in range(len(result[const.ARIMA][1])):\n improvement.append((result[const.ARIMA][1][i]-result[const.KNN][1][i]) / result[const.KNN][1][i])\n plt.plot(x[10:],improvement[10:],'k',label='Improvement over UserKNN Recommender')\n plt.title('Average Precision Improvement over UserKNN Recommender')\n plt.xlabel('Number of recommendations')\n plt.ylabel('Improvement in Average Precision (times)')\n plt.legend()\n indexName = util.getIndexName(1)\n plt.savefig(\"../img/improvement_knn_%s_%s_%d_%d.png\" % (const.DATASET_NAME,indexName,const.TOPIC_NUM,const.TOP_N))\n\n plt.figure(20)\n improvement = []\n for i in range(len(result[const.ARIMA][1])):\n improvement.append((result[const.ARIMA][1][i]-result[const.KNN][1][i]) / result[const.KNN][1][i])\n plt.plot(x[10:],improvement[10:],'k',label='Improvement over UserKNN Recommender')\n plt.title('Average Precision Improvement over UserKNN Recommender')\n plt.xlabel('Number of recommendations')\n plt.ylabel('Improvement in Average Precision (times)')\n plt.legend()\n indexName = util.getIndexName(1)\n plt.savefig(\"../img/improvement_knn_%s_%s_%d_%d.png\" % (const.DATASET_NAME,indexName,const.TOPIC_NUM,const.TOP_N))\n\n plt.figure(21)\n improvement = []\n for i in range(len(result[const.ARIMA][2])):\n improvement.append((result[const.ARIMA][2][i]-result[const.KNN][2][i]) / result[const.KNN][2][i])\n plt.plot(x[10:],improvement[10:],'k',label='Improvement over UserKNN Recommender')\n plt.title('Average F1-Score Improvement over UserKNN Recommender')\n plt.xlabel('Number of recommendations')\n plt.ylabel('Improvement in Average F1-Score (times)')\n plt.legend()\n indexName = util.getIndexName(2)\n plt.savefig(\"../img/improvement_knn_%s_%s_%d_%d.png\" % (const.DATASET_NAME,indexName,const.TOPIC_NUM,const.TOP_N))\n #plt.show()\n logging.info('I am out showResult......')\n\n #plt img of comparing with pure method\n for index in range(5):\n plt.figure(index+50)\n indexName = util.getIndexName(index)\n print indexName\n mids = [const.ARIMA,const.SIMILAR,const.KNN,const.AVG,const.PATTERN,const.MARKOV]\n markerIndex = 0\n for mid in mids:\n if index == 1 or index == 2:\n plt.plot(x[10:],result[mid][index][10:],const.marker1[markerIndex],label=util.getMethodName(mid))\n else:\n plt.plot(x,result[mid][index],const.marker1[markerIndex],label=util.getMethodName(mid))\n markerIndex += 1\n plt.title(\"%s of Different Recommend Algorithms\" % indexName)\n plt.xlabel(\"Number of recommendations\")\n plt.ylabel(indexName)\n plt.legend()\n plt.xlim(1,160)\n plt.savefig(\"../img/all_%s_%s_%d_%d.png\" % (const.DATASET_NAME,indexName,const.TOPIC_NUM,const.TOP_N))\n\n #plt img of comparing with hybrid method\n for index in range(5):\n plt.figure(index+75)\n indexName = util.getIndexName(index)\n mids = [const.ARIMA,const.ALL_HYBRID]\n markerIndex = 0\n for mid in mids:\n if index == 1 or index == 2:\n plt.plot(x[10:],result[mid][index][10:],const.marker[markerIndex],label=util.getMethodName(mid))\n else:\n plt.plot(x,result[mid][index],const.marker[markerIndex],label=util.getMethodName(mid))\n markerIndex += 1\n plt.title(\"%s of Different Recommend Algorithms\" % indexName)\n plt.xlabel(\"Number of recommendations\")\n plt.ylabel(indexName)\n plt.legend()\n plt.xlim(1,160)\n plt.savefig(\"../img/hybrid_only_%s_%s_%d_%d.png\" % (const.DATASET_NAME,indexName,const.TOPIC_NUM,const.TOP_N))\n #plt.show()","def process_output_reports(results, analysis, date_now):\n #PLUG_INS[analysis.plug_in].set_data(analysis.title, file_path, results)\n output = PLUG_INS[analysis.plug_in]()\n file_path = settings.REPORT_PATH+\"/analysis%s_%s_%s_%s_%s_%s_%s\" % (analysis.id, date_now.year, date_now.month, date_now.day, date_now.hour, date_now.minute, date_now.second)\n output.set_data(analysis.title, file_path, results)\n\n result = AnalysisResult(analysis=analysis, output=string.split(output.get_output_file(), \"/\")[-1], run_date=date_now)\n result.save() \n analysis.last_report = date_now\n analysis.save()\n return True","def report(self, results):\n self.notice(\"Test Report\\n\")\n\n for count, group in enumerate(results, 1):\n results = (self._format_test(test, res) for test, res in group)\n results = (', ').join(results)\n self.notice(\"Test group %s:\\t%s\" % (count, results))\n\n self.divider()","def main():\n parser = argparse.ArgumentParser(description=\"Process the results of an experiment.\")\n parser.add_argument(\"experiment\")\n arguments = parser.parse_args()\n path = f\"experiments/{arguments.experiment}\"\n if not os.path.exists(path):\n raise SystemExit(f\"Path {path} does not exists.\")\n\n # For efficiency, one should generate the results from the parts without merging them.\n files = [file for file in os.listdir(path) if os.path.isfile(os.path.join(path, file))]\n frames = []\n for file in files:\n device, experiment, _ = file.split(\".\")\n frame = pandas.read_csv(\n os.path.join(path, file),\n index_col=\"variable\",\n usecols=[\"variable\", \"group_index\", \"value_i\"], dtype={\"value_i\": \"Int64\"}\n )\n frame[\"board\"] = device\n frame[\"experiment\"] = experiment\n frames.append(frame)\n dataframe = pandas.concat(frames)\n frames = None\n\n current_grouping = dataframe.groupby([\"group_index\", \"variable\"])\n \n data = current_grouping.agg([\n numpy.median,\n _percentile_factory(95),\n numpy.mean,\n numpy.std,\n \"count\"\n ])\n\n print(data)\n \n data = data.droplevel([0], axis=1)\n data = data.unstack()\n data.columns = data.columns.map('_'.join)\n data.to_csv(f\"{arguments.experiment}.csv\")","def main():\n\n file_name_base = \"./lab-record/result/fairness/\"\n scenarios = ['lan', 'wan1', 'wan2']\n scenario = scenarios[2]\n\n algorithms = [\"bbr\", \"scalable\", \"bic\", \"highspeed\", \"htcp\", \"hybla\",\n \"illinois\", \"vegas\", \"yeah\"]\n names = [\"BBR\", \"Scalable\", \"BIC\", \"High Speed\",\n \"H-TCP\", \"Hybla\", \"Illinois\", \"Vegas\", \"YeAH\"]\n\n test_types = [\"vs_reno\", \"vs_cubic\", \"vs_itself\"]\n\n fsize = 36\n \n index_reno = []\n index_cubic = []\n index_itself = []\n\n data = []\n \n print 'Loadint statistics for ' + file_name_base + '/' + scenario\n\n for algorithm in algorithms:\n for test in test_types:\n path_base = file_name_base + \"/\" + scenario + \"/\" + test + \"/\" + \\\n algorithm + \"/\"\n if test == \"vs_itself\":\n exp_name = names[algorithms.index(algorithm)] + \"_1\"\n con_name = names[algorithms.index(algorithm)] + \"_2\"\n print path_base + exp_name\n print path_base + con_name\n exp_filename = \"/\" + algorithm + \"_1.log\"\n con_filename = \"/\" + algorithm + \"_2.log\"\n process(path_base, exp_filename, con_filename, index_itself)\n if test == \"vs_reno\":\n exp_name = names[algorithms.index(algorithm)]\n con_name = \"Reno\"\n print path_base + exp_name\n print path_base + con_name\n exp_filename = \"/\" + algorithm + \".log\"\n con_filename = \"/reno.log\"\n process(path_base, exp_filename, con_filename, index_reno)\n if test == \"vs_cubic\":\n con_name = \"CUBIC\"\n exp_name = names[algorithms.index(algorithm)]\n print path_base + exp_name\n print path_base + con_name\n exp_filename = \"/\" + algorithm + \".log\"\n con_filename = \"/cubic.log\"\n process(path_base, exp_filename, con_filename, index_cubic)\n\n size = 9\n x = numpy.arange(size)\n\n total_width, n = 1.2, 2.5\n width = 1.0 / n\n x = x - (total_width - width) / 2\n\n for i in range(0, len(x)):\n x[i] += 0.5 * i\n\n # Exp\n fig = plt.figure()\n\n # Con\n con_reno = plt.bar(x + 0 * width - 1.2,\n index_reno,\n width=width,\n label='Against Reno',\n alpha=0.5,\n color=\"darkorange\")\n\n con_cubic = plt.bar(x + 1 * width - 1.2,\n index_cubic,\n width=width,\n label='Against CUBIC',\n alpha=0.5,\n color=\"lawngreen\")\n\n con_itself = plt.bar(x + 2 * width - 1.2,\n index_itself,\n width=width,\n label='Against Another Same CCA',\n alpha=0.5,\n color=\"dodgerblue\")\n\n # Index\n plt.xticks(x + 1.5 * width - 1.2, [\"BBR\", \"Scalable\", \"BIC\", \"High Speed\",\n \"H-TCP\", \"Hybla\", \"Illinois\", \"Vegas\",\n \"YeAH\"],\n fontsize=fsize,\n rotation=\"45\")\n plt.ylabel(\"Jain`s Fairness Index\", fontsize=fsize)\n plt.yticks(fontsize=fsize)\n plt.ylim(0.5, 1.1)\n\n ax = plt.subplot(111)\n ax.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3,\n ncol=3, mode=\"expand\", borderaxespad=0., fontsize=fsize)\n\n plt.subplots_adjust(left=0.07, right=0.98, top=0.9, bottom=0.2)\n\n plt.show()","def metric_recorded(self, record):\n if record.name in self.headers and self.current_row is not None:\n if record.name == \"learning_rate\" and not record.is_scalar:\n # record is a list of scalars\n value = \",\".join([f\"{lr:.4f}\" for lr in record.value])\n elif record.is_scalar and isinstance(record.value, int):\n value = str(record.value)\n else:\n assert record.is_scalar\n\n value = f\"{record.value:.4f}\"\n\n self.current_row[record.name] = value","def export_data():\n # What we'll keep:\n # - year\n # - total_runners\n # - num males\n # - num females\n # - num states\n # - combined distance\n # - average age (mean)\n # - average pace (mean)\n # - average finish time (mean)\n headers = [\n 'Year', 'Total Runners', 'Males', 'Females', 'States',\n 'Combined Distance', 'Mean Age', 'Mean Pace', 'Mean Finish Time',\n ]\n data = tablib.Dataset(headers=headers)\n\n years = sorted([\n year for year, parser in parsers.PARSERS.items()\n if parser is not None\n ])\n for year in years:\n # parse the the data fot he year\n runners = parsers.parse(year)\n row = []\n if runners:\n row.append(year) # Year\n row.append(len(runners)) # Total Runners\n row.append(len([runner.sex for runner in runners if runner.sex == \"F\"]))\n row.append(len([runner.sex for runner in runners if runner.sex == \"M\"]))\n row.append(len(set(runner.state for runner in runners))) # Sates\n row.append(int(len(runners) * 26.2)) # Combined Distance\n\n # Average age.\n ages = [int(runner.age) for runner in runners if is_numeric(runner.age)]\n row.append(int(mean(ages)))\n\n # Average pace.\n paces = []\n for runner in runners:\n minutes, seconds = runner.pace.split(\":\")\n paces.append(int(seconds) + (int(minutes) * 60))\n mean_pace = int(mean(paces))\n mean_pace_minutes, mean_pace_seconds = divmod(mean_pace, 60)\n mean_pace = \"{}:{}\".format(mean_pace_minutes, mean_pace_seconds)\n row.append(mean_pace)\n\n # Average finish times.\n times = []\n for runner in runners:\n hours, minutes, seconds = runner.time.split(\":\")\n times.append(int(seconds) + (int(minutes) * 60) + (int(hours) * 3600))\n mean_time = int(mean(times))\n minutes, seconds = divmod(mean_time, 60)\n hours, minutes = divmod(minutes, 60)\n mean_time = \"{}:{}:{}\".format(hours, minutes, seconds)\n row.append(mean_time)\n\n # Append to our dataset\n data.append(row)\n print('Calculated data for {}...'.format(year))\n\n with open(\"output.csv\", \"w\") as csvfile:\n csvfile.write(data.csv)\n print(\"Output written to 'output.csv'\")","def analyze(data):\n ## Do welch periodogram here\n pass","def save_results(self):\n results = pd.concat([\n pd.DataFrame(self.IDs.cpu().numpy(), columns= ['ID']), \n pd.DataFrame(self.predicted_labels.cpu().numpy(), columns= ['predicted_label']),\n pd.DataFrame(self.correct_predictions.cpu().numpy(), columns= ['correct_prediction']),\n pd.DataFrame(self.epistemic_uncertainty.cpu().numpy(), columns= ['epistemic_uncertainty']), \n pd.DataFrame(self.aleatoric_uncertainty.cpu().numpy(), columns= ['aleatoric_uncertainty']), \n pd.DataFrame(self.total_uncertainty.cpu().numpy(), columns= ['total_uncertainty']), \n ], axis=1)\n\n create_results_directory()\n results.to_csv('results/{}_{}_results.csv'.format(self.__class__.__name__, datetime.datetime.now().replace(microsecond=0).isoformat()), index=False)","def pipeline_runner():\n # file_parser() # take raw data file and extract columns of interest. remove contaminants.\n entry_parser() # remove duplicates, faulty lines and format the whole thing normally.\n lfq_parser() # replace 0s in lfq reading with random small numbers for t testing purposes\n # open Rstudio and do T testing there\n ROutputFormatter() # reformat R output to something more appealing, add FDR and fold change values","def aggregate_results(results):\n\n for (config,con,dec),folds in results.iteritems():\n m = MODEL_PATTERN.match(config)\n if m:\n mode = m.groupdict()['mode'] # mle, rl, mrt, ...\n model = m.groupdict()['model'] # haem, hacm, hard, ...\n align = m.groupdict()['align'] # crp, cls ...\n else:\n mode, model, align = '', '', ''\n # mean accuracies across seeds for each fold\n foldaccuracies = []\n # we count number of models over folds and seeds\n num_individual_models = 0\n\n for foldname,fold in folds.items():\n if 'Q' in options.mode:\n seedaccurracies = fold.values()[:1] if fold.values() else [] # pick one\n# SUPPORT_STATISTICS[(config,con,dec,model,align,mode,foldname)] += 1\n else:\n seedaccurracies = []\n for seed_acc in fold.values():\n seedaccurracies.append(seed_acc)\n SUPPORT_STATISTICS[(config,con,dec,model,align,mode,foldname)] += 1\n # aggregate on fold level\n fold['__MEAN__'] = float(np.mean(seedaccurracies))\n fold['__SD__'] = float(np.std(seedaccurracies))\n l = len(seedaccurracies)\n num_individual_models += l\n SUPPORT_STATISTICS[(config,con,dec,model,align,mode,'__MEAN__')] += l\n SUPPORT_STATISTICS[(config,con,dec,model,align,mode,'__SD__')] += l\n\n # statistics over seeds for this fold\n fold['__STATS__'] = fold['__MEAN__'], fold['__SD__'], l\n foldaccuracies.append(fold['__MEAN__'])\n # aggregate on (config, condition, decoding) level\n folds['__MEAN__'] = float(np.mean(foldaccuracies))\n folds['__SD__'] = float(np.std(foldaccuracies))\n # statistics over folds for this (config, condition, decoding)\n folds['__STATS__'] = folds['__MEAN__'], folds['__SD__'], num_individual_models","def print_data(self):\n total_score = 0.0\n\n title_game = 'Game'\n title_word = 'Word'\n title_word_status = 'Word Status'\n title_bad_guesses = 'Bad Guesses'\n title_missed_letters = 'Missed Letters'\n title_total_score = 'Total score'\n\n if not record_word:\n print(\"No words played.\")\n else:\n print('%-5s %-10s %-12s %-5s %-5s %s' %(title_game,title_word, title_word_status, title_bad_guesses, title_missed_letters,title_total_score))\n print('---- ---- ------------ ----------- -------------- -----------')\n for x in range(len(record_word)):\n print('%-5s %-10s %-13s %-11s %-13s %.2f'%(record_game[x],record_word[x],record_word_status[x],record_bad_guesses[x],record_missed_letters[x],record_total_score[x]))\n\n for x in range(len(record_total_score)):\n total_score = total_score + record_total_score[x]\n\n print('\\nFinal Score: %.2f' %total_score)","def results_aggregator(self, names):\n\t\tfor name in names:\n\t\t\tresult = self.main(name)\n\t\t\tself.results.append(result)\n\t\t\tprint(\"'%s' has been written to the file.\" % result[0])\n\t\t\t\"\"\"result is formatted name, number, rating, review count\"\"\"","def _after_run_finished(self):\n\n # append lists for this run\n self._mutual_info.append([])\n self._baseline_mutual_info.append([])\n\n self._classifier_accuracy.append([])\n self._baseline_classifier_accuracy.append([])\n\n self._steps.append([])\n\n self._average_boosting_dur.append([])\n self._average_delta.append([])\n self._different_steps.append([])\n\n # get all the measurements from the last run\n last_run_measurements = self._measurement_manager.run_measurements[-1]\n\n # temporary sliding window for computation of one value of the mutual information\n labels_window = []\n outputs_window = []\n baseline_outputs_window = []\n\n print('computing statistics after run...')\n\n # go step-by-step through the last run (single_measurement contains all the values taken in that time-step)\n for single_measurement in last_run_measurements:\n\n # these two measurements have to run with different (lower) frequency\n if 'average_boosting_dur' in single_measurement.keys():\n self._average_boosting_dur[-1].append(single_measurement['average_boosting_dur'])\n self._average_delta[-1].append(single_measurement['average_delta'])\n self._different_steps[-1].append(single_measurement['current_step'])\n\n # pick \"dataset_labels\" (see the init()) from the single_measurement and append one value to the separate list\n labels_window.append(single_measurement['dataset_labels'])\n outputs_window.append(single_measurement['model_outputs'])\n baseline_outputs_window.append(single_measurement['baseline_outputs'])\n\n # wait until the window has enough values\n if len(labels_window) < self._evaluation_period:\n continue\n\n # compute stats in the window and store to the last run (that's the [-1]) at the end (that's the append)\n self._mutual_info[-1].append(\n compute_mutual_information(\n np.array(labels_window),\n np.array(outputs_window),\n self._num_classes,\n data_contains_id=True)\n )\n\n if self._debug_mi:\n self._debug_mutual_info(np.array(labels_window), np.array(outputs_window), self._mutual_info[-1][-1])\n\n self._baseline_mutual_info[-1].append(\n compute_mutual_information(\n np.array(labels_window),\n np.array(baseline_outputs_window),\n self._num_classes,\n data_contains_id=True)\n )\n\n # compute the classifier accuracies (for model and baseline)\n dev = self._topology_adapter.get_device()\n output_dim = self._topology_adapter.get_model_output_size()\n\n labels_tensor = torch.tensor(labels_window, dtype=torch.long, device=dev)\n outputs_tensor = torch.tensor(outputs_window, dtype=torch.long, device=dev)\n baseline_outputs_tensor = torch.tensor(baseline_outputs_window, dtype=torch.long, device=dev)\n\n acc = self._compute_classifier_acc(outputs_tensor, labels_tensor, output_dim)\n self._classifier_accuracy[-1].append(acc)\n\n baseline_acc = self._compute_classifier_acc(baseline_outputs_tensor, labels_tensor, output_dim)\n self._baseline_classifier_accuracy[-1].append(baseline_acc)\n\n # store also step (for the x-axis)\n self._steps[-1].append(single_measurement['current_step'])\n\n # remove the self._sliding_window_stride items from the sliding windows.. (then fill the same amount..)\n for i in range(0, self._sliding_window_stride):\n if len(labels_window) > 0:\n labels_window.pop(0)\n outputs_window.pop(0)\n baseline_outputs_window.pop(0)","def write_results(location, classes, results):\n check_out(location)\n # these are tuples consisting of list of values, mean of the values and std the values\n [accuracies, precisions, recalls, f1scores] = results\n\n file_name = []\n for class_ in classes:\n if class_.endswith('.hdf5'):\n # file_name.append(re.findall('\\d/(.*).hdf5$', class_)[0])\n file_name.append(re.findall('([%.\\w-]*).hdf5$', class_)[0])\n\n with open(os.path.join(location, '-'.join(file_name) + '.results'), 'w') as out_file:\n for class_id, class_ in enumerate(classes):\n out_file.write(file_name[class_id].split('_')[0] + '\\n')\n # out_file.write(infos)\n # out_file.write(' '.join([str(header_item) for header_item in headers[class_id]]))\n out_file.write('\\n\\n')\n\n out_file.write(\"Mean accuracy is {:.2f} with a std of ({:.2f}).\\n\".format(accuracies[1], accuracies[2]))\n out_file.write('\\n'.join([str(accuracy) for accuracy in accuracies[0]]) + '\\n')\n\n if len(classes) == 2:\n out_file.write(\"\\nMean precision is {:.2f} with a std of ({:.2f}).\\n\".format(precisions[1], precisions[2]))\n out_file.write('\\n'.join([str(precision) for precision in precisions[0]]) + '\\n')\n\n out_file.write(\"\\nMean recall is {:.2f} with a std of ({:.2f}).\\n\".format(recalls[1], recalls[2]))\n out_file.write('\\n'.join([str(recall) for recall in recalls[0]]) + '\\n')\n\n out_file.write(\"\\nMean f1 score is {:.2f} with a std of ({:.2f}).\\n\".format(f1scores[1], f1scores[2]))\n out_file.write('\\n'.join([str(f1score) for f1score in f1scores[0]]) + '\\n')","def process_results(training_loss,testing_loss, model_names, params,n_to_average):\n # Unpack the unprocessed results\n # all_train_loss, all_test_loss = unprocessed_results\n\n # Convert to numpy arrays\n # all_train_loss = np.array(all_train_loss)\n all_test_loss = np.array(testing_loss)\n # all_preds = np.stack(all_preds)\n # all_stds = np.stack(all_stds)\n\n # Get order models from best to worst test loss\n sorted_inds = np.argsort(testing_loss)\n\n # Sort all other arrays accordingly\n # sorted_train_loss = all_train_loss[sorted_inds]\n # sorted_test_loss = all_test_loss[sorted_inds]\n # sorted_preds = all_preds[sorted_inds]\n # sorted_stds = all_stds[sorted_inds]\n sorted_model_names = model_names[sorted_inds]\n sorted_model_params=[params[i] for i in sorted_inds]\n # Build a dataframe which summarizes results\n # summary_list = [[model, train_loss, test_loss] for model, train_loss, test_loss in\n # zip(sorted_model_names, sorted_train_loss, sorted_test_loss)]\n # summary_df = pd.DataFrame(summary_list,\n # columns=[\"ModelName\", \"cvTrainingError\", \"cvTestingError\"])\n\n # Generate compound predictions\n # cumulative_preds = np.cumsum(sorted_preds, axis=0)\n # compound_preds = np.empty_like(sorted_preds)\n # for i in range(len(compound_preds)):\n # compound_preds[i] = cumulative_preds[i] / (i + 1)\n #\n # # Pull the requested averaged value\n # mlde_preds = compound_preds[n_to_average - 1]\n top_models_name=sorted_model_names[0:n_to_average]\n top_models_params=sorted_model_params[0:n_to_average]\n sorted_training_loss=training_loss[sorted_inds[0:n_to_average]]\n sorted_testing_loss=testing_loss[sorted_inds[0:n_to_average]]\n return top_models_name,top_models_params,sorted_training_loss,sorted_testing_loss","def evaluate(self, eval_batches, result_dir=None, result_prefix=None, save_full_info=False):\n pred_answers, ref_answers = [], []\n total_loss, total_num = 0, 0\n for b_itx, batch in enumerate(eval_batches):\n feed_dict = {self.p: batch['passage_token_ids'],\n self.q: batch['question_token_ids'],\n self.p_length: batch['passage_length'],\n self.q_length: batch['question_length'],\n self.start_label: batch['start_id'],\n self.end_label: batch['end_id'],\n self.dropout_keep_prob: 1.0,\n self.em: batch['exact_match']}\n batch_size = len(batch['start_id'])\n padded_p_len = len(batch['passage_token_ids'][0])\n padded_p_num = len(batch['passage_token_ids']) / batch_size\n para_ids = []\n for start_id in batch['start_id']:\n para_ids.append(start_id // padded_p_len)\n feed_dict[self.para_label] = para_ids\n content_label = np.zeros([batch_size, padded_p_num * padded_p_len], dtype=int)\n for s_idx, (start_id, end_id) in enumerate(zip(batch['start_id'], batch['end_id'])):\n content_label[s_idx, start_id: end_id+1] = 1\n feed_dict[self.content_label] = content_label\n start_probs, end_probs, content_scores, verif_scores, loss = self.sess.run([self.start_probs, self.end_probs,\n self.concat_content_score, self.reshaped_ans_verif_score,\n self.loss], feed_dict)\n\n total_loss += loss * len(batch['raw_data'])\n total_num += len(batch['raw_data'])\n\n padded_p_len = len(batch['passage_token_ids'][0])\n for s_idx, sample in enumerate(batch['raw_data']):\n start_prob = start_probs[s_idx]\n end_prob = end_probs[s_idx]\n content_score = content_scores[s_idx]\n verif_score = verif_scores[s_idx]\n best_answer = self.find_best_answer_with_verif(sample, start_prob, end_prob,\n content_score, verif_score, padded_p_len)\n if save_full_info:\n sample['pred_answers'] = [best_answer]\n pred_answers.append(sample)\n else:\n pred_answers.append({'question_id': sample['question_id'],\n 'question_type': sample['question_type'],\n 'answers': [best_answer],\n 'entity_answers': [[]],\n 'yesno_answers': []})\n if 'answers' in sample:\n ref_answers.append({'question_id': sample['question_id'],\n 'question_type': sample['question_type'],\n 'answers': sample['answers'],\n 'entity_answers': [[]],\n 'yesno_answers': []})\n\n if result_dir is not None and result_prefix is not None:\n result_file = os.path.join(result_dir, result_prefix + '.json')\n with open(result_file, 'w') as fout:\n for pred_answer in pred_answers:\n fout.write(json.dumps(pred_answer, encoding='utf8', ensure_ascii=False) + '\\n')\n\n self.logger.info('Saving {} results to {}'.format(result_prefix, result_file))\n\n # this average loss is invalid on test set, since we don't have true start_id and end_id\n ave_loss = 1.0 * total_loss / total_num\n # compute the bleu and rouge scores if reference answers is provided\n if len(ref_answers) > 0:\n pred_dict, ref_dict = {}, {}\n for pred, ref in zip(pred_answers, ref_answers):\n question_id = ref['question_id']\n if len(ref['answers']) > 0:\n pred_dict[question_id] = normalize(pred['answers'])\n ref_dict[question_id] = normalize(ref['answers'])\n bleu_rouge = compute_bleu_rouge(pred_dict, ref_dict)\n else:\n bleu_rouge = None\n return ave_loss, bleu_rouge","def run_test(self):\n self.output_analytics = self.run_inference()\n self.output_df = pd.DataFrame(self.output_analytics)","def save_result_all(directory: str, params: Dict[str, Any]):\n # save recall\n outdirectory = os.path.join(directory, 'recall_all.csv')\n fp = open(outdirectory, 'w+')\n models = [('baseline', '_base'), \n ('content-based', '_content'), \n ('CF-item-based', '_CF_item'),\n ('CF-user-based', '_CF_user')\n ]\n for model in models:\n if 'recall'+model[1] in params:\n for topN in params['recall'+model[1]]:\n fp.write(model[0]+','+str(topN)+','+str(params['recall'+model[1]][topN])+'\\n')\n fp.close()\n logging.getLogger(__name__).info('Recall rate saved to ' + outdirectory)\n \n # save RMSE\n outdirectory = os.path.join(directory, 'rmse_all.csv')\n fp = open(outdirectory, 'w+')\n for model in models:\n if 'rmse'+model[1] in params:\n fp.write(model[0]+','+str(params['rmse'+model[1]])+'\\n') \n fp.close()\n logging.getLogger(__name__).info('RMSE saved to ' + outdirectory)","def process_dataset(self):\n\n logger.info('\\n')\n logger.info('=' * 40)\n logger.info('=\\t DeepRank Data Set')\n logger.info('=')\n logger.info('=\\t Training data')\n for f in self.train_database:\n logger.info(f'=\\t -> {f}')\n logger.info('=')\n if self.valid_database:\n logger.info('=\\t Validation data')\n for f in self.valid_database:\n logger.info(f'=\\t -> {f}')\n logger.info('=')\n if self.test_database:\n logger.info('=\\t Test data')\n for f in self.test_database:\n logger.info(f'=\\t -> {f}')\n logger.info('=')\n logger.info('=' * 40 + '\\n')\n sys.stdout.flush()\n\n # check if the files are ok\n self.check_hdf5_files(self.train_database)\n\n if self.valid_database:\n self.valid_database = self.check_hdf5_files(\n self.valid_database)\n\n if self.test_database:\n self.test_database = self.check_hdf5_files(\n self.test_database)\n\n # create the indexing system\n # alows to associate each mol to an index\n # and get fname and mol name from the index\n self.create_index_molecules()\n\n # get the actual feature name\n if self.mapfly:\n self.get_raw_feature_name()\n else:\n self.get_mapped_feature_name()\n\n # get the pairing\n self.get_pairing_feature()\n\n # get grid shape\n self.get_grid_shape()\n\n # get the input shape\n self.get_input_shape()\n\n # get renormalization factor\n if self.normalize_features or self.normalize_targets or self.clip_features:\n if self.mapfly:\n self.compute_norm()\n else:\n self.get_norm()\n\n logger.info('\\n')\n logger.info(\" Data Set Info:\")\n logger.info(\n f' Augmentation : {self.use_rotation} rotations')\n logger.info(\n f' Training set : {self.ntrain} conformations')\n logger.info(\n f' Validation set : {self.nvalid} conformations')\n logger.info(\n f' Test set : {self.ntest} conformations')\n logger.info(f' Number of channels : {self.input_shape[0]}')\n logger.info(f' Grid Size : {self.data_shape[1]}, '\n f'{self.data_shape[2]}, {self.data_shape[3]}')\n sys.stdout.flush()","def parse_log(path_to_log):\n regex_iteration = re.compile('Iteration (\\d+), loss = ([\\.\\deE+-]+)')\n regex_train_output = re.compile('Train net output #(\\d+): (\\S+) = ([\\.\\deE+-]+)')\n regex_learning_rate = re.compile('lr = ([\\.\\deE+-]+)')\n regex_test_output = re.compile('Test net output #(\\d+): detection_eval = ([\\.\\deE+-]+)')\n\n\n # Pick out lines of interest\n iteration = 0\n loss = -1\n learning_rate = 0.001\n train_dict_list = []\n train_row = None\n test_score=0.0\n\n logfile_year = extract_seconds.get_log_created_year(path_to_log)\n with open(path_to_log) as f:\n start_time = extract_seconds.get_start_time(f, logfile_year)\n last_time = start_time\n\n for line in f:\n iteration_match = regex_iteration.search(line)\n if iteration_match:\n iteration = float(iteration_match.group(1))\n loss = float(iteration_match.group(2))\n try:\n time = extract_seconds.extract_datetime_from_line(line,\n logfile_year)\n except:\n # Skip lines with bad formatting, for example when resuming solver\n continue\n\n # if it's another year\n if time.month < last_time.month:\n logfile_year += 1\n time = extract_seconds.extract_datetime_from_line(line, logfile_year)\n last_time = time\n\n seconds = (time - start_time).total_seconds()\n\n learning_rate_match = regex_learning_rate.search(line)\n\n if learning_rate_match:\n learning_rate = float(learning_rate_match.group(1))\n\n test_score_match = regex_test_output.search(line)\n if test_score_match:\n test_score = float(test_score_match.group(2))\n\n train_dict_list, train_row = parse_line_for_net_output(\n regex_train_output, train_row, train_dict_list,\n line, iteration, seconds, learning_rate,loss,test_score\n )\n\n\n return train_dict_list","def evaluate(self):\n scores = []\n scores.append(self.word_analogy())\n print(\"Word Analogy (acc): \", scores[0])\n scores.append(self.word_similarity())\n print(\"Word Similarity (MSE): \", scores[1])\n scores.append(self.concept_categorization())\n print(\"Concept Categorization (purity): \", scores[2])\n scores.append(self.sentiment_analysis())\n print(\"Sentiment Analysis (acc): \", scores[3])\n return scores","def analyze_model(folder, verbose=True):\n hyps = get_hyps(folder)\n table = get_analysis_table(folder, hyps=hyps)\n\n model,metrics = read_model(folder,ret_metrics=True)\n get_analysis_figs(folder, metrics)\n\n train_acc, train_loss = metrics['acc'][-1], metrics['loss'][-1]\n table['train_acc'] = [train_acc]\n table['train_loss'] = [train_loss]\n val_acc, val_loss = metrics['val_acc'][-1], metrics['val_loss'][-1]\n table['val_acc'] = [val_acc]\n table['val_loss'] = [val_loss]\n if verbose:\n print(\"ValAcc: {:05e}, ValLoss: {:05e}\".format(val_acc, val_loss))\n return pd.DataFrame(table)","def save_results(self, data, prefix, mode=\"train\", compute_loss=False):\n # save predictions\n self.save_predictions(prefix, mode)","def score(self, experiment_path: str, result_file: str, **kwargs):\n # Update config parameters with new kwargs\n\n config = torch.load(glob.glob(\n \"{}/run_config*\".format(experiment_path))[0],\n map_location=lambda storage, loc: storage)\n config_parameters = dict(config, **kwargs)\n model = torch.load(glob.glob(\n \"{}/run_model*\".format(experiment_path))[0],\n map_location=lambda storage, loc: storage)\n encoder = torch.load(glob.glob(\n '{}/run_encoder*'.format(experiment_path))[0],\n map_location=lambda storage, loc: storage)\n testlabel = config_parameters['testlabel']\n testdata = config_parameters['testdata']\n # Only a single item to evaluate\n if isinstance(testlabel, list) and len(testlabel) == 1:\n testlabel = testlabel[0]\n if isinstance(testdata, list) and len(testdata) == 1:\n testdata = testdata[0]\n\n labels_df = pd.read_csv(testlabel, sep=' ')\n labels_df['encoded'], encoder = utils.encode_labels(\n labels=labels_df['bintype'], encoder=encoder)\n config_parameters.setdefault('colname', ('filename', 'encoded'))\n dataloader = dataset.getdataloader(\n data_frame=labels_df,\n data_file=testdata,\n num_workers=4,\n batch_size=1, # do not apply any padding\n colname=config_parameters[\n 'colname'] # For other datasets with different key names\n )\n model = model.to(DEVICE).eval()\n genuine_label_idx = encoder.transform(['genuine'])[0]\n\n with torch.no_grad(), open(result_file,\n 'w') as wp, tqdm(total=len(dataloader),\n unit='utts') as pbar:\n datawriter = csv.writer(wp, delimiter=' ')\n datawriter.writerow(['filename', 'score'])\n for batch in dataloader:\n inputs, _, filenames = batch\n inputs = inputs.float().to(DEVICE)\n preds = model(inputs)\n for pred, filename in zip(preds, filenames):\n # Single batchsize\n datawriter.writerow([filename, pred[0].item()])\n pbar.update()\n print(\"Score file can be found at {}\".format(result_file))","def result_handler(raw_output):\n write_log(raw_output[1])\n item_list = []\n line_list = raw_output[1].split('\\n')\n title_list = re.sub(\"\\s{2,}\", \"\\t\", line_list[0]).split(\"\\t\")\n for line in line_list[1:]:\n item_list.append(re.sub(\"\\s{2,}\", \"\\t\", line).split(\"\\t\"))\n return raw_output[0], title_list, item_list","def synthesize_results(parent_dir):\r\n # Aggregate metrics from args.parent_dir directory\r\n metrics = dict()\r\n aggregate_metrics(parent_dir, metrics) # metrics在函数里面被修改\r\n #\r\n table = metrics_to_table(metrics)\r\n # Display the table to terminal\r\n print(table)\r\n\r\n # Save results in parent_dir/results.md\r\n save_file = os.path.join(parent_dir, \"results.md\")\r\n with open(save_file, 'w') as f:\r\n f.write(table)","def generate_learning_reports(self) -> None:\n\n # Generate a report consisting of the evaluation metrics for\n # each sub-experiment comprising each cross-validation\n # experiment for each learner\n (pd.DataFrame(list(chain(*self.cv_learner_stats_)))\n .to_csv(self.stats_report_path_,\n index=False))\n\n # Generate a report consisting of the aggregated evaluation\n # metrics from each cross-validation experiment with each\n # learner\n (self.training_cv_aggregated_stats_\n .to_csv(self.aggregated_stats_report_path_,\n index=False))","def evaluate(model, tokenizer, dataset, lines, output_test_file, batch_size=32):\n sampler = SequentialSampler(dataset)\n dataloader = DataLoader(dataset, sampler=eval_sampler, batch_size=batch_size)\n\n print(\"*** Evaluating ***\")\n eval_loss = 0.0\n num_steps = 0\n preds = None\n out_label_ids = None\n for i, batch in enumerate(dataloader):\n if i % 200 == 199:\n print(\"=\", end=\"\")\n if i % 5000 == 4999:\n print(\"[Step \" + str(i+1) + \" / \" + str(len(dataloader)) + \"] \" )\n model.eval()\n batch = tuple(t.to(device) for t in batch)\n\n with torch.no_grad():\n labels = batch[3]\n outputs = model(input_ids=batch[0], attention_mask=batch[1], labels=labels)\n tmp_eval_loss, logits = outputs[:2]\n eval_loss += tmp_eval_loss.mean().item()\n \n num_steps += 1\n if preds is None:\n preds = logits.detach().cpu().numpy()\n out_label_ids = labels.detach().cpu().numpy()\n else:\n preds = np.append(preds, logits.detach().cpu().numpy(), axis=0)\n out_label_ids = np.append(out_label_ids, labels.detach().cpu().numpy(), axis=0)\n \n eval_loss = eval_loss / num_steps\n \n preds_label = np.argmax(preds, axis=1)\n \n accuracy = (preds_label == out_label_ids).mean()\n output_dir = os.path.dirname(output_test_file)\n if not os.path.exists(output_dir):\n os.makedirs(output_dir)\n with open(output_test_file, \"w\") as writer:\n all_logits = preds.tolist()\n for i, logit in enumerate(all_logits):\n line = '<CODESPLIT>'.join(\n [item.encode('ascii', 'ignore').decode('ascii') for item in lines[i]])\n\n writer.write(line + '<CODESPLIT>' + '<CODESPLIT>'.join([str(l) for l in logit]) + '\\n')\n print(\"Accuracy =\", str(accuracy))\n\n return accuracy","def output(results):\n\n text_file = open(\"problem_1_B_output.txt\", \"w\")\n\n out = \"\"\n\n for i, line in enumerate(results):\n\n string = \"Sample {}: {}, posterior probability of {:.4f}\".format(i + 1,\n line[0],\n line[1])\n\n out += (string + \"\\n\")\n\n text_file.write(out)\n\n text_file.close()","def _store_results(self, accuracy):\n\n self.learning_curve_.append(accuracy)"],"string":"[\n \"def process_results(self, episode, eval):\\n if episode % 10 == 9:\\n ave = np.mean(self.scores[episode - 9:episode])\\n print('Episodes: {}, AveScores: {}, Alpha: {}, Steps: {}'.format(\\n episode + 1, ave, self.alpha.item(), self.step_count))\\n if eval:\\n if episode % 100 == 99:\\n s1 = './' + self.game_name + '/'\\n np.save(s1 + 'scores_eval{}.npy'.format(episode + 1), self.scores)\\n print('Evaluation results saved!')\\n else:\\n if episode % 200 == 199:\\n self.save_episode_models(episode)\\n self.plot_array(episode)\\n print('Model salved!')\\n print('Total {} frames!'.format(self.frames_count))\",\n \"def results_to_file(samples, filename, during_analysis):\\r\\n\\r\\n results = []\\r\\n\\r\\n if hasattr(samples, \\\"log_evidence\\\"):\\r\\n if samples.log_evidence is not None:\\r\\n\\r\\n value = \\\"{:.8f}\\\".format(samples.log_evidence)\\r\\n results += [\\r\\n frm.add_whitespace(str0=\\\"Bayesian Evidence \\\", str1=value, whitespace=90)\\r\\n ]\\r\\n results += [\\\"\\\\n\\\"]\\r\\n\\r\\n value = \\\"{:.8f}\\\".format(max(samples.log_likelihoods))\\r\\n results += [\\r\\n frm.add_whitespace(str0=\\\"Maximum Likelihood \\\", str1=value, whitespace=90)\\r\\n ]\\r\\n results += [\\\"\\\\n\\\\n\\\"]\\r\\n\\r\\n results += [\\\"Maximum Log Likelihood Model:\\\\n\\\\n\\\"]\\r\\n\\r\\n formatter = frm.TextFormatter()\\r\\n\\r\\n for i, prior_path in enumerate(samples.model.unique_prior_paths):\\r\\n formatter.add(\\r\\n (prior_path, format_str().format(samples.max_log_likelihood_vector[i]))\\r\\n )\\r\\n results += [formatter.text + \\\"\\\\n\\\"]\\r\\n\\r\\n if hasattr(samples, \\\"pdf_converged\\\"):\\r\\n\\r\\n if samples.pdf_converged:\\r\\n\\r\\n results += samples_text.summary(samples=samples, sigma=3.0, indent=4, line_length=90)\\r\\n results += [\\\"\\\\n\\\"]\\r\\n results += samples_text.summary(samples=samples, sigma=1.0, indent=4, line_length=90)\\r\\n\\r\\n else:\\r\\n\\r\\n results += [\\r\\n \\\"\\\\n WARNING: The samples have not converged enough to compute a PDF and model errors. \\\\n \\\"\\r\\n \\\"The model below over estimates errors. \\\\n\\\\n\\\"\\r\\n ]\\r\\n results += samples_text.summary(samples=samples, sigma=1.0, indent=4, line_length=90)\\r\\n\\r\\n results += [\\\"\\\\n\\\\ninstances\\\\n\\\"]\\r\\n\\r\\n formatter = frm.TextFormatter()\\r\\n\\r\\n for t in samples.model.path_float_tuples:\\r\\n formatter.add(t)\\r\\n\\r\\n results += [\\\"\\\\n\\\" + formatter.text]\\r\\n\\r\\n frm.output_list_of_strings_to_file(file=filename, list_of_strings=results)\",\n \"def record_results(results, scenario_name, output_file=\\\"ValidationResults.csv\\\"):\\n import pandas as pd\\n if os.path.isfile(output_file):\\n table = pd.read_csv(output_file, index_col=0)\\n else:\\n table = pd.DataFrame({})\\n metrics = {r[2]:r[0] for r in results}\\n index = scenario_name\\n metrics = pd.DataFrame(metrics, index=[index])\\n table = metrics.combine_first(table)\\n table = table[metrics.keys()]\\n table.to_csv(output_file)\",\n \"def writeResults(results, best_params, best_score, modelType, penalty, scoreType,\\\\\\n transformedData, scores=None):\\n\\n strScores = \\\"\\\"\\n\\n if modelType=='NonLinear':\\n for model in results:\\n print(model)\\n strScores += \\\"{:.4} {} {} {}\\\\n\\\".format(model[0]['C'], model[0]['gamma'], model[1], np.std(model[2]))\\n elif modelType=='ElasticNet':\\n for model in results:\\n print(model)\\n strScores += \\\"{:.4} {} {} {}\\\\n\\\".format(model[0]['alpha'], model[0]['l1_ratio'], model[1], np.std(model[2]))\\n\\n elif modelType=='Pipe':\\n for model in results:\\n print(model)\\n if 'classif__C' in model[0].keys():\\n strScores += \\\"{} {:.4} {} {}\\\\n\\\".format(model[0]['csp__n_components'], model[0]['classif__C'], model[1], np.std(model[2]))\\n else:\\n strScores += \\\"{} {:.4} {} {}\\\\n\\\".format(model[0]['csp__n_components'], model[0]['classif__alpha'], model[1], np.std(model[2]))\\n\\n elif modelType=='Ridge':\\n for model in results:\\n print(model)\\n strScores += \\\"{:.4} {} {}\\\\n\\\".format(model[0]['alpha'], model[1], np.std(model[2]))\\n\\n \\n else: #Linear, C is the only parameter\\n for model in results:\\n print(model)\\n strScores += \\\"{:.4} {} {}\\\\n\\\".format(model[0]['C'], model[1], np.std(model[2]))\\n \\n\\n strScores += \\\"Best Params : {} Score CrossVal : {} \\\\n\\\".format(best_params, best_score)\\n\\n if scores:\\n strScores += \\\"{}\\\\n{}\\\\n\\\".format(str(scores['cMatrixTrain']),\\\\\\n str(scores['cMatrixTest']))\\n\\n strScores += \\\"Accuracy Train : {} Accuracy Test : {} \\\\n\\\".format(scores['accTrain'], scores['accTest'])\\n strScores += \\\"F1 Train : {} F1 Test : {} \\\\n\\\".format(scores['f1Train'],\\\\\\n scores['f1Test'])\\n strScores += \\\"Roc_Auc Train : {} Roc_Auc Test : {} \\\\n\\\".format(scores['rocTrain'],scores['rocTest'])\\n else:\\n print(\\\"No Test file\\\")\\n strScores += \\\"\\\\nNo Test file\\\\n=========\\\\n\\\"\\n \\n f = open(\\\"{}{}HyperSelection{}{}{}.txt\\\".format(RESULTS_PATH, penalty, modelType.title(), scoreType.title(), transformedData.title()), 'w')\\n f.write(strScores)\\n f.close()\",\n \"def parse_results(results_file, measure_text):\\r\\n \\r\\n #Read in results file\\r\\n with open(results_file) as f_in:\\r\\n results_text = f_in.readlines()\\r\\n \\r\\n #Set-up output data frame\\r\\n results_df = pd.DataFrame(columns=('effect', 'time_window', 'electrodes'))\\r\\n \\r\\n r = -1 #data frame row index\\r\\n \\r\\n #Parse lines and read relevant results into data frame\\r\\n for i in range(len(results_text)):\\r\\n \\r\\n line = results_text[i]\\r\\n \\r\\n #Starting to parse a new simulation\\r\\n if 'SIMULATION SUMMARY' in line:\\r\\n r += 1\\r\\n \\r\\n #Parse basic simulation parameters\\r\\n elif 'Simulated experiments' in line:\\r\\n (field, value) = line.split('\\\\t')\\r\\n results_df.loc[r, 'n_experiments'] = int(value)\\r\\n \\r\\n elif 'Permutations' in line:\\r\\n (field, value) = line.split('\\\\t')\\r\\n results_df.loc[r, 'n_permutations'] = int(value)\\r\\n \\r\\n elif 'Sample size' in line:\\r\\n (field, value) = line.split('\\\\t')\\r\\n results_df.loc[r, 'n_subjects'] = int(value)\\r\\n \\r\\n elif 'Effect:' in line:\\r\\n (field, value) = line.split('\\\\t')\\r\\n results_df.loc[r, 'effect'] = os.path.basename(value).strip()\\r\\n \\r\\n elif 'Time window' in line:\\r\\n (field, value) = line.split('\\\\t')\\r\\n results_df.loc[r, 'time_window'] = value.strip()\\r\\n \\r\\n elif 'Electrodes:' in line:\\r\\n (field, value) = line.split('\\\\t')\\r\\n results_df.loc[r, 'electrodes'] = value.strip()\\r\\n \\r\\n elif 'Trials =' in line:\\r\\n (field, value) = line.split('\\\\t')\\r\\n results_df.loc[r, 'n_trials'] = int(value.split()[0])\\r\\n \\r\\n elif 'MEAN WINDOW' in line:\\r\\n (field, value) = results_text[i+1].split('\\\\t')\\r\\n results_df.loc[r, 'mean_amp'] = float(value)\\r\\n \\r\\n #Find correction method for current point in file\\r\\n elif 'UNCORRECTED' in line:\\r\\n method = 'uncorrected'\\r\\n elif 'SIDAK' in line:\\r\\n method = 'sidak'\\r\\n elif 'FMAX' in line:\\r\\n method = 'Fmax'\\r\\n elif 'CLUSTER 0.05' in line:\\r\\n method = 'cluster_05'\\r\\n elif 'CLUSTER 0.01' in line:\\r\\n method = 'cluster_01'\\r\\n elif 'BH' in line:\\r\\n method = 'BH'\\r\\n elif 'BY' in line:\\r\\n method = 'BY'\\r\\n elif 'BKY' in line:\\r\\n method = 'BKY'\\r\\n \\r\\n #Get the measure of interst and associate with method (from above)\\r\\n elif measure_text in line:\\r\\n (field, value) = line.split('\\\\t')\\r\\n results_df.loc[r, method] = float(value)\\r\\n \\r\\n return results_df\",\n \"def prepareAccumulatedMetrics(self):\\n displayDF = analyzeMetricsDF(self.resultList)\\n displayDF.to_csv(\\\"data/results.csv\\\")\",\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 _process_results(self, timestamp, results):\\n\\n topic_value = self.create_topic_values(results)\\n\\n _log.debug('Processing Results!')\\n if mode:\\n _log.debug(\\\"ACTUATE ON DEVICE.\\\")\\n actuator_error = False\\n if make_reservations and results.devices:\\n results, actuator_error = self.actuator_request(results)\\n if not actuator_error:\\n self.actuator_set(topic_value)\\n if make_reservations and results.devices and not actuator_error:\\n self.actuator_cancel()\\n\\n for value in results.log_messages:\\n _log.debug(\\\"LOG: {}\\\".format(value))\\n for key, value in results.table_output.items():\\n _log.debug(\\\"TABLE: {}->{}\\\".format(key, value))\\n if output_file_prefix is not None:\\n results = self.create_file_output(results)\\n if command_output_file is not None:\\n self.create_command_file_output(timestamp, topic_value)\\n # if len(results.table_output.keys()):\\n # results = self.publish_analysis_results(results)\\n return results\",\n \"def log_records(self, records, **kwargs):\\n queries = records[\\\"features\\\"][: self.test_dataset.n_queries]\\n docs = records[\\\"features\\\"][self.test_dataset.n_queries :]\\n query_names = records[\\\"audio_names\\\"][: self.test_dataset.n_queries]\\n doc_names = records[\\\"audio_names\\\"][self.test_dataset.n_queries :]\\n results = defaultdict(list)\\n scores = []\\n\\n # Calculate matching scores\\n with ProcessPoolExecutor(self.datarc[\\\"num_workers\\\"]) as executor:\\n futures = []\\n\\n for query, query_name in zip(queries, query_names):\\n query_name = query_name.replace(\\\".wav\\\", \\\"\\\")\\n for doc, doc_name in zip(docs, doc_names):\\n doc_name = doc_name.replace(\\\".wav\\\", \\\"\\\")\\n futures.append(\\n executor.submit(match, query, doc, query_name, doc_name)\\n )\\n\\n for future in tqdm(\\n as_completed(futures), total=len(futures), ncols=0, desc=\\\"DTW\\\"\\n ):\\n query_name, doc_name, score = future.result()\\n results[query_name].append((doc_name, score))\\n scores.append(score)\\n\\n # Determine score threshold\\n scores = sorted(scores)\\n score_thresh = scores[int(0.99 * len(scores))]\\n score_min = scores[0]\\n\\n # Build XML tree\\n root = etree.Element(\\n \\\"stdlist\\\",\\n termlist_filename=\\\"benchmark.stdlist.xml\\\",\\n indexing_time=\\\"1.00\\\",\\n language=\\\"english\\\",\\n index_size=\\\"1\\\",\\n system_id=\\\"benchmark\\\",\\n )\\n for query_name, doc_scores in results.items():\\n term_list = etree.SubElement(\\n root,\\n \\\"detected_termlist\\\",\\n termid=query_name,\\n term_search_time=\\\"1.0\\\",\\n oov_term_count=\\\"1\\\",\\n )\\n for doc_name, score in doc_scores:\\n etree.SubElement(\\n term_list,\\n \\\"term\\\",\\n file=doc_name,\\n channel=\\\"1\\\",\\n tbeg=\\\"0.000\\\",\\n dur=\\\"0.00\\\",\\n score=f\\\"{score - score_min:.4f}\\\",\\n decision=\\\"YES\\\" if score > score_thresh else \\\"NO\\\",\\n )\\n\\n # Output XML\\n tree = etree.ElementTree(root)\\n tree.write(\\n str(self.expdir / \\\"benchmark.stdlist.xml\\\"),\\n encoding=\\\"UTF-8\\\",\\n pretty_print=True,\\n )\",\n \"def after_evaluation(self, epoch, **kw):\\n if self.loss_txt is not None:\\n self.current_row[MetricName(\\\"timestamp\\\")] = f\\\"{datetime.now():%H:%M:%S}\\\"\\n\\n # output log file\\n with open(self.loss_txt, \\\"a\\\") as f:\\n # remove columns where no value was found on the first epoch (could be != 1 if training was resumed)\\n if epoch == self.first_epoch:\\n for k in list(self.headers.keys()):\\n if k not in self.current_row:\\n del self.headers[k]\\n\\n # make headers on epoch 1\\n if epoch == 1:\\n # write header\\n f.write(\\\"\\\\t\\\".join(self.headers.values()) + \\\"\\\\n\\\")\\n\\n for col in self.headers:\\n assert col in self.current_row, str(col) + \\\" \\\" + str(self.current_row.keys())\\n\\n assert all(col in self.current_row for col in self.headers)\\n\\n f.write(\\\"\\\\t\\\".join([str(self.current_row[col]) for col in self.headers]) + \\\"\\\\n\\\")\\n\\n self.current_row = {}\",\n \"def process_sceneset_results(self, training_results, validation_results,\\n tmp_dir):\\n pass\",\n \"def format_det_result():\\n test_results = load(args.test_result)\\n annos = load(args.anno_path)\\n test_videos = annos['test_videos'][0]\\n resolutions = annos['resolution']\\n frm_dets = []\\n for pred in track(test_results, description='formating...'):\\n video_key = pred['video_id'].split('.mp4')[0]\\n frm_num = pred['timestamp']\\n bboxes = pred['pred_instances']['bboxes']\\n cls_scores = pred['pred_instances']['scores']\\n for bbox, cls_score in zip(bboxes, cls_scores):\\n video_idx = test_videos.index(video_key)\\n pred_label = np.argmax(cls_score)\\n score = cls_score[pred_label]\\n h, w = resolutions[video_key]\\n bbox *= np.array([w, h, w, h])\\n instance_result = np.array(\\n [video_idx, frm_num, pred_label, score, *bbox])\\n frm_dets.append(instance_result)\\n frm_dets = np.array(frm_dets)\\n video_tubes = link_tubes(annos, frm_dets, K=1)\\n dump(frm_dets, args.frm_out_path)\\n dump(video_tubes, args.tube_out_path)\",\n \"def log_validation_results(engine):\\n validation_evaluator.run(self.val_dl)\\n metrics: Dict[str, float] = validation_evaluator.state.metrics\\n avg_accuracy: float = metrics['accuracy']\\n avg_bce: float = metrics['bce']\\n pbar.log_message(\\n f'Validation Results - Epoch: {engine.state.epoch} Avg accuracy: {avg_accuracy:.4f} Avg loss: {avg_bce:.4f}')\\n pbar.n = pbar.last_print_n = 0\",\n \"def handleRecord(inputData):\\n # Send it to Numenta detector and get back the results\\n result = model.run(inputData)\\n\\n # Retrieve the anomaly score and write it to a file\\n rawScore = result.inferences[\\\"anomalyScore\\\"]\\n\\n if useLikelihood:\\n # Compute log(anomaly likelihood)\\n anomalyScore = anomalyLikelihood.anomalyProbability(\\n inputData[\\\"value\\\"], rawScore, inputData[\\\"timestamp\\\"])\\n logScore = anomalyLikelihood.computeLogLikelihood(anomalyScore)\\n return (logScore, rawScore)\\n\\n return (rawScore, rawScore)\",\n \"def do_analyse(args):\\n\\n pd.set_option('display.max_columns', None)\\n pd.set_option('display.width', None)\\n\\n score = 'score'\\n\\n # Read in the results, and add a boolean target column.\\n df = pd.read_csv(args.results, index_col=0)\\n df['target'] = df['verify_speaker'] == df['enrol_speaker']\\n\\n # Calculate ideal 0.01% threshold over the multi-session data.\\n nontarget_df = df.loc[df['target'] == False].sort_values(score, ascending=False)\\n nontarget_count = nontarget_df[score].count()\\n th_calc = nontarget_df.iloc[int(nontarget_count * (1 / 10000))][score]\\n\\n # Now filter the data so that we only consider mono-session enrolment and verification.\\n df = df.loc[df['verify_room'] == df['enrol_room']]\\n target_df = df.loc[df['target'] == True].sort_values(score, ascending=False)\\n nontarget_df = df.loc[df['target'] == False].sort_values(score, ascending=False)\\n target_count = target_df[score].count()\\n nontarget_count = nontarget_df[score].count()\\n\\n # Calculate FA/FR for the user-defined threshold.\\n th_user = args.th_user\\n fr_user = target_df.loc[target_df[score] < th_user][score].count()\\n fa_user = nontarget_df.loc[nontarget_df[score] > th_user][score].count()\\n frr_user = fr_user / target_count\\n far_user = fa_user / nontarget_count\\n label_user = 'User Threshold: th {:.4f}, FR {} ({:.3f}%), FA {} ({:.3f}%)'.format(th_user, fr_user, frr_user * 100,\\n fa_user, far_user * 100)\\n\\n # Calculate the FA/FR for the ideal threshold calculated from the multi-session data.\\n fr_calc = target_df.loc[target_df[score] < th_calc][score].count()\\n fa_calc = nontarget_df.loc[nontarget_df[score] > th_calc][score].count()\\n frr_calc = fr_calc / target_count\\n far_calc = fa_calc / nontarget_count\\n label_calc = 'Calc Threshold: th {:.4f}, FR {} ({:.3f}%), FA {} ({:.3f}%)'.format(th_calc, fr_calc, frr_calc * 100,\\n fa_calc, far_calc * 100)\\n\\n # Print the stats.\\n print('\\\\nTarget Stats:')\\n print(target_df[score].describe())\\n print('\\\\nNon-Target Stats:')\\n print(nontarget_df[score].describe())\\n print('\\\\nThresholds:')\\n print(label_user)\\n print(label_calc)\\n\\n # Paint the graphs.\\n paint_graph(score, 'verify_room', df, th_user, label_user, th_calc, label_calc)\\n paint_graph(score, 'enrol_room', df, th_user, label_user, th_calc, label_calc)\\n paint_graph(score, 'verify_speaker', df, th_user, label_user, th_calc, label_calc)\\n paint_graph(score, 'enrol_speaker', df, th_user, label_user, th_calc, label_calc)\",\n \"def run(self):\\n\\n self.preprocess()\\n self.restore_ratings()\\n self.prepare_UI()\\n self.loop_through_units()\\n self.cleanup()\\n\\n print('\\\\nAll Done - results are available in:\\\\n\\\\t{}'.format(self.out_dir))\",\n \"def analyze_data(self):\\n\\n self.truth = self.analyze_folder(\\\"Truth\\\")\\n self.truth.to_csv(self.folder + \\\"/truth.csv\\\")\\n self.false = self.analyze_folder(\\\"False\\\")\\n self.flase.to_csv(self.folder + \\\"/false.csv\\\")\",\n \"def log_results(self, filename=None):\\n\\n self.ad_log['train_auc'] = self.diag['train']['auc'][-1]\\n self.ad_log['train_accuracy'] = self.diag['train']['acc'][-1]\\n self.ad_log['train_time'] = self.train_time\\n\\n self.ad_log['test_auc'] = self.diag['test']['auc'][-1]\\n self.ad_log['test_accuracy'] = self.diag['test']['acc'][-1]\\n self.ad_log['test_time'] = self.test_time\\n\\n self.ad_log.save_to_file(filename=filename)\",\n \"def postprocess_result(self):\\n output_file = self.analyzer_result_file\\n LOG.debug_analyzer(self.analyzer_stdout)\\n tidy_stdout = self.analyzer_stdout.splitlines()\\n generate_plist_from_tidy_result(output_file, tidy_stdout)\\n\\n if self.report_hash_type == 'context-free':\\n report.use_context_free_hashes(output_file)\",\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 analyse_results(self, \\n target_dir, \\n param_file = \\\"TRAINED_PARAMS_END.model\\\",\\n w_norm_file = \\\"W_NORMS.dat\\\",\\n num_to_test = 100,\\n get_means_from = {\\n 'NOISY' :'RECONST_NOISY_ERRORS.dat',\\n 'MISSING':'RECONST_MISSING_ERRORS.dat'\\n },\\n given_inputs = []):\\n \\n path_to_json = os.path.join(target_dir, \\\"PARAMETERS.json\\\")\\n \\n with open(path_to_json, 'r') as json_file:\\n \\n param_dict = json.load(json_file)\\n \\n self.num_hidden = param_dict['GLOBAL']['num_hidden']\\n \\n num_runs = param_dict['GLOBAL']['num_runs']\\n \\n path_to_mix_params = os.path.join(target_dir, \\\"MIX_PARAMS.dat\\\")\\n \\n reg_dict = {}\\n \\n for f_name in param_dict.keys():\\n \\n if f_name != \\\"GLOBAL\\\":\\n reg_dict[param_dict[f_name]['algorithm']] =\\\\\\n param_dict[f_name]['regressor']\\n \\n self.mixture = False\\n \\n E_gaps = {}\\n \\n recon_errors = {}\\n \\n p_tilda_data = {}\\n \\n end_w_norms = {}\\n \\n for field in get_means_from.keys():\\n \\n recon_errors[field] = {}\\n \\n if os.path.exists(path_to_mix_params):\\n \\n self.mixture = True\\n \\n self.mix_params = np.loadtxt(path_to_mix_params)\\n \\n for run_ind in range(num_runs):\\n \\n sub_f1 = \\\"run%s\\\"%run_ind\\n \\n sub_dir = os.path.join(target_dir, sub_f1)\\n \\n if os.path.isdir(sub_dir): \\n print(\\\"Processing %s\\\"%sub_dir)\\n tr_file = \\\"TRAIN_IMAGES.dat\\\"\\n check_input_file = os.path.join(sub_dir, tr_file)\\n \\n if os.path.exists(check_input_file):\\n inputs = np.loadtxt(check_input_file)\\n # to make fair comparison, number of is samples\\n # is set to the number of test inputs\\n self.num_samples = inputs.shape[0]\\n else:\\n \\n if isinstance(given_inputs, np.ndarray):\\n N = given_inputs.shape[0]\\n inds = self.np_rand_gen.choice(N, \\n num_to_test,\\n replace = False)\\n \\n inputs = given_inputs[inds,:]\\n \\n self.num_samples = num_to_test\\n \\n else:\\n print(\\\"Error: %s does not contain file %s\\\"\\n %(sub_dir2, tr_file) +\\\" and given_inputs is []\\\")\\n print(\\\"Script execution will terminate\\\") \\n sys.exit()\\n \\n self.batch_size = inputs.shape[0]\\n \\n if self.mixture:\\n self.set_mixture_means(inputs = inputs)\\n \\n is_samples, _ = self.is_sampler()\\n \\n for sub_f2 in os.listdir(sub_dir):\\n \\n sub_dir2 = os.path.join(sub_dir, sub_f2)\\n \\n if os.path.isdir(sub_dir2):\\n \\n if \\\"_\\\" in sub_f2:\\n spl_str = sub_f2.split(\\\"_\\\")\\n if len(spl_str) > 3:\\n algorithm = spl_str[0]+\\\"_\\\"+spl_str[1]\\n else:\\n algorithm = spl_str[0]\\n \\n else:\\n algorithm = sub_f2\\n \\n field_name =algorithm \\n \\n if reg_dict[algorithm] != None:\\n if reg_dict[algorithm] in sub_f2:\\n if \\\"_\\\" in reg_dict[algorithm]:\\n reg_name_s = reg_dict[algorithm].split(\\\"_\\\")\\n \\n reg_name_s = reg_name_s[0][0].upper() +\\\\\\n reg_name_s[1][0].upper()\\n field_name +=\\\" %s\\\"%reg_name_s\\n else:\\n field_name +=\\\" %s\\\"%reg_dict[algorithm]\\n \\n get_val = sub_f2\\n get_val = get_val.split(reg_dict[algorithm])[1]\\n \\n field_name +=\\\" %s\\\"%get_val\\n \\n if field_name not in E_gaps.keys():\\n \\n E_gaps[field_name] = []\\n \\n if field_name not in p_tilda_data.keys():\\n \\n p_tilda_data[field_name] = []\\n \\n if field_name not in end_w_norms.keys():\\n \\n end_w_norms[field_name] = []\\n \\n par_path = os.path.join(sub_dir2, param_file)\\n \\n w_norms_path = os.path.join(sub_dir2, w_norm_file)\\n \\n w_norms = np.loadtxt(w_norms_path)\\n \\n end_w_norms[field_name].append(w_norms[-1])\\n \\n if os.path.exists(par_path):\\n \\n css_diff, p_tilda_vals =\\\\\\n self.compare_css_terms(x_inputs = inputs,\\n x_samples = is_samples,\\n full_path = par_path)\\n \\n E_gaps[field_name].append(css_diff)\\n \\n mean_val = np.mean(p_tilda_vals[0:self.batch_size])\\n \\n p_tilda_data[field_name].append(mean_val)\\n \\n else:\\n \\n print(\\\"Error: %s does not exist\\\"%par_path)\\n sys.exit()\\n \\n for f_exp in get_means_from.keys():\\n \\n file_name = get_means_from[f_exp]\\n \\n check_err_file = os.path.join(sub_dir2, file_name)\\n \\n errors = np.loadtxt(check_err_file)\\n \\n mean_val = np.mean(errors)\\n \\n if field_name not in recon_errors[f_exp].keys():\\n \\n recon_errors[f_exp][field_name] = []\\n \\n recon_errors[f_exp][field_name].append(mean_val)\\n \\n return E_gaps, recon_errors, p_tilda_data, end_w_norms\",\n \"def log(self, report, epoch):\\n train_return_values = np.asarray([trajectory['reward'].sum()\\n for trajectory in report['training_trajectories']])\\n trajectories_infos = [trajectory['info'] for trajectory in report.pop('training_trajectories')]\\n sum_costs = np.asarray([sum(list(map(lambda info: info.get('cost', 0.0), trajectory)))\\n for trajectory in trajectories_infos])\\n report.update(dict(\\n training_rl_objective=train_return_values.mean(),\\n sum_rewards_stddev=train_return_values.std(),\\n mean_sum_costs=sum_costs.mean()\\n ))\\n training_step = report.pop('total_training_steps')\\n for key, value in report.items():\\n self.training_logger.log_scalar(value, key, training_step)\\n self.training_logger.flush()\",\n \"def analyse(self):\\n pass\",\n \"def _process_results(self):\\n self.portfolio.create_backtest_result_dataframe()\\n stats = self._show_stats()\\n return stats\",\n \"def run(self):\\r\\n history = self.extracter.load_user_history()\\r\\n self.plot_history(history)\\r\\n \\r\\n pp_history = self.analyser.preprocess_history(history)\\r\\n part_worths, attribute_importance, relative_importance = self.analyser.conjoint_analysis(pp_history)\\r\\n self.plot_analysis(part_worths, relative_importance)\\r\\n \\r\\n return history, pp_history, part_worths, relative_importance\",\n \"def record(self, algo, valid_info, test_results=None):\\n\\n model_path = os.path.join(\\n self._working_folder, \\\"hpo_{}.pt\\\".format(len(self._results)))\\n algo.save_model(model_path)\\n test_results_path = ''\\n if test_results is not None:\\n test_results_path = os.path.join(\\n self._working_folder, \\\"test_results_of_hpo_{}.pt\\\".format(len(self._results)))\\n torch.save({'test_results': test_results}, test_results_path)\\n self._results.append((copy.deepcopy(self._cur_config), model_path, valid_info, self._early_stopper.get_cur_step(), test_results_path))\",\n \"def compute_metrics(self, results: list) -> dict:\\n dump(results, self.out_file_path)\\n print_log(\\n f'Results has been saved to {self.out_file_path}.',\\n logger='current')\\n return {}\",\n \"def _record(self):\\r\\n self._plot()\\r\\n self._csvWriter()\\r\\n self._logger()\",\n \"def analyze_predictions(submission_model_path, csv_result):\\n all_data_info = pd.read_csv(path.join(DATA_PATH, 'all_data_info.csv'))\\n submission_model = pd.read_csv(submission_model_path)\\n\\n genre_label = get_genre_labels(True)\\n null_genre = 0\\n null_label = 0\\n exact_labels = []\\n errors = 0\\n progress = progressbar.ProgressBar(\\n maxval=len(submission_model),\\n widgets=[progressbar.Bar('=', '[', ']'), ' ',\\n progressbar.Percentage()])\\n progress.start()\\n for i, row in submission_model.iterrows():\\n progress.update(i + 1)\\n genre = all_data_info[all_data_info['new_filename'] == row['img']][\\n 'genre'].dropna()\\n # some paintings don't have a genre. Checking it\\n if len(genre) < 1:\\n null_genre += 1\\n exact_labels.append(row['label'])\\n continue\\n if genre.values[0] not in genre_label:\\n # No label. It's strange, but let's go on...\\n null_label += 1\\n exact_labels.append(row['label'])\\n continue\\n exact_labels.append(int(genre_label[genre.values[0]]['label']))\\n if row['label'] != exact_labels[-1]:\\n errors += 1\\n progress.finish()\\n\\n submission_model['exact_label'] = exact_labels\\n submission_model.to_csv(csv_result, index=False)\\n\\n # plot_confusion_matrix(submission_model)\\n\\n print('Genre is null: ' + str(null_genre))\\n print('Label is null: ' + str(null_label))\\n print('Errors: ' + str(errors) + ' (out of ' + str(len(submission_model)) +\\n ')')\\n print('Accuracy: ' + str(1 - (errors / float(len(submission_model)))))\",\n \"def process_for_baseline(dat):\\n records = []\\n count = 0\\n for record in dat:\\n # Context\\n context = record['context'].replace(\\\"''\\\", '\\\" ').replace(\\\"``\\\", '\\\" ') # Replace non-standard quotation marks\\n context_tokens = text2tokens(context)\\n spans = convert_idx(context, context_tokens) # (token_start_char_idx, token_end_char_idx)\\n \\n # Question\\n ques = record['question'].replace(\\\"''\\\", '\\\" ').replace(\\\"``\\\", '\\\" ') \\n ques_tokens = text2tokens(ques)\\n \\n # Answers\\n y1, y2 = charIdx_to_tokenIdx(spans, record['answers']['text'], record['answers']['answer_start']) \\n\\n res_dict = {\\\"context_tokens\\\": context_tokens,\\n \\\"ques_tokens\\\": ques_tokens,\\n \\\"answer\\\": record['answers']['text'], \\n \\\"y1s\\\": y1, \\n \\\"y2s\\\": y2,\\n \\\"id\\\": count,\\n 'pubId': record['PubID'],\\n \\\"group\\\": record['group']}\\n records.append(res_dict)\\n count += 1 \\n \\n return records\",\n \"def get_average_scores(self):\\n models = self.eval_parameters['average_experiment']['models']\\n metrics = self.eval_parameters['average_experiment']['metrics_list']\\n metrics_keys = list(metrics.keys())\\n print(r\\\"\\\\begin{table}[]\\\")\\n print(\\\"\\\\centering\\\")\\n print(r\\\"\\\\tiny\\\")\\n print(\\\"\\\\caption{Average results over 85 topics. Each row represents a different run (top 10 runs of each model). Each column represents a different assessments aggregation.}\\\")\\n print(\\\"\\\\label{tab:average_results}\\\")\\n print(r\\\"\\\\begin{tabular}{@{}\\\"+''.join(['l']*(len(metrics_keys)+1))+\\\"@{}}\\\")\\n print(\\\"runid\\\", '&'.join(metric.replace('_','\\\\_')for metric in metrics_keys),sep='&')\\n print(r\\\"\\\\\\\\ \\\\midrule\\\")\\n for model in models:\\n runs = self.get_list_files(self.eval_parameters['average_experiment']['runs_folder'] + model+\\\"/\\\")\\n for file in runs:\\n val = []\\n for metric_id in metrics_keys:\\n out1 = subprocess.check_output(\\n ['../trec_eval-master/trec_eval', '-m', metrics[metric_id]['metric'],\\n metrics[metric_id]['qrels'], file])\\n val += [str(out1.rstrip().split()[2]).replace('b\\\\'', '').replace('\\\\'', '')]\\n print(file.replace(self.eval_parameters['average_experiment']['runs_folder'],'').replace('_','\\\\_'),'&', '&'.join(val),r\\\"\\\\\\\\\\\")\\n print(r\\\"\\\\bottomrule\\\")\\n print(r\\\"\\\\end{tabular}\\\")\\n print(r\\\"\\\\end{table}\\\")\",\n \"def process_recognition(result, filename, output_directory, lexical):\\n if result.reason == speechsdk.ResultReason.RecognizedSpeech:\\n if lexical:\\n text = f\\\"{format(result.text)}\\\\t{json.loads(result.json)['NBest'][0]['Lexical']}\\\"\\n else:\\n text = f\\\"{format(result.text)}\\\"\\n logging.info(f\\\"[INFO] - Recognition successful: {filename} -> {result.text}\\\")\\n elif result.reason == speechsdk.ResultReason.NoMatch:\\n logging.warning(filename + \\\"\\\\t\\\" + f\\\"No speech could be recognized: {result.no_match_details}\\\")\\n text = \\\"\\\"\\n elif result.reason == speechsdk.ResultReason.Canceled:\\n cancellation_details = result.cancellation_details\\n logging.error(filename+\\\"\\\\t\\\"+ f\\\"Speech Recognition canceled: {cancellation_details.reason}\\\")\\n if cancellation_details.reason == speechsdk.CancellationReason.Error:\\n logging.error(f\\\"Error details: {cancellation_details.error_details}\\\")\\n text = \\\"\\\"\\n return text\",\n \"def process(self):\\n self.extract()\\n self.transform()\\n self.load()\",\n \"def convert_format( # pylint: disable=arguments-differ\\n self, results: List[np.ndarray], out_dir: str # type: ignore\\n ) -> None:\\n assert isinstance(results, list), \\\"results must be a list\\\"\\n assert len(results) == len(\\n self\\n ), f\\\"Length of res and dset not equal: {len(results)} != {len(self)}\\\"\\n if not os.path.exists(out_dir):\\n os.makedirs(out_dir)\\n\\n scores_list, segms_list, colors_list = [], [], []\\n det_frames, seg_frames = [], []\\n img_names = []\\n ann_id = 0\\n\\n for img_idx in range(len(self)):\\n index = 0\\n img_name = self.data_infos[img_idx][\\\"file_name\\\"]\\n img_names.append(img_name)\\n det_frame = Frame(name=img_name, labels=[])\\n det_frames.append(det_frame)\\n seg_frame = Frame(name=img_name, labels=[])\\n seg_frames.append(seg_frame)\\n scores, segms, colors = [], [], []\\n\\n det_results, seg_results = results[img_idx]\\n for cat_idx, [cur_det, cur_seg] in enumerate(\\n zip(det_results, seg_results)\\n ):\\n for bbox, segm in zip(cur_det, cur_seg):\\n ann_id += 1\\n index += 1\\n score = bbox[-1]\\n\\n det_label = Label(\\n id=str(ann_id),\\n score=score,\\n box2d=bbox_to_box2d(self.xyxy2xywh(bbox)),\\n category=self.CLASSES[cat_idx],\\n )\\n det_frame.labels.append(det_label) # type: ignore\\n\\n seg_label = Label(id=str(ann_id), index=index, score=score)\\n seg_frame.labels.append(seg_label) # type: ignore\\n\\n scores.append(score)\\n segms.append(segm)\\n colors.append([cat_idx + 1, 0, index >> 8, index & 255])\\n\\n scores_list.append(scores)\\n segms_list.append(segms)\\n colors_list.append(colors)\\n\\n det_out_path = osp.join(out_dir, \\\"det.json\\\")\\n save(det_out_path, det_frames)\\n\\n seg_out_path = osp.join(out_dir, \\\"score.json\\\")\\n seg_frame_dicts = [seg_frame.dict() for seg_frame in seg_frames]\\n with open(seg_out_path, \\\"w\\\", encoding=\\\"utf-8\\\") as fp:\\n json.dump(seg_frame_dicts, fp, indent=2)\\n\\n bitmask_dir = osp.join(out_dir, \\\"bitmasks\\\")\\n mask_merge_parallel(\\n bitmask_dir,\\n img_names,\\n scores_list,\\n segms_list,\\n colors_list,\\n nproc=4,\\n )\",\n \"def record_metrics(metrics, args):\\n with open('interpretation_metrics/model_metrics_{}'.format(args.file_num), 'a') as f:\\n f.write(\\\"META DATA\\\\n\\\")\\n f.write(\\\"---------\\\\n\\\")\\n f.write(\\\"Model Name: {}\\\\n\\\".format(args.model_name))\\n f.write(\\\"Attack Target: {}\\\\n\\\".format(args.attack_target))\\n f.write(\\\"Gradient Model File: {}\\\\n\\\".format(args.gradient_model_file))\\n f.write(\\\"Predictive Model File: {}\\\\n\\\".format(args.predictive_model_file))\\n f.write(\\\"Cuda: {}\\\\n\\\".format(args.cuda))\\n\\n f.write(\\\"\\\\nSIMPLE GRADIENT COMBINED MODEL METRICS\\\\n\\\")\\n f.write(\\\"----------------------------------------\\\\n\\\")\\n for key, val in metrics['simple_gradient_combined'].items():\\n f.write(\\\"{}: {:.3f}\\\\n\\\".format(key, val))\\n\\n f.write(\\\"\\\\nSIMPLE GRADIENT BASELINE MODEL METRICS\\\\n\\\")\\n f.write(\\\"----------------------------------------\\\\n\\\")\\n for key, val in metrics['simple_gradient_baseline'].items():\\n f.write(\\\"{}: {:.3f}\\\\n\\\".format(key, val))\\n\\n f.write(\\\"\\\\nSMOOTH GRADIENT COMBINED MODEL METRICS\\\\n\\\")\\n f.write(\\\"----------------------------------------\\\\n\\\")\\n for key, val in metrics['smooth_gradient_combined'].items():\\n f.write(\\\"{}: {:.3f}\\\\n\\\".format(key, val))\\n\\n f.write(\\\"\\\\nSMOOTH GRADIENT BASELINE MODEL METRICS\\\\n\\\")\\n f.write(\\\"----------------------------------------\\\\n\\\")\\n for key, val in metrics['smooth_gradient_baseline'].items():\\n f.write(\\\"{}: {:.3f}\\\\n\\\".format(key, val))\\n\\n f.write(\\\"\\\\nINTEGRATED GRADIENT COMBINED MODEL METRICS\\\\n\\\")\\n f.write(\\\"--------------------------------------------\\\\n\\\")\\n for key, val in metrics['integr_gradient_combined'].items():\\n f.write(\\\"{}: {:.3f}\\\\n\\\".format(key, val))\\n\\n f.write(\\\"\\\\nINTEGRATED GRADIENT BASELINE MODEL METRICS\\\\n\\\")\\n f.write(\\\"--------------------------------------------\\\\n\\\")\\n for key, val in metrics['integr_gradient_baseline'].items():\\n f.write(\\\"{}: {:.3f}\\\\n\\\".format(key, val))\",\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 run_analyses(y_predict_train, y_train, y_predict, y_test):\\n # calculate metrics\\n _, training_error = output_error(y_predict_train, y_train)\\n (precision, recall, f1, _), testing_error = output_error(y_predict, y_test)\\n \\n # print out metrics\\n print 'Average Precision:', np.average(precision)\\n print 'Average Recall:', np.average(recall)\\n print 'Average F1:', np.average(f1)\\n print 'Training Error:', training_error\\n print 'Testing Error:', testing_error\",\n \"def log_training(self, batch, total_batches, result):\\n metrics = [\\\"loss\\\", \\\"accuracy\\\"]\\n for metric in metrics:\\n if metric not in self.logs:\\n self.logs[metric] = []\\n self.logs[metric].append(result[metric])\\n if batch % self.log_frequency == 0 or batch + 1 == total_batches:\\n print(\\\"Batch {} / {} = {:.2f} %\\\".format(batch, total_batches, 100 * batch / total_batches))\\n print(\\\"{:20}: {}\\\".format(\\\"Global step\\\", result[\\\"global_step\\\"]))\\n print(\\\"{:20}: {:.4e}\\\".format(\\\"Learning rate\\\", result[\\\"learning_rate\\\"]))\\n for metric in metrics:\\n metric_logs = self.logs[metric]\\n average = sum(metric_logs) / len(metric_logs)\\n print(\\\"{:20}: {:.4}\\\".format(\\\"Training \\\" + metric, average))\\n self.logs[metric] = []\\n val_metrics = self.evaluate(self.batches_valid)\\n for k, v in val_metrics.items():\\n print(\\\"{:20}: {:.4}\\\".format(\\\"Validation \\\" + k, v))\",\n \"def parseResults(result):\\n # Split the results based on newline characters\\n results_cut = result.text.split('\\\\n')[12:-49]\\n # Initialize lists of the values to be parsed from results_cut \\n visit_id = []\\n name = []\\n ra_hour = []\\n ra_min = []\\n ra_sec = []\\n dec_deg = []\\n dec_min = []\\n dec_sec = []\\n v_mag = []\\n ra_motion = []\\n dec_motion = []\\n # Iterate through results_cut and append them to the respective lists\\n for line in results_cut:\\n visit_id.append(int(line[6:12]))\\n name.append(line[12:36])\\n ra_hour.append(int(line[38:40]))\\n ra_min.append(int(line[41:43]))\\n ra_sec.append(float(line[44:48]))\\n dec_deg.append(int(line[49:52]))\\n dec_min.append(int(line[53:55]))\\n dec_sec.append(int(line[56:58]))\\n try:\\n v_mag.append(float(line[60:64]))\\n except ValueError:\\n # If there is no reported v_mag for the object, return -99\\n v_mag.append(-99.0)\\n ra_motion.append('%s%i' % (line[84], int(line[82:84])))\\n dec_motion.append('%s%i' % (line[91], int(line[89:91])))\\n # Initialize the pandas dataframe to be returned\\n results_df = pd.DataFrame(np.array([visit_id, name, ra_hour, ra_min, ra_sec, \\n dec_deg, dec_min, dec_sec, v_mag, \\n ra_motion, dec_motion]).T, \\n columns=['visit_id', 'name', 'ra_hour', 'ra_min', 'ra_sec', \\n 'dec_deg', 'dec_min', 'dec_sec', 'v_mag', \\n 'ra_motion', 'dec_motion'])\\n # Add the lists to the dataframe\\n results_df['visit_id'] = pd.to_numeric(results_df['visit_id'])\\n results_df['ra_hour'] = pd.to_numeric(results_df['ra_hour'])\\n results_df['ra_min'] = pd.to_numeric(results_df['ra_min'])\\n results_df['ra_sec'] = pd.to_numeric(results_df['ra_sec'])\\n results_df['dec_deg'] = pd.to_numeric(results_df['dec_deg'])\\n results_df['dec_min'] = pd.to_numeric(results_df['dec_min'])\\n results_df['dec_sec'] = pd.to_numeric(results_df['dec_sec'])\\n results_df['v_mag'] = pd.to_numeric(results_df['v_mag'])\\n results_df['ra_motion'] = pd.to_numeric(results_df['ra_motion'])\\n results_df['dec_motion'] = pd.to_numeric(results_df['dec_motion'])\\n \\n return results_df\",\n \"def write_results(self, results):\\n predictions = open('hmm_results.csv', 'w')\\n predictions.write(\\\"Type,Prediction\\\")\\n for type in results:\\n if type == 'O':\\n continue\\n predictions.write(\\\"\\\\n\\\" + str(type) + \\\",\\\")\\n for interval in results[type]:\\n predictions.write(str(interval) + \\\" \\\")\\n predictions.close()\",\n \"def process(self, results):\\n raise NotImplementedError\",\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 summarize(self):\\n self.smalltalk += \\\"\\\\n Data IDs in this bundle: \\\\n\\\"\\n self._files = {}\\n inv_dict = {}\\n # sort IDs to make sure pdfs are printed in same oder as they were\\n # taken\\n for k, v in self.stage_summaries.items():\\n for qc_id in flatten_list(v):\\n inv_dict[qc_id] = k\\n sorted_ids = list(flatten_list(self.stage_summaries.values()))\\n sorted_ids.sort(key=int)\\n # for stage, value in self.stage_summaries.items():\\n for qc_run_id in sorted_ids:\\n # stage = inv_dict[qc_run_id]\\n # if stage[0:7] == 'failed_':\\n # stage = stage[7:]\\n # try:\\n # s = self.comments[qc_run_id]\\n # except KeyError:\\n # s = ''\\n # self.comments[qc_run_id] = 'Classified as poor result.\\\\n' + s\\n ds = Dataset(qc_run_id, self.db_name)\\n device_name = ds.device_name\\n f_folder = os.path.join(self.db_folder, \\\"tuning_results\\\", device_name)\\n # for qc_run_id in flatten_list(value):\\n self.smalltalk += str(qc_run_id) + \\\", \\\"\\n\\n # filename = stage + '_fit_ds'\\n # filename += str(qc_run_id) + '.png'\\n filename = os.path.join(f_folder, str(ds.ds.guid) + \\\".png\\\")\\n\\n self._files[str(qc_run_id)] = filename\",\n \"def Analyze(self, data):\\n self._AddResult()\",\n \"def parse_log(path_to_log):\\n\\n re_correct_line = re.compile('^\\\\w+\\\\d+')\\n re_iteration = re.compile('Iteration (\\\\d+)')\\n # alexnet\\n #re_output_loss = re.compile('output #\\\\d+: loss = ([\\\\.\\\\d]+)')\\n #re_output_acc = re.compile('output #\\\\d+: accuracy = ([\\\\.\\\\d]+)')\\n\\n #googlenet\\n re_output_loss = re.compile('output #\\\\d+: loss3\\\\/loss3 = ([\\\\.\\\\d]+)')\\n re_output_acc = re.compile('output #\\\\d+: loss3\\\\/top-1 = ([\\\\.\\\\d]+)')\\n\\n re_lr = re.compile('lr = ([\\\\.\\\\d]+)')\\n\\n # Pick out lines of interest\\n iteration = -1\\n test_accuracy = -1\\n learning_rate = float('NaN')\\n acc = float('NaN')\\n train_dict_list = []\\n test_dict_list = []\\n train_dict_names = ('NumIters', 'Loss', 'Accuracy', 'LearningRate', 'Seconds')\\n test_dict_names = ('NumIters', 'Loss', 'Accuracy')\\n\\n is_test_group = False\\n\\n logfile_year = extract_seconds.get_log_created_year(path_to_log)\\n with open(path_to_log) as f:\\n start_time = extract_seconds.get_start_time(f, logfile_year)\\n for line in f:\\n if not re_correct_line.match(line):\\n continue\\n iteration_match = re_iteration.search(line)\\n if iteration_match:\\n iteration = int(iteration_match.group(1))\\n if iteration == -1:\\n # Only look for other stuff if we've found the first iteration\\n continue\\n\\n time = extract_seconds.extract_datetime_from_line(line, logfile_year)\\n seconds = (time - start_time).total_seconds()\\n\\n lr_match = re_lr.search(line)\\n if lr_match:\\n learning_rate = float(lr_match.group(1))\\n output_acc_match = re_output_acc.search(line)\\n if output_acc_match:\\n acc = float(output_acc_match.group(1))\\n\\n output_loss_match = re_output_loss.search(line)\\n if output_loss_match:\\n if get_line_type(line) == 'test':\\n test_loss = float(output_loss_match.group(1))\\n test_dict_list.append({'NumIters': iteration,\\n 'Loss': test_loss,\\n 'Accuracy': acc})\\n else:\\n train_loss = float(output_loss_match.group(1))\\n train_dict_list.append({'NumIters': iteration,\\n 'Loss': train_loss,\\n 'Accuracy': acc,\\n 'LearningRate': learning_rate,\\n 'Seconds': seconds})\\n\\n df_train = pd.DataFrame(columns=train_dict_names)\\n df_test = pd.DataFrame(columns=test_dict_names)\\n\\n for col in train_dict_names:\\n df_train[col] = [d[col] for d in train_dict_list]\\n for col in test_dict_names:\\n df_test[col] = [d[col] for d in test_dict_list]\\n\\n return df_train, df_test\",\n \"def test_make_results_verbose1(self):\\n\\t\\ttest = sentiment.LibraryRun(self.text3, self.lib)\\n\\t\\ttest.do_run()\\n\\t\\ttest.make_results_verbose()\\n\\t\\tobj_ut = test.results_verbose\\n\\t\\tself.assertEqual(obj_ut, [['100', 'not good', 2, -1, 0],\\n\\t\\t\\t['100', 'not very good', 4, -1, 0]])\",\n \"def _evaluate(self, record_video=False):\\n assert record_video == False\\n assert len(self._runners.keys()) == 1\\n num_eval = self._config.num_eval\\n rollouts = {}\\n info_history = Info()\\n\\n for k, runner in self._runners.items():\\n logger.warn(\\\"Run %d evaluations for %s\\\", num_eval, k)\\n for i in range(num_eval):\\n logger.warn(\\\"Evaluate run %d\\\", i + 1)\\n rollout, info, frames = runner.run_episode(is_train=False)\\n\\n rollouts[k] = rollout\\n logger.info(\\n \\\"rollout: %s\\\", {k: v for k, v in info.items() if not \\\"qpos\\\" in k},\\n )\\n\\n info_history.add(info)\\n\\n avg_stats = {}\\n for k, v in info_history.items():\\n if isinstance(v, list) and not isinstance(v[0], wandb.Video):\\n avg_stats[k] = np.mean(v)\\n\\n std_stats = {}\\n for k, v in info_history.items():\\n if isinstance(v, list) and not isinstance(v[0], wandb.Video):\\n std_stats[k] = np.std(v)\\n\\n logger.info(\\n \\\"Evaluation Average stats: %s\\\", {k: v for k, v in avg_stats.items()},\\n )\\n logger.info(\\n \\\"Evaluation Standard Deviation stats: %s\\\",\\n {k: v for k, v in std_stats.items()},\\n )\\n\\n return rollouts, info_history\",\n \"def consolidate_results(path='./Data'):\\n model_files = [load(os.path.join(path, f)) \\n for f in os.listdir(path) if os.path.isfile(os.path.join(path, f)) and f.startswith('model_')]\\n df_final = pd.DataFrame(columns=['model_name','train_accuracy','test_accuracy',\\n 'macro_avg_precision','macro_avg_recall',\\n 'macro_avg_f1-score','weighted_avg_precision',\\n 'weighted_avg_recall','weighted_avg_f1-score'])\\n for model_file in model_files:\\n results = model_file['model_results']\\n class_report = classification_report(results.category, results.pred, output_dict=True)\\n df_final = df_final.append({'model_name':model_file['model_name'],\\n 'train_accuracy':'{0:.2f}'.format(model_file['model_CV'].best_score_),\\n 'test_accuracy':'{0:.2f}'.format(class_report['accuracy']),\\n 'macro_avg_precision':class_report['macro avg']['precision'],\\n 'macro_avg_recall':class_report['macro avg']['recall'],\\n 'macro_avg_f1-score':class_report['macro avg']['f1-score'],\\n 'weighted_avg_precision':class_report['weighted avg']['precision'],\\n 'weighted_avg_recall':class_report['weighted avg']['recall'],\\n 'weighted_avg_f1-score':class_report['weighted avg']['f1-score']\\n },ignore_index=True)\\n return(df_final)\",\n \"def report_csv(self):\\n # type: () -> Optional[AnyStr]\\n if isinstance(self.report_count, int) and self.report_count <= 0:\\n return None\\n if self.score is None or self.true is None:\\n return None\\n pack = list(zip(*[(*pack, )\\n for packs in zip(self.score, self.true, *self.meta.values())\\n for pack in zip(*packs)]))\\n logdata = {key: np.stack(val, axis=0) for key, val in zip([\\\"pred\\\", \\\"target\\\", *self.meta.keys()], pack)}\\n assert all([len(val) == len(logdata[\\\"target\\\"]) for val in logdata.values()]), \\\"messed up unpacking\\\"\\n header = \\\"target_name,target_score\\\"\\n for k in range(self.top_k):\\n header += f\\\",pred_{k + 1}_name,pred_{k + 1}_score\\\"\\n for meta_key in self.log_keys:\\n header += f\\\",{str(meta_key)}\\\"\\n lines = []\\n for sample_idx in range(len(logdata[\\\"target\\\"])):\\n gt_label_idx = int(logdata[\\\"target\\\"][sample_idx])\\n pred_scores = logdata[\\\"pred\\\"][sample_idx]\\n sorted_score_idxs = np.argsort(pred_scores)[::-1]\\n sorted_scores = pred_scores[sorted_score_idxs]\\n if self.conf_threshold is None or gt_label_idx is None or \\\\\\n pred_scores[gt_label_idx] >= self.conf_threshold:\\n if gt_label_idx is not None:\\n entry = f\\\"{self.class_names[gt_label_idx]},{pred_scores[gt_label_idx]:2.4f}\\\"\\n else:\\n entry = f\\\"<unknown>,{0.0:2.4f}\\\"\\n for k in range(self.top_k):\\n entry += f\\\",{self.class_names[sorted_score_idxs[k]]},{sorted_scores[k]:2.4f}\\\"\\n for meta_key in self.log_keys:\\n entry += f\\\",{str(logdata[meta_key][sample_idx])}\\\"\\n lines.append(entry)\\n if isinstance(self.report_count, int) and len(lines) >= self.report_count:\\n break\\n return \\\"\\\\n\\\".join([header, *lines])\",\n \"def metrics(logger, model, X_train, y_train, X_test, y_test):\\n\\n results = dict()\\n y_preds = model.predict(X_test)\\n results['Train Accuracy'] = model.score(X_train, y_train)\\n results['Test Accuracy'] = accuracy_score(y_test, y_preds)\\n results['Precision'] = precision_score(y_test, y_preds)\\n results['Recall'] = recall_score(y_test, y_preds)\\n\\n metric_cols = data_config['params']['metrics_cols']\\n res_df = pd.DataFrame(results.items(), columns=metric_cols)\\n\\n metrics_path = data_config['outputs']['logreg_metrics']\\n res_df.to_csv(metrics_path, index=False)\\n print(f'Metrics saved to {metrics_path}')\\n\\n return\",\n \"def evaluate(self, eval_batches, result_dir=None, result_prefix=None, save_full_info=False):\\n pred_answers, ref_answers = [], []\\n total_loss, total_num = 0, 0\\n for b_itx, batch in enumerate(eval_batches):\\n feed_dict = {self.p: batch['passage_token_ids'],\\n self.q: batch['question_token_ids'],\\n self.p_char: batch['passage_char_ids'],\\n self.q_char: batch['question_char_ids'],\\n self.p_length: batch['passage_length'],\\n self.q_length: batch['question_length'],\\n self.start_label: batch['start_id'],\\n self.end_label: batch['end_id'],\\n self.dropout_keep_prob: 1.0}\\n start_probs, end_probs, loss = self.sess.run([self.start_probs,\\n self.end_probs, self.loss], feed_dict)\\n\\n total_loss += loss * len(batch['raw_data'])\\n total_num += len(batch['raw_data'])\\n\\n padded_p_len = len(batch['passage_token_ids'][0])\\n for sample, start_prob, end_prob in zip(batch['raw_data'], start_probs, end_probs):\\n\\n best_answer = self.find_best_answer(sample, start_prob, end_prob, padded_p_len)\\n if save_full_info:\\n sample['pred_answers'] = [best_answer]\\n pred_answers.append(sample)\\n else:\\n pred_answers.append({'question_id': sample['question_id'],\\n 'question_type': sample['question_type'],\\n 'answers': [best_answer],\\n 'entity_answers': [[]],\\n 'yesno_answers': []})\\n if 'answers' in sample:\\n ref_answers.append({'question_id': sample['question_id'],\\n 'question_type': sample['question_type'],\\n 'answers': sample['answers'],\\n 'entity_answers': [[]],\\n 'yesno_answers': []})\\n\\n if result_dir is not None and result_prefix is not None:\\n result_file = os.path.join(result_dir, result_prefix + '.json')\\n with open(result_file, 'w') as fout:\\n for pred_answer in pred_answers:\\n fout.write(json.dumps(pred_answer, ensure_ascii=False) + '\\\\n')\\n\\n self.logger.info('Saving {} results to {}'.format(result_prefix, result_file))\\n\\n # this average loss is invalid on test set, since we don't have true start_id and end_id\\n ave_loss = 1.0 * total_loss / total_num\\n # compute the bleu and rouge scores if reference answers is provided\\n if len(ref_answers) > 0:\\n pred_dict, ref_dict = {}, {}\\n for pred, ref in zip(pred_answers, ref_answers):\\n question_id = ref['question_id']\\n if len(ref['answers']) > 0:\\n pred_dict[question_id] = normalize(pred['answers'])\\n ref_dict[question_id] = normalize(ref['answers'])\\n bleu_rouge = compute_bleu_rouge(pred_dict, ref_dict)\\n else:\\n bleu_rouge = None\\n return ave_loss, bleu_rouge\",\n \"def save_analysis_results(self):\\r\\n\\r\\n logger.debug('Saving analysis results')\\r\\n\\r\\n segments_nr = len(self.tracked_faces)\\r\\n self.anal_results[c.SEGMENTS_NR_KEY] = segments_nr\\r\\n\\r\\n people_clusters_nr = len(self.recognized_faces)\\r\\n self.anal_results[c.PEOPLE_CLUSTERS_NR_KEY] = people_clusters_nr\\r\\n\\r\\n # Count relevant tags\\r\\n relevant_people_nr = 0\\r\\n for person_dict in self.recognized_faces:\\r\\n\\r\\n tag = person_dict[c.ASSIGNED_TAG_KEY]\\r\\n\\r\\n if tag != c.UNDEFINED_TAG:\\r\\n relevant_people_nr += 1\\r\\n\\r\\n self.anal_results[c.RELEVANT_PEOPLE_NR_KEY] = relevant_people_nr\",\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 output_predictions(pipeline):\\n ##### Write code here #######\\n X_train, y_train_true = load_data_file(TRAIN_FILE)\\n X_dev, y_dev_true = load_data_file(DEV_FILE)\\n X_test, y_test_true = load_data_file(TEST_FILE)\\n\\n #train pipeline with dev and train file\\n pipeline.fit(X=X_train, y=y_train_true)\\n pipeline.fit(X=X_dev, y=y_dev_true)\\n\\n y_pred_test = pipeline.predict(X=X_test)\\n\\n df = pd.DataFrame(y_pred_test)\\n with open('predictions.tsv', 'w'):\\n df.to_csv('predictions.tsv', sep='\\\\t', index=False, header=False)\\n ##### End of your work ######\",\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 analyze(collector):\\n mod_list = [\\\"Oxidation\\\", \\\"Deamidated\\\", \\\"Methyl\\\", \\\"Acetyl\\\", \\\"Phospho\\\"]\\n fieldnames = (\\n [\\\"approach\\\", \\\"count_type\\\", \\\"validation_engine\\\", \\\"unmodified\\\", \\\"multimodified\\\"]\\n + mod_list\\n + [\\\"total\\\"]\\n )\\n\\n csv_writer = csv.DictWriter(open(\\\"ungrouped_results.csv\\\", \\\"w\\\"), fieldnames)\\n csv_writer.writeheader()\\n uc = ursgal.UController()\\n uc.params[\\\"validation_score_field\\\"] = \\\"PEP\\\"\\n uc.params[\\\"bigger_scores_better\\\"] = False\\n\\n # Count the number of identified peptides and PSMs for the different modifications\\n # Spectra with multiple PSMs are sanitized, i.e. only the PSM with best PEP score is counted\\n # and only if the best hit has a PEP that is at least two orders of\\n # magnitude smaller than the others\\n for validation_engine, result_file in collector.items():\\n counter_dict = {\\\"psm\\\": ddict(set), \\\"pep\\\": ddict(set)}\\n grouped_psms = uc._group_psms(\\n result_file, validation_score_field=\\\"PEP\\\", bigger_scores_better=False\\n )\\n for spec_title, grouped_psm_list in grouped_psms.items():\\n best_score, best_line_dict = grouped_psm_list[0]\\n if len(grouped_psm_list) > 1:\\n second_best_score, second_best_line_dict = grouped_psm_list[1]\\n best_peptide_and_mod = (\\n best_line_dict[\\\"Sequence\\\"] + best_line_dict[\\\"Modifications\\\"]\\n )\\n second_best_peptide_and_mod = (\\n second_best_line_dict[\\\"Sequence\\\"]\\n + second_best_line_dict[\\\"Modifications\\\"]\\n )\\n\\n if best_peptide_and_mod == second_best_peptide_and_mod:\\n line_dict = best_line_dict\\n elif best_line_dict[\\\"Sequence\\\"] == second_best_line_dict[\\\"Sequence\\\"]:\\n if best_score == second_best_score:\\n line_dict = best_line_dict\\n else:\\n if (-1 * math.log10(best_score)) - (\\n -1 * math.log10(second_best_score)\\n ) >= 2:\\n line_dict = best_line_dict\\n else:\\n continue\\n else:\\n if (-1 * math.log10(best_score)) - (\\n -1 * math.log10(second_best_score)\\n ) >= 2:\\n line_dict = best_line_dict\\n else:\\n continue\\n else:\\n line_dict = best_line_dict\\n\\n count = 0\\n for mod in mod_list:\\n if mod in line_dict[\\\"Modifications\\\"]:\\n count += 1\\n key_2_add = \\\"\\\"\\n if count == 0:\\n key_2_add = \\\"unmodified\\\"\\n elif count >= 2:\\n key_2_add = \\\"multimodified\\\"\\n elif count == 1:\\n for mod in mod_list:\\n if mod in line_dict[\\\"Modifications\\\"]:\\n key_2_add = mod\\n break\\n # for peptide identification comparison\\n counter_dict[\\\"pep\\\"][key_2_add].add(\\n line_dict[\\\"Sequence\\\"] + line_dict[\\\"Modifications\\\"]\\n )\\n # for PSM comparison\\n counter_dict[\\\"psm\\\"][key_2_add].add(\\n line_dict[\\\"Spectrum Title\\\"]\\n + line_dict[\\\"Sequence\\\"]\\n + line_dict[\\\"Modifications\\\"]\\n )\\n for counter_key, count_dict in counter_dict.items():\\n dict_2_write = {\\n \\\"approach\\\": \\\"ungrouped\\\",\\n \\\"count_type\\\": counter_key,\\n \\\"validation_engine\\\": validation_engine,\\n }\\n total_number = 0\\n for key, obj_set in count_dict.items():\\n dict_2_write[key] = len(obj_set)\\n total_number += len(obj_set)\\n dict_2_write[\\\"total\\\"] = total_number\\n csv_writer.writerow(dict_2_write)\\n return\",\n \"def parse_results_file(filename):\\n\\tfile = open(filename, 'r')\\n\\tpretext=[line for line in file.readlines() if line.strip()]\\n\\tfile.close()\\n\\n\\ttext = []\\n\\tprocessed = []\\n\\tlanguages = 'NONE'\\n\\tID = 'NONE'\\n\\t\\n\\tmoreheader = raw_input('Extra header labels from question field (e.g.: item,condition,factor1,factor2): ')\\n\\tstim_type = raw_input('What type are your stims? (i.e. AcceptabilityJudgment): ')\\n\\toutput_loc = raw_input('Where would you like to put your parsed file? (enter filename path): ')\\n\\t\\n\\t#takes out comments\\n\\tfor line in pretext:\\n\\t\\tif re.match('#', line):\\n\\t\\t\\tcontinue\\n\\t\\telse:\\n\\t\\t\\ttext.append(line)\\n\\n\\tfirst = 1;\\n\\n\\tfor line in range(len(text)):\\n\\t\\t#get their info\\n\\t\\tif re.search('Form', text[line]):\\n\\t\\t\\tif re.search('number', text[line]):\\n\\t\\t\\t\\tID = re.split('number,', text[line])[1].strip()\\n\\t\\t\\telif re.search('age', text[line]):\\n\\t\\t\\t\\tlanguages = re.split('age,', text[line])[1].strip()\\n\\n\\t\\t#looks for the main stimulus type, as entered earlier\\t\\t\\n\\t\\tif re.search(stim_type, text[line]):\\n\\t\\t\\tif first:\\n\\t\\t\\t\\t#print 'first'\\n\\t\\t\\t\\tprocessed.append(str(ID+ ','+languages+','+text[line]))\\n\\t\\t\\t\\tfirst=0\\n\\t\\t\\telse:\\n\\t\\t\\t\\ttoAmend = processed.pop()\\n\\t\\t\\t\\t#print str('toAmend: ' + toAmend)\\n\\t\\t\\t\\ttoAdd=''\\n\\t\\t\\t\\tsplits = re.split('NULL,', text[line])\\n\\t\\t\\t\\tfor thing in splits[1:]:\\n\\t\\t\\t\\t\\tif thing is not '':\\n\\t\\t\\t\\t\\t\\ttoAdd = str(toAdd + ',' + thing.strip(','))\\n\\t\\t\\t\\t#print str('toAdd: ' + toAdd)\\n\\t\\t\\t\\tprocessed.append(str(toAmend.strip()+ toAdd))\\n\\t\\t\\t\\tfirst = 1\\n\\n\\t\\t#if the line is a question line, there's more to append\\n\\t\\tif re.search('Question', text[line]):\\n\\t\\t\\ttoAmend = processed.pop()\\n\\t\\t\\tpart = re.split('\\\\$', text[line])[1]\\n\\t\\t\\tpart.strip('$')\\n\\t\\t\\tparts = part.split('%2C')\\n\\t\\t\\tprocessed.append(str(toAmend.strip()+ ','+ string.join(parts, ',')+'\\\\n'))\\n\\t\\t\\t\\n\\toutput = open(output_loc, 'w')\\n\\n\\theader = 'ID,Languages,Time sent,MD5 Hash of IP Address,Controller,Item Number,Element Number,Type,Group,Stimulus,Answer,RT,'\\n\\n\\toutput.write(str(header+moreheader+'\\\\n'))\\n\\n\\t#put it all into a text file\\n\\tfor line in processed:\\n\\t\\toutput.write(line)\\n\\toutput.close()\",\n \"def log_AD_results(xp_path, learner):\\n\\n log_file = \\\"{}/log.txt\\\".format(xp_path)\\n log = open(log_file, \\\"a\\\")\\n\\n log.write(\\\"Results\\\\n\\\\n\\\")\\n\\n log.write(\\\"Train AUC: {} %\\\\n\\\".format(round(learner.diag['train']['auc'][-1]*100, 4)))\\n log.write(\\\"Train accuracy: {} %\\\\n\\\".format(round(learner.diag['train']['acc'][-1], 4)))\\n log.write(\\\"Train time: {}\\\\n\\\\n\\\".format(round(learner.train_time, 4)))\\n\\n log.write(\\\"Val AUC: {} %\\\\n\\\".format(round(learner.diag['val']['auc'][-1] * 100, 4)))\\n log.write(\\\"Val accuracy: {} %\\\\n\\\\n\\\".format(round(learner.diag['val']['acc'][-1], 4)))\\n\\n log.write(\\\"Test AUC: {} %\\\\n\\\".format(round(learner.diag['test']['auc'][-1]*100, 4)))\\n log.write(\\\"Test accuracy: {} %\\\\n\\\".format(round(learner.diag['test']['acc'][-1], 4)))\\n log.write(\\\"Test time: {}\\\\n\\\".format(round(learner.test_time, 4)))\\n\\n log.write(\\\"\\\\n\\\\n\\\")\\n log.close()\",\n \"def _publish_results(self):\\n\\n doc = Document()\\n date = get_stamp()\\n\\n labels = ExperimentTemplateBase.parameters_to_string(self._topology_parameters_list)\\n\\n title = 'Mutual Information labels vs ' + self._experiment_name\\n self.plot_save(title,\\n self._mutual_info,\\n self._baseline_mutual_info,\\n 'Norm. mutual information',\\n labels, date, self._docs_folder, doc)\\n\\n title = 'Weak classifier accuracy labels vs ' + self._experiment_name\\n self.plot_save(title,\\n self._classifier_accuracy,\\n self._baseline_classifier_accuracy,\\n 'Classifier accuracy',\\n labels, date, self._docs_folder, doc) #, smoothing_size=3)\\n\\n title = 'average delta'\\n f = plot_multiple_runs(\\n self._different_steps[0], # here the X axes are identical\\n self._average_delta,\\n title=title,\\n ylabel='log(delta)',\\n xlabel='steps',\\n labels=labels\\n )\\n add_fig_to_doc(f, path.join(self._docs_folder, title), doc)\\n\\n title = 'average boosting duration'\\n f = plot_multiple_runs(\\n self._different_steps[0],\\n self._average_boosting_dur,\\n title=title,\\n ylabel='duration',\\n xlabel='steps',\\n labels=labels\\n )\\n add_fig_to_doc(f, path.join(self._docs_folder, title), doc)\\n\\n doc.write_file(path.join(self._docs_folder, to_safe_name(self._complete_name() + date + \\\".html\\\")))\\n\\n print('done')\",\n \"def process_iteration_result(self, result, context):\\n self.infile = os.path.join(context.output_directory, 'trace.txt')\\n if os.path.isfile(self.infile):\\n self.logger.debug('Running result_processor \\\"dvfs\\\"')\\n self.outfile = os.path.join(settings.output_directory, 'dvfs.csv')\\n self.flush_parse_initialize()\\n self.calculate()\\n self.percentage()\\n self.generate_csv(context)\\n self.logger.debug('Completed result_processor \\\"dvfs\\\"')\\n else:\\n self.logger.debug('trace.txt not found.')\",\n \"def result():\\n\\n long_description = (\\n \\\"Processing the results from a computed deflex dump file. The \\\"\\n \\\"following functions are available:\\\\n\\\\n\\\"\\n \\\"* calculate_key_values\\\\n\\\"\\n \\\"* something\\\\n\\\"\\n \\\"\\\\nSee the documentation for more details.\\\"\\n )\\n parser = argparse.ArgumentParser(\\n prog=\\\"deflex-results\\\",\\n description=long_description,\\n formatter_class=argparse.RawDescriptionHelpFormatter,\\n )\\n parser.add_argument(\\n \\\"--version\\\", action=\\\"version\\\", version=f\\\"deflex {deflex.__version__}\\\"\\n )\\n parser.add_argument(\\n \\\"function\\\", type=str, help=\\\"Post-processing function to use.\\\"\\n )\\n parser.add_argument(\\\"in_path\\\", type=str, help=\\\"Input file or directory.\\\")\\n parser.add_argument(\\\"out_path\\\", type=str, help=\\\"Output file or directory.\\\")\\n parser.add_argument(\\n \\\"--filetype\\\",\\n dest=\\\"filetype\\\",\\n const=None,\\n default=None,\\n nargs=\\\"?\\\",\\n help=(\\n \\\"The file_type of the output file xlsx or csv. By default the \\\"\\n \\\"suffix of the output file is used, if possible.\\\"\\n ),\\n )\\n args = parser.parse_args()\\n\\n deflex.use_logging()\\n\\n if args.function == \\\"calculate_key_values\\\":\\n results = deflex.restore_results(args.in_path)\\n df = deflex.calculate_key_values(results)\\n elif args.function == \\\"something\\\":\\n df = pd.DataFrame()\\n else:\\n msg = (\\n f\\\"The function {args.function}() is not available with \\\"\\n f\\\"'deflex_results'.\\\"\\n )\\n raise NotImplementedError(msg)\\n\\n if args.filetype is None:\\n args.filetype = args.out_path.split(\\\".\\\")[-1]\\n\\n if args.filetype == \\\"xlsx\\\":\\n df.to_excel(args.out_path)\\n elif args.filetype == \\\"csv\\\":\\n logging.info(f\\\"Results stored in {args.out_path}\\\")\\n df.to_csv(args.out_path)\\n else:\\n raise ValueError(f\\\"File type '{args.filetype}' not valid.\\\")\",\n \"def dataStats(self):\\n print (\\\"Performing statistical analysis of the data\\\")\\n # stuff to do\",\n \"def analyzeResults(self):\\n results = [self.analyzeClusterPerformance(c) for c in self.clusterLabels]\\n rDF = pd.DataFrame(results)\\n self.resultList.append(rDF)\",\n \"def generate_report(path_to_results: str, save_path: str, format: str) -> None:\\n path_to_results = (\\n path_to_results if path_to_results[-1] != \\\"/\\\" else path_to_results[:-1]\\n )\\n files = glob.glob(f\\\"{path_to_results}/*\\\")\\n\\n if len(files) == 0:\\n raise Exception(f\\\"There were no result files in the path {path_to_results}\\\")\\n\\n raw_results = []\\n for result_file in files:\\n with open(result_file, \\\"rb\\\") as handle:\\n raw_results.append(pickle.load(handle))\\n\\n results_dict = []\\n for raw in raw_results:\\n runtime = np.mean([i[\\\"runtime\\\"] for i in raw[\\\"results\\\"]])\\n memory = np.mean(\\n [\\n i[\\\"memory_stats\\\"][\\\"max_memory\\\"]\\n for i in raw[\\\"results\\\"]\\n if i[\\\"memory_stats\\\"] is not None\\n ]\\n )\\n result = {\\n \\\"layer\\\": raw[\\\"layer\\\"],\\n \\\"batch_size\\\": raw[\\\"batch_size\\\"],\\n \\\"num_runs\\\": raw[\\\"num_runs\\\"],\\n \\\"num_repeats\\\": raw[\\\"num_repeats\\\"],\\n \\\"forward_only\\\": raw[\\\"forward_only\\\"],\\n \\\"gsm_mode\\\": raw[\\\"gsm_mode\\\"],\\n \\\"runtime\\\": runtime,\\n \\\"memory\\\": memory,\\n }\\n results_dict.append(result)\\n\\n results = pd.DataFrame(results_dict)\\n\\n results[\\\"gsm_mode\\\"][results[\\\"layer\\\"].str.startswith(\\\"dp\\\")] = (\\n \\\"dp_\\\" + results[\\\"gsm_mode\\\"]\\n )\\n results[\\\"layer\\\"] = results[\\\"layer\\\"].str.replace(\\\"dp\\\", \\\"\\\")\\n\\n pivot = results.pivot_table(\\n index=[\\\"batch_size\\\", \\\"num_runs\\\", \\\"num_repeats\\\", \\\"forward_only\\\", \\\"layer\\\"],\\n columns=[\\\"gsm_mode\\\"],\\n values=[\\\"runtime\\\"],\\n )\\n\\n def add_ratio(df, metric, variant):\\n if variant not in df.columns.get_level_values(\\\"gsm_mode\\\"):\\n for ametric in df.columns.get_level_values(0):\\n df[(ametric, variant)] = np.nan\\n\\n df[(metric, f\\\"{variant}/baseline\\\")] = (\\n df.loc[:, (metric, variant)] / df.loc[:, (metric, \\\"baseline\\\")]\\n )\\n\\n if \\\"baseline\\\" in results[\\\"gsm_mode\\\"].tolist():\\n for m in set(results[\\\"gsm_mode\\\"].tolist()) - {\\\"baseline\\\"}:\\n add_ratio(pivot, \\\"runtime\\\", m)\\n pivot.columns = pivot.columns.set_names(\\\"value\\\", level=1)\\n\\n output = pivot.sort_index(axis=1).sort_values(\\n [\\\"batch_size\\\", \\\"num_runs\\\", \\\"num_repeats\\\", \\\"forward_only\\\"]\\n )\\n if format == \\\"csv\\\":\\n output.to_csv(save_path)\\n else:\\n output.to_pickle(save_path)\",\n \"def after_batch(self):\\n if self.trainer._mode == 'train':\\n with open(os.path.join(self.root_path, 'loss.txt'), 'a+') as fout:\\n fout.write(str(self.trainer._epoch) + '\\\\t' +\\n str(self.trainer._loss.detach().cpu().item()) + '\\\\n')\\n\\n if self.trainer._mode == 'test' and (self.f is not None):\\n for index in range(len(self.trainer._ids)):\\n one_input = self.get_one(self.trainer._input, index)\\n one_output = self.get_one(self.trainer._output, index)\\n\\n res = self.f(one_input, one_output)\\n id = self.trainer._ids[index]\\n\\n self.show(res, id)\",\n \"def collect(self,\\n epoch,\\n test_loss,\\n test_accuracy,\\n training_loss,\\n training_accuracy):\\n print(\\n '\\\\n------------------ Neural Network Testing Result ------------------\\\\n',\\n ' - epoch {0} \\\\n'.format(epoch),\\n ' - test loss {0:1.9f}\\\\n'.format(test_loss),\\n ' - test accuracy {0:1.3f} %\\\\n'.format(test_accuracy),\\n ' - training loss {0:1.9f}\\\\n'.format(training_loss),\\n ' - training accuracy {0:1.3f} %\\\\n'.format(training_accuracy))\\n\\n self.test_accuracies.append(test_accuracy / 100.0)\\n self.training_accuracies.append(training_accuracy / 100.0)\\n self.test_losses.append(test_loss)\\n self.training_losses.append(training_loss)\\n if self.epochs < epoch:\\n self.epochs = epoch\",\n \"def dwn_all_saved_results(request):\\n \\n sources = []\\n for i in Source.objects.filter(user=request.user):\\n sources.append((i.source_id, i.datetime_extracted.strftime('%d/%m/%Y %H:%M'), i.source))\\n \\n data = []\\n for s, timee, s_name in sources:\\n objs = ExtractedRelation.objects.filter(source=s)\\n for i in objs:\\n data.append((i.sentence, i.head, i.tail, i.pred_relation, i.sentiment, i.conf, timee, s_name, i.rel_id, os.path.basename(i.ckpt)))\\n \\n df = pd.DataFrame(data, columns=['Sentence', 'Head', 'Tail', 'Predicted Relation', 'Predicted Sentiment', 'Confidence', 'Extraction Time', 'Source', 'rel_id', 'Checkpoint'])\\n df.to_csv(\\\"temp/all_analysis_results.csv\\\", index=False)\\n \\n return FileResponse(open('temp/all_analysis_results.csv','rb'))\",\n \"def _extract_results(self) -> None:\\n metric_name = self.metric.name\\n for inference_name in ['train', 'test', 'opt']:\\n # TODO: Extract information from self.search_results\\n data = getattr(self.search_results, f'{inference_name}_metric_dict')[metric_name]\\n if all([d is None for d in data]):\\n if inference_name not in OPTIONAL_INFERENCE_CHOICES:\\n raise ValueError(f\\\"Expected {metric_name} score for {inference_name} set\\\"\\n f\\\" to not be None, but got {data}\\\")\\n else:\\n continue\\n self.data[f'single::{inference_name}::{metric_name}'] = np.array(data)\\n\\n if self.ensemble_results.empty() or inference_name == 'opt':\\n continue\\n\\n data = getattr(self.ensemble_results, f'{inference_name}_scores')\\n if all([d is None for d in data]):\\n if inference_name not in OPTIONAL_INFERENCE_CHOICES:\\n raise ValueError(f\\\"Expected {metric_name} score for {inference_name} set\\\"\\n f\\\" to not be None, but got {data}\\\")\\n else:\\n continue\\n self.data[f'ensemble::{inference_name}::{metric_name}'] = np.array(data)\",\n \"def evaluate(self):\\n\\n rew_rl = []\\n for ckpt_path in self._config.eval_ckpt_paths:\\n self._load_ckpt(ckpt_path, self._config.ckpt_num)\\n\\n logger.info(\\n \\\"Run %d evaluations for %d environments\\\",\\n self._config.num_eval,\\n len(self._envs),\\n )\\n rollouts, info = self._evaluate(record_video=self._config.record_video)\\n\\n info_stat = info.get_stat()\\n\\n rew_rl.append(np.mean(info[\\\"rew_rl\\\"]))\\n\\n logger.info(\\\"All Eval Rew Values: %s\\\", rew_rl)\\n logger.info(\\\"Eval Rew RL Average: %f, Std: %f\\\", np.mean(rew_rl), np.std(rew_rl))\\n\\n os.makedirs(\\\"result\\\", exist_ok=True)\\n with h5py.File(\\\"result/{}.hdf5\\\".format(self._config.run_name), \\\"w\\\") as hf:\\n for k, v in info.items():\\n hf.create_dataset(k, data=info[k])\\n with open(\\\"result/{}.txt\\\".format(self._config.run_name), \\\"w\\\") as f:\\n for k, v in info_stat.items():\\n f.write(\\\"{}\\\\t{:.03f} $\\\\\\\\pm$ {:.03f}\\\\n\\\".format(k, v[0], v[1]))\",\n \"def showResult():\\n logging.info('I am in showResult......')\\n filename = \\\"../txt/%s_testall_%d_%d.txt\\\" % (const.DATASET_NAME,const.TOPIC_NUM,const.TOP_N)\\n x = range(1,const.TOP_N,1)\\n result = [[[] for i in range(5)] for i in range(const.METHOD_SIZE)]\\n #read result from file to result\\n if os.path.exists(filename):\\n print '%s is existing......' % filename \\n rFile = open(filename,\\\"r\\\")\\n lines = rFile.readlines()\\n for line in lines:\\n line = line.rstrip('\\\\n')\\n items = line.split(\\\"INFO:\\\")\\n line = items[1]\\n items = line.split(\\\":\\\")\\n ids = items[0]\\n values = items[1]\\n idItems = ids.split(\\\">\\\")\\n mid = int(idItems[0])\\n topN = int(idItems[1])\\n valueItems = values.split()\\n result[mid][0].append(float(valueItems[0]))\\n result[mid][1].append(float(valueItems[1]))\\n result[mid][2].append(float(valueItems[2]))\\n result[mid][3].append(float(valueItems[3]))\\n result[mid][4].append(float(valueItems[4]))\\n rFile.close()\\n else:\\n rFile = open(filename,\\\"w\\\")\\n rFile.close()\\n #if some method is not in file, recreate it\\n for mid in range(const.METHOD_SIZE):\\n if len(result[mid][0]) == 0:\\n recalls,precisions,f1s,maes,rmses = getErrorOfRecMethod(mid)\\n result[mid][0] = recalls\\n result[mid][1] = precisions\\n result[mid][2] = f1s\\n result[mid][3] = maes\\n result[mid][4] = rmses\\n\\n #plt img of comparing with pure method\\n for index in range(5):\\n plt.figure(index)\\n indexName = util.getIndexName(index)\\n mids = [const.ARIMA,const.SIMILAR,const.AVG]\\n markerIndex = 0\\n for mid in mids:\\n if index == 1 or index == 2:\\n plt.plot(x[10:],result[mid][index][10:],const.marker[markerIndex],label=util.getMethodName(mid))\\n else:\\n plt.plot(x,result[mid][index],const.marker[markerIndex],label=util.getMethodName(mid))\\n markerIndex += 1\\n plt.title(\\\"%s of Different Recommend Algorithms(Pure)\\\" % indexName)\\n plt.xlabel(\\\"Number of recommendations\\\")\\n plt.ylabel(indexName)\\n plt.legend()\\n plt.xlim(1,160)\\n plt.savefig(\\\"../img/pure_%s_%s_%d_%d.png\\\" % (const.DATASET_NAME,indexName,const.TOPIC_NUM,const.TOP_N))\\n #plt.show()\\n\\n #plt img of comparing with hybrid method\\n for index in range(5):\\n plt.figure(index+5)\\n indexName = util.getIndexName(index)\\n mids = [const.ARIMA,const.ARIMA_SIMILAR,const.ARIMA_AVG,const.ALL_HYBRID]\\n markerIndex = 0\\n for mid in mids:\\n if index == 1 or index == 2:\\n plt.plot(x[10:],result[mid][index][10:],const.marker[markerIndex],label=util.getMethodName(mid))\\n else:\\n plt.plot(x,result[mid][index],const.marker[markerIndex],label=util.getMethodName(mid))\\n markerIndex += 1\\n plt.title(\\\"%s of Different Recommend Algorithms(Hybrid)\\\" % indexName)\\n plt.xlabel(\\\"Number of recommendations\\\")\\n plt.ylabel(indexName)\\n plt.legend()\\n plt.xlim(1,160)\\n plt.savefig(\\\"../img/hybrid_%s_%s_%d_%d.png\\\" % (const.DATASET_NAME,indexName,const.TOPIC_NUM,const.TOP_N))\\n #plt.show()\\n\\n\\n #plt img of comparing with sequential method\\n for index in range(5):\\n plt.figure(index+10)\\n indexName = util.getIndexName(index)\\n mids = [const.ARIMA,const.KNN,const.PATTERN,const.MARKOV,const.MARKOV_3]\\n markerIndex = 0\\n for mid in mids:\\n if index == 1 or index == 2:\\n plt.plot(x[10:],result[mid][index][10:],const.marker[markerIndex],label=util.getMethodName(mid))\\n else:\\n plt.plot(x,result[mid][index],const.marker[markerIndex],label=util.getMethodName(mid))\\n markerIndex += 1\\n plt.title(\\\"%s of Different Recommend Methods\\\" % indexName)\\n plt.xlabel(\\\"Number of recommendations\\\")\\n plt.ylabel(indexName)\\n plt.legend()\\n plt.xlim(1,160)\\n plt.savefig(\\\"../img/seq_%s_%s_%d_%d.png\\\" % (const.DATASET_NAME,indexName,const.TOPIC_NUM,const.TOP_N))\\n #plt.show()\\n \\n plt.figure(30)\\n plt.plot(x,result[const.ARIMA_SIMILAR][3],'k-.',label=util.getMethodName(const.ARIMA_SIMILAR)) \\n plt.plot(x,result[const.ARIMA_AVG][3],'k+',label=util.getMethodName(const.ARIMA_AVG)) \\n plt.plot(x,result[const.ALL_HYBRID][3],'k',label=util.getMethodName(const.ALL_HYBRID)) \\n plt.title(\\\"MAE of Hybrid Music Recommendation Methods\\\")\\n plt.xlabel(\\\"Number of recommendations\\\")\\n plt.ylabel(\\\"MAE\\\")\\n plt.legend()\\n plt.xlim(1,160)\\n plt.savefig(\\\"../img/local_global_%s_%s_%d_%d.png\\\" % (const.DATASET_NAME,\\\"MAE\\\",const.TOPIC_NUM,const.TOP_N))\\n\\n plt.figure(31)\\n plt.plot(x,result[const.ARIMA_SIMILAR][4],'k-.',label=util.getMethodName(const.ARIMA_SIMILAR)) \\n plt.plot(x,result[const.ARIMA_AVG][4],'k+',label=util.getMethodName(const.ARIMA_AVG)) \\n plt.plot(x,result[const.ALL_HYBRID][4],'k',label=util.getMethodName(const.ALL_HYBRID)) \\n plt.title(\\\"RMSE of Hybrid Music Recommendation Methods\\\")\\n plt.xlabel(\\\"Number of recommendations\\\")\\n plt.ylabel(\\\"RMSE\\\")\\n plt.legend()\\n plt.xlim(1,160)\\n plt.savefig(\\\"../img/local_global_%s_%s_%d_%d.png\\\" % (const.DATASET_NAME,\\\"RMSE\\\",const.TOPIC_NUM,const.TOP_N))\\n\\n plt.figure(19)\\n improvement = []\\n for i in range(len(result[const.ARIMA][1])):\\n improvement.append((result[const.ARIMA][1][i]-result[const.KNN][1][i]) / result[const.KNN][1][i])\\n plt.plot(x[10:],improvement[10:],'k',label='Improvement over UserKNN Recommender')\\n plt.title('Average Precision Improvement over UserKNN Recommender')\\n plt.xlabel('Number of recommendations')\\n plt.ylabel('Improvement in Average Precision (times)')\\n plt.legend()\\n indexName = util.getIndexName(1)\\n plt.savefig(\\\"../img/improvement_knn_%s_%s_%d_%d.png\\\" % (const.DATASET_NAME,indexName,const.TOPIC_NUM,const.TOP_N))\\n\\n plt.figure(20)\\n improvement = []\\n for i in range(len(result[const.ARIMA][1])):\\n improvement.append((result[const.ARIMA][1][i]-result[const.KNN][1][i]) / result[const.KNN][1][i])\\n plt.plot(x[10:],improvement[10:],'k',label='Improvement over UserKNN Recommender')\\n plt.title('Average Precision Improvement over UserKNN Recommender')\\n plt.xlabel('Number of recommendations')\\n plt.ylabel('Improvement in Average Precision (times)')\\n plt.legend()\\n indexName = util.getIndexName(1)\\n plt.savefig(\\\"../img/improvement_knn_%s_%s_%d_%d.png\\\" % (const.DATASET_NAME,indexName,const.TOPIC_NUM,const.TOP_N))\\n\\n plt.figure(21)\\n improvement = []\\n for i in range(len(result[const.ARIMA][2])):\\n improvement.append((result[const.ARIMA][2][i]-result[const.KNN][2][i]) / result[const.KNN][2][i])\\n plt.plot(x[10:],improvement[10:],'k',label='Improvement over UserKNN Recommender')\\n plt.title('Average F1-Score Improvement over UserKNN Recommender')\\n plt.xlabel('Number of recommendations')\\n plt.ylabel('Improvement in Average F1-Score (times)')\\n plt.legend()\\n indexName = util.getIndexName(2)\\n plt.savefig(\\\"../img/improvement_knn_%s_%s_%d_%d.png\\\" % (const.DATASET_NAME,indexName,const.TOPIC_NUM,const.TOP_N))\\n #plt.show()\\n logging.info('I am out showResult......')\\n\\n #plt img of comparing with pure method\\n for index in range(5):\\n plt.figure(index+50)\\n indexName = util.getIndexName(index)\\n print indexName\\n mids = [const.ARIMA,const.SIMILAR,const.KNN,const.AVG,const.PATTERN,const.MARKOV]\\n markerIndex = 0\\n for mid in mids:\\n if index == 1 or index == 2:\\n plt.plot(x[10:],result[mid][index][10:],const.marker1[markerIndex],label=util.getMethodName(mid))\\n else:\\n plt.plot(x,result[mid][index],const.marker1[markerIndex],label=util.getMethodName(mid))\\n markerIndex += 1\\n plt.title(\\\"%s of Different Recommend Algorithms\\\" % indexName)\\n plt.xlabel(\\\"Number of recommendations\\\")\\n plt.ylabel(indexName)\\n plt.legend()\\n plt.xlim(1,160)\\n plt.savefig(\\\"../img/all_%s_%s_%d_%d.png\\\" % (const.DATASET_NAME,indexName,const.TOPIC_NUM,const.TOP_N))\\n\\n #plt img of comparing with hybrid method\\n for index in range(5):\\n plt.figure(index+75)\\n indexName = util.getIndexName(index)\\n mids = [const.ARIMA,const.ALL_HYBRID]\\n markerIndex = 0\\n for mid in mids:\\n if index == 1 or index == 2:\\n plt.plot(x[10:],result[mid][index][10:],const.marker[markerIndex],label=util.getMethodName(mid))\\n else:\\n plt.plot(x,result[mid][index],const.marker[markerIndex],label=util.getMethodName(mid))\\n markerIndex += 1\\n plt.title(\\\"%s of Different Recommend Algorithms\\\" % indexName)\\n plt.xlabel(\\\"Number of recommendations\\\")\\n plt.ylabel(indexName)\\n plt.legend()\\n plt.xlim(1,160)\\n plt.savefig(\\\"../img/hybrid_only_%s_%s_%d_%d.png\\\" % (const.DATASET_NAME,indexName,const.TOPIC_NUM,const.TOP_N))\\n #plt.show()\",\n \"def process_output_reports(results, analysis, date_now):\\n #PLUG_INS[analysis.plug_in].set_data(analysis.title, file_path, results)\\n output = PLUG_INS[analysis.plug_in]()\\n file_path = settings.REPORT_PATH+\\\"/analysis%s_%s_%s_%s_%s_%s_%s\\\" % (analysis.id, date_now.year, date_now.month, date_now.day, date_now.hour, date_now.minute, date_now.second)\\n output.set_data(analysis.title, file_path, results)\\n\\n result = AnalysisResult(analysis=analysis, output=string.split(output.get_output_file(), \\\"/\\\")[-1], run_date=date_now)\\n result.save() \\n analysis.last_report = date_now\\n analysis.save()\\n return True\",\n \"def report(self, results):\\n self.notice(\\\"Test Report\\\\n\\\")\\n\\n for count, group in enumerate(results, 1):\\n results = (self._format_test(test, res) for test, res in group)\\n results = (', ').join(results)\\n self.notice(\\\"Test group %s:\\\\t%s\\\" % (count, results))\\n\\n self.divider()\",\n \"def main():\\n parser = argparse.ArgumentParser(description=\\\"Process the results of an experiment.\\\")\\n parser.add_argument(\\\"experiment\\\")\\n arguments = parser.parse_args()\\n path = f\\\"experiments/{arguments.experiment}\\\"\\n if not os.path.exists(path):\\n raise SystemExit(f\\\"Path {path} does not exists.\\\")\\n\\n # For efficiency, one should generate the results from the parts without merging them.\\n files = [file for file in os.listdir(path) if os.path.isfile(os.path.join(path, file))]\\n frames = []\\n for file in files:\\n device, experiment, _ = file.split(\\\".\\\")\\n frame = pandas.read_csv(\\n os.path.join(path, file),\\n index_col=\\\"variable\\\",\\n usecols=[\\\"variable\\\", \\\"group_index\\\", \\\"value_i\\\"], dtype={\\\"value_i\\\": \\\"Int64\\\"}\\n )\\n frame[\\\"board\\\"] = device\\n frame[\\\"experiment\\\"] = experiment\\n frames.append(frame)\\n dataframe = pandas.concat(frames)\\n frames = None\\n\\n current_grouping = dataframe.groupby([\\\"group_index\\\", \\\"variable\\\"])\\n \\n data = current_grouping.agg([\\n numpy.median,\\n _percentile_factory(95),\\n numpy.mean,\\n numpy.std,\\n \\\"count\\\"\\n ])\\n\\n print(data)\\n \\n data = data.droplevel([0], axis=1)\\n data = data.unstack()\\n data.columns = data.columns.map('_'.join)\\n data.to_csv(f\\\"{arguments.experiment}.csv\\\")\",\n \"def main():\\n\\n file_name_base = \\\"./lab-record/result/fairness/\\\"\\n scenarios = ['lan', 'wan1', 'wan2']\\n scenario = scenarios[2]\\n\\n algorithms = [\\\"bbr\\\", \\\"scalable\\\", \\\"bic\\\", \\\"highspeed\\\", \\\"htcp\\\", \\\"hybla\\\",\\n \\\"illinois\\\", \\\"vegas\\\", \\\"yeah\\\"]\\n names = [\\\"BBR\\\", \\\"Scalable\\\", \\\"BIC\\\", \\\"High Speed\\\",\\n \\\"H-TCP\\\", \\\"Hybla\\\", \\\"Illinois\\\", \\\"Vegas\\\", \\\"YeAH\\\"]\\n\\n test_types = [\\\"vs_reno\\\", \\\"vs_cubic\\\", \\\"vs_itself\\\"]\\n\\n fsize = 36\\n \\n index_reno = []\\n index_cubic = []\\n index_itself = []\\n\\n data = []\\n \\n print 'Loadint statistics for ' + file_name_base + '/' + scenario\\n\\n for algorithm in algorithms:\\n for test in test_types:\\n path_base = file_name_base + \\\"/\\\" + scenario + \\\"/\\\" + test + \\\"/\\\" + \\\\\\n algorithm + \\\"/\\\"\\n if test == \\\"vs_itself\\\":\\n exp_name = names[algorithms.index(algorithm)] + \\\"_1\\\"\\n con_name = names[algorithms.index(algorithm)] + \\\"_2\\\"\\n print path_base + exp_name\\n print path_base + con_name\\n exp_filename = \\\"/\\\" + algorithm + \\\"_1.log\\\"\\n con_filename = \\\"/\\\" + algorithm + \\\"_2.log\\\"\\n process(path_base, exp_filename, con_filename, index_itself)\\n if test == \\\"vs_reno\\\":\\n exp_name = names[algorithms.index(algorithm)]\\n con_name = \\\"Reno\\\"\\n print path_base + exp_name\\n print path_base + con_name\\n exp_filename = \\\"/\\\" + algorithm + \\\".log\\\"\\n con_filename = \\\"/reno.log\\\"\\n process(path_base, exp_filename, con_filename, index_reno)\\n if test == \\\"vs_cubic\\\":\\n con_name = \\\"CUBIC\\\"\\n exp_name = names[algorithms.index(algorithm)]\\n print path_base + exp_name\\n print path_base + con_name\\n exp_filename = \\\"/\\\" + algorithm + \\\".log\\\"\\n con_filename = \\\"/cubic.log\\\"\\n process(path_base, exp_filename, con_filename, index_cubic)\\n\\n size = 9\\n x = numpy.arange(size)\\n\\n total_width, n = 1.2, 2.5\\n width = 1.0 / n\\n x = x - (total_width - width) / 2\\n\\n for i in range(0, len(x)):\\n x[i] += 0.5 * i\\n\\n # Exp\\n fig = plt.figure()\\n\\n # Con\\n con_reno = plt.bar(x + 0 * width - 1.2,\\n index_reno,\\n width=width,\\n label='Against Reno',\\n alpha=0.5,\\n color=\\\"darkorange\\\")\\n\\n con_cubic = plt.bar(x + 1 * width - 1.2,\\n index_cubic,\\n width=width,\\n label='Against CUBIC',\\n alpha=0.5,\\n color=\\\"lawngreen\\\")\\n\\n con_itself = plt.bar(x + 2 * width - 1.2,\\n index_itself,\\n width=width,\\n label='Against Another Same CCA',\\n alpha=0.5,\\n color=\\\"dodgerblue\\\")\\n\\n # Index\\n plt.xticks(x + 1.5 * width - 1.2, [\\\"BBR\\\", \\\"Scalable\\\", \\\"BIC\\\", \\\"High Speed\\\",\\n \\\"H-TCP\\\", \\\"Hybla\\\", \\\"Illinois\\\", \\\"Vegas\\\",\\n \\\"YeAH\\\"],\\n fontsize=fsize,\\n rotation=\\\"45\\\")\\n plt.ylabel(\\\"Jain`s Fairness Index\\\", fontsize=fsize)\\n plt.yticks(fontsize=fsize)\\n plt.ylim(0.5, 1.1)\\n\\n ax = plt.subplot(111)\\n ax.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3,\\n ncol=3, mode=\\\"expand\\\", borderaxespad=0., fontsize=fsize)\\n\\n plt.subplots_adjust(left=0.07, right=0.98, top=0.9, bottom=0.2)\\n\\n plt.show()\",\n \"def metric_recorded(self, record):\\n if record.name in self.headers and self.current_row is not None:\\n if record.name == \\\"learning_rate\\\" and not record.is_scalar:\\n # record is a list of scalars\\n value = \\\",\\\".join([f\\\"{lr:.4f}\\\" for lr in record.value])\\n elif record.is_scalar and isinstance(record.value, int):\\n value = str(record.value)\\n else:\\n assert record.is_scalar\\n\\n value = f\\\"{record.value:.4f}\\\"\\n\\n self.current_row[record.name] = value\",\n \"def export_data():\\n # What we'll keep:\\n # - year\\n # - total_runners\\n # - num males\\n # - num females\\n # - num states\\n # - combined distance\\n # - average age (mean)\\n # - average pace (mean)\\n # - average finish time (mean)\\n headers = [\\n 'Year', 'Total Runners', 'Males', 'Females', 'States',\\n 'Combined Distance', 'Mean Age', 'Mean Pace', 'Mean Finish Time',\\n ]\\n data = tablib.Dataset(headers=headers)\\n\\n years = sorted([\\n year for year, parser in parsers.PARSERS.items()\\n if parser is not None\\n ])\\n for year in years:\\n # parse the the data fot he year\\n runners = parsers.parse(year)\\n row = []\\n if runners:\\n row.append(year) # Year\\n row.append(len(runners)) # Total Runners\\n row.append(len([runner.sex for runner in runners if runner.sex == \\\"F\\\"]))\\n row.append(len([runner.sex for runner in runners if runner.sex == \\\"M\\\"]))\\n row.append(len(set(runner.state for runner in runners))) # Sates\\n row.append(int(len(runners) * 26.2)) # Combined Distance\\n\\n # Average age.\\n ages = [int(runner.age) for runner in runners if is_numeric(runner.age)]\\n row.append(int(mean(ages)))\\n\\n # Average pace.\\n paces = []\\n for runner in runners:\\n minutes, seconds = runner.pace.split(\\\":\\\")\\n paces.append(int(seconds) + (int(minutes) * 60))\\n mean_pace = int(mean(paces))\\n mean_pace_minutes, mean_pace_seconds = divmod(mean_pace, 60)\\n mean_pace = \\\"{}:{}\\\".format(mean_pace_minutes, mean_pace_seconds)\\n row.append(mean_pace)\\n\\n # Average finish times.\\n times = []\\n for runner in runners:\\n hours, minutes, seconds = runner.time.split(\\\":\\\")\\n times.append(int(seconds) + (int(minutes) * 60) + (int(hours) * 3600))\\n mean_time = int(mean(times))\\n minutes, seconds = divmod(mean_time, 60)\\n hours, minutes = divmod(minutes, 60)\\n mean_time = \\\"{}:{}:{}\\\".format(hours, minutes, seconds)\\n row.append(mean_time)\\n\\n # Append to our dataset\\n data.append(row)\\n print('Calculated data for {}...'.format(year))\\n\\n with open(\\\"output.csv\\\", \\\"w\\\") as csvfile:\\n csvfile.write(data.csv)\\n print(\\\"Output written to 'output.csv'\\\")\",\n \"def analyze(data):\\n ## Do welch periodogram here\\n pass\",\n \"def save_results(self):\\n results = pd.concat([\\n pd.DataFrame(self.IDs.cpu().numpy(), columns= ['ID']), \\n pd.DataFrame(self.predicted_labels.cpu().numpy(), columns= ['predicted_label']),\\n pd.DataFrame(self.correct_predictions.cpu().numpy(), columns= ['correct_prediction']),\\n pd.DataFrame(self.epistemic_uncertainty.cpu().numpy(), columns= ['epistemic_uncertainty']), \\n pd.DataFrame(self.aleatoric_uncertainty.cpu().numpy(), columns= ['aleatoric_uncertainty']), \\n pd.DataFrame(self.total_uncertainty.cpu().numpy(), columns= ['total_uncertainty']), \\n ], axis=1)\\n\\n create_results_directory()\\n results.to_csv('results/{}_{}_results.csv'.format(self.__class__.__name__, datetime.datetime.now().replace(microsecond=0).isoformat()), index=False)\",\n \"def pipeline_runner():\\n # file_parser() # take raw data file and extract columns of interest. remove contaminants.\\n entry_parser() # remove duplicates, faulty lines and format the whole thing normally.\\n lfq_parser() # replace 0s in lfq reading with random small numbers for t testing purposes\\n # open Rstudio and do T testing there\\n ROutputFormatter() # reformat R output to something more appealing, add FDR and fold change values\",\n \"def aggregate_results(results):\\n\\n for (config,con,dec),folds in results.iteritems():\\n m = MODEL_PATTERN.match(config)\\n if m:\\n mode = m.groupdict()['mode'] # mle, rl, mrt, ...\\n model = m.groupdict()['model'] # haem, hacm, hard, ...\\n align = m.groupdict()['align'] # crp, cls ...\\n else:\\n mode, model, align = '', '', ''\\n # mean accuracies across seeds for each fold\\n foldaccuracies = []\\n # we count number of models over folds and seeds\\n num_individual_models = 0\\n\\n for foldname,fold in folds.items():\\n if 'Q' in options.mode:\\n seedaccurracies = fold.values()[:1] if fold.values() else [] # pick one\\n# SUPPORT_STATISTICS[(config,con,dec,model,align,mode,foldname)] += 1\\n else:\\n seedaccurracies = []\\n for seed_acc in fold.values():\\n seedaccurracies.append(seed_acc)\\n SUPPORT_STATISTICS[(config,con,dec,model,align,mode,foldname)] += 1\\n # aggregate on fold level\\n fold['__MEAN__'] = float(np.mean(seedaccurracies))\\n fold['__SD__'] = float(np.std(seedaccurracies))\\n l = len(seedaccurracies)\\n num_individual_models += l\\n SUPPORT_STATISTICS[(config,con,dec,model,align,mode,'__MEAN__')] += l\\n SUPPORT_STATISTICS[(config,con,dec,model,align,mode,'__SD__')] += l\\n\\n # statistics over seeds for this fold\\n fold['__STATS__'] = fold['__MEAN__'], fold['__SD__'], l\\n foldaccuracies.append(fold['__MEAN__'])\\n # aggregate on (config, condition, decoding) level\\n folds['__MEAN__'] = float(np.mean(foldaccuracies))\\n folds['__SD__'] = float(np.std(foldaccuracies))\\n # statistics over folds for this (config, condition, decoding)\\n folds['__STATS__'] = folds['__MEAN__'], folds['__SD__'], num_individual_models\",\n \"def print_data(self):\\n total_score = 0.0\\n\\n title_game = 'Game'\\n title_word = 'Word'\\n title_word_status = 'Word Status'\\n title_bad_guesses = 'Bad Guesses'\\n title_missed_letters = 'Missed Letters'\\n title_total_score = 'Total score'\\n\\n if not record_word:\\n print(\\\"No words played.\\\")\\n else:\\n print('%-5s %-10s %-12s %-5s %-5s %s' %(title_game,title_word, title_word_status, title_bad_guesses, title_missed_letters,title_total_score))\\n print('---- ---- ------------ ----------- -------------- -----------')\\n for x in range(len(record_word)):\\n print('%-5s %-10s %-13s %-11s %-13s %.2f'%(record_game[x],record_word[x],record_word_status[x],record_bad_guesses[x],record_missed_letters[x],record_total_score[x]))\\n\\n for x in range(len(record_total_score)):\\n total_score = total_score + record_total_score[x]\\n\\n print('\\\\nFinal Score: %.2f' %total_score)\",\n \"def results_aggregator(self, names):\\n\\t\\tfor name in names:\\n\\t\\t\\tresult = self.main(name)\\n\\t\\t\\tself.results.append(result)\\n\\t\\t\\tprint(\\\"'%s' has been written to the file.\\\" % result[0])\\n\\t\\t\\t\\\"\\\"\\\"result is formatted name, number, rating, review count\\\"\\\"\\\"\",\n \"def _after_run_finished(self):\\n\\n # append lists for this run\\n self._mutual_info.append([])\\n self._baseline_mutual_info.append([])\\n\\n self._classifier_accuracy.append([])\\n self._baseline_classifier_accuracy.append([])\\n\\n self._steps.append([])\\n\\n self._average_boosting_dur.append([])\\n self._average_delta.append([])\\n self._different_steps.append([])\\n\\n # get all the measurements from the last run\\n last_run_measurements = self._measurement_manager.run_measurements[-1]\\n\\n # temporary sliding window for computation of one value of the mutual information\\n labels_window = []\\n outputs_window = []\\n baseline_outputs_window = []\\n\\n print('computing statistics after run...')\\n\\n # go step-by-step through the last run (single_measurement contains all the values taken in that time-step)\\n for single_measurement in last_run_measurements:\\n\\n # these two measurements have to run with different (lower) frequency\\n if 'average_boosting_dur' in single_measurement.keys():\\n self._average_boosting_dur[-1].append(single_measurement['average_boosting_dur'])\\n self._average_delta[-1].append(single_measurement['average_delta'])\\n self._different_steps[-1].append(single_measurement['current_step'])\\n\\n # pick \\\"dataset_labels\\\" (see the init()) from the single_measurement and append one value to the separate list\\n labels_window.append(single_measurement['dataset_labels'])\\n outputs_window.append(single_measurement['model_outputs'])\\n baseline_outputs_window.append(single_measurement['baseline_outputs'])\\n\\n # wait until the window has enough values\\n if len(labels_window) < self._evaluation_period:\\n continue\\n\\n # compute stats in the window and store to the last run (that's the [-1]) at the end (that's the append)\\n self._mutual_info[-1].append(\\n compute_mutual_information(\\n np.array(labels_window),\\n np.array(outputs_window),\\n self._num_classes,\\n data_contains_id=True)\\n )\\n\\n if self._debug_mi:\\n self._debug_mutual_info(np.array(labels_window), np.array(outputs_window), self._mutual_info[-1][-1])\\n\\n self._baseline_mutual_info[-1].append(\\n compute_mutual_information(\\n np.array(labels_window),\\n np.array(baseline_outputs_window),\\n self._num_classes,\\n data_contains_id=True)\\n )\\n\\n # compute the classifier accuracies (for model and baseline)\\n dev = self._topology_adapter.get_device()\\n output_dim = self._topology_adapter.get_model_output_size()\\n\\n labels_tensor = torch.tensor(labels_window, dtype=torch.long, device=dev)\\n outputs_tensor = torch.tensor(outputs_window, dtype=torch.long, device=dev)\\n baseline_outputs_tensor = torch.tensor(baseline_outputs_window, dtype=torch.long, device=dev)\\n\\n acc = self._compute_classifier_acc(outputs_tensor, labels_tensor, output_dim)\\n self._classifier_accuracy[-1].append(acc)\\n\\n baseline_acc = self._compute_classifier_acc(baseline_outputs_tensor, labels_tensor, output_dim)\\n self._baseline_classifier_accuracy[-1].append(baseline_acc)\\n\\n # store also step (for the x-axis)\\n self._steps[-1].append(single_measurement['current_step'])\\n\\n # remove the self._sliding_window_stride items from the sliding windows.. (then fill the same amount..)\\n for i in range(0, self._sliding_window_stride):\\n if len(labels_window) > 0:\\n labels_window.pop(0)\\n outputs_window.pop(0)\\n baseline_outputs_window.pop(0)\",\n \"def write_results(location, classes, results):\\n check_out(location)\\n # these are tuples consisting of list of values, mean of the values and std the values\\n [accuracies, precisions, recalls, f1scores] = results\\n\\n file_name = []\\n for class_ in classes:\\n if class_.endswith('.hdf5'):\\n # file_name.append(re.findall('\\\\d/(.*).hdf5$', class_)[0])\\n file_name.append(re.findall('([%.\\\\w-]*).hdf5$', class_)[0])\\n\\n with open(os.path.join(location, '-'.join(file_name) + '.results'), 'w') as out_file:\\n for class_id, class_ in enumerate(classes):\\n out_file.write(file_name[class_id].split('_')[0] + '\\\\n')\\n # out_file.write(infos)\\n # out_file.write(' '.join([str(header_item) for header_item in headers[class_id]]))\\n out_file.write('\\\\n\\\\n')\\n\\n out_file.write(\\\"Mean accuracy is {:.2f} with a std of ({:.2f}).\\\\n\\\".format(accuracies[1], accuracies[2]))\\n out_file.write('\\\\n'.join([str(accuracy) for accuracy in accuracies[0]]) + '\\\\n')\\n\\n if len(classes) == 2:\\n out_file.write(\\\"\\\\nMean precision is {:.2f} with a std of ({:.2f}).\\\\n\\\".format(precisions[1], precisions[2]))\\n out_file.write('\\\\n'.join([str(precision) for precision in precisions[0]]) + '\\\\n')\\n\\n out_file.write(\\\"\\\\nMean recall is {:.2f} with a std of ({:.2f}).\\\\n\\\".format(recalls[1], recalls[2]))\\n out_file.write('\\\\n'.join([str(recall) for recall in recalls[0]]) + '\\\\n')\\n\\n out_file.write(\\\"\\\\nMean f1 score is {:.2f} with a std of ({:.2f}).\\\\n\\\".format(f1scores[1], f1scores[2]))\\n out_file.write('\\\\n'.join([str(f1score) for f1score in f1scores[0]]) + '\\\\n')\",\n \"def process_results(training_loss,testing_loss, model_names, params,n_to_average):\\n # Unpack the unprocessed results\\n # all_train_loss, all_test_loss = unprocessed_results\\n\\n # Convert to numpy arrays\\n # all_train_loss = np.array(all_train_loss)\\n all_test_loss = np.array(testing_loss)\\n # all_preds = np.stack(all_preds)\\n # all_stds = np.stack(all_stds)\\n\\n # Get order models from best to worst test loss\\n sorted_inds = np.argsort(testing_loss)\\n\\n # Sort all other arrays accordingly\\n # sorted_train_loss = all_train_loss[sorted_inds]\\n # sorted_test_loss = all_test_loss[sorted_inds]\\n # sorted_preds = all_preds[sorted_inds]\\n # sorted_stds = all_stds[sorted_inds]\\n sorted_model_names = model_names[sorted_inds]\\n sorted_model_params=[params[i] for i in sorted_inds]\\n # Build a dataframe which summarizes results\\n # summary_list = [[model, train_loss, test_loss] for model, train_loss, test_loss in\\n # zip(sorted_model_names, sorted_train_loss, sorted_test_loss)]\\n # summary_df = pd.DataFrame(summary_list,\\n # columns=[\\\"ModelName\\\", \\\"cvTrainingError\\\", \\\"cvTestingError\\\"])\\n\\n # Generate compound predictions\\n # cumulative_preds = np.cumsum(sorted_preds, axis=0)\\n # compound_preds = np.empty_like(sorted_preds)\\n # for i in range(len(compound_preds)):\\n # compound_preds[i] = cumulative_preds[i] / (i + 1)\\n #\\n # # Pull the requested averaged value\\n # mlde_preds = compound_preds[n_to_average - 1]\\n top_models_name=sorted_model_names[0:n_to_average]\\n top_models_params=sorted_model_params[0:n_to_average]\\n sorted_training_loss=training_loss[sorted_inds[0:n_to_average]]\\n sorted_testing_loss=testing_loss[sorted_inds[0:n_to_average]]\\n return top_models_name,top_models_params,sorted_training_loss,sorted_testing_loss\",\n \"def evaluate(self, eval_batches, result_dir=None, result_prefix=None, save_full_info=False):\\n pred_answers, ref_answers = [], []\\n total_loss, total_num = 0, 0\\n for b_itx, batch in enumerate(eval_batches):\\n feed_dict = {self.p: batch['passage_token_ids'],\\n self.q: batch['question_token_ids'],\\n self.p_length: batch['passage_length'],\\n self.q_length: batch['question_length'],\\n self.start_label: batch['start_id'],\\n self.end_label: batch['end_id'],\\n self.dropout_keep_prob: 1.0,\\n self.em: batch['exact_match']}\\n batch_size = len(batch['start_id'])\\n padded_p_len = len(batch['passage_token_ids'][0])\\n padded_p_num = len(batch['passage_token_ids']) / batch_size\\n para_ids = []\\n for start_id in batch['start_id']:\\n para_ids.append(start_id // padded_p_len)\\n feed_dict[self.para_label] = para_ids\\n content_label = np.zeros([batch_size, padded_p_num * padded_p_len], dtype=int)\\n for s_idx, (start_id, end_id) in enumerate(zip(batch['start_id'], batch['end_id'])):\\n content_label[s_idx, start_id: end_id+1] = 1\\n feed_dict[self.content_label] = content_label\\n start_probs, end_probs, content_scores, verif_scores, loss = self.sess.run([self.start_probs, self.end_probs,\\n self.concat_content_score, self.reshaped_ans_verif_score,\\n self.loss], feed_dict)\\n\\n total_loss += loss * len(batch['raw_data'])\\n total_num += len(batch['raw_data'])\\n\\n padded_p_len = len(batch['passage_token_ids'][0])\\n for s_idx, sample in enumerate(batch['raw_data']):\\n start_prob = start_probs[s_idx]\\n end_prob = end_probs[s_idx]\\n content_score = content_scores[s_idx]\\n verif_score = verif_scores[s_idx]\\n best_answer = self.find_best_answer_with_verif(sample, start_prob, end_prob,\\n content_score, verif_score, padded_p_len)\\n if save_full_info:\\n sample['pred_answers'] = [best_answer]\\n pred_answers.append(sample)\\n else:\\n pred_answers.append({'question_id': sample['question_id'],\\n 'question_type': sample['question_type'],\\n 'answers': [best_answer],\\n 'entity_answers': [[]],\\n 'yesno_answers': []})\\n if 'answers' in sample:\\n ref_answers.append({'question_id': sample['question_id'],\\n 'question_type': sample['question_type'],\\n 'answers': sample['answers'],\\n 'entity_answers': [[]],\\n 'yesno_answers': []})\\n\\n if result_dir is not None and result_prefix is not None:\\n result_file = os.path.join(result_dir, result_prefix + '.json')\\n with open(result_file, 'w') as fout:\\n for pred_answer in pred_answers:\\n fout.write(json.dumps(pred_answer, encoding='utf8', ensure_ascii=False) + '\\\\n')\\n\\n self.logger.info('Saving {} results to {}'.format(result_prefix, result_file))\\n\\n # this average loss is invalid on test set, since we don't have true start_id and end_id\\n ave_loss = 1.0 * total_loss / total_num\\n # compute the bleu and rouge scores if reference answers is provided\\n if len(ref_answers) > 0:\\n pred_dict, ref_dict = {}, {}\\n for pred, ref in zip(pred_answers, ref_answers):\\n question_id = ref['question_id']\\n if len(ref['answers']) > 0:\\n pred_dict[question_id] = normalize(pred['answers'])\\n ref_dict[question_id] = normalize(ref['answers'])\\n bleu_rouge = compute_bleu_rouge(pred_dict, ref_dict)\\n else:\\n bleu_rouge = None\\n return ave_loss, bleu_rouge\",\n \"def run_test(self):\\n self.output_analytics = self.run_inference()\\n self.output_df = pd.DataFrame(self.output_analytics)\",\n \"def save_result_all(directory: str, params: Dict[str, Any]):\\n # save recall\\n outdirectory = os.path.join(directory, 'recall_all.csv')\\n fp = open(outdirectory, 'w+')\\n models = [('baseline', '_base'), \\n ('content-based', '_content'), \\n ('CF-item-based', '_CF_item'),\\n ('CF-user-based', '_CF_user')\\n ]\\n for model in models:\\n if 'recall'+model[1] in params:\\n for topN in params['recall'+model[1]]:\\n fp.write(model[0]+','+str(topN)+','+str(params['recall'+model[1]][topN])+'\\\\n')\\n fp.close()\\n logging.getLogger(__name__).info('Recall rate saved to ' + outdirectory)\\n \\n # save RMSE\\n outdirectory = os.path.join(directory, 'rmse_all.csv')\\n fp = open(outdirectory, 'w+')\\n for model in models:\\n if 'rmse'+model[1] in params:\\n fp.write(model[0]+','+str(params['rmse'+model[1]])+'\\\\n') \\n fp.close()\\n logging.getLogger(__name__).info('RMSE saved to ' + outdirectory)\",\n \"def process_dataset(self):\\n\\n logger.info('\\\\n')\\n logger.info('=' * 40)\\n logger.info('=\\\\t DeepRank Data Set')\\n logger.info('=')\\n logger.info('=\\\\t Training data')\\n for f in self.train_database:\\n logger.info(f'=\\\\t -> {f}')\\n logger.info('=')\\n if self.valid_database:\\n logger.info('=\\\\t Validation data')\\n for f in self.valid_database:\\n logger.info(f'=\\\\t -> {f}')\\n logger.info('=')\\n if self.test_database:\\n logger.info('=\\\\t Test data')\\n for f in self.test_database:\\n logger.info(f'=\\\\t -> {f}')\\n logger.info('=')\\n logger.info('=' * 40 + '\\\\n')\\n sys.stdout.flush()\\n\\n # check if the files are ok\\n self.check_hdf5_files(self.train_database)\\n\\n if self.valid_database:\\n self.valid_database = self.check_hdf5_files(\\n self.valid_database)\\n\\n if self.test_database:\\n self.test_database = self.check_hdf5_files(\\n self.test_database)\\n\\n # create the indexing system\\n # alows to associate each mol to an index\\n # and get fname and mol name from the index\\n self.create_index_molecules()\\n\\n # get the actual feature name\\n if self.mapfly:\\n self.get_raw_feature_name()\\n else:\\n self.get_mapped_feature_name()\\n\\n # get the pairing\\n self.get_pairing_feature()\\n\\n # get grid shape\\n self.get_grid_shape()\\n\\n # get the input shape\\n self.get_input_shape()\\n\\n # get renormalization factor\\n if self.normalize_features or self.normalize_targets or self.clip_features:\\n if self.mapfly:\\n self.compute_norm()\\n else:\\n self.get_norm()\\n\\n logger.info('\\\\n')\\n logger.info(\\\" Data Set Info:\\\")\\n logger.info(\\n f' Augmentation : {self.use_rotation} rotations')\\n logger.info(\\n f' Training set : {self.ntrain} conformations')\\n logger.info(\\n f' Validation set : {self.nvalid} conformations')\\n logger.info(\\n f' Test set : {self.ntest} conformations')\\n logger.info(f' Number of channels : {self.input_shape[0]}')\\n logger.info(f' Grid Size : {self.data_shape[1]}, '\\n f'{self.data_shape[2]}, {self.data_shape[3]}')\\n sys.stdout.flush()\",\n \"def parse_log(path_to_log):\\n regex_iteration = re.compile('Iteration (\\\\d+), loss = ([\\\\.\\\\deE+-]+)')\\n regex_train_output = re.compile('Train net output #(\\\\d+): (\\\\S+) = ([\\\\.\\\\deE+-]+)')\\n regex_learning_rate = re.compile('lr = ([\\\\.\\\\deE+-]+)')\\n regex_test_output = re.compile('Test net output #(\\\\d+): detection_eval = ([\\\\.\\\\deE+-]+)')\\n\\n\\n # Pick out lines of interest\\n iteration = 0\\n loss = -1\\n learning_rate = 0.001\\n train_dict_list = []\\n train_row = None\\n test_score=0.0\\n\\n logfile_year = extract_seconds.get_log_created_year(path_to_log)\\n with open(path_to_log) as f:\\n start_time = extract_seconds.get_start_time(f, logfile_year)\\n last_time = start_time\\n\\n for line in f:\\n iteration_match = regex_iteration.search(line)\\n if iteration_match:\\n iteration = float(iteration_match.group(1))\\n loss = float(iteration_match.group(2))\\n try:\\n time = extract_seconds.extract_datetime_from_line(line,\\n logfile_year)\\n except:\\n # Skip lines with bad formatting, for example when resuming solver\\n continue\\n\\n # if it's another year\\n if time.month < last_time.month:\\n logfile_year += 1\\n time = extract_seconds.extract_datetime_from_line(line, logfile_year)\\n last_time = time\\n\\n seconds = (time - start_time).total_seconds()\\n\\n learning_rate_match = regex_learning_rate.search(line)\\n\\n if learning_rate_match:\\n learning_rate = float(learning_rate_match.group(1))\\n\\n test_score_match = regex_test_output.search(line)\\n if test_score_match:\\n test_score = float(test_score_match.group(2))\\n\\n train_dict_list, train_row = parse_line_for_net_output(\\n regex_train_output, train_row, train_dict_list,\\n line, iteration, seconds, learning_rate,loss,test_score\\n )\\n\\n\\n return train_dict_list\",\n \"def evaluate(self):\\n scores = []\\n scores.append(self.word_analogy())\\n print(\\\"Word Analogy (acc): \\\", scores[0])\\n scores.append(self.word_similarity())\\n print(\\\"Word Similarity (MSE): \\\", scores[1])\\n scores.append(self.concept_categorization())\\n print(\\\"Concept Categorization (purity): \\\", scores[2])\\n scores.append(self.sentiment_analysis())\\n print(\\\"Sentiment Analysis (acc): \\\", scores[3])\\n return scores\",\n \"def analyze_model(folder, verbose=True):\\n hyps = get_hyps(folder)\\n table = get_analysis_table(folder, hyps=hyps)\\n\\n model,metrics = read_model(folder,ret_metrics=True)\\n get_analysis_figs(folder, metrics)\\n\\n train_acc, train_loss = metrics['acc'][-1], metrics['loss'][-1]\\n table['train_acc'] = [train_acc]\\n table['train_loss'] = [train_loss]\\n val_acc, val_loss = metrics['val_acc'][-1], metrics['val_loss'][-1]\\n table['val_acc'] = [val_acc]\\n table['val_loss'] = [val_loss]\\n if verbose:\\n print(\\\"ValAcc: {:05e}, ValLoss: {:05e}\\\".format(val_acc, val_loss))\\n return pd.DataFrame(table)\",\n \"def save_results(self, data, prefix, mode=\\\"train\\\", compute_loss=False):\\n # save predictions\\n self.save_predictions(prefix, mode)\",\n \"def score(self, experiment_path: str, result_file: str, **kwargs):\\n # Update config parameters with new kwargs\\n\\n config = torch.load(glob.glob(\\n \\\"{}/run_config*\\\".format(experiment_path))[0],\\n map_location=lambda storage, loc: storage)\\n config_parameters = dict(config, **kwargs)\\n model = torch.load(glob.glob(\\n \\\"{}/run_model*\\\".format(experiment_path))[0],\\n map_location=lambda storage, loc: storage)\\n encoder = torch.load(glob.glob(\\n '{}/run_encoder*'.format(experiment_path))[0],\\n map_location=lambda storage, loc: storage)\\n testlabel = config_parameters['testlabel']\\n testdata = config_parameters['testdata']\\n # Only a single item to evaluate\\n if isinstance(testlabel, list) and len(testlabel) == 1:\\n testlabel = testlabel[0]\\n if isinstance(testdata, list) and len(testdata) == 1:\\n testdata = testdata[0]\\n\\n labels_df = pd.read_csv(testlabel, sep=' ')\\n labels_df['encoded'], encoder = utils.encode_labels(\\n labels=labels_df['bintype'], encoder=encoder)\\n config_parameters.setdefault('colname', ('filename', 'encoded'))\\n dataloader = dataset.getdataloader(\\n data_frame=labels_df,\\n data_file=testdata,\\n num_workers=4,\\n batch_size=1, # do not apply any padding\\n colname=config_parameters[\\n 'colname'] # For other datasets with different key names\\n )\\n model = model.to(DEVICE).eval()\\n genuine_label_idx = encoder.transform(['genuine'])[0]\\n\\n with torch.no_grad(), open(result_file,\\n 'w') as wp, tqdm(total=len(dataloader),\\n unit='utts') as pbar:\\n datawriter = csv.writer(wp, delimiter=' ')\\n datawriter.writerow(['filename', 'score'])\\n for batch in dataloader:\\n inputs, _, filenames = batch\\n inputs = inputs.float().to(DEVICE)\\n preds = model(inputs)\\n for pred, filename in zip(preds, filenames):\\n # Single batchsize\\n datawriter.writerow([filename, pred[0].item()])\\n pbar.update()\\n print(\\\"Score file can be found at {}\\\".format(result_file))\",\n \"def result_handler(raw_output):\\n write_log(raw_output[1])\\n item_list = []\\n line_list = raw_output[1].split('\\\\n')\\n title_list = re.sub(\\\"\\\\s{2,}\\\", \\\"\\\\t\\\", line_list[0]).split(\\\"\\\\t\\\")\\n for line in line_list[1:]:\\n item_list.append(re.sub(\\\"\\\\s{2,}\\\", \\\"\\\\t\\\", line).split(\\\"\\\\t\\\"))\\n return raw_output[0], title_list, item_list\",\n \"def synthesize_results(parent_dir):\\r\\n # Aggregate metrics from args.parent_dir directory\\r\\n metrics = dict()\\r\\n aggregate_metrics(parent_dir, metrics) # metrics在函数里面被修改\\r\\n #\\r\\n table = metrics_to_table(metrics)\\r\\n # Display the table to terminal\\r\\n print(table)\\r\\n\\r\\n # Save results in parent_dir/results.md\\r\\n save_file = os.path.join(parent_dir, \\\"results.md\\\")\\r\\n with open(save_file, 'w') as f:\\r\\n f.write(table)\",\n \"def generate_learning_reports(self) -> None:\\n\\n # Generate a report consisting of the evaluation metrics for\\n # each sub-experiment comprising each cross-validation\\n # experiment for each learner\\n (pd.DataFrame(list(chain(*self.cv_learner_stats_)))\\n .to_csv(self.stats_report_path_,\\n index=False))\\n\\n # Generate a report consisting of the aggregated evaluation\\n # metrics from each cross-validation experiment with each\\n # learner\\n (self.training_cv_aggregated_stats_\\n .to_csv(self.aggregated_stats_report_path_,\\n index=False))\",\n \"def evaluate(model, tokenizer, dataset, lines, output_test_file, batch_size=32):\\n sampler = SequentialSampler(dataset)\\n dataloader = DataLoader(dataset, sampler=eval_sampler, batch_size=batch_size)\\n\\n print(\\\"*** Evaluating ***\\\")\\n eval_loss = 0.0\\n num_steps = 0\\n preds = None\\n out_label_ids = None\\n for i, batch in enumerate(dataloader):\\n if i % 200 == 199:\\n print(\\\"=\\\", end=\\\"\\\")\\n if i % 5000 == 4999:\\n print(\\\"[Step \\\" + str(i+1) + \\\" / \\\" + str(len(dataloader)) + \\\"] \\\" )\\n model.eval()\\n batch = tuple(t.to(device) for t in batch)\\n\\n with torch.no_grad():\\n labels = batch[3]\\n outputs = model(input_ids=batch[0], attention_mask=batch[1], labels=labels)\\n tmp_eval_loss, logits = outputs[:2]\\n eval_loss += tmp_eval_loss.mean().item()\\n \\n num_steps += 1\\n if preds is None:\\n preds = logits.detach().cpu().numpy()\\n out_label_ids = labels.detach().cpu().numpy()\\n else:\\n preds = np.append(preds, logits.detach().cpu().numpy(), axis=0)\\n out_label_ids = np.append(out_label_ids, labels.detach().cpu().numpy(), axis=0)\\n \\n eval_loss = eval_loss / num_steps\\n \\n preds_label = np.argmax(preds, axis=1)\\n \\n accuracy = (preds_label == out_label_ids).mean()\\n output_dir = os.path.dirname(output_test_file)\\n if not os.path.exists(output_dir):\\n os.makedirs(output_dir)\\n with open(output_test_file, \\\"w\\\") as writer:\\n all_logits = preds.tolist()\\n for i, logit in enumerate(all_logits):\\n line = '<CODESPLIT>'.join(\\n [item.encode('ascii', 'ignore').decode('ascii') for item in lines[i]])\\n\\n writer.write(line + '<CODESPLIT>' + '<CODESPLIT>'.join([str(l) for l in logit]) + '\\\\n')\\n print(\\\"Accuracy =\\\", str(accuracy))\\n\\n return accuracy\",\n \"def output(results):\\n\\n text_file = open(\\\"problem_1_B_output.txt\\\", \\\"w\\\")\\n\\n out = \\\"\\\"\\n\\n for i, line in enumerate(results):\\n\\n string = \\\"Sample {}: {}, posterior probability of {:.4f}\\\".format(i + 1,\\n line[0],\\n line[1])\\n\\n out += (string + \\\"\\\\n\\\")\\n\\n text_file.write(out)\\n\\n text_file.close()\",\n \"def _store_results(self, accuracy):\\n\\n self.learning_curve_.append(accuracy)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.66430914","0.5997761","0.5993275","0.5946991","0.5923554","0.5910248","0.5840243","0.5838326","0.58113766","0.5800332","0.5793081","0.5748188","0.5714592","0.56949764","0.5694171","0.5687105","0.5685428","0.5673243","0.5668452","0.56547135","0.5650398","0.56455845","0.56054693","0.5601394","0.55860543","0.5586017","0.5560997","0.5558445","0.55452466","0.55432755","0.5538669","0.551625","0.5508339","0.5502987","0.5502758","0.54901844","0.5484556","0.5460583","0.5456064","0.54478884","0.54347736","0.54252106","0.5425097","0.541546","0.54094183","0.5386529","0.5382779","0.5376732","0.5376364","0.5373638","0.53657526","0.53591156","0.53525573","0.5344052","0.53421366","0.5340868","0.53333914","0.5331984","0.53283316","0.53253657","0.5321572","0.53206605","0.53202325","0.53177774","0.5311766","0.53112936","0.53105265","0.53093135","0.5308028","0.53063756","0.5305131","0.5304155","0.53016204","0.53011227","0.52972823","0.5294521","0.5291773","0.5290643","0.52874935","0.5285122","0.5272509","0.52682406","0.5266575","0.5265602","0.526498","0.52562195","0.5254456","0.52539665","0.52513933","0.52477825","0.52397","0.52354896","0.52331924","0.5231853","0.5225322","0.5225089","0.5224345","0.52215606","0.52193373","0.5217572"],"string":"[\n \"0.66430914\",\n \"0.5997761\",\n \"0.5993275\",\n \"0.5946991\",\n \"0.5923554\",\n \"0.5910248\",\n \"0.5840243\",\n \"0.5838326\",\n \"0.58113766\",\n \"0.5800332\",\n \"0.5793081\",\n \"0.5748188\",\n \"0.5714592\",\n \"0.56949764\",\n \"0.5694171\",\n \"0.5687105\",\n \"0.5685428\",\n \"0.5673243\",\n \"0.5668452\",\n \"0.56547135\",\n \"0.5650398\",\n \"0.56455845\",\n \"0.56054693\",\n \"0.5601394\",\n \"0.55860543\",\n \"0.5586017\",\n \"0.5560997\",\n \"0.5558445\",\n \"0.55452466\",\n \"0.55432755\",\n \"0.5538669\",\n \"0.551625\",\n \"0.5508339\",\n \"0.5502987\",\n \"0.5502758\",\n \"0.54901844\",\n \"0.5484556\",\n \"0.5460583\",\n \"0.5456064\",\n \"0.54478884\",\n \"0.54347736\",\n \"0.54252106\",\n \"0.5425097\",\n \"0.541546\",\n \"0.54094183\",\n \"0.5386529\",\n \"0.5382779\",\n \"0.5376732\",\n \"0.5376364\",\n \"0.5373638\",\n \"0.53657526\",\n \"0.53591156\",\n \"0.53525573\",\n \"0.5344052\",\n \"0.53421366\",\n \"0.5340868\",\n \"0.53333914\",\n \"0.5331984\",\n \"0.53283316\",\n \"0.53253657\",\n \"0.5321572\",\n \"0.53206605\",\n \"0.53202325\",\n \"0.53177774\",\n \"0.5311766\",\n \"0.53112936\",\n \"0.53105265\",\n \"0.53093135\",\n \"0.5308028\",\n \"0.53063756\",\n \"0.5305131\",\n \"0.5304155\",\n \"0.53016204\",\n \"0.53011227\",\n \"0.52972823\",\n \"0.5294521\",\n \"0.5291773\",\n \"0.5290643\",\n \"0.52874935\",\n \"0.5285122\",\n \"0.5272509\",\n \"0.52682406\",\n \"0.5266575\",\n \"0.5265602\",\n \"0.526498\",\n \"0.52562195\",\n \"0.5254456\",\n \"0.52539665\",\n \"0.52513933\",\n \"0.52477825\",\n \"0.52397\",\n \"0.52354896\",\n \"0.52331924\",\n \"0.5231853\",\n \"0.5225322\",\n \"0.5225089\",\n \"0.5224345\",\n \"0.52215606\",\n \"0.52193373\",\n \"0.5217572\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.533079"},"document_rank":{"kind":"string","value":"58"}}},{"rowIdx":22480,"cells":{"query":{"kind":"string","value":"Return summary stats of recorded data."},"document":{"kind":"string","value":"def summarize_rec_data(data):\n\n # Warning: not all collectible data has a summary stats implemented below!\n # See get_rec_stats() above!\n\n stats = {}\n\n if 'hc_ro' in data:\n # Entropy across HC units average over samples.\n hc_ro_arr = np.array(list(data['hc_ro'].values()))\n stats['H HC ro'] = utils.entropy(hc_ro_arr.T).mean()\n\n if 'vs_state' in data:\n # Sum of vS reward estimates change (from first to last sample).\n vs_state = data['vs_state']\n stats['d vS'] = sum(vs_state[max(vs_state.keys())] - vs_state[0])\n\n if 'co_occs' in data:\n # Mean entropy of real location and HC state co-occurance frequencies.\n co_occs = data['co_occs'][max(data['co_occs'].keys())]\n stats['H HC co'] = np.nanmean(get_hc_co_occ_entropy(co_occs))\n stats['H loc co'] = np.nanmean(get_loc_co_occ_entropy(co_occs))\n\n return stats"},"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_summary_stats(self):\r\n n = len(self.results)\r\n\r\n if n == 0:\r\n mean = None\r\n stdev = None\r\n\r\n elif n == 1:\r\n mean = numpy.mean(self.results)\r\n stdev = None\r\n\r\n else:\r\n mean = numpy.mean(self.results)\r\n stdev = numpy.std(self.results)\r\n\r\n sum_stats = {'n': n, 'mean': mean, 'stdev': stdev}\r\n\r\n return sum_stats","def dataStats(self):\n print (\"Performing statistical analysis of the data\")\n # stuff to do","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 get_stats(self):\n return self.stats","def summary_data(self):\n data = {\n \"total\": self.total,\n \"card_one_value\": self.cards[0].value,\n \"card_two_value\": self.cards[1].value,\n \"card_one_rank\": self.cards[0].rank,\n \"card_two_rank\": self.cards[1].rank,\n \"cards\": \" \".join([str(card) for card in self.cards]),\n \"soft\": int(self.soft),\n \"from_split\": int(self.from_split),\n \"blackjack\": int(self.blackjack),\n \"num_cards\": len(self.cards),\n \"start_total\": self.cards[0] + self.cards[1],\n \"wager\": int(self.wager),\n \"insurance\": int(self.insurance),\n \"surrender\": int(self.surrender),\n \"double_down\": int(self.double_down),\n \"num_aces\": self.num_aces,\n \"num_hard_aces\": self.num_hard_aces\n }\n return data","def summarize_records(self):\r\n cache = self.retrieve_records_cache()\r\n return \"{} records\".format(\r\n len(cache[\"results\"])\r\n )","def summary_stats(self):\n capital_gains = self.df['values'].iloc[-1].sum() - self.tc.starting_cash\n total_return = capital_gains / self.tc.starting_cash\n days_invested = (self.df.index[-1] - self.df.index[0]).days\n annualized_returns = (total_return + 1) ** (365 / days_invested) - 1\n annualized_volatility = self.df['returns'].std() * (252 ** 0.5)\n sharpe = annualized_returns / annualized_volatility\n num_trades = self.trades.shape[0]\n stats = pd.Series(\n data=[capital_gains, total_return, annualized_returns, annualized_volatility, sharpe, num_trades],\n index=['Capital Gains', 'Total Return', 'Annualized Return', 'Annualized Volatility', 'Sharpe Ratio',\n 'Number of Trades']\n )\n return stats","def get_summarized_results(self):\n stats = [v.stats() for (k, v) in self.examples.items() if v.is_ready()]\n res = self.ExampleClass.average_stats(stats)\n\n res['loss'] = self.loss/self.loss_cnt\n res['recent_loss'] = sum(self.recent_loss_array) / sum(self.recent_loss_bs_array)\n\n return res","def stats(self):\n return self._stats","def statistics(self):\n return self.get_statistics()","def getStats(self):\n\n raise NotImplementedError","def stats(self):\n pass","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 summarize(self):\n return self.df.describe()","def get_stats(self):\n return utils.csv_to_dict(wait(self.proto.stat()))","def stats(self):","def metrics(self):\n\n return six.iteritems(self.__stats_table)","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 summary(self) -> Dict[str, Dict[str, float]]:\n vals: Dict[str, List[float]] = defaultdict(list)\n if not self.steps: # pragma: no cover\n return {}\n\n for timing_dict in self._timings:\n for step in self.steps:\n if step in timing_dict:\n vals[step].append(timing_dict[step])\n summary = {}\n for step in self.steps:\n if vals[step]:\n summary[step] = {\n \"cnt\": len(vals[step]),\n \"sum\": sum(vals[step]),\n \"min\": min(vals[step]),\n \"max\": max(vals[step]),\n \"avg\": sum(vals[step]) / len(vals[step]),\n }\n return summary","def statistics(self):\n return self._statistics","def stats(self) -> Dict:\n return self._stats","def stats(self, **kwargs):\n return self.client.api.stats(self.id, **kwargs)","def summary(self):\n\n # only possible once we've fit a model with statsmodels\n check_is_fitted(self, \"statistics_\")\n sdf = pd.DataFrame(self.statistics_)\n sdf.rename(columns={\"lambda_\": \"lambda\"}, inplace=True)\n return sdf","def summarize(self, data):\n\n return self.summary(data).flatten()","def output_summary_stats(self, filename):\r\n\r\n total_return = self.equity_curve['equity_curve'][-1]\r\n returns = self.equity_curve['returns']\r\n pnl = self.equity_curve['equity_curve']\r\n\r\n sharpe_ratio = create_sharpe_ratio(returns, periods=252)\r\n drawdown, max_dd, dd_duration = create_drawdowns(pnl)\r\n self.equity_curve['drawdown'] = drawdown\r\n\r\n stats = [(\"Total Return\", \"%0.2f%%\" % \\\r\n ((total_return - 1.0) * 100.0)),\r\n (\"Sharpe Ratio\", \"%0.2f%%\" % sharpe_ratio),\r\n (\"Max Drawdown\", \"%0.2f%%\" % (max_dd * 100.0)),\r\n (\"Drawdown Duration\", \"%f\" % dd_duration)]\r\n self.equity_curve.to_csv(filename)\r\n return stats","def statistics(self):\n stats = {}\n fields = {\n 'Hit count:': ('hit_count', Value.from_number),\n 'Miss count:': ('miss_count', Value.from_number),\n 'Hit ratio:': ('hit_ratio', Value.from_percent),\n 'Item count:': ('item_count', Value.from_number),\n 'Total cache size:': ('total_cache_size', Value.from_number),\n 'Oldest item age:': ('oldest_item_age', Value.from_time_ago),\n }\n selector = '#ae-stats-table tr'\n for element in self.doc.cssselect(selector):\n children = list(element)\n assert len(children) == 2, [text(child) for child in children]\n if text(children[0]).strip() in fields:\n # skip rows with invalid or empty cells\n field_name, value_fn = fields[text(children[0]).strip()]\n stats[field_name] = value_fn(text(children[1]))\n # Ensure all fields were filled.\n assert len(stats) == len(fields), (fields.keys(), stats.keys())\n return stats","def get_stats(self):\n return self.manager.get_stats(self)","def get_statistics(self):\n return self.results","def summary(self):\n summaries = self.summaries()\n # Reduce to a single summary with weighted averages for each\n # field except \"total_instances\", which is summed.\n total_instances = sum(d['total_instances'].value() for d in summaries)\n summary = {'total_instances': total_instances}\n for field in ('average_qps', 'average_latency', 'average_memory'):\n instance_weighted_sum = sum(\n d['total_instances'].value() * d[field].value()\n for d in summaries)\n summary[field] = float(instance_weighted_sum) / total_instances\n # Beautify rounding precision to match the App Engine UI.\n summary['average_qps'] = round(summary['average_qps'], 3)\n summary['average_latency_ms'] = round(summary['average_latency'], 1)\n summary['average_memory_mb'] = round(summary['average_memory'], 1)\n del summary['average_latency']\n del summary['average_memory']\n return summary","def print_stats(ds):\n print(\"Dataset Name: \" + ds.name)\n print(\"Dataset Mode: \" + ds.mode)\n print(\"Band Count: \" + str(ds.count))\n print(\"Dataset Width: \" + str(ds.width))\n print(\"Dataset Height: \" + str(ds.height))\n print(\"Dataset Bounds: \", ds.bounds)\n print(\"Dataset Transform: \", ds.transform)\n ul = ds.transform * (0, 0)\n print(\"Upper Left Corner: \", ul)\n lr = ds.transform * (ds.width, ds.height)\n print(\"Lower Right Corner: \", lr)\n {i: dtype for i, dtype in zip(ds.indexes, ds.dtypes)}","def output_stats(self):\n elapsed = self.timer.elapsed.total_seconds()\n count = self.copied + self.errored\n total = self.total\n # Time per key in milliseconds\n avg = round(elapsed / count * 1000, 3)\n # Time remaining in seconds\n remaining = 1.0 * elapsed / count * (total - count)\n # Time remaining in minutes\n remaining = round(remaining / 60.0, 1)\n # Time taken in minutes\n elapsed = round(elapsed / 60.0, 1)\n\n self.log.info(f\"{self.prefix}: {avg}ms avg, {elapsed}min passed, \"\n f\"{remaining}min remaining. ({count:,}/{total:,})\")","def collect_data_stats(data):\n # We re-import this module here because this code will run\n # remotely.\n \n stats_0 = data.shape[0]\n stats_1 = data.sum(axis=0)\n stats_2 = (data**2).sum(axis=0)\n retval = (\n stats_0,\n stats_1,\n stats_2\n )\n return retval","def total_samples(self):\n\n totals = self.recording_data()['totals']\n return totals[0] + totals[1]","def collect_stats(self):\n\n df_avg, self.transport_df, self.customer_df, self.manager_df, self.station_df = self.get_stats_dataframes()\n\n columns = []\n if self.config.simulation_name:\n df_avg[\"Simulation Name\"] = self.config.simulation_name\n columns = [\"Simulation Name\"]\n columns += [\"Avg Waiting Time\", \"Avg Total Time\", \"Simulation Time\"]\n if self.config.max_time:\n df_avg[\"Max Time\"] = self.config.max_time\n columns += [\"Max Time\"]\n columns += [\"Simulation Finished\"]\n self.df_avg = df_avg[columns]","def statistics(self):\n raise NotImplemented()","def dataset_statistics(dataset):\n print (dataset.describe())","def rct_stats(udf_data: UdfData):\n # The dictionary that stores the statistical data\n stats = {}\n # Iterate over each raster collection cube and compute statistical values\n for cube in udf_data.get_datacube_list():\n # make sure to cast the values to floats, otherwise they are not serializable\n stats[cube.id] = dict(\n sum=float(cube.array.sum()),\n mean=float(cube.array.mean()),\n min=float(cube.array.min()),\n max=float(cube.array.max())\n )\n # Create the structured data object\n sd = StructuredData(\n data=stats,\n type=\"dict\",\n description=\"Statistical data sum, min, max and mean for each raster collection cube as dict\",\n )\n # Remove all collections and set the StructuredData list\n udf_data.set_datacube_list(None)\n udf_data.set_structured_data_list([sd, ])","def getStats(data, printStats=False):\n data = list(data)\n d = DataFrame()\n d['Avg'] = [np.mean(data)]\n d['len'] = [len(data)]\n d['min'] = [min(data)]\n d['max'] = [max(data)]\n d['std'] = [np.std(data)]\n d = d.transpose()\n d.columns = ['Stats']\n d = d.transpose()\n if printStats:\n print(d)\n return d","def statistics(self, **_):\n raise NotImplementedError(\"{} doesn't support statistics.\".format(__class__.__name__))","def dataset_statistics(dataset):\n print(dataset.describe())","def dataset_statistics(dataset):\n print(dataset.describe())","def summary(self):\n raise NotImplementedError","def add_stats(self):\n units = self.get_unit_map()\n for metric in self.raw_metrics:\n unit, metric_type = units.get(metric, (DEFAULT_UNIT, DEFAULT_TYPE))\n if metric_type == \"counter\":\n # Unit/Second\n unit = \"/\".join((unit, \"Second\"))\n self.add_derive_value(metric, unit, self.raw_metrics[metric], rate=True)\n else:\n self.add_gauge_value(metric, unit, self.raw_metrics[metric])","def _printSummary(self):\n\t\t### COP OUT\n\t\tif self.params['background'] is True:\n\t\t\tself.stats['count'] += 1\n\t\t\treturn\n\n\t\t### THIS NEEDS TO BECOME MUCH MORE GENERAL, e.g. Peaks\n\t\ttdiff = time.time()-self.stats['startseries']\n\t\tif not self.params['continue'] or tdiff > 0.1:\n\t\t\tcount = self.stats['count']\n\t\t\t#if(count != self.stats['lastcount']):\n\t\t\tsys.stderr.write(\"\\n\\tSUMMARY: \"+self.functionname+\"\\n\")\n\t\t\tself._printLine()\n\t\t\tsys.stderr.write(\"\\tTIME: \\t\"+apDisplay.timeString(tdiff)+\"\\n\")\n\t\t\tself.stats['timesum'] = self.stats['timesum'] + tdiff\n\t\t\tself.stats['timesumsq'] = self.stats['timesumsq'] + (tdiff**2)\n\t\t\ttimesum = self.stats['timesum']\n\t\t\ttimesumsq = self.stats['timesumsq']\n\t\t\tif(count > 1):\n\t\t\t\ttimeavg = float(timesum)/float(count)\n\t\t\t\ttimestdev = math.sqrt(float(count*timesumsq - timesum**2) / float(count*(count-1)))\n\t\t\t\ttimeremain = (float(timeavg)+float(timestdev))*self.stats['seriesleft']\n\t\t\t\tsys.stderr.write(\"\\tAVG TIME: \\t\"+apDisplay.timeString(timeavg,timestdev)+\"\\n\")\n\t\t\t\t#print \"\\t(- TOTAL:\",apDisplay.timeString(timesum),\" -)\"\n\t\t\t\tif(self.stats['seriesleft'] > 0):\n\t\t\t\t\tsys.stderr.write(\"\\t(- REMAINING TIME: \"+apDisplay.timeString(timeremain)+\" for \"\n\t\t\t\t\t\t+str(self.stats['seriesleft'])+\" series -)\\n\")\n\t\t\t#print \"\\tMEM: \",(mem.active()-startmem)/1024,\"M (\",(mem.active()-startmem)/(1024*count),\"M)\"\n\t\t\tself.stats['count'] += 1\n\t\t\tself._printLine()","def get_run_stats(self):\n return self.run_stats","def get_stats(self):\n return {\n \"pings_sent\" : self.ping_count,\n \"measurements\" : self.measurements,\n }","def summary():\n\n summary_result = session.query(Summary.Count, Summary.Total).all()\n session.close()\n\n # Return a List of Column Names (Sample Names)\n return jsonify(summary_result)","def stats(self):\r\n return {}","def get_stats(self):\n result = {\n 'datetime': dt.datetime.now().strftime('%d.%m.%Y %H:%M:%S'),\n 'total': db.session.query(User). \\\n count(),\n 'unverified': db.session.query(User). \\\n filter(db.not_(User.verified)). \\\n count(),\n 'male students': db.session.query(User). \\\n filter(User.sex == Sex.Male,\n User.type == UserType.Student). \\\n count(),\n 'male employees': db.session.query(User). \\\n filter(User.sex == Sex.Male,\n User.type == UserType.Employee). \\\n count(),\n 'male alumni': db.session.query(User). \\\n filter(User.sex == Sex.Male,\n User.type == UserType.Alumni). \\\n count(),\n 'female students': db.session.query(User). \\\n filter(User.sex == Sex.Female,\n User.type == UserType.Student). \\\n count(),\n 'female employees': db.session.query(User). \\\n filter(User.sex == Sex.Female,\n User.type == UserType.Employee). \\\n count(),\n 'female alumni': db.session.query(User). \\\n filter(User.sex == Sex.Female,\n User.type == UserType.Alumni). \\\n count()\n }\n\n return result","def stats(self, **kwargs):\n return stats.stats(self._host, self._session, **kwargs)","def summary(self):\n if hasattr(self,\"_summary\"):\n return self._summary\n else:\n return {}","def aggregate_stats(self):\n if self.split_bn.track_running_stats:\n (\n self.bn.running_mean.data,\n self.bn.running_var.data,\n ) = self._get_aggregated_mean_std(\n self.split_bn.running_mean,\n self.split_bn.running_var,\n self.num_splits,\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\"))","def compute_statistics(self):","def get_stats_summary(self):\n perf_table = spark.table(self.performance_table)\\\n .where(\"yyyy_mm_dd between '{start_date}' and '{end_date}'\"\n .format(start_date = self.start_date, end_date = self.end_date))\\\n .where(\"clicks > 0\")\\\n .where(\"commission_expected_euro <= {max_rpb}\".format(max_rpb = self.max_rpb))\n\n if self.pos == ['All']:\n perf_table = perf_table.groupBy(*self.agg_on)\\\n .agg(f.sum(\"nits_bookings\").alias(\"nits_bookings\")\n ,f.sum(\"commission_expected_euro\").alias(\"nits_commission\")\n ,f.sum(\"bookings\").alias(\"gross_bookings\")\n ,f.sum(\"commission_amount_euro\").alias(\"gross_commission\")\n ,f.sum(\"cost_euro\").alias(\"cost\")\n ,f.sum(\"clicks\").alias(\"clicks\")\n ,f.sum(\"roomnights\").alias(\"roomnights\"))\\\n .withColumn(\"nits_profit\",f.expr(\"nits_commission-cost\"))\\\n .withColumn(\"gross_profit\", f.expr(\"gross_commission-cost\"))\n else:\n filtered_pos = spark.createDataFrame(pd.DataFrame(data = self.pos,\n columns = [\"pos\"]))\n\n perf_table = perf_table.join(filtered_pos, on = \"pos\", how = \"inner\")\\\n .groupBy(*self.agg_on)\\\n .agg(f.sum(\"nits_bookings\").alias(\"nits_bookings\")\n ,f.sum(\"commission_expected_euro\").alias(\"nits_commission\")\n ,f.sum(\"bookings\").alias(\"gross_bookings\")\n ,f.sum(\"commission_amount_euro\").alias(\"gross_commission\")\n ,f.sum(\"cost_euro\").alias(\"cost\")\n ,f.sum(\"clicks\").alias(\"clicks\")\n ,f.sum(\"roomnights\").alias(\"roomnights\"))\\\n .withColumn(\"nits_profit\",f.expr(\"nits_commission-cost\"))\\\n .withColumn(\"gross_profit\", f.expr(\"gross_commission-cost\"))\n\n return (perf_table)","def base_stats(self):\n return (\n self._get_seconds_played_stats_items()\n + self._get_possessions_played_stats_items()\n )","def get_stats(self):\n return scales.getStats()[self.stats_name]","def summary(self):\n summary = defaultdict(int)\n\n for r in self.results:\n summary[r.result] += 1\n\n return summary","def summary(self):\n response = self._get(self.uri_for(\"summary\"))\n return json_to_py(response)","def get_stats(self, datalist, state):\n data = {}\n for i, col in zip(range(5), datalist[0].items()):\n [diff, per_diff, tot_percentage] = self.get_diff_and_percentage(datalist[0].iloc[i], datalist[1].iloc[i],\n state)\n data[col[0]] = {\"value\": datalist[0].iloc[i], \"change\": diff, \"change_per\": per_diff,\n \"tot_percentage\": tot_percentage}\n return data","def summary(self):\n return self._summary","def summary(self):\n return self._summary","def summary(self):\n return self._summary","def summary(self):\n return self._summary","def summary(self):\n return self._summary","def summary(self):\n return self._summary","def summary(self):\n return self._summary","def summary(self):\n return self.pfm","def stats(self):\n return {\"size\": 0, \"maxsize\": 0, \"hits\": 0, \"miss\": 0}","def get_summary_stats(items, attr):\n data = {}\n for item in items:\n stats = models.Stats.query.filter_by(**{attr: item.id})\n data[item.id] = {\n 'ok': sum([int(stat.ok) for stat in stats]),\n 'changed': sum([int(stat.changed) for stat in stats]),\n 'failed': sum([int(stat.failed) for stat in stats]),\n 'skipped': sum([int(stat.skipped) for stat in stats]),\n 'unreachable': sum([int(stat.unreachable) for stat in stats])\n }\n\n # If we're aggregating stats for a playbook, also infer status\n if attr is \"playbook_id\":\n data[item.id]['status'] = _infer_status(item, data[item.id])\n\n return data","def stats():\n return jsonify(shorten.get_stats(get_db(), app.config['MINI_URL_BASE']))","def summary(self, tmin=None, tmax=None, stats='basic'):\n output = {\n 'basic': {\n 'evp': 'Explained variance percentage',\n 'rmse': 'Root mean squared error',\n 'avg_dev': 'Average Deviation',\n 'rsq': 'Pearson R^2',\n 'bic': 'Bayesian Information Criterion',\n 'aic': 'Akaike Information Criterion'},\n }\n\n # get labels and method names for stats output\n if stats == 'all':\n # sort by key, label, method name\n selected_output = sorted([(k, l, f) for k, d in output.items()\n for f, l in d.items()])\n else:\n # sort by name, method name\n selected_output = sorted([(0, l, f) for f, l in\n output[stats].items()])\n\n # compute statistics\n labels_and_values = [(l, getattr(self, f)(tmin=tmin, tmax=tmax))\n for _, l, f in selected_output]\n labels, values = zip(*labels_and_values)\n\n stats = pd.DataFrame(index=list(labels), data=list(values),\n columns=['Value'])\n stats.index.name = 'Statistic'\n return stats","def metadata_reporter(self):\n logging.info('Creating summary report')\n header = '{}\\n'.format(','.join(self.headers))\n # Create a string to store all the results\n data = str()\n for sample in self.metadata:\n # Add the value of the appropriate attribute to the results string\n data += GenObject.returnattr(sample, 'name')\n # SampleName\n data += GenObject.returnattr(sample.run, 'SamplePlate')\n # Genus\n data += GenObject.returnattr(sample.general, 'closestrefseqgenus')\n # SamplePurity\n data += GenObject.returnattr(sample.confindr, 'num_contaminated_snvs')\n # N50\n n50 = GenObject.returnattr(sample.quast, 'N50',\n number=True)\n if n50 != '-,':\n data += n50\n else:\n data += '0,'\n # NumContigs\n data += GenObject.returnattr(sample.quast, 'num_contigs',\n number=True)\n # TotalLength\n data += GenObject.returnattr(sample.quast, 'Total_length',\n number=True)\n # MeanInsertSize\n data += GenObject.returnattr(sample.quast, 'mean_insert',\n number=True)\n # InsertSizeSTD\n data += GenObject.returnattr(sample.quast, 'std_insert',\n number=True)\n # AverageCoverageDepth\n data += GenObject.returnattr(sample.qualimap, 'MeanCoveragedata',\n number=True)\n # CoverageDepthSTD\n data += GenObject.returnattr(sample.qualimap, 'StdCoveragedata',\n number=True)\n # PercentGC\n data += GenObject.returnattr(sample.quast, 'GC',\n number=True)\n # MASH_ReferenceGenome\n data += GenObject.returnattr(sample.mash, 'closestrefseq')\n # MASH_NumMatchingHashes\n data += GenObject.returnattr(sample.mash, 'nummatches')\n # 16S_result\n data += GenObject.returnattr(sample.sixteens_full, 'sixteens_match')\n # 16S PercentID\n data += GenObject.returnattr(sample.sixteens_full, 'percent_id')\n # CoreGenesPresent\n data += GenObject.returnattr(sample.gdcs, 'coreresults')\n # rMLST_Result\n try:\n # If the number of matches to the closest reference profile is 53, return the profile number\n if sample.rmlst.matches == 53:\n if type(sample.rmlst.sequencetype) is list:\n rmlst_seq_type = ';'.join(sorted(sample.rmlst.sequencetype)).rstrip(';') + ','\n else:\n rmlst_seq_type = GenObject.returnattr(sample.rmlst, 'sequencetype')\n rmlst_seq_type = rmlst_seq_type if rmlst_seq_type != 'ND,' else 'new,'\n data += rmlst_seq_type\n else:\n # Otherwise the profile is set to new\n data += 'new,'\n except AttributeError:\n data += 'new,'\n # MLST_Result\n try:\n if sample.mlst.matches == 7:\n if type(sample.mlst.sequencetype) is list:\n mlst_seq_type = ';'.join(sorted(sample.mlst.sequencetype)).rstrip(';') + ','\n else:\n mlst_seq_type = GenObject.returnattr(sample.mlst, 'sequencetype')\n mlst_seq_type = mlst_seq_type if mlst_seq_type != 'ND,' else 'new,'\n data += mlst_seq_type\n else:\n data += 'new,'\n except AttributeError:\n data += 'new,'\n # MLST_gene_X_alleles\n try:\n # Create a set of all the genes present in the results (gene name split from allele)\n gene_set = {gene.split('_')[0] for gene in sample.mlst.combined_metadata_results}\n for gene in sorted(gene_set):\n allele_list = list()\n # Determine all the alleles that are present for each gene\n for allele in sample.mlst.combined_metadata_results:\n if gene in allele:\n allele_list.append(allele.replace(' ', '_'))\n # If there is more than one allele in the sample, add both to the string separated by a ';'\n if len(allele_list) > 1:\n data += '{},'.format(';'.join(allele_list))\n # Otherwise add the only allele\n else:\n data += allele_list[0] + ','\n # If there are fewer than seven matching alleles, add a ND for each missing result\n if len(gene_set) < 7:\n data += (7 - len(gene_set)) * 'ND,'\n except AttributeError:\n # data += '-,-,-,-,-,-,-,'\n data += 'ND,ND,ND,ND,ND,ND,ND,'\n # E_coli_Serotype\n try:\n # If no O-type was found, set the output to be O-untypeable\n if ';'.join(sample.ectyper.o_type) == '-':\n otype = 'O-untypeable'\n else:\n otype = sample.ectyper.o_type\n # Same as above for the H-type\n if ';'.join(sample.ectyper.h_type) == '-':\n htype = 'H-untypeable'\n\n else:\n htype = sample.ectyper.h_type\n serotype = '{otype}:{htype},'.format(otype=otype,\n htype=htype)\n # Add the serotype to the data string unless neither O-type not H-type were found; add ND instead\n data += serotype if serotype != 'O-untypeable:H-untypeable,' else 'ND,'\n except AttributeError:\n data += 'ND,'\n # SISTR_serovar_antigen\n data += GenObject.returnattr(sample.sistr, 'serovar_antigen').rstrip(';')\n # SISTR_serovar_cgMLST\n data += GenObject.returnattr(sample.sistr, 'serovar_cgmlst')\n # SISTR_serogroup\n data += GenObject.returnattr(sample.sistr, 'serogroup')\n # SISTR_h1\n data += GenObject.returnattr(sample.sistr, 'h1').rstrip(';')\n # SISTR_h2\n data += GenObject.returnattr(sample.sistr, 'h2').rstrip(';')\n # SISTR_serovar\n data += GenObject.returnattr(sample.sistr, 'serovar')\n # GeneSeekr_Profile\n try:\n if sample.genesippr.report_output:\n data += ';'.join(sample.genesippr.report_output) + ','\n else:\n data += 'ND,'\n except AttributeError:\n data += 'ND,'\n # Vtyper_Profile\n data += GenObject.returnattr(sample.verotoxin, 'verotoxin_subtypes_set')\n # AMR_Profile and resistant/sensitive status\n if sample.resfinder_assembled.pipelineresults:\n # Profile\n for resistance, resistance_set in sorted(sample.resfinder_assembled.pipelineresults.items()):\n data += '{res}({r_set});'.format(res=resistance.replace(',', ';'),\n r_set=';'.join(sorted(list(resistance_set))))\n data += ','\n # Resistant/Sensitive\n data += 'Resistant,'\n else:\n # Profile\n data += 'ND,'\n # Resistant/Sensitive\n data += 'Sensitive,'\n # Plasmid Result'\n if sample.mobrecon.pipelineresults:\n for plasmid, details in sorted(sample.mobrecon.pipelineresults.items()):\n data += '{plasmid}({details});'.format(plasmid=plasmid,\n details=details)\n data += ','\n else:\n data += 'ND,'\n # TotalPredictedGenes\n data += GenObject.returnattr(sample.prodigal, 'predictedgenestotal',\n number=True)\n # PredictedGenesOver3000bp\n data += GenObject.returnattr(sample.prodigal, 'predictedgenesover3000bp',\n number=True)\n # PredictedGenesOver1000bp\n data += GenObject.returnattr(sample.prodigal, 'predictedgenesover1000bp',\n number=True)\n # PredictedGenesOver500bp\n data += GenObject.returnattr(sample.prodigal, 'predictedgenesover500bp',\n number=True)\n # PredictedGenesUnder500bp\n data += GenObject.returnattr(sample.prodigal, 'predictedgenesunder500bp',\n number=True)\n # AssemblyDate\n data += datetime.now().strftime('%Y-%m-%d') + ','\n # PipelineVersion\n data += self.commit + ','\n # Name of the database used in the analyses\n data += os.path.split(self.reffilepath)[-1] + ','\n # Database download date\n data += self.download_date\n # Append a new line to the end of the results for this sample\n data += '\\n'\n # Replace any NA values with ND\n cleandata = data.replace('NA', 'ND')\n with open(os.path.join(self.reportpath, 'combinedMetadata.csv'), 'w') as metadatareport:\n metadatareport.write(header)\n metadatareport.write(cleandata)","def legacy_reporter(self):\n logging.info('Creating database-friendly summary report')\n header = '{}\\n'.format(','.join(self.legacy_headers))\n # Create a string to store all the results\n data = str()\n for sample in self.metadata:\n # Add the value of the appropriate attribute to the results string\n data += GenObject.returnattr(sample, 'name')\n # SampleName\n data += GenObject.returnattr(sample.run, 'SamplePlate')\n # Genus\n data += GenObject.returnattr(sample.general, 'closestrefseqgenus')\n # SequencingDate\n data += GenObject.returnattr(sample.run, 'Date')\n # Analyst\n data += GenObject.returnattr(sample.run, 'InvestigatorName')\n # Legacy ConFindr clean/contaminated call\n data += 'ND,'\n # N50\n n50 = GenObject.returnattr(sample.quast, 'N50',\n number=True)\n if n50 != '-,':\n data += n50\n else:\n data += '0,'\n # NumContigs\n data += GenObject.returnattr(sample.quast, 'num_contigs',\n number=True)\n # TotalLength\n data += GenObject.returnattr(sample.quast, 'Total_length',\n number=True)\n # MeanInsertSize\n data += GenObject.returnattr(sample.quast, 'mean_insert',\n number=True)\n # InsertSizeSTD\n data += GenObject.returnattr(sample.quast, 'std_insert',\n number=True)\n # AverageCoverageDepth\n data += GenObject.returnattr(sample.qualimap, 'MeanCoveragedata',\n number=True)\n # CoverageDepthSTD\n data += GenObject.returnattr(sample.qualimap, 'StdCoveragedata',\n number=True)\n # PercentGC\n data += GenObject.returnattr(sample.quast, 'GC',\n number=True)\n # MASH_ReferenceGenome\n data += GenObject.returnattr(sample.mash, 'closestrefseq')\n # MASH_NumMatchingHashes\n data += GenObject.returnattr(sample.mash, 'nummatches')\n # 16S_result\n data += GenObject.returnattr(sample.sixteens_full, 'sixteens_match')\n # 16S PercentID\n data += GenObject.returnattr(sample.sixteens_full, 'percent_id')\n # rMLST_Result\n try:\n # If the number of matches to the closest reference profile is 53, return the profile number\n if sample.rmlst.matches == 53:\n if type(sample.rmlst.sequencetype) is list:\n rmlst_seq_type = ';'.join(sorted(sample.rmlst.sequencetype)).rstrip(';') + ','\n else:\n rmlst_seq_type = GenObject.returnattr(sample.rmlst, 'sequencetype')\n rmlst_seq_type = rmlst_seq_type if rmlst_seq_type != 'ND,' else 'new,'\n data += rmlst_seq_type\n else:\n # Otherwise the profile is set to new\n data += 'new,'\n except AttributeError:\n data += 'new,'\n # MLST_Result\n try:\n if sample.mlst.matches == 7:\n if type(sample.mlst.sequencetype) is list:\n mlst_seq_type = ';'.join(sorted(sample.mlst.sequencetype)).rstrip(';') + ','\n else:\n mlst_seq_type = GenObject.returnattr(sample.mlst, 'sequencetype')\n mlst_seq_type = mlst_seq_type if mlst_seq_type != 'ND,' else 'new,'\n data += mlst_seq_type\n else:\n data += 'new,'\n except AttributeError:\n data += 'new,'\n # MLST_gene_X_alleles\n try:\n # Create a set of all the genes present in the results (gene name split from allele)\n gene_set = {gene.split('_')[0] for gene in sample.mlst.combined_metadata_results}\n for gene in sorted(gene_set):\n allele_list = list()\n # Determine all the alleles that are present for each gene\n for allele in sample.mlst.combined_metadata_results:\n if gene in allele:\n allele_list.append(allele.replace(' ', '_'))\n # If there is more than one allele in the sample, add both to the string separated by a ';'\n if len(allele_list) > 1:\n data += '{},'.format(';'.join(allele_list))\n # Otherwise add the only allele\n else:\n data += allele_list[0] + ','\n # If there are fewer than seven matching alleles, add a ND for each missing result\n if len(gene_set) < 7:\n data += (7 - len(gene_set)) * 'ND,'\n except AttributeError:\n # data += '-,-,-,-,-,-,-,'\n data += 'ND,ND,ND,ND,ND,ND,ND,'\n # CoreGenesPresent\n data += GenObject.returnattr(sample.gdcs, 'coreresults')\n # E_coli_Serotype\n try:\n # If no O-type was found, set the output to be O-untypeable\n if ';'.join(sample.ectyper.o_type) == '-':\n otype = 'O-untypeable'\n else:\n otype = sample.ectyper.o_type\n # Same as above for the H-type\n if ';'.join(sample.ectyper.h_type) == '-':\n htype = 'H-untypeable'\n\n else:\n htype = sample.ectyper.h_type\n serotype = '{otype}:{htype},'.format(otype=otype,\n htype=htype)\n # Add the serotype to the data string unless neither O-type not H-type were found; add ND instead\n data += serotype if serotype != 'O-untypeable:H-untypeable,' else 'ND,'\n except AttributeError:\n data += 'ND,'\n # SISTR_serovar_antigen\n data += GenObject.returnattr(sample.sistr, 'serovar_antigen').rstrip(';')\n # SISTR_serovar_cgMLST\n data += GenObject.returnattr(sample.sistr, 'serovar_cgmlst')\n # SISTR_serogroup\n data += GenObject.returnattr(sample.sistr, 'serogroup')\n # SISTR_h1\n data += GenObject.returnattr(sample.sistr, 'h1').rstrip(';')\n # SISTR_h2\n data += GenObject.returnattr(sample.sistr, 'h2').rstrip(';')\n # SISTR_serovar\n data += GenObject.returnattr(sample.sistr, 'serovar')\n # GeneSeekr_Profile\n try:\n if sample.genesippr.report_output:\n data += ';'.join(sample.genesippr.report_output) + ','\n else:\n data += 'ND,'\n except AttributeError:\n data += 'ND,'\n # Vtyper_Profile\n data += GenObject.returnattr(sample.verotoxin, 'verotoxin_subtypes_set')\n # AMR_Profile and resistant/sensitive status\n if sample.resfinder_assembled.pipelineresults:\n # Profile\n for resistance, resistance_set in sorted(sample.resfinder_assembled.pipelineresults.items()):\n data += '{res}({r_set});'.format(res=resistance.replace(',', ';'),\n r_set=';'.join(sorted(list(resistance_set))))\n data += ','\n # Resistant/Sensitive\n data += 'Resistant,'\n else:\n # Profile\n data += 'ND,'\n # Resistant/Sensitive\n data += 'Sensitive,'\n # Plasmid Result'\n if sample.mobrecon.pipelineresults:\n for plasmid, details in sorted(sample.mobrecon.pipelineresults.items()):\n data += '{plasmid}({details});'.format(plasmid=plasmid,\n details=details)\n data += ','\n else:\n data += 'ND,'\n # TotalPredictedGenes\n data += GenObject.returnattr(sample.prodigal, 'predictedgenestotal',\n number=True)\n # PredictedGenesOver3000bp\n data += GenObject.returnattr(sample.prodigal, 'predictedgenesover3000bp',\n number=True)\n # PredictedGenesOver1000bp\n data += GenObject.returnattr(sample.prodigal, 'predictedgenesover1000bp',\n number=True)\n # PredictedGenesOver500bp\n data += GenObject.returnattr(sample.prodigal, 'predictedgenesover500bp',\n number=True)\n # PredictedGenesUnder500bp\n data += GenObject.returnattr(sample.prodigal, 'predictedgenesunder500bp',\n number=True)\n # NumClustersPF\n data += GenObject.returnattr(sample.run, 'NumberofClustersPF')\n # Percentage of reads mapping to PhiX control\n data += GenObject.returnattr(sample.run, 'phix_aligned')\n # Error rate calculated from PhiX control\n data += GenObject.returnattr(sample.run, 'error_rate')\n # LengthForwardRead\n data += GenObject.returnattr(sample.run, 'forwardlength',\n number=True)\n # LengthReverseRead\n data += GenObject.returnattr(sample.run, 'reverselength',\n number=True)\n # Real time strain\n data += GenObject.returnattr(sample.run, 'Description')\n # Flowcell\n data += GenObject.returnattr(sample.run, 'flowcell')\n # MachineName\n data += GenObject.returnattr(sample.run, 'instrument')\n # PipelineVersion\n data += self.commit + ','\n # AssemblyDate\n data += datetime.now().strftime('%Y-%m-%d') + ','\n # SamplePurity\n data += GenObject.returnattr(sample.confindr, 'num_contaminated_snvs')\n # cgMLST\n try:\n if type(sample.cgmlst.sequencetype) is list:\n if sample.cgmlst.sequencetype:\n cgmlst_seq_type = ';'.join(sorted(sample.cgmlst.sequencetype)).rstrip(';') + ','\n else:\n cgmlst_seq_type = 'ND,'\n else:\n cgmlst_seq_type = GenObject.returnattr(sample.cgmlst, 'sequencetype')\n # cgmlst_seq_type = cgmlst_seq_type if cgmlst_seq_type != 'ND,' else 'new,'\n data += cgmlst_seq_type\n except AttributeError:\n data += 'ND,'\n # Name of the database used in the analyses\n data += os.path.split(self.reffilepath)[-1] + ','\n # Database download date\n data += self.download_date\n # Append a new line to the end of the results for this sample\n data += '\\n'\n # Replace any NA values with ND\n cleandata = data.replace('NA', 'ND')\n with open(os.path.join(self.reportpath, 'legacy_combinedMetadata.csv'), 'w') as metadatareport:\n metadatareport.write(header)\n metadatareport.write(cleandata)","def event_stats(self):\n return self.base_stats","def get_stats(self):\n self.stats = self.soup.find('table', id='statsTable')","def print_summary_stats(self) -> None:\n print(\"Number of Users: {}\".format(len(self.all_users)))\n print(\"Number of Utterances: {}\".format(len(self.utterances)))\n print(\"Number of Conversations: {}\".format(len(self.conversations)))","def get_stats(self):\n stats = {\"param_noise_stddev\": self.current_std}\n return stats","def _compute_summarystat(self, data):\n if isinstance(data, pd.DataFrame):\n ma_values = self.kernel_transformer.transform(\n data, masker=self.masker, return_type=\"array\"\n )\n elif isinstance(data, list):\n ma_values = self.masker.transform(data)\n elif isinstance(data, np.ndarray):\n ma_values = data.copy()\n else:\n raise ValueError(f\"Unsupported data type '{type(data)}'\")\n\n stat_values = 1.0 - np.prod(1.0 - ma_values, axis=0)\n return stat_values","def get_stats():\n datasets = [\n (\"../data_processing/data/page2answer_single_abstractive_summ.json\", \"p2a-single-abs\"),\n (\"../data_processing/data/page2answer_single_extractive_summ.json\", \"p2a-single-ext\"),\n (\"../data_processing/data/section2answer_multi_abstractive_summ.json\", \"s2a-multi-abs\"),\n (\"../data_processing/data/page2answer_multi_extractive_summ.json\", \"p2a-multi-ext\"),\n (\"../data_processing/data/section2answer_single_abstractive_summ.json\", \"s2a-single-abs\"),\n (\"../data_processing/data/section2answer_single_extractive_summ.json\", \"s2a-single-ext\"),\n (\"../data_processing/data/section2answer_multi_extractive_summ.json\", \"s2a-multi-ext\"),\n (\"../data_processing/data/question_driven_answer_summarization_primary_dataset.json\", \"complete_dataset\"),\n ]\n\n stats = SummarizationDataStats()\n for dataset in datasets:\n print(dataset[1])\n stats.load_data(dataset[0], dataset[1])\n stats.iterate_data()","def get_summary(self):\n return self.model.summary()","def summarize(self):\n if self.__total_count == 0:\n return \"(count=0)\"\n\n # noinspection PyStringFormat\n return \"(count=%ld,avg=%.2lf,min=%.2lf,max=%.2lf,median=%.2lf)\" % (\n self.count(),\n self.average(),\n self.min(),\n self.max(),\n self.estimate_median(),\n )","def summary(self):\n if len(self.pfm) == 1:\n return self.pfm[0]\n perf_dict = {}\n perf_dict[self.name] = self.pfm[0]\n perf_dict.update({'{}_top{}'.format(self.name, self.topk[idx]): value for idx, value in enumerate(self.pfm)})\n return perf_dict","def get_stats(self):\n # pool.map needs an arg for each function that will be run\n dmx_mean = [self.dmx.mean()] * len(self.genome_paths)\n with ProcessingPool() as pool:\n results = pool.map(genome.mp_stats, self.genome_paths, dmx_mean)\n self.stats = pd.concat(results)\n self.stats.to_csv(self.stats_path)","def event_stats(self):\n pass","def transfer_stats(self):\n tr_sum, tr_max, tr_min, tr_mean, tr_std = self.stats.get_transfer_stats()\n du_sum, du_max, du_min, du_mean, du_std = self.stats.get_duration_stats()\n\n stats_str = \"Summary:\\n\"\n stats_str += \"========\"\n stats_str += \"\"\n stats_str += f\" total steps: {self.stats.nsteps}\"\n stats_str += f\" total data (MB): {(tr_sum / 1024 / 1024)}\"\n stats_str += f\" transfer times(sec): {(du_sum)}\"\n stats_str += f\" throughput (MB/sec): {tr_sum / 1024 / 1024 / du_sum}\"\n\n return stats_str","def calculate_metrics(self):\n self.data_stats = self.sqlContext.read.format(\"org.apache.spark.sql.cassandra\").options(table=self.cassandra_trip_table, keyspace=self.cassandra_keyspace).load()\n self.data_stats = self.data_stats.groupBy(['time_block','day','month','borough_name']).agg(func.avg('num_trips').alias('mean'))","def group1_stats(self):\n return self._group1_stats","def collect_statistics(self, stat_col, data_streams):\n # Calculate all four statistics.\n _, precisions, recalls, f1scores, supports = self.calculate_statistics(data_streams)\n\n # Calculate weighted averages.\n precision_sum = sum([pi*si for (pi,si) in zip(precisions,supports)])\n recall_sum = sum([ri*si for (ri,si) in zip(recalls,supports)])\n f1score_sum = sum([fi*si for (fi,si) in zip(f1scores,supports)])\n support_sum = sum(supports)\n\n if support_sum > 0:\n precision_avg = precision_sum / support_sum \n recall_avg = recall_sum / support_sum\n f1score_avg = f1score_sum / support_sum\n else:\n precision_avg = 0\n recall_avg = 0\n f1score_avg = 0\n\n # Export averages to statistics.\n stat_col[self.key_precision] = precision_avg\n stat_col[self.key_recall] = recall_avg\n stat_col[self.key_f1score] = f1score_avg\n\n # Export support to statistics.\n stat_col[self.key_f1score+'_support'] = support_sum","def statistics(self):\n return self._queue.statistics(self._name)","def _summary(self, name=None):\n if name is None:\n if len(self._tracker_dict.keys()) > 1:\n dataframes = []\n for (_name, tracker) in self._tracker_dict.items():\n summary_df = tracker.series.summary()\n summary_df = summary_df.rename_axis(self.PHASE)\n summary_df[self.SERIES] = _name\n dataframes.append(summary_df.reset_index())\n df = pd.concat(dataframes, ignore_index=True, sort=False)\n return df.set_index([self.SERIES, self.PHASE])\n name = self.MAIN\n return self._tracker(name).series.summary()","def stats(self):\n resp = self.server.request(\"get\", \"/jobs/%s/%s/stats\" %\n (self.sessionid, self.name))\n return self.server.json_body(resp)","def test_summarize(self):\n measurement = self.measurement(self.metric())\n self.assertEqual(\n {\n \"count\": {\"value\": None, \"status\": None},\n \"start\": measurement[\"start\"],\n \"end\": measurement[\"end\"],\n },\n measurement.summarize(),\n )","def stats(self):\n url = client.build_url('stats')\n _, res_json = client.get(url, headers=self.headers)\n\n return res_json","def fields(self):\n return (self._total + self._mean + self._variance\n + self._skew + self._kurtosis)","def get_stats():\r\n stats = {\r\n \"progress_precent\": 100.0*finished_work_units_amount/work_units_amount,\r\n \"results\": None if work_status == Db.WorkStatusNames.finished_work.value else Db.collect_results(),\r\n #If it's already finished, then all the results were already sent to the main server.\r\n }\r\n return stats","def get_statistics(self):\n train_loss, test_loss = self.learner.get_statistics()\n return train_loss, test_loss, np.mean(self.rewards), np.mean(self.surrogate_losses)","def summary(self):\n return self.model.summary()","def transmission_summarise():\n\n study_description = self.processDicomField(dcm_info, \"StudyDescription\")\n study_date = self.processDicomField(dcm_info, \"StudyDate\")\n series_description = self.processDicomField(dcm_info, \"SeriesDescription\")\n patient_id = self.processDicomField(dcm_info, \"PatientID\")\n patient_name = self.processDicomField(dcm_info, \"PatientName\")\n protocol_name = self.processDicomField(dcm_info, \"ProtocolName\")\n d_fileInfo = filesInSeries_determine()\n self.dp.qprint('Summary report:')\n self.dp.qprint('PatientID: %s' % patient_id, level = -1)\n self.dp.qprint('PatientName: %s' % patient_name, level = -1)\n self.dp.qprint('StudyDate: %s' % study_date, level = -1)\n self.dp.qprint('StudyDescription: %s' % study_description, level = -1)\n self.dp.qprint('SeriesDescription: %s' % series_description, level = -1)\n self.dp.qprint('ProtocolName: %s' % protocol_name, level = -1)\n if d_fileInfo['status']:\n self.dp.qprint('Number of files in Series: %d' % d_fileInfo['fileCount'], level = -1)\n self.dp.qprint('Directory size (raw): %d' % d_fileInfo['dirSizeRaw'], level = -1)\n self.dp.qprint('Directory size (human): %s' % d_fileInfo['str_dirSize'], level = -1)","def summary(self, index):\r\n return self.arraydata[index.row()][2]"],"string":"[\n \"def get_summary_stats(self):\\r\\n n = len(self.results)\\r\\n\\r\\n if n == 0:\\r\\n mean = None\\r\\n stdev = None\\r\\n\\r\\n elif n == 1:\\r\\n mean = numpy.mean(self.results)\\r\\n stdev = None\\r\\n\\r\\n else:\\r\\n mean = numpy.mean(self.results)\\r\\n stdev = numpy.std(self.results)\\r\\n\\r\\n sum_stats = {'n': n, 'mean': mean, 'stdev': stdev}\\r\\n\\r\\n return sum_stats\",\n \"def dataStats(self):\\n print (\\\"Performing statistical analysis of the data\\\")\\n # stuff to do\",\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 get_stats(self):\\n return self.stats\",\n \"def summary_data(self):\\n data = {\\n \\\"total\\\": self.total,\\n \\\"card_one_value\\\": self.cards[0].value,\\n \\\"card_two_value\\\": self.cards[1].value,\\n \\\"card_one_rank\\\": self.cards[0].rank,\\n \\\"card_two_rank\\\": self.cards[1].rank,\\n \\\"cards\\\": \\\" \\\".join([str(card) for card in self.cards]),\\n \\\"soft\\\": int(self.soft),\\n \\\"from_split\\\": int(self.from_split),\\n \\\"blackjack\\\": int(self.blackjack),\\n \\\"num_cards\\\": len(self.cards),\\n \\\"start_total\\\": self.cards[0] + self.cards[1],\\n \\\"wager\\\": int(self.wager),\\n \\\"insurance\\\": int(self.insurance),\\n \\\"surrender\\\": int(self.surrender),\\n \\\"double_down\\\": int(self.double_down),\\n \\\"num_aces\\\": self.num_aces,\\n \\\"num_hard_aces\\\": self.num_hard_aces\\n }\\n return data\",\n \"def summarize_records(self):\\r\\n cache = self.retrieve_records_cache()\\r\\n return \\\"{} records\\\".format(\\r\\n len(cache[\\\"results\\\"])\\r\\n )\",\n \"def summary_stats(self):\\n capital_gains = self.df['values'].iloc[-1].sum() - self.tc.starting_cash\\n total_return = capital_gains / self.tc.starting_cash\\n days_invested = (self.df.index[-1] - self.df.index[0]).days\\n annualized_returns = (total_return + 1) ** (365 / days_invested) - 1\\n annualized_volatility = self.df['returns'].std() * (252 ** 0.5)\\n sharpe = annualized_returns / annualized_volatility\\n num_trades = self.trades.shape[0]\\n stats = pd.Series(\\n data=[capital_gains, total_return, annualized_returns, annualized_volatility, sharpe, num_trades],\\n index=['Capital Gains', 'Total Return', 'Annualized Return', 'Annualized Volatility', 'Sharpe Ratio',\\n 'Number of Trades']\\n )\\n return stats\",\n \"def get_summarized_results(self):\\n stats = [v.stats() for (k, v) in self.examples.items() if v.is_ready()]\\n res = self.ExampleClass.average_stats(stats)\\n\\n res['loss'] = self.loss/self.loss_cnt\\n res['recent_loss'] = sum(self.recent_loss_array) / sum(self.recent_loss_bs_array)\\n\\n return res\",\n \"def stats(self):\\n return self._stats\",\n \"def statistics(self):\\n return self.get_statistics()\",\n \"def getStats(self):\\n\\n raise NotImplementedError\",\n \"def stats(self):\\n pass\",\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 summarize(self):\\n return self.df.describe()\",\n \"def get_stats(self):\\n return utils.csv_to_dict(wait(self.proto.stat()))\",\n \"def stats(self):\",\n \"def metrics(self):\\n\\n return six.iteritems(self.__stats_table)\",\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 summary(self) -> Dict[str, Dict[str, float]]:\\n vals: Dict[str, List[float]] = defaultdict(list)\\n if not self.steps: # pragma: no cover\\n return {}\\n\\n for timing_dict in self._timings:\\n for step in self.steps:\\n if step in timing_dict:\\n vals[step].append(timing_dict[step])\\n summary = {}\\n for step in self.steps:\\n if vals[step]:\\n summary[step] = {\\n \\\"cnt\\\": len(vals[step]),\\n \\\"sum\\\": sum(vals[step]),\\n \\\"min\\\": min(vals[step]),\\n \\\"max\\\": max(vals[step]),\\n \\\"avg\\\": sum(vals[step]) / len(vals[step]),\\n }\\n return summary\",\n \"def statistics(self):\\n return self._statistics\",\n \"def stats(self) -> Dict:\\n return self._stats\",\n \"def stats(self, **kwargs):\\n return self.client.api.stats(self.id, **kwargs)\",\n \"def summary(self):\\n\\n # only possible once we've fit a model with statsmodels\\n check_is_fitted(self, \\\"statistics_\\\")\\n sdf = pd.DataFrame(self.statistics_)\\n sdf.rename(columns={\\\"lambda_\\\": \\\"lambda\\\"}, inplace=True)\\n return sdf\",\n \"def summarize(self, data):\\n\\n return self.summary(data).flatten()\",\n \"def output_summary_stats(self, filename):\\r\\n\\r\\n total_return = self.equity_curve['equity_curve'][-1]\\r\\n returns = self.equity_curve['returns']\\r\\n pnl = self.equity_curve['equity_curve']\\r\\n\\r\\n sharpe_ratio = create_sharpe_ratio(returns, periods=252)\\r\\n drawdown, max_dd, dd_duration = create_drawdowns(pnl)\\r\\n self.equity_curve['drawdown'] = drawdown\\r\\n\\r\\n stats = [(\\\"Total Return\\\", \\\"%0.2f%%\\\" % \\\\\\r\\n ((total_return - 1.0) * 100.0)),\\r\\n (\\\"Sharpe Ratio\\\", \\\"%0.2f%%\\\" % sharpe_ratio),\\r\\n (\\\"Max Drawdown\\\", \\\"%0.2f%%\\\" % (max_dd * 100.0)),\\r\\n (\\\"Drawdown Duration\\\", \\\"%f\\\" % dd_duration)]\\r\\n self.equity_curve.to_csv(filename)\\r\\n return stats\",\n \"def statistics(self):\\n stats = {}\\n fields = {\\n 'Hit count:': ('hit_count', Value.from_number),\\n 'Miss count:': ('miss_count', Value.from_number),\\n 'Hit ratio:': ('hit_ratio', Value.from_percent),\\n 'Item count:': ('item_count', Value.from_number),\\n 'Total cache size:': ('total_cache_size', Value.from_number),\\n 'Oldest item age:': ('oldest_item_age', Value.from_time_ago),\\n }\\n selector = '#ae-stats-table tr'\\n for element in self.doc.cssselect(selector):\\n children = list(element)\\n assert len(children) == 2, [text(child) for child in children]\\n if text(children[0]).strip() in fields:\\n # skip rows with invalid or empty cells\\n field_name, value_fn = fields[text(children[0]).strip()]\\n stats[field_name] = value_fn(text(children[1]))\\n # Ensure all fields were filled.\\n assert len(stats) == len(fields), (fields.keys(), stats.keys())\\n return stats\",\n \"def get_stats(self):\\n return self.manager.get_stats(self)\",\n \"def get_statistics(self):\\n return self.results\",\n \"def summary(self):\\n summaries = self.summaries()\\n # Reduce to a single summary with weighted averages for each\\n # field except \\\"total_instances\\\", which is summed.\\n total_instances = sum(d['total_instances'].value() for d in summaries)\\n summary = {'total_instances': total_instances}\\n for field in ('average_qps', 'average_latency', 'average_memory'):\\n instance_weighted_sum = sum(\\n d['total_instances'].value() * d[field].value()\\n for d in summaries)\\n summary[field] = float(instance_weighted_sum) / total_instances\\n # Beautify rounding precision to match the App Engine UI.\\n summary['average_qps'] = round(summary['average_qps'], 3)\\n summary['average_latency_ms'] = round(summary['average_latency'], 1)\\n summary['average_memory_mb'] = round(summary['average_memory'], 1)\\n del summary['average_latency']\\n del summary['average_memory']\\n return summary\",\n \"def print_stats(ds):\\n print(\\\"Dataset Name: \\\" + ds.name)\\n print(\\\"Dataset Mode: \\\" + ds.mode)\\n print(\\\"Band Count: \\\" + str(ds.count))\\n print(\\\"Dataset Width: \\\" + str(ds.width))\\n print(\\\"Dataset Height: \\\" + str(ds.height))\\n print(\\\"Dataset Bounds: \\\", ds.bounds)\\n print(\\\"Dataset Transform: \\\", ds.transform)\\n ul = ds.transform * (0, 0)\\n print(\\\"Upper Left Corner: \\\", ul)\\n lr = ds.transform * (ds.width, ds.height)\\n print(\\\"Lower Right Corner: \\\", lr)\\n {i: dtype for i, dtype in zip(ds.indexes, ds.dtypes)}\",\n \"def output_stats(self):\\n elapsed = self.timer.elapsed.total_seconds()\\n count = self.copied + self.errored\\n total = self.total\\n # Time per key in milliseconds\\n avg = round(elapsed / count * 1000, 3)\\n # Time remaining in seconds\\n remaining = 1.0 * elapsed / count * (total - count)\\n # Time remaining in minutes\\n remaining = round(remaining / 60.0, 1)\\n # Time taken in minutes\\n elapsed = round(elapsed / 60.0, 1)\\n\\n self.log.info(f\\\"{self.prefix}: {avg}ms avg, {elapsed}min passed, \\\"\\n f\\\"{remaining}min remaining. ({count:,}/{total:,})\\\")\",\n \"def collect_data_stats(data):\\n # We re-import this module here because this code will run\\n # remotely.\\n \\n stats_0 = data.shape[0]\\n stats_1 = data.sum(axis=0)\\n stats_2 = (data**2).sum(axis=0)\\n retval = (\\n stats_0,\\n stats_1,\\n stats_2\\n )\\n return retval\",\n \"def total_samples(self):\\n\\n totals = self.recording_data()['totals']\\n return totals[0] + totals[1]\",\n \"def collect_stats(self):\\n\\n df_avg, self.transport_df, self.customer_df, self.manager_df, self.station_df = self.get_stats_dataframes()\\n\\n columns = []\\n if self.config.simulation_name:\\n df_avg[\\\"Simulation Name\\\"] = self.config.simulation_name\\n columns = [\\\"Simulation Name\\\"]\\n columns += [\\\"Avg Waiting Time\\\", \\\"Avg Total Time\\\", \\\"Simulation Time\\\"]\\n if self.config.max_time:\\n df_avg[\\\"Max Time\\\"] = self.config.max_time\\n columns += [\\\"Max Time\\\"]\\n columns += [\\\"Simulation Finished\\\"]\\n self.df_avg = df_avg[columns]\",\n \"def statistics(self):\\n raise NotImplemented()\",\n \"def dataset_statistics(dataset):\\n print (dataset.describe())\",\n \"def rct_stats(udf_data: UdfData):\\n # The dictionary that stores the statistical data\\n stats = {}\\n # Iterate over each raster collection cube and compute statistical values\\n for cube in udf_data.get_datacube_list():\\n # make sure to cast the values to floats, otherwise they are not serializable\\n stats[cube.id] = dict(\\n sum=float(cube.array.sum()),\\n mean=float(cube.array.mean()),\\n min=float(cube.array.min()),\\n max=float(cube.array.max())\\n )\\n # Create the structured data object\\n sd = StructuredData(\\n data=stats,\\n type=\\\"dict\\\",\\n description=\\\"Statistical data sum, min, max and mean for each raster collection cube as dict\\\",\\n )\\n # Remove all collections and set the StructuredData list\\n udf_data.set_datacube_list(None)\\n udf_data.set_structured_data_list([sd, ])\",\n \"def getStats(data, printStats=False):\\n data = list(data)\\n d = DataFrame()\\n d['Avg'] = [np.mean(data)]\\n d['len'] = [len(data)]\\n d['min'] = [min(data)]\\n d['max'] = [max(data)]\\n d['std'] = [np.std(data)]\\n d = d.transpose()\\n d.columns = ['Stats']\\n d = d.transpose()\\n if printStats:\\n print(d)\\n return d\",\n \"def statistics(self, **_):\\n raise NotImplementedError(\\\"{} doesn't support statistics.\\\".format(__class__.__name__))\",\n \"def dataset_statistics(dataset):\\n print(dataset.describe())\",\n \"def dataset_statistics(dataset):\\n print(dataset.describe())\",\n \"def summary(self):\\n raise NotImplementedError\",\n \"def add_stats(self):\\n units = self.get_unit_map()\\n for metric in self.raw_metrics:\\n unit, metric_type = units.get(metric, (DEFAULT_UNIT, DEFAULT_TYPE))\\n if metric_type == \\\"counter\\\":\\n # Unit/Second\\n unit = \\\"/\\\".join((unit, \\\"Second\\\"))\\n self.add_derive_value(metric, unit, self.raw_metrics[metric], rate=True)\\n else:\\n self.add_gauge_value(metric, unit, self.raw_metrics[metric])\",\n \"def _printSummary(self):\\n\\t\\t### COP OUT\\n\\t\\tif self.params['background'] is True:\\n\\t\\t\\tself.stats['count'] += 1\\n\\t\\t\\treturn\\n\\n\\t\\t### THIS NEEDS TO BECOME MUCH MORE GENERAL, e.g. Peaks\\n\\t\\ttdiff = time.time()-self.stats['startseries']\\n\\t\\tif not self.params['continue'] or tdiff > 0.1:\\n\\t\\t\\tcount = self.stats['count']\\n\\t\\t\\t#if(count != self.stats['lastcount']):\\n\\t\\t\\tsys.stderr.write(\\\"\\\\n\\\\tSUMMARY: \\\"+self.functionname+\\\"\\\\n\\\")\\n\\t\\t\\tself._printLine()\\n\\t\\t\\tsys.stderr.write(\\\"\\\\tTIME: \\\\t\\\"+apDisplay.timeString(tdiff)+\\\"\\\\n\\\")\\n\\t\\t\\tself.stats['timesum'] = self.stats['timesum'] + tdiff\\n\\t\\t\\tself.stats['timesumsq'] = self.stats['timesumsq'] + (tdiff**2)\\n\\t\\t\\ttimesum = self.stats['timesum']\\n\\t\\t\\ttimesumsq = self.stats['timesumsq']\\n\\t\\t\\tif(count > 1):\\n\\t\\t\\t\\ttimeavg = float(timesum)/float(count)\\n\\t\\t\\t\\ttimestdev = math.sqrt(float(count*timesumsq - timesum**2) / float(count*(count-1)))\\n\\t\\t\\t\\ttimeremain = (float(timeavg)+float(timestdev))*self.stats['seriesleft']\\n\\t\\t\\t\\tsys.stderr.write(\\\"\\\\tAVG TIME: \\\\t\\\"+apDisplay.timeString(timeavg,timestdev)+\\\"\\\\n\\\")\\n\\t\\t\\t\\t#print \\\"\\\\t(- TOTAL:\\\",apDisplay.timeString(timesum),\\\" -)\\\"\\n\\t\\t\\t\\tif(self.stats['seriesleft'] > 0):\\n\\t\\t\\t\\t\\tsys.stderr.write(\\\"\\\\t(- REMAINING TIME: \\\"+apDisplay.timeString(timeremain)+\\\" for \\\"\\n\\t\\t\\t\\t\\t\\t+str(self.stats['seriesleft'])+\\\" series -)\\\\n\\\")\\n\\t\\t\\t#print \\\"\\\\tMEM: \\\",(mem.active()-startmem)/1024,\\\"M (\\\",(mem.active()-startmem)/(1024*count),\\\"M)\\\"\\n\\t\\t\\tself.stats['count'] += 1\\n\\t\\t\\tself._printLine()\",\n \"def get_run_stats(self):\\n return self.run_stats\",\n \"def get_stats(self):\\n return {\\n \\\"pings_sent\\\" : self.ping_count,\\n \\\"measurements\\\" : self.measurements,\\n }\",\n \"def summary():\\n\\n summary_result = session.query(Summary.Count, Summary.Total).all()\\n session.close()\\n\\n # Return a List of Column Names (Sample Names)\\n return jsonify(summary_result)\",\n \"def stats(self):\\r\\n return {}\",\n \"def get_stats(self):\\n result = {\\n 'datetime': dt.datetime.now().strftime('%d.%m.%Y %H:%M:%S'),\\n 'total': db.session.query(User). \\\\\\n count(),\\n 'unverified': db.session.query(User). \\\\\\n filter(db.not_(User.verified)). \\\\\\n count(),\\n 'male students': db.session.query(User). \\\\\\n filter(User.sex == Sex.Male,\\n User.type == UserType.Student). \\\\\\n count(),\\n 'male employees': db.session.query(User). \\\\\\n filter(User.sex == Sex.Male,\\n User.type == UserType.Employee). \\\\\\n count(),\\n 'male alumni': db.session.query(User). \\\\\\n filter(User.sex == Sex.Male,\\n User.type == UserType.Alumni). \\\\\\n count(),\\n 'female students': db.session.query(User). \\\\\\n filter(User.sex == Sex.Female,\\n User.type == UserType.Student). \\\\\\n count(),\\n 'female employees': db.session.query(User). \\\\\\n filter(User.sex == Sex.Female,\\n User.type == UserType.Employee). \\\\\\n count(),\\n 'female alumni': db.session.query(User). \\\\\\n filter(User.sex == Sex.Female,\\n User.type == UserType.Alumni). \\\\\\n count()\\n }\\n\\n return result\",\n \"def stats(self, **kwargs):\\n return stats.stats(self._host, self._session, **kwargs)\",\n \"def summary(self):\\n if hasattr(self,\\\"_summary\\\"):\\n return self._summary\\n else:\\n return {}\",\n \"def aggregate_stats(self):\\n if self.split_bn.track_running_stats:\\n (\\n self.bn.running_mean.data,\\n self.bn.running_var.data,\\n ) = self._get_aggregated_mean_std(\\n self.split_bn.running_mean,\\n self.split_bn.running_var,\\n self.num_splits,\\n )\",\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 compute_statistics(self):\",\n \"def get_stats_summary(self):\\n perf_table = spark.table(self.performance_table)\\\\\\n .where(\\\"yyyy_mm_dd between '{start_date}' and '{end_date}'\\\"\\n .format(start_date = self.start_date, end_date = self.end_date))\\\\\\n .where(\\\"clicks > 0\\\")\\\\\\n .where(\\\"commission_expected_euro <= {max_rpb}\\\".format(max_rpb = self.max_rpb))\\n\\n if self.pos == ['All']:\\n perf_table = perf_table.groupBy(*self.agg_on)\\\\\\n .agg(f.sum(\\\"nits_bookings\\\").alias(\\\"nits_bookings\\\")\\n ,f.sum(\\\"commission_expected_euro\\\").alias(\\\"nits_commission\\\")\\n ,f.sum(\\\"bookings\\\").alias(\\\"gross_bookings\\\")\\n ,f.sum(\\\"commission_amount_euro\\\").alias(\\\"gross_commission\\\")\\n ,f.sum(\\\"cost_euro\\\").alias(\\\"cost\\\")\\n ,f.sum(\\\"clicks\\\").alias(\\\"clicks\\\")\\n ,f.sum(\\\"roomnights\\\").alias(\\\"roomnights\\\"))\\\\\\n .withColumn(\\\"nits_profit\\\",f.expr(\\\"nits_commission-cost\\\"))\\\\\\n .withColumn(\\\"gross_profit\\\", f.expr(\\\"gross_commission-cost\\\"))\\n else:\\n filtered_pos = spark.createDataFrame(pd.DataFrame(data = self.pos,\\n columns = [\\\"pos\\\"]))\\n\\n perf_table = perf_table.join(filtered_pos, on = \\\"pos\\\", how = \\\"inner\\\")\\\\\\n .groupBy(*self.agg_on)\\\\\\n .agg(f.sum(\\\"nits_bookings\\\").alias(\\\"nits_bookings\\\")\\n ,f.sum(\\\"commission_expected_euro\\\").alias(\\\"nits_commission\\\")\\n ,f.sum(\\\"bookings\\\").alias(\\\"gross_bookings\\\")\\n ,f.sum(\\\"commission_amount_euro\\\").alias(\\\"gross_commission\\\")\\n ,f.sum(\\\"cost_euro\\\").alias(\\\"cost\\\")\\n ,f.sum(\\\"clicks\\\").alias(\\\"clicks\\\")\\n ,f.sum(\\\"roomnights\\\").alias(\\\"roomnights\\\"))\\\\\\n .withColumn(\\\"nits_profit\\\",f.expr(\\\"nits_commission-cost\\\"))\\\\\\n .withColumn(\\\"gross_profit\\\", f.expr(\\\"gross_commission-cost\\\"))\\n\\n return (perf_table)\",\n \"def base_stats(self):\\n return (\\n self._get_seconds_played_stats_items()\\n + self._get_possessions_played_stats_items()\\n )\",\n \"def get_stats(self):\\n return scales.getStats()[self.stats_name]\",\n \"def summary(self):\\n summary = defaultdict(int)\\n\\n for r in self.results:\\n summary[r.result] += 1\\n\\n return summary\",\n \"def summary(self):\\n response = self._get(self.uri_for(\\\"summary\\\"))\\n return json_to_py(response)\",\n \"def get_stats(self, datalist, state):\\n data = {}\\n for i, col in zip(range(5), datalist[0].items()):\\n [diff, per_diff, tot_percentage] = self.get_diff_and_percentage(datalist[0].iloc[i], datalist[1].iloc[i],\\n state)\\n data[col[0]] = {\\\"value\\\": datalist[0].iloc[i], \\\"change\\\": diff, \\\"change_per\\\": per_diff,\\n \\\"tot_percentage\\\": tot_percentage}\\n return data\",\n \"def summary(self):\\n return self._summary\",\n \"def summary(self):\\n return self._summary\",\n \"def summary(self):\\n return self._summary\",\n \"def summary(self):\\n return self._summary\",\n \"def summary(self):\\n return self._summary\",\n \"def summary(self):\\n return self._summary\",\n \"def summary(self):\\n return self._summary\",\n \"def summary(self):\\n return self.pfm\",\n \"def stats(self):\\n return {\\\"size\\\": 0, \\\"maxsize\\\": 0, \\\"hits\\\": 0, \\\"miss\\\": 0}\",\n \"def get_summary_stats(items, attr):\\n data = {}\\n for item in items:\\n stats = models.Stats.query.filter_by(**{attr: item.id})\\n data[item.id] = {\\n 'ok': sum([int(stat.ok) for stat in stats]),\\n 'changed': sum([int(stat.changed) for stat in stats]),\\n 'failed': sum([int(stat.failed) for stat in stats]),\\n 'skipped': sum([int(stat.skipped) for stat in stats]),\\n 'unreachable': sum([int(stat.unreachable) for stat in stats])\\n }\\n\\n # If we're aggregating stats for a playbook, also infer status\\n if attr is \\\"playbook_id\\\":\\n data[item.id]['status'] = _infer_status(item, data[item.id])\\n\\n return data\",\n \"def stats():\\n return jsonify(shorten.get_stats(get_db(), app.config['MINI_URL_BASE']))\",\n \"def summary(self, tmin=None, tmax=None, stats='basic'):\\n output = {\\n 'basic': {\\n 'evp': 'Explained variance percentage',\\n 'rmse': 'Root mean squared error',\\n 'avg_dev': 'Average Deviation',\\n 'rsq': 'Pearson R^2',\\n 'bic': 'Bayesian Information Criterion',\\n 'aic': 'Akaike Information Criterion'},\\n }\\n\\n # get labels and method names for stats output\\n if stats == 'all':\\n # sort by key, label, method name\\n selected_output = sorted([(k, l, f) for k, d in output.items()\\n for f, l in d.items()])\\n else:\\n # sort by name, method name\\n selected_output = sorted([(0, l, f) for f, l in\\n output[stats].items()])\\n\\n # compute statistics\\n labels_and_values = [(l, getattr(self, f)(tmin=tmin, tmax=tmax))\\n for _, l, f in selected_output]\\n labels, values = zip(*labels_and_values)\\n\\n stats = pd.DataFrame(index=list(labels), data=list(values),\\n columns=['Value'])\\n stats.index.name = 'Statistic'\\n return stats\",\n \"def metadata_reporter(self):\\n logging.info('Creating summary report')\\n header = '{}\\\\n'.format(','.join(self.headers))\\n # Create a string to store all the results\\n data = str()\\n for sample in self.metadata:\\n # Add the value of the appropriate attribute to the results string\\n data += GenObject.returnattr(sample, 'name')\\n # SampleName\\n data += GenObject.returnattr(sample.run, 'SamplePlate')\\n # Genus\\n data += GenObject.returnattr(sample.general, 'closestrefseqgenus')\\n # SamplePurity\\n data += GenObject.returnattr(sample.confindr, 'num_contaminated_snvs')\\n # N50\\n n50 = GenObject.returnattr(sample.quast, 'N50',\\n number=True)\\n if n50 != '-,':\\n data += n50\\n else:\\n data += '0,'\\n # NumContigs\\n data += GenObject.returnattr(sample.quast, 'num_contigs',\\n number=True)\\n # TotalLength\\n data += GenObject.returnattr(sample.quast, 'Total_length',\\n number=True)\\n # MeanInsertSize\\n data += GenObject.returnattr(sample.quast, 'mean_insert',\\n number=True)\\n # InsertSizeSTD\\n data += GenObject.returnattr(sample.quast, 'std_insert',\\n number=True)\\n # AverageCoverageDepth\\n data += GenObject.returnattr(sample.qualimap, 'MeanCoveragedata',\\n number=True)\\n # CoverageDepthSTD\\n data += GenObject.returnattr(sample.qualimap, 'StdCoveragedata',\\n number=True)\\n # PercentGC\\n data += GenObject.returnattr(sample.quast, 'GC',\\n number=True)\\n # MASH_ReferenceGenome\\n data += GenObject.returnattr(sample.mash, 'closestrefseq')\\n # MASH_NumMatchingHashes\\n data += GenObject.returnattr(sample.mash, 'nummatches')\\n # 16S_result\\n data += GenObject.returnattr(sample.sixteens_full, 'sixteens_match')\\n # 16S PercentID\\n data += GenObject.returnattr(sample.sixteens_full, 'percent_id')\\n # CoreGenesPresent\\n data += GenObject.returnattr(sample.gdcs, 'coreresults')\\n # rMLST_Result\\n try:\\n # If the number of matches to the closest reference profile is 53, return the profile number\\n if sample.rmlst.matches == 53:\\n if type(sample.rmlst.sequencetype) is list:\\n rmlst_seq_type = ';'.join(sorted(sample.rmlst.sequencetype)).rstrip(';') + ','\\n else:\\n rmlst_seq_type = GenObject.returnattr(sample.rmlst, 'sequencetype')\\n rmlst_seq_type = rmlst_seq_type if rmlst_seq_type != 'ND,' else 'new,'\\n data += rmlst_seq_type\\n else:\\n # Otherwise the profile is set to new\\n data += 'new,'\\n except AttributeError:\\n data += 'new,'\\n # MLST_Result\\n try:\\n if sample.mlst.matches == 7:\\n if type(sample.mlst.sequencetype) is list:\\n mlst_seq_type = ';'.join(sorted(sample.mlst.sequencetype)).rstrip(';') + ','\\n else:\\n mlst_seq_type = GenObject.returnattr(sample.mlst, 'sequencetype')\\n mlst_seq_type = mlst_seq_type if mlst_seq_type != 'ND,' else 'new,'\\n data += mlst_seq_type\\n else:\\n data += 'new,'\\n except AttributeError:\\n data += 'new,'\\n # MLST_gene_X_alleles\\n try:\\n # Create a set of all the genes present in the results (gene name split from allele)\\n gene_set = {gene.split('_')[0] for gene in sample.mlst.combined_metadata_results}\\n for gene in sorted(gene_set):\\n allele_list = list()\\n # Determine all the alleles that are present for each gene\\n for allele in sample.mlst.combined_metadata_results:\\n if gene in allele:\\n allele_list.append(allele.replace(' ', '_'))\\n # If there is more than one allele in the sample, add both to the string separated by a ';'\\n if len(allele_list) > 1:\\n data += '{},'.format(';'.join(allele_list))\\n # Otherwise add the only allele\\n else:\\n data += allele_list[0] + ','\\n # If there are fewer than seven matching alleles, add a ND for each missing result\\n if len(gene_set) < 7:\\n data += (7 - len(gene_set)) * 'ND,'\\n except AttributeError:\\n # data += '-,-,-,-,-,-,-,'\\n data += 'ND,ND,ND,ND,ND,ND,ND,'\\n # E_coli_Serotype\\n try:\\n # If no O-type was found, set the output to be O-untypeable\\n if ';'.join(sample.ectyper.o_type) == '-':\\n otype = 'O-untypeable'\\n else:\\n otype = sample.ectyper.o_type\\n # Same as above for the H-type\\n if ';'.join(sample.ectyper.h_type) == '-':\\n htype = 'H-untypeable'\\n\\n else:\\n htype = sample.ectyper.h_type\\n serotype = '{otype}:{htype},'.format(otype=otype,\\n htype=htype)\\n # Add the serotype to the data string unless neither O-type not H-type were found; add ND instead\\n data += serotype if serotype != 'O-untypeable:H-untypeable,' else 'ND,'\\n except AttributeError:\\n data += 'ND,'\\n # SISTR_serovar_antigen\\n data += GenObject.returnattr(sample.sistr, 'serovar_antigen').rstrip(';')\\n # SISTR_serovar_cgMLST\\n data += GenObject.returnattr(sample.sistr, 'serovar_cgmlst')\\n # SISTR_serogroup\\n data += GenObject.returnattr(sample.sistr, 'serogroup')\\n # SISTR_h1\\n data += GenObject.returnattr(sample.sistr, 'h1').rstrip(';')\\n # SISTR_h2\\n data += GenObject.returnattr(sample.sistr, 'h2').rstrip(';')\\n # SISTR_serovar\\n data += GenObject.returnattr(sample.sistr, 'serovar')\\n # GeneSeekr_Profile\\n try:\\n if sample.genesippr.report_output:\\n data += ';'.join(sample.genesippr.report_output) + ','\\n else:\\n data += 'ND,'\\n except AttributeError:\\n data += 'ND,'\\n # Vtyper_Profile\\n data += GenObject.returnattr(sample.verotoxin, 'verotoxin_subtypes_set')\\n # AMR_Profile and resistant/sensitive status\\n if sample.resfinder_assembled.pipelineresults:\\n # Profile\\n for resistance, resistance_set in sorted(sample.resfinder_assembled.pipelineresults.items()):\\n data += '{res}({r_set});'.format(res=resistance.replace(',', ';'),\\n r_set=';'.join(sorted(list(resistance_set))))\\n data += ','\\n # Resistant/Sensitive\\n data += 'Resistant,'\\n else:\\n # Profile\\n data += 'ND,'\\n # Resistant/Sensitive\\n data += 'Sensitive,'\\n # Plasmid Result'\\n if sample.mobrecon.pipelineresults:\\n for plasmid, details in sorted(sample.mobrecon.pipelineresults.items()):\\n data += '{plasmid}({details});'.format(plasmid=plasmid,\\n details=details)\\n data += ','\\n else:\\n data += 'ND,'\\n # TotalPredictedGenes\\n data += GenObject.returnattr(sample.prodigal, 'predictedgenestotal',\\n number=True)\\n # PredictedGenesOver3000bp\\n data += GenObject.returnattr(sample.prodigal, 'predictedgenesover3000bp',\\n number=True)\\n # PredictedGenesOver1000bp\\n data += GenObject.returnattr(sample.prodigal, 'predictedgenesover1000bp',\\n number=True)\\n # PredictedGenesOver500bp\\n data += GenObject.returnattr(sample.prodigal, 'predictedgenesover500bp',\\n number=True)\\n # PredictedGenesUnder500bp\\n data += GenObject.returnattr(sample.prodigal, 'predictedgenesunder500bp',\\n number=True)\\n # AssemblyDate\\n data += datetime.now().strftime('%Y-%m-%d') + ','\\n # PipelineVersion\\n data += self.commit + ','\\n # Name of the database used in the analyses\\n data += os.path.split(self.reffilepath)[-1] + ','\\n # Database download date\\n data += self.download_date\\n # Append a new line to the end of the results for this sample\\n data += '\\\\n'\\n # Replace any NA values with ND\\n cleandata = data.replace('NA', 'ND')\\n with open(os.path.join(self.reportpath, 'combinedMetadata.csv'), 'w') as metadatareport:\\n metadatareport.write(header)\\n metadatareport.write(cleandata)\",\n \"def legacy_reporter(self):\\n logging.info('Creating database-friendly summary report')\\n header = '{}\\\\n'.format(','.join(self.legacy_headers))\\n # Create a string to store all the results\\n data = str()\\n for sample in self.metadata:\\n # Add the value of the appropriate attribute to the results string\\n data += GenObject.returnattr(sample, 'name')\\n # SampleName\\n data += GenObject.returnattr(sample.run, 'SamplePlate')\\n # Genus\\n data += GenObject.returnattr(sample.general, 'closestrefseqgenus')\\n # SequencingDate\\n data += GenObject.returnattr(sample.run, 'Date')\\n # Analyst\\n data += GenObject.returnattr(sample.run, 'InvestigatorName')\\n # Legacy ConFindr clean/contaminated call\\n data += 'ND,'\\n # N50\\n n50 = GenObject.returnattr(sample.quast, 'N50',\\n number=True)\\n if n50 != '-,':\\n data += n50\\n else:\\n data += '0,'\\n # NumContigs\\n data += GenObject.returnattr(sample.quast, 'num_contigs',\\n number=True)\\n # TotalLength\\n data += GenObject.returnattr(sample.quast, 'Total_length',\\n number=True)\\n # MeanInsertSize\\n data += GenObject.returnattr(sample.quast, 'mean_insert',\\n number=True)\\n # InsertSizeSTD\\n data += GenObject.returnattr(sample.quast, 'std_insert',\\n number=True)\\n # AverageCoverageDepth\\n data += GenObject.returnattr(sample.qualimap, 'MeanCoveragedata',\\n number=True)\\n # CoverageDepthSTD\\n data += GenObject.returnattr(sample.qualimap, 'StdCoveragedata',\\n number=True)\\n # PercentGC\\n data += GenObject.returnattr(sample.quast, 'GC',\\n number=True)\\n # MASH_ReferenceGenome\\n data += GenObject.returnattr(sample.mash, 'closestrefseq')\\n # MASH_NumMatchingHashes\\n data += GenObject.returnattr(sample.mash, 'nummatches')\\n # 16S_result\\n data += GenObject.returnattr(sample.sixteens_full, 'sixteens_match')\\n # 16S PercentID\\n data += GenObject.returnattr(sample.sixteens_full, 'percent_id')\\n # rMLST_Result\\n try:\\n # If the number of matches to the closest reference profile is 53, return the profile number\\n if sample.rmlst.matches == 53:\\n if type(sample.rmlst.sequencetype) is list:\\n rmlst_seq_type = ';'.join(sorted(sample.rmlst.sequencetype)).rstrip(';') + ','\\n else:\\n rmlst_seq_type = GenObject.returnattr(sample.rmlst, 'sequencetype')\\n rmlst_seq_type = rmlst_seq_type if rmlst_seq_type != 'ND,' else 'new,'\\n data += rmlst_seq_type\\n else:\\n # Otherwise the profile is set to new\\n data += 'new,'\\n except AttributeError:\\n data += 'new,'\\n # MLST_Result\\n try:\\n if sample.mlst.matches == 7:\\n if type(sample.mlst.sequencetype) is list:\\n mlst_seq_type = ';'.join(sorted(sample.mlst.sequencetype)).rstrip(';') + ','\\n else:\\n mlst_seq_type = GenObject.returnattr(sample.mlst, 'sequencetype')\\n mlst_seq_type = mlst_seq_type if mlst_seq_type != 'ND,' else 'new,'\\n data += mlst_seq_type\\n else:\\n data += 'new,'\\n except AttributeError:\\n data += 'new,'\\n # MLST_gene_X_alleles\\n try:\\n # Create a set of all the genes present in the results (gene name split from allele)\\n gene_set = {gene.split('_')[0] for gene in sample.mlst.combined_metadata_results}\\n for gene in sorted(gene_set):\\n allele_list = list()\\n # Determine all the alleles that are present for each gene\\n for allele in sample.mlst.combined_metadata_results:\\n if gene in allele:\\n allele_list.append(allele.replace(' ', '_'))\\n # If there is more than one allele in the sample, add both to the string separated by a ';'\\n if len(allele_list) > 1:\\n data += '{},'.format(';'.join(allele_list))\\n # Otherwise add the only allele\\n else:\\n data += allele_list[0] + ','\\n # If there are fewer than seven matching alleles, add a ND for each missing result\\n if len(gene_set) < 7:\\n data += (7 - len(gene_set)) * 'ND,'\\n except AttributeError:\\n # data += '-,-,-,-,-,-,-,'\\n data += 'ND,ND,ND,ND,ND,ND,ND,'\\n # CoreGenesPresent\\n data += GenObject.returnattr(sample.gdcs, 'coreresults')\\n # E_coli_Serotype\\n try:\\n # If no O-type was found, set the output to be O-untypeable\\n if ';'.join(sample.ectyper.o_type) == '-':\\n otype = 'O-untypeable'\\n else:\\n otype = sample.ectyper.o_type\\n # Same as above for the H-type\\n if ';'.join(sample.ectyper.h_type) == '-':\\n htype = 'H-untypeable'\\n\\n else:\\n htype = sample.ectyper.h_type\\n serotype = '{otype}:{htype},'.format(otype=otype,\\n htype=htype)\\n # Add the serotype to the data string unless neither O-type not H-type were found; add ND instead\\n data += serotype if serotype != 'O-untypeable:H-untypeable,' else 'ND,'\\n except AttributeError:\\n data += 'ND,'\\n # SISTR_serovar_antigen\\n data += GenObject.returnattr(sample.sistr, 'serovar_antigen').rstrip(';')\\n # SISTR_serovar_cgMLST\\n data += GenObject.returnattr(sample.sistr, 'serovar_cgmlst')\\n # SISTR_serogroup\\n data += GenObject.returnattr(sample.sistr, 'serogroup')\\n # SISTR_h1\\n data += GenObject.returnattr(sample.sistr, 'h1').rstrip(';')\\n # SISTR_h2\\n data += GenObject.returnattr(sample.sistr, 'h2').rstrip(';')\\n # SISTR_serovar\\n data += GenObject.returnattr(sample.sistr, 'serovar')\\n # GeneSeekr_Profile\\n try:\\n if sample.genesippr.report_output:\\n data += ';'.join(sample.genesippr.report_output) + ','\\n else:\\n data += 'ND,'\\n except AttributeError:\\n data += 'ND,'\\n # Vtyper_Profile\\n data += GenObject.returnattr(sample.verotoxin, 'verotoxin_subtypes_set')\\n # AMR_Profile and resistant/sensitive status\\n if sample.resfinder_assembled.pipelineresults:\\n # Profile\\n for resistance, resistance_set in sorted(sample.resfinder_assembled.pipelineresults.items()):\\n data += '{res}({r_set});'.format(res=resistance.replace(',', ';'),\\n r_set=';'.join(sorted(list(resistance_set))))\\n data += ','\\n # Resistant/Sensitive\\n data += 'Resistant,'\\n else:\\n # Profile\\n data += 'ND,'\\n # Resistant/Sensitive\\n data += 'Sensitive,'\\n # Plasmid Result'\\n if sample.mobrecon.pipelineresults:\\n for plasmid, details in sorted(sample.mobrecon.pipelineresults.items()):\\n data += '{plasmid}({details});'.format(plasmid=plasmid,\\n details=details)\\n data += ','\\n else:\\n data += 'ND,'\\n # TotalPredictedGenes\\n data += GenObject.returnattr(sample.prodigal, 'predictedgenestotal',\\n number=True)\\n # PredictedGenesOver3000bp\\n data += GenObject.returnattr(sample.prodigal, 'predictedgenesover3000bp',\\n number=True)\\n # PredictedGenesOver1000bp\\n data += GenObject.returnattr(sample.prodigal, 'predictedgenesover1000bp',\\n number=True)\\n # PredictedGenesOver500bp\\n data += GenObject.returnattr(sample.prodigal, 'predictedgenesover500bp',\\n number=True)\\n # PredictedGenesUnder500bp\\n data += GenObject.returnattr(sample.prodigal, 'predictedgenesunder500bp',\\n number=True)\\n # NumClustersPF\\n data += GenObject.returnattr(sample.run, 'NumberofClustersPF')\\n # Percentage of reads mapping to PhiX control\\n data += GenObject.returnattr(sample.run, 'phix_aligned')\\n # Error rate calculated from PhiX control\\n data += GenObject.returnattr(sample.run, 'error_rate')\\n # LengthForwardRead\\n data += GenObject.returnattr(sample.run, 'forwardlength',\\n number=True)\\n # LengthReverseRead\\n data += GenObject.returnattr(sample.run, 'reverselength',\\n number=True)\\n # Real time strain\\n data += GenObject.returnattr(sample.run, 'Description')\\n # Flowcell\\n data += GenObject.returnattr(sample.run, 'flowcell')\\n # MachineName\\n data += GenObject.returnattr(sample.run, 'instrument')\\n # PipelineVersion\\n data += self.commit + ','\\n # AssemblyDate\\n data += datetime.now().strftime('%Y-%m-%d') + ','\\n # SamplePurity\\n data += GenObject.returnattr(sample.confindr, 'num_contaminated_snvs')\\n # cgMLST\\n try:\\n if type(sample.cgmlst.sequencetype) is list:\\n if sample.cgmlst.sequencetype:\\n cgmlst_seq_type = ';'.join(sorted(sample.cgmlst.sequencetype)).rstrip(';') + ','\\n else:\\n cgmlst_seq_type = 'ND,'\\n else:\\n cgmlst_seq_type = GenObject.returnattr(sample.cgmlst, 'sequencetype')\\n # cgmlst_seq_type = cgmlst_seq_type if cgmlst_seq_type != 'ND,' else 'new,'\\n data += cgmlst_seq_type\\n except AttributeError:\\n data += 'ND,'\\n # Name of the database used in the analyses\\n data += os.path.split(self.reffilepath)[-1] + ','\\n # Database download date\\n data += self.download_date\\n # Append a new line to the end of the results for this sample\\n data += '\\\\n'\\n # Replace any NA values with ND\\n cleandata = data.replace('NA', 'ND')\\n with open(os.path.join(self.reportpath, 'legacy_combinedMetadata.csv'), 'w') as metadatareport:\\n metadatareport.write(header)\\n metadatareport.write(cleandata)\",\n \"def event_stats(self):\\n return self.base_stats\",\n \"def get_stats(self):\\n self.stats = self.soup.find('table', id='statsTable')\",\n \"def print_summary_stats(self) -> None:\\n print(\\\"Number of Users: {}\\\".format(len(self.all_users)))\\n print(\\\"Number of Utterances: {}\\\".format(len(self.utterances)))\\n print(\\\"Number of Conversations: {}\\\".format(len(self.conversations)))\",\n \"def get_stats(self):\\n stats = {\\\"param_noise_stddev\\\": self.current_std}\\n return stats\",\n \"def _compute_summarystat(self, data):\\n if isinstance(data, pd.DataFrame):\\n ma_values = self.kernel_transformer.transform(\\n data, masker=self.masker, return_type=\\\"array\\\"\\n )\\n elif isinstance(data, list):\\n ma_values = self.masker.transform(data)\\n elif isinstance(data, np.ndarray):\\n ma_values = data.copy()\\n else:\\n raise ValueError(f\\\"Unsupported data type '{type(data)}'\\\")\\n\\n stat_values = 1.0 - np.prod(1.0 - ma_values, axis=0)\\n return stat_values\",\n \"def get_stats():\\n datasets = [\\n (\\\"../data_processing/data/page2answer_single_abstractive_summ.json\\\", \\\"p2a-single-abs\\\"),\\n (\\\"../data_processing/data/page2answer_single_extractive_summ.json\\\", \\\"p2a-single-ext\\\"),\\n (\\\"../data_processing/data/section2answer_multi_abstractive_summ.json\\\", \\\"s2a-multi-abs\\\"),\\n (\\\"../data_processing/data/page2answer_multi_extractive_summ.json\\\", \\\"p2a-multi-ext\\\"),\\n (\\\"../data_processing/data/section2answer_single_abstractive_summ.json\\\", \\\"s2a-single-abs\\\"),\\n (\\\"../data_processing/data/section2answer_single_extractive_summ.json\\\", \\\"s2a-single-ext\\\"),\\n (\\\"../data_processing/data/section2answer_multi_extractive_summ.json\\\", \\\"s2a-multi-ext\\\"),\\n (\\\"../data_processing/data/question_driven_answer_summarization_primary_dataset.json\\\", \\\"complete_dataset\\\"),\\n ]\\n\\n stats = SummarizationDataStats()\\n for dataset in datasets:\\n print(dataset[1])\\n stats.load_data(dataset[0], dataset[1])\\n stats.iterate_data()\",\n \"def get_summary(self):\\n return self.model.summary()\",\n \"def summarize(self):\\n if self.__total_count == 0:\\n return \\\"(count=0)\\\"\\n\\n # noinspection PyStringFormat\\n return \\\"(count=%ld,avg=%.2lf,min=%.2lf,max=%.2lf,median=%.2lf)\\\" % (\\n self.count(),\\n self.average(),\\n self.min(),\\n self.max(),\\n self.estimate_median(),\\n )\",\n \"def summary(self):\\n if len(self.pfm) == 1:\\n return self.pfm[0]\\n perf_dict = {}\\n perf_dict[self.name] = self.pfm[0]\\n perf_dict.update({'{}_top{}'.format(self.name, self.topk[idx]): value for idx, value in enumerate(self.pfm)})\\n return perf_dict\",\n \"def get_stats(self):\\n # pool.map needs an arg for each function that will be run\\n dmx_mean = [self.dmx.mean()] * len(self.genome_paths)\\n with ProcessingPool() as pool:\\n results = pool.map(genome.mp_stats, self.genome_paths, dmx_mean)\\n self.stats = pd.concat(results)\\n self.stats.to_csv(self.stats_path)\",\n \"def event_stats(self):\\n pass\",\n \"def transfer_stats(self):\\n tr_sum, tr_max, tr_min, tr_mean, tr_std = self.stats.get_transfer_stats()\\n du_sum, du_max, du_min, du_mean, du_std = self.stats.get_duration_stats()\\n\\n stats_str = \\\"Summary:\\\\n\\\"\\n stats_str += \\\"========\\\"\\n stats_str += \\\"\\\"\\n stats_str += f\\\" total steps: {self.stats.nsteps}\\\"\\n stats_str += f\\\" total data (MB): {(tr_sum / 1024 / 1024)}\\\"\\n stats_str += f\\\" transfer times(sec): {(du_sum)}\\\"\\n stats_str += f\\\" throughput (MB/sec): {tr_sum / 1024 / 1024 / du_sum}\\\"\\n\\n return stats_str\",\n \"def calculate_metrics(self):\\n self.data_stats = self.sqlContext.read.format(\\\"org.apache.spark.sql.cassandra\\\").options(table=self.cassandra_trip_table, keyspace=self.cassandra_keyspace).load()\\n self.data_stats = self.data_stats.groupBy(['time_block','day','month','borough_name']).agg(func.avg('num_trips').alias('mean'))\",\n \"def group1_stats(self):\\n return self._group1_stats\",\n \"def collect_statistics(self, stat_col, data_streams):\\n # Calculate all four statistics.\\n _, precisions, recalls, f1scores, supports = self.calculate_statistics(data_streams)\\n\\n # Calculate weighted averages.\\n precision_sum = sum([pi*si for (pi,si) in zip(precisions,supports)])\\n recall_sum = sum([ri*si for (ri,si) in zip(recalls,supports)])\\n f1score_sum = sum([fi*si for (fi,si) in zip(f1scores,supports)])\\n support_sum = sum(supports)\\n\\n if support_sum > 0:\\n precision_avg = precision_sum / support_sum \\n recall_avg = recall_sum / support_sum\\n f1score_avg = f1score_sum / support_sum\\n else:\\n precision_avg = 0\\n recall_avg = 0\\n f1score_avg = 0\\n\\n # Export averages to statistics.\\n stat_col[self.key_precision] = precision_avg\\n stat_col[self.key_recall] = recall_avg\\n stat_col[self.key_f1score] = f1score_avg\\n\\n # Export support to statistics.\\n stat_col[self.key_f1score+'_support'] = support_sum\",\n \"def statistics(self):\\n return self._queue.statistics(self._name)\",\n \"def _summary(self, name=None):\\n if name is None:\\n if len(self._tracker_dict.keys()) > 1:\\n dataframes = []\\n for (_name, tracker) in self._tracker_dict.items():\\n summary_df = tracker.series.summary()\\n summary_df = summary_df.rename_axis(self.PHASE)\\n summary_df[self.SERIES] = _name\\n dataframes.append(summary_df.reset_index())\\n df = pd.concat(dataframes, ignore_index=True, sort=False)\\n return df.set_index([self.SERIES, self.PHASE])\\n name = self.MAIN\\n return self._tracker(name).series.summary()\",\n \"def stats(self):\\n resp = self.server.request(\\\"get\\\", \\\"/jobs/%s/%s/stats\\\" %\\n (self.sessionid, self.name))\\n return self.server.json_body(resp)\",\n \"def test_summarize(self):\\n measurement = self.measurement(self.metric())\\n self.assertEqual(\\n {\\n \\\"count\\\": {\\\"value\\\": None, \\\"status\\\": None},\\n \\\"start\\\": measurement[\\\"start\\\"],\\n \\\"end\\\": measurement[\\\"end\\\"],\\n },\\n measurement.summarize(),\\n )\",\n \"def stats(self):\\n url = client.build_url('stats')\\n _, res_json = client.get(url, headers=self.headers)\\n\\n return res_json\",\n \"def fields(self):\\n return (self._total + self._mean + self._variance\\n + self._skew + self._kurtosis)\",\n \"def get_stats():\\r\\n stats = {\\r\\n \\\"progress_precent\\\": 100.0*finished_work_units_amount/work_units_amount,\\r\\n \\\"results\\\": None if work_status == Db.WorkStatusNames.finished_work.value else Db.collect_results(),\\r\\n #If it's already finished, then all the results were already sent to the main server.\\r\\n }\\r\\n return stats\",\n \"def get_statistics(self):\\n train_loss, test_loss = self.learner.get_statistics()\\n return train_loss, test_loss, np.mean(self.rewards), np.mean(self.surrogate_losses)\",\n \"def summary(self):\\n return self.model.summary()\",\n \"def transmission_summarise():\\n\\n study_description = self.processDicomField(dcm_info, \\\"StudyDescription\\\")\\n study_date = self.processDicomField(dcm_info, \\\"StudyDate\\\")\\n series_description = self.processDicomField(dcm_info, \\\"SeriesDescription\\\")\\n patient_id = self.processDicomField(dcm_info, \\\"PatientID\\\")\\n patient_name = self.processDicomField(dcm_info, \\\"PatientName\\\")\\n protocol_name = self.processDicomField(dcm_info, \\\"ProtocolName\\\")\\n d_fileInfo = filesInSeries_determine()\\n self.dp.qprint('Summary report:')\\n self.dp.qprint('PatientID: %s' % patient_id, level = -1)\\n self.dp.qprint('PatientName: %s' % patient_name, level = -1)\\n self.dp.qprint('StudyDate: %s' % study_date, level = -1)\\n self.dp.qprint('StudyDescription: %s' % study_description, level = -1)\\n self.dp.qprint('SeriesDescription: %s' % series_description, level = -1)\\n self.dp.qprint('ProtocolName: %s' % protocol_name, level = -1)\\n if d_fileInfo['status']:\\n self.dp.qprint('Number of files in Series: %d' % d_fileInfo['fileCount'], level = -1)\\n self.dp.qprint('Directory size (raw): %d' % d_fileInfo['dirSizeRaw'], level = -1)\\n self.dp.qprint('Directory size (human): %s' % d_fileInfo['str_dirSize'], level = -1)\",\n \"def summary(self, index):\\r\\n return self.arraydata[index.row()][2]\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7375736","0.71892005","0.68082","0.675289","0.67228687","0.6701924","0.66873115","0.66815513","0.6668492","0.6614487","0.66084427","0.65882283","0.6587884","0.6563548","0.65191305","0.6514346","0.6482753","0.64782995","0.64565426","0.6439179","0.6433357","0.6425168","0.64190054","0.64093673","0.64065367","0.63871205","0.6371112","0.63682556","0.6367421","0.6364423","0.6347924","0.6328534","0.63189524","0.6310208","0.6298601","0.62959176","0.6289546","0.6276874","0.6264346","0.62640494","0.62640494","0.6257208","0.6240132","0.6227932","0.62015444","0.6178269","0.61686367","0.61250746","0.61221963","0.61196893","0.6115925","0.6106327","0.6100726","0.60970306","0.6095315","0.60950637","0.60878104","0.6085095","0.6079067","0.60681874","0.6067883","0.6067883","0.6067883","0.6067883","0.6067883","0.6067883","0.6067883","0.6060967","0.6058207","0.60416746","0.6040736","0.6039389","0.60365045","0.60311323","0.6028247","0.60256517","0.6018247","0.60119146","0.60061693","0.60043263","0.60026824","0.60015535","0.5999876","0.59988385","0.59944844","0.5994373","0.59903234","0.5985249","0.5980209","0.59731936","0.59694606","0.5963268","0.59607863","0.5959949","0.59573746","0.5954855","0.5947602","0.5938857","0.593487","0.5930907"],"string":"[\n \"0.7375736\",\n \"0.71892005\",\n \"0.68082\",\n \"0.675289\",\n \"0.67228687\",\n \"0.6701924\",\n \"0.66873115\",\n \"0.66815513\",\n \"0.6668492\",\n \"0.6614487\",\n \"0.66084427\",\n \"0.65882283\",\n \"0.6587884\",\n \"0.6563548\",\n \"0.65191305\",\n \"0.6514346\",\n \"0.6482753\",\n \"0.64782995\",\n \"0.64565426\",\n \"0.6439179\",\n \"0.6433357\",\n \"0.6425168\",\n \"0.64190054\",\n \"0.64093673\",\n \"0.64065367\",\n \"0.63871205\",\n \"0.6371112\",\n \"0.63682556\",\n \"0.6367421\",\n \"0.6364423\",\n \"0.6347924\",\n \"0.6328534\",\n \"0.63189524\",\n \"0.6310208\",\n \"0.6298601\",\n \"0.62959176\",\n \"0.6289546\",\n \"0.6276874\",\n \"0.6264346\",\n \"0.62640494\",\n \"0.62640494\",\n \"0.6257208\",\n \"0.6240132\",\n \"0.6227932\",\n \"0.62015444\",\n \"0.6178269\",\n \"0.61686367\",\n \"0.61250746\",\n \"0.61221963\",\n \"0.61196893\",\n \"0.6115925\",\n \"0.6106327\",\n \"0.6100726\",\n \"0.60970306\",\n \"0.6095315\",\n \"0.60950637\",\n \"0.60878104\",\n \"0.6085095\",\n \"0.6079067\",\n \"0.60681874\",\n \"0.6067883\",\n \"0.6067883\",\n \"0.6067883\",\n \"0.6067883\",\n \"0.6067883\",\n \"0.6067883\",\n \"0.6067883\",\n \"0.6060967\",\n \"0.6058207\",\n \"0.60416746\",\n \"0.6040736\",\n \"0.6039389\",\n \"0.60365045\",\n \"0.60311323\",\n \"0.6028247\",\n \"0.60256517\",\n \"0.6018247\",\n \"0.60119146\",\n \"0.60061693\",\n \"0.60043263\",\n \"0.60026824\",\n \"0.60015535\",\n \"0.5999876\",\n \"0.59988385\",\n \"0.59944844\",\n \"0.5994373\",\n \"0.59903234\",\n \"0.5985249\",\n \"0.5980209\",\n \"0.59731936\",\n \"0.59694606\",\n \"0.5963268\",\n \"0.59607863\",\n \"0.5959949\",\n \"0.59573746\",\n \"0.5954855\",\n \"0.5947602\",\n \"0.5938857\",\n \"0.593487\",\n \"0.5930907\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6649093"},"document_rank":{"kind":"string","value":"9"}}},{"rowIdx":22481,"cells":{"query":{"kind":"string","value":"Format recorded simulation data."},"document":{"kind":"string","value":"def format_rec_data(tr_data, village, HC, gs_pars, vfeatures, idx_pars=[]):\n\n print('\\nFormatting recorded data...')\n ret_list = []\n\n gs_xvec, gs_yvec = [utils.get_copy(gs_pars[v]) for v in ['xvec', 'yvec']]\n\n if 'anim_data' in tr_data:\n\n anim_data = pd.DataFrame(tr_data['anim_data']).T\n ret_list.append(anim_data)\n\n if 'stim_data' in tr_data:\n\n # Motor input.\n mot_keys = ['umot', 'vmot']\n mot_data = {i: pd.DataFrame({k: d[k] for k in mot_keys}).unstack()\n for i, d in tr_data['stim_data'].items() if d is not None}\n mot_data = pd.DataFrame(mot_data).T\n mot_data.columns.set_levels(['x', 'y'], level=1, inplace=True)\n ret_list.append(mot_data)\n\n # Visual input.\n vis_keys = ['ovis', 'vvis']\n vis_data = {i: pd.DataFrame({k: d[k] for k in vis_keys}).unstack()\n for i, d in tr_data['stim_data'].items() if d is not None}\n vis_data = pd.DataFrame(vis_data).T\n vis_data.columns.set_levels(vfeatures, level=1, inplace=True)\n ret_list.append(vis_data)\n\n if 'gs_state' in tr_data:\n\n gs_state = {k: pd.DataFrame(gs, columns=gs_xvec, index=gs_yvec)\n for k, gs in tr_data['gs_state'].items()}\n gs_state = pd.concat(gs_state)\n gs_state.columns.rename('x', inplace=True)\n gs_state.index.rename('y', level=-1, inplace=True)\n ret_list.append(gs_state)\n\n if 'hc_state' in tr_data:\n\n hc_state = {k: pd.DataFrame(hc, index=HC.s_names)\n for k, hc in tr_data['hc_state'].items()}\n hc_state = pd.concat(hc_state)\n hc_state.index.rename('loc', level=-1, inplace=True)\n hc_state = hc_state[HC.s_types] # reorder columns\n ret_list.append(hc_state)\n\n if 'vs_state' in tr_data:\n\n vs_state = {k: pd.Series(vc, index=HC.s_names)\n for k, vc in tr_data['vs_state'].items()}\n vs_state = pd.concat(vs_state)\n ret_list.append(vs_state)\n\n if 'dls_state' in tr_data:\n\n dls_state = {k: pd.DataFrame(dls, columns=HC.s_names, index=village.U)\n for k, dls in tr_data['dls_state'].items()}\n dls_state = pd.concat(dls_state)\n dls_state.columns.rename('s', inplace=True)\n dls_state.index.rename('u', level=-1, inplace=True)\n ret_list.append(dls_state)\n\n # Set index level names.\n idx_lvl_names = idx_pars + ['step']\n for df in ret_list:\n levels = (None if not isinstance(df.index, pd.core.index.MultiIndex)\n else list(range(len(idx_lvl_names))))\n df.index.set_names(idx_lvl_names, level=levels, inplace=True)\n\n return ret_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 format(self, data):","def format_data(self, data):","def _formatData(self):\r\n assert self._runData is not None\r\n\r\n # Getting Axes data into separate lists\r\n x=[]; y=[]; z=[]\r\n for i in range(len(self._runData)):\r\n ySet = []; xSet = []; zSet = []\r\n for _ in range(len(self._runData[i][1][0])):\r\n ySet.append(self._runData[i][0])\r\n y.append(ySet)\r\n xSet.append(self._runData[i][1][0])\r\n x.append(xSet)\r\n zSet.append(self._runData[i][1][1])\r\n z.append(zSet)\r\n\r\n # Reduce extra brackets\r\n xnew = []; znew = []\r\n for i in range(len(x)):\r\n xnew.append(x[i][0])\r\n znew.append(z[i][0])\r\n x = xnew; z = znew\r\n\r\n self._frequency = x\r\n self._voltages = y\r\n self._intensity = z","def write(self, path):\n\n with open(path, 'w') as file:\n file.write(f\"\\n{self.subject}\")\n file.write(8 * '\\n')\n file.write(\"0\")\n file.write(8 * '\\n')\n file.write(self.data.date[0].strftime(\"%d.%m.%Y\"))\n file.write(7 * '\\n')\n file.write(\"Unknown Line\")\n file.write(26 * '\\n')\n file.write(self.valid_measurements + \"\\n\")\n printing_df = self.data.drop(columns=['date', 'time'])\n printing_df['hours'] = self.data.time.map(lambda x: x.strftime(\"%H\"))\n printing_df['minutes'] = self.data.time.map(lambda x: x.strftime(\"%M\"))\n order = ['hours', 'minutes', 'SYS(mmHg)', 'DIA(mmHg)', 'UNKNOW_1', 'UNKNOW_2', 'CODE', 'UNKNOW_3']\n printing_df = printing_df[order]\n printing_df.fillna(-9999, inplace=True)\n printing_df.replace('EB', -9998, inplace=True)\n printing_df.replace('AB', -9997, inplace=True)\n printing_df[['SYS(mmHg)', 'DIA(mmHg)', 'UNKNOW_1', 'UNKNOW_2', 'CODE', 'UNKNOW_3']] = printing_df[\n ['SYS(mmHg)', 'DIA(mmHg)', 'UNKNOW_1', 'UNKNOW_2', 'CODE', 'UNKNOW_3']].astype(int).astype(str)\n printing_df.replace('-9999', '\"\"', inplace=True)\n printing_df.replace('-9998', '\"EB\"', inplace=True)\n printing_df.replace('-9997', '\"AB\"', inplace=True)\n printing_df.to_csv(file, header=None, index=None, quoting=csv.QUOTE_NONE, line_terminator='\\n')\n\n xml_node = ET.Element('XML')\n xml_node.extend(self._dict_to_etree(self.metadata))\n xml_line = ET.tostring(xml_node, encoding=\"unicode\")\n file.write(xml_line)","def coregister_formatted():\r\n\r\n print(\"begin coregister_formatted\")\r\n\r\n # check all records for pairs using the recordBegin time\r\n pair_records()\r\n\r\n # establish the beginning and end of the coregistered records\r\n define_pairedRecords()\r\n\r\n # coregister paired data in a single csv\r\n format_coregister()\r\n\r\n\r\n print(\"completed segment_formatted\")","def _record(self):\r\n self._plot()\r\n self._csvWriter()\r\n self._logger()","def convert_simulation_results(self, gdat_filename, out_filename, out_format):\n data = self.read_simulation_results(gdat_filename)\n if out_format == 'csv':\n data.to_csv(out_filename)\n elif out_format == 'tsv':\n data.to_csv(out_filename, sep='\\t')\n else:\n raise NotImplementedError('Unsupported output format {}'.format(out_format))","def create_file(self, type_simu=0, simulation_counter=0):\n portion_duration = self.individuals[0].portion_duration\n nbr_portions = self.individuals[0].nbr_portions\n arriveeX = self.individuals[0].arriveeX*nbr_portions*4.0/20\n arriveeY = self.individuals[0].arriveeY*nbr_portions*4.0/20\n\n if type_simu == 0:\n repertoire = (f\"simulation_data/Simu {simulation_counter} : \"\n f\"type_simu=0, nbr_individuals={self.nbr_individuals}, \"\n f\"mutation_factor={self.mutation_factor}, portion_duration={portion_duration}, \"\n f\"nbr_portions={nbr_portions}, arrivee=({arriveeX},{arriveeY})\")\n else:\n repertoire = (f\"simulation_data/Simu {simulation_counter} : \"\n f\"type_simu=1, nbr_individuals={self.nbr_individuals}, \"\n f\"mutation_factor={self.mutation_factor}, portion_duration={portion_duration}, \"\n f\"nbr_portions={nbr_portions}, accepted_radius={self.accepted_radius}, \"\n f\"arrivee=({arriveeX},{arriveeY})\")\n\n fname = \"data_simu.csv\"\n \n try: \n os.makedirs(repertoire)\n except OSError:\n if not os.path.isdir(repertoire):\n Raise\n\n with open(repertoire+\"/\"+fname, \"a\") as file:\n list_col = [\"Génération\", 'Individu', 'Score']\n for portion in range(self.individuals[0].nbr_portions):\n for roue in range(4):\n list_col.append('Gene_' + str(portion) + 'roue_' + str(roue))\n if type_simu == 1:\n list_col.append('individuals_under_threshold')\n writer = csv.writer(file)\n writer.writerow(list_col)\n\n return repertoire","def format(self):\n ...","def format():\n train_data.info() # retreiving general info about train data\n print('Number of rows and columns of train set:', train_data.shape)\n print('dtype: ', train_data.dtypes) # analyse data type of each column\n print('First 3 rows of the train data:', train_data.head(3))","def export_simulation_data(self, ok):\n res, ok = QtGui.QInputDialog.getText(self,\n u\"PyMoskito\",\n u\"Please specify regime a name\",\n QtGui.QLineEdit.Normal,\n self._regimes[self._current_regime_index][\"Name\"])\n if not ok:\n return\n\n name = unicode(res.toUtf8(), encoding=\"utf-8\")\n if not name:\n self._logger.warning(u\"empty regime name specified!\")\n\n self._save_data(name)","def data_format(self):\n for description in SENSOR_TYPES:\n if description.key not in self.conditions:\n continue\n attr_key = description.key\n try:\n if description.primary_key in self.raw_data:\n sensor_value = self.raw_data[description.primary_key][\n description.sensor_key\n ]\n # Format sensor for better readability\n if attr_key == ATTR_NEW_VERSION and sensor_value == \"0.0.0.0\":\n sensor_value = \"Latest\"\n elif attr_key == ATTR_UPTIME:\n sensor_value = round(sensor_value / (3600 * 24), 2)\n elif attr_key == ATTR_LAST_RESTART:\n last_restart = dt_util.now() - timedelta(seconds=sensor_value)\n sensor_value = last_restart.strftime(\"%Y-%m-%d %H:%M:%S\")\n elif attr_key == ATTR_STATUS:\n if sensor_value:\n sensor_value = \"Online\"\n else:\n sensor_value = \"Offline\"\n elif (\n attr_key == ATTR_LOCAL_IP and not self.raw_data[\"wan\"][\"online\"]\n ):\n sensor_value = None\n\n self.data[attr_key] = sensor_value\n except KeyError:\n _LOGGER.error(\n (\n \"Router does not support %s field. \"\n \"Please remove %s from monitored_conditions\"\n ),\n description.sensor_key,\n attr_key,\n )\n self.data[attr_key] = None","def _format_data(self) -> None:\n for row in self._db_data:\n if row['age_start'] is None:\n continue\n # entry = {'x': 'Celkem', 'y': int(row['count'])}\n elif row['age_start'] == 95:\n entry = {'x': f\"{int(row['age_start'])}+\", 'y': int(row['count'])}\n else:\n entry = {'x': f\"{int(row['age_start'])}-{int(row['age_start'])+4}\", 'y': int(row['count'])}\n self.return_data['data'].append(entry)","def as_dfile(self):\n rows = [\"Dose NumAnimals Response Stdev\"]\n for i, v in enumerate(self.doses):\n if i >= self.num_dose_groups:\n continue\n rows.append(\"%f %d %f %f\" % (self.doses[i], self.ns[i], self.means[i], self.stdevs[i]))\n return \"\\n\".join(rows)","def format_and_write(*, fname, header, data, df_sigma, positions):\n # Reorder to all positions at each timesample a la .txt file format\n row_list = []\n for idx_date, date_val in enumerate(data['date']):\n for idx_pos, pos in enumerate(positions):\n dict_row = {}\n if not df_sigma.empty: # assume MF has uncertainties\n dict_row.update(\n {'theta': pos[0],\n 'phi': pos[1],\n 'Year': date_val.year,\n 'Month': date_val.month,\n 'Time': mjd2000(date_val),\n 'r': pos[2],\n # -Z = B_r\n 'B_r': -data.iloc[idx_date].filter(\n regex='Z_' + str(idx_pos).zfill(3)).values[0],\n # -X = B_theta\n 'B_theta': -data.iloc[idx_date].filter(\n regex='X_' + str(idx_pos).zfill(3)).values[0],\n # Y = B_phi\n 'B_phi': data.iloc[idx_date].filter(\n regex='Y_' + str(idx_pos).zfill(3)).values[0]\n })\n else: # assume SV has empty df instead of uncertainties\n dict_row.update(\n {'theta': pos[0],\n 'phi': pos[1],\n 'Year': date_val.year,\n 'Month': date_val.month,\n 'Time': mjd2000(date_val),\n 'r': pos[2],\n # -Z = B_r\n 'B_r': -data.iloc[idx_date].filter(\n regex='dz_' + str(idx_pos).zfill(3)).values[0],\n # -X = B_theta\n 'B_theta': -data.iloc[idx_date].filter(\n regex='dx_' + str(idx_pos).zfill(3)).values[0],\n # Y = B_phi\n 'B_phi': data.iloc[idx_date].filter(\n regex='dy_' + str(idx_pos).zfill(3)).values[0]\n })\n row_list.append(dict_row)\n data_to_write = pd.DataFrame(row_list)\n if not df_sigma.empty:\n data_to_write = pd.concat([data_to_write.reset_index(drop=True),\n df_sigma.reset_index(drop=True)], axis=1)\n\n with open(fname, 'w') as file:\n file.write(header)\n # data_to_write.to_csv(fname, na_rep='NaN',\n # index=False, header=False,\n # mode='a', sep='\\t')\n for idx, row in data_to_write.iterrows():\n if not df_sigma.empty:\n file.write((\n '%9.5f%13.5f%9d%5d%14.4f%12.2f%15.5f%15.5f%15.5f%10.4f%10.4f%10.4f%12.0f\\n')\n % (row['theta'], row['phi'], row['Year'], row['Month'],\n row['Time'], row['r'], row['B_r'], row['B_theta'],\n row['B_phi'], row['sigma_r'], row['sigma_theta'],\n row['sigma_phi'], row['N_data']))\n else:\n file.write((\n '%9.5f%13.5f%9d%5d%14.4f%12.2f%15.5f%15.5f%15.5f\\n')\n % (row['theta'], row['phi'], row['Year'], row['Month'],\n row['Time'], row['r'], row['B_r'], row['B_theta'],\n row['B_phi']))","def save(self, filename, format = \"text\"):\n #\n # preparation of data\n #\n Data = []\n Data.append(['Name','Spectrum','Select','MDV','Std'])\n for fragment in self.mdv.keys():\n for isotope_number in self.mdv[fragment].keys():\n if self.mdv[fragment][isotope_number]['use'] == 'use':\n use_or_no = 1\n else:\n use_or_no = 0\n Data.append([fragment,\n str(isotope_number),\n str(use_or_no),\n str(self.mdv[fragment][isotope_number]['ratio']),\n str(self.mdv[fragment][isotope_number]['std'])])\n try:\n with open(filename, 'w', newline='') as f:\n import csv\n if format == \"text\":\n writer = csv.writer(f, delimiter='\\t')\n writer.writerows(Data)\n if format == \"csv\":\n writer = csv.writer(f, dialect='excel')\n writer.writerows(Data)\n except:\n return False\n\n return True","def get_data_format(self):\n return {\n \"timestamp\": self.timestamp,\n \"measure\": self.measure,\n \"symbolic\": self.symbolic\n }","def write_to_csv(self):\n\n dump_list = []\n\n # add rows one by one, each as a list, even if only 1 element\n\n dump_list.append([\"test execution ID\",self.ID])\n dump_list.append([\"test execution name\",self.name])\n\n dump_list.append([\"test definition ID\",self.test_def_ID])\n test_def_name = get_indexed_item_from_file(self.test_def_ID, FILE_TEST_DEFINITIONS)\n dump_list.append([\"test definition name\",test_def_name])\n\n dump_list.append([\"associated challenge execution ID\",self.challenge_exec_ID])\n dump_list.append([\"user ID\",self.user_ID])\n\n if self.start_time != None:\n dump_list.append([\"test start time\",self.start_time.strftime(\"%Y-%m-%d %H:%M:%S\")])\n\n if self.finish_time != None:\n dump_list.append([\"test finish time\",self.finish_time.strftime(\"%Y-%m-%d %H:%M:%S\")])\n\n if self.challenge_start_time != None:\n dump_list.append([\"challenge stop time\",self.challenge_start_time.strftime(\"%Y-%m-%d %H:%M:%S\")])\n if self.restoration_detection_time != None:\n dump_list.append([\"restoration detection time\",self.restoration_detection_time.strftime(\"%Y-%m-%d %H:%M:%S\")])\n if self.recovery_time != None:\n if self.recovery_time.value != None:\n if type(self.recovery_time.value)==timedelta:\n # timedelta: days and seconds are attributes, total_seconds() is a method\n dump_list.append([\"MEASURED RECOVERY TIME (s)\",self.recovery_time.value.total_seconds()])\n rtday = self.recovery_time.value.days\n rthrs = self.recovery_time.value.seconds // 3600\n rtmin = (self.recovery_time.value.seconds % 3600) // 60\n rtsec = self.recovery_time.value.seconds % 60\n rtmil = self.recovery_time.value.microseconds\n dump_list.append([\"MEASURED RECOVERY TIME (days, hours, mins, seconds, microseconds)\",\n rtday, rthrs, rtmin, rtsec, rtmil])\n\n if self.associated_metric_values.length() > 0 :\n dump_list.append([\"Metric Values:\"])\n for item in self.associated_metric_values.get_timestamped_metric_values_as_strings():\n dump_list.append([item])\n\n if self.log.length() > 0 :\n dump_list.append([\"Log:\"])\n for item in self.log.get_timestamped_strings():\n dump_list.append([item])\n\n if self.CLI_responses.length() > 0 :\n dump_list.append([\"CLI responses:\"])\n for item in self.CLI_responses.get_timestamped_strings():\n dump_list.append([item])\n\n if self.API_responses.length() > 0 :\n dump_list.append([\"API responses:\"])\n for item in self.API_responses.get_timestamped_strings():\n dump_list.append([item])\n\n try:\n # output CSV file name: testDefExec + ID + start time + .csv\n file_name = \"testDefExec\" + \"{0:0=3d}\".format(self.test_def_ID) + \"-\" + self.start_time.strftime(\"%Y-%m-%d-%H-%M-%S\") + \".csv\"\n with open(file_name, \"w\", newline=\"\") as file:\n csv_file_writer = csv.writer(file)\n csv_file_writer.writerows(dump_list)\n except Exception as e:\n print(type(e), e)\n sys.exit()","def __str__(self):\n s = self.__repr__()\n data = s.split(',')\n header = 'PDB id| macro mol.| symmetry | all | year | experiment type'\n l = len(header)\n # test data length is correct\n title = header.split('|')\n data = data[:4] + data[7:]\n assert len(data) == len(title)\n # analyze the header string and create a template for the data\n # find size of each column\n s_pos = [i for i in range(l) if header[i] == '|']\n s_pos.insert(0,0)\n s_pos.append(l-1)\n s_lengths = [s_pos[i+1]-s_pos[i] for i in range(len(s_pos)-1)]\n d_length = [len(d) for d in data]\n # make sure data length is not longer than the title length\n s_lengths = [max(x,y) for x,y in zip(s_lengths,d_length)]\n # create the template\n s_template = ['{:^%d}'%x for x in s_lengths]\n s_template = '|'.join(s_template)\n # combine header and results to a table\n s_out = s_template.format(*title) + '\\n'\n l = len(s_out)\n s_out += '-'*l + '\\n'\n s_out += s_template.format(*data) + '\\n'\n s_out = ('{:^%d}\\n'%l).format('non-bonded overlaps') + s_out\n return s_out","def dumpData(self,out):\n #--Get sizes\n for record in self.records:\n #--Text\n if record.name == 'NAME':\n #--Truncate text?\n if len(self.text) > 511:\n self.text = self.text[:511]\n record.data = self.text\n record.size = len(self.text)\n #--Speaker\n elif record.name == 'ONAM':\n record.data = self.spId+'\\x00'\n record.size = len(self.spId) + 1\n record.getSize()\n record.dump(out)","def to_file(self, namefile=None):\n # Observational data\n if namefile is None:\n folder = ''\n file = self.name.replace(' ', '_')+'.dat'\n else:\n folder = os.path.dirname(namefile)\n file = os.path.basename(namefile)\n data = np.array([(self.time*u.s + self.tref).jd, self.time, self.flux, self.model, self.flux-self.model])\n colunm_names = ['Time JD', 'Time relative to {} UTC in seconds'.format(self.tref.iso),\n 'Observational Flux', 'Modelled Flux', 'Residual O-C']\n np.savetxt(os.path.join(folder, file), data.T, fmt='%11.8f')\n f = open(os.path.join(folder, file) + '.label', 'w')\n for i, name in enumerate(colunm_names):\n f.write('Column {}: {}\\n'.format(i+1, name))\n f.close()\n # Complete Model\n if hasattr(self, 'model_geometric'):\n data_model = np.array([(self.time_model*u.s + self.tref).jd, self.time_model, self.model_geometric,\n self.model_fresnel, self.model_star])\n colunm_names_model = ['Model time JD', 'Model time relative to {} UTC in seconds'.format(self.tref.iso),\n 'Geometric Model', 'Model with Fresnel diffraction', 'Model with star diameter']\n np.savetxt(os.path.join(folder, 'model_'+file), data_model.T, fmt='%11.8f')\n f = open(os.path.join(folder, 'model_'+file)+'.label', 'w')\n for i, name in enumerate(colunm_names_model):\n f.write('Column {}: {}\\n'.format(i+1, name))\n f.close()","def _format_data(self):\n formatted_data = []\n\n for row in self._data_agg_by_mean_value.iterrows():\n \n car_make = row[0]\n mean_car_value = round(row[1][0], 2)\n formatted_data.append({'car_make': car_make, 'mean_car_value': mean_car_value})\n\n return formatted_data","def format(self, time, key_to_fluxes, names):\n yield [self._flag_char + 'time' + self.__time_fmt.format(time)] * 3\n yield [self._flag_char + 'data'] * 3\n # for name, current in zip(names, current):\n\n for itar_1, name_i in enumerate(names):\n for jtar_1, name_j in enumerate(names):\n if not self.ok_write(itar_1, jtar_1): continue\n\n \"\"\"fluxes = [None for k in self.__decomps]\n for i, pot_type in enumerate(self.__decomps):\n fluxes[i] = key_to_fluxes[pot_type][itar_1, jtar_1]\n\n fluxes_ar = numpy.array(fluxes).transpose()\n fluxes_ar = fluxes_ar.reshape(self.__dim, -1)\n\n line = [self.__data_fmt.format(name_i, name_j, *k)\n for k in fluxes_ar]\n \"\"\"\n\n fluxes = numpy.zeros([self.__num_decomps, self.__dim])\n for i, pot_type in enumerate(self.__decomps):\n fluxes[i] = key_to_fluxes[pot_type][itar_1, jtar_1]\n\n line = [self.__data_fmt.format(name_i, name_j, *fluxes[:,i])\n for i, axis in enumerate(self.__axes)]\n yield line","def _text_write_preprocess(self):\n self.check()\n\n max_name_len = np.max([len(name) for name in self.name])\n fieldtypes = [\"U\" + str(max_name_len), \"f8\", \"f8\"]\n comp_names = self._get_lon_lat_component_names()\n frame_obj = self._get_frame_obj()\n frame_desc_str = _get_frame_desc_str(frame_obj)\n\n component_fieldnames = []\n for comp_name in comp_names:\n # This will add e.g. ra_J2000 and dec_J2000 for FK5\n component_fieldnames.append(comp_name + \"_\" + frame_desc_str)\n fieldnames = [\"source_id\"] + component_fieldnames\n stokes_names = [\"I\", \"Q\", \"U\", \"V\"]\n fieldshapes = [()] * 3\n\n if self.stokes_error is not None:\n stokes_error_names = [(f\"{k}_error\") for k in [\"I\", \"Q\", \"U\", \"V\"]]\n\n n_stokes = 0\n stokes_keep = []\n for si, total in enumerate(np.nansum(self.stokes.to(\"Jy\"), axis=(1, 2))):\n if total > 0:\n fieldnames.append(stokes_names[si])\n fieldshapes.append((self.Nfreqs,))\n fieldtypes.append(\"f8\")\n if self.stokes_error is not None:\n fieldnames.append(stokes_error_names[si])\n fieldshapes.append((self.Nfreqs,))\n fieldtypes.append(\"f8\")\n n_stokes += 1\n stokes_keep.append(total > 0)\n\n assert n_stokes >= 1, \"No components with nonzero flux.\"\n\n if self.freq_array is not None:\n if self.spectral_type == \"subband\":\n fieldnames.append(\"subband_frequency\")\n else:\n fieldnames.append(\"frequency\")\n fieldtypes.append(\"f8\")\n fieldshapes.extend([(self.Nfreqs,)])\n elif self.reference_frequency is not None:\n fieldnames.extend([(\"reference_frequency\")])\n fieldtypes.extend([\"f8\"])\n fieldshapes.extend([()] * n_stokes + [()])\n if self.spectral_index is not None:\n fieldnames.append(\"spectral_index\")\n fieldtypes.append(\"f8\")\n fieldshapes.append(())\n\n if hasattr(self, \"_rise_lst\"):\n fieldnames.append(\"rise_lst\")\n fieldtypes.append(\"f8\")\n fieldshapes.append(())\n\n if hasattr(self, \"_set_lst\"):\n fieldnames.append(\"set_lst\")\n fieldtypes.append(\"f8\")\n fieldshapes.append(())\n\n dt = np.dtype(list(zip(fieldnames, fieldtypes, fieldshapes)))\n\n arr = np.empty(self.Ncomponents, dtype=dt)\n arr[\"source_id\"] = self.name\n\n for comp_ind, comp in enumerate(comp_names):\n arr[component_fieldnames[comp_ind]] = getattr(self.skycoord, comp).deg\n\n for ii in range(4):\n if stokes_keep[ii]:\n arr[stokes_names[ii]] = self.stokes[ii].T.to(\"Jy\").value\n if self.stokes_error is not None:\n arr[stokes_error_names[ii]] = self.stokes_error[ii].T.to(\"Jy\").value\n\n if self.freq_array is not None:\n if self.spectral_type == \"subband\":\n arr[\"subband_frequency\"] = self.freq_array.to(\"Hz\").value\n else:\n arr[\"frequency\"] = self.freq_array.to(\"Hz\").value\n elif self.reference_frequency is not None:\n arr[\"reference_frequency\"] = self.reference_frequency.to(\"Hz\").value\n if self.spectral_index is not None:\n arr[\"spectral_index\"] = self.spectral_index\n\n if hasattr(self, \"_rise_lst\"):\n arr[\"rise_lst\"] = self._rise_lst\n if hasattr(self, \"_set_lst\"):\n arr[\"set_lst\"] = self._set_lst\n\n return arr","def format(self, record):\n\n\n if not hasattr(record, 'filename_'):\n record.file_indicator = '-'\n else:\n record.file_indicator = os.path.relpath(record.filename_.strip(),\n self.study_dir)\n record.line_indicator = self.format_aggregated(\n record,\n 'line_number',\n ' line %d:',\n ' lines [%s]:',\n optional=True)\n record.column_indicator = self.format_aggregated(\n record,\n 'column_number',\n ' column %d:',\n ' columns [%s]:',\n optional=True)\n record.cause_indicator = self.format_aggregated(\n record,\n 'cause',\n \"; value encountered: '%s'\",\n \"; values encountered: ['%s']\",\n join_string=\"', '\",\n optional=True)\n\n # format the string based on these fields\n formatted_result = super(LogfileStyleFormatter, self).format(record)\n\n # prepend an empty line if the filename is different than before\n current_filename = getattr(record, 'filename_', '')\n if (self.previous_filename is not None and\n current_filename != self.previous_filename):\n formatted_result = '\\n' + formatted_result\n self.previous_filename = current_filename\n\n return formatted_result","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 format(self, record):\n row = [self.formatTime(record, self.datefmt), record.name, record.levelname]\n keys = filter(self.filterer, record.__dict__)\n extra = [record.__dict__[k] for k in keys]\n\n self.writer.writerow(row + extra + [record.getMessage()])\n data = self.output.getvalue()\n self.output.truncate(0)\n self.output.seek(0)\n return data.strip()","def formatted_data(self):\n return self._formatted_data","def _write_report_content(self):\n self.worksheet.batch_update(self.report_data)\n gsfmt.format_cell_ranges(self.worksheet, self.report_formatting)","def general_simulation_data(self):\n iterations = {}\n nstates = {}\n natoms = {}\n for phase in self.phases:\n positions = self.ncfiles[phase].variables['positions']\n iterations[phase], nstates[phase], natoms[phase], spatial = positions.shape\n\n leniter = max(len('Iterations'), *[len(str(i)) for i in iterations.values()]) + 2\n lenstates = max(len('States'), *[len(str(i)) for i in nstates.values()]) + 2\n lennatoms = max(len('Num Atoms'), *[len(str(i)) for i in natoms.values()]) + 2\n lenleftcol = max(len('Phase'), *[len(phase) for phase in self.phases]) + 2\n\n lines = []\n headstring = ''\n headstring += ('{:^' + '{}'.format(lenleftcol) + '}').format('Phase') + '|'\n headstring += ('{:^' + '{}'.format(leniter) + '}').format('Iterations') + '|'\n headstring += ('{:^' + '{}'.format(lenstates) + '}').format('States') + '|'\n headstring += ('{:^' + '{}'.format(lennatoms) + '}').format('Num Atoms')\n lines.append(headstring)\n lenline = len(headstring)\n topdiv = '=' * lenline\n lines.append(topdiv)\n for phase in self.phases:\n phasestring = ''\n phasestring += ('{:^' + '{}'.format(lenleftcol) + '}').format(phase) + '|'\n phasestring += ('{:^' + '{}'.format(leniter) + '}').format(iterations[phase]) + '|'\n phasestring += ('{:^' + '{}'.format(lenstates) + '}').format(nstates[phase]) + '|'\n phasestring += ('{:^' + '{}'.format(lennatoms) + '}').format(natoms[phase])\n lines.append(phasestring)\n lines.append('-' * lenline)\n\n for line in lines:\n print(line)\n self.iterations = iterations\n self._general_run = True","def to_dat(self, **kwargs):\n return self.write(file_format=\"dat\", **kwargs)","def format(self, data):\r\n for name, value in sorted(data.items()):\r\n line = '{name} = {value}\\n'.format(\r\n name=name,\r\n value=value,\r\n )\r\n yield line","def gen_sample_report():\n sample_report().save()","def write(self): \n # Open csv file\n with open(self.file_name, 'w', newline='') as file:\n self._writer = csv.writer(file)\n \n # Write header rows\n# self.write_sim_header_data(self.trace.sim.get_data())\n \n # Write trace table\n self._writer.writerow(['Record #', 'Rep', 'Time',\n 'Priority', 'Record Type', 'Name'])\n for trace_record in self.trace._record_list:\n self._writer.writerow(trace_record.get_row())\n file.close()","def run(self):\r\n #print 'WriteFITS.run'\r\n\r\n # construct the name of the file\r\n runtime = self.previous_results['runtime']\r\n fitsname = '%s.fits' % runtime\r\n\r\n # get list of instrument observations\r\n observe = self.previous_results['observe']\r\n obs_timeline = observe['observed_timeline']\r\n observed_times = obs_timeline.keys()\r\n observed_times.sort()\r\n\r\n # construct lists of the values to be stored in each Table column\r\n for t in observed_times:\r\n timelist = []\r\n smec_position = []\r\n smec_nominal_position = []\r\n flag = []\r\n data = []\r\n pointing1_x = []\r\n pointing1_y = []\r\n pointing2_x = []\r\n pointing2_y = []\r\n\r\n config = obs_timeline[t]\r\n\r\n timelist.append(config.time)\r\n smec_position.append(config.smec_position)\r\n smec_nominal_position.append(config.smec_nominal_position)\r\n flag.append(config.flag)\r\n data.append(config.data)\r\n pointing1_x.append(config.pointing1_x)\r\n pointing1_y.append(config.pointing1_y)\r\n pointing2_x.append(config.pointing2_x)\r\n pointing2_y.append(config.pointing2_y)\r\n\r\n # create a Header object and primary HDU - this just contains\r\n # some very basic, general information\r\n prihdr = pyfits.Header()\r\n prihdr['COMMENT'] = 'This FITS file was created by pyfiins at %s' % \\\r\n runtime\r\n prihdu = pyfits.PrimaryHDU(header=prihdr)\r\n\r\n # create list of Header Data Unit objects, include the primary HDU\r\n hdulist = pyfits.HDUList([prihdu])\r\n\r\n # create an HDU to contain the Table and append it to the list\r\n hdulist.append(pyfits.BinTableHDU.from_columns(\r\n pyfits.ColDefs([\r\n pyfits.Column(name='Time', format='D',\r\n array=np.array(timelist)),\r\n pyfits.Column(name='SMEC Position', format='E',\r\n array=np.array(smec_position)),\r\n pyfits.Column(name='SMEC Nominal Position', format='E',\r\n array=np.array(smec_nominal_position)),\r\n pyfits.Column(name='Flag', format='L',\r\n array=np.array(flag)),\r\n pyfits.Column(name='Data', format='E',\r\n array=np.array(data)),\r\n pyfits.Column(name='Pointing1 X', format='E',\r\n array=np.array(pointing1_x)),\r\n pyfits.Column(name='Pointing1 Y', format='E',\r\n array=np.array(pointing1_y)),\r\n pyfits.Column(name='Pointing2 X', format='E',\r\n array=np.array(pointing2_x)),\r\n pyfits.Column(name='Pointing2 Y', format='E',\r\n array=np.array(pointing2_y))])))\r\n\r\n # write the HDU list to a file\r\n hdulist.writeto(fitsname, clobber=True)\r\n self.result['fitsfile'] = fitsname\r\n\r\n return self.result","def output_data(self):\n if not self.is_record:\n logging.error('Env: no record to output!')\n else:\n control_data = pd.DataFrame(self.control_data)\n control_data.to_csv(self.output_path + ('%s_%s_control.csv' % (self.name, self.agent)))","def update_record(self, d: dict) -> None:\n super().update_record(d)\n d.update(\n dataset_doses=str_list(self.doses),\n dataset_ns=str_list(self.ns),\n dataset_stdevs=str_list(self.stdevs),\n dataset_means=str_list(self.means),\n )","def _format_sample_sheet_data(sample_ids, i7_name, i7_seq, i5_name, i5_seq,\n wells=None, sample_plate='', sample_proj='',\n description=None, lanes=[1], sep=','):\n if (len(sample_ids) != len(i7_name) != len(i7_seq) !=\n len(i5_name) != len(i5_seq)):\n raise ValueError('Sample information lengths are not all equal')\n\n if wells is None:\n wells = [''] * len(sample_ids)\n if description is None:\n description = [''] * len(sample_ids)\n\n data = [sep.join([\n 'Lane', 'Sample_ID', 'Sample_Name', 'Sample_Plate', 'Sample_Well',\n 'I7_Index_ID', 'index', 'I5_Index_ID', 'index2', 'Sample_Project',\n 'Description'])]\n\n for lane in lanes:\n for i, sample in enumerate(sample_ids):\n line = sep.join([str(lane), sample, sample, sample_plate,\n wells[i], i7_name[i], i7_seq[i], i5_name[i],\n i5_seq[i], sample_proj, description[i]])\n data.append(line)\n\n return '\\n'.join(data)","def _phot_format_for_save(self, names = ('MJD', 'flux', 'flux_err', 'filter'), formats = ('.3f','.5g', '.5g', ''),\n filters = False, verbose = False, sort=False):\n\n\n if sort:\n save_table = self.phot[names]\n save_table = save_table[save_table.argsort()]\n else:\n save_table = self.phot[names]\n\n for z in zip(names, formats):\n save_table[z[0]].format = z[1]\n\n if filters:\n save_table = save_table[np.in1d(save_table[\"filter\"], filters)]\n\n if verbose:\n print(save_table)\n return save_table","def recorder(self):\n if not self.record_mode:\n return\n self.record_value['X1'].append(self.X1) # 每一代记录一个X1，shape(particles_num,dim)\n self.record_value['V1'].append(self.V1) # 每一代记录一个V1，shape(particles_num,dim)\n self.record_value['Y1'].append(self.Y1) # 每一代记录一个Y1，shape(particles_num,dim)\n self.record_value['X2'].append(self.X2) # 每一代记录一个X2，shape(particles_num,dim)\n self.record_value['V2'].append(self.V2) # 每一代记录一个V2，shape(particles_num,dim)\n self.record_value['Y2'].append(self.Y2) # 每一代记录一个Y2，shape(particles_num,dim)\n self.record_value['w'].append(self.w) # 每一代记录一个w，shape(particles_num,dim)\n self.record_value['pop01'].append(self.pop01) # 每一代记录一个种群规模，shape(max_iter,1)\n self.record_value['pop02'].append(self.pop02) # 每一代记录一个种群规模，shape(max_iter,1)\n self.record_value['gbest_GPSO'].append(self.gbest_y1) # 每一代记录一个子种群最优，shape(max_iter,1)\n self.record_value['gbest_LPSO'].append(self.gbest_y2) # 每一代记录一个种群最优，shape(max_iter,1)\n self.record_value['gbest_All'].append(self.gbestY) # 每一代记录一个Y，shape(max_iter,1)","def print_data(self):\n total_score = 0.0\n\n title_game = 'Game'\n title_word = 'Word'\n title_word_status = 'Word Status'\n title_bad_guesses = 'Bad Guesses'\n title_missed_letters = 'Missed Letters'\n title_total_score = 'Total score'\n\n if not record_word:\n print(\"No words played.\")\n else:\n print('%-5s %-10s %-12s %-5s %-5s %s' %(title_game,title_word, title_word_status, title_bad_guesses, title_missed_letters,title_total_score))\n print('---- ---- ------------ ----------- -------------- -----------')\n for x in range(len(record_word)):\n print('%-5s %-10s %-13s %-11s %-13s %.2f'%(record_game[x],record_word[x],record_word_status[x],record_bad_guesses[x],record_missed_letters[x],record_total_score[x]))\n\n for x in range(len(record_total_score)):\n total_score = total_score + record_total_score[x]\n\n print('\\nFinal Score: %.2f' %total_score)","def create_sim_data_file(outdata_dir, sim_file):\n # Open output file, write header\n outfile = open(sim_file, 'w')\n #outfile.write(\"realization, Magnitude, Mechanism,\"\n # \"Distance, Station Name,\")\n outfile.write(\"%10.5f\" % (PERIODS[0]))\n for period in PERIODS[1:]:\n outfile.write(\",%10.5f\" % period)\n outfile.write(\"\\n\")\n\n realizations = sorted(os.listdir(outdata_dir))\n for realization in realizations:\n basedir = os.path.join(outdata_dir, realization)\n stations = sorted(glob.glob(\"%s%s*.rd50\" % (basedir, os.sep)))\n for station in stations:\n #st_id = os.path.basename(station)\n # Find just the numeric part of the station id\n #st_id = st_id[st_id.find('-')+1:st_id.find(\".rd50\")]\n input_file = open(station, 'r')\n # Write first part of line\n #outfile.write(\"%s, %2.1f, %s, %d, %s, \" %\n # (realization, MAGNITUDE, MECH, DIST, st_id))\n add_comma = 0\n for line in input_file:\n line = line.strip()\n # Skip comments\n if line.startswith(\"#\"):\n continue\n pieces = [float(item) for item in line.split()]\n # Write RotD50 value to file\n if add_comma:\n outfile.write(\",%10.7f\" % (pieces[3]))\n else:\n add_comma = 1\n outfile.write(\"%10.7f\" % (pieces[3]))\n # Done with this file\n input_file.close()\n # Add newline to outfile\n outfile.write(\"\\n\")\n # All done!\n outfile.close()","def simulation_finished(self, data):\n self._logger.info(u\"simulation finished\")\n self.statusLabel.setText(u\"simulation finished.\")\n\n self.actSimulate.setDisabled(False)\n self.shortRunSimulation.setEnabled(True)\n self.shortRunRegimeBatch.setEnabled(True)\n self.actPlayPause.setDisabled(False)\n self.shortPlayPause.setEnabled(True)\n self.shortSaveResult.setEnabled(True)\n self.actStop.setDisabled(False)\n self.actSave.setDisabled(False)\n self.speedDial.setDisabled(False)\n self.timeSlider.setDisabled(False)\n\n self.sim.simulationProgressChanged.disconnect(self.guiProgress.setValue)\n self.statusBar().removeWidget(self.guiProgress)\n\n self.timeSlider.triggerAction(QtGui.QAbstractSlider.SliderToMinimum)\n\n self.currentDataset = data\n self._read_results()\n self._update_data_list()\n self._update_plots()\n\n self.stop_animation()\n # self.playAnimation()\n\n if self.runningBatch:\n regime_name = self._regimes[self._current_regime_index][\"Name\"]\n self._save_data(regime_name)\n self.regimeFinished.emit()\n else:\n self.actExecuteRegimes.setDisabled(False)","def _write_csv(self, file_name, metadata, dates, data, disclaimer,\n float_fmt):\n\n version = '# file_format: pysonde csv format version 1.0\\n'\n header = [version]\n #prepend parameter list and units with single #\n param_header = '# datetime, '\n unit_header = '# yyyy/mm/dd HH:MM:SS, '\n dtype_fmts = ['|S19']\n fmt = '%s, '\n for param in np.sort(data.keys()):\n param_header += param + ', '\n try:\n unit_header += data[param].dimensionality.keys()[0].symbol + \\\n ', '\n except:\n unit_header += 'nd, '\n fill_value = float(metadata['fill_value']) * data[param].units\n data[param][np.isnan(data[param])] = fill_value\n dtype_fmts.append('f8')\n fmt += float_fmt + ', '\n\n #prepend disclaimer and metadata with ##\n for line in disclaimer.splitlines():\n header.append('# disclaimer: ' + line + '\\n')\n\n #for key,val in metadata.items():\n # if not isinstance(val, np.ndarray):\n # header.append('# ' + str(key) + ': ' + str(val) + '\\n')\n # else:\n # param_header += key + ', '\n # unit_header += 'n/a, '\n # dtype_fmts.append(val.dtype)\n # fmt += '%s, '\n for key in np.sort(metadata.keys()):\n if not isinstance(metadata[key], np.ndarray):\n header.append('# %s: %s\\n' % (str(key), str(metadata[key])))\n\n else:\n param_header += key + ', '\n unit_header += 'n/a, '\n dtype_fmts.append(metadata[key].dtype)\n fmt += '%s, '\n\n #remove trailing commas\n param_header = param_header[:-2] + '\\n'\n unit_header = unit_header[:-2] + '\\n'\n fmt = fmt[:-2]\n\n header.append('# timezone: ' + str(self.default_tzinfo) + '\\n')\n header.append(param_header)\n header.append(unit_header)\n\n dtype = np.dtype({\n 'names': param_header.replace(' ', '').strip('#\\n').split(','),\n 'formats': dtype_fmts})\n\n write_data = np.zeros(dates.size, dtype=dtype)\n write_data['datetime'] = np.array(\n [datetime.datetime.strftime(dt, '%Y/%m/%d %H:%M:%S')\n for dt in dates])\n\n for key, val in metadata.items():\n if isinstance(val, np.ndarray):\n write_data[key] = val\n\n for param in data.keys():\n write_data[param] = data[param]\n\n #start writing file\n fid = open(file_name, 'w')\n fid.writelines(header)\n np.savetxt(fid, write_data, fmt=fmt)\n fid.close()","def generatePandasRecordLayout(resource,workspace,\n file=sys.stdout,\n format=\"fixed\",\n instance=\"\",\n useClassificationBank=False,\n importPd=False\n):\n\n # Init\n bank = resource.getBank()\n id = resource.getId()\n print(\"Pandas: Generating Record \"+id)\n\n # Collect start/end and variable names\n fwf_colspecs=[]\n fwf_widths=[]\n fwf_names=[]\n fwf_converters=[]\n\n nextStart=1 # use for running column width in case start/end are not provided\n index=0\n for layout in workspace.getResources(type=\"layout\",bank=id):\n if format==\"fixed\":\n # FIXED FORMAT\n # Gather basic properties\n start=layout.getPropertyValue(\"start\")\n if start: start = int(start)\n end=layout.getPropertyValue(\"end\")\n if end: end = int(end)\n width=layout.getPropertyValue(\"width\")\n if width: width=int(width)\n \n # compute missing width/start/end properties\n if not start: start = nextStart\n if start and (not end) and width: end = start+width-1\n if not start or not end:\n layout.dump()\n raise RuntimeError(\"width/start/end not found for \"+layout.source+\" \"+layout.getName())\n if not width: width = start-end+1\n \n # record value\n fwf_colspecs.append((start-1,end)) # for pandas, position is zero based and end is exclusive\n fwf_widths.append(width)\n\n # keep track of where we are\n nextStart = start + width\n\n # data type\n converter = \"str\"\n datatype=None\n if layout.hasProperty(\"datatype\"):\n datatype = layout.getPropertyValue(\"datatype\")\n elif layout.hasProperty(\"datatype[generic]\"):\n datatype = layout.getPropertyValue(\"datatype[generic]\")\n if datatype and re.match(\"^N.*\",datatype,re.IGNORECASE):\n if layout.hasProperty(\"decimals\") and layout.getPropertyValue(\"decimals\")==0:\n converter=\"int\"\n else:\n converter=\"float\"\n fwf_converters.append(converter)\n\n else:\n # DELIMITED\n pass\n index += 1\n # label\n fwf_names.append(layout.getName())\n ### for\n\n print(\"import pandas as pd\",file=file)\n print(file=file)\n print(\"data=pd.read_fwf('\"+instance+\"'\",sep='',file=file)\n print(\",colspecs=\",fwf_colspecs,sep='',file=file)\n #print(\",widths=\",fwf_widths,sep='',file=file)\n print(\",names=\",fwf_names,sep='',file=file)\n converters=\"{\"\n for index,value in enumerate(fwf_converters):\n if index>0: converters+=\",\"\n converters+=str(index)+\":\"+value\n converters+=\"}\"\n print(\",converters=\",converters,sep='',file=file)\n print(\")\",file=file)\n print(file=file)\n print(\"print(data.head())\",file=file)\n print(\"print(data.describe())\",file=file)","def __mdformat(self, dat):\n\t\tif type(dat) is types.FloatType:\n\t\t\treturn round(dat, 3)\n\t\telse:\n\t\t\treturn dat","def save_data(self):\n\n #\n # t=self.t[0:-1:self.R].reshape([self.t[0:-1:self.R].shape[0],1])\n\n def deterministic_data():\n t = self.dt * self.tau\n Ueem1 = self.Xeem[:, 0]\n Ueem2 = self.Xeem[:, 1]\n Ueem3 = self.Xeem[:, 2]\n Uem1 = self.Xem[:, 0]\n Uem2 = self.Xem[:, 1]\n Uem3 = self.Xem[:, 2]\n Ustk1 = self.Xstkm[:, 0]\n Ustk2 = self.Xstkm[:, 1]\n Ustk3 = self.Xstkm[:, 2]\n tagPar = np.array([\n 'k = ',\n 'r = ',\n 'T0 = ',\n 'N = ',\n 'R = ',\n 'T = ',\n 'dt = ',\n 'Dt = ',\n 'L = ',\n 'gamma = ',\n 'eta = ',\n 'lambda =',\n 'delta = ',\n 'beta = ',\n 'a = ',\n 'N0 = ',\n 'u = ',\n 'sigma1 = ',\n 'sigma2 = ',\n 'x01 = ',\n 'x02 = ',\n 'x03 = ',\n ])\n ParValues = np.array([\n self.k,\n self.r,\n self.T0,\n self.N,\n self.R,\n self.T,\n self.dt,\n self.Dt,\n self.L,\n self.gamma,\n self.eta,\n self.Lambda,\n self.delta,\n self.beta,\n self.a,\n self.NN,\n self.u,\n self.sigma1,\n self.sigma2,\n self.Xzero[0, 0],\n self.Xzero[0, 1],\n self.Xzero[0, 2]\n ])\n strPrefix = str(self.Dt)\n name1 = 'DetParameters' + strPrefix + '.txt'\n name2 = 'DetSolution' + strPrefix + '.txt'\n name3 = 'DetRefSolution' + str(self.dt) + '.txt'\n\n PARAMETERS = np.column_stack((tagPar, ParValues))\n np.savetxt(name1, PARAMETERS, delimiter=\" \", fmt=\"%s\")\n np.savetxt(name2,\n np.transpose(\n (\n t, Uem1, Uem2, Uem3, Ustk1, Ustk2, Ustk3,\n )\n ), fmt='%1.8f', delimiter='\\t')\n np.savetxt(name3,\n np.transpose(\n (\n self.t, Ueem1, Ueem2, Ueem3,\n )\n ), fmt='%1.8f', delimiter='\\t')\n\n def stochastic_data():\n \"\"\"\n t = self.dt * self.tau\n Ueem1 = self.Xeem[:, 0]\n Ueem2 = self.Xeem[:, 1]\n Ueem3 = self.Xeem[:, 2]\n Uem1 = self.Xem[:, 0]\n Uem2 = self.Xem[:, 1]\n Uem3 = self.Xem[:, 2]\n Ustk1 = self.Xstkm[:, 0]\n Ustk2 = self.Xstkm[:, 1]\n Ustk3 = self.Xstkm[:, 2]\n Utem1 = self.Xtem[:, 0]\n Utem2 = self.Xtem[:, 1]\n Utem3 = self.Xtem[:, 2]\n \"\"\"\n\n tagPar = np.array([\n 'k = ',\n 'r = ',\n 'T0 = ',\n 'N = ',\n 'R = ',\n 'T = ',\n 'dt = ',\n 'Dt = ',\n 'L = ',\n 'gamma = ',\n 'eta = ',\n 'lambda =',\n 'delta = ',\n 'beta = ',\n 'a = ',\n 'N0 = ',\n 'u = ',\n 'sigma1 = ',\n 'sigma2 = ',\n 'x01 = ',\n 'x02 = ',\n 'x03 = ',\n ])\n ParValues = np.array([\n self.k,\n self.r,\n self.T0,\n self.N,\n self.R,\n self.T,\n self.dt,\n self.Dt,\n self.L,\n self.gamma,\n self.eta,\n self.Lambda,\n self.delta,\n self.beta,\n self.a,\n self.NN,\n self.u,\n self.sigma1,\n self.sigma2,\n self.Xzero[0, 0],\n self.Xzero[0, 1],\n self.Xzero[0, 2]\n ])\n strPrefix = str(self.Dt)\n name1 = 'StoParameters' + strPrefix + '.txt'\n '''\n name2 = 'StoSolution' + strPrefix + '.txt'\n name3 = 'StoRefSolution' + str(self.dt) + '.txt'\n '''\n PARAMETERS = np.column_stack((tagPar, ParValues))\n np.savetxt(name1, PARAMETERS, delimiter=\" \", fmt=\"%s\")\n '''\n np.save(name2,\n np.transpose(\n (\n t, Uem1, Uem2, Uem3, Ustk1, Ustk2, Ustk3, Utem1,\n Utem2, Utem3\n )\n ))\n np.savetxt(name3,\n np.transpose(\n (\n self.t, Ueem1, Ueem2, Ueem3\n )\n ))\n if self.sigma1 == 0.0:\n if self.sigma2 == 0.0:\n DeterministicData()\n return\n StochasticData()\n '''\n\n return","def recordData( numNeurons, Soma ):\n\tvec = {}\n\t#Record appropriate number of potentials.\n\tfor var in range(0,numNeurons):\n \t\tvec[str(var + 1)] = h.Vector()\n \t\tvec[str(var + 1)].record(Soma[var](0.5)._ref_v)\n\t#Record time.\n\tvec['t '] = h.Vector()\n\tvec ['t '].record(h._ref_t)\n\treturn vec","def __str__(self):\n if self.filename:\n filename = self.filename\n else:\n filename = 'Unknown'\n if self.endian == '<':\n endian = 'Little Endian'\n else:\n endian = 'Big Endian'\n ret_val = ('FILE: %s\\nRecord Offset: %i byte\\n' +\n 'Header Endianness: %s\\n\\n') % \\\n (filename, self.record_offset, endian)\n ret_val += 'FIXED SECTION OF DATA HEADER\\n'\n for key in self.fixed_header.keys():\n ret_val += '\\t%s: %s\\n' % (key, self.fixed_header[key])\n ret_val += '\\nBLOCKETTES\\n'\n for key in self.blockettes.keys():\n ret_val += '\\t%i:' % key\n if not len(self.blockettes[key]):\n ret_val += '\\tNOT YET IMPLEMENTED\\n'\n for _i, blkt_key in enumerate(self.blockettes[key].keys()):\n if _i == 0:\n tabs = '\\t'\n else:\n tabs = '\\t\\t'\n ret_val += '%s%s: %s\\n' % (tabs, blkt_key,\n self.blockettes[key][blkt_key])\n ret_val += '\\nCALCULATED VALUES\\n'\n ret_val += '\\tCorrected Starttime: %s\\n' % self.corrected_starttime\n return ret_val","def record(self):\n # TODO: record the data","def tabulate(self) -> str:\n items = [\n ('Number of stations', self._num_stations),\n ('Loss probability', self.drop_prob),\n ]\n\n for node in range(self._num_stations):\n items.append((f'[[ STATION #{node} ]]', ''))\n\n ssize = self.system_size[node]\n qsize = self.queue_size[node]\n busy = self.busy[node]\n\n ssize_pmf = [ssize.pmf(x) for x in range(ssize.truncated_at + 1)]\n qsize_pmf = [qsize.pmf(x) for x in range(qsize.truncated_at + 1)]\n busy_pmf = [busy.pmf(x) for x in range(busy.truncated_at + 1)]\n\n items.extend([\n ('System size PMF', str_array(ssize_pmf)),\n ('System size average', ssize.mean),\n ('System size std.dev.', ssize.std),\n ('Queue size PMF', str_array(qsize_pmf)),\n ('Queue size average', qsize.mean),\n ('Queue size std.dev.', qsize.std),\n ('Busy PMF', str_array(busy_pmf)),\n ('Utilization', self.get_utilization(node)),\n ('Drop probability', self.drop_prob[node]),\n ('Delivery probability', self.delivery_prob[node]),\n ('Departures, average', self.departures[node].avg),\n ('Departures, std.dev.', self.departures[node].std),\n ('Response time, average', self.response_time[node].avg),\n ('Response time, std.dev.', self.response_time[node].std),\n ('Wait time, average', self.wait_time[node].avg),\n ('Wait time, std.dev.', self.wait_time[node].std),\n ('End-to-end delays, average', self.delivery_delays[node].avg),\n ('End-to-end delays, std.dev.', self.delivery_delays[node].std),\n ])\n return tabulate(items, headers=('Param', 'Value'))","def transmission_summarise():\n\n study_description = self.processDicomField(dcm_info, \"StudyDescription\")\n study_date = self.processDicomField(dcm_info, \"StudyDate\")\n series_description = self.processDicomField(dcm_info, \"SeriesDescription\")\n patient_id = self.processDicomField(dcm_info, \"PatientID\")\n patient_name = self.processDicomField(dcm_info, \"PatientName\")\n protocol_name = self.processDicomField(dcm_info, \"ProtocolName\")\n d_fileInfo = filesInSeries_determine()\n self.dp.qprint('Summary report:')\n self.dp.qprint('PatientID: %s' % patient_id, level = -1)\n self.dp.qprint('PatientName: %s' % patient_name, level = -1)\n self.dp.qprint('StudyDate: %s' % study_date, level = -1)\n self.dp.qprint('StudyDescription: %s' % study_description, level = -1)\n self.dp.qprint('SeriesDescription: %s' % series_description, level = -1)\n self.dp.qprint('ProtocolName: %s' % protocol_name, level = -1)\n if d_fileInfo['status']:\n self.dp.qprint('Number of files in Series: %d' % d_fileInfo['fileCount'], level = -1)\n self.dp.qprint('Directory size (raw): %d' % d_fileInfo['dirSizeRaw'], level = -1)\n self.dp.qprint('Directory size (human): %s' % d_fileInfo['str_dirSize'], level = -1)","def save_data(self) -> None:\n # Construct a grid in physical space\n rvals = np.logspace(start=-3,\n stop=2.5,\n num=21,\n endpoint=True)\n # Compute C, D, K1 and F on that grid\n Cvals = np.array([self.compute_C(r, Suppression.RAW) for r in rvals])\n Dvals = np.array([self.compute_D(r, Suppression.RAW) for r in rvals])\n K1vals = np.array([self.compute_K1(r, Suppression.RAW) for r in rvals])\n Fvals = np.array([self.compute_F(r, Suppression.RAW) for r in rvals])\n # Save them to file\n df = pd.DataFrame([rvals, Cvals[:, 0], Dvals[:, 0], K1vals[:, 0], Fvals[:, 0],\n Cvals[:, 1], Dvals[:, 1], K1vals[:, 1], Fvals[:, 1]]).transpose()\n df.columns = ['r', 'C(r)', 'D(r)', 'K1(r)', 'F(r)', 'dC(r)', 'dD(r)', 'dK1(r)', 'dF(r)']\n df.to_csv(self.file_path(self.filename + '.csv'), index=False)","def _format(self):\n output = f\"\\n{color('>>> DUMP')} from {self.filename}: {color(f'L{self.line}')} in {color(f'{self.method}()')}\"\n\n for name, obj in self.objects.items():\n output += f\"\\n\\n{color(f' - {name}:')}\\n\"\n output += f\" {pformat(obj, width=110, indent=4)}\"\n\n output += color(\"\\n\\n<<< END\")\n return output","def format_report_header(self):","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 dumpData(self,out):\n #--Get sizes and dump into dataIO\n out.packSub0('NAME',self.cellName)\n #--Hack: Insert data record if necessary\n for record in self.records:\n if record.name == 'DATA': break\n else:\n self.records.insert(0,SubRecord('DATA',0))\n #--Top Records\n for record in self.records:\n if record.name == 'DATA':\n record.setData(struct.pack('3i',self.flags,self.gridX,self.gridY))\n record.getSize()\n record.dump(out)\n #--Objects\n inTempObjects = False\n for object in self.getObjects().list():\n #--Begin temp objects?\n if not inTempObjects and (object in self.tempObjects):\n out.packSub('NAM0','i',len(self.tempObjects))\n inTempObjects = True\n (iMod,iObj,objId,objRecords) = object\n for record in objRecords:\n #--FRMR/NAME placeholder?\n if isinstance(record,Cell_Frmr):\n out.pack('4si','FRMR',4)\n out.write(struct.pack('i',iObj)[:3])\n out.pack('B',iMod)\n out.packSub0('NAME',objId)\n else:\n record.getSize()\n record.dump(out)\n #--End Records\n for endRecord in self.endRecords:\n endRecord.getSize()\n endRecord.dump(out)","def __str__(self):\n cols = {'theta': self.theta, 'std': self.stde, 'tstat': self.tstat}\n res = pd.DataFrame(cols, index=self.names)[['theta', 'std', 'tstat']]\n res_str = res.to_string(float_format=lambda x: '%.4f' % x)\n width = len(res_str) // (res.shape[0] + 1)\n show = '-' * 60\n show += '\\nGMM final results:\\n'\n show += width * '-' + '\\n'\n show += res_str\n show += '\\n' + width * '-'\n show += '\\nJ-stat = %0.2f' % self.jstat\n show += '\\ndf = ' + str(self.degf)\n show += '\\np-value = %0.2f' % self.jpval\n show += '\\n' + '-' * 60\n return show","def generate(self):\n\n # create temporary folder for copying image files\n try:\n os.mkdir(ReportGenerator.TEMP_FOLDER)\n except FileExistsError:\n shutil.rmtree(ReportGenerator.TEMP_FOLDER)\n os.mkdir(ReportGenerator.TEMP_FOLDER)\n\n # find all simulations to be included in report\n group_directory = self.config['group_directory']\n sim_directory = self.config['sim_directory']\n assert (group_directory or sim_directory) and not(group_directory and sim_directory), \\\n \"A group directory or a simulation directory must be specified, but not both\"\n\n if group_directory:\n assert os.path.exists(f'results/{group_directory}'), \"ERROR: Group directory does not exist\"\n\n sim_list = os.listdir(f'results/{group_directory}')\n sim_label = group_directory.split('-')[0]\n sim_list = [f'{group_directory}/{x}' for x in sim_list if sim_label in x]\n\n # filters\n if self.config['filters']:\n \n seed_filters = self.config['filters']['seed']\n dilution_filters = self.config['filters']['dilution']\n order_filters = self.config['filters']['order']\n\n for symbol, filters in zip(['S', 'D', 'O'], [seed_filters, dilution_filters, order_filters]):\n if type(filters) == int:\n sim_list = [x for x in sim_list if f'{symbol}{filters}' in x.split('-')[2]]\n elif type(filters) == list:\n sim_list = [x for x in sim_list if any([f\"{symbol}{f}\" in x.split('-')[-2] for f in filters])]\n\n if len(sim_list) == 0:\n raise Exception('Simulation filters resulted in no satisfactory simulations')\n\n else:\n assert os.path.exists(f'results/{sim_directory}'), \"ERROR: Simulation directory does not exist\"\n sim_list = [sim_directory]\n\n # REPORT HEADER\n self.add_heading(level=1, text=self.config['title'])\n now = datetime.now()\n self.add_text(text=f'Report generated on {now.strftime(\"%B %d, %Y\")} at {now.strftime(\"%H:%M:%S\")}')\n\n # REPORT CONTENTS\n for i, sim in enumerate(sim_list):\n for content in self.config['content']:\n content_type = list(content.keys())[0]\n params = content[content_type]\n\n self.add_content(content_type, params, sim)\n\n if i+1 < len(sim_list):\n self.add_content(content_type='break')\n\n # CREATE HTML TEMPLATE\n base_template = self.templateEnv.get_template(f'{ReportGenerator.COMPONENTS_FOLDER}/base.html')\n base_output = base_template.render(content='\\n\\n'.join(self.contents))\n with open(f'{ReportGenerator.TEMP_FOLDER}/template.html', 'w') as html_file:\n html_file.write(base_output)\n\n # CREATE PDF FROM TEMPLATE\n if group_directory:\n HTML(f'{ReportGenerator.TEMP_FOLDER}/template.html').write_pdf(\n f'results/{group_directory}/{self.config[\"title\"]}.pdf', stylesheets=['reports/style.css'])\n else:\n HTML(f'{ReportGenerator.TEMP_FOLDER}/template.html').write_pdf(\n f'results/{sim_directory}/{self.config[\"title\"]}.pdf', stylesheets=['reports/style.css'])\n\n # delete temp folder for storing image files\n shutil.rmtree(f'{ReportGenerator.TEMP_FOLDER}')\n return None","def data_format(self):\n # Run without arguments\n if len(sys.argv) == 1:\n sys.exit(usage(None))\n # Test input file argument\n if self.input and not os.path.isfile(self.input):\n print(self.input)\n print(os.path.isfile(self.input))","def printObj(self):\n return 'patient_id:{}, medication:{}, frequency:{}, start_dt:{},'\n 'end_dt:{}, noti_type:{}'.format(\n self.patients.data,\n self.medication.data,\n self.frequency.data,\n self.start_dt,\n self.end_dt.data,\n self.noti_type.data)","def update_record(self, d: dict) -> None:\n super().update_record(d)\n d.update(\n dataset_individual_doses=str_list(self.individual_doses),\n dataset_responses=str_list(self.responses),\n dataset_doses=str_list(self.doses),\n dataset_ns=str_list(self.ns),\n dataset_stdevs=str_list(self.stdevs),\n dataset_means=str_list(self.means),\n )","def generate_visualization_dict(self):\n self._data = {}\n self._data['name'] = self.name\n self._data['type'] = self.__repr__()\n self._data['color'] = self._color_rgb\n\n try:\n self._data['simulation_matrix'] = \\\n self._visualization_matrix.tolist()\n\n except:\n #Not sure which error to call here.\n raise RuntimeError('''Please call the numerical\n transformation methods,\n before generating simulation dict ''')\n\n\n return self._data","def format(self, table):\n #return table.data.to_json()\n m = table.as_array()\n rank = len(m.shape)\n is_table = len(table.headers)<=5 or (len(table.headers)>5 and (table.headers[0] != '0' or table.headers[1] != '1' or table.headers[2] != '2' ))\n\n if rank<3 and is_table:\n v = []\n for i in range(len(table.headers)):\n vv = {\n 'offset': table.offset,\n 'header': table.headers[i],\n 'type': table.types[i],\n 'data': _replace_nans(m[:,i].tolist()) if rank>1 else _replace_nans(m.tolist()),\n }\n if table.sizes is not None:\n vv[\"size\"] = table.sizes[0]\n v.append(vv)\n else:\n # if hasattr(data, \"strip\") or \\\n # (not hasattr(data, \"__getitem__\") and \\\n # not hasattr(data, \"__iter__\")):\n # # data is not a list/tuple => wrap it\n # data = [ data ]\n v = {\n 'offset': table.offset,\n #'headers': table.headers,\n 'type': table.types[0],\n 'data': _replace_nans(m.tolist()),\n }\n if table.sizes is not None:\n v[\"size\"] = table.sizes\n\n return json.dumps(v, cls=ExtEncoder)","def write_report(self):\n\n def report_array(f, label, array):\n f.write(label)\n for val in array:\n f.write('{:.4f},\\t'.format(val))\n f.write('\\n')\n\n report_file = FLAGS.report_file\n\n with open(report_file, 'w') as f:\n f.write('Mean Error 2D: {}\\n'.format(\n safe_divide(self._error_2d, self._matched)))\n f.write('Mean 3D IoU: {}\\n'.format(\n safe_divide(self._iou_3d, self._matched)))\n f.write('Mean Azimuth Error: {}\\n'.format(\n safe_divide(self._azimuth_error, self._matched)))\n f.write('Mean Polar Error: {}\\n'.format(\n safe_divide(self._polar_error, self._matched)))\n\n f.write('\\n')\n f.write('IoU Thresholds: ')\n for threshold in self._iou_thresholds:\n f.write('{:.4f},\\t'.format(threshold))\n f.write('\\n')\n report_array(f, 'AP @3D IoU : ', self._iou_ap.aps)\n\n f.write('\\n')\n f.write('2D Thresholds : ')\n for threshold in self._pixel_thresholds:\n f.write('{:.4f},\\t'.format(threshold * 0.1))\n f.write('\\n')\n report_array(f, 'AP @2D Pixel : ', self._pixel_ap.aps)\n f.write('\\n')\n\n f.write('Azimuth Thresh: ')\n for threshold in self._azimuth_thresholds:\n f.write('{:.4f},\\t'.format(threshold * 0.1))\n f.write('\\n')\n report_array(f, 'AP @Azimuth : ', self._azimuth_ap.aps)\n f.write('\\n')\n\n f.write('Polar Thresh : ')\n for threshold in self._polar_thresholds:\n f.write('{:.4f},\\t'.format(threshold * 0.1))\n f.write('\\n')\n report_array(f, 'AP @Polar : ', self._polar_ap.aps)","def print(self):\n df = self.gen_test()\n # print(df)\n df.to_csv('some_dated_file.csv', index=False)\n return df","def test_format_data(self, meteo):\n meteo.config.climate.meteo.station_id = '889'\n meteo.data['air_temperature'] = [\n (datetime.datetime(2011, 9, 25, i, 0, 0), 215.0)\n for i in range(24)]\n line = next(meteo.format_data('air_temperature'))\n assert line == '889 2011 09 25 42' + ' 215.00' * 24 + '\\n'","def simulate_logging(self):\n data = self.generator.generate_data()\n data_json = json.dumps(data)\n\n part_id = self.get_highest_id() + 1\n component_id = 1\n\n # Add new part\n insert_command = \"INSERT INTO data (time, part_id, component_id, processed, classified, data)\" \\\n \" VALUES ('{}', {}, {}, {}, '{}', '{}')\".format('now()',\n part_id,\n component_id,\n 'False',\n 'False',\n data_json)\n\n self.cursor.execute(insert_command)\n\n string = \"Neuer Datenbankeintrag: time={},\" \\\n \" part_id={}, component_id={}, data={}\".format(datetime.datetime,\n part_id,\n component_id,\n data_json)\n\n return string","def as_dfile(self):\n rows = [\"Dose Response\"]\n for dose, response in zip(self.individual_doses, self.responses, strict=True):\n dose_idx = self.doses.index(dose)\n if dose_idx >= self.num_dose_groups:\n continue\n rows.append(f\"{dose:f} {response:f}\")\n return \"\\n\".join(rows)","def report(self):\r\n # Compose the list of report_column names required for \r\n # summary_report.dsw.DictWriter()\r\n sr = self.summary_report\r\n dict_leader = sr.dict_leader\r\n dict_out = sr.dict_out\r\n report_column_names = []\r\n if dict_leader is not None and dict_out is not None:\r\n for key,value in dict_leader.iteritems():\r\n #print \"Adding report_column_name(from dict_leader)=\",key\r\n report_column_names.append(key)\r\n dict_out[key] = value\r\n # We have to initialize the DictWriter with the report_column_names\r\n # below. \r\n # Also need matched coord_val and var names for calling node_report()\r\n # below,\r\n # so we do this duplication of storage of names. \r\n coord_var_names = []\r\n coord_val_names = []\r\n for idx, column_name in enumerate(self.column_names):\r\n var_name = \"Var_%s\" % str(idx+1)\r\n report_column_names.append(var_name)\r\n coord_var_names.append(var_name)\r\n val_name = \"Val_%s\" % str(idx+1)\r\n report_column_names.append(val_name)\r\n coord_val_names.append(val_name)\r\n # Add the entry report_column_names\r\n report_column_names += self.EntryClass.report_column_names\r\n # Instantiate dsw.DictWriter with report column names\r\n # 4 lines follow for quick test output\r\n columns_string = \"\"; sep = \"\"\r\n for i,cn in enumerate(report_column_names):\r\n columns_string += sep + cn\r\n sep = \", \"\r\n if sr.dsw_full_report is not None:\r\n # Instantiate the dict writer to write only one-row at a time,\r\n # rather than buffer the entire report in memory before\r\n # outputting, to reduce memory footprint of \r\n # large reports.\r\n # The caller assumes responsibility to sort such a large report \r\n # as needed, and to produce a view of only the 'max_bad' rows, \r\n # if needed; for example, by loading the full report\r\n # into a sql table and after it is populated by this routine, \r\n # using its facilities to sort and manipulate the report rows.\r\n dict_writer = (self.summary_report.dsw_full_report\r\n .dict_writer(report_column_names))\r\n if sr.write_header: \r\n # write the header row\r\n dict_writer.writeheader()\r\n else:\r\n dict_writer = None\r\n # Accrue output data values for a buffered report, separate from a \r\n # report that node_report may write, row by row, using dict_writer. \r\n # The output collected here may be further quickly sorted and \r\n # examined without having to reread the file that dict_writer \r\n # writes to.\r\n # Coord data output is formatted in node_report().\r\n # node_report() adds final entries column data to dict_out for \r\n # node coords and entry, and\r\n # if an entry has output, calls dict_writer to write it.\r\n is_entry, outrows = self.node_report(\r\n self.grand, \r\n dict_out=self.summary_report.dict_out, \r\n dict_writer=dict_writer,\r\n coord_var_names=coord_var_names, \r\n coord_val_names=coord_val_names)\r\n return outrows","def output(self):\n to_write = 'S '\n \"\"\"\n print self.def_field\n for key in self.def_field:\n print key,\"=\", self.def_field[key]\n \"\"\"\n to_write += str(self.offset[0] + self.def_field['x1'])+' '\n to_write += str(self.offset[1] + self.def_field['y1'])+' '\n to_write += str(self.offset[0] + self.def_field['x2'])+' '\n to_write += str(self.offset[1] + self.def_field['y2'])+' '\n to_write += str(self.def_field['part'])+' '\n to_write += str(self.def_field['dmg'])+' '\n to_write += str(self.def_field['pen'])+' '\n to_write += self.def_field['fill']+'\\n'\n return to_write","def _format_data(self, data: np.ndarray) -> np.ndarray:\n self._n_circs = 0\n self._n_shots = 0\n self._n_slots = 0\n self._n_iq = 0\n\n # identify shape\n try:\n # level1 single-shot data\n self._n_circs, self._n_shots, self._n_slots, self._n_iq = data.shape\n except ValueError:\n try:\n # level1 data averaged over shots\n self._n_circs, self._n_slots, self._n_iq = data.shape\n except ValueError as ex:\n raise DataProcessorError(\n f\"Data given to {self.__class__.__name__} is not likely level1 data.\"\n ) from ex\n\n if self._validate:\n if self._n_iq != 2:\n raise DataProcessorError(\n f\"IQ data given to {self.__class__.__name__} does not have two-dimensions \"\n f\"(I and Q). Instead, {self._n_iq} dimensions were found.\"\n )\n\n return data","def Info2Save(self):\n\n if self.ds.sat == 'Kepler':\n snr_header = 'SNR_' + self.ds.sat\n prob_header = 'Pdet_' + self.ds.sat\n\n elif self.ds.sat == 'TESS':\n snr_header = 'SNR_' + self.ds.sat + str(ds.Tobs)\n prob_header = 'Pdet_' + self.ds.sat + str(ds.Tobs)\n\n\n # check if file exists. If it does, add columns/write over columns\n # if the files doesn't exist, make it\n if os.path.exists(self.probfile):\n save = pd.read_csv(self.probfile)\n save[snr_header], save[prob_header] = [self.snr_modes, self.prob]\n\n else:\n save = pd.DataFrame({'f0' :self.ds.mode_id['f0'],\n snr_header :self.snr_modes,\n prob_header:self.prob})\n save.sort_values(['f0'], axis=0, ascending=True, inplace=True)\n save = save.ix[:, ['f0', snr_header, prob_header]]\n\n #print self.ds.sat, ds.Tobs\n #print save\n\n save.to_csv(self.probfile, index=False)","def _writeRecord(self, path, name, data):\n file_path = os.path.join(path, name)\n with open(file_path, 'w') as f:\n for item in data:\n f.write(str(item)+'\\t')\n f.write('\\n')","def report(self,f):\n f.write(\"sectorsize: %d\\n\" % self.sectorsize)\n for run in sorted(self.db):\n f.write(\" [@%8d ; %8d]\\n\" % (run.img_offset,run.bytes))\n f.write(\"total entries in database: %d\\n\\n\" % len(r))","def _convert_and_save_to_FILE_pdbformat(self, cur, FILE, supress_model_separation=False):\n\n\n rows = cur.fetchall()\n if not supress_model_separation:\n pdb_previous = rows[0]['pdbID']; struc_previous = rows[0]['strucID']\n FILE.write(\"MODEL\\n\")\n for row in rows:\n pdb=row['pdbID']; struc=row['strucID']\n if pdb!=pdb_previous or struc!=struc_previous:\n FILE.write(\"ENDMDL\\n\")\n FILE.write(\"MODEL\\n\")\n line = self._morph_db_row_to_pdb_line(row)\n FILE.write(line+\"\\n\")\n pdb_previous = row['pdbID']; struc_previous = row['strucID']\n FILE.write('ENDMDL')\n else:\n FILE.write(\"MODEL\\n\")\n for row in rows:\n line = self._morph_db_row_to_pdb_line(row)\n FILE.write(line+\"\\n\")\n FILE.write('ENDMDL')\n FILE.close()","def generate_extra_data(self):\n self.data[\"male_initial\"], self.data[\"female_initial\"] = \\\n self.get_initial_student_count()\n \n date_line = '<p class=\"report-title\"> %s</p>' \\\n %(self.start_date.strftime(\"%B %Y\"))\n row1 = \"\"\"\n <table>\n <tr class=\"tblRow\"><td>%s</td><td>Enrollment For Year</td>\n <td>Male:</td><td>%d</td><td>Female:</td><td>%d</td>\n <td>Total:</td><td>%d</td></tr>\n \"\"\" %(unicode(self.school), self.data[\"male_initial\"], \n self.data[\"female_initial\"], \n self.data[\"male_initial\"] + self.data[\"female_initial\"])\n row2 = \"\"\"\n <tr class=\"tblOddRow\"><td>%s</td><td>Enrollment For Month</td>\n <td>Male:</td><td>%d</td><td>Female:</td><td>%d</td>\n <td>Total:</td><td>%d</td></tr>\n \"\"\" %(unicode(self.section), self.data[\"male_current\"], \n self.data[\"female_current\"],\n self.data[\"male_current\"] + self.data[\"female_current\"])\n row3 = \"\"\"\n <tr class=\"tblRow\"><td>%s</td><td>Average Attendance</td>\n <td>Male:</td><td>%.1f</td><td>Female:</td><td>%.1f</td>\n <td>Total:</td><td>%.1f</td></tr>\n \"\"\" %(\"Secondary\", self.data[\"aa_male\"], self.data[\"aa_female\"] ,\n self.data[\"aa_combined\"])\n row4 =\"\"\"\n <tr class=\"tblOddRow\"><td>%s</td><td>Percentage of Attendance</td>\n <td>Male:</td><td>%.1f %% </td><td>Female:</td><td>%.1f %% </td>\n <td>Total:</td><td>%.1f %% </td></tr>\n \"\"\" %(unicode(self.school.municipality), self.data[\"pa_male\"], \n self.data[\"pa_female\"], self.data[\"pa_combined\"])\n row5 = \"\"\"\n <tr class=\"tblRow\"><td>School Days: %d</td><td>Percentage of Enrollment</td>\n <td>Male:</td><td>%.1f %% </td><td>Female:</td><td>%.1f %% </td>\n <td>Total:</td><td>%.1f %% </td></tr>\n </table>\n \"\"\" %(self.data[\"num_school_days\"], \n self.data[\"male_current\"] * 100.0 / self.data[\"male_initial\"],\n self.data[\"female_current\"] * 100.0 / \n self.data[\"female_initial\"],\n (self.data[\"male_current\"] + self.data[\"female_current\"]) * \n 100.0 /\n (self.data[\"male_initial\"] + self.data[\"female_initial\"]))\n self.extra_data = date_line + row1 + row2 + row3 + row4 + row5","def save(self):\n\n print('Bye!')\n try:\n with open('records.txt', 'w') as fh:\n fh.write(str(self._initial_money))\n for n in self._records:\n fh.write('\\n'+n)\n except OSError:\n sys.stderr.write('Cannot open file.\\n')","def _format_data(self, data: np.ndarray) -> np.ndarray:\n\n self._n_shots = len(data[0])\n self._n_circuits = len(data)\n\n if self._validate:\n if data.shape[:2] != (self._n_circuits, self._n_shots):\n raise DataProcessorError(\n f\"The datum given to {self.__class__.__name__} does not convert \"\n \"of an array with dimension (number of circuit, number of shots).\"\n )\n\n return data","def _write_csv(self):\n\n # add the label to the header\n if self.input_data.get_value(InputType.TIME_PERIOD) == 'all':\n self.header.append('Date')\n else:\n self.header.append('sample id')\n\n key_list = []\n\n for i, cube in enumerate(self.cube_list):\n if self.input_data.get_value(InputType.TIME_PERIOD) == 'all':\n self._write_sample_with_date(cube, i, key_list)\n else:\n self._write_sample(cube, i, key_list)\n\n output_data_file_path = self._get_full_file_name()\n self._write_data_dict(output_data_file_path, key_list)\n\n return [output_data_file_path]","def save_simulation_file(self):\n a = self.ui.inputfile.text()\n a = self.get_root_file_name(a)\n a = a.split('_a.txt')\n output_suffix = self.ui.output_suffix.text()\n simfile_name = self.input_dir+'/'+sgGL.SIMFILES_PATH + a[0] + '_' +\\\n sgcom.create_file_suffix(self.algorithm,output_suffix,self.ciclos)+\\\n '.sim'\n simulation_selected_filename = QtGui.QFileDialog.getSaveFileName(self,\n \"Save simulation parameters\",\n simfile_name)\n if len(simulation_selected_filename)>0:\n simulation_params.write2file(simulation_selected_filename)","def dump(self):\n outputs = [\"Sequence : %s\" % self.name]\n if self.curr_value:\n outputs.append(\" start : %d\" % self.curr_value)\n outputs.append(\" minimum : %d\" % self.min_value)\n outputs.append(\" maximum : %d\" % self.max_value)\n if self.increment_by > 1:\n outputs.append(\" increment : %d\" % self.increment_by)\n return \"\\n\".join(outputs)","def flush(self):\n # spectra/*.csv | Age distribution of various subpopulations (e.g. population that died of genetic causes)\n for key, val in self.collection.items():\n with open(self.paths[\"visor_spectra\"] / f\"{key}.csv\", \"ab\") as f:\n array = np.array(val)\n np.savetxt(f, [array], delimiter=\",\", fmt=\"%i\")\n\n # Reinitialize the collection\n self.collection = copy.deepcopy(self._collection)","def generate_report(path_to_results: str, save_path: str, format: str) -> None:\n path_to_results = (\n path_to_results if path_to_results[-1] != \"/\" else path_to_results[:-1]\n )\n files = glob.glob(f\"{path_to_results}/*\")\n\n if len(files) == 0:\n raise Exception(f\"There were no result files in the path {path_to_results}\")\n\n raw_results = []\n for result_file in files:\n with open(result_file, \"rb\") as handle:\n raw_results.append(pickle.load(handle))\n\n results_dict = []\n for raw in raw_results:\n runtime = np.mean([i[\"runtime\"] for i in raw[\"results\"]])\n memory = np.mean(\n [\n i[\"memory_stats\"][\"max_memory\"]\n for i in raw[\"results\"]\n if i[\"memory_stats\"] is not None\n ]\n )\n result = {\n \"layer\": raw[\"layer\"],\n \"batch_size\": raw[\"batch_size\"],\n \"num_runs\": raw[\"num_runs\"],\n \"num_repeats\": raw[\"num_repeats\"],\n \"forward_only\": raw[\"forward_only\"],\n \"gsm_mode\": raw[\"gsm_mode\"],\n \"runtime\": runtime,\n \"memory\": memory,\n }\n results_dict.append(result)\n\n results = pd.DataFrame(results_dict)\n\n results[\"gsm_mode\"][results[\"layer\"].str.startswith(\"dp\")] = (\n \"dp_\" + results[\"gsm_mode\"]\n )\n results[\"layer\"] = results[\"layer\"].str.replace(\"dp\", \"\")\n\n pivot = results.pivot_table(\n index=[\"batch_size\", \"num_runs\", \"num_repeats\", \"forward_only\", \"layer\"],\n columns=[\"gsm_mode\"],\n values=[\"runtime\"],\n )\n\n def add_ratio(df, metric, variant):\n if variant not in df.columns.get_level_values(\"gsm_mode\"):\n for ametric in df.columns.get_level_values(0):\n df[(ametric, variant)] = np.nan\n\n df[(metric, f\"{variant}/baseline\")] = (\n df.loc[:, (metric, variant)] / df.loc[:, (metric, \"baseline\")]\n )\n\n if \"baseline\" in results[\"gsm_mode\"].tolist():\n for m in set(results[\"gsm_mode\"].tolist()) - {\"baseline\"}:\n add_ratio(pivot, \"runtime\", m)\n pivot.columns = pivot.columns.set_names(\"value\", level=1)\n\n output = pivot.sort_index(axis=1).sort_values(\n [\"batch_size\", \"num_runs\", \"num_repeats\", \"forward_only\"]\n )\n if format == \"csv\":\n output.to_csv(save_path)\n else:\n output.to_pickle(save_path)","def _format_data(self, data: np.ndarray) -> np.ndarray:\n self._n_shots = 0\n\n # identify shape\n try:\n # level1 single-shot data\n self._n_circs, self._n_shots, self._n_slots, self._n_iq = data.shape\n except ValueError:\n try:\n # level1 data averaged over shots\n self._n_circs, self._n_slots, self._n_iq = data.shape\n except ValueError as ex:\n raise DataProcessorError(\n f\"Data given to {self.__class__.__name__} is not likely level1 data.\"\n ) from ex\n\n if self._validate:\n if data.shape[-1] != 2:\n raise DataProcessorError(\n f\"IQ data given to {self.__class__.__name__} must be a multi-dimensional array\"\n \"of dimension [d0, d1, ..., 2] in which the last dimension \"\n \"corresponds to IQ elements.\"\n f\"Input data contains element with length {data.shape[-1]} != 2.\"\n )\n\n return data","def _writeOutput(self):\n head = \"Station\\tX\\tY\\tZ\\tUEast\\tUNorth\\tUUp\\tSigEast\\tSigNorth\\tSigUp\\n\"\n outFmt = \"%s\" + 9 * \"\\t%g\" + \"\\n\"\n\n f = open(self.outputFile, 'w')\n f.write(head)\n\n for stationNum in range(self.numStations):\n outLine = outFmt % (self.stations[stationNum],\n self.coords[stationNum, 0], self.coords[stationNum, 1],\n self.coords[stationNum, 2],\n self.dispNoise[stationNum, 0],\n self.dispNoise[stationNum, 1],\n self.dispNoise[stationNum, 2],\n self.sigmaEast, self.sigmaNorth, self.sigmaUp)\n f.write(outLine)\n\n f.close()\n\n return","def save(self, filename, precision='%15.07e', encoding='utf-8'):\n\n # when creating empty st object, cols is not set yet\n if not hasattr(self, 'cols'):\n if self.main_data_sets[1][1].shape[1]==19:\n self.cols = stc.split()\n elif self.main_data_sets[1][1].shape[1]==30:\n self.cols = fpm.split()\n else:\n c = self.main_data_sets[1][1].shape[1]\n raise TypeError(f'St input needs 19 (iso) or 30 (aniso/fpm) cols, not {c}')\n\n colwidth = len(precision % 1)\n sep = '=' * colwidth * len(self.cols) + '\\n'\n colhead = ''.join([k.center(colwidth) for k in self.cols]) + '\\n'\n\n nsets = len(self.main_data_sets)\n # fails if dirname is empty string\n if len(os.path.dirname(filename)) > 0:\n os.makedirs(os.path.dirname(filename), exist_ok=True)\n with open(filename, 'w', encoding=encoding) as fid:\n fid.write('%i ; number of sets, Nset\\n' % nsets)\n for mset, set_data_dict in self.main_data_sets.items():\n fid.write('#%i ; set number\\n' % mset)\n for set, set_array in set_data_dict.items():\n dstr = self.to_str(mset=mset, set=set, precision=precision)\n npoints = self.main_data_sets[mset][set].shape[0]\n fid.write(sep + colhead + sep)\n fid.write('$%i %i\\n' % (set, npoints))\n fid.write(dstr + '\\n')","def write (self, path):\n\t\ts=[]; add=s.append\n\t\tadd ('\\t'.join (self.schema))\n\t\tfor record in self.data:\n\t\t\tadd (record.asTabDelimitedRecord())\n\t\t\n\t\t# f = open (path, 'w')\n\t\tf = codecs.open(path, 'w', 'utf-8')\n\t\tf.write (self.linesep.join (s))\n\t\tf.close()\n\t\tprint (\"data written to \" + path)","def tabular_data(self):\n path = CFG.GRAPHS_DIR\n chdir(path)\n\n if self.experiment_count == 1:\n f = open(self.tablefile, 'w')\n f.write(self.print_border_line())\n f.write(self.table_header())\n f.write(self.print_border_line())\n f.write(self.pretty_string(\"Functions\"))\n f.write(self.pretty_string(\"Batch Size\"))\n f.write(self.pretty_string(\"Training (%)\"))\n f.write(self.pretty_string(\"Testing (%)\", True))\n f.write('\\n')\n f.write(self.print_border_line())\n f.close()\n\n f = open(self.tablefile, 'a')\n f.write(self.pretty_string(self.function_name))\n f.write(self.pretty_string(str(self.batch_size)))\n f.write(self.pretty_string(self.tr_mean_str))\n f.write(self.pretty_string(self.test_mean_str, True))\n f.write('\\n')\n f.close()","def generate_student_report(self):\n \n period_type = self.parameter_dict.get(\"period_type\", \"monthly\")\n insert_gender_markers = self.parameter_dict.get(\n \"insert_gender_markers\", False)\n period = [(self.start_date,self.end_date)]\n for student in self.students:\n self.table_data.append(self._generate_single_student_report_line(\n student,period, False))\n self.keys_list.append(\"\")\n self.table_descriptor = \\\n [('name','string','Name'),\n ('days_present','number', 'Days Present'),\n ('percent_present', 'number', '% Present')]","def toJSON(self):\r\n\r\n jsonToRet = []\r\n rowJson = []\r\n matrixJson = []\r\n\r\n if len(self.slctData) > 100:\r\n self.getSimMatSummary(100)\r\n jsonToRet.append(self.summaryOrdering)\r\n for i in range(0,len(self.simMatSmm)):\r\n for n in self.simMatSmm[i]:\r\n rowJson.append(n)\r\n matrixJson.append(rowJson)\r\n rowJson = []\r\n jsonToRet.append(matrixJson)\r\n\r\n jsonToRet.append(self.patchOrdering)\r\n # jsonToRet = []\r\n rowJson = []\r\n matrixJson = []\r\n\r\n for i in range(0,len(self.simMat)):\r\n for n in self.simMat[i]:\r\n rowJson.append(n)\r\n matrixJson.append(rowJson)\r\n rowJson = []\r\n jsonToRet.append(matrixJson)\r\n return jsonToRet","def generate_record(self, data_dictionaries, group_by):\n result = {}\n\n for one_measurement in data_dictionaries:\n time = one_measurement['datetime']\n\n if isinstance(time, str):\n if self.timezone:\n time = arrow.get(time).shift(hours=6) # TODO: fix utc conversion\n else:\n time = arrow.get(time)\n\n record = Record(self.name, self.lat, self.lon, self.height, time)\n\n del one_measurement['datetime']\n\n one_measurement = {k: float(v) for k, v in one_measurement.items()}\n\n record.merge(one_measurement)\n\n key = group_by(time)\n \n if key == '2016-04-01_00':\n break\n\n record_string = record.little_r_report()\n\n try:\n result[key].append(record_string)\n except KeyError:\n result[key] = [record_string]\n\n return result","def data_saving(self, sol):\r\n # Only export data once per time-step. We do this on the conduction\r\n # step.\r\n if self.save_rule is not None:\r\n save_rule_true = self.save_rule(self.step, self.d_T)\r\n else:\r\n save_rule_true = True\r\n \r\n if self.cond == True:\r\n series = \"Temperature\"\r\n elif self.rad is not None:\r\n series = (\"Radiation\", self.fq_list[self.rad])\r\n else:\r\n # before sim starts... EARLY EXIT\r\n return\r\n\r\n if self.cond == True and save_rule_true:\r\n # Save data to file with step no.\r\n # First, generate dictionaries with {nid:value}\r\n\r\n # CASE 1: Export mesh = FEM mesh (ie, no enrichment, easier!)\r\n if self.export_mesh is None:\r\n data_temp = {}\r\n for nid in self.mesh.nodes.keys():\r\n idx = self.node_map.tag_to_idx((nid, 0))\r\n data_temp[nid] = self.lst_tmp[idx]\r\n data_rad = {}\r\n for i in self.fq_list:\r\n data_rad[i] = {}\r\n for nid in self.mesh.nodes.keys():\r\n idx = self.node_map.tag_to_idx((nid, 0))\r\n for i in range(0, len(self.fq_list)):\r\n data_rad[self.fq_list[i]][nid] = self.lst_rad[i][idx]\r\n # End CASE 1 - see after case two for finishing export.\r\n\r\n\r\n # CASE 2: Exporting to a different mesh to the the XFEM / FEM\r\n # mesh. \r\n else:\r\n # We need to a mapping from global to local element \r\n # coordinates. We'll do this once and then store it.\r\n # We store it in self.export_to_elem dictionary.\r\n if self.export_to_elem is None:\r\n self.export_to_elem = \\\r\n self.mesh.project_points(self.export_mesh.nodes,\r\n failure_rule='closest')\r\n # Dictionaries to export:\r\n data_temp = {}\r\n data_rad = {}\r\n # Setup frequency data:\r\n for i in self.fq_list:\r\n data_rad[i] = {}\r\n\r\n for node_id, expt_data in self.export_to_elem.items():\r\n # Unpack the value of the dictionary value for clarity:\r\n elem = expt_data[0]\r\n eta = expt_data[1] # local coord\r\n\r\n # Get element / solution indexes:\r\n val = elem.eval_elem(self.node_map, self.lst_tmp, [eta])[0]\r\n data_temp[node_id] = val\r\n # And for all the frequencies:\r\n for i in range(0, len(self.fq_list)):\r\n data_rad[self.fq_list[i]][node_id] \\\r\n = elem.eval_elem(self.node_map,\r\n self.lst_rad[i],\r\n [eta])[0]\r\n # END CASE 2\r\n\r\n expt_data = {\"Temperature\": data_temp}\r\n for freq, nvals in data_rad.items():\r\n expt_data[str(freq * 10) + \"THz\"] = data_rad[freq]\r\n\r\n # Send to be exported as a VTK.\r\n if self.export_mesh is None:\r\n self.mesh.export_to_vtk(self.save_path + str(self.step),\r\n expt_data)\r\n else:\r\n self.export_mesh.export_to_vtk(self.save_path + str(self.step),\r\n expt_data)\r\n\r\n try:\r\n container = self.saved_data[series]\r\n except:\r\n self.saved_data[series] = {}\r\n container = self.saved_data[series]\r\n if self.step % 10 == 0 or self.step < 10:\r\n container[self.step] = saved_data(sol, self.step, self.current_T)","def writeOut(self):\n # import time\n self.outHeader = self.srcHeader\n for line in self.outHeader:\n self.outFile.write(line + '\\n')\n # now = time.asctime(time.localtime(time.time()))\n # self.outFile.write('%% -- %s -- Written to new alog' % now)\n for time_s in sorted(self.outData):\n for sens in self.outData[time_s]:\n for meas in self.outData[time_s][sens]:\n valu = self.outData[time_s][sens][meas]\n msg_list = [str(time_s), meas, sens, str(valu)]\n line_string = reconstructLine(msg_list)\n self.outFile.write(line_string + '\\n')","def write_results_dat(self, output_path):\n\n def fstr(nb):\n data = '%E' % nb\n if data == 'NAN':\n nb, power = 0,0\n else:\n nb, power = data.split('E')\n nb = float(nb) /10\n power = int(power) + 1\n return '%.5fE%+03i' %(nb,power)\n\n line = '%s %s %s %i %i %i %i %s %s %s %s %s %i\\n' % (fstr(self.axsec), fstr(self.xerru), \n fstr(self.xerrc), self.nevents, self.nw, self.maxit, self.nunwgt,\n fstr(self.luminosity), fstr(self.wgt), fstr(self.xsec), fstr(self.maxwgt),\n fstr(self.th_maxwgt), self.th_nunwgt) \n fsock = open(output_path,'w') \n fsock.writelines(line)\n for i in range(len(self.ysec_iter)):\n line = '%s %s %s %s %s %s\\n' % (i+1, self.ysec_iter[i], self.yerr_iter[i], \n self.eff_iter[i], self.maxwgt_iter[i], self.yasec_iter[i]) \n fsock.writelines(line)","def _logging_smm(self, stats_obs, stats_sim):\n fname = 'monitoring.estimagic.smm.info'\n if self.num_evals == 1 and os.path.exists(fname):\n os.unlink(fname)\n\n with open(fname, 'a+') as outfile:\n\n fmt_ = '\\n\\n{:>8}{:>15}\\n\\n'\n outfile.write(fmt_.format('EVALUATION', self.num_evals))\n\n fmt_ = '{:>8}' + '{:>15}' * 4 + '\\n\\n'\n info = ['Moment', 'Observed', 'Simulated', 'Difference', 'Weight']\n outfile.write(fmt_.format(*info))\n\n for i, moment in enumerate(stats_obs):\n\n stat_obs, stat_sim = stats_obs[i], stats_sim[i]\n info = [i, stat_obs, stat_sim, abs(stat_obs - stat_sim), self.weighing_matrix[i, i]]\n\n fmt_ = '{:>8}' + '{:15.5f}' * 4 + '\\n'\n outfile.write(fmt_.format(*info))","def _write_times(parameters):\n data = parameters[\"times\"]\n data = data if numpy.ndim(data) else [data]\n\n # Formats\n fmt = block_to_format[\"TIMES\"]\n fmt1 = str2format(fmt[1])\n fmt2 = str2format(fmt[2])\n\n # Record 1\n out = write_record([len(data)], fmt1)\n\n # Record 2\n out += write_record(data, fmt2, multi=True)\n\n return out","def bgs_write_simdata(sim, overwrite=False):\n from desispec.io.util import makepath\n from desispec.io.util import write_bintable\n\n simdatafile = os.path.join(sim.simdir, \n 'bgs_{}_simdata.fits'.format(sim.simid))\n makepath(simdatafile)\n\n cols = [\n ('SEED', 'S20'),\n ('NSPEC', 'i4'),\n ('EXPTIME', 'f4'),\n ('AIRMASS', 'f4'),\n ('SEEING', 'f4'),\n ('MOONFRAC', 'f4'),\n ('MOONSEP', 'f4'),\n ('MOONALT', 'f4')]\n\n simdata = Table(np.zeros(sim.nsim, dtype=cols))\n simdata['EXPTIME'].unit = 's'\n simdata['SEEING'].unit = 'arcsec'\n simdata['MOONSEP'].unit = 'deg'\n simdata['MOONALT'].unit = 'deg'\n\n simdata['SEED'] = sim.seed\n simdata['NSPEC'] = sim.nspec\n simdata['AIRMASS'] = sim.airmass\n simdata['SEEING'] = sim.seeing\n simdata['MOONALT'] = sim.moonalt\n simdata['MOONSEP'] = sim.moonsep\n simdata['MOONFRAC'] = sim.moonfrac\n simdata['EXPTIME'] = sim.exptime\n\n if overwrite or not os.path.isfile(simdatafile):\n print('Writing {}'.format(simdatafile))\n write_bintable(simdatafile, simdata, extname='SIMDATA', clobber=True)\n\n return simdata","def create_data(self) -> str:\r\n s = self.scale\r\n mini, maxi = self.get_min_max()\r\n diff = maxi - mini\r\n\r\n output = \"const data = {\\n\"\r\n\r\n # Create the data for the scatters\r\n # TODO: If it's not interactive, labels shouldn't be exported.\r\n for name, data in self.scatters_data.items():\r\n mapping = self.scatters[name][\"mapping\"]\r\n colormaps = self.scatters[name][\"colormap\"]\r\n cmaps = [None] * len(colormaps)\r\n\r\n for i, colormap in enumerate(colormaps):\r\n if isinstance(colormap, str):\r\n cmaps[i] = plt.cm.get_cmap(colormap)\r\n else:\r\n cmaps[i] = colormap\r\n\r\n output += name + \": {\\n\"\r\n x_norm = [round(s * (x - mini) / diff, 3) for x in data[mapping[\"x\"]]]\r\n output += \"x: [\" + \",\".join(map(str, x_norm)) + \"],\\n\"\r\n\r\n y_norm = [round(s * (y - mini) / diff, 3) for y in data[mapping[\"y\"]]]\r\n output += \"y: [\" + \",\".join(map(str, y_norm)) + \"],\\n\"\r\n\r\n z_norm = [round(s * (z - mini) / diff, 3) for z in data[mapping[\"z\"]]]\r\n output += \"z: [\" + \",\".join(map(str, z_norm)) + \"],\\n\"\r\n\r\n if mapping[\"labels\"] in data:\r\n fmt_labels = [\"'{0}'\".format(s) for s in data[mapping[\"labels\"]]]\r\n output += \"labels: [\" + \",\".join(fmt_labels) + \"],\\n\"\r\n\r\n if mapping[\"s\"] in data:\r\n output += \"s: [\"\r\n\r\n for series in range(len(data[mapping[\"s\"]])):\r\n output += (\r\n \"[\"\r\n + \",\".join(map(str, np.round(data[mapping[\"s\"]][series], 3)))\r\n + \"],\\n\"\r\n )\r\n\r\n output += \"],\\n\"\r\n\r\n output += \"colors: [\\n\"\r\n for series in range(len(data[mapping[\"c\"]])):\r\n output += \"{\\n\"\r\n if mapping[\"cs\"] in data:\r\n colors = np.array(\r\n [cmaps[series](x) for x in data[mapping[\"c\"]][series]]\r\n )\r\n\r\n for i, c in enumerate(colors):\r\n hsl = np.array(colour.rgb2hsl(c[:3]))\r\n hsl[1] = hsl[1] - hsl[1] * data[mapping[\"cs\"]][series][i]\r\n colors[i] = np.append(np.array(colour.hsl2rgb(hsl)), 1.0)\r\n\r\n colors = np.round(colors * 255.0)\r\n\r\n output += (\r\n \"r: [\" + \",\".join(map(str, map(int, colors[:, 0]))) + \"],\\n\"\r\n )\r\n output += (\r\n \"g: [\" + \",\".join(map(str, map(int, colors[:, 1]))) + \"],\\n\"\r\n )\r\n output += (\r\n \"b: [\" + \",\".join(map(str, map(int, colors[:, 2]))) + \"],\\n\"\r\n )\r\n elif mapping[\"c\"] in data:\r\n colors = np.array(\r\n [cmaps[series](x) for x in data[mapping[\"c\"]][series]]\r\n )\r\n colors = np.round(colors * 255.0)\r\n output += (\r\n \"r: [\" + \",\".join(map(str, map(int, colors[:, 0]))) + \"],\\n\"\r\n )\r\n output += (\r\n \"g: [\" + \",\".join(map(str, map(int, colors[:, 1]))) + \"],\\n\"\r\n )\r\n output += (\r\n \"b: [\" + \",\".join(map(str, map(int, colors[:, 2]))) + \"],\\n\"\r\n )\r\n output += \"},\\n\"\r\n\r\n output += \"]\"\r\n output += \"},\\n\"\r\n\r\n for name, data in self.trees_data.items():\r\n mapping = self.trees[name][\"mapping\"]\r\n point_helper = self.trees[name][\"point_helper\"]\r\n\r\n output += name + \": {\\n\"\r\n\r\n if point_helper is not None and point_helper in self.scatters_data:\r\n scatter = self.scatters_data[point_helper]\r\n scatter_mapping = self.scatters[point_helper][\"mapping\"]\r\n\r\n x_t = []\r\n y_t = []\r\n z_t = []\r\n\r\n for i in range(len(data[mapping[\"from\"]])):\r\n x_t.append(scatter[scatter_mapping[\"x\"]][data[mapping[\"from\"]][i]])\r\n x_t.append(scatter[scatter_mapping[\"x\"]][data[mapping[\"to\"]][i]])\r\n y_t.append(scatter[scatter_mapping[\"y\"]][data[mapping[\"from\"]][i]])\r\n y_t.append(scatter[scatter_mapping[\"y\"]][data[mapping[\"to\"]][i]])\r\n z_t.append(scatter[scatter_mapping[\"z\"]][data[mapping[\"from\"]][i]])\r\n z_t.append(scatter[scatter_mapping[\"z\"]][data[mapping[\"to\"]][i]])\r\n\r\n x_norm = [round(s * (x - mini) / diff, 3) for x in x_t]\r\n output += f\"x: [\" + \",\".join(map(str, x_norm)) + \"],\\n\"\r\n\r\n y_norm = [round(s * (y - mini) / diff, 3) for y in y_t]\r\n output += \"y: [\" + \",\".join(map(str, y_norm)) + \"],\\n\"\r\n\r\n z_norm = [round(s * (z - mini) / diff, 3) for z in z_t]\r\n output += \"z: [\" + \",\".join(map(str, z_norm)) + \"],\\n\"\r\n else:\r\n x_norm = [round(s * (x - mini) / diff, 3) for x in data[mapping[\"x\"]]]\r\n output += \"x: [\" + \",\".join(map(str, x_norm)) + \"],\\n\"\r\n\r\n y_norm = [round(s * (y - mini) / diff, 3) for y in data[mapping[\"y\"]]]\r\n output += \"y: [\" + \",\".join(map(str, y_norm)) + \"],\\n\"\r\n\r\n z_norm = [round(s * (z - mini) / diff, 3) for z in data[mapping[\"z\"]]]\r\n\r\n output += \"z: [\" + \",\".join(map(str, z_norm)) + \"],\\n\"\r\n\r\n if mapping[\"c\"] in data:\r\n colormap = self.trees[name][\"colormap\"]\r\n cmap = None\r\n if isinstance(colormap, str):\r\n cmap = plt.cm.get_cmap(colormap)\r\n else:\r\n cmap = colormap\r\n\r\n colors = np.array([cmap(x) for x in data[mapping[\"c\"]]])\r\n colors = np.round(colors * 255.0)\r\n output += \"r: [\" + \",\".join(map(str, colors[:, 0])) + \"],\\n\"\r\n output += \"g: [\" + \",\".join(map(str, colors[:, 1])) + \"],\\n\"\r\n output += \"b: [\" + \",\".join(map(str, colors[:, 2])) + \"],\\n\"\r\n\r\n output += \"},\\n\"\r\n\r\n output += \"};\\n\"\r\n\r\n return output","def format_data(self, raw_data):\n opz = raw_data.copy()\n opz['datetime'] = pd.to_datetime(opz['Datum-tijd'], format='%Y-%m-%dT%H:%M:%SZ')\n opz.drop(['Datum-tijd'],axis=1, inplace=True)\n opz['dag']=opz['datetime'].dt.day\n opz['tijd'] = opz['datetime'].dt.time\n #voeg open/dicht data toe en bepaal momenten waarop dit wisselt\n opz['Opzetstuk Noord (°)'] = opz['Opzetstuk Noord (°)'].str.replace(',', '.').astype(float)\n opz['Opzetstuk Zuid (°)'] = opz['Opzetstuk Zuid (°)'].str.replace(',', '.').astype(float)\n opz['Opzetstuk Noord (°)'].fillna(opz['Opzetstuk Zuid (°)'], inplace=True)\n opz['Opzetstuk Zuid (°)'].fillna(opz['Opzetstuk Noord (°)'], inplace=True)\n return opz"],"string":"[\n \"def format(self, data):\",\n \"def format_data(self, data):\",\n \"def _formatData(self):\\r\\n assert self._runData is not None\\r\\n\\r\\n # Getting Axes data into separate lists\\r\\n x=[]; y=[]; z=[]\\r\\n for i in range(len(self._runData)):\\r\\n ySet = []; xSet = []; zSet = []\\r\\n for _ in range(len(self._runData[i][1][0])):\\r\\n ySet.append(self._runData[i][0])\\r\\n y.append(ySet)\\r\\n xSet.append(self._runData[i][1][0])\\r\\n x.append(xSet)\\r\\n zSet.append(self._runData[i][1][1])\\r\\n z.append(zSet)\\r\\n\\r\\n # Reduce extra brackets\\r\\n xnew = []; znew = []\\r\\n for i in range(len(x)):\\r\\n xnew.append(x[i][0])\\r\\n znew.append(z[i][0])\\r\\n x = xnew; z = znew\\r\\n\\r\\n self._frequency = x\\r\\n self._voltages = y\\r\\n self._intensity = z\",\n \"def write(self, path):\\n\\n with open(path, 'w') as file:\\n file.write(f\\\"\\\\n{self.subject}\\\")\\n file.write(8 * '\\\\n')\\n file.write(\\\"0\\\")\\n file.write(8 * '\\\\n')\\n file.write(self.data.date[0].strftime(\\\"%d.%m.%Y\\\"))\\n file.write(7 * '\\\\n')\\n file.write(\\\"Unknown Line\\\")\\n file.write(26 * '\\\\n')\\n file.write(self.valid_measurements + \\\"\\\\n\\\")\\n printing_df = self.data.drop(columns=['date', 'time'])\\n printing_df['hours'] = self.data.time.map(lambda x: x.strftime(\\\"%H\\\"))\\n printing_df['minutes'] = self.data.time.map(lambda x: x.strftime(\\\"%M\\\"))\\n order = ['hours', 'minutes', 'SYS(mmHg)', 'DIA(mmHg)', 'UNKNOW_1', 'UNKNOW_2', 'CODE', 'UNKNOW_3']\\n printing_df = printing_df[order]\\n printing_df.fillna(-9999, inplace=True)\\n printing_df.replace('EB', -9998, inplace=True)\\n printing_df.replace('AB', -9997, inplace=True)\\n printing_df[['SYS(mmHg)', 'DIA(mmHg)', 'UNKNOW_1', 'UNKNOW_2', 'CODE', 'UNKNOW_3']] = printing_df[\\n ['SYS(mmHg)', 'DIA(mmHg)', 'UNKNOW_1', 'UNKNOW_2', 'CODE', 'UNKNOW_3']].astype(int).astype(str)\\n printing_df.replace('-9999', '\\\"\\\"', inplace=True)\\n printing_df.replace('-9998', '\\\"EB\\\"', inplace=True)\\n printing_df.replace('-9997', '\\\"AB\\\"', inplace=True)\\n printing_df.to_csv(file, header=None, index=None, quoting=csv.QUOTE_NONE, line_terminator='\\\\n')\\n\\n xml_node = ET.Element('XML')\\n xml_node.extend(self._dict_to_etree(self.metadata))\\n xml_line = ET.tostring(xml_node, encoding=\\\"unicode\\\")\\n file.write(xml_line)\",\n \"def coregister_formatted():\\r\\n\\r\\n print(\\\"begin coregister_formatted\\\")\\r\\n\\r\\n # check all records for pairs using the recordBegin time\\r\\n pair_records()\\r\\n\\r\\n # establish the beginning and end of the coregistered records\\r\\n define_pairedRecords()\\r\\n\\r\\n # coregister paired data in a single csv\\r\\n format_coregister()\\r\\n\\r\\n\\r\\n print(\\\"completed segment_formatted\\\")\",\n \"def _record(self):\\r\\n self._plot()\\r\\n self._csvWriter()\\r\\n self._logger()\",\n \"def convert_simulation_results(self, gdat_filename, out_filename, out_format):\\n data = self.read_simulation_results(gdat_filename)\\n if out_format == 'csv':\\n data.to_csv(out_filename)\\n elif out_format == 'tsv':\\n data.to_csv(out_filename, sep='\\\\t')\\n else:\\n raise NotImplementedError('Unsupported output format {}'.format(out_format))\",\n \"def create_file(self, type_simu=0, simulation_counter=0):\\n portion_duration = self.individuals[0].portion_duration\\n nbr_portions = self.individuals[0].nbr_portions\\n arriveeX = self.individuals[0].arriveeX*nbr_portions*4.0/20\\n arriveeY = self.individuals[0].arriveeY*nbr_portions*4.0/20\\n\\n if type_simu == 0:\\n repertoire = (f\\\"simulation_data/Simu {simulation_counter} : \\\"\\n f\\\"type_simu=0, nbr_individuals={self.nbr_individuals}, \\\"\\n f\\\"mutation_factor={self.mutation_factor}, portion_duration={portion_duration}, \\\"\\n f\\\"nbr_portions={nbr_portions}, arrivee=({arriveeX},{arriveeY})\\\")\\n else:\\n repertoire = (f\\\"simulation_data/Simu {simulation_counter} : \\\"\\n f\\\"type_simu=1, nbr_individuals={self.nbr_individuals}, \\\"\\n f\\\"mutation_factor={self.mutation_factor}, portion_duration={portion_duration}, \\\"\\n f\\\"nbr_portions={nbr_portions}, accepted_radius={self.accepted_radius}, \\\"\\n f\\\"arrivee=({arriveeX},{arriveeY})\\\")\\n\\n fname = \\\"data_simu.csv\\\"\\n \\n try: \\n os.makedirs(repertoire)\\n except OSError:\\n if not os.path.isdir(repertoire):\\n Raise\\n\\n with open(repertoire+\\\"/\\\"+fname, \\\"a\\\") as file:\\n list_col = [\\\"Génération\\\", 'Individu', 'Score']\\n for portion in range(self.individuals[0].nbr_portions):\\n for roue in range(4):\\n list_col.append('Gene_' + str(portion) + 'roue_' + str(roue))\\n if type_simu == 1:\\n list_col.append('individuals_under_threshold')\\n writer = csv.writer(file)\\n writer.writerow(list_col)\\n\\n return repertoire\",\n \"def format(self):\\n ...\",\n \"def format():\\n train_data.info() # retreiving general info about train data\\n print('Number of rows and columns of train set:', train_data.shape)\\n print('dtype: ', train_data.dtypes) # analyse data type of each column\\n print('First 3 rows of the train data:', train_data.head(3))\",\n \"def export_simulation_data(self, ok):\\n res, ok = QtGui.QInputDialog.getText(self,\\n u\\\"PyMoskito\\\",\\n u\\\"Please specify regime a name\\\",\\n QtGui.QLineEdit.Normal,\\n self._regimes[self._current_regime_index][\\\"Name\\\"])\\n if not ok:\\n return\\n\\n name = unicode(res.toUtf8(), encoding=\\\"utf-8\\\")\\n if not name:\\n self._logger.warning(u\\\"empty regime name specified!\\\")\\n\\n self._save_data(name)\",\n \"def data_format(self):\\n for description in SENSOR_TYPES:\\n if description.key not in self.conditions:\\n continue\\n attr_key = description.key\\n try:\\n if description.primary_key in self.raw_data:\\n sensor_value = self.raw_data[description.primary_key][\\n description.sensor_key\\n ]\\n # Format sensor for better readability\\n if attr_key == ATTR_NEW_VERSION and sensor_value == \\\"0.0.0.0\\\":\\n sensor_value = \\\"Latest\\\"\\n elif attr_key == ATTR_UPTIME:\\n sensor_value = round(sensor_value / (3600 * 24), 2)\\n elif attr_key == ATTR_LAST_RESTART:\\n last_restart = dt_util.now() - timedelta(seconds=sensor_value)\\n sensor_value = last_restart.strftime(\\\"%Y-%m-%d %H:%M:%S\\\")\\n elif attr_key == ATTR_STATUS:\\n if sensor_value:\\n sensor_value = \\\"Online\\\"\\n else:\\n sensor_value = \\\"Offline\\\"\\n elif (\\n attr_key == ATTR_LOCAL_IP and not self.raw_data[\\\"wan\\\"][\\\"online\\\"]\\n ):\\n sensor_value = None\\n\\n self.data[attr_key] = sensor_value\\n except KeyError:\\n _LOGGER.error(\\n (\\n \\\"Router does not support %s field. \\\"\\n \\\"Please remove %s from monitored_conditions\\\"\\n ),\\n description.sensor_key,\\n attr_key,\\n )\\n self.data[attr_key] = None\",\n \"def _format_data(self) -> None:\\n for row in self._db_data:\\n if row['age_start'] is None:\\n continue\\n # entry = {'x': 'Celkem', 'y': int(row['count'])}\\n elif row['age_start'] == 95:\\n entry = {'x': f\\\"{int(row['age_start'])}+\\\", 'y': int(row['count'])}\\n else:\\n entry = {'x': f\\\"{int(row['age_start'])}-{int(row['age_start'])+4}\\\", 'y': int(row['count'])}\\n self.return_data['data'].append(entry)\",\n \"def as_dfile(self):\\n rows = [\\\"Dose NumAnimals Response Stdev\\\"]\\n for i, v in enumerate(self.doses):\\n if i >= self.num_dose_groups:\\n continue\\n rows.append(\\\"%f %d %f %f\\\" % (self.doses[i], self.ns[i], self.means[i], self.stdevs[i]))\\n return \\\"\\\\n\\\".join(rows)\",\n \"def format_and_write(*, fname, header, data, df_sigma, positions):\\n # Reorder to all positions at each timesample a la .txt file format\\n row_list = []\\n for idx_date, date_val in enumerate(data['date']):\\n for idx_pos, pos in enumerate(positions):\\n dict_row = {}\\n if not df_sigma.empty: # assume MF has uncertainties\\n dict_row.update(\\n {'theta': pos[0],\\n 'phi': pos[1],\\n 'Year': date_val.year,\\n 'Month': date_val.month,\\n 'Time': mjd2000(date_val),\\n 'r': pos[2],\\n # -Z = B_r\\n 'B_r': -data.iloc[idx_date].filter(\\n regex='Z_' + str(idx_pos).zfill(3)).values[0],\\n # -X = B_theta\\n 'B_theta': -data.iloc[idx_date].filter(\\n regex='X_' + str(idx_pos).zfill(3)).values[0],\\n # Y = B_phi\\n 'B_phi': data.iloc[idx_date].filter(\\n regex='Y_' + str(idx_pos).zfill(3)).values[0]\\n })\\n else: # assume SV has empty df instead of uncertainties\\n dict_row.update(\\n {'theta': pos[0],\\n 'phi': pos[1],\\n 'Year': date_val.year,\\n 'Month': date_val.month,\\n 'Time': mjd2000(date_val),\\n 'r': pos[2],\\n # -Z = B_r\\n 'B_r': -data.iloc[idx_date].filter(\\n regex='dz_' + str(idx_pos).zfill(3)).values[0],\\n # -X = B_theta\\n 'B_theta': -data.iloc[idx_date].filter(\\n regex='dx_' + str(idx_pos).zfill(3)).values[0],\\n # Y = B_phi\\n 'B_phi': data.iloc[idx_date].filter(\\n regex='dy_' + str(idx_pos).zfill(3)).values[0]\\n })\\n row_list.append(dict_row)\\n data_to_write = pd.DataFrame(row_list)\\n if not df_sigma.empty:\\n data_to_write = pd.concat([data_to_write.reset_index(drop=True),\\n df_sigma.reset_index(drop=True)], axis=1)\\n\\n with open(fname, 'w') as file:\\n file.write(header)\\n # data_to_write.to_csv(fname, na_rep='NaN',\\n # index=False, header=False,\\n # mode='a', sep='\\\\t')\\n for idx, row in data_to_write.iterrows():\\n if not df_sigma.empty:\\n file.write((\\n '%9.5f%13.5f%9d%5d%14.4f%12.2f%15.5f%15.5f%15.5f%10.4f%10.4f%10.4f%12.0f\\\\n')\\n % (row['theta'], row['phi'], row['Year'], row['Month'],\\n row['Time'], row['r'], row['B_r'], row['B_theta'],\\n row['B_phi'], row['sigma_r'], row['sigma_theta'],\\n row['sigma_phi'], row['N_data']))\\n else:\\n file.write((\\n '%9.5f%13.5f%9d%5d%14.4f%12.2f%15.5f%15.5f%15.5f\\\\n')\\n % (row['theta'], row['phi'], row['Year'], row['Month'],\\n row['Time'], row['r'], row['B_r'], row['B_theta'],\\n row['B_phi']))\",\n \"def save(self, filename, format = \\\"text\\\"):\\n #\\n # preparation of data\\n #\\n Data = []\\n Data.append(['Name','Spectrum','Select','MDV','Std'])\\n for fragment in self.mdv.keys():\\n for isotope_number in self.mdv[fragment].keys():\\n if self.mdv[fragment][isotope_number]['use'] == 'use':\\n use_or_no = 1\\n else:\\n use_or_no = 0\\n Data.append([fragment,\\n str(isotope_number),\\n str(use_or_no),\\n str(self.mdv[fragment][isotope_number]['ratio']),\\n str(self.mdv[fragment][isotope_number]['std'])])\\n try:\\n with open(filename, 'w', newline='') as f:\\n import csv\\n if format == \\\"text\\\":\\n writer = csv.writer(f, delimiter='\\\\t')\\n writer.writerows(Data)\\n if format == \\\"csv\\\":\\n writer = csv.writer(f, dialect='excel')\\n writer.writerows(Data)\\n except:\\n return False\\n\\n return True\",\n \"def get_data_format(self):\\n return {\\n \\\"timestamp\\\": self.timestamp,\\n \\\"measure\\\": self.measure,\\n \\\"symbolic\\\": self.symbolic\\n }\",\n \"def write_to_csv(self):\\n\\n dump_list = []\\n\\n # add rows one by one, each as a list, even if only 1 element\\n\\n dump_list.append([\\\"test execution ID\\\",self.ID])\\n dump_list.append([\\\"test execution name\\\",self.name])\\n\\n dump_list.append([\\\"test definition ID\\\",self.test_def_ID])\\n test_def_name = get_indexed_item_from_file(self.test_def_ID, FILE_TEST_DEFINITIONS)\\n dump_list.append([\\\"test definition name\\\",test_def_name])\\n\\n dump_list.append([\\\"associated challenge execution ID\\\",self.challenge_exec_ID])\\n dump_list.append([\\\"user ID\\\",self.user_ID])\\n\\n if self.start_time != None:\\n dump_list.append([\\\"test start time\\\",self.start_time.strftime(\\\"%Y-%m-%d %H:%M:%S\\\")])\\n\\n if self.finish_time != None:\\n dump_list.append([\\\"test finish time\\\",self.finish_time.strftime(\\\"%Y-%m-%d %H:%M:%S\\\")])\\n\\n if self.challenge_start_time != None:\\n dump_list.append([\\\"challenge stop time\\\",self.challenge_start_time.strftime(\\\"%Y-%m-%d %H:%M:%S\\\")])\\n if self.restoration_detection_time != None:\\n dump_list.append([\\\"restoration detection time\\\",self.restoration_detection_time.strftime(\\\"%Y-%m-%d %H:%M:%S\\\")])\\n if self.recovery_time != None:\\n if self.recovery_time.value != None:\\n if type(self.recovery_time.value)==timedelta:\\n # timedelta: days and seconds are attributes, total_seconds() is a method\\n dump_list.append([\\\"MEASURED RECOVERY TIME (s)\\\",self.recovery_time.value.total_seconds()])\\n rtday = self.recovery_time.value.days\\n rthrs = self.recovery_time.value.seconds // 3600\\n rtmin = (self.recovery_time.value.seconds % 3600) // 60\\n rtsec = self.recovery_time.value.seconds % 60\\n rtmil = self.recovery_time.value.microseconds\\n dump_list.append([\\\"MEASURED RECOVERY TIME (days, hours, mins, seconds, microseconds)\\\",\\n rtday, rthrs, rtmin, rtsec, rtmil])\\n\\n if self.associated_metric_values.length() > 0 :\\n dump_list.append([\\\"Metric Values:\\\"])\\n for item in self.associated_metric_values.get_timestamped_metric_values_as_strings():\\n dump_list.append([item])\\n\\n if self.log.length() > 0 :\\n dump_list.append([\\\"Log:\\\"])\\n for item in self.log.get_timestamped_strings():\\n dump_list.append([item])\\n\\n if self.CLI_responses.length() > 0 :\\n dump_list.append([\\\"CLI responses:\\\"])\\n for item in self.CLI_responses.get_timestamped_strings():\\n dump_list.append([item])\\n\\n if self.API_responses.length() > 0 :\\n dump_list.append([\\\"API responses:\\\"])\\n for item in self.API_responses.get_timestamped_strings():\\n dump_list.append([item])\\n\\n try:\\n # output CSV file name: testDefExec + ID + start time + .csv\\n file_name = \\\"testDefExec\\\" + \\\"{0:0=3d}\\\".format(self.test_def_ID) + \\\"-\\\" + self.start_time.strftime(\\\"%Y-%m-%d-%H-%M-%S\\\") + \\\".csv\\\"\\n with open(file_name, \\\"w\\\", newline=\\\"\\\") as file:\\n csv_file_writer = csv.writer(file)\\n csv_file_writer.writerows(dump_list)\\n except Exception as e:\\n print(type(e), e)\\n sys.exit()\",\n \"def __str__(self):\\n s = self.__repr__()\\n data = s.split(',')\\n header = 'PDB id| macro mol.| symmetry | all | year | experiment type'\\n l = len(header)\\n # test data length is correct\\n title = header.split('|')\\n data = data[:4] + data[7:]\\n assert len(data) == len(title)\\n # analyze the header string and create a template for the data\\n # find size of each column\\n s_pos = [i for i in range(l) if header[i] == '|']\\n s_pos.insert(0,0)\\n s_pos.append(l-1)\\n s_lengths = [s_pos[i+1]-s_pos[i] for i in range(len(s_pos)-1)]\\n d_length = [len(d) for d in data]\\n # make sure data length is not longer than the title length\\n s_lengths = [max(x,y) for x,y in zip(s_lengths,d_length)]\\n # create the template\\n s_template = ['{:^%d}'%x for x in s_lengths]\\n s_template = '|'.join(s_template)\\n # combine header and results to a table\\n s_out = s_template.format(*title) + '\\\\n'\\n l = len(s_out)\\n s_out += '-'*l + '\\\\n'\\n s_out += s_template.format(*data) + '\\\\n'\\n s_out = ('{:^%d}\\\\n'%l).format('non-bonded overlaps') + s_out\\n return s_out\",\n \"def dumpData(self,out):\\n #--Get sizes\\n for record in self.records:\\n #--Text\\n if record.name == 'NAME':\\n #--Truncate text?\\n if len(self.text) > 511:\\n self.text = self.text[:511]\\n record.data = self.text\\n record.size = len(self.text)\\n #--Speaker\\n elif record.name == 'ONAM':\\n record.data = self.spId+'\\\\x00'\\n record.size = len(self.spId) + 1\\n record.getSize()\\n record.dump(out)\",\n \"def to_file(self, namefile=None):\\n # Observational data\\n if namefile is None:\\n folder = ''\\n file = self.name.replace(' ', '_')+'.dat'\\n else:\\n folder = os.path.dirname(namefile)\\n file = os.path.basename(namefile)\\n data = np.array([(self.time*u.s + self.tref).jd, self.time, self.flux, self.model, self.flux-self.model])\\n colunm_names = ['Time JD', 'Time relative to {} UTC in seconds'.format(self.tref.iso),\\n 'Observational Flux', 'Modelled Flux', 'Residual O-C']\\n np.savetxt(os.path.join(folder, file), data.T, fmt='%11.8f')\\n f = open(os.path.join(folder, file) + '.label', 'w')\\n for i, name in enumerate(colunm_names):\\n f.write('Column {}: {}\\\\n'.format(i+1, name))\\n f.close()\\n # Complete Model\\n if hasattr(self, 'model_geometric'):\\n data_model = np.array([(self.time_model*u.s + self.tref).jd, self.time_model, self.model_geometric,\\n self.model_fresnel, self.model_star])\\n colunm_names_model = ['Model time JD', 'Model time relative to {} UTC in seconds'.format(self.tref.iso),\\n 'Geometric Model', 'Model with Fresnel diffraction', 'Model with star diameter']\\n np.savetxt(os.path.join(folder, 'model_'+file), data_model.T, fmt='%11.8f')\\n f = open(os.path.join(folder, 'model_'+file)+'.label', 'w')\\n for i, name in enumerate(colunm_names_model):\\n f.write('Column {}: {}\\\\n'.format(i+1, name))\\n f.close()\",\n \"def _format_data(self):\\n formatted_data = []\\n\\n for row in self._data_agg_by_mean_value.iterrows():\\n \\n car_make = row[0]\\n mean_car_value = round(row[1][0], 2)\\n formatted_data.append({'car_make': car_make, 'mean_car_value': mean_car_value})\\n\\n return formatted_data\",\n \"def format(self, time, key_to_fluxes, names):\\n yield [self._flag_char + 'time' + self.__time_fmt.format(time)] * 3\\n yield [self._flag_char + 'data'] * 3\\n # for name, current in zip(names, current):\\n\\n for itar_1, name_i in enumerate(names):\\n for jtar_1, name_j in enumerate(names):\\n if not self.ok_write(itar_1, jtar_1): continue\\n\\n \\\"\\\"\\\"fluxes = [None for k in self.__decomps]\\n for i, pot_type in enumerate(self.__decomps):\\n fluxes[i] = key_to_fluxes[pot_type][itar_1, jtar_1]\\n\\n fluxes_ar = numpy.array(fluxes).transpose()\\n fluxes_ar = fluxes_ar.reshape(self.__dim, -1)\\n\\n line = [self.__data_fmt.format(name_i, name_j, *k)\\n for k in fluxes_ar]\\n \\\"\\\"\\\"\\n\\n fluxes = numpy.zeros([self.__num_decomps, self.__dim])\\n for i, pot_type in enumerate(self.__decomps):\\n fluxes[i] = key_to_fluxes[pot_type][itar_1, jtar_1]\\n\\n line = [self.__data_fmt.format(name_i, name_j, *fluxes[:,i])\\n for i, axis in enumerate(self.__axes)]\\n yield line\",\n \"def _text_write_preprocess(self):\\n self.check()\\n\\n max_name_len = np.max([len(name) for name in self.name])\\n fieldtypes = [\\\"U\\\" + str(max_name_len), \\\"f8\\\", \\\"f8\\\"]\\n comp_names = self._get_lon_lat_component_names()\\n frame_obj = self._get_frame_obj()\\n frame_desc_str = _get_frame_desc_str(frame_obj)\\n\\n component_fieldnames = []\\n for comp_name in comp_names:\\n # This will add e.g. ra_J2000 and dec_J2000 for FK5\\n component_fieldnames.append(comp_name + \\\"_\\\" + frame_desc_str)\\n fieldnames = [\\\"source_id\\\"] + component_fieldnames\\n stokes_names = [\\\"I\\\", \\\"Q\\\", \\\"U\\\", \\\"V\\\"]\\n fieldshapes = [()] * 3\\n\\n if self.stokes_error is not None:\\n stokes_error_names = [(f\\\"{k}_error\\\") for k in [\\\"I\\\", \\\"Q\\\", \\\"U\\\", \\\"V\\\"]]\\n\\n n_stokes = 0\\n stokes_keep = []\\n for si, total in enumerate(np.nansum(self.stokes.to(\\\"Jy\\\"), axis=(1, 2))):\\n if total > 0:\\n fieldnames.append(stokes_names[si])\\n fieldshapes.append((self.Nfreqs,))\\n fieldtypes.append(\\\"f8\\\")\\n if self.stokes_error is not None:\\n fieldnames.append(stokes_error_names[si])\\n fieldshapes.append((self.Nfreqs,))\\n fieldtypes.append(\\\"f8\\\")\\n n_stokes += 1\\n stokes_keep.append(total > 0)\\n\\n assert n_stokes >= 1, \\\"No components with nonzero flux.\\\"\\n\\n if self.freq_array is not None:\\n if self.spectral_type == \\\"subband\\\":\\n fieldnames.append(\\\"subband_frequency\\\")\\n else:\\n fieldnames.append(\\\"frequency\\\")\\n fieldtypes.append(\\\"f8\\\")\\n fieldshapes.extend([(self.Nfreqs,)])\\n elif self.reference_frequency is not None:\\n fieldnames.extend([(\\\"reference_frequency\\\")])\\n fieldtypes.extend([\\\"f8\\\"])\\n fieldshapes.extend([()] * n_stokes + [()])\\n if self.spectral_index is not None:\\n fieldnames.append(\\\"spectral_index\\\")\\n fieldtypes.append(\\\"f8\\\")\\n fieldshapes.append(())\\n\\n if hasattr(self, \\\"_rise_lst\\\"):\\n fieldnames.append(\\\"rise_lst\\\")\\n fieldtypes.append(\\\"f8\\\")\\n fieldshapes.append(())\\n\\n if hasattr(self, \\\"_set_lst\\\"):\\n fieldnames.append(\\\"set_lst\\\")\\n fieldtypes.append(\\\"f8\\\")\\n fieldshapes.append(())\\n\\n dt = np.dtype(list(zip(fieldnames, fieldtypes, fieldshapes)))\\n\\n arr = np.empty(self.Ncomponents, dtype=dt)\\n arr[\\\"source_id\\\"] = self.name\\n\\n for comp_ind, comp in enumerate(comp_names):\\n arr[component_fieldnames[comp_ind]] = getattr(self.skycoord, comp).deg\\n\\n for ii in range(4):\\n if stokes_keep[ii]:\\n arr[stokes_names[ii]] = self.stokes[ii].T.to(\\\"Jy\\\").value\\n if self.stokes_error is not None:\\n arr[stokes_error_names[ii]] = self.stokes_error[ii].T.to(\\\"Jy\\\").value\\n\\n if self.freq_array is not None:\\n if self.spectral_type == \\\"subband\\\":\\n arr[\\\"subband_frequency\\\"] = self.freq_array.to(\\\"Hz\\\").value\\n else:\\n arr[\\\"frequency\\\"] = self.freq_array.to(\\\"Hz\\\").value\\n elif self.reference_frequency is not None:\\n arr[\\\"reference_frequency\\\"] = self.reference_frequency.to(\\\"Hz\\\").value\\n if self.spectral_index is not None:\\n arr[\\\"spectral_index\\\"] = self.spectral_index\\n\\n if hasattr(self, \\\"_rise_lst\\\"):\\n arr[\\\"rise_lst\\\"] = self._rise_lst\\n if hasattr(self, \\\"_set_lst\\\"):\\n arr[\\\"set_lst\\\"] = self._set_lst\\n\\n return arr\",\n \"def format(self, record):\\n\\n\\n if not hasattr(record, 'filename_'):\\n record.file_indicator = '-'\\n else:\\n record.file_indicator = os.path.relpath(record.filename_.strip(),\\n self.study_dir)\\n record.line_indicator = self.format_aggregated(\\n record,\\n 'line_number',\\n ' line %d:',\\n ' lines [%s]:',\\n optional=True)\\n record.column_indicator = self.format_aggregated(\\n record,\\n 'column_number',\\n ' column %d:',\\n ' columns [%s]:',\\n optional=True)\\n record.cause_indicator = self.format_aggregated(\\n record,\\n 'cause',\\n \\\"; value encountered: '%s'\\\",\\n \\\"; values encountered: ['%s']\\\",\\n join_string=\\\"', '\\\",\\n optional=True)\\n\\n # format the string based on these fields\\n formatted_result = super(LogfileStyleFormatter, self).format(record)\\n\\n # prepend an empty line if the filename is different than before\\n current_filename = getattr(record, 'filename_', '')\\n if (self.previous_filename is not None and\\n current_filename != self.previous_filename):\\n formatted_result = '\\\\n' + formatted_result\\n self.previous_filename = current_filename\\n\\n return formatted_result\",\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 format(self, record):\\n row = [self.formatTime(record, self.datefmt), record.name, record.levelname]\\n keys = filter(self.filterer, record.__dict__)\\n extra = [record.__dict__[k] for k in keys]\\n\\n self.writer.writerow(row + extra + [record.getMessage()])\\n data = self.output.getvalue()\\n self.output.truncate(0)\\n self.output.seek(0)\\n return data.strip()\",\n \"def formatted_data(self):\\n return self._formatted_data\",\n \"def _write_report_content(self):\\n self.worksheet.batch_update(self.report_data)\\n gsfmt.format_cell_ranges(self.worksheet, self.report_formatting)\",\n \"def general_simulation_data(self):\\n iterations = {}\\n nstates = {}\\n natoms = {}\\n for phase in self.phases:\\n positions = self.ncfiles[phase].variables['positions']\\n iterations[phase], nstates[phase], natoms[phase], spatial = positions.shape\\n\\n leniter = max(len('Iterations'), *[len(str(i)) for i in iterations.values()]) + 2\\n lenstates = max(len('States'), *[len(str(i)) for i in nstates.values()]) + 2\\n lennatoms = max(len('Num Atoms'), *[len(str(i)) for i in natoms.values()]) + 2\\n lenleftcol = max(len('Phase'), *[len(phase) for phase in self.phases]) + 2\\n\\n lines = []\\n headstring = ''\\n headstring += ('{:^' + '{}'.format(lenleftcol) + '}').format('Phase') + '|'\\n headstring += ('{:^' + '{}'.format(leniter) + '}').format('Iterations') + '|'\\n headstring += ('{:^' + '{}'.format(lenstates) + '}').format('States') + '|'\\n headstring += ('{:^' + '{}'.format(lennatoms) + '}').format('Num Atoms')\\n lines.append(headstring)\\n lenline = len(headstring)\\n topdiv = '=' * lenline\\n lines.append(topdiv)\\n for phase in self.phases:\\n phasestring = ''\\n phasestring += ('{:^' + '{}'.format(lenleftcol) + '}').format(phase) + '|'\\n phasestring += ('{:^' + '{}'.format(leniter) + '}').format(iterations[phase]) + '|'\\n phasestring += ('{:^' + '{}'.format(lenstates) + '}').format(nstates[phase]) + '|'\\n phasestring += ('{:^' + '{}'.format(lennatoms) + '}').format(natoms[phase])\\n lines.append(phasestring)\\n lines.append('-' * lenline)\\n\\n for line in lines:\\n print(line)\\n self.iterations = iterations\\n self._general_run = True\",\n \"def to_dat(self, **kwargs):\\n return self.write(file_format=\\\"dat\\\", **kwargs)\",\n \"def format(self, data):\\r\\n for name, value in sorted(data.items()):\\r\\n line = '{name} = {value}\\\\n'.format(\\r\\n name=name,\\r\\n value=value,\\r\\n )\\r\\n yield line\",\n \"def gen_sample_report():\\n sample_report().save()\",\n \"def write(self): \\n # Open csv file\\n with open(self.file_name, 'w', newline='') as file:\\n self._writer = csv.writer(file)\\n \\n # Write header rows\\n# self.write_sim_header_data(self.trace.sim.get_data())\\n \\n # Write trace table\\n self._writer.writerow(['Record #', 'Rep', 'Time',\\n 'Priority', 'Record Type', 'Name'])\\n for trace_record in self.trace._record_list:\\n self._writer.writerow(trace_record.get_row())\\n file.close()\",\n \"def run(self):\\r\\n #print 'WriteFITS.run'\\r\\n\\r\\n # construct the name of the file\\r\\n runtime = self.previous_results['runtime']\\r\\n fitsname = '%s.fits' % runtime\\r\\n\\r\\n # get list of instrument observations\\r\\n observe = self.previous_results['observe']\\r\\n obs_timeline = observe['observed_timeline']\\r\\n observed_times = obs_timeline.keys()\\r\\n observed_times.sort()\\r\\n\\r\\n # construct lists of the values to be stored in each Table column\\r\\n for t in observed_times:\\r\\n timelist = []\\r\\n smec_position = []\\r\\n smec_nominal_position = []\\r\\n flag = []\\r\\n data = []\\r\\n pointing1_x = []\\r\\n pointing1_y = []\\r\\n pointing2_x = []\\r\\n pointing2_y = []\\r\\n\\r\\n config = obs_timeline[t]\\r\\n\\r\\n timelist.append(config.time)\\r\\n smec_position.append(config.smec_position)\\r\\n smec_nominal_position.append(config.smec_nominal_position)\\r\\n flag.append(config.flag)\\r\\n data.append(config.data)\\r\\n pointing1_x.append(config.pointing1_x)\\r\\n pointing1_y.append(config.pointing1_y)\\r\\n pointing2_x.append(config.pointing2_x)\\r\\n pointing2_y.append(config.pointing2_y)\\r\\n\\r\\n # create a Header object and primary HDU - this just contains\\r\\n # some very basic, general information\\r\\n prihdr = pyfits.Header()\\r\\n prihdr['COMMENT'] = 'This FITS file was created by pyfiins at %s' % \\\\\\r\\n runtime\\r\\n prihdu = pyfits.PrimaryHDU(header=prihdr)\\r\\n\\r\\n # create list of Header Data Unit objects, include the primary HDU\\r\\n hdulist = pyfits.HDUList([prihdu])\\r\\n\\r\\n # create an HDU to contain the Table and append it to the list\\r\\n hdulist.append(pyfits.BinTableHDU.from_columns(\\r\\n pyfits.ColDefs([\\r\\n pyfits.Column(name='Time', format='D',\\r\\n array=np.array(timelist)),\\r\\n pyfits.Column(name='SMEC Position', format='E',\\r\\n array=np.array(smec_position)),\\r\\n pyfits.Column(name='SMEC Nominal Position', format='E',\\r\\n array=np.array(smec_nominal_position)),\\r\\n pyfits.Column(name='Flag', format='L',\\r\\n array=np.array(flag)),\\r\\n pyfits.Column(name='Data', format='E',\\r\\n array=np.array(data)),\\r\\n pyfits.Column(name='Pointing1 X', format='E',\\r\\n array=np.array(pointing1_x)),\\r\\n pyfits.Column(name='Pointing1 Y', format='E',\\r\\n array=np.array(pointing1_y)),\\r\\n pyfits.Column(name='Pointing2 X', format='E',\\r\\n array=np.array(pointing2_x)),\\r\\n pyfits.Column(name='Pointing2 Y', format='E',\\r\\n array=np.array(pointing2_y))])))\\r\\n\\r\\n # write the HDU list to a file\\r\\n hdulist.writeto(fitsname, clobber=True)\\r\\n self.result['fitsfile'] = fitsname\\r\\n\\r\\n return self.result\",\n \"def output_data(self):\\n if not self.is_record:\\n logging.error('Env: no record to output!')\\n else:\\n control_data = pd.DataFrame(self.control_data)\\n control_data.to_csv(self.output_path + ('%s_%s_control.csv' % (self.name, self.agent)))\",\n \"def update_record(self, d: dict) -> None:\\n super().update_record(d)\\n d.update(\\n dataset_doses=str_list(self.doses),\\n dataset_ns=str_list(self.ns),\\n dataset_stdevs=str_list(self.stdevs),\\n dataset_means=str_list(self.means),\\n )\",\n \"def _format_sample_sheet_data(sample_ids, i7_name, i7_seq, i5_name, i5_seq,\\n wells=None, sample_plate='', sample_proj='',\\n description=None, lanes=[1], sep=','):\\n if (len(sample_ids) != len(i7_name) != len(i7_seq) !=\\n len(i5_name) != len(i5_seq)):\\n raise ValueError('Sample information lengths are not all equal')\\n\\n if wells is None:\\n wells = [''] * len(sample_ids)\\n if description is None:\\n description = [''] * len(sample_ids)\\n\\n data = [sep.join([\\n 'Lane', 'Sample_ID', 'Sample_Name', 'Sample_Plate', 'Sample_Well',\\n 'I7_Index_ID', 'index', 'I5_Index_ID', 'index2', 'Sample_Project',\\n 'Description'])]\\n\\n for lane in lanes:\\n for i, sample in enumerate(sample_ids):\\n line = sep.join([str(lane), sample, sample, sample_plate,\\n wells[i], i7_name[i], i7_seq[i], i5_name[i],\\n i5_seq[i], sample_proj, description[i]])\\n data.append(line)\\n\\n return '\\\\n'.join(data)\",\n \"def _phot_format_for_save(self, names = ('MJD', 'flux', 'flux_err', 'filter'), formats = ('.3f','.5g', '.5g', ''),\\n filters = False, verbose = False, sort=False):\\n\\n\\n if sort:\\n save_table = self.phot[names]\\n save_table = save_table[save_table.argsort()]\\n else:\\n save_table = self.phot[names]\\n\\n for z in zip(names, formats):\\n save_table[z[0]].format = z[1]\\n\\n if filters:\\n save_table = save_table[np.in1d(save_table[\\\"filter\\\"], filters)]\\n\\n if verbose:\\n print(save_table)\\n return save_table\",\n \"def recorder(self):\\n if not self.record_mode:\\n return\\n self.record_value['X1'].append(self.X1) # 每一代记录一个X1，shape(particles_num,dim)\\n self.record_value['V1'].append(self.V1) # 每一代记录一个V1，shape(particles_num,dim)\\n self.record_value['Y1'].append(self.Y1) # 每一代记录一个Y1，shape(particles_num,dim)\\n self.record_value['X2'].append(self.X2) # 每一代记录一个X2，shape(particles_num,dim)\\n self.record_value['V2'].append(self.V2) # 每一代记录一个V2，shape(particles_num,dim)\\n self.record_value['Y2'].append(self.Y2) # 每一代记录一个Y2，shape(particles_num,dim)\\n self.record_value['w'].append(self.w) # 每一代记录一个w，shape(particles_num,dim)\\n self.record_value['pop01'].append(self.pop01) # 每一代记录一个种群规模，shape(max_iter,1)\\n self.record_value['pop02'].append(self.pop02) # 每一代记录一个种群规模，shape(max_iter,1)\\n self.record_value['gbest_GPSO'].append(self.gbest_y1) # 每一代记录一个子种群最优，shape(max_iter,1)\\n self.record_value['gbest_LPSO'].append(self.gbest_y2) # 每一代记录一个种群最优，shape(max_iter,1)\\n self.record_value['gbest_All'].append(self.gbestY) # 每一代记录一个Y，shape(max_iter,1)\",\n \"def print_data(self):\\n total_score = 0.0\\n\\n title_game = 'Game'\\n title_word = 'Word'\\n title_word_status = 'Word Status'\\n title_bad_guesses = 'Bad Guesses'\\n title_missed_letters = 'Missed Letters'\\n title_total_score = 'Total score'\\n\\n if not record_word:\\n print(\\\"No words played.\\\")\\n else:\\n print('%-5s %-10s %-12s %-5s %-5s %s' %(title_game,title_word, title_word_status, title_bad_guesses, title_missed_letters,title_total_score))\\n print('---- ---- ------------ ----------- -------------- -----------')\\n for x in range(len(record_word)):\\n print('%-5s %-10s %-13s %-11s %-13s %.2f'%(record_game[x],record_word[x],record_word_status[x],record_bad_guesses[x],record_missed_letters[x],record_total_score[x]))\\n\\n for x in range(len(record_total_score)):\\n total_score = total_score + record_total_score[x]\\n\\n print('\\\\nFinal Score: %.2f' %total_score)\",\n \"def create_sim_data_file(outdata_dir, sim_file):\\n # Open output file, write header\\n outfile = open(sim_file, 'w')\\n #outfile.write(\\\"realization, Magnitude, Mechanism,\\\"\\n # \\\"Distance, Station Name,\\\")\\n outfile.write(\\\"%10.5f\\\" % (PERIODS[0]))\\n for period in PERIODS[1:]:\\n outfile.write(\\\",%10.5f\\\" % period)\\n outfile.write(\\\"\\\\n\\\")\\n\\n realizations = sorted(os.listdir(outdata_dir))\\n for realization in realizations:\\n basedir = os.path.join(outdata_dir, realization)\\n stations = sorted(glob.glob(\\\"%s%s*.rd50\\\" % (basedir, os.sep)))\\n for station in stations:\\n #st_id = os.path.basename(station)\\n # Find just the numeric part of the station id\\n #st_id = st_id[st_id.find('-')+1:st_id.find(\\\".rd50\\\")]\\n input_file = open(station, 'r')\\n # Write first part of line\\n #outfile.write(\\\"%s, %2.1f, %s, %d, %s, \\\" %\\n # (realization, MAGNITUDE, MECH, DIST, st_id))\\n add_comma = 0\\n for line in input_file:\\n line = line.strip()\\n # Skip comments\\n if line.startswith(\\\"#\\\"):\\n continue\\n pieces = [float(item) for item in line.split()]\\n # Write RotD50 value to file\\n if add_comma:\\n outfile.write(\\\",%10.7f\\\" % (pieces[3]))\\n else:\\n add_comma = 1\\n outfile.write(\\\"%10.7f\\\" % (pieces[3]))\\n # Done with this file\\n input_file.close()\\n # Add newline to outfile\\n outfile.write(\\\"\\\\n\\\")\\n # All done!\\n outfile.close()\",\n \"def simulation_finished(self, data):\\n self._logger.info(u\\\"simulation finished\\\")\\n self.statusLabel.setText(u\\\"simulation finished.\\\")\\n\\n self.actSimulate.setDisabled(False)\\n self.shortRunSimulation.setEnabled(True)\\n self.shortRunRegimeBatch.setEnabled(True)\\n self.actPlayPause.setDisabled(False)\\n self.shortPlayPause.setEnabled(True)\\n self.shortSaveResult.setEnabled(True)\\n self.actStop.setDisabled(False)\\n self.actSave.setDisabled(False)\\n self.speedDial.setDisabled(False)\\n self.timeSlider.setDisabled(False)\\n\\n self.sim.simulationProgressChanged.disconnect(self.guiProgress.setValue)\\n self.statusBar().removeWidget(self.guiProgress)\\n\\n self.timeSlider.triggerAction(QtGui.QAbstractSlider.SliderToMinimum)\\n\\n self.currentDataset = data\\n self._read_results()\\n self._update_data_list()\\n self._update_plots()\\n\\n self.stop_animation()\\n # self.playAnimation()\\n\\n if self.runningBatch:\\n regime_name = self._regimes[self._current_regime_index][\\\"Name\\\"]\\n self._save_data(regime_name)\\n self.regimeFinished.emit()\\n else:\\n self.actExecuteRegimes.setDisabled(False)\",\n \"def _write_csv(self, file_name, metadata, dates, data, disclaimer,\\n float_fmt):\\n\\n version = '# file_format: pysonde csv format version 1.0\\\\n'\\n header = [version]\\n #prepend parameter list and units with single #\\n param_header = '# datetime, '\\n unit_header = '# yyyy/mm/dd HH:MM:SS, '\\n dtype_fmts = ['|S19']\\n fmt = '%s, '\\n for param in np.sort(data.keys()):\\n param_header += param + ', '\\n try:\\n unit_header += data[param].dimensionality.keys()[0].symbol + \\\\\\n ', '\\n except:\\n unit_header += 'nd, '\\n fill_value = float(metadata['fill_value']) * data[param].units\\n data[param][np.isnan(data[param])] = fill_value\\n dtype_fmts.append('f8')\\n fmt += float_fmt + ', '\\n\\n #prepend disclaimer and metadata with ##\\n for line in disclaimer.splitlines():\\n header.append('# disclaimer: ' + line + '\\\\n')\\n\\n #for key,val in metadata.items():\\n # if not isinstance(val, np.ndarray):\\n # header.append('# ' + str(key) + ': ' + str(val) + '\\\\n')\\n # else:\\n # param_header += key + ', '\\n # unit_header += 'n/a, '\\n # dtype_fmts.append(val.dtype)\\n # fmt += '%s, '\\n for key in np.sort(metadata.keys()):\\n if not isinstance(metadata[key], np.ndarray):\\n header.append('# %s: %s\\\\n' % (str(key), str(metadata[key])))\\n\\n else:\\n param_header += key + ', '\\n unit_header += 'n/a, '\\n dtype_fmts.append(metadata[key].dtype)\\n fmt += '%s, '\\n\\n #remove trailing commas\\n param_header = param_header[:-2] + '\\\\n'\\n unit_header = unit_header[:-2] + '\\\\n'\\n fmt = fmt[:-2]\\n\\n header.append('# timezone: ' + str(self.default_tzinfo) + '\\\\n')\\n header.append(param_header)\\n header.append(unit_header)\\n\\n dtype = np.dtype({\\n 'names': param_header.replace(' ', '').strip('#\\\\n').split(','),\\n 'formats': dtype_fmts})\\n\\n write_data = np.zeros(dates.size, dtype=dtype)\\n write_data['datetime'] = np.array(\\n [datetime.datetime.strftime(dt, '%Y/%m/%d %H:%M:%S')\\n for dt in dates])\\n\\n for key, val in metadata.items():\\n if isinstance(val, np.ndarray):\\n write_data[key] = val\\n\\n for param in data.keys():\\n write_data[param] = data[param]\\n\\n #start writing file\\n fid = open(file_name, 'w')\\n fid.writelines(header)\\n np.savetxt(fid, write_data, fmt=fmt)\\n fid.close()\",\n \"def generatePandasRecordLayout(resource,workspace,\\n file=sys.stdout,\\n format=\\\"fixed\\\",\\n instance=\\\"\\\",\\n useClassificationBank=False,\\n importPd=False\\n):\\n\\n # Init\\n bank = resource.getBank()\\n id = resource.getId()\\n print(\\\"Pandas: Generating Record \\\"+id)\\n\\n # Collect start/end and variable names\\n fwf_colspecs=[]\\n fwf_widths=[]\\n fwf_names=[]\\n fwf_converters=[]\\n\\n nextStart=1 # use for running column width in case start/end are not provided\\n index=0\\n for layout in workspace.getResources(type=\\\"layout\\\",bank=id):\\n if format==\\\"fixed\\\":\\n # FIXED FORMAT\\n # Gather basic properties\\n start=layout.getPropertyValue(\\\"start\\\")\\n if start: start = int(start)\\n end=layout.getPropertyValue(\\\"end\\\")\\n if end: end = int(end)\\n width=layout.getPropertyValue(\\\"width\\\")\\n if width: width=int(width)\\n \\n # compute missing width/start/end properties\\n if not start: start = nextStart\\n if start and (not end) and width: end = start+width-1\\n if not start or not end:\\n layout.dump()\\n raise RuntimeError(\\\"width/start/end not found for \\\"+layout.source+\\\" \\\"+layout.getName())\\n if not width: width = start-end+1\\n \\n # record value\\n fwf_colspecs.append((start-1,end)) # for pandas, position is zero based and end is exclusive\\n fwf_widths.append(width)\\n\\n # keep track of where we are\\n nextStart = start + width\\n\\n # data type\\n converter = \\\"str\\\"\\n datatype=None\\n if layout.hasProperty(\\\"datatype\\\"):\\n datatype = layout.getPropertyValue(\\\"datatype\\\")\\n elif layout.hasProperty(\\\"datatype[generic]\\\"):\\n datatype = layout.getPropertyValue(\\\"datatype[generic]\\\")\\n if datatype and re.match(\\\"^N.*\\\",datatype,re.IGNORECASE):\\n if layout.hasProperty(\\\"decimals\\\") and layout.getPropertyValue(\\\"decimals\\\")==0:\\n converter=\\\"int\\\"\\n else:\\n converter=\\\"float\\\"\\n fwf_converters.append(converter)\\n\\n else:\\n # DELIMITED\\n pass\\n index += 1\\n # label\\n fwf_names.append(layout.getName())\\n ### for\\n\\n print(\\\"import pandas as pd\\\",file=file)\\n print(file=file)\\n print(\\\"data=pd.read_fwf('\\\"+instance+\\\"'\\\",sep='',file=file)\\n print(\\\",colspecs=\\\",fwf_colspecs,sep='',file=file)\\n #print(\\\",widths=\\\",fwf_widths,sep='',file=file)\\n print(\\\",names=\\\",fwf_names,sep='',file=file)\\n converters=\\\"{\\\"\\n for index,value in enumerate(fwf_converters):\\n if index>0: converters+=\\\",\\\"\\n converters+=str(index)+\\\":\\\"+value\\n converters+=\\\"}\\\"\\n print(\\\",converters=\\\",converters,sep='',file=file)\\n print(\\\")\\\",file=file)\\n print(file=file)\\n print(\\\"print(data.head())\\\",file=file)\\n print(\\\"print(data.describe())\\\",file=file)\",\n \"def __mdformat(self, dat):\\n\\t\\tif type(dat) is types.FloatType:\\n\\t\\t\\treturn round(dat, 3)\\n\\t\\telse:\\n\\t\\t\\treturn dat\",\n \"def save_data(self):\\n\\n #\\n # t=self.t[0:-1:self.R].reshape([self.t[0:-1:self.R].shape[0],1])\\n\\n def deterministic_data():\\n t = self.dt * self.tau\\n Ueem1 = self.Xeem[:, 0]\\n Ueem2 = self.Xeem[:, 1]\\n Ueem3 = self.Xeem[:, 2]\\n Uem1 = self.Xem[:, 0]\\n Uem2 = self.Xem[:, 1]\\n Uem3 = self.Xem[:, 2]\\n Ustk1 = self.Xstkm[:, 0]\\n Ustk2 = self.Xstkm[:, 1]\\n Ustk3 = self.Xstkm[:, 2]\\n tagPar = np.array([\\n 'k = ',\\n 'r = ',\\n 'T0 = ',\\n 'N = ',\\n 'R = ',\\n 'T = ',\\n 'dt = ',\\n 'Dt = ',\\n 'L = ',\\n 'gamma = ',\\n 'eta = ',\\n 'lambda =',\\n 'delta = ',\\n 'beta = ',\\n 'a = ',\\n 'N0 = ',\\n 'u = ',\\n 'sigma1 = ',\\n 'sigma2 = ',\\n 'x01 = ',\\n 'x02 = ',\\n 'x03 = ',\\n ])\\n ParValues = np.array([\\n self.k,\\n self.r,\\n self.T0,\\n self.N,\\n self.R,\\n self.T,\\n self.dt,\\n self.Dt,\\n self.L,\\n self.gamma,\\n self.eta,\\n self.Lambda,\\n self.delta,\\n self.beta,\\n self.a,\\n self.NN,\\n self.u,\\n self.sigma1,\\n self.sigma2,\\n self.Xzero[0, 0],\\n self.Xzero[0, 1],\\n self.Xzero[0, 2]\\n ])\\n strPrefix = str(self.Dt)\\n name1 = 'DetParameters' + strPrefix + '.txt'\\n name2 = 'DetSolution' + strPrefix + '.txt'\\n name3 = 'DetRefSolution' + str(self.dt) + '.txt'\\n\\n PARAMETERS = np.column_stack((tagPar, ParValues))\\n np.savetxt(name1, PARAMETERS, delimiter=\\\" \\\", fmt=\\\"%s\\\")\\n np.savetxt(name2,\\n np.transpose(\\n (\\n t, Uem1, Uem2, Uem3, Ustk1, Ustk2, Ustk3,\\n )\\n ), fmt='%1.8f', delimiter='\\\\t')\\n np.savetxt(name3,\\n np.transpose(\\n (\\n self.t, Ueem1, Ueem2, Ueem3,\\n )\\n ), fmt='%1.8f', delimiter='\\\\t')\\n\\n def stochastic_data():\\n \\\"\\\"\\\"\\n t = self.dt * self.tau\\n Ueem1 = self.Xeem[:, 0]\\n Ueem2 = self.Xeem[:, 1]\\n Ueem3 = self.Xeem[:, 2]\\n Uem1 = self.Xem[:, 0]\\n Uem2 = self.Xem[:, 1]\\n Uem3 = self.Xem[:, 2]\\n Ustk1 = self.Xstkm[:, 0]\\n Ustk2 = self.Xstkm[:, 1]\\n Ustk3 = self.Xstkm[:, 2]\\n Utem1 = self.Xtem[:, 0]\\n Utem2 = self.Xtem[:, 1]\\n Utem3 = self.Xtem[:, 2]\\n \\\"\\\"\\\"\\n\\n tagPar = np.array([\\n 'k = ',\\n 'r = ',\\n 'T0 = ',\\n 'N = ',\\n 'R = ',\\n 'T = ',\\n 'dt = ',\\n 'Dt = ',\\n 'L = ',\\n 'gamma = ',\\n 'eta = ',\\n 'lambda =',\\n 'delta = ',\\n 'beta = ',\\n 'a = ',\\n 'N0 = ',\\n 'u = ',\\n 'sigma1 = ',\\n 'sigma2 = ',\\n 'x01 = ',\\n 'x02 = ',\\n 'x03 = ',\\n ])\\n ParValues = np.array([\\n self.k,\\n self.r,\\n self.T0,\\n self.N,\\n self.R,\\n self.T,\\n self.dt,\\n self.Dt,\\n self.L,\\n self.gamma,\\n self.eta,\\n self.Lambda,\\n self.delta,\\n self.beta,\\n self.a,\\n self.NN,\\n self.u,\\n self.sigma1,\\n self.sigma2,\\n self.Xzero[0, 0],\\n self.Xzero[0, 1],\\n self.Xzero[0, 2]\\n ])\\n strPrefix = str(self.Dt)\\n name1 = 'StoParameters' + strPrefix + '.txt'\\n '''\\n name2 = 'StoSolution' + strPrefix + '.txt'\\n name3 = 'StoRefSolution' + str(self.dt) + '.txt'\\n '''\\n PARAMETERS = np.column_stack((tagPar, ParValues))\\n np.savetxt(name1, PARAMETERS, delimiter=\\\" \\\", fmt=\\\"%s\\\")\\n '''\\n np.save(name2,\\n np.transpose(\\n (\\n t, Uem1, Uem2, Uem3, Ustk1, Ustk2, Ustk3, Utem1,\\n Utem2, Utem3\\n )\\n ))\\n np.savetxt(name3,\\n np.transpose(\\n (\\n self.t, Ueem1, Ueem2, Ueem3\\n )\\n ))\\n if self.sigma1 == 0.0:\\n if self.sigma2 == 0.0:\\n DeterministicData()\\n return\\n StochasticData()\\n '''\\n\\n return\",\n \"def recordData( numNeurons, Soma ):\\n\\tvec = {}\\n\\t#Record appropriate number of potentials.\\n\\tfor var in range(0,numNeurons):\\n \\t\\tvec[str(var + 1)] = h.Vector()\\n \\t\\tvec[str(var + 1)].record(Soma[var](0.5)._ref_v)\\n\\t#Record time.\\n\\tvec['t '] = h.Vector()\\n\\tvec ['t '].record(h._ref_t)\\n\\treturn vec\",\n \"def __str__(self):\\n if self.filename:\\n filename = self.filename\\n else:\\n filename = 'Unknown'\\n if self.endian == '<':\\n endian = 'Little Endian'\\n else:\\n endian = 'Big Endian'\\n ret_val = ('FILE: %s\\\\nRecord Offset: %i byte\\\\n' +\\n 'Header Endianness: %s\\\\n\\\\n') % \\\\\\n (filename, self.record_offset, endian)\\n ret_val += 'FIXED SECTION OF DATA HEADER\\\\n'\\n for key in self.fixed_header.keys():\\n ret_val += '\\\\t%s: %s\\\\n' % (key, self.fixed_header[key])\\n ret_val += '\\\\nBLOCKETTES\\\\n'\\n for key in self.blockettes.keys():\\n ret_val += '\\\\t%i:' % key\\n if not len(self.blockettes[key]):\\n ret_val += '\\\\tNOT YET IMPLEMENTED\\\\n'\\n for _i, blkt_key in enumerate(self.blockettes[key].keys()):\\n if _i == 0:\\n tabs = '\\\\t'\\n else:\\n tabs = '\\\\t\\\\t'\\n ret_val += '%s%s: %s\\\\n' % (tabs, blkt_key,\\n self.blockettes[key][blkt_key])\\n ret_val += '\\\\nCALCULATED VALUES\\\\n'\\n ret_val += '\\\\tCorrected Starttime: %s\\\\n' % self.corrected_starttime\\n return ret_val\",\n \"def record(self):\\n # TODO: record the data\",\n \"def tabulate(self) -> str:\\n items = [\\n ('Number of stations', self._num_stations),\\n ('Loss probability', self.drop_prob),\\n ]\\n\\n for node in range(self._num_stations):\\n items.append((f'[[ STATION #{node} ]]', ''))\\n\\n ssize = self.system_size[node]\\n qsize = self.queue_size[node]\\n busy = self.busy[node]\\n\\n ssize_pmf = [ssize.pmf(x) for x in range(ssize.truncated_at + 1)]\\n qsize_pmf = [qsize.pmf(x) for x in range(qsize.truncated_at + 1)]\\n busy_pmf = [busy.pmf(x) for x in range(busy.truncated_at + 1)]\\n\\n items.extend([\\n ('System size PMF', str_array(ssize_pmf)),\\n ('System size average', ssize.mean),\\n ('System size std.dev.', ssize.std),\\n ('Queue size PMF', str_array(qsize_pmf)),\\n ('Queue size average', qsize.mean),\\n ('Queue size std.dev.', qsize.std),\\n ('Busy PMF', str_array(busy_pmf)),\\n ('Utilization', self.get_utilization(node)),\\n ('Drop probability', self.drop_prob[node]),\\n ('Delivery probability', self.delivery_prob[node]),\\n ('Departures, average', self.departures[node].avg),\\n ('Departures, std.dev.', self.departures[node].std),\\n ('Response time, average', self.response_time[node].avg),\\n ('Response time, std.dev.', self.response_time[node].std),\\n ('Wait time, average', self.wait_time[node].avg),\\n ('Wait time, std.dev.', self.wait_time[node].std),\\n ('End-to-end delays, average', self.delivery_delays[node].avg),\\n ('End-to-end delays, std.dev.', self.delivery_delays[node].std),\\n ])\\n return tabulate(items, headers=('Param', 'Value'))\",\n \"def transmission_summarise():\\n\\n study_description = self.processDicomField(dcm_info, \\\"StudyDescription\\\")\\n study_date = self.processDicomField(dcm_info, \\\"StudyDate\\\")\\n series_description = self.processDicomField(dcm_info, \\\"SeriesDescription\\\")\\n patient_id = self.processDicomField(dcm_info, \\\"PatientID\\\")\\n patient_name = self.processDicomField(dcm_info, \\\"PatientName\\\")\\n protocol_name = self.processDicomField(dcm_info, \\\"ProtocolName\\\")\\n d_fileInfo = filesInSeries_determine()\\n self.dp.qprint('Summary report:')\\n self.dp.qprint('PatientID: %s' % patient_id, level = -1)\\n self.dp.qprint('PatientName: %s' % patient_name, level = -1)\\n self.dp.qprint('StudyDate: %s' % study_date, level = -1)\\n self.dp.qprint('StudyDescription: %s' % study_description, level = -1)\\n self.dp.qprint('SeriesDescription: %s' % series_description, level = -1)\\n self.dp.qprint('ProtocolName: %s' % protocol_name, level = -1)\\n if d_fileInfo['status']:\\n self.dp.qprint('Number of files in Series: %d' % d_fileInfo['fileCount'], level = -1)\\n self.dp.qprint('Directory size (raw): %d' % d_fileInfo['dirSizeRaw'], level = -1)\\n self.dp.qprint('Directory size (human): %s' % d_fileInfo['str_dirSize'], level = -1)\",\n \"def save_data(self) -> None:\\n # Construct a grid in physical space\\n rvals = np.logspace(start=-3,\\n stop=2.5,\\n num=21,\\n endpoint=True)\\n # Compute C, D, K1 and F on that grid\\n Cvals = np.array([self.compute_C(r, Suppression.RAW) for r in rvals])\\n Dvals = np.array([self.compute_D(r, Suppression.RAW) for r in rvals])\\n K1vals = np.array([self.compute_K1(r, Suppression.RAW) for r in rvals])\\n Fvals = np.array([self.compute_F(r, Suppression.RAW) for r in rvals])\\n # Save them to file\\n df = pd.DataFrame([rvals, Cvals[:, 0], Dvals[:, 0], K1vals[:, 0], Fvals[:, 0],\\n Cvals[:, 1], Dvals[:, 1], K1vals[:, 1], Fvals[:, 1]]).transpose()\\n df.columns = ['r', 'C(r)', 'D(r)', 'K1(r)', 'F(r)', 'dC(r)', 'dD(r)', 'dK1(r)', 'dF(r)']\\n df.to_csv(self.file_path(self.filename + '.csv'), index=False)\",\n \"def _format(self):\\n output = f\\\"\\\\n{color('>>> DUMP')} from {self.filename}: {color(f'L{self.line}')} in {color(f'{self.method}()')}\\\"\\n\\n for name, obj in self.objects.items():\\n output += f\\\"\\\\n\\\\n{color(f' - {name}:')}\\\\n\\\"\\n output += f\\\" {pformat(obj, width=110, indent=4)}\\\"\\n\\n output += color(\\\"\\\\n\\\\n<<< END\\\")\\n return output\",\n \"def format_report_header(self):\",\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 dumpData(self,out):\\n #--Get sizes and dump into dataIO\\n out.packSub0('NAME',self.cellName)\\n #--Hack: Insert data record if necessary\\n for record in self.records:\\n if record.name == 'DATA': break\\n else:\\n self.records.insert(0,SubRecord('DATA',0))\\n #--Top Records\\n for record in self.records:\\n if record.name == 'DATA':\\n record.setData(struct.pack('3i',self.flags,self.gridX,self.gridY))\\n record.getSize()\\n record.dump(out)\\n #--Objects\\n inTempObjects = False\\n for object in self.getObjects().list():\\n #--Begin temp objects?\\n if not inTempObjects and (object in self.tempObjects):\\n out.packSub('NAM0','i',len(self.tempObjects))\\n inTempObjects = True\\n (iMod,iObj,objId,objRecords) = object\\n for record in objRecords:\\n #--FRMR/NAME placeholder?\\n if isinstance(record,Cell_Frmr):\\n out.pack('4si','FRMR',4)\\n out.write(struct.pack('i',iObj)[:3])\\n out.pack('B',iMod)\\n out.packSub0('NAME',objId)\\n else:\\n record.getSize()\\n record.dump(out)\\n #--End Records\\n for endRecord in self.endRecords:\\n endRecord.getSize()\\n endRecord.dump(out)\",\n \"def __str__(self):\\n cols = {'theta': self.theta, 'std': self.stde, 'tstat': self.tstat}\\n res = pd.DataFrame(cols, index=self.names)[['theta', 'std', 'tstat']]\\n res_str = res.to_string(float_format=lambda x: '%.4f' % x)\\n width = len(res_str) // (res.shape[0] + 1)\\n show = '-' * 60\\n show += '\\\\nGMM final results:\\\\n'\\n show += width * '-' + '\\\\n'\\n show += res_str\\n show += '\\\\n' + width * '-'\\n show += '\\\\nJ-stat = %0.2f' % self.jstat\\n show += '\\\\ndf = ' + str(self.degf)\\n show += '\\\\np-value = %0.2f' % self.jpval\\n show += '\\\\n' + '-' * 60\\n return show\",\n \"def generate(self):\\n\\n # create temporary folder for copying image files\\n try:\\n os.mkdir(ReportGenerator.TEMP_FOLDER)\\n except FileExistsError:\\n shutil.rmtree(ReportGenerator.TEMP_FOLDER)\\n os.mkdir(ReportGenerator.TEMP_FOLDER)\\n\\n # find all simulations to be included in report\\n group_directory = self.config['group_directory']\\n sim_directory = self.config['sim_directory']\\n assert (group_directory or sim_directory) and not(group_directory and sim_directory), \\\\\\n \\\"A group directory or a simulation directory must be specified, but not both\\\"\\n\\n if group_directory:\\n assert os.path.exists(f'results/{group_directory}'), \\\"ERROR: Group directory does not exist\\\"\\n\\n sim_list = os.listdir(f'results/{group_directory}')\\n sim_label = group_directory.split('-')[0]\\n sim_list = [f'{group_directory}/{x}' for x in sim_list if sim_label in x]\\n\\n # filters\\n if self.config['filters']:\\n \\n seed_filters = self.config['filters']['seed']\\n dilution_filters = self.config['filters']['dilution']\\n order_filters = self.config['filters']['order']\\n\\n for symbol, filters in zip(['S', 'D', 'O'], [seed_filters, dilution_filters, order_filters]):\\n if type(filters) == int:\\n sim_list = [x for x in sim_list if f'{symbol}{filters}' in x.split('-')[2]]\\n elif type(filters) == list:\\n sim_list = [x for x in sim_list if any([f\\\"{symbol}{f}\\\" in x.split('-')[-2] for f in filters])]\\n\\n if len(sim_list) == 0:\\n raise Exception('Simulation filters resulted in no satisfactory simulations')\\n\\n else:\\n assert os.path.exists(f'results/{sim_directory}'), \\\"ERROR: Simulation directory does not exist\\\"\\n sim_list = [sim_directory]\\n\\n # REPORT HEADER\\n self.add_heading(level=1, text=self.config['title'])\\n now = datetime.now()\\n self.add_text(text=f'Report generated on {now.strftime(\\\"%B %d, %Y\\\")} at {now.strftime(\\\"%H:%M:%S\\\")}')\\n\\n # REPORT CONTENTS\\n for i, sim in enumerate(sim_list):\\n for content in self.config['content']:\\n content_type = list(content.keys())[0]\\n params = content[content_type]\\n\\n self.add_content(content_type, params, sim)\\n\\n if i+1 < len(sim_list):\\n self.add_content(content_type='break')\\n\\n # CREATE HTML TEMPLATE\\n base_template = self.templateEnv.get_template(f'{ReportGenerator.COMPONENTS_FOLDER}/base.html')\\n base_output = base_template.render(content='\\\\n\\\\n'.join(self.contents))\\n with open(f'{ReportGenerator.TEMP_FOLDER}/template.html', 'w') as html_file:\\n html_file.write(base_output)\\n\\n # CREATE PDF FROM TEMPLATE\\n if group_directory:\\n HTML(f'{ReportGenerator.TEMP_FOLDER}/template.html').write_pdf(\\n f'results/{group_directory}/{self.config[\\\"title\\\"]}.pdf', stylesheets=['reports/style.css'])\\n else:\\n HTML(f'{ReportGenerator.TEMP_FOLDER}/template.html').write_pdf(\\n f'results/{sim_directory}/{self.config[\\\"title\\\"]}.pdf', stylesheets=['reports/style.css'])\\n\\n # delete temp folder for storing image files\\n shutil.rmtree(f'{ReportGenerator.TEMP_FOLDER}')\\n return None\",\n \"def data_format(self):\\n # Run without arguments\\n if len(sys.argv) == 1:\\n sys.exit(usage(None))\\n # Test input file argument\\n if self.input and not os.path.isfile(self.input):\\n print(self.input)\\n print(os.path.isfile(self.input))\",\n \"def printObj(self):\\n return 'patient_id:{}, medication:{}, frequency:{}, start_dt:{},'\\n 'end_dt:{}, noti_type:{}'.format(\\n self.patients.data,\\n self.medication.data,\\n self.frequency.data,\\n self.start_dt,\\n self.end_dt.data,\\n self.noti_type.data)\",\n \"def update_record(self, d: dict) -> None:\\n super().update_record(d)\\n d.update(\\n dataset_individual_doses=str_list(self.individual_doses),\\n dataset_responses=str_list(self.responses),\\n dataset_doses=str_list(self.doses),\\n dataset_ns=str_list(self.ns),\\n dataset_stdevs=str_list(self.stdevs),\\n dataset_means=str_list(self.means),\\n )\",\n \"def generate_visualization_dict(self):\\n self._data = {}\\n self._data['name'] = self.name\\n self._data['type'] = self.__repr__()\\n self._data['color'] = self._color_rgb\\n\\n try:\\n self._data['simulation_matrix'] = \\\\\\n self._visualization_matrix.tolist()\\n\\n except:\\n #Not sure which error to call here.\\n raise RuntimeError('''Please call the numerical\\n transformation methods,\\n before generating simulation dict ''')\\n\\n\\n return self._data\",\n \"def format(self, table):\\n #return table.data.to_json()\\n m = table.as_array()\\n rank = len(m.shape)\\n is_table = len(table.headers)<=5 or (len(table.headers)>5 and (table.headers[0] != '0' or table.headers[1] != '1' or table.headers[2] != '2' ))\\n\\n if rank<3 and is_table:\\n v = []\\n for i in range(len(table.headers)):\\n vv = {\\n 'offset': table.offset,\\n 'header': table.headers[i],\\n 'type': table.types[i],\\n 'data': _replace_nans(m[:,i].tolist()) if rank>1 else _replace_nans(m.tolist()),\\n }\\n if table.sizes is not None:\\n vv[\\\"size\\\"] = table.sizes[0]\\n v.append(vv)\\n else:\\n # if hasattr(data, \\\"strip\\\") or \\\\\\n # (not hasattr(data, \\\"__getitem__\\\") and \\\\\\n # not hasattr(data, \\\"__iter__\\\")):\\n # # data is not a list/tuple => wrap it\\n # data = [ data ]\\n v = {\\n 'offset': table.offset,\\n #'headers': table.headers,\\n 'type': table.types[0],\\n 'data': _replace_nans(m.tolist()),\\n }\\n if table.sizes is not None:\\n v[\\\"size\\\"] = table.sizes\\n\\n return json.dumps(v, cls=ExtEncoder)\",\n \"def write_report(self):\\n\\n def report_array(f, label, array):\\n f.write(label)\\n for val in array:\\n f.write('{:.4f},\\\\t'.format(val))\\n f.write('\\\\n')\\n\\n report_file = FLAGS.report_file\\n\\n with open(report_file, 'w') as f:\\n f.write('Mean Error 2D: {}\\\\n'.format(\\n safe_divide(self._error_2d, self._matched)))\\n f.write('Mean 3D IoU: {}\\\\n'.format(\\n safe_divide(self._iou_3d, self._matched)))\\n f.write('Mean Azimuth Error: {}\\\\n'.format(\\n safe_divide(self._azimuth_error, self._matched)))\\n f.write('Mean Polar Error: {}\\\\n'.format(\\n safe_divide(self._polar_error, self._matched)))\\n\\n f.write('\\\\n')\\n f.write('IoU Thresholds: ')\\n for threshold in self._iou_thresholds:\\n f.write('{:.4f},\\\\t'.format(threshold))\\n f.write('\\\\n')\\n report_array(f, 'AP @3D IoU : ', self._iou_ap.aps)\\n\\n f.write('\\\\n')\\n f.write('2D Thresholds : ')\\n for threshold in self._pixel_thresholds:\\n f.write('{:.4f},\\\\t'.format(threshold * 0.1))\\n f.write('\\\\n')\\n report_array(f, 'AP @2D Pixel : ', self._pixel_ap.aps)\\n f.write('\\\\n')\\n\\n f.write('Azimuth Thresh: ')\\n for threshold in self._azimuth_thresholds:\\n f.write('{:.4f},\\\\t'.format(threshold * 0.1))\\n f.write('\\\\n')\\n report_array(f, 'AP @Azimuth : ', self._azimuth_ap.aps)\\n f.write('\\\\n')\\n\\n f.write('Polar Thresh : ')\\n for threshold in self._polar_thresholds:\\n f.write('{:.4f},\\\\t'.format(threshold * 0.1))\\n f.write('\\\\n')\\n report_array(f, 'AP @Polar : ', self._polar_ap.aps)\",\n \"def print(self):\\n df = self.gen_test()\\n # print(df)\\n df.to_csv('some_dated_file.csv', index=False)\\n return df\",\n \"def test_format_data(self, meteo):\\n meteo.config.climate.meteo.station_id = '889'\\n meteo.data['air_temperature'] = [\\n (datetime.datetime(2011, 9, 25, i, 0, 0), 215.0)\\n for i in range(24)]\\n line = next(meteo.format_data('air_temperature'))\\n assert line == '889 2011 09 25 42' + ' 215.00' * 24 + '\\\\n'\",\n \"def simulate_logging(self):\\n data = self.generator.generate_data()\\n data_json = json.dumps(data)\\n\\n part_id = self.get_highest_id() + 1\\n component_id = 1\\n\\n # Add new part\\n insert_command = \\\"INSERT INTO data (time, part_id, component_id, processed, classified, data)\\\" \\\\\\n \\\" VALUES ('{}', {}, {}, {}, '{}', '{}')\\\".format('now()',\\n part_id,\\n component_id,\\n 'False',\\n 'False',\\n data_json)\\n\\n self.cursor.execute(insert_command)\\n\\n string = \\\"Neuer Datenbankeintrag: time={},\\\" \\\\\\n \\\" part_id={}, component_id={}, data={}\\\".format(datetime.datetime,\\n part_id,\\n component_id,\\n data_json)\\n\\n return string\",\n \"def as_dfile(self):\\n rows = [\\\"Dose Response\\\"]\\n for dose, response in zip(self.individual_doses, self.responses, strict=True):\\n dose_idx = self.doses.index(dose)\\n if dose_idx >= self.num_dose_groups:\\n continue\\n rows.append(f\\\"{dose:f} {response:f}\\\")\\n return \\\"\\\\n\\\".join(rows)\",\n \"def report(self):\\r\\n # Compose the list of report_column names required for \\r\\n # summary_report.dsw.DictWriter()\\r\\n sr = self.summary_report\\r\\n dict_leader = sr.dict_leader\\r\\n dict_out = sr.dict_out\\r\\n report_column_names = []\\r\\n if dict_leader is not None and dict_out is not None:\\r\\n for key,value in dict_leader.iteritems():\\r\\n #print \\\"Adding report_column_name(from dict_leader)=\\\",key\\r\\n report_column_names.append(key)\\r\\n dict_out[key] = value\\r\\n # We have to initialize the DictWriter with the report_column_names\\r\\n # below. \\r\\n # Also need matched coord_val and var names for calling node_report()\\r\\n # below,\\r\\n # so we do this duplication of storage of names. \\r\\n coord_var_names = []\\r\\n coord_val_names = []\\r\\n for idx, column_name in enumerate(self.column_names):\\r\\n var_name = \\\"Var_%s\\\" % str(idx+1)\\r\\n report_column_names.append(var_name)\\r\\n coord_var_names.append(var_name)\\r\\n val_name = \\\"Val_%s\\\" % str(idx+1)\\r\\n report_column_names.append(val_name)\\r\\n coord_val_names.append(val_name)\\r\\n # Add the entry report_column_names\\r\\n report_column_names += self.EntryClass.report_column_names\\r\\n # Instantiate dsw.DictWriter with report column names\\r\\n # 4 lines follow for quick test output\\r\\n columns_string = \\\"\\\"; sep = \\\"\\\"\\r\\n for i,cn in enumerate(report_column_names):\\r\\n columns_string += sep + cn\\r\\n sep = \\\", \\\"\\r\\n if sr.dsw_full_report is not None:\\r\\n # Instantiate the dict writer to write only one-row at a time,\\r\\n # rather than buffer the entire report in memory before\\r\\n # outputting, to reduce memory footprint of \\r\\n # large reports.\\r\\n # The caller assumes responsibility to sort such a large report \\r\\n # as needed, and to produce a view of only the 'max_bad' rows, \\r\\n # if needed; for example, by loading the full report\\r\\n # into a sql table and after it is populated by this routine, \\r\\n # using its facilities to sort and manipulate the report rows.\\r\\n dict_writer = (self.summary_report.dsw_full_report\\r\\n .dict_writer(report_column_names))\\r\\n if sr.write_header: \\r\\n # write the header row\\r\\n dict_writer.writeheader()\\r\\n else:\\r\\n dict_writer = None\\r\\n # Accrue output data values for a buffered report, separate from a \\r\\n # report that node_report may write, row by row, using dict_writer. \\r\\n # The output collected here may be further quickly sorted and \\r\\n # examined without having to reread the file that dict_writer \\r\\n # writes to.\\r\\n # Coord data output is formatted in node_report().\\r\\n # node_report() adds final entries column data to dict_out for \\r\\n # node coords and entry, and\\r\\n # if an entry has output, calls dict_writer to write it.\\r\\n is_entry, outrows = self.node_report(\\r\\n self.grand, \\r\\n dict_out=self.summary_report.dict_out, \\r\\n dict_writer=dict_writer,\\r\\n coord_var_names=coord_var_names, \\r\\n coord_val_names=coord_val_names)\\r\\n return outrows\",\n \"def output(self):\\n to_write = 'S '\\n \\\"\\\"\\\"\\n print self.def_field\\n for key in self.def_field:\\n print key,\\\"=\\\", self.def_field[key]\\n \\\"\\\"\\\"\\n to_write += str(self.offset[0] + self.def_field['x1'])+' '\\n to_write += str(self.offset[1] + self.def_field['y1'])+' '\\n to_write += str(self.offset[0] + self.def_field['x2'])+' '\\n to_write += str(self.offset[1] + self.def_field['y2'])+' '\\n to_write += str(self.def_field['part'])+' '\\n to_write += str(self.def_field['dmg'])+' '\\n to_write += str(self.def_field['pen'])+' '\\n to_write += self.def_field['fill']+'\\\\n'\\n return to_write\",\n \"def _format_data(self, data: np.ndarray) -> np.ndarray:\\n self._n_circs = 0\\n self._n_shots = 0\\n self._n_slots = 0\\n self._n_iq = 0\\n\\n # identify shape\\n try:\\n # level1 single-shot data\\n self._n_circs, self._n_shots, self._n_slots, self._n_iq = data.shape\\n except ValueError:\\n try:\\n # level1 data averaged over shots\\n self._n_circs, self._n_slots, self._n_iq = data.shape\\n except ValueError as ex:\\n raise DataProcessorError(\\n f\\\"Data given to {self.__class__.__name__} is not likely level1 data.\\\"\\n ) from ex\\n\\n if self._validate:\\n if self._n_iq != 2:\\n raise DataProcessorError(\\n f\\\"IQ data given to {self.__class__.__name__} does not have two-dimensions \\\"\\n f\\\"(I and Q). Instead, {self._n_iq} dimensions were found.\\\"\\n )\\n\\n return data\",\n \"def Info2Save(self):\\n\\n if self.ds.sat == 'Kepler':\\n snr_header = 'SNR_' + self.ds.sat\\n prob_header = 'Pdet_' + self.ds.sat\\n\\n elif self.ds.sat == 'TESS':\\n snr_header = 'SNR_' + self.ds.sat + str(ds.Tobs)\\n prob_header = 'Pdet_' + self.ds.sat + str(ds.Tobs)\\n\\n\\n # check if file exists. If it does, add columns/write over columns\\n # if the files doesn't exist, make it\\n if os.path.exists(self.probfile):\\n save = pd.read_csv(self.probfile)\\n save[snr_header], save[prob_header] = [self.snr_modes, self.prob]\\n\\n else:\\n save = pd.DataFrame({'f0' :self.ds.mode_id['f0'],\\n snr_header :self.snr_modes,\\n prob_header:self.prob})\\n save.sort_values(['f0'], axis=0, ascending=True, inplace=True)\\n save = save.ix[:, ['f0', snr_header, prob_header]]\\n\\n #print self.ds.sat, ds.Tobs\\n #print save\\n\\n save.to_csv(self.probfile, index=False)\",\n \"def _writeRecord(self, path, name, data):\\n file_path = os.path.join(path, name)\\n with open(file_path, 'w') as f:\\n for item in data:\\n f.write(str(item)+'\\\\t')\\n f.write('\\\\n')\",\n \"def report(self,f):\\n f.write(\\\"sectorsize: %d\\\\n\\\" % self.sectorsize)\\n for run in sorted(self.db):\\n f.write(\\\" [@%8d ; %8d]\\\\n\\\" % (run.img_offset,run.bytes))\\n f.write(\\\"total entries in database: %d\\\\n\\\\n\\\" % len(r))\",\n \"def _convert_and_save_to_FILE_pdbformat(self, cur, FILE, supress_model_separation=False):\\n\\n\\n rows = cur.fetchall()\\n if not supress_model_separation:\\n pdb_previous = rows[0]['pdbID']; struc_previous = rows[0]['strucID']\\n FILE.write(\\\"MODEL\\\\n\\\")\\n for row in rows:\\n pdb=row['pdbID']; struc=row['strucID']\\n if pdb!=pdb_previous or struc!=struc_previous:\\n FILE.write(\\\"ENDMDL\\\\n\\\")\\n FILE.write(\\\"MODEL\\\\n\\\")\\n line = self._morph_db_row_to_pdb_line(row)\\n FILE.write(line+\\\"\\\\n\\\")\\n pdb_previous = row['pdbID']; struc_previous = row['strucID']\\n FILE.write('ENDMDL')\\n else:\\n FILE.write(\\\"MODEL\\\\n\\\")\\n for row in rows:\\n line = self._morph_db_row_to_pdb_line(row)\\n FILE.write(line+\\\"\\\\n\\\")\\n FILE.write('ENDMDL')\\n FILE.close()\",\n \"def generate_extra_data(self):\\n self.data[\\\"male_initial\\\"], self.data[\\\"female_initial\\\"] = \\\\\\n self.get_initial_student_count()\\n \\n date_line = '<p class=\\\"report-title\\\"> %s</p>' \\\\\\n %(self.start_date.strftime(\\\"%B %Y\\\"))\\n row1 = \\\"\\\"\\\"\\n <table>\\n <tr class=\\\"tblRow\\\"><td>%s</td><td>Enrollment For Year</td>\\n <td>Male:</td><td>%d</td><td>Female:</td><td>%d</td>\\n <td>Total:</td><td>%d</td></tr>\\n \\\"\\\"\\\" %(unicode(self.school), self.data[\\\"male_initial\\\"], \\n self.data[\\\"female_initial\\\"], \\n self.data[\\\"male_initial\\\"] + self.data[\\\"female_initial\\\"])\\n row2 = \\\"\\\"\\\"\\n <tr class=\\\"tblOddRow\\\"><td>%s</td><td>Enrollment For Month</td>\\n <td>Male:</td><td>%d</td><td>Female:</td><td>%d</td>\\n <td>Total:</td><td>%d</td></tr>\\n \\\"\\\"\\\" %(unicode(self.section), self.data[\\\"male_current\\\"], \\n self.data[\\\"female_current\\\"],\\n self.data[\\\"male_current\\\"] + self.data[\\\"female_current\\\"])\\n row3 = \\\"\\\"\\\"\\n <tr class=\\\"tblRow\\\"><td>%s</td><td>Average Attendance</td>\\n <td>Male:</td><td>%.1f</td><td>Female:</td><td>%.1f</td>\\n <td>Total:</td><td>%.1f</td></tr>\\n \\\"\\\"\\\" %(\\\"Secondary\\\", self.data[\\\"aa_male\\\"], self.data[\\\"aa_female\\\"] ,\\n self.data[\\\"aa_combined\\\"])\\n row4 =\\\"\\\"\\\"\\n <tr class=\\\"tblOddRow\\\"><td>%s</td><td>Percentage of Attendance</td>\\n <td>Male:</td><td>%.1f %% </td><td>Female:</td><td>%.1f %% </td>\\n <td>Total:</td><td>%.1f %% </td></tr>\\n \\\"\\\"\\\" %(unicode(self.school.municipality), self.data[\\\"pa_male\\\"], \\n self.data[\\\"pa_female\\\"], self.data[\\\"pa_combined\\\"])\\n row5 = \\\"\\\"\\\"\\n <tr class=\\\"tblRow\\\"><td>School Days: %d</td><td>Percentage of Enrollment</td>\\n <td>Male:</td><td>%.1f %% </td><td>Female:</td><td>%.1f %% </td>\\n <td>Total:</td><td>%.1f %% </td></tr>\\n </table>\\n \\\"\\\"\\\" %(self.data[\\\"num_school_days\\\"], \\n self.data[\\\"male_current\\\"] * 100.0 / self.data[\\\"male_initial\\\"],\\n self.data[\\\"female_current\\\"] * 100.0 / \\n self.data[\\\"female_initial\\\"],\\n (self.data[\\\"male_current\\\"] + self.data[\\\"female_current\\\"]) * \\n 100.0 /\\n (self.data[\\\"male_initial\\\"] + self.data[\\\"female_initial\\\"]))\\n self.extra_data = date_line + row1 + row2 + row3 + row4 + row5\",\n \"def save(self):\\n\\n print('Bye!')\\n try:\\n with open('records.txt', 'w') as fh:\\n fh.write(str(self._initial_money))\\n for n in self._records:\\n fh.write('\\\\n'+n)\\n except OSError:\\n sys.stderr.write('Cannot open file.\\\\n')\",\n \"def _format_data(self, data: np.ndarray) -> np.ndarray:\\n\\n self._n_shots = len(data[0])\\n self._n_circuits = len(data)\\n\\n if self._validate:\\n if data.shape[:2] != (self._n_circuits, self._n_shots):\\n raise DataProcessorError(\\n f\\\"The datum given to {self.__class__.__name__} does not convert \\\"\\n \\\"of an array with dimension (number of circuit, number of shots).\\\"\\n )\\n\\n return data\",\n \"def _write_csv(self):\\n\\n # add the label to the header\\n if self.input_data.get_value(InputType.TIME_PERIOD) == 'all':\\n self.header.append('Date')\\n else:\\n self.header.append('sample id')\\n\\n key_list = []\\n\\n for i, cube in enumerate(self.cube_list):\\n if self.input_data.get_value(InputType.TIME_PERIOD) == 'all':\\n self._write_sample_with_date(cube, i, key_list)\\n else:\\n self._write_sample(cube, i, key_list)\\n\\n output_data_file_path = self._get_full_file_name()\\n self._write_data_dict(output_data_file_path, key_list)\\n\\n return [output_data_file_path]\",\n \"def save_simulation_file(self):\\n a = self.ui.inputfile.text()\\n a = self.get_root_file_name(a)\\n a = a.split('_a.txt')\\n output_suffix = self.ui.output_suffix.text()\\n simfile_name = self.input_dir+'/'+sgGL.SIMFILES_PATH + a[0] + '_' +\\\\\\n sgcom.create_file_suffix(self.algorithm,output_suffix,self.ciclos)+\\\\\\n '.sim'\\n simulation_selected_filename = QtGui.QFileDialog.getSaveFileName(self,\\n \\\"Save simulation parameters\\\",\\n simfile_name)\\n if len(simulation_selected_filename)>0:\\n simulation_params.write2file(simulation_selected_filename)\",\n \"def dump(self):\\n outputs = [\\\"Sequence : %s\\\" % self.name]\\n if self.curr_value:\\n outputs.append(\\\" start : %d\\\" % self.curr_value)\\n outputs.append(\\\" minimum : %d\\\" % self.min_value)\\n outputs.append(\\\" maximum : %d\\\" % self.max_value)\\n if self.increment_by > 1:\\n outputs.append(\\\" increment : %d\\\" % self.increment_by)\\n return \\\"\\\\n\\\".join(outputs)\",\n \"def flush(self):\\n # spectra/*.csv | Age distribution of various subpopulations (e.g. population that died of genetic causes)\\n for key, val in self.collection.items():\\n with open(self.paths[\\\"visor_spectra\\\"] / f\\\"{key}.csv\\\", \\\"ab\\\") as f:\\n array = np.array(val)\\n np.savetxt(f, [array], delimiter=\\\",\\\", fmt=\\\"%i\\\")\\n\\n # Reinitialize the collection\\n self.collection = copy.deepcopy(self._collection)\",\n \"def generate_report(path_to_results: str, save_path: str, format: str) -> None:\\n path_to_results = (\\n path_to_results if path_to_results[-1] != \\\"/\\\" else path_to_results[:-1]\\n )\\n files = glob.glob(f\\\"{path_to_results}/*\\\")\\n\\n if len(files) == 0:\\n raise Exception(f\\\"There were no result files in the path {path_to_results}\\\")\\n\\n raw_results = []\\n for result_file in files:\\n with open(result_file, \\\"rb\\\") as handle:\\n raw_results.append(pickle.load(handle))\\n\\n results_dict = []\\n for raw in raw_results:\\n runtime = np.mean([i[\\\"runtime\\\"] for i in raw[\\\"results\\\"]])\\n memory = np.mean(\\n [\\n i[\\\"memory_stats\\\"][\\\"max_memory\\\"]\\n for i in raw[\\\"results\\\"]\\n if i[\\\"memory_stats\\\"] is not None\\n ]\\n )\\n result = {\\n \\\"layer\\\": raw[\\\"layer\\\"],\\n \\\"batch_size\\\": raw[\\\"batch_size\\\"],\\n \\\"num_runs\\\": raw[\\\"num_runs\\\"],\\n \\\"num_repeats\\\": raw[\\\"num_repeats\\\"],\\n \\\"forward_only\\\": raw[\\\"forward_only\\\"],\\n \\\"gsm_mode\\\": raw[\\\"gsm_mode\\\"],\\n \\\"runtime\\\": runtime,\\n \\\"memory\\\": memory,\\n }\\n results_dict.append(result)\\n\\n results = pd.DataFrame(results_dict)\\n\\n results[\\\"gsm_mode\\\"][results[\\\"layer\\\"].str.startswith(\\\"dp\\\")] = (\\n \\\"dp_\\\" + results[\\\"gsm_mode\\\"]\\n )\\n results[\\\"layer\\\"] = results[\\\"layer\\\"].str.replace(\\\"dp\\\", \\\"\\\")\\n\\n pivot = results.pivot_table(\\n index=[\\\"batch_size\\\", \\\"num_runs\\\", \\\"num_repeats\\\", \\\"forward_only\\\", \\\"layer\\\"],\\n columns=[\\\"gsm_mode\\\"],\\n values=[\\\"runtime\\\"],\\n )\\n\\n def add_ratio(df, metric, variant):\\n if variant not in df.columns.get_level_values(\\\"gsm_mode\\\"):\\n for ametric in df.columns.get_level_values(0):\\n df[(ametric, variant)] = np.nan\\n\\n df[(metric, f\\\"{variant}/baseline\\\")] = (\\n df.loc[:, (metric, variant)] / df.loc[:, (metric, \\\"baseline\\\")]\\n )\\n\\n if \\\"baseline\\\" in results[\\\"gsm_mode\\\"].tolist():\\n for m in set(results[\\\"gsm_mode\\\"].tolist()) - {\\\"baseline\\\"}:\\n add_ratio(pivot, \\\"runtime\\\", m)\\n pivot.columns = pivot.columns.set_names(\\\"value\\\", level=1)\\n\\n output = pivot.sort_index(axis=1).sort_values(\\n [\\\"batch_size\\\", \\\"num_runs\\\", \\\"num_repeats\\\", \\\"forward_only\\\"]\\n )\\n if format == \\\"csv\\\":\\n output.to_csv(save_path)\\n else:\\n output.to_pickle(save_path)\",\n \"def _format_data(self, data: np.ndarray) -> np.ndarray:\\n self._n_shots = 0\\n\\n # identify shape\\n try:\\n # level1 single-shot data\\n self._n_circs, self._n_shots, self._n_slots, self._n_iq = data.shape\\n except ValueError:\\n try:\\n # level1 data averaged over shots\\n self._n_circs, self._n_slots, self._n_iq = data.shape\\n except ValueError as ex:\\n raise DataProcessorError(\\n f\\\"Data given to {self.__class__.__name__} is not likely level1 data.\\\"\\n ) from ex\\n\\n if self._validate:\\n if data.shape[-1] != 2:\\n raise DataProcessorError(\\n f\\\"IQ data given to {self.__class__.__name__} must be a multi-dimensional array\\\"\\n \\\"of dimension [d0, d1, ..., 2] in which the last dimension \\\"\\n \\\"corresponds to IQ elements.\\\"\\n f\\\"Input data contains element with length {data.shape[-1]} != 2.\\\"\\n )\\n\\n return data\",\n \"def _writeOutput(self):\\n head = \\\"Station\\\\tX\\\\tY\\\\tZ\\\\tUEast\\\\tUNorth\\\\tUUp\\\\tSigEast\\\\tSigNorth\\\\tSigUp\\\\n\\\"\\n outFmt = \\\"%s\\\" + 9 * \\\"\\\\t%g\\\" + \\\"\\\\n\\\"\\n\\n f = open(self.outputFile, 'w')\\n f.write(head)\\n\\n for stationNum in range(self.numStations):\\n outLine = outFmt % (self.stations[stationNum],\\n self.coords[stationNum, 0], self.coords[stationNum, 1],\\n self.coords[stationNum, 2],\\n self.dispNoise[stationNum, 0],\\n self.dispNoise[stationNum, 1],\\n self.dispNoise[stationNum, 2],\\n self.sigmaEast, self.sigmaNorth, self.sigmaUp)\\n f.write(outLine)\\n\\n f.close()\\n\\n return\",\n \"def save(self, filename, precision='%15.07e', encoding='utf-8'):\\n\\n # when creating empty st object, cols is not set yet\\n if not hasattr(self, 'cols'):\\n if self.main_data_sets[1][1].shape[1]==19:\\n self.cols = stc.split()\\n elif self.main_data_sets[1][1].shape[1]==30:\\n self.cols = fpm.split()\\n else:\\n c = self.main_data_sets[1][1].shape[1]\\n raise TypeError(f'St input needs 19 (iso) or 30 (aniso/fpm) cols, not {c}')\\n\\n colwidth = len(precision % 1)\\n sep = '=' * colwidth * len(self.cols) + '\\\\n'\\n colhead = ''.join([k.center(colwidth) for k in self.cols]) + '\\\\n'\\n\\n nsets = len(self.main_data_sets)\\n # fails if dirname is empty string\\n if len(os.path.dirname(filename)) > 0:\\n os.makedirs(os.path.dirname(filename), exist_ok=True)\\n with open(filename, 'w', encoding=encoding) as fid:\\n fid.write('%i ; number of sets, Nset\\\\n' % nsets)\\n for mset, set_data_dict in self.main_data_sets.items():\\n fid.write('#%i ; set number\\\\n' % mset)\\n for set, set_array in set_data_dict.items():\\n dstr = self.to_str(mset=mset, set=set, precision=precision)\\n npoints = self.main_data_sets[mset][set].shape[0]\\n fid.write(sep + colhead + sep)\\n fid.write('$%i %i\\\\n' % (set, npoints))\\n fid.write(dstr + '\\\\n')\",\n \"def write (self, path):\\n\\t\\ts=[]; add=s.append\\n\\t\\tadd ('\\\\t'.join (self.schema))\\n\\t\\tfor record in self.data:\\n\\t\\t\\tadd (record.asTabDelimitedRecord())\\n\\t\\t\\n\\t\\t# f = open (path, 'w')\\n\\t\\tf = codecs.open(path, 'w', 'utf-8')\\n\\t\\tf.write (self.linesep.join (s))\\n\\t\\tf.close()\\n\\t\\tprint (\\\"data written to \\\" + path)\",\n \"def tabular_data(self):\\n path = CFG.GRAPHS_DIR\\n chdir(path)\\n\\n if self.experiment_count == 1:\\n f = open(self.tablefile, 'w')\\n f.write(self.print_border_line())\\n f.write(self.table_header())\\n f.write(self.print_border_line())\\n f.write(self.pretty_string(\\\"Functions\\\"))\\n f.write(self.pretty_string(\\\"Batch Size\\\"))\\n f.write(self.pretty_string(\\\"Training (%)\\\"))\\n f.write(self.pretty_string(\\\"Testing (%)\\\", True))\\n f.write('\\\\n')\\n f.write(self.print_border_line())\\n f.close()\\n\\n f = open(self.tablefile, 'a')\\n f.write(self.pretty_string(self.function_name))\\n f.write(self.pretty_string(str(self.batch_size)))\\n f.write(self.pretty_string(self.tr_mean_str))\\n f.write(self.pretty_string(self.test_mean_str, True))\\n f.write('\\\\n')\\n f.close()\",\n \"def generate_student_report(self):\\n \\n period_type = self.parameter_dict.get(\\\"period_type\\\", \\\"monthly\\\")\\n insert_gender_markers = self.parameter_dict.get(\\n \\\"insert_gender_markers\\\", False)\\n period = [(self.start_date,self.end_date)]\\n for student in self.students:\\n self.table_data.append(self._generate_single_student_report_line(\\n student,period, False))\\n self.keys_list.append(\\\"\\\")\\n self.table_descriptor = \\\\\\n [('name','string','Name'),\\n ('days_present','number', 'Days Present'),\\n ('percent_present', 'number', '% Present')]\",\n \"def toJSON(self):\\r\\n\\r\\n jsonToRet = []\\r\\n rowJson = []\\r\\n matrixJson = []\\r\\n\\r\\n if len(self.slctData) > 100:\\r\\n self.getSimMatSummary(100)\\r\\n jsonToRet.append(self.summaryOrdering)\\r\\n for i in range(0,len(self.simMatSmm)):\\r\\n for n in self.simMatSmm[i]:\\r\\n rowJson.append(n)\\r\\n matrixJson.append(rowJson)\\r\\n rowJson = []\\r\\n jsonToRet.append(matrixJson)\\r\\n\\r\\n jsonToRet.append(self.patchOrdering)\\r\\n # jsonToRet = []\\r\\n rowJson = []\\r\\n matrixJson = []\\r\\n\\r\\n for i in range(0,len(self.simMat)):\\r\\n for n in self.simMat[i]:\\r\\n rowJson.append(n)\\r\\n matrixJson.append(rowJson)\\r\\n rowJson = []\\r\\n jsonToRet.append(matrixJson)\\r\\n return jsonToRet\",\n \"def generate_record(self, data_dictionaries, group_by):\\n result = {}\\n\\n for one_measurement in data_dictionaries:\\n time = one_measurement['datetime']\\n\\n if isinstance(time, str):\\n if self.timezone:\\n time = arrow.get(time).shift(hours=6) # TODO: fix utc conversion\\n else:\\n time = arrow.get(time)\\n\\n record = Record(self.name, self.lat, self.lon, self.height, time)\\n\\n del one_measurement['datetime']\\n\\n one_measurement = {k: float(v) for k, v in one_measurement.items()}\\n\\n record.merge(one_measurement)\\n\\n key = group_by(time)\\n \\n if key == '2016-04-01_00':\\n break\\n\\n record_string = record.little_r_report()\\n\\n try:\\n result[key].append(record_string)\\n except KeyError:\\n result[key] = [record_string]\\n\\n return result\",\n \"def data_saving(self, sol):\\r\\n # Only export data once per time-step. We do this on the conduction\\r\\n # step.\\r\\n if self.save_rule is not None:\\r\\n save_rule_true = self.save_rule(self.step, self.d_T)\\r\\n else:\\r\\n save_rule_true = True\\r\\n \\r\\n if self.cond == True:\\r\\n series = \\\"Temperature\\\"\\r\\n elif self.rad is not None:\\r\\n series = (\\\"Radiation\\\", self.fq_list[self.rad])\\r\\n else:\\r\\n # before sim starts... EARLY EXIT\\r\\n return\\r\\n\\r\\n if self.cond == True and save_rule_true:\\r\\n # Save data to file with step no.\\r\\n # First, generate dictionaries with {nid:value}\\r\\n\\r\\n # CASE 1: Export mesh = FEM mesh (ie, no enrichment, easier!)\\r\\n if self.export_mesh is None:\\r\\n data_temp = {}\\r\\n for nid in self.mesh.nodes.keys():\\r\\n idx = self.node_map.tag_to_idx((nid, 0))\\r\\n data_temp[nid] = self.lst_tmp[idx]\\r\\n data_rad = {}\\r\\n for i in self.fq_list:\\r\\n data_rad[i] = {}\\r\\n for nid in self.mesh.nodes.keys():\\r\\n idx = self.node_map.tag_to_idx((nid, 0))\\r\\n for i in range(0, len(self.fq_list)):\\r\\n data_rad[self.fq_list[i]][nid] = self.lst_rad[i][idx]\\r\\n # End CASE 1 - see after case two for finishing export.\\r\\n\\r\\n\\r\\n # CASE 2: Exporting to a different mesh to the the XFEM / FEM\\r\\n # mesh. \\r\\n else:\\r\\n # We need to a mapping from global to local element \\r\\n # coordinates. We'll do this once and then store it.\\r\\n # We store it in self.export_to_elem dictionary.\\r\\n if self.export_to_elem is None:\\r\\n self.export_to_elem = \\\\\\r\\n self.mesh.project_points(self.export_mesh.nodes,\\r\\n failure_rule='closest')\\r\\n # Dictionaries to export:\\r\\n data_temp = {}\\r\\n data_rad = {}\\r\\n # Setup frequency data:\\r\\n for i in self.fq_list:\\r\\n data_rad[i] = {}\\r\\n\\r\\n for node_id, expt_data in self.export_to_elem.items():\\r\\n # Unpack the value of the dictionary value for clarity:\\r\\n elem = expt_data[0]\\r\\n eta = expt_data[1] # local coord\\r\\n\\r\\n # Get element / solution indexes:\\r\\n val = elem.eval_elem(self.node_map, self.lst_tmp, [eta])[0]\\r\\n data_temp[node_id] = val\\r\\n # And for all the frequencies:\\r\\n for i in range(0, len(self.fq_list)):\\r\\n data_rad[self.fq_list[i]][node_id] \\\\\\r\\n = elem.eval_elem(self.node_map,\\r\\n self.lst_rad[i],\\r\\n [eta])[0]\\r\\n # END CASE 2\\r\\n\\r\\n expt_data = {\\\"Temperature\\\": data_temp}\\r\\n for freq, nvals in data_rad.items():\\r\\n expt_data[str(freq * 10) + \\\"THz\\\"] = data_rad[freq]\\r\\n\\r\\n # Send to be exported as a VTK.\\r\\n if self.export_mesh is None:\\r\\n self.mesh.export_to_vtk(self.save_path + str(self.step),\\r\\n expt_data)\\r\\n else:\\r\\n self.export_mesh.export_to_vtk(self.save_path + str(self.step),\\r\\n expt_data)\\r\\n\\r\\n try:\\r\\n container = self.saved_data[series]\\r\\n except:\\r\\n self.saved_data[series] = {}\\r\\n container = self.saved_data[series]\\r\\n if self.step % 10 == 0 or self.step < 10:\\r\\n container[self.step] = saved_data(sol, self.step, self.current_T)\",\n \"def writeOut(self):\\n # import time\\n self.outHeader = self.srcHeader\\n for line in self.outHeader:\\n self.outFile.write(line + '\\\\n')\\n # now = time.asctime(time.localtime(time.time()))\\n # self.outFile.write('%% -- %s -- Written to new alog' % now)\\n for time_s in sorted(self.outData):\\n for sens in self.outData[time_s]:\\n for meas in self.outData[time_s][sens]:\\n valu = self.outData[time_s][sens][meas]\\n msg_list = [str(time_s), meas, sens, str(valu)]\\n line_string = reconstructLine(msg_list)\\n self.outFile.write(line_string + '\\\\n')\",\n \"def write_results_dat(self, output_path):\\n\\n def fstr(nb):\\n data = '%E' % nb\\n if data == 'NAN':\\n nb, power = 0,0\\n else:\\n nb, power = data.split('E')\\n nb = float(nb) /10\\n power = int(power) + 1\\n return '%.5fE%+03i' %(nb,power)\\n\\n line = '%s %s %s %i %i %i %i %s %s %s %s %s %i\\\\n' % (fstr(self.axsec), fstr(self.xerru), \\n fstr(self.xerrc), self.nevents, self.nw, self.maxit, self.nunwgt,\\n fstr(self.luminosity), fstr(self.wgt), fstr(self.xsec), fstr(self.maxwgt),\\n fstr(self.th_maxwgt), self.th_nunwgt) \\n fsock = open(output_path,'w') \\n fsock.writelines(line)\\n for i in range(len(self.ysec_iter)):\\n line = '%s %s %s %s %s %s\\\\n' % (i+1, self.ysec_iter[i], self.yerr_iter[i], \\n self.eff_iter[i], self.maxwgt_iter[i], self.yasec_iter[i]) \\n fsock.writelines(line)\",\n \"def _logging_smm(self, stats_obs, stats_sim):\\n fname = 'monitoring.estimagic.smm.info'\\n if self.num_evals == 1 and os.path.exists(fname):\\n os.unlink(fname)\\n\\n with open(fname, 'a+') as outfile:\\n\\n fmt_ = '\\\\n\\\\n{:>8}{:>15}\\\\n\\\\n'\\n outfile.write(fmt_.format('EVALUATION', self.num_evals))\\n\\n fmt_ = '{:>8}' + '{:>15}' * 4 + '\\\\n\\\\n'\\n info = ['Moment', 'Observed', 'Simulated', 'Difference', 'Weight']\\n outfile.write(fmt_.format(*info))\\n\\n for i, moment in enumerate(stats_obs):\\n\\n stat_obs, stat_sim = stats_obs[i], stats_sim[i]\\n info = [i, stat_obs, stat_sim, abs(stat_obs - stat_sim), self.weighing_matrix[i, i]]\\n\\n fmt_ = '{:>8}' + '{:15.5f}' * 4 + '\\\\n'\\n outfile.write(fmt_.format(*info))\",\n \"def _write_times(parameters):\\n data = parameters[\\\"times\\\"]\\n data = data if numpy.ndim(data) else [data]\\n\\n # Formats\\n fmt = block_to_format[\\\"TIMES\\\"]\\n fmt1 = str2format(fmt[1])\\n fmt2 = str2format(fmt[2])\\n\\n # Record 1\\n out = write_record([len(data)], fmt1)\\n\\n # Record 2\\n out += write_record(data, fmt2, multi=True)\\n\\n return out\",\n \"def bgs_write_simdata(sim, overwrite=False):\\n from desispec.io.util import makepath\\n from desispec.io.util import write_bintable\\n\\n simdatafile = os.path.join(sim.simdir, \\n 'bgs_{}_simdata.fits'.format(sim.simid))\\n makepath(simdatafile)\\n\\n cols = [\\n ('SEED', 'S20'),\\n ('NSPEC', 'i4'),\\n ('EXPTIME', 'f4'),\\n ('AIRMASS', 'f4'),\\n ('SEEING', 'f4'),\\n ('MOONFRAC', 'f4'),\\n ('MOONSEP', 'f4'),\\n ('MOONALT', 'f4')]\\n\\n simdata = Table(np.zeros(sim.nsim, dtype=cols))\\n simdata['EXPTIME'].unit = 's'\\n simdata['SEEING'].unit = 'arcsec'\\n simdata['MOONSEP'].unit = 'deg'\\n simdata['MOONALT'].unit = 'deg'\\n\\n simdata['SEED'] = sim.seed\\n simdata['NSPEC'] = sim.nspec\\n simdata['AIRMASS'] = sim.airmass\\n simdata['SEEING'] = sim.seeing\\n simdata['MOONALT'] = sim.moonalt\\n simdata['MOONSEP'] = sim.moonsep\\n simdata['MOONFRAC'] = sim.moonfrac\\n simdata['EXPTIME'] = sim.exptime\\n\\n if overwrite or not os.path.isfile(simdatafile):\\n print('Writing {}'.format(simdatafile))\\n write_bintable(simdatafile, simdata, extname='SIMDATA', clobber=True)\\n\\n return simdata\",\n \"def create_data(self) -> str:\\r\\n s = self.scale\\r\\n mini, maxi = self.get_min_max()\\r\\n diff = maxi - mini\\r\\n\\r\\n output = \\\"const data = {\\\\n\\\"\\r\\n\\r\\n # Create the data for the scatters\\r\\n # TODO: If it's not interactive, labels shouldn't be exported.\\r\\n for name, data in self.scatters_data.items():\\r\\n mapping = self.scatters[name][\\\"mapping\\\"]\\r\\n colormaps = self.scatters[name][\\\"colormap\\\"]\\r\\n cmaps = [None] * len(colormaps)\\r\\n\\r\\n for i, colormap in enumerate(colormaps):\\r\\n if isinstance(colormap, str):\\r\\n cmaps[i] = plt.cm.get_cmap(colormap)\\r\\n else:\\r\\n cmaps[i] = colormap\\r\\n\\r\\n output += name + \\\": {\\\\n\\\"\\r\\n x_norm = [round(s * (x - mini) / diff, 3) for x in data[mapping[\\\"x\\\"]]]\\r\\n output += \\\"x: [\\\" + \\\",\\\".join(map(str, x_norm)) + \\\"],\\\\n\\\"\\r\\n\\r\\n y_norm = [round(s * (y - mini) / diff, 3) for y in data[mapping[\\\"y\\\"]]]\\r\\n output += \\\"y: [\\\" + \\\",\\\".join(map(str, y_norm)) + \\\"],\\\\n\\\"\\r\\n\\r\\n z_norm = [round(s * (z - mini) / diff, 3) for z in data[mapping[\\\"z\\\"]]]\\r\\n output += \\\"z: [\\\" + \\\",\\\".join(map(str, z_norm)) + \\\"],\\\\n\\\"\\r\\n\\r\\n if mapping[\\\"labels\\\"] in data:\\r\\n fmt_labels = [\\\"'{0}'\\\".format(s) for s in data[mapping[\\\"labels\\\"]]]\\r\\n output += \\\"labels: [\\\" + \\\",\\\".join(fmt_labels) + \\\"],\\\\n\\\"\\r\\n\\r\\n if mapping[\\\"s\\\"] in data:\\r\\n output += \\\"s: [\\\"\\r\\n\\r\\n for series in range(len(data[mapping[\\\"s\\\"]])):\\r\\n output += (\\r\\n \\\"[\\\"\\r\\n + \\\",\\\".join(map(str, np.round(data[mapping[\\\"s\\\"]][series], 3)))\\r\\n + \\\"],\\\\n\\\"\\r\\n )\\r\\n\\r\\n output += \\\"],\\\\n\\\"\\r\\n\\r\\n output += \\\"colors: [\\\\n\\\"\\r\\n for series in range(len(data[mapping[\\\"c\\\"]])):\\r\\n output += \\\"{\\\\n\\\"\\r\\n if mapping[\\\"cs\\\"] in data:\\r\\n colors = np.array(\\r\\n [cmaps[series](x) for x in data[mapping[\\\"c\\\"]][series]]\\r\\n )\\r\\n\\r\\n for i, c in enumerate(colors):\\r\\n hsl = np.array(colour.rgb2hsl(c[:3]))\\r\\n hsl[1] = hsl[1] - hsl[1] * data[mapping[\\\"cs\\\"]][series][i]\\r\\n colors[i] = np.append(np.array(colour.hsl2rgb(hsl)), 1.0)\\r\\n\\r\\n colors = np.round(colors * 255.0)\\r\\n\\r\\n output += (\\r\\n \\\"r: [\\\" + \\\",\\\".join(map(str, map(int, colors[:, 0]))) + \\\"],\\\\n\\\"\\r\\n )\\r\\n output += (\\r\\n \\\"g: [\\\" + \\\",\\\".join(map(str, map(int, colors[:, 1]))) + \\\"],\\\\n\\\"\\r\\n )\\r\\n output += (\\r\\n \\\"b: [\\\" + \\\",\\\".join(map(str, map(int, colors[:, 2]))) + \\\"],\\\\n\\\"\\r\\n )\\r\\n elif mapping[\\\"c\\\"] in data:\\r\\n colors = np.array(\\r\\n [cmaps[series](x) for x in data[mapping[\\\"c\\\"]][series]]\\r\\n )\\r\\n colors = np.round(colors * 255.0)\\r\\n output += (\\r\\n \\\"r: [\\\" + \\\",\\\".join(map(str, map(int, colors[:, 0]))) + \\\"],\\\\n\\\"\\r\\n )\\r\\n output += (\\r\\n \\\"g: [\\\" + \\\",\\\".join(map(str, map(int, colors[:, 1]))) + \\\"],\\\\n\\\"\\r\\n )\\r\\n output += (\\r\\n \\\"b: [\\\" + \\\",\\\".join(map(str, map(int, colors[:, 2]))) + \\\"],\\\\n\\\"\\r\\n )\\r\\n output += \\\"},\\\\n\\\"\\r\\n\\r\\n output += \\\"]\\\"\\r\\n output += \\\"},\\\\n\\\"\\r\\n\\r\\n for name, data in self.trees_data.items():\\r\\n mapping = self.trees[name][\\\"mapping\\\"]\\r\\n point_helper = self.trees[name][\\\"point_helper\\\"]\\r\\n\\r\\n output += name + \\\": {\\\\n\\\"\\r\\n\\r\\n if point_helper is not None and point_helper in self.scatters_data:\\r\\n scatter = self.scatters_data[point_helper]\\r\\n scatter_mapping = self.scatters[point_helper][\\\"mapping\\\"]\\r\\n\\r\\n x_t = []\\r\\n y_t = []\\r\\n z_t = []\\r\\n\\r\\n for i in range(len(data[mapping[\\\"from\\\"]])):\\r\\n x_t.append(scatter[scatter_mapping[\\\"x\\\"]][data[mapping[\\\"from\\\"]][i]])\\r\\n x_t.append(scatter[scatter_mapping[\\\"x\\\"]][data[mapping[\\\"to\\\"]][i]])\\r\\n y_t.append(scatter[scatter_mapping[\\\"y\\\"]][data[mapping[\\\"from\\\"]][i]])\\r\\n y_t.append(scatter[scatter_mapping[\\\"y\\\"]][data[mapping[\\\"to\\\"]][i]])\\r\\n z_t.append(scatter[scatter_mapping[\\\"z\\\"]][data[mapping[\\\"from\\\"]][i]])\\r\\n z_t.append(scatter[scatter_mapping[\\\"z\\\"]][data[mapping[\\\"to\\\"]][i]])\\r\\n\\r\\n x_norm = [round(s * (x - mini) / diff, 3) for x in x_t]\\r\\n output += f\\\"x: [\\\" + \\\",\\\".join(map(str, x_norm)) + \\\"],\\\\n\\\"\\r\\n\\r\\n y_norm = [round(s * (y - mini) / diff, 3) for y in y_t]\\r\\n output += \\\"y: [\\\" + \\\",\\\".join(map(str, y_norm)) + \\\"],\\\\n\\\"\\r\\n\\r\\n z_norm = [round(s * (z - mini) / diff, 3) for z in z_t]\\r\\n output += \\\"z: [\\\" + \\\",\\\".join(map(str, z_norm)) + \\\"],\\\\n\\\"\\r\\n else:\\r\\n x_norm = [round(s * (x - mini) / diff, 3) for x in data[mapping[\\\"x\\\"]]]\\r\\n output += \\\"x: [\\\" + \\\",\\\".join(map(str, x_norm)) + \\\"],\\\\n\\\"\\r\\n\\r\\n y_norm = [round(s * (y - mini) / diff, 3) for y in data[mapping[\\\"y\\\"]]]\\r\\n output += \\\"y: [\\\" + \\\",\\\".join(map(str, y_norm)) + \\\"],\\\\n\\\"\\r\\n\\r\\n z_norm = [round(s * (z - mini) / diff, 3) for z in data[mapping[\\\"z\\\"]]]\\r\\n\\r\\n output += \\\"z: [\\\" + \\\",\\\".join(map(str, z_norm)) + \\\"],\\\\n\\\"\\r\\n\\r\\n if mapping[\\\"c\\\"] in data:\\r\\n colormap = self.trees[name][\\\"colormap\\\"]\\r\\n cmap = None\\r\\n if isinstance(colormap, str):\\r\\n cmap = plt.cm.get_cmap(colormap)\\r\\n else:\\r\\n cmap = colormap\\r\\n\\r\\n colors = np.array([cmap(x) for x in data[mapping[\\\"c\\\"]]])\\r\\n colors = np.round(colors * 255.0)\\r\\n output += \\\"r: [\\\" + \\\",\\\".join(map(str, colors[:, 0])) + \\\"],\\\\n\\\"\\r\\n output += \\\"g: [\\\" + \\\",\\\".join(map(str, colors[:, 1])) + \\\"],\\\\n\\\"\\r\\n output += \\\"b: [\\\" + \\\",\\\".join(map(str, colors[:, 2])) + \\\"],\\\\n\\\"\\r\\n\\r\\n output += \\\"},\\\\n\\\"\\r\\n\\r\\n output += \\\"};\\\\n\\\"\\r\\n\\r\\n return output\",\n \"def format_data(self, raw_data):\\n opz = raw_data.copy()\\n opz['datetime'] = pd.to_datetime(opz['Datum-tijd'], format='%Y-%m-%dT%H:%M:%SZ')\\n opz.drop(['Datum-tijd'],axis=1, inplace=True)\\n opz['dag']=opz['datetime'].dt.day\\n opz['tijd'] = opz['datetime'].dt.time\\n #voeg open/dicht data toe en bepaal momenten waarop dit wisselt\\n opz['Opzetstuk Noord (°)'] = opz['Opzetstuk Noord (°)'].str.replace(',', '.').astype(float)\\n opz['Opzetstuk Zuid (°)'] = opz['Opzetstuk Zuid (°)'].str.replace(',', '.').astype(float)\\n opz['Opzetstuk Noord (°)'].fillna(opz['Opzetstuk Zuid (°)'], inplace=True)\\n opz['Opzetstuk Zuid (°)'].fillna(opz['Opzetstuk Noord (°)'], inplace=True)\\n return opz\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6369223","0.6365222","0.585794","0.5771995","0.5722368","0.5701128","0.5615324","0.56003875","0.5577038","0.55621386","0.5557003","0.5556837","0.55493116","0.5504945","0.5502198","0.54964334","0.5491324","0.5473072","0.5443231","0.54316455","0.5413139","0.54070723","0.535886","0.53488433","0.5322872","0.53065187","0.52990854","0.52826375","0.5278614","0.5269334","0.5268921","0.5265093","0.5264546","0.5257704","0.5252975","0.5249222","0.52280563","0.5220573","0.52203393","0.5212401","0.520895","0.5206615","0.51972204","0.519633","0.5196077","0.519378","0.5190677","0.5188719","0.5179449","0.5176586","0.5162077","0.5148358","0.51461655","0.51444036","0.5144074","0.5141943","0.5138669","0.5128991","0.5124639","0.51242167","0.5116084","0.5115979","0.5114022","0.51097435","0.51051795","0.5095362","0.5095318","0.50928074","0.5092255","0.50880826","0.5084488","0.5077939","0.5076919","0.5075148","0.5067052","0.5066252","0.5065601","0.50590265","0.50590074","0.50577724","0.50557435","0.50554323","0.5053653","0.5052958","0.5052759","0.5049019","0.50442964","0.50323796","0.5028235","0.5026216","0.5011812","0.5008895","0.5007958","0.5000325","0.49998462","0.49985367","0.4995906","0.4991033","0.4987765","0.49874344"],"string":"[\n \"0.6369223\",\n \"0.6365222\",\n \"0.585794\",\n \"0.5771995\",\n \"0.5722368\",\n \"0.5701128\",\n \"0.5615324\",\n \"0.56003875\",\n \"0.5577038\",\n \"0.55621386\",\n \"0.5557003\",\n \"0.5556837\",\n \"0.55493116\",\n \"0.5504945\",\n \"0.5502198\",\n \"0.54964334\",\n \"0.5491324\",\n \"0.5473072\",\n \"0.5443231\",\n \"0.54316455\",\n \"0.5413139\",\n \"0.54070723\",\n \"0.535886\",\n \"0.53488433\",\n \"0.5322872\",\n \"0.53065187\",\n \"0.52990854\",\n \"0.52826375\",\n \"0.5278614\",\n \"0.5269334\",\n \"0.5268921\",\n \"0.5265093\",\n \"0.5264546\",\n \"0.5257704\",\n \"0.5252975\",\n \"0.5249222\",\n \"0.52280563\",\n \"0.5220573\",\n \"0.52203393\",\n \"0.5212401\",\n \"0.520895\",\n \"0.5206615\",\n \"0.51972204\",\n \"0.519633\",\n \"0.5196077\",\n \"0.519378\",\n \"0.5190677\",\n \"0.5188719\",\n \"0.5179449\",\n \"0.5176586\",\n \"0.5162077\",\n \"0.5148358\",\n \"0.51461655\",\n \"0.51444036\",\n \"0.5144074\",\n \"0.5141943\",\n \"0.5138669\",\n \"0.5128991\",\n \"0.5124639\",\n \"0.51242167\",\n \"0.5116084\",\n \"0.5115979\",\n \"0.5114022\",\n \"0.51097435\",\n \"0.51051795\",\n \"0.5095362\",\n \"0.5095318\",\n \"0.50928074\",\n \"0.5092255\",\n \"0.50880826\",\n \"0.5084488\",\n \"0.5077939\",\n \"0.5076919\",\n \"0.5075148\",\n \"0.5067052\",\n \"0.5066252\",\n \"0.5065601\",\n \"0.50590265\",\n \"0.50590074\",\n \"0.50577724\",\n \"0.50557435\",\n \"0.50554323\",\n \"0.5053653\",\n \"0.5052958\",\n \"0.5052759\",\n \"0.5049019\",\n \"0.50442964\",\n \"0.50323796\",\n \"0.5028235\",\n \"0.5026216\",\n \"0.5011812\",\n \"0.5008895\",\n \"0.5007958\",\n \"0.5000325\",\n \"0.49998462\",\n \"0.49985367\",\n \"0.4995906\",\n \"0.4991033\",\n \"0.4987765\",\n \"0.49874344\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.51327187"},"document_rank":{"kind":"string","value":"57"}}},{"rowIdx":22482,"cells":{"query":{"kind":"string","value":"Report progress of simulation."},"document":{"kind":"string","value":"def report_progr(i, n, freq=1000):\n\n if not i % freq:\n print('\\t{} / {}'.format(str(i).rjust(5), n))\n # print('{}%'.format(int(100*istep/nsteps)))"},"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 reportProgress(self):\n \n pass","def report_scenario_progress(self):\n pass","def report_progress(self):\r\n stats = self.simulation_stats.stats\r\n solutions = len(self.solutions)\r\n round = self.round\r\n scores = stats[round]\r\n best_score = min(scores)\r\n avg_score = sum(scores) / solutions\r\n line = f\"Round: {round}\\t\\tBest Score: {best_score}\\t\\t Average Score: {avg_score}\"\r\n print(line)","def report_step_progress(self, step):\n pass","def _print_progress(self):\n \n print 'Completed %d of %d' %(self.progress_id, self.total_work)\n self.progress_id += 1","def update_progress(self, done):\r\n if done % 100 == 0:\r\n print >>sys.stderr, \" %d processed, run time %d secs\" % (done, (datetime.now() - self.started_at).seconds)","def PrintProgress(self):\n print ' Examined %d nodes, found %d unique...' % (\n self.nodes_examined, self.unique_nodes\n )","def report_scenario_completed(self):\n self.report_scenario_progress()","def report_scenario_progress(self):\n if not self.current_scenario:\n return # SKIP: No results to report for first scenario.\n # -- NORMAL-CASE:\n status_name = self.current_scenario.status.name\n dot_status = self.dot_status[status_name]\n if status_name == \"failed\":\n # MAYBE TODO: self.failures.append(result)\n pass\n self.stream.write(dot_status)\n self.stream.flush()","def progress(self):\n if self.running:\n pass\n else:\n self._engine.progress()","def performSimulation(self):\n \n if self.parameters['verbose']:\n print(\"=====================\\nStarting simulation with parameters\\n\",self.parameters)\n print(\"=====================\\nInitial Graph\\n\")\n self.showState()\n print(\"=====================\")\n\n while self.parameters['steps'] > 0:\n if self.parameters['verbose']: print(\"Performing step\")\n self.performStep()\n if self.parameters['verbose']: self.showState()\n\n if self.parameters['verbose']:\n print(\"=====================\\nFinished Simulation\\n\\nResult graph:\")\n self.showState()\n #self.showGraph(self.parameters['file_name'])\n #self.showState()\n #self.showStats()","def show_progress(self, game):\n if self.verbose:\n if self.params.eval_interval is not None and (self.episode % self.params.eval_interval == 0):\n self._print_progress()\n # evaluate one run\n state = game.reset()\n self.tracker.reset()\n done = False\n while not done:\n controls = self.sess.run(self.graph.target_actor_outputs,\n feed_dict={self.graph.states: np.array([state])}).reshape(-1)\n state, reward, done, _ = game.step(controls)\n self.tracker.step(reward)\n print('****** ', self.tracker.total_reward, self.tracker.discounted_rewards, ' ******')","def transfer_progress(self, stats):","def show_progress(self, game):\n if self.verbose:\n if self.params.eval_interval is not None and (self.episode % self.params.eval_interval == 0):\n self.print_progress()\n # evaluate one run\n state = game.reset()\n self.tracker.reset()\n done = False\n while not done:\n q_values = self.sess.run(self.graph.target_q_values, feed_dict={self.graph.states: [state]})\n action = np.argmax(q_values)\n state, reward, done, _ = game.step(action)\n self.tracker.step(reward)\n print('****** ', self.tracker.total_reward, self.tracker.discounted_rewards, ' ******')","def run_simulator(self):\n\n self.update_settings()\n\n # Pass in the progress bar and the master so that the simulator can\n # update the progress bar and then refresh the screen when the progress\n # checkpoints are hit\n\n self.sim_results = self.sim.run(self.progress_bar, self.master)\n self.graph_results()","def update_progress(self):\n report = self.build_progress_report()\n self.conduit.set_progress(report)","def update_progress(self):\n report = self.build_progress_report()\n self.conduit.set_progress(report)","def progress(self, arg, num_done, info=''):\n pass","def report_step_progress(self, step):\n dot_status = self.dot_status[step.status.name]\n if step.status == Status.failed:\n if (step.exception and\n not isinstance(step.exception, AssertionError)):\n # -- ISA-ERROR: Some Exception\n dot_status = self.dot_status[\"error\"]\n step.feature = self.current_feature\n step.scenario = self.current_scenario\n self.failures.append(step)\n self.stream.write(dot_status)\n self.stream.flush()","def wait_progress(self):\n pass","def wait_progress(self):\n pass","def doProgress(self,progress,message):\n pass","def _PrintStatus(self):\n comment = 'iter %i' % (self.n_iter)\n self.gfile.write(fileio.GetPrintCoordsXyzString(\n self.mol.atoms, comment, 14, 8))\n self.PrintEnergy(self.n_iter)\n self.gfile.flush()\n self.efile.flush()","def _print_progress(self):\n if self.current_training_size % 1000 == 0:\n print(self.current_training_size, end='')\n elif self.current_training_size % 100 == 0:\n print('.', end='')","def status(self) -> NoReturn:\n\n curr_status= self.percent_done()\n while(curr_status < 100):\n\n update_status(name=self.name, status=curr_status)\n time.sleep(0.5)\n\n curr_status = self.percent_done()\n\n update_status(name=self.name, status=curr_status)","def progress(self, msg):\n logging.info(\"UI-Test: \" + msg)\n with step(\"UI test progress: \" + msg):\n pass\n if len(self.state) > 0:\n self.state += \"\\n\"\n self.state += \"UI: \" + msg","def Step(self):\n lock = threading.Lock()\n\n lock.acquire()\n \n for schedulee in self._schedulees:\n\n schedulee.Step(self.current_simulation_time)\n\n if self.simulation_verbose:\n\n schedulee.Report()\n \n # Update the current global simulation time and current time step\n # at the end of a step.\n self.current_simulation_time += self.time_step\n\n self.current_step += 1\n \n lock.release()","def run():\n step = 0\n while traci.simulation.getMinExpectedNumber() > 0:\n traci.simulationStep()\n step+=1\n traci.close()\n sys.stdout.flush()","def _log_progress(self):\n self.num_of_requests_in_pipeline += 1\n if self.num_of_requests_in_pipeline % 20 == 0:\n print('-' * 200)\n print(f'DB PIPELINE: {self.num_of_requests_in_pipeline} items wenth though pipeline.')\n print('-' * 200)","def check_sim(self):\n if self.index % 5 == 0:\n print (\"Timesteps Completed: {} out of {}\".format(self.index, self.timesteps))\n if self.index == self.timesteps:\n print (\"Simulation Completed\")\n self.end_simulation()\n self.index += 1","def report_progress(self, group_num, group_length):\r\n first_sim_in_group = (group_num * group_length) + 1\r\n last_sim_in_group = first_sim_in_group + group_length - 1\r\n if first_sim_in_group < last_sim_in_group:\r\n print(f\"{datetime.now()}\\t{first_sim_in_group}-{last_sim_in_group}\\n\")\r\n else:\r\n print(f\"{datetime.now()}\\t{first_sim_in_group}\\n\")","def _setProgress(self):\n\n self.progress = (self.iteration, self.iterationCount)","def report(self, individuals):\n print(f'Iteration: {self.current}')\n self.current += 1","def report_trial(self):\n pass","def progress_status(self):\n from tqdm import tqdm\n pbar_a = tqdm(total=len(self.jobs), position=0)\n pbar_a.set_description('Submitted jobs ...')\n pbar_b = tqdm(total=self.n_submit_script, position=1)\n pbar_b.set_description('Running jobs ...')\n pbar_c = tqdm(total=self.n_submit_script, position=2)\n pbar_c.set_description('Completed jobs ...')\n pbar_d = tqdm(total=self.n_submit_script, position=3)\n pbar_d.set_description('Failed? jobs ...')\n while self.n_completed < self.n_submit_script:\n pbar_a.n = self.n_submitted\n pbar_b.n = self.n_running\n pbar_c.n = self.n_completed\n pbar_d.n = self.n_failed + self.n_unknown\n pbar_a.refresh()\n pbar_b.refresh()\n pbar_c.refresh()\n pbar_d.refresh()\n sleep(5)\n self.update_status()","def update(self, steps):\n self.launch_progress += (steps)/self.total","def report_build_progress(self, build_id, current, total, group_name='',\n status_line=''):\n pass","def reporting(self, sol):\r\n if self.cond == True:\r\n time = python_time.clock()\r\n dt = time - self._reporting_statics.time\r\n\r\n def rp(txt):\r\n print(\"ht3_solver:\\t\" + txt)\r\n\r\n if self._reporting_statics.last_report - time < 0:\r\n rp(\"Completed step \" + str(self.step - 1) + \" in \" \\\r\n + str(dt) + \" s.\")\r\n steps_rem = (self.max_T - self.current_T) / self.d_T\r\n completion = 1 - steps_rem / (self.step + steps_rem)\r\n rp(str(int(completion * 100)) + \"% complete.\")\r\n more_steps = np.ceil((self.max_T - self.current_T) / self.d_T)\r\n more_time = more_steps * dt\r\n exp_fin = python_time.asctime(python_time.localtime(\r\n python_time.time() + int(more_time)))\r\n rp(\"Expected completion is \" + exp_fin)\r\n print(\"\\n\")\r\n rp(\"Starting step \" + str(self.step) + \".\")\r\n self._reporting_statics.last_report = time\r\n self._reporting_statics.time = time","def print_progress(self, info_dict):\n if self.n_print != 0:\n t = info_dict['t']\n if t == 1 or t % self.n_print == 0:\n string = 'Iteration {0}'.format(str(t).rjust(len(str(self.n_iter))))\n string += ' [{0}%]'.format(str(int(t / self.n_iter * 100)).rjust(3))\n print(string)","def report():\n Robot.report()","def run(self):\n self._display_sims(self._compute_sims())","def calc_progress(self):\n if self.is_prepared():\n self._sync_info_from_disk()\n self._num_sown_batches = len(\n glob.glob(\n os.path.join(self.location, \"batches\", BTCH_NM.format(\"*\"))\n )\n )\n self._num_results = len(\n glob.glob(\n os.path.join(self.location, \"results\", RSLT_NM.format(\"*\"))\n )\n )\n else:\n self._num_sown_batches = -1\n self._num_results = -1","def final_report(self):\n print('Final Count for', self.reason, self.successes, 'of', self.tests, 'tests passed')","def progress(self):\n return self.runProgress","def progress(self):\n return self.runProgress","def _print_progress(self):\n print(\n 'E {} S {} TR {:6.2f} G {:6.2f} Reg {:6.5f} Loss {:6.5f} AvgQ {:6.2f}'\n ' MinR {:6.2f} MaxR {:6.2f}'.format(\n self.episode, self.episode_step, self.tracker.total_reward, self.tracker.discounted_rewards,\n self.reg_loss_val, self.critic_loss_val, self.mean_q_val,\n self.tracker.min_reward, self.tracker.max_reward))","def finished_sim(self):\n raise NotImplementedError(\n \"finished_sim function not reimplemented form base class\")","def print_progress(self, i, current_params):\n for split in range(1,11):\n if i == (round(self.iterations/10*split)-1):\n post = -self.neg_posterior(current_params)\n approx = self.create_normal_logq(current_params)\n diff = post - approx\n if not self.quiet_progress:\n print(str(split) + \"0% done : ELBO is \" + str(diff) + \", p(y,z) is \" + str(post) + \", q(z) is \" + str(approx))","def print_progress(self, i, current_params):\n for split in range(1,11):\n if i == (round(self.iterations/10*split)-1):\n post = -self.full_neg_posterior(current_params)\n approx = self.create_normal_logq(current_params)\n diff = post - approx\n if not self.quiet_progress:\n print(str(split) + \"0% done : ELBO is \" + str(diff) + \", p(y,z) is \" + str(post) + \", q(z) is \" + str(approx))","def start_progress_bar(self):\r\n self.progress[\"value\"] = self.progress_step","def emit_status(self):\n next_job_count = len(self.fe.get_next_jobs())\n if next_job_count:\n emoji = \"🤔\"\n else:\n emoji = \"👌\"\n remaining = len(self.fe.get_current_network())\n\n pct = (self.total_job_count - remaining) / self.total_job_count\n print(\n f\"{emoji} ———— {next_job_count} jobs running, {remaining} remaining ({int(100*pct)}%). \",\n end=\"\\r\",\n )","def report():\n pass","def simulation_finished(self, data):\n self._logger.info(u\"simulation finished\")\n self.statusLabel.setText(u\"simulation finished.\")\n\n self.actSimulate.setDisabled(False)\n self.shortRunSimulation.setEnabled(True)\n self.shortRunRegimeBatch.setEnabled(True)\n self.actPlayPause.setDisabled(False)\n self.shortPlayPause.setEnabled(True)\n self.shortSaveResult.setEnabled(True)\n self.actStop.setDisabled(False)\n self.actSave.setDisabled(False)\n self.speedDial.setDisabled(False)\n self.timeSlider.setDisabled(False)\n\n self.sim.simulationProgressChanged.disconnect(self.guiProgress.setValue)\n self.statusBar().removeWidget(self.guiProgress)\n\n self.timeSlider.triggerAction(QtGui.QAbstractSlider.SliderToMinimum)\n\n self.currentDataset = data\n self._read_results()\n self._update_data_list()\n self._update_plots()\n\n self.stop_animation()\n # self.playAnimation()\n\n if self.runningBatch:\n regime_name = self._regimes[self._current_regime_index][\"Name\"]\n self._save_data(regime_name)\n self.regimeFinished.emit()\n else:\n self.actExecuteRegimes.setDisabled(False)","def sync(self):\n print(\"{} started\".format(self))\n time.sleep(0.5)\n print(\"{} done\".format(self))\n return \"Result of {}\".format(self)","def report_end(self):\r\n print(f\"\\n\\n\\n{datetime.now()}\\tDone\\n\")","def start_simulation(self):\n regime_name = str(self.regime_list.item(self._current_regime_index).text())\n self.statusLabel.setText(u\"simulating {}\".format(regime_name))\n self._logger.info(u\"Simulating: {}\".format(regime_name))\n\n self.actSimulate.setDisabled(True)\n self.shortRunSimulation.setEnabled(False)\n self.shortRunRegimeBatch.setEnabled(False)\n self.actExecuteRegimes.setDisabled(True)\n self.guiProgress = QtGui.QProgressBar(self)\n self.sim.simulationProgressChanged.connect(self.guiProgress.setValue)\n self.statusBar().addWidget(self.guiProgress)\n self.runSimulation.emit()","def testSimCompletes(self):\n sim = Simulation()\n self.assertEqual(25, sim.run_simple(1, 11, \"output\", 0.1, 2, 10))","def getProgress(self):","def complete(self):\n self.chunk_percentage = [1.0] * self.total_steps\n self.progress_updated.emit(self.percentage)\n self.initialized = False","def updateProgress (self, iteration, total, prefix='Progress', suffix='complete', decimals=1, length=100, fill='█', printEnd=\"\\r\"):\n if iteration == 0:\n self.start_time = timer()\n ETC = '' #Estimated Time to Completion\n if (iteration/total)*100 >= self.updates[self.update_counter]:\n elapsed = timer() - self.start_time\n if iteration != 0:\n minutes = int((elapsed * total/iteration - elapsed)//60)\n seconds = int((elapsed * total/iteration - elapsed)%60)\n ETC = \"(~{:d} mins {:d}s left)\".format(minutes, seconds)\n percent = (\"{0:.\" + str(decimals) + \"f}\").format(100 * (iteration / float(total)))\n filledLength = int(length * iteration // total)\n bar = fill * filledLength + '-' * (length - filledLength)\n # Unfortunately \\r doesn't work in the pycharm console, so we have to reprint the whole bar everytime,\n # clogging the console.\n #print(f'\\r{prefix} |{bar}| {percent}% {suffix} {ETC}', end = printEnd)\n print(f'{prefix} |{bar}| {percent}% {suffix} {ETC}')\n # Print New Line on Complete\n if iteration == total:\n print()\n self.update_counter += 1","def _iteration_changed(self):\n if self.showProgressBar:\n try:\n self._setProgress()\n except:\n # may fail when switching from training to inference\n from dbgp.client import brk; brk(port=9011)\n pass","def report_percent_complete(self):\n meta_str = 'Bundling percent complete: ' + \\\n str(int(self.percent_complete))\n\n TaskComm.set_state('PROGRESS', meta_str)","def printProgress(self, percentage):\n #print '%s\\r' % ' '*20, # clean up row\n #print '%3d%% ' % percentage, # ending with comma prevents newline from being appended\n sys.stdout.flush()","def report_learning_progress(istep, nsteps, VC_HC, GS_HC, norm, mrh, mhh,\n rop=None):\n\n rep = '{}%'.format(int(100*istep/nsteps)).rjust(4)\n l_vc_hc = analysis.VC_HC_norm(VC_HC, norm).mean()\n l_gs_hc = analysis.GS_HC_norm(GS_HC, norm).mean()\n prog = '{} | VC-HC: {:.3f}, GS-HC: {:.3f}'.format(rep, l_vc_hc, l_gs_hc)\n prog += ' | H real: {:.2f}, H HC: {:.2f}'.format(mrh, mhh)\n if rop is not None:\n prog += ' | ro pow: {:.1f}'.format(rop)\n print(prog)","def run(self):\n\n inside = 0\n for draws in range(1, self.data['samples']):\n # generate points and check whether they are inside the unit circle\n r1 = random.random()\n r2 = random.random()\n if r1 ** 2 + r2 ** 2 < 1.0:\n inside += 1\n\n # every 1000 iterations, update status\n if draws % 1000 != 0:\n continue\n\n # debug\n yield self.emit('log', {'draws': draws, 'inside': inside})\n\n # calculate pi and its uncertainty given the current draws\n p = inside / draws\n pi = {\n 'estimate': 4.0 * inside / draws,\n 'uncertainty': 4.0 * math.sqrt(draws * p * (1.0 - p)) / draws,\n }\n\n # send status to frontend\n yield self.set_state(pi=pi)\n\n yield self.emit('log', {'action': 'done'})","def next(self):\n print(f\" {colored('[', 'yellow')}{bold(self.progress[self.pos])}{colored(']', 'yellow')} \"\n f\"{bold('Processing, please wait...')}\",\n end=\"\\r\",\n flush=True\n )\n self.increment()","def report(self):\n\n job_summary = {}\n for job in self._jobs:\n \n if job.step_name not in job_summary:\n job_summary[ job.step_name ] = {}\n job_summary[ job.step_name ][ 'DONE' ] = 0\n job_summary[ job.step_name ][ 'RUNNING' ] = 0\n job_summary[ job.step_name ][ 'QUEUING' ] = 0\n job_summary[ job.step_name ][ 'FAILED' ] = 0\n job_summary[ job.step_name ][ 'UNKNOWN' ] = 0\n job_summary[ job.step_name ][ 'max_mem' ] = 0\n job_summary[ job.step_name ][ 'cputime' ] = 0\n\n if job.status == Job_status.FINISHED:\n job_summary[ job.step_name ][ 'DONE' ] += 1\n if job.cputime is not None:\n job_summary[ job.step_name ]['cputime'] += int(job.cputime)\n\n if job.max_memory is not None and job.max_memory > job_summary[ job.step_name ][ 'max_mem']:\n job_summary[ job.step_name ][ 'max_mem'] = int(job.max_memory)\n\n elif job.status == Job_status.RUNNING:\n job_summary[ job.step_name ][ 'RUNNING' ] += 1\n elif job.status == Job_status.QUEUEING or job.status == Job_status.SUBMITTED:\n job_summary[ job.step_name ][ 'QUEUING' ] += 1\n elif job.status == Job_status.FAILED or job.status == Job_status.NO_RESTART:\n job_summary[ job.step_name ][ 'FAILED' ] += 1\n else:\n job_summary[ job.step_name ][ 'UNKNOWN' ] += 1\n\n\n\n local_time = strftime(\"%d/%m/%Y %H:%M\", time.localtime())\n \n\n pickle_file = \"{}.{}\".format(self.pipeline.project_name, self.pipeline._pid)\n\n print(\"[{} @{} {}]\".format( local_time,self.pipeline._hostname , pickle_file))\n\n print(\"{:20} || {:12} || {:12} || {:2s} {:2s} {:2s} {:2s} {:2s}\".format(\"Run stats\", \"Runtime\", \"Max Mem\", \"D\",\"R\",\"Q\",\"F\",\"U\"))\n\n for step in sorted(self.pipeline._workflow._analysis_order, key=self.pipeline._workflow._analysis_order.__getitem__):\n if step not in job_summary:\n continue\n\n print(\"{:20} || {:12} || {:12} || {:02d}/{:02d}/{:02d}/{:02d}/{:02d}\".format(step, \n self.format_time(job_summary[ step ]['cputime']),\n self.format_memory(job_summary[ step ]['max_mem']),\n job_summary[ step ][ 'DONE' ],\n job_summary[ step ][ 'RUNNING' ],\n job_summary[ step ][ 'QUEUING' ],\n job_summary[ step ][ 'FAILED' ],\n job_summary[ step ][ 'UNKNOWN' ]))","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 done(self):\n log.debug(\"Test run concluded\")\n if self._startTime is not None:\n self.report['startTime'] = self._startTime\n self.report['runTime'] = time.time() - self._startTime\n self.report['testsRun'] = self.testsRun\n self.report['tests'] = self._tests\n self.writeReport()","def _printProgress(self, progress):\n if not self._quiet:\n sys.stdout.write('\\rWriting store to CSV: [{0:50s}] {1:.2f}% '.format('#' * int(progress * 50.0), progress * 100.0))\n sys.stdout.flush()","def progress(self, progress):\n\n self._progress = progress","def progress(self, progress):\n\n self._progress = progress","def set_progress(self, progress: float):","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 progress(self, loaded, total, msg=''):\n\n self.fire('progress', {\n 'loaded': loaded,\n 'total': total,\n 'msg': msg\n })","def print_progress(self, current_acq=0):\n # The following method is always called because it includes the\n # no-progress checks.\n percdone = self.get_percdone(current_acq=current_acq)\n if self.verbose() and percdone != np.nan:\n elapsed_time = time.time() - self.begintime\n t_left = round((100. - percdone) / (percdone) *\n elapsed_time, 1) if percdone != 0 else '??'\n t_end = time.strftime('%H:%M:%S', time.localtime(time.time() +\n + t_left)) if percdone != 0 else '??'\n # The trailing spaces are to overwrite some characters in case the\n # previous progress message was longer. (Due to \\r, the string\n # output will start at the beginning of the current line and\n # each character of the new string will overwrite a character\n # of the previous output in the current line.)\n progress_message = (\n \"\\r{timestamp}\\t{percdone}% completed \\telapsed time: \"\n \"{t_elapsed}s \\ttime left: {t_left}s\\t(until {t_end}) \"\n \"\").format(\n timestamp=time.strftime('%H:%M:%S', time.localtime()),\n percdone=int(percdone),\n t_elapsed=round(elapsed_time, 1),\n t_left=t_left,\n t_end=t_end,)\n\n if percdone != 100 or current_acq:\n end_char = ''\n else:\n end_char = '\\n'\n print('\\r', progress_message, end=end_char)","def tick(self):\n self.current_count += 1\n self.progress(self.current_count)","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 status(self):\n\t\tstatus = self.thread.status()\n#\t\tprint_array(status)\n\t\tmessage = [\"------ RSYNC PROGRESS ------ \"]\n\t\tif self.log_message:\n\t\t\tmessage.append(self.log_message)\n\t\tmessage.append(\"Current file: %s\" % status['current_file'])\n\t\tmessage.append(\"\\tBytes Copied: %s\" % status['bytes_copied'])\n\t\tmessage.append(\"\\tPercent Done: %s\" % status['percent_done'])\n\t\tmessage.append(\"\\tTransfer Rate: %s\" % status['transfer_rate'])\n\t\tmessage.append(\"\\tTime Remaining: %s\" % status['est_remain'])\n\t\tmessage.append(\"\\tTransfer Number: %s\" % status['xfer_num'])\n\t\tmessage.append(\"\\tTransfers Remaining: %s\" % status['xfer_remain'])\n\t\tmessage.append(\"\\tTransfers Total: %s\" % status['xfer_total'])\n\t\tmessage.append(\"\\t----------------------------------\")\n\t\ttry:\n\t\t\toverall_percent = int(round((int(status['xfer_num'])*1.0)/int(status['xfer_total']),2)*100)\n\t\texcept: overall_percent = 0\n\t\tmessage.append(\"\\tTotal Rsync done: %s%%\\n\" % overall_percent)\n\t\tp = open(self.progress_file,'w+',0)\n\t\tfor line in message:\n\t\t\t#print line\n\t\t\tp.write(\"%s\\n\" % line)\n\t\tp.flush()\n\t\tp.close()","def print_progress(self):\n print(\n '\\rE {} S {} TR {:6.2f} G {:6.2f} Loss {:6.5f} AvgQ {:6.2f}'\n ' MinR {:6.2f} MaxR {:6.2f}'.format(\n self.episode, self.episode_step,\n self.tracker.total_reward, self.tracker.discounted_rewards,\n self.loss_val, self.total_max_q / self.episode_step,\n self.tracker.min_reward, self.tracker.max_reward,\n end=\"\"))","def print_progress(c, t, K, t_end, flag):\r\n if (t > (t_end / 20.0)) and flag == True:\r\n print('1/20 of the simulation is done.')\r\n flag = False\r\n\r\n # debugging information\r\n print('\\n======================\\nTime: %g' % (t))\r\n print('Clones:\\n', c)\r\n print('\\nInteractions in Matrix K:\\n', np.array(K))\r\n print('Dimension of Matrix K:\\t%s' % str(K.shape))\r\n return flag","def OnProgress(bytes_read, total_bytes, percent):\n sys.stdout.write(\"progress: %.2f%% \\r\" % (percent))\n sys.stdout.flush()","def progressbar(self, complete = 0.0):\n\n if self.is_subprocess:\n sys.stderr.write(\"%f\\n\" % complete)\n sys.stderr.flush()\n else:\n gdal.TermProgress_nocb(complete)","def setReportProgress(self, newReportProgress):\n \n pass","def progress(self, i, num_episodes, last_avg, start, end):\n percentage = (i / num_episodes) * 100.0\n self.log(\n \"Epoch {:>5}/{:<5} [{:>5.1f}%] Avg reward: {:+.3f}\".format(\n i,\n num_episodes,\n percentage,\n last_avg), end='')\n if percentage == 0.0:\n self.log(' Est.time: UNKNOWN')\n return\n elapsed = end - start\n remaining = ((100. - percentage) * elapsed) / percentage\n self.log(' Est.time: {}'.format(self.timer(remaining)))","def _on_progress(self, num):\n self._num_progresses += num\n self._log.info(\"Progress incrementing by {}\".format(num))\n self._host_comms.send_msg(\"progress\", num)","def progress(self) -> int:\n return int(round(100 * self.somme() / self.finances))","def print_progress(remaining_pairs, G):\n total_pairs = G.number_of_nodes()**2\n uncomputed_pairs = len(remaining_pairs)\n print(1 - uncomputed_pairs/total_pairs)","def simulation_step(self):\n if self.data_valid.get():\n print(\"Output pin %s writing %s\" % (self.name, self.debug_data.get()))","def print_progress(self):\n\n if not self.verbose:\n return\n\n elapsed_time = time.time() - self._start_time\n\n if self._is_completed():\n # Make sure that we get a last message telling us we are done\n self._print(\n f\"Done {self.n_completed_tasks:3d} out of \"\n f\"{self.n_completed_tasks:3d} | elapsed: \"\n f\"{short_format_time(elapsed_time)} finished\"\n )\n return\n\n # Original job iterator becomes None once it has been fully\n # consumed : at this point we know the total number of jobs and we are\n # able to display an estimation of the remaining time based on already\n # completed jobs. Otherwise, we simply display the number of completed\n # tasks.\n elif self._original_iterator is not None:\n if _verbosity_filter(self.n_dispatched_batches, self.verbose):\n return\n self._print(\n f\"Done {self.n_completed_tasks:3d} tasks | elapsed: \"\n f\"{short_format_time(elapsed_time)}\"\n )\n else:\n index = self.n_completed_tasks\n # We are finished dispatching\n total_tasks = self.n_dispatched_tasks\n # We always display the first loop\n if not index == 0:\n # Display depending on the number of remaining items\n # A message as soon as we finish dispatching, cursor is 0\n cursor = (total_tasks - index + 1 -\n self._pre_dispatch_amount)\n frequency = (total_tasks // self.verbose) + 1\n is_last_item = (index + 1 == total_tasks)\n if (is_last_item or cursor % frequency):\n return\n remaining_time = (elapsed_time / index) * \\\n (self.n_dispatched_tasks - index * 1.0)\n # only display status if remaining time is greater or equal to 0\n self._print(\n f\"Done {index:3d} out of {total_tasks:3d} | elapsed: \"\n f\"{short_format_time(elapsed_time)} remaining: \"\n f\"{short_format_time(remaining_time)}\"\n )","def simulate(self):\r\n\r\n for index in tqdm(range(self.steps)):\r\n\r\n S = 0.1 - 0.1 / self.steps * (index + 1)\r\n T = 0.5 / (np.log(2 + 0.2 * index))\r\n\r\n self.move(T, S)\r\n self.t_change.append(T)\r\n self.s_change.append(S)\r\n tot = calculate_total_energy(self.current_config)\r\n self.energies.append(tot)","def report(self):\r\n print(\"\".join(self.memory), self.error, self.steps)","def status(self, loop=False, delay=10):\n if not self._submit:\n raise Exception('The workfow has not started its execution yet.\\n'\n 'Please, check if the workflow is planned and submitted for execution.')\n seq = False\n\n while True:\n out, err = subprocess.Popen('pegasus-status -l %s' % self.submit_dir, stdout=subprocess.PIPE,\n stderr=subprocess.PIPE, shell=True, cwd=self.base_dir).communicate()\n if err:\n raise Exception(err)\n\n for line in out.decode('utf8').split('\\n'):\n if 'UNRDY' in line:\n seq = True\n elif seq:\n seq = False\n v = line.split()\n\n state = v[8]\n if state == 'Success':\n state = '\\x1b[1;32m' + state + '\\x1b[0m'\n elif state == 'Failure':\n state = '\\x1b[1;31m' + state + '\\x1b[0m'\n\n progress = '\\x1b[1;34m' + 'Progress: ' + v[7] + '%\\x1b[0m (' + state + ')'\n completed = '\\x1b[1;32mCompleted: ' + v[5] + '\\x1b[0m'\n queued = '\\x1b[1;33mQueued: ' + v[1] + '\\x1b[0m'\n running = '\\x1b[1;36mRunning: ' + v[3] + '\\x1b[0m'\n fail = '\\x1b[1;31mFailed: ' + v[6] + '\\x1b[0m'\n\n st = progress + '\\t(' + completed + ', ' + queued + ', ' + running + ', ' + fail + ')'\n print('%s\\r' % st, end='')\n break\n\n if not loop or 'Success' in out.decode('utf8') or 'Failure' in out.decode('utf8'):\n break\n time.sleep(delay)","def __show_progress(self, _cur_file_idx, _file_count):\n if (self.__is_show_proegress == False):\n return\n\n if(_file_count == 0):\n raise StandardError('no file found.')\n\n # show progress for each 5% (20 steps)\n digit = math.modf(math.log10(_file_count))[1]\n if(digit < 3):\n print \"prog: [{0}%] {1}/{2}\".format((100 * _cur_file_idx) /_file_count,\n _cur_file_idx, _file_count)\n else:\n digit = digit - 2\n skipstep10 = math.pow(10, digit)\n if ((_cur_file_idx % skipstep10) == 0):\n print \"prog: [{0}%] {1}/{2}\".format((100 * _cur_file_idx) /_file_count,\n _cur_file_idx, _file_count)","def notify_progress(self, progress_data):\n pass # pragma: no cover","def progress(self, value):\n self.step = float(value)\n self._draw()","def step_print(self, prefix=\"\", suffix=\"\"):\n (progress, current_run_time, expected_time, expected_rest_time) = self.step()\n clear_output(True)\n if prefix != \"\":\n print(prefix)\n print(\"Current progress: {:3.1f} %\".format(progress) )\n print(\"#\" * int(progress*0.3))\n seconds, minutes, hours = self._calc_times(current_run_time)\n print(\"Current run time: {:02d}:{:02d}:{:02d}\".format(hours,minutes,seconds))\n seconds, minutes, hours = self._calc_times(expected_rest_time)\n print(\"Expected time to finish: {:02d}:{:02d}:{:02d}\".format(hours,minutes,seconds))\n if suffix != \"\":\n print(suffix)","def Report(self):\n return True","def generate(self):\n if not (os.path.exists(self.target_path.get()) and self.target_file.get()):\n messagebox.showerror(\"Invalid Arguments\",\n \"At least one path was not valid.\")\n return\n self.progress.grid(row=100, columnspan=3, sticky=\"ews\")\n self.update()\n thread = threading.Thread(\n target=folder_structure_backup.iterate_and_save,\n args=(self.target_path.get(), self.target_file.get(),\n \"big\" if not self.fastmode.get() else \"fast\"))\n thread.daemon = True\n thread.start()\n while thread.is_alive():\n self.progress.step(amount=2)\n self.progress.update()\n self.update()\n time.sleep(0.1)\n self.progress.grid_forget()\n self.finished = True\n self.destroy()\n self.quit()","def launch_status(self):\n print(\n f\"Starting job with {len(self.fe.get_network())} jobs total. \",\n end=\"\\r\",\n )","def run_tests(self):\n with self.report.timer.record(\"run\"):\n self.result.report.extend(self._run_tests())"],"string":"[\n \"def reportProgress(self):\\n \\n pass\",\n \"def report_scenario_progress(self):\\n pass\",\n \"def report_progress(self):\\r\\n stats = self.simulation_stats.stats\\r\\n solutions = len(self.solutions)\\r\\n round = self.round\\r\\n scores = stats[round]\\r\\n best_score = min(scores)\\r\\n avg_score = sum(scores) / solutions\\r\\n line = f\\\"Round: {round}\\\\t\\\\tBest Score: {best_score}\\\\t\\\\t Average Score: {avg_score}\\\"\\r\\n print(line)\",\n \"def report_step_progress(self, step):\\n pass\",\n \"def _print_progress(self):\\n \\n print 'Completed %d of %d' %(self.progress_id, self.total_work)\\n self.progress_id += 1\",\n \"def update_progress(self, done):\\r\\n if done % 100 == 0:\\r\\n print >>sys.stderr, \\\" %d processed, run time %d secs\\\" % (done, (datetime.now() - self.started_at).seconds)\",\n \"def PrintProgress(self):\\n print ' Examined %d nodes, found %d unique...' % (\\n self.nodes_examined, self.unique_nodes\\n )\",\n \"def report_scenario_completed(self):\\n self.report_scenario_progress()\",\n \"def report_scenario_progress(self):\\n if not self.current_scenario:\\n return # SKIP: No results to report for first scenario.\\n # -- NORMAL-CASE:\\n status_name = self.current_scenario.status.name\\n dot_status = self.dot_status[status_name]\\n if status_name == \\\"failed\\\":\\n # MAYBE TODO: self.failures.append(result)\\n pass\\n self.stream.write(dot_status)\\n self.stream.flush()\",\n \"def progress(self):\\n if self.running:\\n pass\\n else:\\n self._engine.progress()\",\n \"def performSimulation(self):\\n \\n if self.parameters['verbose']:\\n print(\\\"=====================\\\\nStarting simulation with parameters\\\\n\\\",self.parameters)\\n print(\\\"=====================\\\\nInitial Graph\\\\n\\\")\\n self.showState()\\n print(\\\"=====================\\\")\\n\\n while self.parameters['steps'] > 0:\\n if self.parameters['verbose']: print(\\\"Performing step\\\")\\n self.performStep()\\n if self.parameters['verbose']: self.showState()\\n\\n if self.parameters['verbose']:\\n print(\\\"=====================\\\\nFinished Simulation\\\\n\\\\nResult graph:\\\")\\n self.showState()\\n #self.showGraph(self.parameters['file_name'])\\n #self.showState()\\n #self.showStats()\",\n \"def show_progress(self, game):\\n if self.verbose:\\n if self.params.eval_interval is not None and (self.episode % self.params.eval_interval == 0):\\n self._print_progress()\\n # evaluate one run\\n state = game.reset()\\n self.tracker.reset()\\n done = False\\n while not done:\\n controls = self.sess.run(self.graph.target_actor_outputs,\\n feed_dict={self.graph.states: np.array([state])}).reshape(-1)\\n state, reward, done, _ = game.step(controls)\\n self.tracker.step(reward)\\n print('****** ', self.tracker.total_reward, self.tracker.discounted_rewards, ' ******')\",\n \"def transfer_progress(self, stats):\",\n \"def show_progress(self, game):\\n if self.verbose:\\n if self.params.eval_interval is not None and (self.episode % self.params.eval_interval == 0):\\n self.print_progress()\\n # evaluate one run\\n state = game.reset()\\n self.tracker.reset()\\n done = False\\n while not done:\\n q_values = self.sess.run(self.graph.target_q_values, feed_dict={self.graph.states: [state]})\\n action = np.argmax(q_values)\\n state, reward, done, _ = game.step(action)\\n self.tracker.step(reward)\\n print('****** ', self.tracker.total_reward, self.tracker.discounted_rewards, ' ******')\",\n \"def run_simulator(self):\\n\\n self.update_settings()\\n\\n # Pass in the progress bar and the master so that the simulator can\\n # update the progress bar and then refresh the screen when the progress\\n # checkpoints are hit\\n\\n self.sim_results = self.sim.run(self.progress_bar, self.master)\\n self.graph_results()\",\n \"def update_progress(self):\\n report = self.build_progress_report()\\n self.conduit.set_progress(report)\",\n \"def update_progress(self):\\n report = self.build_progress_report()\\n self.conduit.set_progress(report)\",\n \"def progress(self, arg, num_done, info=''):\\n pass\",\n \"def report_step_progress(self, step):\\n dot_status = self.dot_status[step.status.name]\\n if step.status == Status.failed:\\n if (step.exception and\\n not isinstance(step.exception, AssertionError)):\\n # -- ISA-ERROR: Some Exception\\n dot_status = self.dot_status[\\\"error\\\"]\\n step.feature = self.current_feature\\n step.scenario = self.current_scenario\\n self.failures.append(step)\\n self.stream.write(dot_status)\\n self.stream.flush()\",\n \"def wait_progress(self):\\n pass\",\n \"def wait_progress(self):\\n pass\",\n \"def doProgress(self,progress,message):\\n pass\",\n \"def _PrintStatus(self):\\n comment = 'iter %i' % (self.n_iter)\\n self.gfile.write(fileio.GetPrintCoordsXyzString(\\n self.mol.atoms, comment, 14, 8))\\n self.PrintEnergy(self.n_iter)\\n self.gfile.flush()\\n self.efile.flush()\",\n \"def _print_progress(self):\\n if self.current_training_size % 1000 == 0:\\n print(self.current_training_size, end='')\\n elif self.current_training_size % 100 == 0:\\n print('.', end='')\",\n \"def status(self) -> NoReturn:\\n\\n curr_status= self.percent_done()\\n while(curr_status < 100):\\n\\n update_status(name=self.name, status=curr_status)\\n time.sleep(0.5)\\n\\n curr_status = self.percent_done()\\n\\n update_status(name=self.name, status=curr_status)\",\n \"def progress(self, msg):\\n logging.info(\\\"UI-Test: \\\" + msg)\\n with step(\\\"UI test progress: \\\" + msg):\\n pass\\n if len(self.state) > 0:\\n self.state += \\\"\\\\n\\\"\\n self.state += \\\"UI: \\\" + msg\",\n \"def Step(self):\\n lock = threading.Lock()\\n\\n lock.acquire()\\n \\n for schedulee in self._schedulees:\\n\\n schedulee.Step(self.current_simulation_time)\\n\\n if self.simulation_verbose:\\n\\n schedulee.Report()\\n \\n # Update the current global simulation time and current time step\\n # at the end of a step.\\n self.current_simulation_time += self.time_step\\n\\n self.current_step += 1\\n \\n lock.release()\",\n \"def run():\\n step = 0\\n while traci.simulation.getMinExpectedNumber() > 0:\\n traci.simulationStep()\\n step+=1\\n traci.close()\\n sys.stdout.flush()\",\n \"def _log_progress(self):\\n self.num_of_requests_in_pipeline += 1\\n if self.num_of_requests_in_pipeline % 20 == 0:\\n print('-' * 200)\\n print(f'DB PIPELINE: {self.num_of_requests_in_pipeline} items wenth though pipeline.')\\n print('-' * 200)\",\n \"def check_sim(self):\\n if self.index % 5 == 0:\\n print (\\\"Timesteps Completed: {} out of {}\\\".format(self.index, self.timesteps))\\n if self.index == self.timesteps:\\n print (\\\"Simulation Completed\\\")\\n self.end_simulation()\\n self.index += 1\",\n \"def report_progress(self, group_num, group_length):\\r\\n first_sim_in_group = (group_num * group_length) + 1\\r\\n last_sim_in_group = first_sim_in_group + group_length - 1\\r\\n if first_sim_in_group < last_sim_in_group:\\r\\n print(f\\\"{datetime.now()}\\\\t{first_sim_in_group}-{last_sim_in_group}\\\\n\\\")\\r\\n else:\\r\\n print(f\\\"{datetime.now()}\\\\t{first_sim_in_group}\\\\n\\\")\",\n \"def _setProgress(self):\\n\\n self.progress = (self.iteration, self.iterationCount)\",\n \"def report(self, individuals):\\n print(f'Iteration: {self.current}')\\n self.current += 1\",\n \"def report_trial(self):\\n pass\",\n \"def progress_status(self):\\n from tqdm import tqdm\\n pbar_a = tqdm(total=len(self.jobs), position=0)\\n pbar_a.set_description('Submitted jobs ...')\\n pbar_b = tqdm(total=self.n_submit_script, position=1)\\n pbar_b.set_description('Running jobs ...')\\n pbar_c = tqdm(total=self.n_submit_script, position=2)\\n pbar_c.set_description('Completed jobs ...')\\n pbar_d = tqdm(total=self.n_submit_script, position=3)\\n pbar_d.set_description('Failed? jobs ...')\\n while self.n_completed < self.n_submit_script:\\n pbar_a.n = self.n_submitted\\n pbar_b.n = self.n_running\\n pbar_c.n = self.n_completed\\n pbar_d.n = self.n_failed + self.n_unknown\\n pbar_a.refresh()\\n pbar_b.refresh()\\n pbar_c.refresh()\\n pbar_d.refresh()\\n sleep(5)\\n self.update_status()\",\n \"def update(self, steps):\\n self.launch_progress += (steps)/self.total\",\n \"def report_build_progress(self, build_id, current, total, group_name='',\\n status_line=''):\\n pass\",\n \"def reporting(self, sol):\\r\\n if self.cond == True:\\r\\n time = python_time.clock()\\r\\n dt = time - self._reporting_statics.time\\r\\n\\r\\n def rp(txt):\\r\\n print(\\\"ht3_solver:\\\\t\\\" + txt)\\r\\n\\r\\n if self._reporting_statics.last_report - time < 0:\\r\\n rp(\\\"Completed step \\\" + str(self.step - 1) + \\\" in \\\" \\\\\\r\\n + str(dt) + \\\" s.\\\")\\r\\n steps_rem = (self.max_T - self.current_T) / self.d_T\\r\\n completion = 1 - steps_rem / (self.step + steps_rem)\\r\\n rp(str(int(completion * 100)) + \\\"% complete.\\\")\\r\\n more_steps = np.ceil((self.max_T - self.current_T) / self.d_T)\\r\\n more_time = more_steps * dt\\r\\n exp_fin = python_time.asctime(python_time.localtime(\\r\\n python_time.time() + int(more_time)))\\r\\n rp(\\\"Expected completion is \\\" + exp_fin)\\r\\n print(\\\"\\\\n\\\")\\r\\n rp(\\\"Starting step \\\" + str(self.step) + \\\".\\\")\\r\\n self._reporting_statics.last_report = time\\r\\n self._reporting_statics.time = time\",\n \"def print_progress(self, info_dict):\\n if self.n_print != 0:\\n t = info_dict['t']\\n if t == 1 or t % self.n_print == 0:\\n string = 'Iteration {0}'.format(str(t).rjust(len(str(self.n_iter))))\\n string += ' [{0}%]'.format(str(int(t / self.n_iter * 100)).rjust(3))\\n print(string)\",\n \"def report():\\n Robot.report()\",\n \"def run(self):\\n self._display_sims(self._compute_sims())\",\n \"def calc_progress(self):\\n if self.is_prepared():\\n self._sync_info_from_disk()\\n self._num_sown_batches = len(\\n glob.glob(\\n os.path.join(self.location, \\\"batches\\\", BTCH_NM.format(\\\"*\\\"))\\n )\\n )\\n self._num_results = len(\\n glob.glob(\\n os.path.join(self.location, \\\"results\\\", RSLT_NM.format(\\\"*\\\"))\\n )\\n )\\n else:\\n self._num_sown_batches = -1\\n self._num_results = -1\",\n \"def final_report(self):\\n print('Final Count for', self.reason, self.successes, 'of', self.tests, 'tests passed')\",\n \"def progress(self):\\n return self.runProgress\",\n \"def progress(self):\\n return self.runProgress\",\n \"def _print_progress(self):\\n print(\\n 'E {} S {} TR {:6.2f} G {:6.2f} Reg {:6.5f} Loss {:6.5f} AvgQ {:6.2f}'\\n ' MinR {:6.2f} MaxR {:6.2f}'.format(\\n self.episode, self.episode_step, self.tracker.total_reward, self.tracker.discounted_rewards,\\n self.reg_loss_val, self.critic_loss_val, self.mean_q_val,\\n self.tracker.min_reward, self.tracker.max_reward))\",\n \"def finished_sim(self):\\n raise NotImplementedError(\\n \\\"finished_sim function not reimplemented form base class\\\")\",\n \"def print_progress(self, i, current_params):\\n for split in range(1,11):\\n if i == (round(self.iterations/10*split)-1):\\n post = -self.neg_posterior(current_params)\\n approx = self.create_normal_logq(current_params)\\n diff = post - approx\\n if not self.quiet_progress:\\n print(str(split) + \\\"0% done : ELBO is \\\" + str(diff) + \\\", p(y,z) is \\\" + str(post) + \\\", q(z) is \\\" + str(approx))\",\n \"def print_progress(self, i, current_params):\\n for split in range(1,11):\\n if i == (round(self.iterations/10*split)-1):\\n post = -self.full_neg_posterior(current_params)\\n approx = self.create_normal_logq(current_params)\\n diff = post - approx\\n if not self.quiet_progress:\\n print(str(split) + \\\"0% done : ELBO is \\\" + str(diff) + \\\", p(y,z) is \\\" + str(post) + \\\", q(z) is \\\" + str(approx))\",\n \"def start_progress_bar(self):\\r\\n self.progress[\\\"value\\\"] = self.progress_step\",\n \"def emit_status(self):\\n next_job_count = len(self.fe.get_next_jobs())\\n if next_job_count:\\n emoji = \\\"🤔\\\"\\n else:\\n emoji = \\\"👌\\\"\\n remaining = len(self.fe.get_current_network())\\n\\n pct = (self.total_job_count - remaining) / self.total_job_count\\n print(\\n f\\\"{emoji} ———— {next_job_count} jobs running, {remaining} remaining ({int(100*pct)}%). \\\",\\n end=\\\"\\\\r\\\",\\n )\",\n \"def report():\\n pass\",\n \"def simulation_finished(self, data):\\n self._logger.info(u\\\"simulation finished\\\")\\n self.statusLabel.setText(u\\\"simulation finished.\\\")\\n\\n self.actSimulate.setDisabled(False)\\n self.shortRunSimulation.setEnabled(True)\\n self.shortRunRegimeBatch.setEnabled(True)\\n self.actPlayPause.setDisabled(False)\\n self.shortPlayPause.setEnabled(True)\\n self.shortSaveResult.setEnabled(True)\\n self.actStop.setDisabled(False)\\n self.actSave.setDisabled(False)\\n self.speedDial.setDisabled(False)\\n self.timeSlider.setDisabled(False)\\n\\n self.sim.simulationProgressChanged.disconnect(self.guiProgress.setValue)\\n self.statusBar().removeWidget(self.guiProgress)\\n\\n self.timeSlider.triggerAction(QtGui.QAbstractSlider.SliderToMinimum)\\n\\n self.currentDataset = data\\n self._read_results()\\n self._update_data_list()\\n self._update_plots()\\n\\n self.stop_animation()\\n # self.playAnimation()\\n\\n if self.runningBatch:\\n regime_name = self._regimes[self._current_regime_index][\\\"Name\\\"]\\n self._save_data(regime_name)\\n self.regimeFinished.emit()\\n else:\\n self.actExecuteRegimes.setDisabled(False)\",\n \"def sync(self):\\n print(\\\"{} started\\\".format(self))\\n time.sleep(0.5)\\n print(\\\"{} done\\\".format(self))\\n return \\\"Result of {}\\\".format(self)\",\n \"def report_end(self):\\r\\n print(f\\\"\\\\n\\\\n\\\\n{datetime.now()}\\\\tDone\\\\n\\\")\",\n \"def start_simulation(self):\\n regime_name = str(self.regime_list.item(self._current_regime_index).text())\\n self.statusLabel.setText(u\\\"simulating {}\\\".format(regime_name))\\n self._logger.info(u\\\"Simulating: {}\\\".format(regime_name))\\n\\n self.actSimulate.setDisabled(True)\\n self.shortRunSimulation.setEnabled(False)\\n self.shortRunRegimeBatch.setEnabled(False)\\n self.actExecuteRegimes.setDisabled(True)\\n self.guiProgress = QtGui.QProgressBar(self)\\n self.sim.simulationProgressChanged.connect(self.guiProgress.setValue)\\n self.statusBar().addWidget(self.guiProgress)\\n self.runSimulation.emit()\",\n \"def testSimCompletes(self):\\n sim = Simulation()\\n self.assertEqual(25, sim.run_simple(1, 11, \\\"output\\\", 0.1, 2, 10))\",\n \"def getProgress(self):\",\n \"def complete(self):\\n self.chunk_percentage = [1.0] * self.total_steps\\n self.progress_updated.emit(self.percentage)\\n self.initialized = False\",\n \"def updateProgress (self, iteration, total, prefix='Progress', suffix='complete', decimals=1, length=100, fill='█', printEnd=\\\"\\\\r\\\"):\\n if iteration == 0:\\n self.start_time = timer()\\n ETC = '' #Estimated Time to Completion\\n if (iteration/total)*100 >= self.updates[self.update_counter]:\\n elapsed = timer() - self.start_time\\n if iteration != 0:\\n minutes = int((elapsed * total/iteration - elapsed)//60)\\n seconds = int((elapsed * total/iteration - elapsed)%60)\\n ETC = \\\"(~{:d} mins {:d}s left)\\\".format(minutes, seconds)\\n percent = (\\\"{0:.\\\" + str(decimals) + \\\"f}\\\").format(100 * (iteration / float(total)))\\n filledLength = int(length * iteration // total)\\n bar = fill * filledLength + '-' * (length - filledLength)\\n # Unfortunately \\\\r doesn't work in the pycharm console, so we have to reprint the whole bar everytime,\\n # clogging the console.\\n #print(f'\\\\r{prefix} |{bar}| {percent}% {suffix} {ETC}', end = printEnd)\\n print(f'{prefix} |{bar}| {percent}% {suffix} {ETC}')\\n # Print New Line on Complete\\n if iteration == total:\\n print()\\n self.update_counter += 1\",\n \"def _iteration_changed(self):\\n if self.showProgressBar:\\n try:\\n self._setProgress()\\n except:\\n # may fail when switching from training to inference\\n from dbgp.client import brk; brk(port=9011)\\n pass\",\n \"def report_percent_complete(self):\\n meta_str = 'Bundling percent complete: ' + \\\\\\n str(int(self.percent_complete))\\n\\n TaskComm.set_state('PROGRESS', meta_str)\",\n \"def printProgress(self, percentage):\\n #print '%s\\\\r' % ' '*20, # clean up row\\n #print '%3d%% ' % percentage, # ending with comma prevents newline from being appended\\n sys.stdout.flush()\",\n \"def report_learning_progress(istep, nsteps, VC_HC, GS_HC, norm, mrh, mhh,\\n rop=None):\\n\\n rep = '{}%'.format(int(100*istep/nsteps)).rjust(4)\\n l_vc_hc = analysis.VC_HC_norm(VC_HC, norm).mean()\\n l_gs_hc = analysis.GS_HC_norm(GS_HC, norm).mean()\\n prog = '{} | VC-HC: {:.3f}, GS-HC: {:.3f}'.format(rep, l_vc_hc, l_gs_hc)\\n prog += ' | H real: {:.2f}, H HC: {:.2f}'.format(mrh, mhh)\\n if rop is not None:\\n prog += ' | ro pow: {:.1f}'.format(rop)\\n print(prog)\",\n \"def run(self):\\n\\n inside = 0\\n for draws in range(1, self.data['samples']):\\n # generate points and check whether they are inside the unit circle\\n r1 = random.random()\\n r2 = random.random()\\n if r1 ** 2 + r2 ** 2 < 1.0:\\n inside += 1\\n\\n # every 1000 iterations, update status\\n if draws % 1000 != 0:\\n continue\\n\\n # debug\\n yield self.emit('log', {'draws': draws, 'inside': inside})\\n\\n # calculate pi and its uncertainty given the current draws\\n p = inside / draws\\n pi = {\\n 'estimate': 4.0 * inside / draws,\\n 'uncertainty': 4.0 * math.sqrt(draws * p * (1.0 - p)) / draws,\\n }\\n\\n # send status to frontend\\n yield self.set_state(pi=pi)\\n\\n yield self.emit('log', {'action': 'done'})\",\n \"def next(self):\\n print(f\\\" {colored('[', 'yellow')}{bold(self.progress[self.pos])}{colored(']', 'yellow')} \\\"\\n f\\\"{bold('Processing, please wait...')}\\\",\\n end=\\\"\\\\r\\\",\\n flush=True\\n )\\n self.increment()\",\n \"def report(self):\\n\\n job_summary = {}\\n for job in self._jobs:\\n \\n if job.step_name not in job_summary:\\n job_summary[ job.step_name ] = {}\\n job_summary[ job.step_name ][ 'DONE' ] = 0\\n job_summary[ job.step_name ][ 'RUNNING' ] = 0\\n job_summary[ job.step_name ][ 'QUEUING' ] = 0\\n job_summary[ job.step_name ][ 'FAILED' ] = 0\\n job_summary[ job.step_name ][ 'UNKNOWN' ] = 0\\n job_summary[ job.step_name ][ 'max_mem' ] = 0\\n job_summary[ job.step_name ][ 'cputime' ] = 0\\n\\n if job.status == Job_status.FINISHED:\\n job_summary[ job.step_name ][ 'DONE' ] += 1\\n if job.cputime is not None:\\n job_summary[ job.step_name ]['cputime'] += int(job.cputime)\\n\\n if job.max_memory is not None and job.max_memory > job_summary[ job.step_name ][ 'max_mem']:\\n job_summary[ job.step_name ][ 'max_mem'] = int(job.max_memory)\\n\\n elif job.status == Job_status.RUNNING:\\n job_summary[ job.step_name ][ 'RUNNING' ] += 1\\n elif job.status == Job_status.QUEUEING or job.status == Job_status.SUBMITTED:\\n job_summary[ job.step_name ][ 'QUEUING' ] += 1\\n elif job.status == Job_status.FAILED or job.status == Job_status.NO_RESTART:\\n job_summary[ job.step_name ][ 'FAILED' ] += 1\\n else:\\n job_summary[ job.step_name ][ 'UNKNOWN' ] += 1\\n\\n\\n\\n local_time = strftime(\\\"%d/%m/%Y %H:%M\\\", time.localtime())\\n \\n\\n pickle_file = \\\"{}.{}\\\".format(self.pipeline.project_name, self.pipeline._pid)\\n\\n print(\\\"[{} @{} {}]\\\".format( local_time,self.pipeline._hostname , pickle_file))\\n\\n print(\\\"{:20} || {:12} || {:12} || {:2s} {:2s} {:2s} {:2s} {:2s}\\\".format(\\\"Run stats\\\", \\\"Runtime\\\", \\\"Max Mem\\\", \\\"D\\\",\\\"R\\\",\\\"Q\\\",\\\"F\\\",\\\"U\\\"))\\n\\n for step in sorted(self.pipeline._workflow._analysis_order, key=self.pipeline._workflow._analysis_order.__getitem__):\\n if step not in job_summary:\\n continue\\n\\n print(\\\"{:20} || {:12} || {:12} || {:02d}/{:02d}/{:02d}/{:02d}/{:02d}\\\".format(step, \\n self.format_time(job_summary[ step ]['cputime']),\\n self.format_memory(job_summary[ step ]['max_mem']),\\n job_summary[ step ][ 'DONE' ],\\n job_summary[ step ][ 'RUNNING' ],\\n job_summary[ step ][ 'QUEUING' ],\\n job_summary[ step ][ 'FAILED' ],\\n job_summary[ step ][ 'UNKNOWN' ]))\",\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 done(self):\\n log.debug(\\\"Test run concluded\\\")\\n if self._startTime is not None:\\n self.report['startTime'] = self._startTime\\n self.report['runTime'] = time.time() - self._startTime\\n self.report['testsRun'] = self.testsRun\\n self.report['tests'] = self._tests\\n self.writeReport()\",\n \"def _printProgress(self, progress):\\n if not self._quiet:\\n sys.stdout.write('\\\\rWriting store to CSV: [{0:50s}] {1:.2f}% '.format('#' * int(progress * 50.0), progress * 100.0))\\n sys.stdout.flush()\",\n \"def progress(self, progress):\\n\\n self._progress = progress\",\n \"def progress(self, progress):\\n\\n self._progress = progress\",\n \"def set_progress(self, progress: float):\",\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 progress(self, loaded, total, msg=''):\\n\\n self.fire('progress', {\\n 'loaded': loaded,\\n 'total': total,\\n 'msg': msg\\n })\",\n \"def print_progress(self, current_acq=0):\\n # The following method is always called because it includes the\\n # no-progress checks.\\n percdone = self.get_percdone(current_acq=current_acq)\\n if self.verbose() and percdone != np.nan:\\n elapsed_time = time.time() - self.begintime\\n t_left = round((100. - percdone) / (percdone) *\\n elapsed_time, 1) if percdone != 0 else '??'\\n t_end = time.strftime('%H:%M:%S', time.localtime(time.time() +\\n + t_left)) if percdone != 0 else '??'\\n # The trailing spaces are to overwrite some characters in case the\\n # previous progress message was longer. (Due to \\\\r, the string\\n # output will start at the beginning of the current line and\\n # each character of the new string will overwrite a character\\n # of the previous output in the current line.)\\n progress_message = (\\n \\\"\\\\r{timestamp}\\\\t{percdone}% completed \\\\telapsed time: \\\"\\n \\\"{t_elapsed}s \\\\ttime left: {t_left}s\\\\t(until {t_end}) \\\"\\n \\\"\\\").format(\\n timestamp=time.strftime('%H:%M:%S', time.localtime()),\\n percdone=int(percdone),\\n t_elapsed=round(elapsed_time, 1),\\n t_left=t_left,\\n t_end=t_end,)\\n\\n if percdone != 100 or current_acq:\\n end_char = ''\\n else:\\n end_char = '\\\\n'\\n print('\\\\r', progress_message, end=end_char)\",\n \"def tick(self):\\n self.current_count += 1\\n self.progress(self.current_count)\",\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 status(self):\\n\\t\\tstatus = self.thread.status()\\n#\\t\\tprint_array(status)\\n\\t\\tmessage = [\\\"------ RSYNC PROGRESS ------ \\\"]\\n\\t\\tif self.log_message:\\n\\t\\t\\tmessage.append(self.log_message)\\n\\t\\tmessage.append(\\\"Current file: %s\\\" % status['current_file'])\\n\\t\\tmessage.append(\\\"\\\\tBytes Copied: %s\\\" % status['bytes_copied'])\\n\\t\\tmessage.append(\\\"\\\\tPercent Done: %s\\\" % status['percent_done'])\\n\\t\\tmessage.append(\\\"\\\\tTransfer Rate: %s\\\" % status['transfer_rate'])\\n\\t\\tmessage.append(\\\"\\\\tTime Remaining: %s\\\" % status['est_remain'])\\n\\t\\tmessage.append(\\\"\\\\tTransfer Number: %s\\\" % status['xfer_num'])\\n\\t\\tmessage.append(\\\"\\\\tTransfers Remaining: %s\\\" % status['xfer_remain'])\\n\\t\\tmessage.append(\\\"\\\\tTransfers Total: %s\\\" % status['xfer_total'])\\n\\t\\tmessage.append(\\\"\\\\t----------------------------------\\\")\\n\\t\\ttry:\\n\\t\\t\\toverall_percent = int(round((int(status['xfer_num'])*1.0)/int(status['xfer_total']),2)*100)\\n\\t\\texcept: overall_percent = 0\\n\\t\\tmessage.append(\\\"\\\\tTotal Rsync done: %s%%\\\\n\\\" % overall_percent)\\n\\t\\tp = open(self.progress_file,'w+',0)\\n\\t\\tfor line in message:\\n\\t\\t\\t#print line\\n\\t\\t\\tp.write(\\\"%s\\\\n\\\" % line)\\n\\t\\tp.flush()\\n\\t\\tp.close()\",\n \"def print_progress(self):\\n print(\\n '\\\\rE {} S {} TR {:6.2f} G {:6.2f} Loss {:6.5f} AvgQ {:6.2f}'\\n ' MinR {:6.2f} MaxR {:6.2f}'.format(\\n self.episode, self.episode_step,\\n self.tracker.total_reward, self.tracker.discounted_rewards,\\n self.loss_val, self.total_max_q / self.episode_step,\\n self.tracker.min_reward, self.tracker.max_reward,\\n end=\\\"\\\"))\",\n \"def print_progress(c, t, K, t_end, flag):\\r\\n if (t > (t_end / 20.0)) and flag == True:\\r\\n print('1/20 of the simulation is done.')\\r\\n flag = False\\r\\n\\r\\n # debugging information\\r\\n print('\\\\n======================\\\\nTime: %g' % (t))\\r\\n print('Clones:\\\\n', c)\\r\\n print('\\\\nInteractions in Matrix K:\\\\n', np.array(K))\\r\\n print('Dimension of Matrix K:\\\\t%s' % str(K.shape))\\r\\n return flag\",\n \"def OnProgress(bytes_read, total_bytes, percent):\\n sys.stdout.write(\\\"progress: %.2f%% \\\\r\\\" % (percent))\\n sys.stdout.flush()\",\n \"def progressbar(self, complete = 0.0):\\n\\n if self.is_subprocess:\\n sys.stderr.write(\\\"%f\\\\n\\\" % complete)\\n sys.stderr.flush()\\n else:\\n gdal.TermProgress_nocb(complete)\",\n \"def setReportProgress(self, newReportProgress):\\n \\n pass\",\n \"def progress(self, i, num_episodes, last_avg, start, end):\\n percentage = (i / num_episodes) * 100.0\\n self.log(\\n \\\"Epoch {:>5}/{:<5} [{:>5.1f}%] Avg reward: {:+.3f}\\\".format(\\n i,\\n num_episodes,\\n percentage,\\n last_avg), end='')\\n if percentage == 0.0:\\n self.log(' Est.time: UNKNOWN')\\n return\\n elapsed = end - start\\n remaining = ((100. - percentage) * elapsed) / percentage\\n self.log(' Est.time: {}'.format(self.timer(remaining)))\",\n \"def _on_progress(self, num):\\n self._num_progresses += num\\n self._log.info(\\\"Progress incrementing by {}\\\".format(num))\\n self._host_comms.send_msg(\\\"progress\\\", num)\",\n \"def progress(self) -> int:\\n return int(round(100 * self.somme() / self.finances))\",\n \"def print_progress(remaining_pairs, G):\\n total_pairs = G.number_of_nodes()**2\\n uncomputed_pairs = len(remaining_pairs)\\n print(1 - uncomputed_pairs/total_pairs)\",\n \"def simulation_step(self):\\n if self.data_valid.get():\\n print(\\\"Output pin %s writing %s\\\" % (self.name, self.debug_data.get()))\",\n \"def print_progress(self):\\n\\n if not self.verbose:\\n return\\n\\n elapsed_time = time.time() - self._start_time\\n\\n if self._is_completed():\\n # Make sure that we get a last message telling us we are done\\n self._print(\\n f\\\"Done {self.n_completed_tasks:3d} out of \\\"\\n f\\\"{self.n_completed_tasks:3d} | elapsed: \\\"\\n f\\\"{short_format_time(elapsed_time)} finished\\\"\\n )\\n return\\n\\n # Original job iterator becomes None once it has been fully\\n # consumed : at this point we know the total number of jobs and we are\\n # able to display an estimation of the remaining time based on already\\n # completed jobs. Otherwise, we simply display the number of completed\\n # tasks.\\n elif self._original_iterator is not None:\\n if _verbosity_filter(self.n_dispatched_batches, self.verbose):\\n return\\n self._print(\\n f\\\"Done {self.n_completed_tasks:3d} tasks | elapsed: \\\"\\n f\\\"{short_format_time(elapsed_time)}\\\"\\n )\\n else:\\n index = self.n_completed_tasks\\n # We are finished dispatching\\n total_tasks = self.n_dispatched_tasks\\n # We always display the first loop\\n if not index == 0:\\n # Display depending on the number of remaining items\\n # A message as soon as we finish dispatching, cursor is 0\\n cursor = (total_tasks - index + 1 -\\n self._pre_dispatch_amount)\\n frequency = (total_tasks // self.verbose) + 1\\n is_last_item = (index + 1 == total_tasks)\\n if (is_last_item or cursor % frequency):\\n return\\n remaining_time = (elapsed_time / index) * \\\\\\n (self.n_dispatched_tasks - index * 1.0)\\n # only display status if remaining time is greater or equal to 0\\n self._print(\\n f\\\"Done {index:3d} out of {total_tasks:3d} | elapsed: \\\"\\n f\\\"{short_format_time(elapsed_time)} remaining: \\\"\\n f\\\"{short_format_time(remaining_time)}\\\"\\n )\",\n \"def simulate(self):\\r\\n\\r\\n for index in tqdm(range(self.steps)):\\r\\n\\r\\n S = 0.1 - 0.1 / self.steps * (index + 1)\\r\\n T = 0.5 / (np.log(2 + 0.2 * index))\\r\\n\\r\\n self.move(T, S)\\r\\n self.t_change.append(T)\\r\\n self.s_change.append(S)\\r\\n tot = calculate_total_energy(self.current_config)\\r\\n self.energies.append(tot)\",\n \"def report(self):\\r\\n print(\\\"\\\".join(self.memory), self.error, self.steps)\",\n \"def status(self, loop=False, delay=10):\\n if not self._submit:\\n raise Exception('The workfow has not started its execution yet.\\\\n'\\n 'Please, check if the workflow is planned and submitted for execution.')\\n seq = False\\n\\n while True:\\n out, err = subprocess.Popen('pegasus-status -l %s' % self.submit_dir, stdout=subprocess.PIPE,\\n stderr=subprocess.PIPE, shell=True, cwd=self.base_dir).communicate()\\n if err:\\n raise Exception(err)\\n\\n for line in out.decode('utf8').split('\\\\n'):\\n if 'UNRDY' in line:\\n seq = True\\n elif seq:\\n seq = False\\n v = line.split()\\n\\n state = v[8]\\n if state == 'Success':\\n state = '\\\\x1b[1;32m' + state + '\\\\x1b[0m'\\n elif state == 'Failure':\\n state = '\\\\x1b[1;31m' + state + '\\\\x1b[0m'\\n\\n progress = '\\\\x1b[1;34m' + 'Progress: ' + v[7] + '%\\\\x1b[0m (' + state + ')'\\n completed = '\\\\x1b[1;32mCompleted: ' + v[5] + '\\\\x1b[0m'\\n queued = '\\\\x1b[1;33mQueued: ' + v[1] + '\\\\x1b[0m'\\n running = '\\\\x1b[1;36mRunning: ' + v[3] + '\\\\x1b[0m'\\n fail = '\\\\x1b[1;31mFailed: ' + v[6] + '\\\\x1b[0m'\\n\\n st = progress + '\\\\t(' + completed + ', ' + queued + ', ' + running + ', ' + fail + ')'\\n print('%s\\\\r' % st, end='')\\n break\\n\\n if not loop or 'Success' in out.decode('utf8') or 'Failure' in out.decode('utf8'):\\n break\\n time.sleep(delay)\",\n \"def __show_progress(self, _cur_file_idx, _file_count):\\n if (self.__is_show_proegress == False):\\n return\\n\\n if(_file_count == 0):\\n raise StandardError('no file found.')\\n\\n # show progress for each 5% (20 steps)\\n digit = math.modf(math.log10(_file_count))[1]\\n if(digit < 3):\\n print \\\"prog: [{0}%] {1}/{2}\\\".format((100 * _cur_file_idx) /_file_count,\\n _cur_file_idx, _file_count)\\n else:\\n digit = digit - 2\\n skipstep10 = math.pow(10, digit)\\n if ((_cur_file_idx % skipstep10) == 0):\\n print \\\"prog: [{0}%] {1}/{2}\\\".format((100 * _cur_file_idx) /_file_count,\\n _cur_file_idx, _file_count)\",\n \"def notify_progress(self, progress_data):\\n pass # pragma: no cover\",\n \"def progress(self, value):\\n self.step = float(value)\\n self._draw()\",\n \"def step_print(self, prefix=\\\"\\\", suffix=\\\"\\\"):\\n (progress, current_run_time, expected_time, expected_rest_time) = self.step()\\n clear_output(True)\\n if prefix != \\\"\\\":\\n print(prefix)\\n print(\\\"Current progress: {:3.1f} %\\\".format(progress) )\\n print(\\\"#\\\" * int(progress*0.3))\\n seconds, minutes, hours = self._calc_times(current_run_time)\\n print(\\\"Current run time: {:02d}:{:02d}:{:02d}\\\".format(hours,minutes,seconds))\\n seconds, minutes, hours = self._calc_times(expected_rest_time)\\n print(\\\"Expected time to finish: {:02d}:{:02d}:{:02d}\\\".format(hours,minutes,seconds))\\n if suffix != \\\"\\\":\\n print(suffix)\",\n \"def Report(self):\\n return True\",\n \"def generate(self):\\n if not (os.path.exists(self.target_path.get()) and self.target_file.get()):\\n messagebox.showerror(\\\"Invalid Arguments\\\",\\n \\\"At least one path was not valid.\\\")\\n return\\n self.progress.grid(row=100, columnspan=3, sticky=\\\"ews\\\")\\n self.update()\\n thread = threading.Thread(\\n target=folder_structure_backup.iterate_and_save,\\n args=(self.target_path.get(), self.target_file.get(),\\n \\\"big\\\" if not self.fastmode.get() else \\\"fast\\\"))\\n thread.daemon = True\\n thread.start()\\n while thread.is_alive():\\n self.progress.step(amount=2)\\n self.progress.update()\\n self.update()\\n time.sleep(0.1)\\n self.progress.grid_forget()\\n self.finished = True\\n self.destroy()\\n self.quit()\",\n \"def launch_status(self):\\n print(\\n f\\\"Starting job with {len(self.fe.get_network())} jobs total. \\\",\\n end=\\\"\\\\r\\\",\\n )\",\n \"def run_tests(self):\\n with self.report.timer.record(\\\"run\\\"):\\n self.result.report.extend(self._run_tests())\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.78183013","0.7616774","0.7516212","0.7346008","0.7162934","0.7073537","0.6910481","0.687008","0.68546647","0.6802137","0.670709","0.6668817","0.6607123","0.6585042","0.65652347","0.6525252","0.6525252","0.6443862","0.6428839","0.64268047","0.64268047","0.6393495","0.6392124","0.63848823","0.63789344","0.63725215","0.63588065","0.63489836","0.6345064","0.6308253","0.6283535","0.62776935","0.62774193","0.62387735","0.6235235","0.61925197","0.61649716","0.61565155","0.6146791","0.6095169","0.60931385","0.6090776","0.60882163","0.6073822","0.6073822","0.6068076","0.60609365","0.6058709","0.6055146","0.60513437","0.6048602","0.60424584","0.603918","0.6032513","0.6014221","0.6012158","0.59989667","0.59911346","0.598666","0.5985771","0.597716","0.5972806","0.596346","0.59412026","0.5934525","0.5933998","0.59281594","0.5908498","0.5894072","0.5888559","0.58812565","0.58812565","0.58787256","0.58732986","0.58724576","0.5867011","0.5864893","0.5860311","0.5855759","0.58471775","0.5837498","0.5835548","0.5823085","0.5814926","0.5813099","0.58052295","0.5781134","0.57793885","0.5778703","0.57739043","0.5763907","0.57596606","0.5758618","0.57580763","0.57572365","0.57505333","0.57478845","0.5735953","0.57357955","0.5727255","0.5726268"],"string":"[\n \"0.78183013\",\n \"0.7616774\",\n \"0.7516212\",\n \"0.7346008\",\n \"0.7162934\",\n \"0.7073537\",\n \"0.6910481\",\n \"0.687008\",\n \"0.68546647\",\n \"0.6802137\",\n \"0.670709\",\n \"0.6668817\",\n \"0.6607123\",\n \"0.6585042\",\n \"0.65652347\",\n \"0.6525252\",\n \"0.6525252\",\n \"0.6443862\",\n \"0.6428839\",\n \"0.64268047\",\n \"0.64268047\",\n \"0.6393495\",\n \"0.6392124\",\n \"0.63848823\",\n \"0.63789344\",\n \"0.63725215\",\n \"0.63588065\",\n \"0.63489836\",\n \"0.6345064\",\n \"0.6308253\",\n \"0.6283535\",\n \"0.62776935\",\n \"0.62774193\",\n \"0.62387735\",\n \"0.6235235\",\n \"0.61925197\",\n \"0.61649716\",\n \"0.61565155\",\n \"0.6146791\",\n \"0.6095169\",\n \"0.60931385\",\n \"0.6090776\",\n \"0.60882163\",\n \"0.6073822\",\n \"0.6073822\",\n \"0.6068076\",\n \"0.60609365\",\n \"0.6058709\",\n \"0.6055146\",\n \"0.60513437\",\n \"0.6048602\",\n \"0.60424584\",\n \"0.603918\",\n \"0.6032513\",\n \"0.6014221\",\n \"0.6012158\",\n \"0.59989667\",\n \"0.59911346\",\n \"0.598666\",\n \"0.5985771\",\n \"0.597716\",\n \"0.5972806\",\n \"0.596346\",\n \"0.59412026\",\n \"0.5934525\",\n \"0.5933998\",\n \"0.59281594\",\n \"0.5908498\",\n \"0.5894072\",\n \"0.5888559\",\n \"0.58812565\",\n \"0.58812565\",\n \"0.58787256\",\n \"0.58732986\",\n \"0.58724576\",\n \"0.5867011\",\n \"0.5864893\",\n \"0.5860311\",\n \"0.5855759\",\n \"0.58471775\",\n \"0.5837498\",\n \"0.5835548\",\n \"0.5823085\",\n \"0.5814926\",\n \"0.5813099\",\n \"0.58052295\",\n \"0.5781134\",\n \"0.57793885\",\n \"0.5778703\",\n \"0.57739043\",\n \"0.5763907\",\n \"0.57596606\",\n \"0.5758618\",\n \"0.57580763\",\n \"0.57572365\",\n \"0.57505333\",\n \"0.57478845\",\n \"0.5735953\",\n \"0.57357955\",\n \"0.5727255\",\n \"0.5726268\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":22483,"cells":{"query":{"kind":"string","value":"Report progress during learning simulations."},"document":{"kind":"string","value":"def report_learning_progress(istep, nsteps, VC_HC, GS_HC, norm, mrh, mhh,\n rop=None):\n\n rep = '{}%'.format(int(100*istep/nsteps)).rjust(4)\n l_vc_hc = analysis.VC_HC_norm(VC_HC, norm).mean()\n l_gs_hc = analysis.GS_HC_norm(GS_HC, norm).mean()\n prog = '{} | VC-HC: {:.3f}, GS-HC: {:.3f}'.format(rep, l_vc_hc, l_gs_hc)\n prog += ' | H real: {:.2f}, H HC: {:.2f}'.format(mrh, mhh)\n if rop is not None:\n prog += ' | ro pow: {:.1f}'.format(rop)\n print(prog)"},"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 reportProgress(self):\n \n pass","def report_scenario_progress(self):\n pass","def show_progress(self, game):\n if self.verbose:\n if self.params.eval_interval is not None and (self.episode % self.params.eval_interval == 0):\n self._print_progress()\n # evaluate one run\n state = game.reset()\n self.tracker.reset()\n done = False\n while not done:\n controls = self.sess.run(self.graph.target_actor_outputs,\n feed_dict={self.graph.states: np.array([state])}).reshape(-1)\n state, reward, done, _ = game.step(controls)\n self.tracker.step(reward)\n print('****** ', self.tracker.total_reward, self.tracker.discounted_rewards, ' ******')","def report_progress(self):\r\n stats = self.simulation_stats.stats\r\n solutions = len(self.solutions)\r\n round = self.round\r\n scores = stats[round]\r\n best_score = min(scores)\r\n avg_score = sum(scores) / solutions\r\n line = f\"Round: {round}\\t\\tBest Score: {best_score}\\t\\t Average Score: {avg_score}\"\r\n print(line)","def show_progress(self, game):\n if self.verbose:\n if self.params.eval_interval is not None and (self.episode % self.params.eval_interval == 0):\n self.print_progress()\n # evaluate one run\n state = game.reset()\n self.tracker.reset()\n done = False\n while not done:\n q_values = self.sess.run(self.graph.target_q_values, feed_dict={self.graph.states: [state]})\n action = np.argmax(q_values)\n state, reward, done, _ = game.step(action)\n self.tracker.step(reward)\n print('****** ', self.tracker.total_reward, self.tracker.discounted_rewards, ' ******')","def PrintProgress(self):\n print ' Examined %d nodes, found %d unique...' % (\n self.nodes_examined, self.unique_nodes\n )","def report_step_progress(self, step):\n pass","def update_progress(self, done):\r\n if done % 100 == 0:\r\n print >>sys.stderr, \" %d processed, run time %d secs\" % (done, (datetime.now() - self.started_at).seconds)","def _iteration_changed(self):\n if self.showProgressBar:\n try:\n self._setProgress()\n except:\n # may fail when switching from training to inference\n from dbgp.client import brk; brk(port=9011)\n pass","def report_scenario_progress(self):\n if not self.current_scenario:\n return # SKIP: No results to report for first scenario.\n # -- NORMAL-CASE:\n status_name = self.current_scenario.status.name\n dot_status = self.dot_status[status_name]\n if status_name == \"failed\":\n # MAYBE TODO: self.failures.append(result)\n pass\n self.stream.write(dot_status)\n self.stream.flush()","def learn(self, Nsim, progress_bar=False):\n\n if progress_bar:\n iterations = tqdm(range(Nsim))\n else:\n iterations = range(Nsim)\n for _ in iterations:\n self.grow_tree()","def _print_progress(self):\n \n print 'Completed %d of %d' %(self.progress_id, self.total_work)\n self.progress_id += 1","def report_progress(self, samples, labels,\n epoch_no, counter_name, t_start):\n # Calculate network accuracy\n result = self.sess.run( [self.network_prediction], feed_dict={\n self.x: samples, self.y_trgt: labels,\n self.fc1_keep_prob: 1.0 })\n pred = result[0]\n\n # Loop for each output class\n avg_accuracy = 0\n for c in range(self.n_output_classes):\n # Calculate true/false pos/neg\n true_pos = np.sum( pred[labels[:,c]==1]==c )\n false_pos = np.sum( pred[labels[:,c]==0]==c )\n false_neg = np.sum( pred[labels[:,c]==1]!=c )\n true_neg = np.sum( pred[labels[:,c]==0]!=c )\n\n # Calculate accuracy, precision, recall, F1\n accuracy = (true_pos+true_neg) / len(pred)\n precision = np.nan_to_num(true_pos / (true_pos+false_pos))\n recall = np.nan_to_num(true_pos / (true_pos+false_neg))\n F1 = np.nan_to_num(2 * ((precision*recall)/(precision+recall)))\n self.accuracy_list[c].append(float(accuracy))\n self.precision_list[c].append(float(precision))\n self.recall_list[c].append(float(recall))\n self.F1_list[c].append(float(F1))\n self.n_class_samples_list[c].append(int(self.n_class_samples_trained[c]))\n avg_accuracy += accuracy\n self.n_samples_list.append(int(self.n_samples_trained))\n\n t_curr = time.time()\n self.log('{} {:4d}: Acc = {:6.4f} (t={})'.format( \\\n counter_name, epoch_no, avg_accuracy/self.n_output_classes,\n str(datetime.timedelta(seconds=np.round(t_curr-t_start))) ),\n no_enter=True, overwrite_last=True)","def report_scenario_completed(self):\n self.report_scenario_progress()","def progress(self, i, num_episodes, last_avg, start, end):\n percentage = (i / num_episodes) * 100.0\n self.log(\n \"Epoch {:>5}/{:<5} [{:>5.1f}%] Avg reward: {:+.3f}\".format(\n i,\n num_episodes,\n percentage,\n last_avg), end='')\n if percentage == 0.0:\n self.log(' Est.time: UNKNOWN')\n return\n elapsed = end - start\n remaining = ((100. - percentage) * elapsed) / percentage\n self.log(' Est.time: {}'.format(self.timer(remaining)))","def _setProgress(self):\n\n self.progress = (self.iteration, self.iterationCount)","def report_step_progress(self, step):\n dot_status = self.dot_status[step.status.name]\n if step.status == Status.failed:\n if (step.exception and\n not isinstance(step.exception, AssertionError)):\n # -- ISA-ERROR: Some Exception\n dot_status = self.dot_status[\"error\"]\n step.feature = self.current_feature\n step.scenario = self.current_scenario\n self.failures.append(step)\n self.stream.write(dot_status)\n self.stream.flush()","def _print_progress(self):\n if self.current_training_size % 1000 == 0:\n print(self.current_training_size, end='')\n elif self.current_training_size % 100 == 0:\n print('.', end='')","def finish_learning(self):\n pass","def progress(self):\n if self.running:\n pass\n else:\n self._engine.progress()","def _log_progress(self):\n self.num_of_requests_in_pipeline += 1\n if self.num_of_requests_in_pipeline % 20 == 0:\n print('-' * 200)\n print(f'DB PIPELINE: {self.num_of_requests_in_pipeline} items wenth though pipeline.')\n print('-' * 200)","def _on_step(self) -> None:\n self._n_calls += 1\n # Account for multiple environments\n # each call to step() corresponds to n_envs transitions\n if self._n_calls % max(self.target_update_interval // self.n_envs, 1) == 0:\n polyak_update(self.q_net.parameters(), self.q_net_target.parameters(), self.tau)\n # Copy running stats, see GH issue #996\n polyak_update(self.batch_norm_stats, self.batch_norm_stats_target, 1.0)\n\n self.exploration_rate = self.exploration_schedule(self._current_progress_remaining)\n self.logger.record(\"rollout/exploration_rate\", self.exploration_rate)","def status(self) -> NoReturn:\n\n curr_status= self.percent_done()\n while(curr_status < 100):\n\n update_status(name=self.name, status=curr_status)\n time.sleep(0.5)\n\n curr_status = self.percent_done()\n\n update_status(name=self.name, status=curr_status)","def _log_progress(self, t):\n\n # Run the update only 2 step before the actual logging happens in order to\n # make sure that the most recent possible values will be stored in\n # self.summary. This is a hacky workaround in order to support OffPolicyAgent\n # which runs 2 threads without coordination\n if (t+2) % self.log_freq == 0 and self.learn_started:\n episode_rewards = self.env_monitor.get_episode_rewards()\n self.episode_rewards = np.asarray(episode_rewards)\n if self.episode_rewards.size > 0:\n self.mean_ep_rew = np.mean(episode_rewards[-self.stats_n:])\n self.best_mean_ep_rew = max(self.best_mean_ep_rew, self.mean_ep_rew)\n\n if t % self.log_freq == 0 and self.learn_started:\n stats_logger.info(\"\")\n for s, lambda_v in self.log_info:\n stats_logger.info(s.format(lambda_v(t)))\n stats_logger.info(\"\")\n\n if self.summary:\n # Log with TensorBoard\n self.tb_writer.add_summary(self.summary, global_step=t)","def transfer_progress(self, stats):","def progress(self, arg, num_done, info=''):\n pass","def update(self, steps):\n self.launch_progress += (steps)/self.total","def performSimulation(self):\n \n if self.parameters['verbose']:\n print(\"=====================\\nStarting simulation with parameters\\n\",self.parameters)\n print(\"=====================\\nInitial Graph\\n\")\n self.showState()\n print(\"=====================\")\n\n while self.parameters['steps'] > 0:\n if self.parameters['verbose']: print(\"Performing step\")\n self.performStep()\n if self.parameters['verbose']: self.showState()\n\n if self.parameters['verbose']:\n print(\"=====================\\nFinished Simulation\\n\\nResult graph:\")\n self.showState()\n #self.showGraph(self.parameters['file_name'])\n #self.showState()\n #self.showStats()","def report_trial(self):\n pass","def wait_progress(self):\n pass","def wait_progress(self):\n pass","def final(self, state):\n deltaReward = state.getScore() - self.lastState.getScore()\n self.observeTransition(self.lastState, self.lastAction, state, deltaReward)\n self.stopEpisode()\n\n # Make sure we have this var\n if not 'episodeStartTime' in self.__dict__:\n self.episodeStartTime = time.time()\n if not 'lastWindowAccumRewards' in self.__dict__:\n self.lastWindowAccumRewards = 0.0\n self.lastWindowAccumRewards += state.getScore()\n\n NUM_EPS_UPDATE = 100\n if self.episodesSoFar % NUM_EPS_UPDATE == 0:\n print 'Reinforcement Learning Status:'\n windowAvg = self.lastWindowAccumRewards / float(NUM_EPS_UPDATE)\n if self.episodesSoFar <= self.numTraining:\n trainAvg = self.accumTrainRewards / float(self.episodesSoFar)\n print '\\tCompleted %d out of %d training episodes' % (\n self.episodesSoFar,self.numTraining)\n print '\\tAverage Rewards over all training: %.2f' % (\n trainAvg)\n else:\n testAvg = float(self.accumTestRewards) / (self.episodesSoFar - self.numTraining)\n print '\\tCompleted %d test episodes' % (self.episodesSoFar - self.numTraining)\n print '\\tAverage Rewards over testing: %.2f' % testAvg\n print '\\tAverage Rewards for last %d episodes: %.2f' % (\n NUM_EPS_UPDATE,windowAvg)\n print '\\tEpisode took %.2f seconds' % (time.time() - self.episodeStartTime)\n self.lastWindowAccumRewards = 0.0\n self.episodeStartTime = time.time()\n\n if self.episodesSoFar == self.numTraining:\n msg = 'Training Done (turning off epsilon and alpha)'\n print '%s\\n%s' % (msg,'-' * len(msg))","def check_sim(self):\n if self.index % 5 == 0:\n print (\"Timesteps Completed: {} out of {}\".format(self.index, self.timesteps))\n if self.index == self.timesteps:\n print (\"Simulation Completed\")\n self.end_simulation()\n self.index += 1","def get_learn_after_each_trial(self):\r\n return 0","def __progress(self, dataset, input_function):\n batchsize = 100\n total_loss = 0\n total_correct = 0\n total_selected = 0\n total_relevant = 0\n total_correct_relevant = 0\n total_batches = 0\n for batch in self.__iterate_minibatches(dataset, batchsize):\n inputs, targets = batch\n l, c, s, r, h = input_function(inputs, targets)\n total_loss += l\n total_correct += c\n total_selected += s\n total_relevant += r\n total_correct_relevant += h\n total_batches += 1\n avg_loss = total_loss / total_batches\n precision = total_correct_relevant / total_selected\n recall = total_correct_relevant / total_relevant\n accuracy = total_correct / (batchsize * total_batches)\n return avg_loss, precision, recall, accuracy","def progress(self, msg):\n logging.info(\"UI-Test: \" + msg)\n with step(\"UI test progress: \" + msg):\n pass\n if len(self.state) > 0:\n self.state += \"\\n\"\n self.state += \"UI: \" + msg","def print_progress(self, i, current_params):\n for split in range(1,11):\n if i == (round(self.iterations/10*split)-1):\n post = -self.neg_posterior(current_params)\n approx = self.create_normal_logq(current_params)\n diff = post - approx\n if not self.quiet_progress:\n print(str(split) + \"0% done : ELBO is \" + str(diff) + \", p(y,z) is \" + str(post) + \", q(z) is \" + str(approx))","def finished_tests(self):\n self.testing = 0","def plot_training_progress(self):\n if self.score_record:\n plt.plot(self.score_record)\n plt.ylabel('Average score')\n plt.xlabel('Episode')\n plt.title('Average score for last 100 episodes in the training phase')\n else:\n print('No progress made yet...')","def print_progress(self, i, current_params):\n for split in range(1,11):\n if i == (round(self.iterations/10*split)-1):\n post = -self.full_neg_posterior(current_params)\n approx = self.create_normal_logq(current_params)\n diff = post - approx\n if not self.quiet_progress:\n print(str(split) + \"0% done : ELBO is \" + str(diff) + \", p(y,z) is \" + str(post) + \", q(z) is \" + str(approx))","def _step(self):\n title()\n self.runCount = 1\n self.experiment.pause = False\n self._runExperiment()\n self.pause = True","def on_train_end(self, agent, **kwargs):\n duration = timeit.default_timer() - self.train_start\n print('done, took {:.3f} seconds'.format(duration))","def updates(_progress, _tag, _summary):\n log.msg(\"%d%%: %s\" % (_progress, _summary))","def step(self):\n # Fast learning\n task_embedding = self._ilp.infer_task()\n\n # Posterior update\n #self._skip_flag = self._is_graph_same(task_embedding, self._prev_task_embedding)\n self._skip_flag = False # XXX do not skip test\n if not self._skip_flag:\n self._grprop.observe_task(task_embedding)\n self._prev_task_embedding = task_embedding\n else:\n print(\"skipping!\")","def report(self, individuals):\n print(f'Iteration: {self.current}')\n self.current += 1","def report_training_progress(scores_01, traj_err_01, elt_err_01, R_01):\n \n # Unpack inputs pairs\n scores0, scores1 = scores_01\n traj_err0, traj_err1 = traj_err_01\n elt_err0, elt_err1 = elt_err_01\n R0, R1, = R_01\n\n # Unpack score components\n raw_score0 = scores0[:,0]\n raw_score1 = scores1[:,0]\n mu0 = scores0[:,1]\n mu1 = scores1[:,1]\n sigma0 = scores0[:,2]\n sigma1 = scores1[:,2]\n objective0 = scores0[:,3]\n objective1 = scores1[:,3]\n t_score0 = scores0[:,5]\n t_score1 = scores1[:,5]\n \n # Calculations\n eff_obs0 = raw_score0 - mu0\n eff_obs1 = raw_score1 - mu1\n\n # Changes in trajectory errors\n report_model_attribute_change(traj_err0, traj_err1, mask_good, 'Trajectory Error (AU)', dp=6)\n report_model_attribute_change(raw_score0, raw_score1, mask_good, 'Raw Score', dp=0)\n report_model_attribute_change(mu0, mu1, mask_good, 'Mu', dp=0)\n report_model_attribute_change(eff_obs0, eff_obs1, mask_good, 'Effective Observations', dp=0)\n report_model_attribute_change(sigma0, sigma1, mask_good, 'Sigma Squared', dp=0)\n report_model_attribute_change(t_score0, t_score1, mask_good, 't-score', dp=1)\n report_model_attribute_change(objective0, objective1, mask_good, 'Objective Function', dp=0)\n report_model_attribute_change(np.rad2deg(R0), np.rad2deg(R1), mask_good, 'Resolution R (degrees)', dp=4)\n \n # Unpack element errors\n err_a0 = elt_err0[:,0]\n err_a1 = elt_err1[:,0]\n err_e0 = elt_err0[:,1]\n err_e1 = elt_err1[:,1]\n err_inc0 = elt_err0[:,2]\n err_inc1 = elt_err1[:,2]\n # err_Omega0 = elt_err0[:,3]\n # err_Omega1 = elt_err1[:,3]\n # err_omega0 = elt_err0[:,4]\n # err_omega1 = elt_err1[:,4]\n err_f0 = elt_err0[:,5]\n err_f1 = elt_err1[:,5]\n \n # Report element errors\n report_model_attribute_change(err_a0, err_a1, mask_good, 'Error in semi-major axis, a', dp=6)\n report_model_attribute_change(err_e0, err_e1, mask_good, 'Error in eccentricity, e', dp=6)\n report_model_attribute_change(err_inc0, err_inc1, mask_good, 'Error in inclination, inc', dp=6)\n report_model_attribute_change(err_f0, err_f1, mask_good, 'Error in true anomaly, f', dp=6)\n \n # Changes in element errors\n d_elt_err_g = np.mean(d_elt_err[mask_good], axis=0)\n d_elt_err_b = np.mean(d_elt_err[mask_bad], axis=0)\n\n print(f'\\nChange in Orbital Element error by Category:')\n print(f'(Angles shown in degrees)')\n print(' a e inc Omega omega f')\n print(f'd_err_g: {d_elt_err_g[0]:+8.6f}, {d_elt_err_g[1]:+8.6f}, {d_elt_err_g[2]:+8.6f}, '\n f'{d_elt_err_g[3]:+8.6f}, {d_elt_err_g[4]:+8.6f}, {d_elt_err_g[5]:+8.6f}, ')\n print(f'd_err_b: {d_elt_err_b[0]:+8.6f}, {d_elt_err_b[1]:+8.6f}, {d_elt_err_b[2]:+8.6f}, '\n f'{d_elt_err_b[3]:+8.6f}, {d_elt_err_b[4]:+8.6f}, {d_elt_err_b[5]:+8.6f}, ')","def update_progress(self):\n report = self.build_progress_report()\n self.conduit.set_progress(report)","def update_progress(self):\n report = self.build_progress_report()\n self.conduit.set_progress(report)","def doProgress(self,progress,message):\n pass","def run(self):\n for i in range(self.exploration_steps):\n self.single_step(i)\n if self.save_checkpoints:\n self.save_results()\n self.save_results()\n if self.verbose:\n print(\"\\nExploration completed\")\n return","def run_simulator(self):\n\n self.update_settings()\n\n # Pass in the progress bar and the master so that the simulator can\n # update the progress bar and then refresh the screen when the progress\n # checkpoints are hit\n\n self.sim_results = self.sim.run(self.progress_bar, self.master)\n self.graph_results()","def test_recalculate_progress(self):\n self._build_sample_graph()\n self._create_lessons() # 3 lessons in unit 1\n self.student = models.Student(user_id='1')\n self._create_linear_progress() # Lesson 1 and 2 completed\n self.lesson1.properties[SKILLS_KEY] = [self.sa.id]\n self.lesson2.properties[SKILLS_KEY] = [self.sb.id]\n self.lesson3.properties[SKILLS_KEY] = [self.sa.id,\n self.sc.id]\n self.course.save()\n\n tracker = SkillCompletionTracker(self.course)\n lprogress_tracker = UnitLessonCompletionTracker(self.course)\n lprogress = lprogress_tracker.get_or_create_progress(self.student)\n expected = {\n self.sa: tracker.IN_PROGRESS,\n self.sb: tracker.COMPLETED,\n self.sc: tracker.NOT_ATTEMPTED\n }\n for skill, expected_progress in expected.iteritems():\n self.assertEqual(expected_progress,\n tracker.recalculate_progress(lprogress_tracker,\n lprogress, skill))","def learn(self):\n\n for i in range(self.args.n_iters):\n diff = self.iteration()\n\n if diff < self.args.epsilon:\n self.save(self.save_path, i)\n break\n elif (i + 1) % self.args.save_frequency == 0:\n self.save(self.save_path, i)","def run():\n step = 0\n while traci.simulation.getMinExpectedNumber() > 0:\n traci.simulationStep()\n step+=1\n traci.close()\n sys.stdout.flush()","def getProgress(self):","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 get_learning_completion(self):\n return min(1.0, self.step / Parameters.FINAL_EXPLORATION)","def calc_progress(self):\n if self.is_prepared():\n self._sync_info_from_disk()\n self._num_sown_batches = len(\n glob.glob(\n os.path.join(self.location, \"batches\", BTCH_NM.format(\"*\"))\n )\n )\n self._num_results = len(\n glob.glob(\n os.path.join(self.location, \"results\", RSLT_NM.format(\"*\"))\n )\n )\n else:\n self._num_sown_batches = -1\n self._num_results = -1","def complete(self):\n self.chunk_percentage = [1.0] * self.total_steps\n self.progress_updated.emit(self.percentage)\n self.initialized = False","def run():\n\n Number_repetitions = 1\n Rate = np.zeros((Number_repetitions,1))\n Rate20 = np.zeros((Number_repetitions,1))\n Penalty20 = np.zeros((Number_repetitions, 1))\n\n # Loop to average\n for idx in np.arange(0,Number_repetitions,1):\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\n # Now simulate it\n sim = Simulator(e, update_delay=0.0000001, display=True) # 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=100) # 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 # I've edited the enviroment variable to do the plot creating an completions array\n completions = np.array(e.completions)\n rate = float(completions.sum())/float((len(completions)))\n rate20 = float(completions[-20:].sum())/20\n\n Rate[idx] = rate\n Rate20[idx] = rate20\n\n Wrong = np.array(a.wrong_moves_per_run[-20:]).mean()\n Penalty20[idx] = Wrong\n\n plt.scatter(np.arange(0,len(completions)),completions)\n plt.plot(Wrong)\n plt.xlabel('Trial')\n plt.ylabel('1 = Get in the destination, 0 = did not get')\n plt.title('Reiforcement learning progress')\n plt.legend(['Rate of completion: ' + str(rate) + '. Rate last 20: ' + str(rate20) + '.Mean penalty last 20: ' + str(Wrong)])\n plt.show()\n\n #print 'Accuracy: ' + str(Rate) + '. Mean: ' + str(np.mean(Rate))\n #print 'Mean 20: ' + str(np.mean(Rate20))#'Accuracy 20: ' + str(Rate20) + '. Mean 20: ' + str(np.mean(Rate20))\n #print 'Mean_penalty: ' + str(np.mean(Penalty20))\n\n # Print state table with actions\n #t = 0\n #for state in a.states:\n #print 'State ' + str(state) + '. Best action: ' + str((str(np.argmax(a.QTable[t][:]))))\n #t += 1","def report_feature_completed(self):\n pass","def emit_status(self):\n next_job_count = len(self.fe.get_next_jobs())\n if next_job_count:\n emoji = \"🤔\"\n else:\n emoji = \"👌\"\n remaining = len(self.fe.get_current_network())\n\n pct = (self.total_job_count - remaining) / self.total_job_count\n print(\n f\"{emoji} ———— {next_job_count} jobs running, {remaining} remaining ({int(100*pct)}%). \",\n end=\"\\r\",\n )","def finished_sim(self):\n raise NotImplementedError(\n \"finished_sim function not reimplemented form base class\")","def set_progress(self, progress: float):","def progress_status(self):\n from tqdm import tqdm\n pbar_a = tqdm(total=len(self.jobs), position=0)\n pbar_a.set_description('Submitted jobs ...')\n pbar_b = tqdm(total=self.n_submit_script, position=1)\n pbar_b.set_description('Running jobs ...')\n pbar_c = tqdm(total=self.n_submit_script, position=2)\n pbar_c.set_description('Completed jobs ...')\n pbar_d = tqdm(total=self.n_submit_script, position=3)\n pbar_d.set_description('Failed? jobs ...')\n while self.n_completed < self.n_submit_script:\n pbar_a.n = self.n_submitted\n pbar_b.n = self.n_running\n pbar_c.n = self.n_completed\n pbar_d.n = self.n_failed + self.n_unknown\n pbar_a.refresh()\n pbar_b.refresh()\n pbar_c.refresh()\n pbar_d.refresh()\n sleep(5)\n self.update_status()","def on_train_begin(self):\n self.epoch_tqdm = self.tqdm(total=self.trainer.total_epochs,\n unit='epoch',\n leave=True,\n position=0,\n ascii=self.ascii)\n self.epoch_bar = self.epoch_tqdm.__enter__()\n self.last_step = 0","def perform_step(self) -> None:\n self.n_it = self.n_it + 1\n self.update_learning_rate()\n observed_gradient = self.get_observed_gradient(self.theta)\n latent_gradient = self.compute_latent_gradient(observed_gradient)\n eta = np.random.normal(0.0, np.sqrt(self.epsilon), self.dim_latent)\n self.omega = self.omega + self.epsilon / 2.0 * latent_gradient + eta\n self.theta = self.gplvm_model.predict(self.omega)[0]\n return","def launch_status(self):\n print(\n f\"Starting job with {len(self.fe.get_network())} jobs total. \",\n end=\"\\r\",\n )","def test_get_skill_progress(self):\n self._build_sample_graph()\n self._add_student_and_progress()\n tracker = SkillCompletionTracker()\n result = tracker.get_skills_progress(\n self.student, [self.sa.id, self.sb.id, self.sc.id])\n self.assertEqual(SkillCompletionTracker.COMPLETED,\n result[self.sa.id][0])\n self.assertEqual(SkillCompletionTracker.IN_PROGRESS,\n result[self.sb.id][0])\n self.assertEqual(SkillCompletionTracker.NOT_ATTEMPTED,\n result[self.sc.id][0])","def final_report(self):\n print('Final Count for', self.reason, self.successes, 'of', self.tests, 'tests passed')","def progress(self):\n return self.runProgress","def progress(self):\n return self.runProgress","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 testingProgressFunc(state, action, text, tick):\n pass\n #print \"testingProgressFunc\", state, action, text, tick","def _progress(self, walker):\n\n raise NotImplementedError","def progress(loss, epoch, batch, batch_size, dataset_size):\n batches = math.ceil(float(dataset_size) / batch_size)\n count = batch * batch_size\n bar_len = 40\n filled_len = int(round(bar_len * count / float(dataset_size)))\n\n bar = '=' * filled_len + '-' * (bar_len - filled_len)\n\n status = 'Epoch {}, Loss: {:.4f}'.format(epoch, loss)\n _progress_str = \"\\r \\r [{}] ...{}\".format(bar, status)\n sys.stdout.write(_progress_str)\n sys.stdout.flush()\n\n if batch == batches:\n print()","def setProgress(self, prog):\n\t\tself.progress = prog","def progress(self, progress):\n\n self._progress = progress","def progress(self, progress):\n\n self._progress = progress","def dump_iteration(self,status):\n L = self.level\n stats.add_to_stats(step=status.step, time=status.time, iter=status.iter, type='residual',\n value=L.status.residual)\n pass","def _progress(self, num_completed_batches, data_loader):\n return '[{}/{} ({:.0f}%)]'.format(num_completed_batches, len(data_loader),\n 100.0 * num_completed_batches / len(data_loader))","def test_progress(self):\r\n # Navigate to the progress page from the info page\r\n self.course_info_page.visit()\r\n self.tab_nav.go_to_tab('Progress')\r\n\r\n # We haven't answered any problems yet, so assume scores are zero\r\n # Only problems should have scores; so there should be 2 scores.\r\n CHAPTER = 'Test Section'\r\n SECTION = 'Test Subsection'\r\n EXPECTED_SCORES = [(0, 3), (0, 1)]\r\n\r\n actual_scores = self.progress_page.scores(CHAPTER, SECTION)\r\n self.assertEqual(actual_scores, EXPECTED_SCORES)","def report_progress(self, group_num, group_length):\r\n first_sim_in_group = (group_num * group_length) + 1\r\n last_sim_in_group = first_sim_in_group + group_length - 1\r\n if first_sim_in_group < last_sim_in_group:\r\n print(f\"{datetime.now()}\\t{first_sim_in_group}-{last_sim_in_group}\\n\")\r\n else:\r\n print(f\"{datetime.now()}\\t{first_sim_in_group}\\n\")","def on_train_end(self, logs):\n duration = timeit.default_timer() - self.train_start\n print('done, took {:.3f} seconds'.format(duration))","def _iteration(self, data_loader):\n\n iter_rho = AverageMeter('Iter rho')\n iter_nmse = AverageMeter('Iter nmse')\n iter_loss = AverageMeter('Iter loss')\n iter_time = AverageMeter('Iter time')\n time_tmp = time.time()\n\n for batch_idx, (sparse_gt, raw_gt) in enumerate(data_loader):\n sparse_gt = sparse_gt.to(self.device)\n sparse_pred = self.model(sparse_gt)\n loss = self.criterion(sparse_pred, sparse_gt)\n rho, nmse = evaluator(sparse_pred, sparse_gt, raw_gt)\n\n # Log and visdom update\n iter_loss.update(loss)\n iter_rho.update(rho)\n iter_nmse.update(nmse)\n iter_time.update(time.time() - time_tmp)\n time_tmp = time.time()\n\n # plot progress\n if (batch_idx + 1) % self.print_freq == 0:\n logger.info(f'[{batch_idx + 1}/{len(data_loader)}] '\n f'loss: {iter_loss.avg:.3e} | rho: {iter_rho.avg:.3e} | '\n f'NMSE: {iter_nmse.avg:.3e} | time: {iter_time.avg:.3f}')\n\n logger.info(f'=> Test rho:{iter_rho.avg:.3e} NMSE: {iter_nmse.avg:.3e}\\n')\n\n return iter_loss.avg, iter_rho.avg, iter_nmse.avg","def simulation_finished(self, data):\n self._logger.info(u\"simulation finished\")\n self.statusLabel.setText(u\"simulation finished.\")\n\n self.actSimulate.setDisabled(False)\n self.shortRunSimulation.setEnabled(True)\n self.shortRunRegimeBatch.setEnabled(True)\n self.actPlayPause.setDisabled(False)\n self.shortPlayPause.setEnabled(True)\n self.shortSaveResult.setEnabled(True)\n self.actStop.setDisabled(False)\n self.actSave.setDisabled(False)\n self.speedDial.setDisabled(False)\n self.timeSlider.setDisabled(False)\n\n self.sim.simulationProgressChanged.disconnect(self.guiProgress.setValue)\n self.statusBar().removeWidget(self.guiProgress)\n\n self.timeSlider.triggerAction(QtGui.QAbstractSlider.SliderToMinimum)\n\n self.currentDataset = data\n self._read_results()\n self._update_data_list()\n self._update_plots()\n\n self.stop_animation()\n # self.playAnimation()\n\n if self.runningBatch:\n regime_name = self._regimes[self._current_regime_index][\"Name\"]\n self._save_data(regime_name)\n self.regimeFinished.emit()\n else:\n self.actExecuteRegimes.setDisabled(False)","def run(self) -> None:\n for episode in range(1, self.episodes + 1):\n print('Episode:', episode)\n steps, state_action_history = self.run_one_episode()\n self.steps_per_episode.append(steps)\n if episode % parameters.CACHING_INTERVAL == 0 or steps < 1000:\n visualize.animate_track(state_action_history, f'agent-{episode}')\n\n print('Training completed.')\n visualize.plot_steps_per_episode(self.steps_per_episode)\n visualize.plot_epsilon(self.agent.epsilon_history)\n\n if parameters.VISUALIZE_FINAL_GAME:\n print('Showing one episode with the greedy strategy.')\n self.agent.epsilon = 0\n steps, state_action_history = self.run_one_episode()\n print(f'Episode completed in {steps} steps.')\n visualize.animate_track(state_action_history)","def final(self, state):\n\n deltaReward = state.getScore() - self.pre_state.getScore()\n self.observeTransition(self.pre_state, self.action, state, deltaReward, True)\n self.stopEpisode()\n\n # Make sure we have this var\n if not 'episodeStartTime' in self.__dict__:\n self.episodeStartTime = time.time()\n if not 'lastWindowAccumRewards' in self.__dict__:\n self.lastWindowAccumRewards = 0.0\n self.lastWindowAccumRewards += state.getScore()\n\n NUM_EPS_UPDATE = 10000\n if self.episodesSoFar % NUM_EPS_UPDATE == 0:\n print('episode: ', self.episodesSoFar)\n print('Saving model...')\n self.model.save(MODEL_PATH)\n print('Reinforcement Learning Status:')\n windowAvg = self.lastWindowAccumRewards / float(NUM_EPS_UPDATE)\n if self.episodesSoFar <= self.numTraining:\n trainAvg = self.accumTrainRewards / float(self.episodesSoFar)\n print('\\tCompleted %d out of %d training episodes' % (self.episodesSoFar, self.numTraining))\n print('\\tAverage Rewards over all training: %.2f' % (trainAvg))\n\n else:\n testAvg = float(self.accumTestRewards) / (self.episodesSoFar - self.numTraining)\n print('\\tCompleted %d test episodes' % (self.episodesSoFar - self.numTraining))\n\n print('\\tAverage Rewards over testing: %.2f' % testAvg)\n\n print('\\tAverage Rewards for last %d episodes: %.2f' % (NUM_EPS_UPDATE, windowAvg))\n\n print('\\tEpisode took %.2f seconds' % (time.time() - self.episodeStartTime))\n\n self.lastWindowAccumRewards = 0.0\n self.episodeStartTime = time.time()\n\n if self.episodesSoFar == self.numTraining:\n msg = 'Training Done (turning off epsilon and alpha)'\n print('%s\\n%s' % (msg, '-' * len(msg)))","def _on_progress(self, num):\n self._num_progresses += num\n self._log.info(\"Progress incrementing by {}\".format(num))\n self._host_comms.send_msg(\"progress\", num)","def run(self):\n self._display_sims(self._compute_sims())","def step(self, estim: EstimBase) -> None:\n _, results = estim.get_last_results()\n results = [self.to_metrics(res) for res in results]\n self.metrics.extend(results)\n if len(self.metrics) >= len(self.population):\n self.population = self._mating(self.population)\n self.metrics = []","def record_stats(self, move_is_viable, move_is_accepted):\n if move_is_viable:\n self.viablesteps += 1\n if move_is_accepted:\n self.acceptedsteps += 1\n self.steps += 1","def print_progress(remaining_pairs, G):\n total_pairs = G.number_of_nodes()**2\n uncomputed_pairs = len(remaining_pairs)\n print(1 - uncomputed_pairs/total_pairs)","def completeStep(self):\n self.chunk_percentage[self.current_step - 1] = self.current_chunk_size\n self.progress_updated.emit(self.percentage)\n if self.total_steps == self.current_step:\n self.initialized = False","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 status_update(frame_number, tot_frames):\r\n if frame_number == 1:\r\n print(\"Starting analysis of %d frames...\" %tot_frames)\r\n\r\n if frame_number % 100 == 0:\r\n print(\"%d\" %frame_number, end='')\r\n sys.stdout.flush()\r\n elif frame_number % 10 == 0:\r\n print(\".\", end='')\r\n sys.stdout.flush()\r\n\r\n if frame_number == tot_frames:\r\n print (\"End of video reached successfully.\")","def onTrainLoopTaken(self, agent):\n pass","def start_progress_bar(self):\r\n self.progress[\"value\"] = self.progress_step","def _report_step(self, learning_rate, step, train_stats=None,\n valid_stats=None):\n if self.report_manager is not None:\n return self.report_manager.report_step(\n learning_rate, step, train_stats=train_stats,\n valid_stats=valid_stats)","def on_epoch_begin(self, epoch, logs={}):\n self.current_progress = 0\n self.loss = 0\n self.accuracy = 0"],"string":"[\n \"def reportProgress(self):\\n \\n pass\",\n \"def report_scenario_progress(self):\\n pass\",\n \"def show_progress(self, game):\\n if self.verbose:\\n if self.params.eval_interval is not None and (self.episode % self.params.eval_interval == 0):\\n self._print_progress()\\n # evaluate one run\\n state = game.reset()\\n self.tracker.reset()\\n done = False\\n while not done:\\n controls = self.sess.run(self.graph.target_actor_outputs,\\n feed_dict={self.graph.states: np.array([state])}).reshape(-1)\\n state, reward, done, _ = game.step(controls)\\n self.tracker.step(reward)\\n print('****** ', self.tracker.total_reward, self.tracker.discounted_rewards, ' ******')\",\n \"def report_progress(self):\\r\\n stats = self.simulation_stats.stats\\r\\n solutions = len(self.solutions)\\r\\n round = self.round\\r\\n scores = stats[round]\\r\\n best_score = min(scores)\\r\\n avg_score = sum(scores) / solutions\\r\\n line = f\\\"Round: {round}\\\\t\\\\tBest Score: {best_score}\\\\t\\\\t Average Score: {avg_score}\\\"\\r\\n print(line)\",\n \"def show_progress(self, game):\\n if self.verbose:\\n if self.params.eval_interval is not None and (self.episode % self.params.eval_interval == 0):\\n self.print_progress()\\n # evaluate one run\\n state = game.reset()\\n self.tracker.reset()\\n done = False\\n while not done:\\n q_values = self.sess.run(self.graph.target_q_values, feed_dict={self.graph.states: [state]})\\n action = np.argmax(q_values)\\n state, reward, done, _ = game.step(action)\\n self.tracker.step(reward)\\n print('****** ', self.tracker.total_reward, self.tracker.discounted_rewards, ' ******')\",\n \"def PrintProgress(self):\\n print ' Examined %d nodes, found %d unique...' % (\\n self.nodes_examined, self.unique_nodes\\n )\",\n \"def report_step_progress(self, step):\\n pass\",\n \"def update_progress(self, done):\\r\\n if done % 100 == 0:\\r\\n print >>sys.stderr, \\\" %d processed, run time %d secs\\\" % (done, (datetime.now() - self.started_at).seconds)\",\n \"def _iteration_changed(self):\\n if self.showProgressBar:\\n try:\\n self._setProgress()\\n except:\\n # may fail when switching from training to inference\\n from dbgp.client import brk; brk(port=9011)\\n pass\",\n \"def report_scenario_progress(self):\\n if not self.current_scenario:\\n return # SKIP: No results to report for first scenario.\\n # -- NORMAL-CASE:\\n status_name = self.current_scenario.status.name\\n dot_status = self.dot_status[status_name]\\n if status_name == \\\"failed\\\":\\n # MAYBE TODO: self.failures.append(result)\\n pass\\n self.stream.write(dot_status)\\n self.stream.flush()\",\n \"def learn(self, Nsim, progress_bar=False):\\n\\n if progress_bar:\\n iterations = tqdm(range(Nsim))\\n else:\\n iterations = range(Nsim)\\n for _ in iterations:\\n self.grow_tree()\",\n \"def _print_progress(self):\\n \\n print 'Completed %d of %d' %(self.progress_id, self.total_work)\\n self.progress_id += 1\",\n \"def report_progress(self, samples, labels,\\n epoch_no, counter_name, t_start):\\n # Calculate network accuracy\\n result = self.sess.run( [self.network_prediction], feed_dict={\\n self.x: samples, self.y_trgt: labels,\\n self.fc1_keep_prob: 1.0 })\\n pred = result[0]\\n\\n # Loop for each output class\\n avg_accuracy = 0\\n for c in range(self.n_output_classes):\\n # Calculate true/false pos/neg\\n true_pos = np.sum( pred[labels[:,c]==1]==c )\\n false_pos = np.sum( pred[labels[:,c]==0]==c )\\n false_neg = np.sum( pred[labels[:,c]==1]!=c )\\n true_neg = np.sum( pred[labels[:,c]==0]!=c )\\n\\n # Calculate accuracy, precision, recall, F1\\n accuracy = (true_pos+true_neg) / len(pred)\\n precision = np.nan_to_num(true_pos / (true_pos+false_pos))\\n recall = np.nan_to_num(true_pos / (true_pos+false_neg))\\n F1 = np.nan_to_num(2 * ((precision*recall)/(precision+recall)))\\n self.accuracy_list[c].append(float(accuracy))\\n self.precision_list[c].append(float(precision))\\n self.recall_list[c].append(float(recall))\\n self.F1_list[c].append(float(F1))\\n self.n_class_samples_list[c].append(int(self.n_class_samples_trained[c]))\\n avg_accuracy += accuracy\\n self.n_samples_list.append(int(self.n_samples_trained))\\n\\n t_curr = time.time()\\n self.log('{} {:4d}: Acc = {:6.4f} (t={})'.format( \\\\\\n counter_name, epoch_no, avg_accuracy/self.n_output_classes,\\n str(datetime.timedelta(seconds=np.round(t_curr-t_start))) ),\\n no_enter=True, overwrite_last=True)\",\n \"def report_scenario_completed(self):\\n self.report_scenario_progress()\",\n \"def progress(self, i, num_episodes, last_avg, start, end):\\n percentage = (i / num_episodes) * 100.0\\n self.log(\\n \\\"Epoch {:>5}/{:<5} [{:>5.1f}%] Avg reward: {:+.3f}\\\".format(\\n i,\\n num_episodes,\\n percentage,\\n last_avg), end='')\\n if percentage == 0.0:\\n self.log(' Est.time: UNKNOWN')\\n return\\n elapsed = end - start\\n remaining = ((100. - percentage) * elapsed) / percentage\\n self.log(' Est.time: {}'.format(self.timer(remaining)))\",\n \"def _setProgress(self):\\n\\n self.progress = (self.iteration, self.iterationCount)\",\n \"def report_step_progress(self, step):\\n dot_status = self.dot_status[step.status.name]\\n if step.status == Status.failed:\\n if (step.exception and\\n not isinstance(step.exception, AssertionError)):\\n # -- ISA-ERROR: Some Exception\\n dot_status = self.dot_status[\\\"error\\\"]\\n step.feature = self.current_feature\\n step.scenario = self.current_scenario\\n self.failures.append(step)\\n self.stream.write(dot_status)\\n self.stream.flush()\",\n \"def _print_progress(self):\\n if self.current_training_size % 1000 == 0:\\n print(self.current_training_size, end='')\\n elif self.current_training_size % 100 == 0:\\n print('.', end='')\",\n \"def finish_learning(self):\\n pass\",\n \"def progress(self):\\n if self.running:\\n pass\\n else:\\n self._engine.progress()\",\n \"def _log_progress(self):\\n self.num_of_requests_in_pipeline += 1\\n if self.num_of_requests_in_pipeline % 20 == 0:\\n print('-' * 200)\\n print(f'DB PIPELINE: {self.num_of_requests_in_pipeline} items wenth though pipeline.')\\n print('-' * 200)\",\n \"def _on_step(self) -> None:\\n self._n_calls += 1\\n # Account for multiple environments\\n # each call to step() corresponds to n_envs transitions\\n if self._n_calls % max(self.target_update_interval // self.n_envs, 1) == 0:\\n polyak_update(self.q_net.parameters(), self.q_net_target.parameters(), self.tau)\\n # Copy running stats, see GH issue #996\\n polyak_update(self.batch_norm_stats, self.batch_norm_stats_target, 1.0)\\n\\n self.exploration_rate = self.exploration_schedule(self._current_progress_remaining)\\n self.logger.record(\\\"rollout/exploration_rate\\\", self.exploration_rate)\",\n \"def status(self) -> NoReturn:\\n\\n curr_status= self.percent_done()\\n while(curr_status < 100):\\n\\n update_status(name=self.name, status=curr_status)\\n time.sleep(0.5)\\n\\n curr_status = self.percent_done()\\n\\n update_status(name=self.name, status=curr_status)\",\n \"def _log_progress(self, t):\\n\\n # Run the update only 2 step before the actual logging happens in order to\\n # make sure that the most recent possible values will be stored in\\n # self.summary. This is a hacky workaround in order to support OffPolicyAgent\\n # which runs 2 threads without coordination\\n if (t+2) % self.log_freq == 0 and self.learn_started:\\n episode_rewards = self.env_monitor.get_episode_rewards()\\n self.episode_rewards = np.asarray(episode_rewards)\\n if self.episode_rewards.size > 0:\\n self.mean_ep_rew = np.mean(episode_rewards[-self.stats_n:])\\n self.best_mean_ep_rew = max(self.best_mean_ep_rew, self.mean_ep_rew)\\n\\n if t % self.log_freq == 0 and self.learn_started:\\n stats_logger.info(\\\"\\\")\\n for s, lambda_v in self.log_info:\\n stats_logger.info(s.format(lambda_v(t)))\\n stats_logger.info(\\\"\\\")\\n\\n if self.summary:\\n # Log with TensorBoard\\n self.tb_writer.add_summary(self.summary, global_step=t)\",\n \"def transfer_progress(self, stats):\",\n \"def progress(self, arg, num_done, info=''):\\n pass\",\n \"def update(self, steps):\\n self.launch_progress += (steps)/self.total\",\n \"def performSimulation(self):\\n \\n if self.parameters['verbose']:\\n print(\\\"=====================\\\\nStarting simulation with parameters\\\\n\\\",self.parameters)\\n print(\\\"=====================\\\\nInitial Graph\\\\n\\\")\\n self.showState()\\n print(\\\"=====================\\\")\\n\\n while self.parameters['steps'] > 0:\\n if self.parameters['verbose']: print(\\\"Performing step\\\")\\n self.performStep()\\n if self.parameters['verbose']: self.showState()\\n\\n if self.parameters['verbose']:\\n print(\\\"=====================\\\\nFinished Simulation\\\\n\\\\nResult graph:\\\")\\n self.showState()\\n #self.showGraph(self.parameters['file_name'])\\n #self.showState()\\n #self.showStats()\",\n \"def report_trial(self):\\n pass\",\n \"def wait_progress(self):\\n pass\",\n \"def wait_progress(self):\\n pass\",\n \"def final(self, state):\\n deltaReward = state.getScore() - self.lastState.getScore()\\n self.observeTransition(self.lastState, self.lastAction, state, deltaReward)\\n self.stopEpisode()\\n\\n # Make sure we have this var\\n if not 'episodeStartTime' in self.__dict__:\\n self.episodeStartTime = time.time()\\n if not 'lastWindowAccumRewards' in self.__dict__:\\n self.lastWindowAccumRewards = 0.0\\n self.lastWindowAccumRewards += state.getScore()\\n\\n NUM_EPS_UPDATE = 100\\n if self.episodesSoFar % NUM_EPS_UPDATE == 0:\\n print 'Reinforcement Learning Status:'\\n windowAvg = self.lastWindowAccumRewards / float(NUM_EPS_UPDATE)\\n if self.episodesSoFar <= self.numTraining:\\n trainAvg = self.accumTrainRewards / float(self.episodesSoFar)\\n print '\\\\tCompleted %d out of %d training episodes' % (\\n self.episodesSoFar,self.numTraining)\\n print '\\\\tAverage Rewards over all training: %.2f' % (\\n trainAvg)\\n else:\\n testAvg = float(self.accumTestRewards) / (self.episodesSoFar - self.numTraining)\\n print '\\\\tCompleted %d test episodes' % (self.episodesSoFar - self.numTraining)\\n print '\\\\tAverage Rewards over testing: %.2f' % testAvg\\n print '\\\\tAverage Rewards for last %d episodes: %.2f' % (\\n NUM_EPS_UPDATE,windowAvg)\\n print '\\\\tEpisode took %.2f seconds' % (time.time() - self.episodeStartTime)\\n self.lastWindowAccumRewards = 0.0\\n self.episodeStartTime = time.time()\\n\\n if self.episodesSoFar == self.numTraining:\\n msg = 'Training Done (turning off epsilon and alpha)'\\n print '%s\\\\n%s' % (msg,'-' * len(msg))\",\n \"def check_sim(self):\\n if self.index % 5 == 0:\\n print (\\\"Timesteps Completed: {} out of {}\\\".format(self.index, self.timesteps))\\n if self.index == self.timesteps:\\n print (\\\"Simulation Completed\\\")\\n self.end_simulation()\\n self.index += 1\",\n \"def get_learn_after_each_trial(self):\\r\\n return 0\",\n \"def __progress(self, dataset, input_function):\\n batchsize = 100\\n total_loss = 0\\n total_correct = 0\\n total_selected = 0\\n total_relevant = 0\\n total_correct_relevant = 0\\n total_batches = 0\\n for batch in self.__iterate_minibatches(dataset, batchsize):\\n inputs, targets = batch\\n l, c, s, r, h = input_function(inputs, targets)\\n total_loss += l\\n total_correct += c\\n total_selected += s\\n total_relevant += r\\n total_correct_relevant += h\\n total_batches += 1\\n avg_loss = total_loss / total_batches\\n precision = total_correct_relevant / total_selected\\n recall = total_correct_relevant / total_relevant\\n accuracy = total_correct / (batchsize * total_batches)\\n return avg_loss, precision, recall, accuracy\",\n \"def progress(self, msg):\\n logging.info(\\\"UI-Test: \\\" + msg)\\n with step(\\\"UI test progress: \\\" + msg):\\n pass\\n if len(self.state) > 0:\\n self.state += \\\"\\\\n\\\"\\n self.state += \\\"UI: \\\" + msg\",\n \"def print_progress(self, i, current_params):\\n for split in range(1,11):\\n if i == (round(self.iterations/10*split)-1):\\n post = -self.neg_posterior(current_params)\\n approx = self.create_normal_logq(current_params)\\n diff = post - approx\\n if not self.quiet_progress:\\n print(str(split) + \\\"0% done : ELBO is \\\" + str(diff) + \\\", p(y,z) is \\\" + str(post) + \\\", q(z) is \\\" + str(approx))\",\n \"def finished_tests(self):\\n self.testing = 0\",\n \"def plot_training_progress(self):\\n if self.score_record:\\n plt.plot(self.score_record)\\n plt.ylabel('Average score')\\n plt.xlabel('Episode')\\n plt.title('Average score for last 100 episodes in the training phase')\\n else:\\n print('No progress made yet...')\",\n \"def print_progress(self, i, current_params):\\n for split in range(1,11):\\n if i == (round(self.iterations/10*split)-1):\\n post = -self.full_neg_posterior(current_params)\\n approx = self.create_normal_logq(current_params)\\n diff = post - approx\\n if not self.quiet_progress:\\n print(str(split) + \\\"0% done : ELBO is \\\" + str(diff) + \\\", p(y,z) is \\\" + str(post) + \\\", q(z) is \\\" + str(approx))\",\n \"def _step(self):\\n title()\\n self.runCount = 1\\n self.experiment.pause = False\\n self._runExperiment()\\n self.pause = True\",\n \"def on_train_end(self, agent, **kwargs):\\n duration = timeit.default_timer() - self.train_start\\n print('done, took {:.3f} seconds'.format(duration))\",\n \"def updates(_progress, _tag, _summary):\\n log.msg(\\\"%d%%: %s\\\" % (_progress, _summary))\",\n \"def step(self):\\n # Fast learning\\n task_embedding = self._ilp.infer_task()\\n\\n # Posterior update\\n #self._skip_flag = self._is_graph_same(task_embedding, self._prev_task_embedding)\\n self._skip_flag = False # XXX do not skip test\\n if not self._skip_flag:\\n self._grprop.observe_task(task_embedding)\\n self._prev_task_embedding = task_embedding\\n else:\\n print(\\\"skipping!\\\")\",\n \"def report(self, individuals):\\n print(f'Iteration: {self.current}')\\n self.current += 1\",\n \"def report_training_progress(scores_01, traj_err_01, elt_err_01, R_01):\\n \\n # Unpack inputs pairs\\n scores0, scores1 = scores_01\\n traj_err0, traj_err1 = traj_err_01\\n elt_err0, elt_err1 = elt_err_01\\n R0, R1, = R_01\\n\\n # Unpack score components\\n raw_score0 = scores0[:,0]\\n raw_score1 = scores1[:,0]\\n mu0 = scores0[:,1]\\n mu1 = scores1[:,1]\\n sigma0 = scores0[:,2]\\n sigma1 = scores1[:,2]\\n objective0 = scores0[:,3]\\n objective1 = scores1[:,3]\\n t_score0 = scores0[:,5]\\n t_score1 = scores1[:,5]\\n \\n # Calculations\\n eff_obs0 = raw_score0 - mu0\\n eff_obs1 = raw_score1 - mu1\\n\\n # Changes in trajectory errors\\n report_model_attribute_change(traj_err0, traj_err1, mask_good, 'Trajectory Error (AU)', dp=6)\\n report_model_attribute_change(raw_score0, raw_score1, mask_good, 'Raw Score', dp=0)\\n report_model_attribute_change(mu0, mu1, mask_good, 'Mu', dp=0)\\n report_model_attribute_change(eff_obs0, eff_obs1, mask_good, 'Effective Observations', dp=0)\\n report_model_attribute_change(sigma0, sigma1, mask_good, 'Sigma Squared', dp=0)\\n report_model_attribute_change(t_score0, t_score1, mask_good, 't-score', dp=1)\\n report_model_attribute_change(objective0, objective1, mask_good, 'Objective Function', dp=0)\\n report_model_attribute_change(np.rad2deg(R0), np.rad2deg(R1), mask_good, 'Resolution R (degrees)', dp=4)\\n \\n # Unpack element errors\\n err_a0 = elt_err0[:,0]\\n err_a1 = elt_err1[:,0]\\n err_e0 = elt_err0[:,1]\\n err_e1 = elt_err1[:,1]\\n err_inc0 = elt_err0[:,2]\\n err_inc1 = elt_err1[:,2]\\n # err_Omega0 = elt_err0[:,3]\\n # err_Omega1 = elt_err1[:,3]\\n # err_omega0 = elt_err0[:,4]\\n # err_omega1 = elt_err1[:,4]\\n err_f0 = elt_err0[:,5]\\n err_f1 = elt_err1[:,5]\\n \\n # Report element errors\\n report_model_attribute_change(err_a0, err_a1, mask_good, 'Error in semi-major axis, a', dp=6)\\n report_model_attribute_change(err_e0, err_e1, mask_good, 'Error in eccentricity, e', dp=6)\\n report_model_attribute_change(err_inc0, err_inc1, mask_good, 'Error in inclination, inc', dp=6)\\n report_model_attribute_change(err_f0, err_f1, mask_good, 'Error in true anomaly, f', dp=6)\\n \\n # Changes in element errors\\n d_elt_err_g = np.mean(d_elt_err[mask_good], axis=0)\\n d_elt_err_b = np.mean(d_elt_err[mask_bad], axis=0)\\n\\n print(f'\\\\nChange in Orbital Element error by Category:')\\n print(f'(Angles shown in degrees)')\\n print(' a e inc Omega omega f')\\n print(f'd_err_g: {d_elt_err_g[0]:+8.6f}, {d_elt_err_g[1]:+8.6f}, {d_elt_err_g[2]:+8.6f}, '\\n f'{d_elt_err_g[3]:+8.6f}, {d_elt_err_g[4]:+8.6f}, {d_elt_err_g[5]:+8.6f}, ')\\n print(f'd_err_b: {d_elt_err_b[0]:+8.6f}, {d_elt_err_b[1]:+8.6f}, {d_elt_err_b[2]:+8.6f}, '\\n f'{d_elt_err_b[3]:+8.6f}, {d_elt_err_b[4]:+8.6f}, {d_elt_err_b[5]:+8.6f}, ')\",\n \"def update_progress(self):\\n report = self.build_progress_report()\\n self.conduit.set_progress(report)\",\n \"def update_progress(self):\\n report = self.build_progress_report()\\n self.conduit.set_progress(report)\",\n \"def doProgress(self,progress,message):\\n pass\",\n \"def run(self):\\n for i in range(self.exploration_steps):\\n self.single_step(i)\\n if self.save_checkpoints:\\n self.save_results()\\n self.save_results()\\n if self.verbose:\\n print(\\\"\\\\nExploration completed\\\")\\n return\",\n \"def run_simulator(self):\\n\\n self.update_settings()\\n\\n # Pass in the progress bar and the master so that the simulator can\\n # update the progress bar and then refresh the screen when the progress\\n # checkpoints are hit\\n\\n self.sim_results = self.sim.run(self.progress_bar, self.master)\\n self.graph_results()\",\n \"def test_recalculate_progress(self):\\n self._build_sample_graph()\\n self._create_lessons() # 3 lessons in unit 1\\n self.student = models.Student(user_id='1')\\n self._create_linear_progress() # Lesson 1 and 2 completed\\n self.lesson1.properties[SKILLS_KEY] = [self.sa.id]\\n self.lesson2.properties[SKILLS_KEY] = [self.sb.id]\\n self.lesson3.properties[SKILLS_KEY] = [self.sa.id,\\n self.sc.id]\\n self.course.save()\\n\\n tracker = SkillCompletionTracker(self.course)\\n lprogress_tracker = UnitLessonCompletionTracker(self.course)\\n lprogress = lprogress_tracker.get_or_create_progress(self.student)\\n expected = {\\n self.sa: tracker.IN_PROGRESS,\\n self.sb: tracker.COMPLETED,\\n self.sc: tracker.NOT_ATTEMPTED\\n }\\n for skill, expected_progress in expected.iteritems():\\n self.assertEqual(expected_progress,\\n tracker.recalculate_progress(lprogress_tracker,\\n lprogress, skill))\",\n \"def learn(self):\\n\\n for i in range(self.args.n_iters):\\n diff = self.iteration()\\n\\n if diff < self.args.epsilon:\\n self.save(self.save_path, i)\\n break\\n elif (i + 1) % self.args.save_frequency == 0:\\n self.save(self.save_path, i)\",\n \"def run():\\n step = 0\\n while traci.simulation.getMinExpectedNumber() > 0:\\n traci.simulationStep()\\n step+=1\\n traci.close()\\n sys.stdout.flush()\",\n \"def getProgress(self):\",\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 get_learning_completion(self):\\n return min(1.0, self.step / Parameters.FINAL_EXPLORATION)\",\n \"def calc_progress(self):\\n if self.is_prepared():\\n self._sync_info_from_disk()\\n self._num_sown_batches = len(\\n glob.glob(\\n os.path.join(self.location, \\\"batches\\\", BTCH_NM.format(\\\"*\\\"))\\n )\\n )\\n self._num_results = len(\\n glob.glob(\\n os.path.join(self.location, \\\"results\\\", RSLT_NM.format(\\\"*\\\"))\\n )\\n )\\n else:\\n self._num_sown_batches = -1\\n self._num_results = -1\",\n \"def complete(self):\\n self.chunk_percentage = [1.0] * self.total_steps\\n self.progress_updated.emit(self.percentage)\\n self.initialized = False\",\n \"def run():\\n\\n Number_repetitions = 1\\n Rate = np.zeros((Number_repetitions,1))\\n Rate20 = np.zeros((Number_repetitions,1))\\n Penalty20 = np.zeros((Number_repetitions, 1))\\n\\n # Loop to average\\n for idx in np.arange(0,Number_repetitions,1):\\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\\n # Now simulate it\\n sim = Simulator(e, update_delay=0.0000001, display=True) # 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=100) # 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 # I've edited the enviroment variable to do the plot creating an completions array\\n completions = np.array(e.completions)\\n rate = float(completions.sum())/float((len(completions)))\\n rate20 = float(completions[-20:].sum())/20\\n\\n Rate[idx] = rate\\n Rate20[idx] = rate20\\n\\n Wrong = np.array(a.wrong_moves_per_run[-20:]).mean()\\n Penalty20[idx] = Wrong\\n\\n plt.scatter(np.arange(0,len(completions)),completions)\\n plt.plot(Wrong)\\n plt.xlabel('Trial')\\n plt.ylabel('1 = Get in the destination, 0 = did not get')\\n plt.title('Reiforcement learning progress')\\n plt.legend(['Rate of completion: ' + str(rate) + '. Rate last 20: ' + str(rate20) + '.Mean penalty last 20: ' + str(Wrong)])\\n plt.show()\\n\\n #print 'Accuracy: ' + str(Rate) + '. Mean: ' + str(np.mean(Rate))\\n #print 'Mean 20: ' + str(np.mean(Rate20))#'Accuracy 20: ' + str(Rate20) + '. Mean 20: ' + str(np.mean(Rate20))\\n #print 'Mean_penalty: ' + str(np.mean(Penalty20))\\n\\n # Print state table with actions\\n #t = 0\\n #for state in a.states:\\n #print 'State ' + str(state) + '. Best action: ' + str((str(np.argmax(a.QTable[t][:]))))\\n #t += 1\",\n \"def report_feature_completed(self):\\n pass\",\n \"def emit_status(self):\\n next_job_count = len(self.fe.get_next_jobs())\\n if next_job_count:\\n emoji = \\\"🤔\\\"\\n else:\\n emoji = \\\"👌\\\"\\n remaining = len(self.fe.get_current_network())\\n\\n pct = (self.total_job_count - remaining) / self.total_job_count\\n print(\\n f\\\"{emoji} ———— {next_job_count} jobs running, {remaining} remaining ({int(100*pct)}%). \\\",\\n end=\\\"\\\\r\\\",\\n )\",\n \"def finished_sim(self):\\n raise NotImplementedError(\\n \\\"finished_sim function not reimplemented form base class\\\")\",\n \"def set_progress(self, progress: float):\",\n \"def progress_status(self):\\n from tqdm import tqdm\\n pbar_a = tqdm(total=len(self.jobs), position=0)\\n pbar_a.set_description('Submitted jobs ...')\\n pbar_b = tqdm(total=self.n_submit_script, position=1)\\n pbar_b.set_description('Running jobs ...')\\n pbar_c = tqdm(total=self.n_submit_script, position=2)\\n pbar_c.set_description('Completed jobs ...')\\n pbar_d = tqdm(total=self.n_submit_script, position=3)\\n pbar_d.set_description('Failed? jobs ...')\\n while self.n_completed < self.n_submit_script:\\n pbar_a.n = self.n_submitted\\n pbar_b.n = self.n_running\\n pbar_c.n = self.n_completed\\n pbar_d.n = self.n_failed + self.n_unknown\\n pbar_a.refresh()\\n pbar_b.refresh()\\n pbar_c.refresh()\\n pbar_d.refresh()\\n sleep(5)\\n self.update_status()\",\n \"def on_train_begin(self):\\n self.epoch_tqdm = self.tqdm(total=self.trainer.total_epochs,\\n unit='epoch',\\n leave=True,\\n position=0,\\n ascii=self.ascii)\\n self.epoch_bar = self.epoch_tqdm.__enter__()\\n self.last_step = 0\",\n \"def perform_step(self) -> None:\\n self.n_it = self.n_it + 1\\n self.update_learning_rate()\\n observed_gradient = self.get_observed_gradient(self.theta)\\n latent_gradient = self.compute_latent_gradient(observed_gradient)\\n eta = np.random.normal(0.0, np.sqrt(self.epsilon), self.dim_latent)\\n self.omega = self.omega + self.epsilon / 2.0 * latent_gradient + eta\\n self.theta = self.gplvm_model.predict(self.omega)[0]\\n return\",\n \"def launch_status(self):\\n print(\\n f\\\"Starting job with {len(self.fe.get_network())} jobs total. \\\",\\n end=\\\"\\\\r\\\",\\n )\",\n \"def test_get_skill_progress(self):\\n self._build_sample_graph()\\n self._add_student_and_progress()\\n tracker = SkillCompletionTracker()\\n result = tracker.get_skills_progress(\\n self.student, [self.sa.id, self.sb.id, self.sc.id])\\n self.assertEqual(SkillCompletionTracker.COMPLETED,\\n result[self.sa.id][0])\\n self.assertEqual(SkillCompletionTracker.IN_PROGRESS,\\n result[self.sb.id][0])\\n self.assertEqual(SkillCompletionTracker.NOT_ATTEMPTED,\\n result[self.sc.id][0])\",\n \"def final_report(self):\\n print('Final Count for', self.reason, self.successes, 'of', self.tests, 'tests passed')\",\n \"def progress(self):\\n return self.runProgress\",\n \"def progress(self):\\n return self.runProgress\",\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 testingProgressFunc(state, action, text, tick):\\n pass\\n #print \\\"testingProgressFunc\\\", state, action, text, tick\",\n \"def _progress(self, walker):\\n\\n raise NotImplementedError\",\n \"def progress(loss, epoch, batch, batch_size, dataset_size):\\n batches = math.ceil(float(dataset_size) / batch_size)\\n count = batch * batch_size\\n bar_len = 40\\n filled_len = int(round(bar_len * count / float(dataset_size)))\\n\\n bar = '=' * filled_len + '-' * (bar_len - filled_len)\\n\\n status = 'Epoch {}, Loss: {:.4f}'.format(epoch, loss)\\n _progress_str = \\\"\\\\r \\\\r [{}] ...{}\\\".format(bar, status)\\n sys.stdout.write(_progress_str)\\n sys.stdout.flush()\\n\\n if batch == batches:\\n print()\",\n \"def setProgress(self, prog):\\n\\t\\tself.progress = prog\",\n \"def progress(self, progress):\\n\\n self._progress = progress\",\n \"def progress(self, progress):\\n\\n self._progress = progress\",\n \"def dump_iteration(self,status):\\n L = self.level\\n stats.add_to_stats(step=status.step, time=status.time, iter=status.iter, type='residual',\\n value=L.status.residual)\\n pass\",\n \"def _progress(self, num_completed_batches, data_loader):\\n return '[{}/{} ({:.0f}%)]'.format(num_completed_batches, len(data_loader),\\n 100.0 * num_completed_batches / len(data_loader))\",\n \"def test_progress(self):\\r\\n # Navigate to the progress page from the info page\\r\\n self.course_info_page.visit()\\r\\n self.tab_nav.go_to_tab('Progress')\\r\\n\\r\\n # We haven't answered any problems yet, so assume scores are zero\\r\\n # Only problems should have scores; so there should be 2 scores.\\r\\n CHAPTER = 'Test Section'\\r\\n SECTION = 'Test Subsection'\\r\\n EXPECTED_SCORES = [(0, 3), (0, 1)]\\r\\n\\r\\n actual_scores = self.progress_page.scores(CHAPTER, SECTION)\\r\\n self.assertEqual(actual_scores, EXPECTED_SCORES)\",\n \"def report_progress(self, group_num, group_length):\\r\\n first_sim_in_group = (group_num * group_length) + 1\\r\\n last_sim_in_group = first_sim_in_group + group_length - 1\\r\\n if first_sim_in_group < last_sim_in_group:\\r\\n print(f\\\"{datetime.now()}\\\\t{first_sim_in_group}-{last_sim_in_group}\\\\n\\\")\\r\\n else:\\r\\n print(f\\\"{datetime.now()}\\\\t{first_sim_in_group}\\\\n\\\")\",\n \"def on_train_end(self, logs):\\n duration = timeit.default_timer() - self.train_start\\n print('done, took {:.3f} seconds'.format(duration))\",\n \"def _iteration(self, data_loader):\\n\\n iter_rho = AverageMeter('Iter rho')\\n iter_nmse = AverageMeter('Iter nmse')\\n iter_loss = AverageMeter('Iter loss')\\n iter_time = AverageMeter('Iter time')\\n time_tmp = time.time()\\n\\n for batch_idx, (sparse_gt, raw_gt) in enumerate(data_loader):\\n sparse_gt = sparse_gt.to(self.device)\\n sparse_pred = self.model(sparse_gt)\\n loss = self.criterion(sparse_pred, sparse_gt)\\n rho, nmse = evaluator(sparse_pred, sparse_gt, raw_gt)\\n\\n # Log and visdom update\\n iter_loss.update(loss)\\n iter_rho.update(rho)\\n iter_nmse.update(nmse)\\n iter_time.update(time.time() - time_tmp)\\n time_tmp = time.time()\\n\\n # plot progress\\n if (batch_idx + 1) % self.print_freq == 0:\\n logger.info(f'[{batch_idx + 1}/{len(data_loader)}] '\\n f'loss: {iter_loss.avg:.3e} | rho: {iter_rho.avg:.3e} | '\\n f'NMSE: {iter_nmse.avg:.3e} | time: {iter_time.avg:.3f}')\\n\\n logger.info(f'=> Test rho:{iter_rho.avg:.3e} NMSE: {iter_nmse.avg:.3e}\\\\n')\\n\\n return iter_loss.avg, iter_rho.avg, iter_nmse.avg\",\n \"def simulation_finished(self, data):\\n self._logger.info(u\\\"simulation finished\\\")\\n self.statusLabel.setText(u\\\"simulation finished.\\\")\\n\\n self.actSimulate.setDisabled(False)\\n self.shortRunSimulation.setEnabled(True)\\n self.shortRunRegimeBatch.setEnabled(True)\\n self.actPlayPause.setDisabled(False)\\n self.shortPlayPause.setEnabled(True)\\n self.shortSaveResult.setEnabled(True)\\n self.actStop.setDisabled(False)\\n self.actSave.setDisabled(False)\\n self.speedDial.setDisabled(False)\\n self.timeSlider.setDisabled(False)\\n\\n self.sim.simulationProgressChanged.disconnect(self.guiProgress.setValue)\\n self.statusBar().removeWidget(self.guiProgress)\\n\\n self.timeSlider.triggerAction(QtGui.QAbstractSlider.SliderToMinimum)\\n\\n self.currentDataset = data\\n self._read_results()\\n self._update_data_list()\\n self._update_plots()\\n\\n self.stop_animation()\\n # self.playAnimation()\\n\\n if self.runningBatch:\\n regime_name = self._regimes[self._current_regime_index][\\\"Name\\\"]\\n self._save_data(regime_name)\\n self.regimeFinished.emit()\\n else:\\n self.actExecuteRegimes.setDisabled(False)\",\n \"def run(self) -> None:\\n for episode in range(1, self.episodes + 1):\\n print('Episode:', episode)\\n steps, state_action_history = self.run_one_episode()\\n self.steps_per_episode.append(steps)\\n if episode % parameters.CACHING_INTERVAL == 0 or steps < 1000:\\n visualize.animate_track(state_action_history, f'agent-{episode}')\\n\\n print('Training completed.')\\n visualize.plot_steps_per_episode(self.steps_per_episode)\\n visualize.plot_epsilon(self.agent.epsilon_history)\\n\\n if parameters.VISUALIZE_FINAL_GAME:\\n print('Showing one episode with the greedy strategy.')\\n self.agent.epsilon = 0\\n steps, state_action_history = self.run_one_episode()\\n print(f'Episode completed in {steps} steps.')\\n visualize.animate_track(state_action_history)\",\n \"def final(self, state):\\n\\n deltaReward = state.getScore() - self.pre_state.getScore()\\n self.observeTransition(self.pre_state, self.action, state, deltaReward, True)\\n self.stopEpisode()\\n\\n # Make sure we have this var\\n if not 'episodeStartTime' in self.__dict__:\\n self.episodeStartTime = time.time()\\n if not 'lastWindowAccumRewards' in self.__dict__:\\n self.lastWindowAccumRewards = 0.0\\n self.lastWindowAccumRewards += state.getScore()\\n\\n NUM_EPS_UPDATE = 10000\\n if self.episodesSoFar % NUM_EPS_UPDATE == 0:\\n print('episode: ', self.episodesSoFar)\\n print('Saving model...')\\n self.model.save(MODEL_PATH)\\n print('Reinforcement Learning Status:')\\n windowAvg = self.lastWindowAccumRewards / float(NUM_EPS_UPDATE)\\n if self.episodesSoFar <= self.numTraining:\\n trainAvg = self.accumTrainRewards / float(self.episodesSoFar)\\n print('\\\\tCompleted %d out of %d training episodes' % (self.episodesSoFar, self.numTraining))\\n print('\\\\tAverage Rewards over all training: %.2f' % (trainAvg))\\n\\n else:\\n testAvg = float(self.accumTestRewards) / (self.episodesSoFar - self.numTraining)\\n print('\\\\tCompleted %d test episodes' % (self.episodesSoFar - self.numTraining))\\n\\n print('\\\\tAverage Rewards over testing: %.2f' % testAvg)\\n\\n print('\\\\tAverage Rewards for last %d episodes: %.2f' % (NUM_EPS_UPDATE, windowAvg))\\n\\n print('\\\\tEpisode took %.2f seconds' % (time.time() - self.episodeStartTime))\\n\\n self.lastWindowAccumRewards = 0.0\\n self.episodeStartTime = time.time()\\n\\n if self.episodesSoFar == self.numTraining:\\n msg = 'Training Done (turning off epsilon and alpha)'\\n print('%s\\\\n%s' % (msg, '-' * len(msg)))\",\n \"def _on_progress(self, num):\\n self._num_progresses += num\\n self._log.info(\\\"Progress incrementing by {}\\\".format(num))\\n self._host_comms.send_msg(\\\"progress\\\", num)\",\n \"def run(self):\\n self._display_sims(self._compute_sims())\",\n \"def step(self, estim: EstimBase) -> None:\\n _, results = estim.get_last_results()\\n results = [self.to_metrics(res) for res in results]\\n self.metrics.extend(results)\\n if len(self.metrics) >= len(self.population):\\n self.population = self._mating(self.population)\\n self.metrics = []\",\n \"def record_stats(self, move_is_viable, move_is_accepted):\\n if move_is_viable:\\n self.viablesteps += 1\\n if move_is_accepted:\\n self.acceptedsteps += 1\\n self.steps += 1\",\n \"def print_progress(remaining_pairs, G):\\n total_pairs = G.number_of_nodes()**2\\n uncomputed_pairs = len(remaining_pairs)\\n print(1 - uncomputed_pairs/total_pairs)\",\n \"def completeStep(self):\\n self.chunk_percentage[self.current_step - 1] = self.current_chunk_size\\n self.progress_updated.emit(self.percentage)\\n if self.total_steps == self.current_step:\\n self.initialized = False\",\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 status_update(frame_number, tot_frames):\\r\\n if frame_number == 1:\\r\\n print(\\\"Starting analysis of %d frames...\\\" %tot_frames)\\r\\n\\r\\n if frame_number % 100 == 0:\\r\\n print(\\\"%d\\\" %frame_number, end='')\\r\\n sys.stdout.flush()\\r\\n elif frame_number % 10 == 0:\\r\\n print(\\\".\\\", end='')\\r\\n sys.stdout.flush()\\r\\n\\r\\n if frame_number == tot_frames:\\r\\n print (\\\"End of video reached successfully.\\\")\",\n \"def onTrainLoopTaken(self, agent):\\n pass\",\n \"def start_progress_bar(self):\\r\\n self.progress[\\\"value\\\"] = self.progress_step\",\n \"def _report_step(self, learning_rate, step, train_stats=None,\\n valid_stats=None):\\n if self.report_manager is not None:\\n return self.report_manager.report_step(\\n learning_rate, step, train_stats=train_stats,\\n valid_stats=valid_stats)\",\n \"def on_epoch_begin(self, epoch, logs={}):\\n self.current_progress = 0\\n self.loss = 0\\n self.accuracy = 0\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.71725804","0.70678365","0.6930796","0.6844124","0.6836328","0.68133104","0.67061037","0.6654687","0.6649508","0.64450794","0.64312387","0.6404172","0.6400389","0.6358474","0.63549787","0.6334084","0.63017714","0.62951624","0.62896836","0.62857735","0.62044835","0.618807","0.6170468","0.6149956","0.6133795","0.6125196","0.61123466","0.60986644","0.60872895","0.6070884","0.6070884","0.60689175","0.6054667","0.602755","0.6021483","0.601321","0.60084784","0.6007653","0.6002673","0.5995682","0.59732413","0.59727967","0.5937054","0.592546","0.59197587","0.59009534","0.5900141","0.5900141","0.5899254","0.589715","0.5895863","0.5872288","0.58708","0.5867828","0.58599514","0.5847893","0.58462274","0.58388996","0.5829316","0.58229727","0.581852","0.5808263","0.58057845","0.58040047","0.5787184","0.57637525","0.57614833","0.5758385","0.5748723","0.57425857","0.5740845","0.5740845","0.5731057","0.5724654","0.5718236","0.5711087","0.5706751","0.56922525","0.56922525","0.56872636","0.5658295","0.5648733","0.56416154","0.5638052","0.563432","0.5623441","0.5615885","0.5596386","0.5587377","0.5583295","0.55818456","0.55776864","0.5577327","0.5563863","0.5558692","0.55571806","0.5553643","0.55516076","0.55500656","0.5548267"],"string":"[\n \"0.71725804\",\n \"0.70678365\",\n \"0.6930796\",\n \"0.6844124\",\n \"0.6836328\",\n \"0.68133104\",\n \"0.67061037\",\n \"0.6654687\",\n \"0.6649508\",\n \"0.64450794\",\n \"0.64312387\",\n \"0.6404172\",\n \"0.6400389\",\n \"0.6358474\",\n \"0.63549787\",\n \"0.6334084\",\n \"0.63017714\",\n \"0.62951624\",\n \"0.62896836\",\n \"0.62857735\",\n \"0.62044835\",\n \"0.618807\",\n \"0.6170468\",\n \"0.6149956\",\n \"0.6133795\",\n \"0.6125196\",\n \"0.61123466\",\n \"0.60986644\",\n \"0.60872895\",\n \"0.6070884\",\n \"0.6070884\",\n \"0.60689175\",\n \"0.6054667\",\n \"0.602755\",\n \"0.6021483\",\n \"0.601321\",\n \"0.60084784\",\n \"0.6007653\",\n \"0.6002673\",\n \"0.5995682\",\n \"0.59732413\",\n \"0.59727967\",\n \"0.5937054\",\n \"0.592546\",\n \"0.59197587\",\n \"0.59009534\",\n \"0.5900141\",\n \"0.5900141\",\n \"0.5899254\",\n \"0.589715\",\n \"0.5895863\",\n \"0.5872288\",\n \"0.58708\",\n \"0.5867828\",\n \"0.58599514\",\n \"0.5847893\",\n \"0.58462274\",\n \"0.58388996\",\n \"0.5829316\",\n \"0.58229727\",\n \"0.581852\",\n \"0.5808263\",\n \"0.58057845\",\n \"0.58040047\",\n \"0.5787184\",\n \"0.57637525\",\n \"0.57614833\",\n \"0.5758385\",\n \"0.5748723\",\n \"0.57425857\",\n \"0.5740845\",\n \"0.5740845\",\n \"0.5731057\",\n \"0.5724654\",\n \"0.5718236\",\n \"0.5711087\",\n \"0.5706751\",\n \"0.56922525\",\n \"0.56922525\",\n \"0.56872636\",\n \"0.5658295\",\n \"0.5648733\",\n \"0.56416154\",\n \"0.5638052\",\n \"0.563432\",\n \"0.5623441\",\n \"0.5615885\",\n \"0.5596386\",\n \"0.5587377\",\n \"0.5583295\",\n \"0.55818456\",\n \"0.55776864\",\n \"0.5577327\",\n \"0.5563863\",\n \"0.5558692\",\n \"0.55571806\",\n \"0.5553643\",\n \"0.55516076\",\n \"0.55500656\",\n \"0.5548267\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6581055"},"document_rank":{"kind":"string","value":"9"}}},{"rowIdx":22484,"cells":{"query":{"kind":"string","value":"Return parameterized folder name of simulation."},"document":{"kind":"string","value":"def sim_dir_name(fdir, nsteps, stim_pars, hc_pars, gs_pars, str_pars):\n\n fn = ('nsteps_{}_GSHCsharp_{}'.format(nsteps, hc_pars['gs_hc_sharp']) +\n '_hcpow_{}'.format(hc_pars['ro_pow']) +\n '_msig_{}'.format(int(stim_pars['mot_sig'])) +\n '_vbeta_{}'.format(stim_pars['vis_beta']) +\n '_lambda_{:.1f}'.format(str_pars['gamma']))\n fn = fdir + 'navigation/' + utils.format_to_fname(fn) + '/'\n return fn"},"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 folder(self, step=None):\n if step is None:\n return self._obs_group_folder / self._obs_folder\n else:\n return Path(step) / self._obs_group_folder / self._obs_folder","def folder(self, step=None):\n if step is None:\n return self._obs_group_folder / self._obs_folder\n else:\n return Path(step) / self._obs_group_folder / self._obs_folder","def folder(self, step=None):\n if step is None:\n return self._obs_group_folder / self._obs_folder\n else:\n return Path(step) / self._obs_group_folder / self._obs_folder","def simulation_dir(self):\n try:\n return (self.output_directory / self.sim_id).expand()\n except AttributeError:\n return Path()","def folder(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"folder\")","def folder(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"folder\")","def create_summary_folder_name():\n dir_str = '' # ADD PATH\n time_stamp_str = time.strftime(\"%a, %d %b %Y %H:%M:%S/\", time.gmtime())\n param_str = ''\n return dir_str + time_stamp_str + param_str","def name_folder_data(data):\n string = 'results/S0(%s)_sig(%s)_r(%s)_T(%s)_K(%s)' % data\n return string","def media_folder_name(self):\n raise NotImplementedError","def folder(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"folder\")","def folder(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"folder\")","def get_folder():\n return input(\"Folder: \")","def shpname(self):\n _, tail = os.path.split(self.url)\n return self.folder + ('/' + tail[:-4]) * 2","def gen_folder(base, e, f, a, k):\n \n return base + 'eps_' + str(e) + '_fp_' + str(f) + \\\n '_areak_' + str(a) + '_kappa_' + str(k)","def obsfoldername(self, folder_name_beamctl_type=True, source_name=None):\n if not folder_name_beamctl_type:\n obsextname = self.filenametime\n obsextname += \"_\" + self.ldat_type\n else:\n self.source_name = source_name\n obsextname = obsfileinfo2filefolder(vars(self))\n return obsextname","def Directory(self) -> str:","def folder_runnum():\n now = datetime.datetime.now()\n runnum = 1\n while True:\n folder_name = f\"astroNN_{now.month:0{2}d}{now.day:0{2}d}_run{runnum:0{3}d}\"\n if not os.path.exists(folder_name):\n break\n else:\n runnum += 1\n\n return folder_name","def set_folder_name(self, folder_name=None):\n\n if folder_name == None:\n folder_name = 'Abstract-OneDim/'\n self.params['folder_name'] = folder_name\n else:\n self.params['folder_name'] = folder_name\n print 'Folder name:', self.params['folder_name']","def get_directory() -> str:\n return directory","def world_name(self) -> str:\n return os.path.basename(self.path)","def get_directory(self):\n path = os.path.join(settings.SUBMISSION_DIR, \n str(self.course.semester), \n str(self.course.code), \n \"Sec.%d\" % self.course.section,\n self.code)\n return path.replace(\" \", \"_\")","def getFolderPath(self) -> unicode:\n ...","def root_folder_name(asmtSubmission):\n\n userID = asmtSubmission.by.djangoUser.username\n\n # If multiple submissions permitted, need to be able to distinguish\n # So add the current time down to the second.\n # We are not using assignment UUID because that truncates the file name.\n userID = userID + \"_\" + strftime(\"%Y-%m-%d_%H-%M-%S\")\n\n return userID","def logdir(self):\n return osp.join('runs/', self.net_name, '')","def name_final_path(out_img_folder):\n if out_img_folder == None:\n return \"./.out_hidden_images\"\n else:\n return out_img_folder","def get(self):\n return self.directory_name","def create_savename(self):\n \n savename = self.config.get('grid', 'dir') + self.fname.split('/')[-1]\n newsuffix = '_gridded_%ix%ix%i.nc' % (self.nx, self.ny, self.nz)\n savename = savename.replace('.nc', newsuffix)\n \n return savename","def create_out_dir_name(params):\n\n current_timestamp = timestamp()\n out_dir = os.path.join('out', current_timestamp)\n return out_dir","def get_output_folder_name(argi=2, root_folder=\"\"):\n # First tries to read the output folder name from argv[2]\n try:\n output_folder = sys.argv[argi]\n except IndexError:\n # If argv[argi] was not passed, asks the user for the output folder.\n output_folder = root_folder\n output_folder += input(\"Output folder path was not informed. Please inform:\\n\"\n \"{}\".format(root_folder))\n\n # Adds the SEP (/ or \\\\) character to the end of the folder name.\n if output_folder[-len(SEP):] != SEP:\n output_folder += SEP\n\n # Checks if the folder does not exist. Creates it, in this case.\n if not os.path.exists(output_folder):\n os.system(\"mkdir -p '{}'\".format(output_folder))\n\n return output_folder","def get_res_dir_name(random, nx=None, ny=None, p_state=None, p_path=None,\n **kws):\n\n if random:\n fdir = 'random_env/'\n env_dir = 'nx{}_ny{}_pstate{}_ppath{}'.format(nx, ny, p_state, p_path)\n fdir += utils.format_to_fname(env_dir) + '/'\n else:\n fdir = 'village/'\n\n return fdir","def directory_id(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"directory_id\")","def _get_station_filename():\n output_dir = os.path.join(output, state, station)\n if not os.path.isdir(output_dir):\n logger.debug(\"Creating directory %s\", output_dir)\n os.makedirs(output_dir)\n return os.path.join(output_dir, \"%s.%s\" % (c_time, format))","def _get_models_name(data_dir):\n call_folders = [x[0] for x in os.walk(data_dir)][1::]\n folder = []\n for i in call_folders:\n temp = i.split('\\\\')\n folder.append(temp[1])\n print('The models to ensemble are {}'.format(folder))\n return folder","def absolute_folder_name(self):\n return 'music_decompose/media/{0}'.format(self.media_folder_name)","def workspace_name(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"workspace_name\")","def workspace_name(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"workspace_name\")","def workspace_name(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"workspace_name\")","def get_directory(self, subdir=None):\n path = settings.SUBMISSION_DIR / str(self.assignment.id) / str(self.id)\n if subdir:\n path = path / subdir\n\n return path","def folder(self):\n return os.path.join(\n td.project.folder.replace(\"/\", os.sep), # incase we're on windows\n \"component_repos\",\n self.name)","def directory_id(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"directory_id\")","def common_folder_path(folder: str,) -> str:\n return \"folders/{folder}\".format(folder=folder,)","def get_created_adjustment_folder_name(self):\n global adjustment_folder_name\n return adjustment_folder_name","def compute_model_name(self, params, folder=None):\n folder = folder or self.output_dir\n\n prefix = os.path.basename(params['graph'])\n name = \"_\".join([\"{}-{}\".format(k, v) \n for k, v in sorted(params.items())\n if k not in [\"graph\"]])\n\n model_name = '-'.join([prefix, name])\n full_name = os.path.join(folder, model_name)\n\n return full_name","def dir_name(self):\n return self._dir","def path(self, prefix, args=()):\n dir_path = self.dir.path(prefix, args)\n return os.path.join(dir_path, self.name)","def directory(self) -> str:\n return self._values.get(\"directory\")","def use_name(self):\n projection = proj.get_projection(self.conf.projections, self.spec)\n if not projection:\n projection = self.conf.default_projections[\"all\"]\n\n name = self.spec.format(projection)\n # Not everybody is working on linux...\n parts = name.split(\"/\")\n name = os.path.join(*parts)\n # Add optional suffixes based on constraints\n path_elements = [name] + self.conf.suffixes\n return \"-\".join(path_elements)","def folder_name(self): \n folders = []\n for folder in self.folders:\n folders.append(folder)\n return folders","def get_root():\n\n return 'data/simulators/mg1'","def folder_str(f):\n if not os.path.exists(f):\n raise argparse.ArgumentTypeError('\"%s\" does not exist, you must create this folder' % f)\n return f","def get_name(self):\n return self.client._perform_json(\"GET\", \"/project-folders/%s\" % self.project_folder_id).get(\"name\", None)","def get_data_output_name(prefix: str):\n return settings.data_dir + 'standardized_data_' + prefix","def create_experiment_folder(path_out, dir_name, name='', stamp_unique=True):\n assert os.path.exists(path_out), 'missing base folder \"%s\"' % path_out\n date = time.gmtime()\n if isinstance(name, str) and name:\n dir_name = '{}_{}'.format(dir_name, name)\n path_exp = os.path.join(path_out, dir_name)\n if stamp_unique:\n path_exp += '_' + time.strftime(FORMAT_DATE_TIME, date)\n path_created = None\n while not path_created:\n logging.warning('particular out folder already exists')\n if path_created is not None:\n path_exp += '-' + str(np.random.randint(0, 100))\n path_created = create_folder(path_exp, ok_existing=False)\n else:\n path_created = create_folder(path_exp, ok_existing=False)\n logging.info('created experiment folder \"%r\"', path_created)\n return path_exp","def path_name(self):","def directory_name(initial_name):\n import string\n allowed_chars = string.digits + string.ascii_letters + \" _.\"\n result_name = \"\"\n for ch in initial_name:\n if allowed_chars.find(ch) != -1:\n result_name += ch\n return result_name if result_name != \"\" else \"course_folder\"","def _dir_out(self):\n ens_label = utils.io.ens_label(self.ens_mem)\n return os.path.join(self.proj.direc_out, self.proj.name,\n self.model.name, self.run.name,\n ens_label, self.name)","def name(self):\n try:\n return self._name\n except AttributeError:\n if self.is_task:\n try:\n return self.pos_str\n except:\n return os.path.basename(self.workdir)\n else:\n return os.path.basename(self.workdir)","def getDirectory(nameP:str) -> str:\n nameObject = list(nameP.split(\" \"))\n lengName = len(nameObject)\n if (lengName >= 2):\n concat = \"\"\n nameProject = (concat.join(nameObject))\n else:\n nameProject = nameP\n\n lo = os.getcwd()\n # print(lo)\n if platform == 'linux' or platform == 'linux2':\n li = list(lo.split(\"/\"))\n var1 = li[0]\n var2 = li[1]\n var3 = li[2]\n pathC = (\"{0}/{1}/{2}/Documents/Respaldos/{3}\").format(var1, var2, var3, nameProject)\n return pathC\n elif platform == 'darwin':\n li = list(lo.split(\"/\"))\n var1 = li[0] \n var2 = li[1]\n var3 = li[2]\n pathC = (\"{0}/{1}/{2}/Documents/Respaldos/{3}\").format(var1, var2, var3, nameProject)\n return pathC\n elif platform == 'win32': \n li = list(lo.split(\"\\\\\"))\n var1 = li[0]\n var2 = li[1]\n var3 = li[2]\n pathC = (\"{0}/{1}/{2}/Documents/Respaldos/{3}\").format(var1, var2, var3, nameProject)\n return pathC","def prepare_folder(self) -> str:\n base_folder = self.config['info']['folder']\n today_folder = f'{datetime.today():%Y-%m-%d}'\n folder = os.path.join(base_folder, today_folder)\n if not os.path.isdir(folder):\n os.makedirs(folder)\n return folder","def get_scene_folder():\n\n flg = logging.getLogger(\"lettuce.xgenSetup.get_scene_folder\")\n\n file_name = mc.file(q=True, sceneName=True)\n\n head, tail = os.path.split(file_name)\n\n flg.info(\"Scene fileName: {}\".format(tail))\n flg.info(\"Scene directory: {}\".format(head))\n\n return head","def get_dummy_folder() -> str:\n dummy_folder = os.path.join(os.getcwd(), 'dummy_test_folder')\n return dummy_folder","def displayname(self):\n if self.path.is_dir():\n if (is_uuid(self.path.parts[-1])):\n self.is_uuid_folder = True\n return self.path.name + '/'\n elif is_proj(self.path.parts[-1]):\n return f'{bcolors.BOLD}' + self.path.name + f'{bcolors.ENDC}'\n return self.path.name","def get_name(self) -> str:\n return os.path.split(os.getcwd())[-1]","def gen_folders(rho, kappa, km, pa, analysis, dbase, analysisdbase):\n \n path1 = 'density_' + + str(rho) + \"_kappa_\" + \\\n str(kappa) + \"_km_\" + str(km) + \"_panti_\" + str(pa)\n path2 = analysis + '_density_' + + str(rho) + \"_kappa_\" + \\\n str(kappa) + \"_km_\" + str(km) + \"_panti_\" + str(pa) + '.txt' \n datafolder = dbase + path1 + '/'\n analysisfile = analysisdbase + path2 \n\n return datafolder, analysisfile","def getFolderItemName(self) -> unicode:\n ...","def folder(self) -> pulumi.Output[Optional['outputs.ChangeDataCaptureResponseFolder']]:\n return pulumi.get(self, \"folder\")","def set_folder(tipo):\n loc = path.join(pardir, 'SHOTS')\n makedirs(loc, exist_ok=True)\n # 'data_type' is the subfolder, depending on data type. default is a shot.\n data_type = 'shots'\n if tipo == 'mirror':\n data_type = 'tests'\n elif 'test' in tipo:\n data_type = 'tests'\n elif tipo == 'clean':\n data_type = 'cleaning_plasma'\n folder = path.join(loc, data_type)\n makedirs(folder, exist_ok=True)\n return path.join(loc, data_type)","def directory_id(self) -> pulumi.Output[Optional[str]]:\n return pulumi.get(self, \"directory_id\")","def get_path(self):\n definition = self.client._perform_json(\"GET\", \"/project-folders/%s\" % self.project_folder_id)\n parent_id = definition.get(\"parentId\", None)\n if parent_id is not None:\n parent = DSSProjectFolder(self.client, parent_id)\n path = parent.get_path()\n return (\"\" if path == \"/\" else path) + \"/\" + definition.get(\"name\", \"\")\n else:\n return \"/\"","def _get_subdir(self, dmc, year, trigger, pt):\n full_trigger = get_full_trigger(trigger)\n return '/'.join([dmc, str(year), full_trigger, str(pt)])","def get_sim_file(cls, root_dir, constants):\n return os.path.join(root_dir, '%s.hdf5' % constants['mos_type'])","def get_sim_file(cls, root_dir, constants):\n return os.path.join(root_dir, '%s.hdf5' % constants['mos_type'])","def get_directory(self) -> Path:\n path = Path(str(self.max_nesting_lvl))\n path /= str(self.num_snps)\n path /= self.read_quality\n path /= str(self.coverage)\n path /= str(self.denovo_kmer_size)\n return path","def get_replay_folder(self):\n\n images_with_labels_folder = self.base_folder_path + \"/data/replay_images_and_labels/\"\n return images_with_labels_folder","def generate_dir_soolin(channel, energy=13):\n uid = getpass.getuser()\n return \"/hdfs/user/%s/NMSSMPheno/MG5_aMC/%s\" % (uid, generate_subdir(channel, energy))","def path(name, folder= \"~/data/LJSpeech-1.0\"):\n return join(expanduser(folder), \"wavs\", name + \".wav\")","def get_directory_names():\n prefix = FLAGS.dataset + \"-\" + FLAGS.uid + \"-\" + FLAGS.method\n\n # Use the number specified on the command line (higher precedence than --debug)\n if FLAGS.debugnum >= 0:\n attempt = FLAGS.debugnum\n print(\"Debugging attempt:\", attempt)\n\n prefix += \"-\" + str(attempt)\n model_dir = os.path.join(FLAGS.modeldir, prefix)\n log_dir = os.path.join(FLAGS.logdir, prefix)\n # Find last one, increment number\n elif FLAGS.debug:\n attempt = file_utils.last_modified_number(FLAGS.logdir, prefix + \"*\")\n attempt = attempt + 1 if attempt is not None else 1\n print(\"Debugging attempt:\", attempt)\n\n prefix += \"-\" + str(attempt)\n model_dir = os.path.join(FLAGS.modeldir, prefix)\n log_dir = os.path.join(FLAGS.logdir, prefix)\n # If no debugging modes, use the model and log directory with only the \"prefix\"\n # (even though it's not actually a prefix in this case, it's the whole name)\n elif FLAGS.subdir:\n model_dir = os.path.join(FLAGS.modeldir, prefix)\n log_dir = os.path.join(FLAGS.logdir, prefix)\n else:\n model_dir = FLAGS.modeldir\n log_dir = FLAGS.logdir\n\n return model_dir, log_dir","def dir(self) -> str:\n return f'{os.path.dirname(self.path)}/'.lstrip('/')","def get_path(\n layer: LayerType,\n batch_size: int,\n num_runs: int,\n num_repeats: int,\n gsm_mode: str,\n random_seed: Optional[int] = None,\n forward_only: bool = False,\n root: str = \"./results/raw/\",\n suffix: str = \"\",\n) -> str:\n pickle_name = f\"{layer}_mode_{gsm_mode}_bs_{batch_size}_runs_{num_runs}_repeats_{num_repeats}_seed_{random_seed}\"\n if forward_only:\n pickle_name += \"_forward_only\"\n\n if len(suffix) and not suffix.startswith(\"_\"):\n suffix = f\"_{suffix}\"\n\n return f\"{root}{pickle_name}{suffix}.pkl\"","def working_directory(self):\n project_full_path = \"/\".join(self.file_name.split(\"/\")[:-1])\n file_name = self.file_name.split(\"/\")[-1]\n if \".h5\" in file_name:\n file_name = file_name.split(\".h5\")[0]\n file_name += \"_hdf5\"\n if self.h5_path[0] == \"/\":\n h5_path = self.h5_path[1:]\n else:\n h5_path = self.h5_path\n return posixpath.join(project_full_path, file_name, h5_path)","def data_abex_results_iteration_dir(experiment_name: str, iteration: int) -> Path: # pragma: no cover\n return data_abex_results_dir(experiment_name) / iteration_name(iteration)","def getFSGroupDir(self, desired=None):\n role = self.getRole(desired)\n groupDir = self.config.get(\"FileMan\",\"homedir\") + role[\"roleName\"]\n return groupDir","def get_folder(self):\n return os.path.join(\n settings.PRIVATE_STORAGE_ROOT, Exam.EXAM_FILES_LOCATION,\n str(self.unique_id)[0:2])","def song_directory_path(instance, filename):\n return f\"music/songs/{instance.id}_{instance.name}_{filename}\"","def GcsDirname(path):\n return os.path.dirname(path)","def get_dag_directory(self) -> str:\n if isinstance(self._dag_directory, Path):\n return str(self._dag_directory.resolve())\n else:\n return str(self._dag_directory)","def experiment_dir(experiment_name: str) -> Path: # pragma: no cover\n return EXPERIMENTS_DIR / experiment_name","def season_folder(cls, season):\r\n\r\n\t\t'''# Google Drive downloads replace these characters automatically\r\n\t\t# I'm implementing this in the code as well for convenience\r\n\t\tseason = season.replace(\"&\", \"_\")\r\n\t\tseason = season.replace(\"'\", \"_\")'''\r\n\r\n\t\t# Folder names are ANSI versions of the season name\r\n\t\t# This is important in names like \"Lé Unicorn\" which get\r\n\t\t# converted incorrectly as folder names\r\n\t\tseason = season.encode(encoding=\"utf-8\")\r\n\t\tseason = season.decode(encoding=\"cp1252\", errors=\"ignore\")\r\n\r\n\t\treturn season","def path_helper(location, date, time, slc_dir='slc', data_dir='/media/bup/Data'):\n\n base_folder = data_dir + '/' + location + '/' + date + '/'\n name = date + '_' + time\n def_path = base_folder + slc_dir + '/' + name\n return def_path","def common_folder_path(\n folder: str,\n ) -> str:\n return \"folders/{folder}\".format(\n folder=folder,\n )","def name(self):\n return self.attributes.workspace.name","def get_path(network):\n\n # Just verify wich network was passed to the function and address the right path\n if network == 'vgg16':\n path = 'experiments/vgg-16/'\n\n elif network == 'smallnet':\n path = 'experiments/smallnet/'\n\n elif network == 'inception_v3':\n path = 'experiments/inception_v3/'\n\n elif network == 'c3d':\n path = 'experiments/c3d/'\n\n elif network == 'dense':\n path = 'experiments/dense/'\n\n elif network == 'lstm':\n path = 'experiments/lstm/'\n\n else:\n raise ValueError('Network \"%s\" not found!' % (network))\n\n return path","def name(self):\n return self.path.stem","def create_experiment_folder(path_out, dir_name, name='', stamp_unique=True):\n if not os.path.isdir(path_out):\n raise FileNotFoundError('missing base folder \"%s\"' % path_out)\n date = time.gmtime()\n if isinstance(name, str) and name:\n dir_name = '%s_%s' % (dir_name, name)\n # if you require time stamp\n if stamp_unique:\n path_stamp = time.strftime(FORMAT_DATE_TIME, date)\n # prepare experiment path with initial timestamp - now\n path_exp = os.path.join(path_out, '%s_%s' % (dir_name, path_stamp))\n if os.path.isdir(path_exp):\n logging.warning('particular out folder already exists')\n path_exp += '-' + str(uuid.uuid4().hex)\n else:\n path_exp = os.path.join(path_out, dir_name)\n path_created = create_folder(path_exp, ok_existing=False)\n logging.info('created experiment folder \"%r\"', path_created)\n return path_exp","def folder(self) -> Optional[pulumi.Input['ChangeDataCaptureFolderArgs']]:\n return pulumi.get(self, \"folder\")","def get_name(name):\n return \"/\".join([settings.CONF['path'], name])","def step_param(self):\n if self.variable_name is None:\n return self.step_name\n elif self.step_name is None:\n return self.variable_name\n else:\n return '{step}__{var}'.format(\n step=self.step_name, var=self.variable_name)","def _directory_subdir(hemi, month_num):\n month_dir = '{:02}_{}'.format(month_num, cal.month_abbr[month_num])\n return os.path.join(nt.by_name(hemi)['long_name'],\n 'monthly', month_dir)","def root_name(file_name, file_id):\n if file_id is not None:\n return \"{}{}\".format(R_DIR, file_name.format(file_id))\n else:\n return \"{}{}\".format(R_DIR, file_name)","def log_dir_name(learning_rate, dense_layers, nodes, activation):\t\n\t# the dir-name for the TensorBoard log-dir\n\ts = \"./2_logs/lr_{0:.0e}_layers{1}_nodes{2}_{3}/\"\n\tlog_dir = s.format(learning_rate, dense_layers, nodes, activation)\n\n\treturn log_dir"],"string":"[\n \"def folder(self, step=None):\\n if step is None:\\n return self._obs_group_folder / self._obs_folder\\n else:\\n return Path(step) / self._obs_group_folder / self._obs_folder\",\n \"def folder(self, step=None):\\n if step is None:\\n return self._obs_group_folder / self._obs_folder\\n else:\\n return Path(step) / self._obs_group_folder / self._obs_folder\",\n \"def folder(self, step=None):\\n if step is None:\\n return self._obs_group_folder / self._obs_folder\\n else:\\n return Path(step) / self._obs_group_folder / self._obs_folder\",\n \"def simulation_dir(self):\\n try:\\n return (self.output_directory / self.sim_id).expand()\\n except AttributeError:\\n return Path()\",\n \"def folder(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"folder\\\")\",\n \"def folder(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"folder\\\")\",\n \"def create_summary_folder_name():\\n dir_str = '' # ADD PATH\\n time_stamp_str = time.strftime(\\\"%a, %d %b %Y %H:%M:%S/\\\", time.gmtime())\\n param_str = ''\\n return dir_str + time_stamp_str + param_str\",\n \"def name_folder_data(data):\\n string = 'results/S0(%s)_sig(%s)_r(%s)_T(%s)_K(%s)' % data\\n return string\",\n \"def media_folder_name(self):\\n raise NotImplementedError\",\n \"def folder(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"folder\\\")\",\n \"def folder(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"folder\\\")\",\n \"def get_folder():\\n return input(\\\"Folder: \\\")\",\n \"def shpname(self):\\n _, tail = os.path.split(self.url)\\n return self.folder + ('/' + tail[:-4]) * 2\",\n \"def gen_folder(base, e, f, a, k):\\n \\n return base + 'eps_' + str(e) + '_fp_' + str(f) + \\\\\\n '_areak_' + str(a) + '_kappa_' + str(k)\",\n \"def obsfoldername(self, folder_name_beamctl_type=True, source_name=None):\\n if not folder_name_beamctl_type:\\n obsextname = self.filenametime\\n obsextname += \\\"_\\\" + self.ldat_type\\n else:\\n self.source_name = source_name\\n obsextname = obsfileinfo2filefolder(vars(self))\\n return obsextname\",\n \"def Directory(self) -> str:\",\n \"def folder_runnum():\\n now = datetime.datetime.now()\\n runnum = 1\\n while True:\\n folder_name = f\\\"astroNN_{now.month:0{2}d}{now.day:0{2}d}_run{runnum:0{3}d}\\\"\\n if not os.path.exists(folder_name):\\n break\\n else:\\n runnum += 1\\n\\n return folder_name\",\n \"def set_folder_name(self, folder_name=None):\\n\\n if folder_name == None:\\n folder_name = 'Abstract-OneDim/'\\n self.params['folder_name'] = folder_name\\n else:\\n self.params['folder_name'] = folder_name\\n print 'Folder name:', self.params['folder_name']\",\n \"def get_directory() -> str:\\n return directory\",\n \"def world_name(self) -> str:\\n return os.path.basename(self.path)\",\n \"def get_directory(self):\\n path = os.path.join(settings.SUBMISSION_DIR, \\n str(self.course.semester), \\n str(self.course.code), \\n \\\"Sec.%d\\\" % self.course.section,\\n self.code)\\n return path.replace(\\\" \\\", \\\"_\\\")\",\n \"def getFolderPath(self) -> unicode:\\n ...\",\n \"def root_folder_name(asmtSubmission):\\n\\n userID = asmtSubmission.by.djangoUser.username\\n\\n # If multiple submissions permitted, need to be able to distinguish\\n # So add the current time down to the second.\\n # We are not using assignment UUID because that truncates the file name.\\n userID = userID + \\\"_\\\" + strftime(\\\"%Y-%m-%d_%H-%M-%S\\\")\\n\\n return userID\",\n \"def logdir(self):\\n return osp.join('runs/', self.net_name, '')\",\n \"def name_final_path(out_img_folder):\\n if out_img_folder == None:\\n return \\\"./.out_hidden_images\\\"\\n else:\\n return out_img_folder\",\n \"def get(self):\\n return self.directory_name\",\n \"def create_savename(self):\\n \\n savename = self.config.get('grid', 'dir') + self.fname.split('/')[-1]\\n newsuffix = '_gridded_%ix%ix%i.nc' % (self.nx, self.ny, self.nz)\\n savename = savename.replace('.nc', newsuffix)\\n \\n return savename\",\n \"def create_out_dir_name(params):\\n\\n current_timestamp = timestamp()\\n out_dir = os.path.join('out', current_timestamp)\\n return out_dir\",\n \"def get_output_folder_name(argi=2, root_folder=\\\"\\\"):\\n # First tries to read the output folder name from argv[2]\\n try:\\n output_folder = sys.argv[argi]\\n except IndexError:\\n # If argv[argi] was not passed, asks the user for the output folder.\\n output_folder = root_folder\\n output_folder += input(\\\"Output folder path was not informed. Please inform:\\\\n\\\"\\n \\\"{}\\\".format(root_folder))\\n\\n # Adds the SEP (/ or \\\\\\\\) character to the end of the folder name.\\n if output_folder[-len(SEP):] != SEP:\\n output_folder += SEP\\n\\n # Checks if the folder does not exist. Creates it, in this case.\\n if not os.path.exists(output_folder):\\n os.system(\\\"mkdir -p '{}'\\\".format(output_folder))\\n\\n return output_folder\",\n \"def get_res_dir_name(random, nx=None, ny=None, p_state=None, p_path=None,\\n **kws):\\n\\n if random:\\n fdir = 'random_env/'\\n env_dir = 'nx{}_ny{}_pstate{}_ppath{}'.format(nx, ny, p_state, p_path)\\n fdir += utils.format_to_fname(env_dir) + '/'\\n else:\\n fdir = 'village/'\\n\\n return fdir\",\n \"def directory_id(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"directory_id\\\")\",\n \"def _get_station_filename():\\n output_dir = os.path.join(output, state, station)\\n if not os.path.isdir(output_dir):\\n logger.debug(\\\"Creating directory %s\\\", output_dir)\\n os.makedirs(output_dir)\\n return os.path.join(output_dir, \\\"%s.%s\\\" % (c_time, format))\",\n \"def _get_models_name(data_dir):\\n call_folders = [x[0] for x in os.walk(data_dir)][1::]\\n folder = []\\n for i in call_folders:\\n temp = i.split('\\\\\\\\')\\n folder.append(temp[1])\\n print('The models to ensemble are {}'.format(folder))\\n return folder\",\n \"def absolute_folder_name(self):\\n return 'music_decompose/media/{0}'.format(self.media_folder_name)\",\n \"def workspace_name(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"workspace_name\\\")\",\n \"def workspace_name(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"workspace_name\\\")\",\n \"def workspace_name(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"workspace_name\\\")\",\n \"def get_directory(self, subdir=None):\\n path = settings.SUBMISSION_DIR / str(self.assignment.id) / str(self.id)\\n if subdir:\\n path = path / subdir\\n\\n return path\",\n \"def folder(self):\\n return os.path.join(\\n td.project.folder.replace(\\\"/\\\", os.sep), # incase we're on windows\\n \\\"component_repos\\\",\\n self.name)\",\n \"def directory_id(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"directory_id\\\")\",\n \"def common_folder_path(folder: str,) -> str:\\n return \\\"folders/{folder}\\\".format(folder=folder,)\",\n \"def get_created_adjustment_folder_name(self):\\n global adjustment_folder_name\\n return adjustment_folder_name\",\n \"def compute_model_name(self, params, folder=None):\\n folder = folder or self.output_dir\\n\\n prefix = os.path.basename(params['graph'])\\n name = \\\"_\\\".join([\\\"{}-{}\\\".format(k, v) \\n for k, v in sorted(params.items())\\n if k not in [\\\"graph\\\"]])\\n\\n model_name = '-'.join([prefix, name])\\n full_name = os.path.join(folder, model_name)\\n\\n return full_name\",\n \"def dir_name(self):\\n return self._dir\",\n \"def path(self, prefix, args=()):\\n dir_path = self.dir.path(prefix, args)\\n return os.path.join(dir_path, self.name)\",\n \"def directory(self) -> str:\\n return self._values.get(\\\"directory\\\")\",\n \"def use_name(self):\\n projection = proj.get_projection(self.conf.projections, self.spec)\\n if not projection:\\n projection = self.conf.default_projections[\\\"all\\\"]\\n\\n name = self.spec.format(projection)\\n # Not everybody is working on linux...\\n parts = name.split(\\\"/\\\")\\n name = os.path.join(*parts)\\n # Add optional suffixes based on constraints\\n path_elements = [name] + self.conf.suffixes\\n return \\\"-\\\".join(path_elements)\",\n \"def folder_name(self): \\n folders = []\\n for folder in self.folders:\\n folders.append(folder)\\n return folders\",\n \"def get_root():\\n\\n return 'data/simulators/mg1'\",\n \"def folder_str(f):\\n if not os.path.exists(f):\\n raise argparse.ArgumentTypeError('\\\"%s\\\" does not exist, you must create this folder' % f)\\n return f\",\n \"def get_name(self):\\n return self.client._perform_json(\\\"GET\\\", \\\"/project-folders/%s\\\" % self.project_folder_id).get(\\\"name\\\", None)\",\n \"def get_data_output_name(prefix: str):\\n return settings.data_dir + 'standardized_data_' + prefix\",\n \"def create_experiment_folder(path_out, dir_name, name='', stamp_unique=True):\\n assert os.path.exists(path_out), 'missing base folder \\\"%s\\\"' % path_out\\n date = time.gmtime()\\n if isinstance(name, str) and name:\\n dir_name = '{}_{}'.format(dir_name, name)\\n path_exp = os.path.join(path_out, dir_name)\\n if stamp_unique:\\n path_exp += '_' + time.strftime(FORMAT_DATE_TIME, date)\\n path_created = None\\n while not path_created:\\n logging.warning('particular out folder already exists')\\n if path_created is not None:\\n path_exp += '-' + str(np.random.randint(0, 100))\\n path_created = create_folder(path_exp, ok_existing=False)\\n else:\\n path_created = create_folder(path_exp, ok_existing=False)\\n logging.info('created experiment folder \\\"%r\\\"', path_created)\\n return path_exp\",\n \"def path_name(self):\",\n \"def directory_name(initial_name):\\n import string\\n allowed_chars = string.digits + string.ascii_letters + \\\" _.\\\"\\n result_name = \\\"\\\"\\n for ch in initial_name:\\n if allowed_chars.find(ch) != -1:\\n result_name += ch\\n return result_name if result_name != \\\"\\\" else \\\"course_folder\\\"\",\n \"def _dir_out(self):\\n ens_label = utils.io.ens_label(self.ens_mem)\\n return os.path.join(self.proj.direc_out, self.proj.name,\\n self.model.name, self.run.name,\\n ens_label, self.name)\",\n \"def name(self):\\n try:\\n return self._name\\n except AttributeError:\\n if self.is_task:\\n try:\\n return self.pos_str\\n except:\\n return os.path.basename(self.workdir)\\n else:\\n return os.path.basename(self.workdir)\",\n \"def getDirectory(nameP:str) -> str:\\n nameObject = list(nameP.split(\\\" \\\"))\\n lengName = len(nameObject)\\n if (lengName >= 2):\\n concat = \\\"\\\"\\n nameProject = (concat.join(nameObject))\\n else:\\n nameProject = nameP\\n\\n lo = os.getcwd()\\n # print(lo)\\n if platform == 'linux' or platform == 'linux2':\\n li = list(lo.split(\\\"/\\\"))\\n var1 = li[0]\\n var2 = li[1]\\n var3 = li[2]\\n pathC = (\\\"{0}/{1}/{2}/Documents/Respaldos/{3}\\\").format(var1, var2, var3, nameProject)\\n return pathC\\n elif platform == 'darwin':\\n li = list(lo.split(\\\"/\\\"))\\n var1 = li[0] \\n var2 = li[1]\\n var3 = li[2]\\n pathC = (\\\"{0}/{1}/{2}/Documents/Respaldos/{3}\\\").format(var1, var2, var3, nameProject)\\n return pathC\\n elif platform == 'win32': \\n li = list(lo.split(\\\"\\\\\\\\\\\"))\\n var1 = li[0]\\n var2 = li[1]\\n var3 = li[2]\\n pathC = (\\\"{0}/{1}/{2}/Documents/Respaldos/{3}\\\").format(var1, var2, var3, nameProject)\\n return pathC\",\n \"def prepare_folder(self) -> str:\\n base_folder = self.config['info']['folder']\\n today_folder = f'{datetime.today():%Y-%m-%d}'\\n folder = os.path.join(base_folder, today_folder)\\n if not os.path.isdir(folder):\\n os.makedirs(folder)\\n return folder\",\n \"def get_scene_folder():\\n\\n flg = logging.getLogger(\\\"lettuce.xgenSetup.get_scene_folder\\\")\\n\\n file_name = mc.file(q=True, sceneName=True)\\n\\n head, tail = os.path.split(file_name)\\n\\n flg.info(\\\"Scene fileName: {}\\\".format(tail))\\n flg.info(\\\"Scene directory: {}\\\".format(head))\\n\\n return head\",\n \"def get_dummy_folder() -> str:\\n dummy_folder = os.path.join(os.getcwd(), 'dummy_test_folder')\\n return dummy_folder\",\n \"def displayname(self):\\n if self.path.is_dir():\\n if (is_uuid(self.path.parts[-1])):\\n self.is_uuid_folder = True\\n return self.path.name + '/'\\n elif is_proj(self.path.parts[-1]):\\n return f'{bcolors.BOLD}' + self.path.name + f'{bcolors.ENDC}'\\n return self.path.name\",\n \"def get_name(self) -> str:\\n return os.path.split(os.getcwd())[-1]\",\n \"def gen_folders(rho, kappa, km, pa, analysis, dbase, analysisdbase):\\n \\n path1 = 'density_' + + str(rho) + \\\"_kappa_\\\" + \\\\\\n str(kappa) + \\\"_km_\\\" + str(km) + \\\"_panti_\\\" + str(pa)\\n path2 = analysis + '_density_' + + str(rho) + \\\"_kappa_\\\" + \\\\\\n str(kappa) + \\\"_km_\\\" + str(km) + \\\"_panti_\\\" + str(pa) + '.txt' \\n datafolder = dbase + path1 + '/'\\n analysisfile = analysisdbase + path2 \\n\\n return datafolder, analysisfile\",\n \"def getFolderItemName(self) -> unicode:\\n ...\",\n \"def folder(self) -> pulumi.Output[Optional['outputs.ChangeDataCaptureResponseFolder']]:\\n return pulumi.get(self, \\\"folder\\\")\",\n \"def set_folder(tipo):\\n loc = path.join(pardir, 'SHOTS')\\n makedirs(loc, exist_ok=True)\\n # 'data_type' is the subfolder, depending on data type. default is a shot.\\n data_type = 'shots'\\n if tipo == 'mirror':\\n data_type = 'tests'\\n elif 'test' in tipo:\\n data_type = 'tests'\\n elif tipo == 'clean':\\n data_type = 'cleaning_plasma'\\n folder = path.join(loc, data_type)\\n makedirs(folder, exist_ok=True)\\n return path.join(loc, data_type)\",\n \"def directory_id(self) -> pulumi.Output[Optional[str]]:\\n return pulumi.get(self, \\\"directory_id\\\")\",\n \"def get_path(self):\\n definition = self.client._perform_json(\\\"GET\\\", \\\"/project-folders/%s\\\" % self.project_folder_id)\\n parent_id = definition.get(\\\"parentId\\\", None)\\n if parent_id is not None:\\n parent = DSSProjectFolder(self.client, parent_id)\\n path = parent.get_path()\\n return (\\\"\\\" if path == \\\"/\\\" else path) + \\\"/\\\" + definition.get(\\\"name\\\", \\\"\\\")\\n else:\\n return \\\"/\\\"\",\n \"def _get_subdir(self, dmc, year, trigger, pt):\\n full_trigger = get_full_trigger(trigger)\\n return '/'.join([dmc, str(year), full_trigger, str(pt)])\",\n \"def get_sim_file(cls, root_dir, constants):\\n return os.path.join(root_dir, '%s.hdf5' % constants['mos_type'])\",\n \"def get_sim_file(cls, root_dir, constants):\\n return os.path.join(root_dir, '%s.hdf5' % constants['mos_type'])\",\n \"def get_directory(self) -> Path:\\n path = Path(str(self.max_nesting_lvl))\\n path /= str(self.num_snps)\\n path /= self.read_quality\\n path /= str(self.coverage)\\n path /= str(self.denovo_kmer_size)\\n return path\",\n \"def get_replay_folder(self):\\n\\n images_with_labels_folder = self.base_folder_path + \\\"/data/replay_images_and_labels/\\\"\\n return images_with_labels_folder\",\n \"def generate_dir_soolin(channel, energy=13):\\n uid = getpass.getuser()\\n return \\\"/hdfs/user/%s/NMSSMPheno/MG5_aMC/%s\\\" % (uid, generate_subdir(channel, energy))\",\n \"def path(name, folder= \\\"~/data/LJSpeech-1.0\\\"):\\n return join(expanduser(folder), \\\"wavs\\\", name + \\\".wav\\\")\",\n \"def get_directory_names():\\n prefix = FLAGS.dataset + \\\"-\\\" + FLAGS.uid + \\\"-\\\" + FLAGS.method\\n\\n # Use the number specified on the command line (higher precedence than --debug)\\n if FLAGS.debugnum >= 0:\\n attempt = FLAGS.debugnum\\n print(\\\"Debugging attempt:\\\", attempt)\\n\\n prefix += \\\"-\\\" + str(attempt)\\n model_dir = os.path.join(FLAGS.modeldir, prefix)\\n log_dir = os.path.join(FLAGS.logdir, prefix)\\n # Find last one, increment number\\n elif FLAGS.debug:\\n attempt = file_utils.last_modified_number(FLAGS.logdir, prefix + \\\"*\\\")\\n attempt = attempt + 1 if attempt is not None else 1\\n print(\\\"Debugging attempt:\\\", attempt)\\n\\n prefix += \\\"-\\\" + str(attempt)\\n model_dir = os.path.join(FLAGS.modeldir, prefix)\\n log_dir = os.path.join(FLAGS.logdir, prefix)\\n # If no debugging modes, use the model and log directory with only the \\\"prefix\\\"\\n # (even though it's not actually a prefix in this case, it's the whole name)\\n elif FLAGS.subdir:\\n model_dir = os.path.join(FLAGS.modeldir, prefix)\\n log_dir = os.path.join(FLAGS.logdir, prefix)\\n else:\\n model_dir = FLAGS.modeldir\\n log_dir = FLAGS.logdir\\n\\n return model_dir, log_dir\",\n \"def dir(self) -> str:\\n return f'{os.path.dirname(self.path)}/'.lstrip('/')\",\n \"def get_path(\\n layer: LayerType,\\n batch_size: int,\\n num_runs: int,\\n num_repeats: int,\\n gsm_mode: str,\\n random_seed: Optional[int] = None,\\n forward_only: bool = False,\\n root: str = \\\"./results/raw/\\\",\\n suffix: str = \\\"\\\",\\n) -> str:\\n pickle_name = f\\\"{layer}_mode_{gsm_mode}_bs_{batch_size}_runs_{num_runs}_repeats_{num_repeats}_seed_{random_seed}\\\"\\n if forward_only:\\n pickle_name += \\\"_forward_only\\\"\\n\\n if len(suffix) and not suffix.startswith(\\\"_\\\"):\\n suffix = f\\\"_{suffix}\\\"\\n\\n return f\\\"{root}{pickle_name}{suffix}.pkl\\\"\",\n \"def working_directory(self):\\n project_full_path = \\\"/\\\".join(self.file_name.split(\\\"/\\\")[:-1])\\n file_name = self.file_name.split(\\\"/\\\")[-1]\\n if \\\".h5\\\" in file_name:\\n file_name = file_name.split(\\\".h5\\\")[0]\\n file_name += \\\"_hdf5\\\"\\n if self.h5_path[0] == \\\"/\\\":\\n h5_path = self.h5_path[1:]\\n else:\\n h5_path = self.h5_path\\n return posixpath.join(project_full_path, file_name, h5_path)\",\n \"def data_abex_results_iteration_dir(experiment_name: str, iteration: int) -> Path: # pragma: no cover\\n return data_abex_results_dir(experiment_name) / iteration_name(iteration)\",\n \"def getFSGroupDir(self, desired=None):\\n role = self.getRole(desired)\\n groupDir = self.config.get(\\\"FileMan\\\",\\\"homedir\\\") + role[\\\"roleName\\\"]\\n return groupDir\",\n \"def get_folder(self):\\n return os.path.join(\\n settings.PRIVATE_STORAGE_ROOT, Exam.EXAM_FILES_LOCATION,\\n str(self.unique_id)[0:2])\",\n \"def song_directory_path(instance, filename):\\n return f\\\"music/songs/{instance.id}_{instance.name}_{filename}\\\"\",\n \"def GcsDirname(path):\\n return os.path.dirname(path)\",\n \"def get_dag_directory(self) -> str:\\n if isinstance(self._dag_directory, Path):\\n return str(self._dag_directory.resolve())\\n else:\\n return str(self._dag_directory)\",\n \"def experiment_dir(experiment_name: str) -> Path: # pragma: no cover\\n return EXPERIMENTS_DIR / experiment_name\",\n \"def season_folder(cls, season):\\r\\n\\r\\n\\t\\t'''# Google Drive downloads replace these characters automatically\\r\\n\\t\\t# I'm implementing this in the code as well for convenience\\r\\n\\t\\tseason = season.replace(\\\"&\\\", \\\"_\\\")\\r\\n\\t\\tseason = season.replace(\\\"'\\\", \\\"_\\\")'''\\r\\n\\r\\n\\t\\t# Folder names are ANSI versions of the season name\\r\\n\\t\\t# This is important in names like \\\"Lé Unicorn\\\" which get\\r\\n\\t\\t# converted incorrectly as folder names\\r\\n\\t\\tseason = season.encode(encoding=\\\"utf-8\\\")\\r\\n\\t\\tseason = season.decode(encoding=\\\"cp1252\\\", errors=\\\"ignore\\\")\\r\\n\\r\\n\\t\\treturn season\",\n \"def path_helper(location, date, time, slc_dir='slc', data_dir='/media/bup/Data'):\\n\\n base_folder = data_dir + '/' + location + '/' + date + '/'\\n name = date + '_' + time\\n def_path = base_folder + slc_dir + '/' + name\\n return def_path\",\n \"def common_folder_path(\\n folder: str,\\n ) -> str:\\n return \\\"folders/{folder}\\\".format(\\n folder=folder,\\n )\",\n \"def name(self):\\n return self.attributes.workspace.name\",\n \"def get_path(network):\\n\\n # Just verify wich network was passed to the function and address the right path\\n if network == 'vgg16':\\n path = 'experiments/vgg-16/'\\n\\n elif network == 'smallnet':\\n path = 'experiments/smallnet/'\\n\\n elif network == 'inception_v3':\\n path = 'experiments/inception_v3/'\\n\\n elif network == 'c3d':\\n path = 'experiments/c3d/'\\n\\n elif network == 'dense':\\n path = 'experiments/dense/'\\n\\n elif network == 'lstm':\\n path = 'experiments/lstm/'\\n\\n else:\\n raise ValueError('Network \\\"%s\\\" not found!' % (network))\\n\\n return path\",\n \"def name(self):\\n return self.path.stem\",\n \"def create_experiment_folder(path_out, dir_name, name='', stamp_unique=True):\\n if not os.path.isdir(path_out):\\n raise FileNotFoundError('missing base folder \\\"%s\\\"' % path_out)\\n date = time.gmtime()\\n if isinstance(name, str) and name:\\n dir_name = '%s_%s' % (dir_name, name)\\n # if you require time stamp\\n if stamp_unique:\\n path_stamp = time.strftime(FORMAT_DATE_TIME, date)\\n # prepare experiment path with initial timestamp - now\\n path_exp = os.path.join(path_out, '%s_%s' % (dir_name, path_stamp))\\n if os.path.isdir(path_exp):\\n logging.warning('particular out folder already exists')\\n path_exp += '-' + str(uuid.uuid4().hex)\\n else:\\n path_exp = os.path.join(path_out, dir_name)\\n path_created = create_folder(path_exp, ok_existing=False)\\n logging.info('created experiment folder \\\"%r\\\"', path_created)\\n return path_exp\",\n \"def folder(self) -> Optional[pulumi.Input['ChangeDataCaptureFolderArgs']]:\\n return pulumi.get(self, \\\"folder\\\")\",\n \"def get_name(name):\\n return \\\"/\\\".join([settings.CONF['path'], name])\",\n \"def step_param(self):\\n if self.variable_name is None:\\n return self.step_name\\n elif self.step_name is None:\\n return self.variable_name\\n else:\\n return '{step}__{var}'.format(\\n step=self.step_name, var=self.variable_name)\",\n \"def _directory_subdir(hemi, month_num):\\n month_dir = '{:02}_{}'.format(month_num, cal.month_abbr[month_num])\\n return os.path.join(nt.by_name(hemi)['long_name'],\\n 'monthly', month_dir)\",\n \"def root_name(file_name, file_id):\\n if file_id is not None:\\n return \\\"{}{}\\\".format(R_DIR, file_name.format(file_id))\\n else:\\n return \\\"{}{}\\\".format(R_DIR, file_name)\",\n \"def log_dir_name(learning_rate, dense_layers, nodes, activation):\\t\\n\\t# the dir-name for the TensorBoard log-dir\\n\\ts = \\\"./2_logs/lr_{0:.0e}_layers{1}_nodes{2}_{3}/\\\"\\n\\tlog_dir = s.format(learning_rate, dense_layers, nodes, activation)\\n\\n\\treturn log_dir\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.67199093","0.67199093","0.67199093","0.6616217","0.656852","0.6482704","0.6465053","0.62389404","0.62153554","0.615684","0.615684","0.6155915","0.6129946","0.61195314","0.6086348","0.6066995","0.60657847","0.6050361","0.5979955","0.58812314","0.5849302","0.5787726","0.5781995","0.5774175","0.57503974","0.5748798","0.57204646","0.56990576","0.5698982","0.5697836","0.5682192","0.5679128","0.56749624","0.5666471","0.5661427","0.5661427","0.5661427","0.5646803","0.5628245","0.5621009","0.562046","0.56203216","0.56095773","0.5602906","0.55952555","0.55805016","0.55768484","0.5555749","0.55529696","0.55516046","0.55511355","0.55443734","0.5542208","0.5529539","0.5514689","0.5512961","0.5503393","0.5496223","0.5494444","0.5493568","0.5490586","0.54868823","0.5482387","0.54550964","0.5450656","0.54495895","0.5448713","0.5448402","0.54404366","0.5434719","0.54196787","0.54196787","0.54150087","0.5407906","0.5400207","0.5396948","0.539088","0.53834826","0.53820443","0.53749263","0.5369115","0.5357478","0.5356796","0.5352794","0.53495467","0.5349518","0.5340856","0.5335227","0.5332413","0.53257513","0.53245926","0.5322627","0.5314949","0.53141594","0.5304142","0.5302019","0.52882177","0.5284552","0.52784985","0.5275186"],"string":"[\n \"0.67199093\",\n \"0.67199093\",\n \"0.67199093\",\n \"0.6616217\",\n \"0.656852\",\n \"0.6482704\",\n \"0.6465053\",\n \"0.62389404\",\n \"0.62153554\",\n \"0.615684\",\n \"0.615684\",\n \"0.6155915\",\n \"0.6129946\",\n \"0.61195314\",\n \"0.6086348\",\n \"0.6066995\",\n \"0.60657847\",\n \"0.6050361\",\n \"0.5979955\",\n \"0.58812314\",\n \"0.5849302\",\n \"0.5787726\",\n \"0.5781995\",\n \"0.5774175\",\n \"0.57503974\",\n \"0.5748798\",\n \"0.57204646\",\n \"0.56990576\",\n \"0.5698982\",\n \"0.5697836\",\n \"0.5682192\",\n \"0.5679128\",\n \"0.56749624\",\n \"0.5666471\",\n \"0.5661427\",\n \"0.5661427\",\n \"0.5661427\",\n \"0.5646803\",\n \"0.5628245\",\n \"0.5621009\",\n \"0.562046\",\n \"0.56203216\",\n \"0.56095773\",\n \"0.5602906\",\n \"0.55952555\",\n \"0.55805016\",\n \"0.55768484\",\n \"0.5555749\",\n \"0.55529696\",\n \"0.55516046\",\n \"0.55511355\",\n \"0.55443734\",\n \"0.5542208\",\n \"0.5529539\",\n \"0.5514689\",\n \"0.5512961\",\n \"0.5503393\",\n \"0.5496223\",\n \"0.5494444\",\n \"0.5493568\",\n \"0.5490586\",\n \"0.54868823\",\n \"0.5482387\",\n \"0.54550964\",\n \"0.5450656\",\n \"0.54495895\",\n \"0.5448713\",\n \"0.5448402\",\n \"0.54404366\",\n \"0.5434719\",\n \"0.54196787\",\n \"0.54196787\",\n \"0.54150087\",\n \"0.5407906\",\n \"0.5400207\",\n \"0.5396948\",\n \"0.539088\",\n \"0.53834826\",\n \"0.53820443\",\n \"0.53749263\",\n \"0.5369115\",\n \"0.5357478\",\n \"0.5356796\",\n \"0.5352794\",\n \"0.53495467\",\n \"0.5349518\",\n \"0.5340856\",\n \"0.5335227\",\n \"0.5332413\",\n \"0.53257513\",\n \"0.53245926\",\n \"0.5322627\",\n \"0.5314949\",\n \"0.53141594\",\n \"0.5304142\",\n \"0.5302019\",\n \"0.52882177\",\n \"0.5284552\",\n \"0.52784985\",\n \"0.5275186\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.659045"},"document_rank":{"kind":"string","value":"4"}}},{"rowIdx":22485,"cells":{"query":{"kind":"string","value":"Return folder name for simulation results."},"document":{"kind":"string","value":"def get_res_dir_name(random, nx=None, ny=None, p_state=None, p_path=None,\n **kws):\n\n if random:\n fdir = 'random_env/'\n env_dir = 'nx{}_ny{}_pstate{}_ppath{}'.format(nx, ny, p_state, p_path)\n fdir += utils.format_to_fname(env_dir) + '/'\n else:\n fdir = 'village/'\n\n return fdir"},"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 folder(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"folder\")","def simulation_dir(self):\n try:\n return (self.output_directory / self.sim_id).expand()\n except AttributeError:\n return Path()","def name_folder_data(data):\n string = 'results/S0(%s)_sig(%s)_r(%s)_T(%s)_K(%s)' % data\n return string","def dir_results(assignment, user):\n return os.path.join(repository, assignment, user, 'results')","def data_abex_results_iteration_dir(experiment_name: str, iteration: int) -> Path: # pragma: no cover\n return data_abex_results_dir(experiment_name) / iteration_name(iteration)","def folder(self, step=None):\n if step is None:\n return self._obs_group_folder / self._obs_folder\n else:\n return Path(step) / self._obs_group_folder / self._obs_folder","def folder(self, step=None):\n if step is None:\n return self._obs_group_folder / self._obs_folder\n else:\n return Path(step) / self._obs_group_folder / self._obs_folder","def folder(self, step=None):\n if step is None:\n return self._obs_group_folder / self._obs_folder\n else:\n return Path(step) / self._obs_group_folder / self._obs_folder","def create_summary_folder_name():\n dir_str = '' # ADD PATH\n time_stamp_str = time.strftime(\"%a, %d %b %Y %H:%M:%S/\", time.gmtime())\n param_str = ''\n return dir_str + time_stamp_str + param_str","def results_directory(self, run_config):\n suite_dir_name = '{}_{}'.format(run_config['test_suite_start_time'],\n run_config['test_id'])\n datetime_str = datetime.datetime.now().strftime('%Y%m%dT%H%M%S')\n test_result_dir = '{}'.format(datetime_str)\n result_dir = os.path.join(self.workspace, 'results', suite_dir_name,\n test_result_dir)\n\n # Creates workspace and default log folder\n if not os.path.exists(result_dir):\n print('Making results directory:{}'.format(result_dir))\n os.makedirs(result_dir)\n\n return result_dir","def folder(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"folder\")","def get_resultdir(project_obj, result_folder):\n description = project_obj.description.replace(' ', '') # remove any spaces in the project name\n return 'documents/%s/%s/reports/%s' % (str(project_obj.date.date()), description, result_folder)","def _get_results_path(self):\n # if we already have the results path set, please return it\n if self._results_path is not None:\n return self._results_path\n\n self._validate_results_path()\n\n path = self.results_path\n\n if path is None:\n for i in range(1, 10001):\n name = f\"AutoML_{i}\"\n if not os.path.exists(name):\n self.create_dir(name)\n self._results_path = name\n return name\n # If it got here, could not create, raise expection\n raise AutoMLException(\"Cannot create directory for AutoML results\")\n elif os.path.exists(self.results_path) and os.path.exists(\n os.path.join(self.results_path, \"params.json\")\n ): # AutoML already loaded, return path\n self._results_path = path\n return path\n # Dir does not exist, create it\n elif not os.path.exists(path):\n self.create_dir(path)\n self._results_path = path\n return path\n # Dir exists and is empty, use it\n elif os.path.exists(path) and not len(os.listdir(path)):\n self._results_path = path\n return path\n elif os.path.exists(path) and len(os.listdir(path)):\n raise AutoMLException(\n f\"Cannot set directory for AutoML. Directory '{path}' is not empty.\"\n )\n\n raise AutoMLException(\"Cannot set directory for AutoML results\")","def media_folder_name(self):\n raise NotImplementedError","def Directory(self) -> str:","def get_directory(self):\n path = os.path.join(settings.SUBMISSION_DIR, \n str(self.course.semester), \n str(self.course.code), \n \"Sec.%d\" % self.course.section,\n self.code)\n return path.replace(\" \", \"_\")","def get_output_folder(self):\n return os.path.join(self.root_output_folder, self.base_fish_folder)","def _output_path(name):\n output = Path(\"../Analysis Results/\").joinpath(str(date.today()))\n output.mkdir(parents=True, exist_ok=True)\n return output.joinpath(f\"{name}.png\")","def _dir_out(self):\n ens_label = utils.io.ens_label(self.ens_mem)\n return os.path.join(self.proj.direc_out, self.proj.name,\n self.model.name, self.run.name,\n ens_label, self.name)","def build_result_folder(timestamp=str(int(time.time()))):\n out_path = os.path.abspath(os.path.join(os.path.curdir, \"runs\", timestamp))\n print(\"Writing to {}\\n\".format(out_path))\n\n data_path = os.path.abspath(os.path.join(out_path, \"data\"))\n evaluation_path = os.path.abspath(os.path.join(out_path, \"evaluation\"))\n\n if not os.path.exists(out_path):\n os.makedirs(data_path)\n os.makedirs(evaluation_path)\n return out_path","def folder(self) -> pulumi.Output[Optional['outputs.ChangeDataCaptureResponseFolder']]:\n return pulumi.get(self, \"folder\")","def folder_name(self): \n folders = []\n for folder in self.folders:\n folders.append(folder)\n return folders","def folder_runnum():\n now = datetime.datetime.now()\n runnum = 1\n while True:\n folder_name = f\"astroNN_{now.month:0{2}d}{now.day:0{2}d}_run{runnum:0{3}d}\"\n if not os.path.exists(folder_name):\n break\n else:\n runnum += 1\n\n return folder_name","def data_abex_results_dir(experiment_name: str) -> Path: # pragma: no cover\n return experiment_dir(experiment_name) / \"Results\"","def getOutputFileName(aDirectory, aSelenium, aSuite):\n\n name = \\\n aSelenium.mOperatingSystem + \"_\" + \\\n aSelenium.mBrowser + \"_\" + \\\n aSelenium.mBrowserMode + \"_\" + \\\n aSelenium.mFont + \"_\" + \\\n aSelenium.mOutputJax\n\n if resultsExist(aDirectory + name):\n i = 1\n while (resultsExist(aDirectory + name + \"-\" + str(i)) and\n i < MAX_TEST_RESULTS_WITH_SAME_NAME):\n i = i + 1\n name += \"-\" + str(i)\n\n if (aSuite.mTaskHandler != None):\n sendOutputFileName(name);\n\n return name","def get_directory() -> str:\n return directory","def folder(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"folder\")","def folder(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"folder\")","def getFolderPath(self) -> unicode:\n ...","def logdir(self):\n return osp.join('runs/', self.net_name, '')","def get_replay_folder(self):\n\n images_with_labels_folder = self.base_folder_path + \"/data/replay_images_and_labels/\"\n return images_with_labels_folder","def out_dir(self) -> str:\n return self._out_dir","def shpname(self):\n _, tail = os.path.split(self.url)\n return self.folder + ('/' + tail[:-4]) * 2","def _get_models_name(data_dir):\n call_folders = [x[0] for x in os.walk(data_dir)][1::]\n folder = []\n for i in call_folders:\n temp = i.split('\\\\')\n folder.append(temp[1])\n print('The models to ensemble are {}'.format(folder))\n return folder","def directory(self) -> str:\n return self._values.get(\"directory\")","def create_output_dir(self, cfg: dict) -> str:\n output_dir = cfg.get(\"output\").get(\"output_dir\")\n time_sfx = cfg.get(\"output\").get(\"time_suffix\", True)\n if not os.path.isabs(output_dir):\n output_dir = os.path.join(self.repo_path, output_dir)\n subdir = self.project_name\n if time_sfx:\n cur_time = get_cur_time_str()\n subdir = f\"{subdir}_{cur_time}\"\n output_dir = os.path.join(output_dir, subdir) # type: str\n if check_dir(output_dir, make_if_not=True):\n logger.info(\"Results will be in {}\".format(output_dir))\n else:\n exit(ErrorCode.PATH_ERROR)\n return output_dir","def name_final_path(out_img_folder):\n if out_img_folder == None:\n return \"./.out_hidden_images\"\n else:\n return out_img_folder","def get(self):\n return self.directory_name","def get_results_directory(self, high_level_directory):\n batch_num_dir_name = \"BN{}\".format(self.config_manager.get(ConfigManager.CONFIG_BATCH_MO_NUMBER))\n serial_num_dir_name = \"SN{}-{}\".format(self.config_manager.get(ConfigManager.CONFIG_BATCH_MO_NUMBER),\n self.config_manager.get(ConfigManager.CONFIG_SERIAL_NUMBER))\n results_path = os.path.join(high_level_directory, batch_num_dir_name, serial_num_dir_name)\n os.makedirs(results_path, exist_ok=True, mode=0o660)\n \n return results_path","def results_filename(self): # FIXME: was called output_name in util\n assert self.filename.endswith(\".krun\")\n return self.filename[:-5] + \"_results.json.bz2\"","def get_name(self):\n return self.client._perform_json(\"GET\", \"/project-folders/%s\" % self.project_folder_id).get(\"name\", None)","def outputs(self):\n return {\"path_to_result_folder\": File_IO(\n self.node.outputs[0])}","def results(results_dir):\n\n if not isinstance(results_dir, str):\n raise TypeError(\"results_dir must be a string!\")\n\n if not os.path.exists(results_dir):\n os.makedirs(results_dir)\n\n return results_dir","def obsfoldername(self, folder_name_beamctl_type=True, source_name=None):\n if not folder_name_beamctl_type:\n obsextname = self.filenametime\n obsextname += \"_\" + self.ldat_type\n else:\n self.source_name = source_name\n obsextname = obsfileinfo2filefolder(vars(self))\n return obsextname","def test_get_result_directory(self):\n pass","def get_output_folder(parent_dir, env_name):\n os.makedirs(parent_dir, exist_ok=True)\n experiment_id = 0\n for folder_name in os.listdir(parent_dir):\n if not os.path.isdir(os.path.join(parent_dir, folder_name)):\n continue\n try:\n folder_name = int(folder_name.split('-evaluate')[-1])\n if folder_name > experiment_id:\n experiment_id = folder_name\n except:\n pass\n experiment_id += 1\n\n parent_dir = os.path.join(parent_dir, env_name)\n parent_dir = parent_dir + '-evaluate{}'.format(experiment_id)\n return parent_dir","def get_folder(self):\n return os.path.join(\n settings.PRIVATE_STORAGE_ROOT, Exam.EXAM_FILES_LOCATION,\n str(self.unique_id)[0:2])","def output_path(self) -> str:\n if self._output_path is None:\n if not self._root_folder:\n self._root_folder = self._env.experiments_folder\n folder = os.path.join(self._root_folder, self.key)\n\n if not os.path.exists(folder):\n os.makedirs(folder)\n\n self._output_path = folder\n\n return self._output_path","def directory_id(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"directory_id\")","def get_data_output_name(prefix: str):\n return settings.data_dir + 'standardized_data_' + prefix","def get_directory(self, subdir=None):\n path = settings.SUBMISSION_DIR / str(self.assignment.id) / str(self.id)\n if subdir:\n path = path / subdir\n\n return path","def dir_name(self):\n return self._dir","def get_directory(self) -> Path:\n path = Path(str(self.max_nesting_lvl))\n path /= str(self.num_snps)\n path /= self.read_quality\n path /= str(self.coverage)\n path /= str(self.denovo_kmer_size)\n return path","def root_folder_name(asmtSubmission):\n\n userID = asmtSubmission.by.djangoUser.username\n\n # If multiple submissions permitted, need to be able to distinguish\n # So add the current time down to the second.\n # We are not using assignment UUID because that truncates the file name.\n userID = userID + \"_\" + strftime(\"%Y-%m-%d_%H-%M-%S\")\n\n return userID","def output_dir(self):\n return os.path.join(self._sandbox, 'output' + os.path.sep)","def results_dir(filename = None):\n path = 'results'\n if os.path.isdir(path):\n if not os.access(path, os.R_OK | os.W_OK):\n raise EnvironmentError(\"{0} is not readable or writable\".format(os.path.abspath(path)))\n return os.path.join(path, filename) if filename else path\n os.mkdir(path) # raises if it fails\n return os.path.join(path, filename) if filename else path","def create_experiment_folder(path_out, dir_name, name='', stamp_unique=True):\n assert os.path.exists(path_out), 'missing base folder \"%s\"' % path_out\n date = time.gmtime()\n if isinstance(name, str) and name:\n dir_name = '{}_{}'.format(dir_name, name)\n path_exp = os.path.join(path_out, dir_name)\n if stamp_unique:\n path_exp += '_' + time.strftime(FORMAT_DATE_TIME, date)\n path_created = None\n while not path_created:\n logging.warning('particular out folder already exists')\n if path_created is not None:\n path_exp += '-' + str(np.random.randint(0, 100))\n path_created = create_folder(path_exp, ok_existing=False)\n else:\n path_created = create_folder(path_exp, ok_existing=False)\n logging.info('created experiment folder \"%r\"', path_created)\n return path_exp","def get_output_folder_name(argi=2, root_folder=\"\"):\n # First tries to read the output folder name from argv[2]\n try:\n output_folder = sys.argv[argi]\n except IndexError:\n # If argv[argi] was not passed, asks the user for the output folder.\n output_folder = root_folder\n output_folder += input(\"Output folder path was not informed. Please inform:\\n\"\n \"{}\".format(root_folder))\n\n # Adds the SEP (/ or \\\\) character to the end of the folder name.\n if output_folder[-len(SEP):] != SEP:\n output_folder += SEP\n\n # Checks if the folder does not exist. Creates it, in this case.\n if not os.path.exists(output_folder):\n os.system(\"mkdir -p '{}'\".format(output_folder))\n\n return output_folder","def get_created_adjustment_folder_name(self):\n global adjustment_folder_name\n return adjustment_folder_name","def gen_folder(base, e, f, a, k):\n \n return base + 'eps_' + str(e) + '_fp_' + str(f) + \\\n '_areak_' + str(a) + '_kappa_' + str(k)","def absolute_folder_name(self):\n return 'music_decompose/media/{0}'.format(self.media_folder_name)","def res_file_directory(username,code,run_number,results_dir=\"results\",res_file_subdir=os.path.join(\"results\",\"res\")):\n\n group_home = os.environ.get(\"GROUP_HOME\")\n if (type(group_home) is not str):\n raise(ValueError(\"Need to set environment variable GROUP_HOME\"))\n\n res_directory = os.path.join(group_home,results_dir,username,code,\"run\"+run_number)\n if (res_file_subdir is not None):\n res_directory = os.path.join(res_directory,res_file_subdir)\n\n return res_directory","def get_dummy_folder() -> str:\n dummy_folder = os.path.join(os.getcwd(), 'dummy_test_folder')\n return dummy_folder","def data_directories(self):\n\n return [simulation.outdir for simulation in self.simulations]","def sim_dir_name(fdir, nsteps, stim_pars, hc_pars, gs_pars, str_pars):\n\n fn = ('nsteps_{}_GSHCsharp_{}'.format(nsteps, hc_pars['gs_hc_sharp']) +\n '_hcpow_{}'.format(hc_pars['ro_pow']) +\n '_msig_{}'.format(int(stim_pars['mot_sig'])) +\n '_vbeta_{}'.format(stim_pars['vis_beta']) +\n '_lambda_{:.1f}'.format(str_pars['gamma']))\n fn = fdir + 'navigation/' + utils.format_to_fname(fn) + '/'\n return fn","def getFolderItemName(self) -> unicode:\n ...","def folder(self):\n return os.path.join(\n td.project.folder.replace(\"/\", os.sep), # incase we're on windows\n \"component_repos\",\n self.name)","def get_folder():\n return input(\"Folder: \")","def form_results():\n folder_name = \"/{0}_{1}_{2}_{3}_{4}_{5}_Basic_NN_Classifier\". \\\n format(datetime.datetime.now(), z_dim, learning_rate, batch_size, n_epochs, beta1)\n tensorboard_path = results_path + folder_name + '/Tensorboard'\n saved_model_path = results_path + folder_name + '/Saved_models/'\n log_path = results_path + folder_name + '/log'\n if not os.path.exists(results_path + folder_name):\n os.mkdir(results_path + folder_name)\n os.mkdir(tensorboard_path)\n os.mkdir(saved_model_path)\n os.mkdir(log_path)\n return tensorboard_path, saved_model_path, log_path","def create_output_loc(self):\n self.output_name = [self.args.xml_out, 'gatk4_' + self.json_file['name'].lower().split(' ')[0] + '.xml']\n if not self.args.xml_out.endswith('/'):\n return '/'.join(self.output_name)\n else:\n return ''.join(self.output_name)","def get_output_folder(parent_dir, env_name):\n os.makedirs(parent_dir, exist_ok=True)\n experiment_id = 0\n for folder_name in os.listdir(parent_dir):\n if not os.path.isdir(os.path.join(parent_dir, folder_name)):\n continue\n try:\n folder_name = int(folder_name.split('-run')[-1])\n if folder_name > experiment_id:\n experiment_id = folder_name\n except:\n pass\n experiment_id += 1\n\n parent_dir = os.path.join(parent_dir, env_name)\n parent_dir = parent_dir + '-run{}'.format(experiment_id)\n return parent_dir","def prepare_folder(self) -> str:\n base_folder = self.config['info']['folder']\n today_folder = f'{datetime.today():%Y-%m-%d}'\n folder = os.path.join(base_folder, today_folder)\n if not os.path.isdir(folder):\n os.makedirs(folder)\n return folder","def set_folder_name(self, folder_name=None):\n\n if folder_name == None:\n folder_name = 'Abstract-OneDim/'\n self.params['folder_name'] = folder_name\n else:\n self.params['folder_name'] = folder_name\n print 'Folder name:', self.params['folder_name']","def build_new_paths(FOLDER_NAME):\n RES_FOLDER = './results'\n XP_FOLDER = os.path.join(RES_FOLDER, FOLDER_NAME)\n return XP_FOLDER","def _get_station_filename():\n output_dir = os.path.join(output, state, station)\n if not os.path.isdir(output_dir):\n logger.debug(\"Creating directory %s\", output_dir)\n os.makedirs(output_dir)\n return os.path.join(output_dir, \"%s.%s\" % (c_time, format))","def group_results(results_dir, group_name):\n\n if isinstance(group_name, str):\n group_dir = os.path.join(results_dir, group_name)\n else:\n raise TypeError('Type of variable group_name is required '\n 'to be a string, type(group_name) '\n f'= {type(group_name)}')\n if os.path.exists(group_dir):\n shutil.rmtree(group_dir)\n os.makedirs(group_dir)\n return group_dir","def test_get_result_directories(self):\n pass","def get_score_dir(cfg):\n return os.path.join(\n BASE_DATA_DIR,\n \"capt\",\n \"pH\" + str(cfg.pH),\n str(cfg.mut))","def getOutputFolder(analysesFolder):\n i = 1\n outputFolder = os.path.join(analysesFolder, \"Output_\" + str(i))\n while os.path.exists(outputFolder):\n i += 1\n outputFolder = os.path.join(analysesFolder, \"Output_\" + str(i))\n\n os.mkdir(outputFolder)\n return outputFolder","def _get_folder(self):\n # type: () -> str\n headers = Headers({\"content-type\": \"application/json\", \"accept\": \"application/json\"})\n response = self.connection.api_call(\n \"GET\", [\"v1\", \"resources\", self.id, \"folderpath\"], headers=headers\n )\n\n return response.json().get(\"path\")","def data_loader_new_directory_name(self, working_dir: Path):\n\n wd = SnowfakeryWorkingDirectory(working_dir)\n key = wd.index\n if key not in self.cached_counts:\n self.cached_counts[key] = wd.get_record_counts()\n\n if not self.run_until.sobject_name:\n return working_dir\n\n count = self.cached_counts[key][self.run_until.sobject_name]\n\n path, _ = str(working_dir).rsplit(\"_\", 1)\n new_working_dir = Path(path + \"_\" + str(count))\n return new_working_dir","def gen_folders(rho, kappa, km, pa, analysis, dbase, analysisdbase):\n \n path1 = 'density_' + + str(rho) + \"_kappa_\" + \\\n str(kappa) + \"_km_\" + str(km) + \"_panti_\" + str(pa)\n path2 = analysis + '_density_' + + str(rho) + \"_kappa_\" + \\\n str(kappa) + \"_km_\" + str(km) + \"_panti_\" + str(pa) + '.txt' \n datafolder = dbase + path1 + '/'\n analysisfile = analysisdbase + path2 \n\n return datafolder, analysisfile","def __create_folder(self, stamp_unique=True):\n if 'path_out' not in self.params:\n raise ValueError('missing \"path_out\" among %r' % self.params.keys())\n # create results folder for experiments\n path_exp = create_experiment_folder(\n self.params.get('path_out'), self.__class__.__name__, self.params.get('name'), stamp_unique\n )\n self.params['path_exp'] = path_exp\n save_config_yaml(os.path.join(path_exp, self.NAME_CONFIG_YAML), self.params)","def displayname(self):\n if self.path.is_dir():\n if (is_uuid(self.path.parts[-1])):\n self.is_uuid_folder = True\n return self.path.name + '/'\n elif is_proj(self.path.parts[-1]):\n return f'{bcolors.BOLD}' + self.path.name + f'{bcolors.ENDC}'\n return self.path.name","def working_directory(self):\n project_full_path = \"/\".join(self.file_name.split(\"/\")[:-1])\n file_name = self.file_name.split(\"/\")[-1]\n if \".h5\" in file_name:\n file_name = file_name.split(\".h5\")[0]\n file_name += \"_hdf5\"\n if self.h5_path[0] == \"/\":\n h5_path = self.h5_path[1:]\n else:\n h5_path = self.h5_path\n return posixpath.join(project_full_path, file_name, h5_path)","def get_output_workspace_names(self):\n mode = ''\n if self._corrections_workspace != '' and self._can_workspace != '':\n mode = 'Correct_1'\n elif self._corrections_workspace != '':\n mode = 'Corrected'\n else:\n mode = 'Subtract_1'\n\n workspace_name_stem = 'irs1_graphite002_%s' % mode\n\n output_workspaces = {\n 'reduced_workspace': workspace_name_stem + '_red',\n 'rqw_workspace': workspace_name_stem + '_rqw',\n }\n\n if self._can_workspace != '':\n output_workspaces['result_workspace'] = workspace_name_stem + '_Result'\n\n return output_workspaces","def outputdir():\n return __OUTPUT_DIR__","def get_output_dir(imdb, net):\n path = os.path.abspath(os.path.join(__C.ROOT_DIR, 'output', __C.EXP_DIR, imdb.name))\n if net is None:\n return path\n else:\n return os.path.join(path, net.name)","def directory(self):\n if not self.query[\"dataset\"]:\n raise ValueError(\"At least a dataset must be selected\")\n if not self.query[\"filter\"]:\n if self.query[\"item\"]:\n return \"{}/{}\".format(self.query[\"dataset\"], self.query[\"item\"])\n else:\n return self.query[\"dataset\"]\n else:\n if not self.query[\"item\"]:\n raise ValueError(\"You cannot set a filter without selecting an item\")\n else:\n return \"{}/{}/{}\".format(*self.query.values())","def get_output_folder(parent_dir, env_name):\n os.makedirs(parent_dir, exist_ok=True)\n experiment_id = 0\n for folder_name in os.listdir(parent_dir):\n if not os.path.isdir(os.path.join(parent_dir, folder_name)):\n continue\n try:\n folder_name = int(folder_name.split('-run')[-1])\n if folder_name > experiment_id:\n experiment_id = folder_name\n except:\n pass\n experiment_id += 1\n\n parent_dir = os.path.join(parent_dir, env_name)\n parent_dir = parent_dir + '-run{}'.format(experiment_id)\n os.makedirs(parent_dir, exist_ok=True)\n return parent_dir","def create_dir_structure():\n LOG.info('In create_dir_structure')\n OutputWrite.change_to_script_directory(__file__)\n path = os.path.abspath(os.path.join('..', 'results',\n global_constants.TEXT_BOARD,\n global_constants.TEXT_INTERFACE,\n global_constants.TEXT_DEVICE,\n global_constants.TEST_EXECUTION_NAME\n ))\n LOG.debug('Path to be Created = {0}'.format(path))\n os.makedirs(path, exist_ok=True, mode=0o755)\n for item in global_constants.TEST_CASE_LIST_NAMES:\n in_path = os.path.exists(os.path.join(path, item))\n if not os.path.exists(in_path):\n LOG.debug('Path with Test Case name = {0}'.format(in_path))\n os.mkdir(in_path)\n LOG.debug('Path = {0}'.format(path))\n return path","def outdir_str(d):\n f = folder_str(d)\n logs_dir = os.path.join(f, 'logs')\n try:\n if not os.path.exists(logs_dir):\n os.makedirs(logs_dir)\n except OSError:\n raise argparse.ArgumentTypeError('could not create \"%s\" directory' % logs_dir)\n return f","def get_trial_dir() -> str:\n return logging.root._log_dir # type: ignore","def get_log_folder(cls, test_suite_name):\n if not test_suite_name:\n test_suite_name = os.path.splitext(os.path.basename(sys.modules['__main__'].__file__))[0]\n sdk_path = cls.get_sdk_path()\n log_folder = os.path.join(sdk_path, \"TEST_LOGS\",\n test_suite_name +\n time.strftime(\"_%m%d_%H_%M_%S\", time.localtime(LOG_FOLDER_TIMESTAMP)))\n if not os.path.exists(log_folder):\n os.makedirs(log_folder)\n return log_folder","def _getResultsFileName(self, toilPath):\n return os.path.join(toilPath, \"results.txt\")","def directory_id(self) -> pulumi.Output[Optional[str]]:\n return pulumi.get(self, \"directory_id\")","def get_directory_names():\n prefix = FLAGS.dataset + \"-\" + FLAGS.uid + \"-\" + FLAGS.method\n\n # Use the number specified on the command line (higher precedence than --debug)\n if FLAGS.debugnum >= 0:\n attempt = FLAGS.debugnum\n print(\"Debugging attempt:\", attempt)\n\n prefix += \"-\" + str(attempt)\n model_dir = os.path.join(FLAGS.modeldir, prefix)\n log_dir = os.path.join(FLAGS.logdir, prefix)\n # Find last one, increment number\n elif FLAGS.debug:\n attempt = file_utils.last_modified_number(FLAGS.logdir, prefix + \"*\")\n attempt = attempt + 1 if attempt is not None else 1\n print(\"Debugging attempt:\", attempt)\n\n prefix += \"-\" + str(attempt)\n model_dir = os.path.join(FLAGS.modeldir, prefix)\n log_dir = os.path.join(FLAGS.logdir, prefix)\n # If no debugging modes, use the model and log directory with only the \"prefix\"\n # (even though it's not actually a prefix in this case, it's the whole name)\n elif FLAGS.subdir:\n model_dir = os.path.join(FLAGS.modeldir, prefix)\n log_dir = os.path.join(FLAGS.logdir, prefix)\n else:\n model_dir = FLAGS.modeldir\n log_dir = FLAGS.logdir\n\n return model_dir, log_dir","def form_results():\n folder_name = \"/{0}_{1}_{2}_{3}_{4}_{5}_Adversarial_Autoencoder\". \\\n format(time.strftime(\"%Y-%m-%d %Hh%Mm%Ss\", time.localtime()), z_dim,\n learning_rate, batch_size, n_epochs, beta1)\n tensorboard_path = results_path + folder_name + '/Tensorboard'\n saved_model_path = results_path + folder_name + '/Saved_models/'\n log_path = results_path + folder_name + '/log'\n if not os.path.exists(results_path + folder_name):\n os.mkdir(results_path + folder_name)\n os.mkdir(tensorboard_path)\n os.mkdir(saved_model_path)\n os.mkdir(log_path)\n return tensorboard_path, saved_model_path, log_path","def get_data_folder():\n data_folder = './MedData/'\n\n if not os.path.isdir(data_folder):\n os.makedirs(data_folder)\n\n return data_folder","def get_summary_writer_log_dir(args: argparse.Namespace) -> str:\n tb_log_dir_prefix = f'CNN_bn_mode={args.mode}_dropout={args.dropout}_bs={args.batch_size}_lr={args.learning_rate}_run_'\n i = 0\n while i < 1000:\n tb_log_dir = args.log_dir / (tb_log_dir_prefix + str(i))\n if not tb_log_dir.exists():\n return str(tb_log_dir)\n i += 1\n return str(tb_log_dir)","def get_sim_file(cls, root_dir, constants):\n return os.path.join(root_dir, '%s.hdf5' % constants['mos_type'])"],"string":"[\n \"def folder(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"folder\\\")\",\n \"def simulation_dir(self):\\n try:\\n return (self.output_directory / self.sim_id).expand()\\n except AttributeError:\\n return Path()\",\n \"def name_folder_data(data):\\n string = 'results/S0(%s)_sig(%s)_r(%s)_T(%s)_K(%s)' % data\\n return string\",\n \"def dir_results(assignment, user):\\n return os.path.join(repository, assignment, user, 'results')\",\n \"def data_abex_results_iteration_dir(experiment_name: str, iteration: int) -> Path: # pragma: no cover\\n return data_abex_results_dir(experiment_name) / iteration_name(iteration)\",\n \"def folder(self, step=None):\\n if step is None:\\n return self._obs_group_folder / self._obs_folder\\n else:\\n return Path(step) / self._obs_group_folder / self._obs_folder\",\n \"def folder(self, step=None):\\n if step is None:\\n return self._obs_group_folder / self._obs_folder\\n else:\\n return Path(step) / self._obs_group_folder / self._obs_folder\",\n \"def folder(self, step=None):\\n if step is None:\\n return self._obs_group_folder / self._obs_folder\\n else:\\n return Path(step) / self._obs_group_folder / self._obs_folder\",\n \"def create_summary_folder_name():\\n dir_str = '' # ADD PATH\\n time_stamp_str = time.strftime(\\\"%a, %d %b %Y %H:%M:%S/\\\", time.gmtime())\\n param_str = ''\\n return dir_str + time_stamp_str + param_str\",\n \"def results_directory(self, run_config):\\n suite_dir_name = '{}_{}'.format(run_config['test_suite_start_time'],\\n run_config['test_id'])\\n datetime_str = datetime.datetime.now().strftime('%Y%m%dT%H%M%S')\\n test_result_dir = '{}'.format(datetime_str)\\n result_dir = os.path.join(self.workspace, 'results', suite_dir_name,\\n test_result_dir)\\n\\n # Creates workspace and default log folder\\n if not os.path.exists(result_dir):\\n print('Making results directory:{}'.format(result_dir))\\n os.makedirs(result_dir)\\n\\n return result_dir\",\n \"def folder(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"folder\\\")\",\n \"def get_resultdir(project_obj, result_folder):\\n description = project_obj.description.replace(' ', '') # remove any spaces in the project name\\n return 'documents/%s/%s/reports/%s' % (str(project_obj.date.date()), description, result_folder)\",\n \"def _get_results_path(self):\\n # if we already have the results path set, please return it\\n if self._results_path is not None:\\n return self._results_path\\n\\n self._validate_results_path()\\n\\n path = self.results_path\\n\\n if path is None:\\n for i in range(1, 10001):\\n name = f\\\"AutoML_{i}\\\"\\n if not os.path.exists(name):\\n self.create_dir(name)\\n self._results_path = name\\n return name\\n # If it got here, could not create, raise expection\\n raise AutoMLException(\\\"Cannot create directory for AutoML results\\\")\\n elif os.path.exists(self.results_path) and os.path.exists(\\n os.path.join(self.results_path, \\\"params.json\\\")\\n ): # AutoML already loaded, return path\\n self._results_path = path\\n return path\\n # Dir does not exist, create it\\n elif not os.path.exists(path):\\n self.create_dir(path)\\n self._results_path = path\\n return path\\n # Dir exists and is empty, use it\\n elif os.path.exists(path) and not len(os.listdir(path)):\\n self._results_path = path\\n return path\\n elif os.path.exists(path) and len(os.listdir(path)):\\n raise AutoMLException(\\n f\\\"Cannot set directory for AutoML. Directory '{path}' is not empty.\\\"\\n )\\n\\n raise AutoMLException(\\\"Cannot set directory for AutoML results\\\")\",\n \"def media_folder_name(self):\\n raise NotImplementedError\",\n \"def Directory(self) -> str:\",\n \"def get_directory(self):\\n path = os.path.join(settings.SUBMISSION_DIR, \\n str(self.course.semester), \\n str(self.course.code), \\n \\\"Sec.%d\\\" % self.course.section,\\n self.code)\\n return path.replace(\\\" \\\", \\\"_\\\")\",\n \"def get_output_folder(self):\\n return os.path.join(self.root_output_folder, self.base_fish_folder)\",\n \"def _output_path(name):\\n output = Path(\\\"../Analysis Results/\\\").joinpath(str(date.today()))\\n output.mkdir(parents=True, exist_ok=True)\\n return output.joinpath(f\\\"{name}.png\\\")\",\n \"def _dir_out(self):\\n ens_label = utils.io.ens_label(self.ens_mem)\\n return os.path.join(self.proj.direc_out, self.proj.name,\\n self.model.name, self.run.name,\\n ens_label, self.name)\",\n \"def build_result_folder(timestamp=str(int(time.time()))):\\n out_path = os.path.abspath(os.path.join(os.path.curdir, \\\"runs\\\", timestamp))\\n print(\\\"Writing to {}\\\\n\\\".format(out_path))\\n\\n data_path = os.path.abspath(os.path.join(out_path, \\\"data\\\"))\\n evaluation_path = os.path.abspath(os.path.join(out_path, \\\"evaluation\\\"))\\n\\n if not os.path.exists(out_path):\\n os.makedirs(data_path)\\n os.makedirs(evaluation_path)\\n return out_path\",\n \"def folder(self) -> pulumi.Output[Optional['outputs.ChangeDataCaptureResponseFolder']]:\\n return pulumi.get(self, \\\"folder\\\")\",\n \"def folder_name(self): \\n folders = []\\n for folder in self.folders:\\n folders.append(folder)\\n return folders\",\n \"def folder_runnum():\\n now = datetime.datetime.now()\\n runnum = 1\\n while True:\\n folder_name = f\\\"astroNN_{now.month:0{2}d}{now.day:0{2}d}_run{runnum:0{3}d}\\\"\\n if not os.path.exists(folder_name):\\n break\\n else:\\n runnum += 1\\n\\n return folder_name\",\n \"def data_abex_results_dir(experiment_name: str) -> Path: # pragma: no cover\\n return experiment_dir(experiment_name) / \\\"Results\\\"\",\n \"def getOutputFileName(aDirectory, aSelenium, aSuite):\\n\\n name = \\\\\\n aSelenium.mOperatingSystem + \\\"_\\\" + \\\\\\n aSelenium.mBrowser + \\\"_\\\" + \\\\\\n aSelenium.mBrowserMode + \\\"_\\\" + \\\\\\n aSelenium.mFont + \\\"_\\\" + \\\\\\n aSelenium.mOutputJax\\n\\n if resultsExist(aDirectory + name):\\n i = 1\\n while (resultsExist(aDirectory + name + \\\"-\\\" + str(i)) and\\n i < MAX_TEST_RESULTS_WITH_SAME_NAME):\\n i = i + 1\\n name += \\\"-\\\" + str(i)\\n\\n if (aSuite.mTaskHandler != None):\\n sendOutputFileName(name);\\n\\n return name\",\n \"def get_directory() -> str:\\n return directory\",\n \"def folder(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"folder\\\")\",\n \"def folder(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"folder\\\")\",\n \"def getFolderPath(self) -> unicode:\\n ...\",\n \"def logdir(self):\\n return osp.join('runs/', self.net_name, '')\",\n \"def get_replay_folder(self):\\n\\n images_with_labels_folder = self.base_folder_path + \\\"/data/replay_images_and_labels/\\\"\\n return images_with_labels_folder\",\n \"def out_dir(self) -> str:\\n return self._out_dir\",\n \"def shpname(self):\\n _, tail = os.path.split(self.url)\\n return self.folder + ('/' + tail[:-4]) * 2\",\n \"def _get_models_name(data_dir):\\n call_folders = [x[0] for x in os.walk(data_dir)][1::]\\n folder = []\\n for i in call_folders:\\n temp = i.split('\\\\\\\\')\\n folder.append(temp[1])\\n print('The models to ensemble are {}'.format(folder))\\n return folder\",\n \"def directory(self) -> str:\\n return self._values.get(\\\"directory\\\")\",\n \"def create_output_dir(self, cfg: dict) -> str:\\n output_dir = cfg.get(\\\"output\\\").get(\\\"output_dir\\\")\\n time_sfx = cfg.get(\\\"output\\\").get(\\\"time_suffix\\\", True)\\n if not os.path.isabs(output_dir):\\n output_dir = os.path.join(self.repo_path, output_dir)\\n subdir = self.project_name\\n if time_sfx:\\n cur_time = get_cur_time_str()\\n subdir = f\\\"{subdir}_{cur_time}\\\"\\n output_dir = os.path.join(output_dir, subdir) # type: str\\n if check_dir(output_dir, make_if_not=True):\\n logger.info(\\\"Results will be in {}\\\".format(output_dir))\\n else:\\n exit(ErrorCode.PATH_ERROR)\\n return output_dir\",\n \"def name_final_path(out_img_folder):\\n if out_img_folder == None:\\n return \\\"./.out_hidden_images\\\"\\n else:\\n return out_img_folder\",\n \"def get(self):\\n return self.directory_name\",\n \"def get_results_directory(self, high_level_directory):\\n batch_num_dir_name = \\\"BN{}\\\".format(self.config_manager.get(ConfigManager.CONFIG_BATCH_MO_NUMBER))\\n serial_num_dir_name = \\\"SN{}-{}\\\".format(self.config_manager.get(ConfigManager.CONFIG_BATCH_MO_NUMBER),\\n self.config_manager.get(ConfigManager.CONFIG_SERIAL_NUMBER))\\n results_path = os.path.join(high_level_directory, batch_num_dir_name, serial_num_dir_name)\\n os.makedirs(results_path, exist_ok=True, mode=0o660)\\n \\n return results_path\",\n \"def results_filename(self): # FIXME: was called output_name in util\\n assert self.filename.endswith(\\\".krun\\\")\\n return self.filename[:-5] + \\\"_results.json.bz2\\\"\",\n \"def get_name(self):\\n return self.client._perform_json(\\\"GET\\\", \\\"/project-folders/%s\\\" % self.project_folder_id).get(\\\"name\\\", None)\",\n \"def outputs(self):\\n return {\\\"path_to_result_folder\\\": File_IO(\\n self.node.outputs[0])}\",\n \"def results(results_dir):\\n\\n if not isinstance(results_dir, str):\\n raise TypeError(\\\"results_dir must be a string!\\\")\\n\\n if not os.path.exists(results_dir):\\n os.makedirs(results_dir)\\n\\n return results_dir\",\n \"def obsfoldername(self, folder_name_beamctl_type=True, source_name=None):\\n if not folder_name_beamctl_type:\\n obsextname = self.filenametime\\n obsextname += \\\"_\\\" + self.ldat_type\\n else:\\n self.source_name = source_name\\n obsextname = obsfileinfo2filefolder(vars(self))\\n return obsextname\",\n \"def test_get_result_directory(self):\\n pass\",\n \"def get_output_folder(parent_dir, env_name):\\n os.makedirs(parent_dir, exist_ok=True)\\n experiment_id = 0\\n for folder_name in os.listdir(parent_dir):\\n if not os.path.isdir(os.path.join(parent_dir, folder_name)):\\n continue\\n try:\\n folder_name = int(folder_name.split('-evaluate')[-1])\\n if folder_name > experiment_id:\\n experiment_id = folder_name\\n except:\\n pass\\n experiment_id += 1\\n\\n parent_dir = os.path.join(parent_dir, env_name)\\n parent_dir = parent_dir + '-evaluate{}'.format(experiment_id)\\n return parent_dir\",\n \"def get_folder(self):\\n return os.path.join(\\n settings.PRIVATE_STORAGE_ROOT, Exam.EXAM_FILES_LOCATION,\\n str(self.unique_id)[0:2])\",\n \"def output_path(self) -> str:\\n if self._output_path is None:\\n if not self._root_folder:\\n self._root_folder = self._env.experiments_folder\\n folder = os.path.join(self._root_folder, self.key)\\n\\n if not os.path.exists(folder):\\n os.makedirs(folder)\\n\\n self._output_path = folder\\n\\n return self._output_path\",\n \"def directory_id(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"directory_id\\\")\",\n \"def get_data_output_name(prefix: str):\\n return settings.data_dir + 'standardized_data_' + prefix\",\n \"def get_directory(self, subdir=None):\\n path = settings.SUBMISSION_DIR / str(self.assignment.id) / str(self.id)\\n if subdir:\\n path = path / subdir\\n\\n return path\",\n \"def dir_name(self):\\n return self._dir\",\n \"def get_directory(self) -> Path:\\n path = Path(str(self.max_nesting_lvl))\\n path /= str(self.num_snps)\\n path /= self.read_quality\\n path /= str(self.coverage)\\n path /= str(self.denovo_kmer_size)\\n return path\",\n \"def root_folder_name(asmtSubmission):\\n\\n userID = asmtSubmission.by.djangoUser.username\\n\\n # If multiple submissions permitted, need to be able to distinguish\\n # So add the current time down to the second.\\n # We are not using assignment UUID because that truncates the file name.\\n userID = userID + \\\"_\\\" + strftime(\\\"%Y-%m-%d_%H-%M-%S\\\")\\n\\n return userID\",\n \"def output_dir(self):\\n return os.path.join(self._sandbox, 'output' + os.path.sep)\",\n \"def results_dir(filename = None):\\n path = 'results'\\n if os.path.isdir(path):\\n if not os.access(path, os.R_OK | os.W_OK):\\n raise EnvironmentError(\\\"{0} is not readable or writable\\\".format(os.path.abspath(path)))\\n return os.path.join(path, filename) if filename else path\\n os.mkdir(path) # raises if it fails\\n return os.path.join(path, filename) if filename else path\",\n \"def create_experiment_folder(path_out, dir_name, name='', stamp_unique=True):\\n assert os.path.exists(path_out), 'missing base folder \\\"%s\\\"' % path_out\\n date = time.gmtime()\\n if isinstance(name, str) and name:\\n dir_name = '{}_{}'.format(dir_name, name)\\n path_exp = os.path.join(path_out, dir_name)\\n if stamp_unique:\\n path_exp += '_' + time.strftime(FORMAT_DATE_TIME, date)\\n path_created = None\\n while not path_created:\\n logging.warning('particular out folder already exists')\\n if path_created is not None:\\n path_exp += '-' + str(np.random.randint(0, 100))\\n path_created = create_folder(path_exp, ok_existing=False)\\n else:\\n path_created = create_folder(path_exp, ok_existing=False)\\n logging.info('created experiment folder \\\"%r\\\"', path_created)\\n return path_exp\",\n \"def get_output_folder_name(argi=2, root_folder=\\\"\\\"):\\n # First tries to read the output folder name from argv[2]\\n try:\\n output_folder = sys.argv[argi]\\n except IndexError:\\n # If argv[argi] was not passed, asks the user for the output folder.\\n output_folder = root_folder\\n output_folder += input(\\\"Output folder path was not informed. Please inform:\\\\n\\\"\\n \\\"{}\\\".format(root_folder))\\n\\n # Adds the SEP (/ or \\\\\\\\) character to the end of the folder name.\\n if output_folder[-len(SEP):] != SEP:\\n output_folder += SEP\\n\\n # Checks if the folder does not exist. Creates it, in this case.\\n if not os.path.exists(output_folder):\\n os.system(\\\"mkdir -p '{}'\\\".format(output_folder))\\n\\n return output_folder\",\n \"def get_created_adjustment_folder_name(self):\\n global adjustment_folder_name\\n return adjustment_folder_name\",\n \"def gen_folder(base, e, f, a, k):\\n \\n return base + 'eps_' + str(e) + '_fp_' + str(f) + \\\\\\n '_areak_' + str(a) + '_kappa_' + str(k)\",\n \"def absolute_folder_name(self):\\n return 'music_decompose/media/{0}'.format(self.media_folder_name)\",\n \"def res_file_directory(username,code,run_number,results_dir=\\\"results\\\",res_file_subdir=os.path.join(\\\"results\\\",\\\"res\\\")):\\n\\n group_home = os.environ.get(\\\"GROUP_HOME\\\")\\n if (type(group_home) is not str):\\n raise(ValueError(\\\"Need to set environment variable GROUP_HOME\\\"))\\n\\n res_directory = os.path.join(group_home,results_dir,username,code,\\\"run\\\"+run_number)\\n if (res_file_subdir is not None):\\n res_directory = os.path.join(res_directory,res_file_subdir)\\n\\n return res_directory\",\n \"def get_dummy_folder() -> str:\\n dummy_folder = os.path.join(os.getcwd(), 'dummy_test_folder')\\n return dummy_folder\",\n \"def data_directories(self):\\n\\n return [simulation.outdir for simulation in self.simulations]\",\n \"def sim_dir_name(fdir, nsteps, stim_pars, hc_pars, gs_pars, str_pars):\\n\\n fn = ('nsteps_{}_GSHCsharp_{}'.format(nsteps, hc_pars['gs_hc_sharp']) +\\n '_hcpow_{}'.format(hc_pars['ro_pow']) +\\n '_msig_{}'.format(int(stim_pars['mot_sig'])) +\\n '_vbeta_{}'.format(stim_pars['vis_beta']) +\\n '_lambda_{:.1f}'.format(str_pars['gamma']))\\n fn = fdir + 'navigation/' + utils.format_to_fname(fn) + '/'\\n return fn\",\n \"def getFolderItemName(self) -> unicode:\\n ...\",\n \"def folder(self):\\n return os.path.join(\\n td.project.folder.replace(\\\"/\\\", os.sep), # incase we're on windows\\n \\\"component_repos\\\",\\n self.name)\",\n \"def get_folder():\\n return input(\\\"Folder: \\\")\",\n \"def form_results():\\n folder_name = \\\"/{0}_{1}_{2}_{3}_{4}_{5}_Basic_NN_Classifier\\\". \\\\\\n format(datetime.datetime.now(), z_dim, learning_rate, batch_size, n_epochs, beta1)\\n tensorboard_path = results_path + folder_name + '/Tensorboard'\\n saved_model_path = results_path + folder_name + '/Saved_models/'\\n log_path = results_path + folder_name + '/log'\\n if not os.path.exists(results_path + folder_name):\\n os.mkdir(results_path + folder_name)\\n os.mkdir(tensorboard_path)\\n os.mkdir(saved_model_path)\\n os.mkdir(log_path)\\n return tensorboard_path, saved_model_path, log_path\",\n \"def create_output_loc(self):\\n self.output_name = [self.args.xml_out, 'gatk4_' + self.json_file['name'].lower().split(' ')[0] + '.xml']\\n if not self.args.xml_out.endswith('/'):\\n return '/'.join(self.output_name)\\n else:\\n return ''.join(self.output_name)\",\n \"def get_output_folder(parent_dir, env_name):\\n os.makedirs(parent_dir, exist_ok=True)\\n experiment_id = 0\\n for folder_name in os.listdir(parent_dir):\\n if not os.path.isdir(os.path.join(parent_dir, folder_name)):\\n continue\\n try:\\n folder_name = int(folder_name.split('-run')[-1])\\n if folder_name > experiment_id:\\n experiment_id = folder_name\\n except:\\n pass\\n experiment_id += 1\\n\\n parent_dir = os.path.join(parent_dir, env_name)\\n parent_dir = parent_dir + '-run{}'.format(experiment_id)\\n return parent_dir\",\n \"def prepare_folder(self) -> str:\\n base_folder = self.config['info']['folder']\\n today_folder = f'{datetime.today():%Y-%m-%d}'\\n folder = os.path.join(base_folder, today_folder)\\n if not os.path.isdir(folder):\\n os.makedirs(folder)\\n return folder\",\n \"def set_folder_name(self, folder_name=None):\\n\\n if folder_name == None:\\n folder_name = 'Abstract-OneDim/'\\n self.params['folder_name'] = folder_name\\n else:\\n self.params['folder_name'] = folder_name\\n print 'Folder name:', self.params['folder_name']\",\n \"def build_new_paths(FOLDER_NAME):\\n RES_FOLDER = './results'\\n XP_FOLDER = os.path.join(RES_FOLDER, FOLDER_NAME)\\n return XP_FOLDER\",\n \"def _get_station_filename():\\n output_dir = os.path.join(output, state, station)\\n if not os.path.isdir(output_dir):\\n logger.debug(\\\"Creating directory %s\\\", output_dir)\\n os.makedirs(output_dir)\\n return os.path.join(output_dir, \\\"%s.%s\\\" % (c_time, format))\",\n \"def group_results(results_dir, group_name):\\n\\n if isinstance(group_name, str):\\n group_dir = os.path.join(results_dir, group_name)\\n else:\\n raise TypeError('Type of variable group_name is required '\\n 'to be a string, type(group_name) '\\n f'= {type(group_name)}')\\n if os.path.exists(group_dir):\\n shutil.rmtree(group_dir)\\n os.makedirs(group_dir)\\n return group_dir\",\n \"def test_get_result_directories(self):\\n pass\",\n \"def get_score_dir(cfg):\\n return os.path.join(\\n BASE_DATA_DIR,\\n \\\"capt\\\",\\n \\\"pH\\\" + str(cfg.pH),\\n str(cfg.mut))\",\n \"def getOutputFolder(analysesFolder):\\n i = 1\\n outputFolder = os.path.join(analysesFolder, \\\"Output_\\\" + str(i))\\n while os.path.exists(outputFolder):\\n i += 1\\n outputFolder = os.path.join(analysesFolder, \\\"Output_\\\" + str(i))\\n\\n os.mkdir(outputFolder)\\n return outputFolder\",\n \"def _get_folder(self):\\n # type: () -> str\\n headers = Headers({\\\"content-type\\\": \\\"application/json\\\", \\\"accept\\\": \\\"application/json\\\"})\\n response = self.connection.api_call(\\n \\\"GET\\\", [\\\"v1\\\", \\\"resources\\\", self.id, \\\"folderpath\\\"], headers=headers\\n )\\n\\n return response.json().get(\\\"path\\\")\",\n \"def data_loader_new_directory_name(self, working_dir: Path):\\n\\n wd = SnowfakeryWorkingDirectory(working_dir)\\n key = wd.index\\n if key not in self.cached_counts:\\n self.cached_counts[key] = wd.get_record_counts()\\n\\n if not self.run_until.sobject_name:\\n return working_dir\\n\\n count = self.cached_counts[key][self.run_until.sobject_name]\\n\\n path, _ = str(working_dir).rsplit(\\\"_\\\", 1)\\n new_working_dir = Path(path + \\\"_\\\" + str(count))\\n return new_working_dir\",\n \"def gen_folders(rho, kappa, km, pa, analysis, dbase, analysisdbase):\\n \\n path1 = 'density_' + + str(rho) + \\\"_kappa_\\\" + \\\\\\n str(kappa) + \\\"_km_\\\" + str(km) + \\\"_panti_\\\" + str(pa)\\n path2 = analysis + '_density_' + + str(rho) + \\\"_kappa_\\\" + \\\\\\n str(kappa) + \\\"_km_\\\" + str(km) + \\\"_panti_\\\" + str(pa) + '.txt' \\n datafolder = dbase + path1 + '/'\\n analysisfile = analysisdbase + path2 \\n\\n return datafolder, analysisfile\",\n \"def __create_folder(self, stamp_unique=True):\\n if 'path_out' not in self.params:\\n raise ValueError('missing \\\"path_out\\\" among %r' % self.params.keys())\\n # create results folder for experiments\\n path_exp = create_experiment_folder(\\n self.params.get('path_out'), self.__class__.__name__, self.params.get('name'), stamp_unique\\n )\\n self.params['path_exp'] = path_exp\\n save_config_yaml(os.path.join(path_exp, self.NAME_CONFIG_YAML), self.params)\",\n \"def displayname(self):\\n if self.path.is_dir():\\n if (is_uuid(self.path.parts[-1])):\\n self.is_uuid_folder = True\\n return self.path.name + '/'\\n elif is_proj(self.path.parts[-1]):\\n return f'{bcolors.BOLD}' + self.path.name + f'{bcolors.ENDC}'\\n return self.path.name\",\n \"def working_directory(self):\\n project_full_path = \\\"/\\\".join(self.file_name.split(\\\"/\\\")[:-1])\\n file_name = self.file_name.split(\\\"/\\\")[-1]\\n if \\\".h5\\\" in file_name:\\n file_name = file_name.split(\\\".h5\\\")[0]\\n file_name += \\\"_hdf5\\\"\\n if self.h5_path[0] == \\\"/\\\":\\n h5_path = self.h5_path[1:]\\n else:\\n h5_path = self.h5_path\\n return posixpath.join(project_full_path, file_name, h5_path)\",\n \"def get_output_workspace_names(self):\\n mode = ''\\n if self._corrections_workspace != '' and self._can_workspace != '':\\n mode = 'Correct_1'\\n elif self._corrections_workspace != '':\\n mode = 'Corrected'\\n else:\\n mode = 'Subtract_1'\\n\\n workspace_name_stem = 'irs1_graphite002_%s' % mode\\n\\n output_workspaces = {\\n 'reduced_workspace': workspace_name_stem + '_red',\\n 'rqw_workspace': workspace_name_stem + '_rqw',\\n }\\n\\n if self._can_workspace != '':\\n output_workspaces['result_workspace'] = workspace_name_stem + '_Result'\\n\\n return output_workspaces\",\n \"def outputdir():\\n return __OUTPUT_DIR__\",\n \"def get_output_dir(imdb, net):\\n path = os.path.abspath(os.path.join(__C.ROOT_DIR, 'output', __C.EXP_DIR, imdb.name))\\n if net is None:\\n return path\\n else:\\n return os.path.join(path, net.name)\",\n \"def directory(self):\\n if not self.query[\\\"dataset\\\"]:\\n raise ValueError(\\\"At least a dataset must be selected\\\")\\n if not self.query[\\\"filter\\\"]:\\n if self.query[\\\"item\\\"]:\\n return \\\"{}/{}\\\".format(self.query[\\\"dataset\\\"], self.query[\\\"item\\\"])\\n else:\\n return self.query[\\\"dataset\\\"]\\n else:\\n if not self.query[\\\"item\\\"]:\\n raise ValueError(\\\"You cannot set a filter without selecting an item\\\")\\n else:\\n return \\\"{}/{}/{}\\\".format(*self.query.values())\",\n \"def get_output_folder(parent_dir, env_name):\\n os.makedirs(parent_dir, exist_ok=True)\\n experiment_id = 0\\n for folder_name in os.listdir(parent_dir):\\n if not os.path.isdir(os.path.join(parent_dir, folder_name)):\\n continue\\n try:\\n folder_name = int(folder_name.split('-run')[-1])\\n if folder_name > experiment_id:\\n experiment_id = folder_name\\n except:\\n pass\\n experiment_id += 1\\n\\n parent_dir = os.path.join(parent_dir, env_name)\\n parent_dir = parent_dir + '-run{}'.format(experiment_id)\\n os.makedirs(parent_dir, exist_ok=True)\\n return parent_dir\",\n \"def create_dir_structure():\\n LOG.info('In create_dir_structure')\\n OutputWrite.change_to_script_directory(__file__)\\n path = os.path.abspath(os.path.join('..', 'results',\\n global_constants.TEXT_BOARD,\\n global_constants.TEXT_INTERFACE,\\n global_constants.TEXT_DEVICE,\\n global_constants.TEST_EXECUTION_NAME\\n ))\\n LOG.debug('Path to be Created = {0}'.format(path))\\n os.makedirs(path, exist_ok=True, mode=0o755)\\n for item in global_constants.TEST_CASE_LIST_NAMES:\\n in_path = os.path.exists(os.path.join(path, item))\\n if not os.path.exists(in_path):\\n LOG.debug('Path with Test Case name = {0}'.format(in_path))\\n os.mkdir(in_path)\\n LOG.debug('Path = {0}'.format(path))\\n return path\",\n \"def outdir_str(d):\\n f = folder_str(d)\\n logs_dir = os.path.join(f, 'logs')\\n try:\\n if not os.path.exists(logs_dir):\\n os.makedirs(logs_dir)\\n except OSError:\\n raise argparse.ArgumentTypeError('could not create \\\"%s\\\" directory' % logs_dir)\\n return f\",\n \"def get_trial_dir() -> str:\\n return logging.root._log_dir # type: ignore\",\n \"def get_log_folder(cls, test_suite_name):\\n if not test_suite_name:\\n test_suite_name = os.path.splitext(os.path.basename(sys.modules['__main__'].__file__))[0]\\n sdk_path = cls.get_sdk_path()\\n log_folder = os.path.join(sdk_path, \\\"TEST_LOGS\\\",\\n test_suite_name +\\n time.strftime(\\\"_%m%d_%H_%M_%S\\\", time.localtime(LOG_FOLDER_TIMESTAMP)))\\n if not os.path.exists(log_folder):\\n os.makedirs(log_folder)\\n return log_folder\",\n \"def _getResultsFileName(self, toilPath):\\n return os.path.join(toilPath, \\\"results.txt\\\")\",\n \"def directory_id(self) -> pulumi.Output[Optional[str]]:\\n return pulumi.get(self, \\\"directory_id\\\")\",\n \"def get_directory_names():\\n prefix = FLAGS.dataset + \\\"-\\\" + FLAGS.uid + \\\"-\\\" + FLAGS.method\\n\\n # Use the number specified on the command line (higher precedence than --debug)\\n if FLAGS.debugnum >= 0:\\n attempt = FLAGS.debugnum\\n print(\\\"Debugging attempt:\\\", attempt)\\n\\n prefix += \\\"-\\\" + str(attempt)\\n model_dir = os.path.join(FLAGS.modeldir, prefix)\\n log_dir = os.path.join(FLAGS.logdir, prefix)\\n # Find last one, increment number\\n elif FLAGS.debug:\\n attempt = file_utils.last_modified_number(FLAGS.logdir, prefix + \\\"*\\\")\\n attempt = attempt + 1 if attempt is not None else 1\\n print(\\\"Debugging attempt:\\\", attempt)\\n\\n prefix += \\\"-\\\" + str(attempt)\\n model_dir = os.path.join(FLAGS.modeldir, prefix)\\n log_dir = os.path.join(FLAGS.logdir, prefix)\\n # If no debugging modes, use the model and log directory with only the \\\"prefix\\\"\\n # (even though it's not actually a prefix in this case, it's the whole name)\\n elif FLAGS.subdir:\\n model_dir = os.path.join(FLAGS.modeldir, prefix)\\n log_dir = os.path.join(FLAGS.logdir, prefix)\\n else:\\n model_dir = FLAGS.modeldir\\n log_dir = FLAGS.logdir\\n\\n return model_dir, log_dir\",\n \"def form_results():\\n folder_name = \\\"/{0}_{1}_{2}_{3}_{4}_{5}_Adversarial_Autoencoder\\\". \\\\\\n format(time.strftime(\\\"%Y-%m-%d %Hh%Mm%Ss\\\", time.localtime()), z_dim,\\n learning_rate, batch_size, n_epochs, beta1)\\n tensorboard_path = results_path + folder_name + '/Tensorboard'\\n saved_model_path = results_path + folder_name + '/Saved_models/'\\n log_path = results_path + folder_name + '/log'\\n if not os.path.exists(results_path + folder_name):\\n os.mkdir(results_path + folder_name)\\n os.mkdir(tensorboard_path)\\n os.mkdir(saved_model_path)\\n os.mkdir(log_path)\\n return tensorboard_path, saved_model_path, log_path\",\n \"def get_data_folder():\\n data_folder = './MedData/'\\n\\n if not os.path.isdir(data_folder):\\n os.makedirs(data_folder)\\n\\n return data_folder\",\n \"def get_summary_writer_log_dir(args: argparse.Namespace) -> str:\\n tb_log_dir_prefix = f'CNN_bn_mode={args.mode}_dropout={args.dropout}_bs={args.batch_size}_lr={args.learning_rate}_run_'\\n i = 0\\n while i < 1000:\\n tb_log_dir = args.log_dir / (tb_log_dir_prefix + str(i))\\n if not tb_log_dir.exists():\\n return str(tb_log_dir)\\n i += 1\\n return str(tb_log_dir)\",\n \"def get_sim_file(cls, root_dir, constants):\\n return os.path.join(root_dir, '%s.hdf5' % constants['mos_type'])\"\n]","isConversational":false},"negative_scores":{"kind":"list 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\"0.7222843\",\n \"0.6902778\",\n \"0.6742704\",\n \"0.6699133\",\n \"0.66006047\",\n \"0.65905297\",\n \"0.65905297\",\n \"0.65905297\",\n \"0.6492835\",\n \"0.6459841\",\n \"0.6366473\",\n \"0.63570267\",\n \"0.630044\",\n \"0.62975377\",\n \"0.6291892\",\n \"0.6234195\",\n \"0.621634\",\n \"0.6193216\",\n \"0.6178031\",\n \"0.617218\",\n \"0.6141358\",\n \"0.6109404\",\n \"0.6105047\",\n \"0.60924554\",\n \"0.6091118\",\n \"0.6089549\",\n \"0.6050834\",\n \"0.6050834\",\n \"0.6026644\",\n \"0.6024488\",\n \"0.6023893\",\n \"0.59755677\",\n \"0.59689355\",\n \"0.5939075\",\n \"0.59323263\",\n \"0.59250206\",\n \"0.592016\",\n \"0.59108436\",\n \"0.5907922\",\n \"0.5906631\",\n \"0.5903925\",\n \"0.5897117\",\n \"0.58940464\",\n \"0.58807045\",\n \"0.58751804\",\n \"0.58746535\",\n \"0.5866869\",\n \"0.5841393\",\n \"0.58362126\",\n \"0.58263147\",\n \"0.58262336\",\n \"0.58214706\",\n \"0.5820171\",\n \"0.57913053\",\n \"0.57873905\",\n \"0.5787001\",\n \"0.5770624\",\n \"0.5764197\",\n \"0.57633907\",\n \"0.5746529\",\n \"0.57458836\",\n \"0.5734052\",\n \"0.57268053\",\n \"0.572193\",\n \"0.57149607\",\n \"0.5709387\",\n \"0.570616\",\n \"0.57021713\",\n \"0.569972\",\n \"0.5699571\",\n \"0.56852084\",\n \"0.5678696\",\n \"0.567303\",\n \"0.5669866\",\n \"0.56663233\",\n \"0.56655955\",\n \"0.5663897\",\n \"0.56594896\",\n \"0.5658853\",\n \"0.5645245\",\n \"0.5642517\",\n \"0.5642117\",\n \"0.5624711\",\n \"0.562364\",\n \"0.56191236\",\n \"0.56143725\",\n \"0.56057215\",\n \"0.5604239\",\n \"0.55986583\",\n \"0.5597545\",\n \"0.5585555\",\n \"0.5581951\",\n \"0.55815154\",\n \"0.5579493\",\n \"0.5573815\",\n \"0.5565662\",\n \"0.55588675\",\n \"0.5558855\",\n \"0.55466\",\n \"0.5542589\",\n \"0.553945\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":22486,"cells":{"query":{"kind":"string","value":"Return name of connectivity configuration."},"document":{"kind":"string","value":"def conn_config_name(conn_config):\n\n n_conn_config = [cname for cname, on in conn_config.items() if on]\n n_conn_config = ' + '.join(n_conn_config)\n return n_conn_config"},"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 name(self):\n return self._config_name","def get_config_name(self): # pragma: no cover\n pass","def name(self):\n return self._config.get(CONF_NAME)","def connection_name(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"connection_name\")","def connection_name(self) -> str:\n return pulumi.get(self, \"connection_name\")","def access_configuration_name(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"access_configuration_name\")","def name(self) -> str:\n return self.config_name or self.host_name or self.dev_id or DEVICE_DEFAULT_NAME","def _getConfigName(self):\n return \"%s_processCoadd_config\" % (self.config.coaddName,)","def __str__(self):\n return str(self._config.name)","def antenny_which_config(self):\n return self.antenny_config.get_name()","def name(self):\n return f'{self._config[CONF_NAME]} {self._typeconf[\"name\"]}'","def name(self):\n return self.config[\"name\"]","def connection_name(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"connection_name\")","def name(self):\n return 'Connected Devices'","def __str__(self):\n \n return self.net.ifconfig()","def _getConfigName(self):\n pass","def name(self) -> str:\n name = self._config[\"name\"]\n assert isinstance(name, str) # noqa: S101\n return name","def access_configuration_name(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"access_configuration_name\")","def getConnectionName(self):\n return self.system","def config_file_name(self):\n return self._config_file_name","def access_configuration_name(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"access_configuration_name\")","def configuration_configmap_name(self) -> Optional[str]:\n return pulumi.get(self, \"configuration_configmap_name\")","def configuration_set_name(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"configuration_set_name\")","def module_name(self):\n return self.config_section","def get_connections_accountname(self):\n account_info = self.get_account()\n return getattr(account_info, 'account_name', None)","def get_config_connection(self):\n return self.m_connection.config","def get_name(self):\n if 'label' in self.configs_:\n return self.configs_['label']\n return self.configs_['id']","def get_config_descr(self, name):\n return self.configs[name][1]","def kafka_configuration_name(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"kafka_configuration_name\")","def get_config_name():\n name = CONFIG_FILE_NAME\n for i, arg in enumerate(sys.argv):\n if arg.startswith('--config'):\n if arg == '--config':\n return sys.argv[i + 1]\n else:\n name = sys.argv[i].split('--config')[1]\n if name[0] == '=':\n name = name[1:]\n return name\n\n return name","def name(self):\n return self._config.backend_name","def get_name(self):\n \n return 'Socket/IP'","def get_config_file_name(self):\n argv = sys.argv\n config_type = \"dev\" # default configuration type\n if None != argv and len(argv) > 1 :\n config_type = argv[1]\n config_file = config_type + \".cfg\"\n logger.info(\"get_config_file_name() return : \" + config_file)\n return config_file","def connection_prefix(self) -> pulumi.Output[Optional[str]]:\n return pulumi.get(self, \"connection_prefix\")","def connection_string_prefix(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"connection_string_prefix\")","def __str__(self):\n return self.config[\"name\"]","def filename(self):\n return f'{self._peer.interface}.conf'","def configuration(self) -> str:\n return self._configuration","def get_name(self):\n\t\treturn call_sdk_function('PrlSrvCfgDev_GetName', self.handle)","def topology_name(self):\n return self._topology_name","def configuration_set_name(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"configuration_set_name\")","def __str__(self):\n if self.connected_to.get_size() != 0:\n s = ''\n for connection in self.connected_to:\n s += f\"{self.name} -> {connection.get_name()} \"\n return s\n return self.name","def connection_string(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"connection_string\")","def connection_string(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"connection_string\")","def configuration_set_name(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"configuration_set_name\")","def name(self):\n return self.config.get('name') or f\"{self.id.replace('_', ' ').title()}\"","def name(self):\n return self.config.get('name') or f\"{self.id.replace('_', ' ').title()}\"","def config_id(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"config_id\")","def name(self):\n return '{} {}'.format(self._device,\n self._endpoint)","def vpc_connector_name(self) -> pulumi.Output[Optional[str]]:\n return pulumi.get(self, \"vpc_connector_name\")","def get_name(self):\n \n return 'TCP/IP Server'","def network(self) -> str:\n return pulumi.get(self, \"network\")","def name(self):\n if self.resource.is_client:\n return f\"{self.network.name} {self.resource.name_connection_type} {SWITCH_TYPES[self.variable][0]}\"\n elif self.resource.is_eero or self.resource.is_profile:\n return f\"{self.network.name} {self.resource.name} {SWITCH_TYPES[self.variable][0]}\"\n return f\"{self.resource.name} {SWITCH_TYPES[self.variable][0]}\"","def connector_name(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"connector_name\")","def config(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"config\")","def get_mp_config_name(self, mp_config):\n if not isinstance(mp_config, list):\n mp_configs = [mp_config]\n else:\n mp_configs = mp_config\n\n for mp_config_name in mp_configs:\n if self.has_option('config', mp_config_name):\n return mp_config_name\n\n return None","def get_network_name_on_vc(options):\n network = get_network_on_vc(options)\n if network:\n return network.name","def get_config(self, name):\n return self.configs[name][0]","def __str__(self):\n return describe_connection(self)","def connector_mapping_name(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"connector_mapping_name\")","def name(self):\n return self.device.name()","def configFilename(self):\n return self.name()+'.py'","def name(self) -> str:\n return f\"{self._inst} NAT {self._data['name']}\"","def account_connection_string(self) -> str:\n return pulumi.get(self, \"account_connection_string\")","def network_config(self) -> 'outputs.NetworkConfigResponse':\n return pulumi.get(self, \"network_config\")","def network_config(self) -> 'outputs.NetworkConfigResponse':\n return pulumi.get(self, \"network_config\")","def get_network_name(self): # type: () -> str\n networks = self.get_network_names()\n\n if not networks:\n raise ApplicationError('No network found for Docker container: %s.' % self.id)\n\n if len(networks) > 1:\n raise ApplicationError('Found multiple networks for Docker container %s instead of only one: %s' % (self.id, ', '.join(networks)))\n\n return networks[0]","def network_configuration(self) -> pulumi.Output['outputs.ServiceNetworkConfiguration']:\n return pulumi.get(self, \"network_configuration\")","def name(self) -> str:\n return self._alias or f\"Nut-{self._host}\"","def config_mode(self):\n return \"\"","def configuration_profile(self) -> Optional[str]:\n return pulumi.get(self, \"configuration_profile\")","def get_name(self):\n\t\treturn call_sdk_function('PrlVmCfg_GetName', self.handle)","def get_name(self):\n \n return 'TCP/IP Client'","def connection_bus(self) -> str:\n return self.properties[DBUS_ATTR_CONNECTION_BUS]","def name(self):\n return self._device.description_pretty()","def connection_id(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"connection_id\")","def connection_string(self) -> pulumi.Output[Optional[str]]:\n return pulumi.get(self, \"connection_string\")","def name(self) -> str:\n return self.dev.label","def getConfigFileName(self):\n return self._configFileName","def computer_network_name(self) -> str:\n return self._computer_network_name","def name(self):\n return self._device.name","def name(self):\n return self._device.name","def name(self):\n return self._device.name","def name(self):\n if self._connection.location_names:\n return '{} {} {}'.format(self._device.location2, self._device.location, self._device.name)\n else:\n return self._device.name","def wallet_config(self) -> str:\n\n return self._wallet_config","def get_coil_config_section(cls) -> Optional[str]:\n return None","def connection_prefix(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"connection_prefix\")","def connection_prefix(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"connection_prefix\")","def details(self):\n return self.sock.getsockname()","def name(self) -> str:\n return self._device.name or self._device.mac","def get_name(self):\n return \"catkin\"","def cloud_services_network_name(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"cloud_services_network_name\")","def config(self):\n return self.namespace['config']","def layer_protocol_name(self) -> str:\n return self._layer_protocol_name","def mmo_config_server_replset_name(self, mmo_connection):\n replset_name = None\n c = mmo_connection[\"admin\"].command(\"getCmdLineOpts\")[\"parsed\"][\"sharding\"][\"configDB\"]\n for item in c.split(\",\"):\n if \"/\" in item: # cfg Replset server\n replset_name = item.partition(\"/\")[0]\n break\n return replset_name","def connection(entity) -> str:\n return entity.__connection__","def connection_string_prefix(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"connection_string_prefix\")","def name(self) -> str:\n return f\"{self._inst} port {self._data[self._sid_data['sid_name']]}\"","def getName(self):\n return _libsbml.Port_getName(self)","def name(self):\n return self._meural_device[\"alias\"]"],"string":"[\n \"def name(self):\\n return self._config_name\",\n \"def get_config_name(self): # pragma: no cover\\n pass\",\n \"def name(self):\\n return self._config.get(CONF_NAME)\",\n \"def connection_name(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"connection_name\\\")\",\n \"def connection_name(self) -> str:\\n return pulumi.get(self, \\\"connection_name\\\")\",\n \"def access_configuration_name(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"access_configuration_name\\\")\",\n \"def name(self) -> str:\\n return self.config_name or self.host_name or self.dev_id or DEVICE_DEFAULT_NAME\",\n \"def _getConfigName(self):\\n return \\\"%s_processCoadd_config\\\" % (self.config.coaddName,)\",\n \"def __str__(self):\\n return str(self._config.name)\",\n \"def antenny_which_config(self):\\n return self.antenny_config.get_name()\",\n \"def name(self):\\n return f'{self._config[CONF_NAME]} {self._typeconf[\\\"name\\\"]}'\",\n \"def name(self):\\n return self.config[\\\"name\\\"]\",\n \"def connection_name(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"connection_name\\\")\",\n \"def name(self):\\n return 'Connected Devices'\",\n \"def __str__(self):\\n \\n return self.net.ifconfig()\",\n \"def _getConfigName(self):\\n pass\",\n \"def name(self) -> str:\\n name = self._config[\\\"name\\\"]\\n assert isinstance(name, str) # noqa: S101\\n return name\",\n \"def access_configuration_name(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"access_configuration_name\\\")\",\n \"def getConnectionName(self):\\n return self.system\",\n \"def config_file_name(self):\\n return self._config_file_name\",\n \"def access_configuration_name(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"access_configuration_name\\\")\",\n \"def configuration_configmap_name(self) -> Optional[str]:\\n return pulumi.get(self, \\\"configuration_configmap_name\\\")\",\n \"def configuration_set_name(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"configuration_set_name\\\")\",\n \"def module_name(self):\\n return self.config_section\",\n \"def get_connections_accountname(self):\\n account_info = self.get_account()\\n return getattr(account_info, 'account_name', None)\",\n \"def get_config_connection(self):\\n return self.m_connection.config\",\n \"def get_name(self):\\n if 'label' in self.configs_:\\n return self.configs_['label']\\n return self.configs_['id']\",\n \"def get_config_descr(self, name):\\n return self.configs[name][1]\",\n \"def kafka_configuration_name(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"kafka_configuration_name\\\")\",\n \"def get_config_name():\\n name = CONFIG_FILE_NAME\\n for i, arg in enumerate(sys.argv):\\n if arg.startswith('--config'):\\n if arg == '--config':\\n return sys.argv[i + 1]\\n else:\\n name = sys.argv[i].split('--config')[1]\\n if name[0] == '=':\\n name = name[1:]\\n return name\\n\\n return name\",\n \"def name(self):\\n return self._config.backend_name\",\n \"def get_name(self):\\n \\n return 'Socket/IP'\",\n \"def get_config_file_name(self):\\n argv = sys.argv\\n config_type = \\\"dev\\\" # default configuration type\\n if None != argv and len(argv) > 1 :\\n config_type = argv[1]\\n config_file = config_type + \\\".cfg\\\"\\n logger.info(\\\"get_config_file_name() return : \\\" + config_file)\\n return config_file\",\n \"def connection_prefix(self) -> pulumi.Output[Optional[str]]:\\n return pulumi.get(self, \\\"connection_prefix\\\")\",\n \"def connection_string_prefix(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"connection_string_prefix\\\")\",\n \"def __str__(self):\\n return self.config[\\\"name\\\"]\",\n \"def filename(self):\\n return f'{self._peer.interface}.conf'\",\n \"def configuration(self) -> str:\\n return self._configuration\",\n \"def get_name(self):\\n\\t\\treturn call_sdk_function('PrlSrvCfgDev_GetName', self.handle)\",\n \"def topology_name(self):\\n return self._topology_name\",\n \"def configuration_set_name(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"configuration_set_name\\\")\",\n \"def __str__(self):\\n if self.connected_to.get_size() != 0:\\n s = ''\\n for connection in self.connected_to:\\n s += f\\\"{self.name} -> {connection.get_name()} \\\"\\n return s\\n return self.name\",\n \"def connection_string(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"connection_string\\\")\",\n \"def connection_string(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"connection_string\\\")\",\n \"def configuration_set_name(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"configuration_set_name\\\")\",\n \"def name(self):\\n return self.config.get('name') or f\\\"{self.id.replace('_', ' ').title()}\\\"\",\n \"def name(self):\\n return self.config.get('name') or f\\\"{self.id.replace('_', ' ').title()}\\\"\",\n \"def config_id(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"config_id\\\")\",\n \"def name(self):\\n return '{} {}'.format(self._device,\\n self._endpoint)\",\n \"def vpc_connector_name(self) -> pulumi.Output[Optional[str]]:\\n return pulumi.get(self, \\\"vpc_connector_name\\\")\",\n \"def get_name(self):\\n \\n return 'TCP/IP Server'\",\n \"def network(self) -> str:\\n return pulumi.get(self, \\\"network\\\")\",\n \"def name(self):\\n if self.resource.is_client:\\n return f\\\"{self.network.name} {self.resource.name_connection_type} {SWITCH_TYPES[self.variable][0]}\\\"\\n elif self.resource.is_eero or self.resource.is_profile:\\n return f\\\"{self.network.name} {self.resource.name} {SWITCH_TYPES[self.variable][0]}\\\"\\n return f\\\"{self.resource.name} {SWITCH_TYPES[self.variable][0]}\\\"\",\n \"def connector_name(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"connector_name\\\")\",\n \"def config(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"config\\\")\",\n \"def get_mp_config_name(self, mp_config):\\n if not isinstance(mp_config, list):\\n mp_configs = [mp_config]\\n else:\\n mp_configs = mp_config\\n\\n for mp_config_name in mp_configs:\\n if self.has_option('config', mp_config_name):\\n return mp_config_name\\n\\n return None\",\n \"def get_network_name_on_vc(options):\\n network = get_network_on_vc(options)\\n if network:\\n return network.name\",\n \"def get_config(self, name):\\n return self.configs[name][0]\",\n \"def __str__(self):\\n return describe_connection(self)\",\n \"def connector_mapping_name(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"connector_mapping_name\\\")\",\n \"def name(self):\\n return self.device.name()\",\n \"def configFilename(self):\\n return self.name()+'.py'\",\n \"def name(self) -> str:\\n return f\\\"{self._inst} NAT {self._data['name']}\\\"\",\n \"def account_connection_string(self) -> str:\\n return pulumi.get(self, \\\"account_connection_string\\\")\",\n \"def network_config(self) -> 'outputs.NetworkConfigResponse':\\n return pulumi.get(self, \\\"network_config\\\")\",\n \"def network_config(self) -> 'outputs.NetworkConfigResponse':\\n return pulumi.get(self, \\\"network_config\\\")\",\n \"def get_network_name(self): # type: () -> str\\n networks = self.get_network_names()\\n\\n if not networks:\\n raise ApplicationError('No network found for Docker container: %s.' % self.id)\\n\\n if len(networks) > 1:\\n raise ApplicationError('Found multiple networks for Docker container %s instead of only one: %s' % (self.id, ', '.join(networks)))\\n\\n return networks[0]\",\n \"def network_configuration(self) -> pulumi.Output['outputs.ServiceNetworkConfiguration']:\\n return pulumi.get(self, \\\"network_configuration\\\")\",\n \"def name(self) -> str:\\n return self._alias or f\\\"Nut-{self._host}\\\"\",\n \"def config_mode(self):\\n return \\\"\\\"\",\n \"def configuration_profile(self) -> Optional[str]:\\n return pulumi.get(self, \\\"configuration_profile\\\")\",\n \"def get_name(self):\\n\\t\\treturn call_sdk_function('PrlVmCfg_GetName', self.handle)\",\n \"def get_name(self):\\n \\n return 'TCP/IP Client'\",\n \"def connection_bus(self) -> str:\\n return self.properties[DBUS_ATTR_CONNECTION_BUS]\",\n \"def name(self):\\n return self._device.description_pretty()\",\n \"def connection_id(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"connection_id\\\")\",\n \"def connection_string(self) -> pulumi.Output[Optional[str]]:\\n return pulumi.get(self, \\\"connection_string\\\")\",\n \"def name(self) -> str:\\n return self.dev.label\",\n \"def getConfigFileName(self):\\n return self._configFileName\",\n \"def computer_network_name(self) -> str:\\n return self._computer_network_name\",\n \"def name(self):\\n return self._device.name\",\n \"def name(self):\\n return self._device.name\",\n \"def name(self):\\n return self._device.name\",\n \"def name(self):\\n if self._connection.location_names:\\n return '{} {} {}'.format(self._device.location2, self._device.location, self._device.name)\\n else:\\n return self._device.name\",\n \"def wallet_config(self) -> str:\\n\\n return self._wallet_config\",\n \"def get_coil_config_section(cls) -> Optional[str]:\\n return None\",\n \"def connection_prefix(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"connection_prefix\\\")\",\n \"def connection_prefix(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"connection_prefix\\\")\",\n \"def details(self):\\n return self.sock.getsockname()\",\n \"def name(self) -> str:\\n return self._device.name or self._device.mac\",\n \"def get_name(self):\\n return \\\"catkin\\\"\",\n \"def cloud_services_network_name(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"cloud_services_network_name\\\")\",\n \"def config(self):\\n return self.namespace['config']\",\n \"def layer_protocol_name(self) -> str:\\n return self._layer_protocol_name\",\n \"def mmo_config_server_replset_name(self, mmo_connection):\\n replset_name = None\\n c = mmo_connection[\\\"admin\\\"].command(\\\"getCmdLineOpts\\\")[\\\"parsed\\\"][\\\"sharding\\\"][\\\"configDB\\\"]\\n for item in c.split(\\\",\\\"):\\n if \\\"/\\\" in item: # cfg Replset server\\n replset_name = item.partition(\\\"/\\\")[0]\\n break\\n return replset_name\",\n \"def connection(entity) -> str:\\n return entity.__connection__\",\n \"def connection_string_prefix(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"connection_string_prefix\\\")\",\n \"def name(self) -> str:\\n return f\\\"{self._inst} port {self._data[self._sid_data['sid_name']]}\\\"\",\n \"def getName(self):\\n return _libsbml.Port_getName(self)\",\n \"def name(self):\\n return self._meural_device[\\\"alias\\\"]\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.77077055","0.7493195","0.745413","0.72979784","0.7289367","0.7035228","0.6940678","0.685062","0.6848523","0.6846582","0.6830779","0.68089354","0.6791377","0.675848","0.67513496","0.67148095","0.66720396","0.6649063","0.66395855","0.6601104","0.6592569","0.6562268","0.6521006","0.6456873","0.63926756","0.6379958","0.6332176","0.6320637","0.6283928","0.62714064","0.62558323","0.6214694","0.62109387","0.62042576","0.620313","0.62013745","0.61882114","0.61555904","0.61416554","0.61385953","0.61215323","0.6100427","0.60878617","0.60878617","0.608396","0.6081219","0.6081219","0.6078385","0.6077722","0.605294","0.60470647","0.60456073","0.60209715","0.6015747","0.59961295","0.5992714","0.5991083","0.5985822","0.5965448","0.5964509","0.59589636","0.59431726","0.594167","0.59345007","0.592926","0.592926","0.5923176","0.59180576","0.5896314","0.58914065","0.5855742","0.5855408","0.5847119","0.5834964","0.5834217","0.58339363","0.5830976","0.5828966","0.5825337","0.5821157","0.5819977","0.5819977","0.5819977","0.5801477","0.5797406","0.5786881","0.577232","0.577232","0.5770035","0.57697856","0.57624036","0.5753333","0.5746591","0.5739904","0.57149625","0.57053626","0.5695812","0.569483","0.56860006","0.567542"],"string":"[\n \"0.77077055\",\n \"0.7493195\",\n \"0.745413\",\n \"0.72979784\",\n \"0.7289367\",\n \"0.7035228\",\n \"0.6940678\",\n \"0.685062\",\n \"0.6848523\",\n \"0.6846582\",\n \"0.6830779\",\n \"0.68089354\",\n \"0.6791377\",\n \"0.675848\",\n \"0.67513496\",\n \"0.67148095\",\n \"0.66720396\",\n \"0.6649063\",\n \"0.66395855\",\n \"0.6601104\",\n \"0.6592569\",\n \"0.6562268\",\n \"0.6521006\",\n \"0.6456873\",\n \"0.63926756\",\n \"0.6379958\",\n \"0.6332176\",\n \"0.6320637\",\n \"0.6283928\",\n \"0.62714064\",\n \"0.62558323\",\n \"0.6214694\",\n \"0.62109387\",\n \"0.62042576\",\n \"0.620313\",\n \"0.62013745\",\n \"0.61882114\",\n \"0.61555904\",\n \"0.61416554\",\n \"0.61385953\",\n \"0.61215323\",\n \"0.6100427\",\n \"0.60878617\",\n \"0.60878617\",\n \"0.608396\",\n \"0.6081219\",\n \"0.6081219\",\n \"0.6078385\",\n \"0.6077722\",\n \"0.605294\",\n \"0.60470647\",\n \"0.60456073\",\n \"0.60209715\",\n \"0.6015747\",\n \"0.59961295\",\n \"0.5992714\",\n \"0.5991083\",\n \"0.5985822\",\n \"0.5965448\",\n \"0.5964509\",\n \"0.59589636\",\n \"0.59431726\",\n \"0.594167\",\n \"0.59345007\",\n \"0.592926\",\n \"0.592926\",\n \"0.5923176\",\n \"0.59180576\",\n \"0.5896314\",\n \"0.58914065\",\n \"0.5855742\",\n \"0.5855408\",\n \"0.5847119\",\n \"0.5834964\",\n \"0.5834217\",\n \"0.58339363\",\n \"0.5830976\",\n \"0.5828966\",\n \"0.5825337\",\n \"0.5821157\",\n \"0.5819977\",\n \"0.5819977\",\n \"0.5819977\",\n \"0.5801477\",\n \"0.5797406\",\n \"0.5786881\",\n \"0.577232\",\n \"0.577232\",\n \"0.5770035\",\n \"0.57697856\",\n \"0.57624036\",\n \"0.5753333\",\n \"0.5746591\",\n \"0.5739904\",\n \"0.57149625\",\n \"0.57053626\",\n \"0.5695812\",\n \"0.569483\",\n \"0.56860006\",\n \"0.567542\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7347803"},"document_rank":{"kind":"string","value":"3"}}},{"rowIdx":22487,"cells":{"query":{"kind":"string","value":"Return world state HC state cooccurance matrix."},"document":{"kind":"string","value":"def get_co_occ_mat(s_hc_ml, n_s_real, n_s_hc):\n\n co_occs = np.zeros((n_s_hc, n_s_real))\n for idx, n in s_hc_ml.items():\n co_occs[idx] = n\n\n return co_occs"},"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 CTMCtoStormpy(h):\n\tstate_labelling = _buildStateLabeling(h)\n\ttransition_matrix = deepcopy(h.matrix)\n\te = array([h.e(s) for s in range(h.nb_states)])\n\ttransition_matrix /= e[:,newaxis]\n\ttransition_matrix = st.build_sparse_matrix(transition_matrix)\n\tcomponents = st.SparseModelComponents(transition_matrix=transition_matrix,\n\t\t\t\t\t\t\t\t\t\t state_labeling=state_labelling,\n\t\t\t\t\t\t\t\t\t\t rate_transitions=True)\n\tcomponents.exit_rates = e\n\tctmc = st.storage.SparseCtmc(components)\n\treturn ctmc","def _get_obs(self):\n # return np.concatenate((self.world.state[:6], self.world.state[7:13]))\n return np.concatenate((self.world.state, np.zeros(7)))\n # return self.world.state","def get_cov_matrix_states(self):\n cov = numpy.diag(numpy.zeros(self.get_num_variables()))\n i = 0\n for v in self.variables:\n cov[i,i] = v.get_covariance()\n i += 1\n return cov","def _excitonic_coft_all(self,SS,AG):\n \n # SystemBathInteraction\n sbi = AG.get_SystemBathInteraction()\n # CorrelationFunctionMatrix\n cfm = sbi.CC\n \n c0 = AG.monomers[0].get_egcf((0,1))\n Nt = len(c0)\n \n Nst = AG.HamOp.dim\n ct = numpy.zeros((Nst,Nt),dtype=numpy.complex128)\n\n # electronic states corresponding to single excited states\n import time\n timecount = 0\n elst = numpy.where(AG.which_band == 1)[0]\n start = time.time()\n for el1 in elst:\n for el2 in elst:\n coft = cfm.get_coft(el1-1,el2-1)\n start2 = time.time()\n for kk in AG.vibindices[el1]:\n for ll in AG.vibindices[el2]:\n ct[:,:] += numpy.dot(\n numpy.expand_dims((SS[kk,:]**2)*(SS[ll,:]**2),axis=1),\n numpy.expand_dims(coft,axis=0))\n stop2 = time.time()\n timecount += stop2 - start2\n stop = time.time()\n print(stop-start,stop-start - timecount)\n return ct","def c_():\r\n c = np.array([[0, 0], [0, 100], [100, 100], [100, 80], [20, 80],\r\n [20, 20], [100, 20], [100, 0], [0, 0]])\r\n return c","def get_system_state_matrix(self):\n\n # Create empty matrix to describe system state\n A = zeros((9,9))\n\n # Q-dot terms\n A[0:3,3:6] = 0.5*identity(3)\n\n # X and Y gravity terms and Z rotation\n A[3:6,0:3] = matrix([\n [2*self.params['g']*self.params['lp']*self.params['Mp']/(self.params['Ix']), 0, 0],\n [0, 2*self.params['g']*self.params['lp']*self.params['Mp']/(self.params['Iy']), 0],\n [0, 0, 2*self.params['g']*self.params['Fspin_z']/(self.params['Iz'])]\n ])\n # Friction in pendulum rotation on pendulum\n A[3:6,3:6] = matrix([\n [self.params['Bx']/(self.params['Ix']), 0, 0],\n [0, self.params['By']/(self.params['Iy']), 0],\n [0, 0, self.params['Bz']/(self.params['Iz'])]\n ])\n # Friction in wheel rotation on pendulum\n A[3:6,6:9] = matrix([\n [-self.params['Bw']/(self.params['Ix']), 0, 0],\n [0, -self.params['Bw']/(self.params['Iy']), 0],\n [0, 0, -self.params['Bw']/(self.params['Iz'])]\n ])\n # Friction in wheel rotation on wheel\n A[6:9,6:9] = -self.params['Bw']/self.params['Iw']*identity(3)\n\n return A","def coherence(self):\r\n\r\n #XXX Calculate this from the standard output, instead of recalculating\r\n #the coherence:\r\n\r\n tseries_length = self.input.data.shape[0]\r\n spectrum_length = self.spectrum.shape[-1]\r\n coherence = np.zeros((tseries_length,\r\n tseries_length,\r\n spectrum_length))\r\n\r\n for i in range(tseries_length):\r\n for j in range(i, tseries_length):\r\n coherence[i][j] = tsa.coherence_spec(self.spectrum[i][j],\r\n self.spectrum[i][i],\r\n self.spectrum[j][j])\r\n\r\n idx = tril_indices(tseries_length, -1)\r\n coherence[idx[0], idx[1], ...] = coherence[idx[1], idx[0], ...].conj()\r\n\r\n return coherence","def chol_covar(self) -> np.ndarray:\n if self._chol_covar is None:\n self._chol_covar = batched_cholesky(self.covar)\n return self._chol_covar","def get_global_stiffness_matrix(self) -> NDArray[np.float64]:\n\n s = self.sin()\n c = self.cos()\n matrix_helper = [\n c ** 2,\n c * s,\n -(c ** 2),\n -c * s,\n c * s,\n s ** 2,\n -c * s,\n -(s ** 2),\n -(c ** 2),\n -c * s,\n c ** 2,\n c * s,\n -c * s,\n -(s ** 2),\n c * s,\n s ** 2,\n ]\n return (\n self.youngs_modulus\n * self.area\n / self.get_length()\n * np.array(matrix_helper, dtype=np.float64).reshape(4, 4)\n )","def coaccessible_components(self):\n DG = self.digraph().reverse()\n coaccessible_states = DG.breadth_first_search(\n [_.label() for _ in self.iter_final_states()])\n return self.induced_sub_finite_state_machine(\n [self.state(_) for _ in coaccessible_states])","def get_features_critic(state):\n # reshape to make it a matrix with one row (so we can transpose it later)\n p, v = state\n p_v = np.array([p, v]).reshape((1, -1)).T\n X = np.array([p_v - c_entry.T for c_entry in C])\n inv_cov = np.linalg.inv(np.diag([0.04, 0.0004]))\n phi = np.array([np.exp(-(xi.T @ inv_cov @ xi) / 2) for xi in X])\n\n return np.squeeze(phi) # get rid of 2 unnecessary dimensions","def cofactorMatrix(self):\n returnvalue = Matrix()\n for i in range(self._height):\n newRow = list()\n for j in range(self._width):\n newRow.append(self.cofactor(i, j))\n returnvalue.addRow(*newRow)\n return returnvalue","def scc(self):\n if not self.is_square:\n raise DMNonSquareMatrixError('Matrix must be square for scc')\n\n return self.rep.scc()","def state_descriptor(self, state, player):\n\n\t\tboard = np.copy(state.board)\n\n\t\tnp.place(board, board == -1, 0)\n\t\tnp.place(board, board == player, 3)\n\t\tnp.place(board, board == 3 - player, -1)\n\t\tnp.place(board, board == 3, 1)\n\n\t\tmy_cows = state.cows[player - 1]\n\t\ten_cows = state.cows[2 - player]\n\n\t\treturn (self.totuple(board), my_cows, en_cows)","def bc_outgoing_mat(n, h, k):\n \n d = [1.0, 2.0j*k*h]\n i = [n-1, n-1]\n j = [n-2, n-1]\n return scipy.sparse.coo_matrix((d, (i, j)))","def cofactor_matrix(self):\n resp = []\n len_b = len(self.take_vec())\n for i in range(self.order):\n _matrix = aux.cofactor(self.take_matrix(),\n (i, self.order-1)\n )\n _resp = math.pow(-1, len_b-1)\n _resp = _resp * np.linalg.det(_matrix)\n _resp = _resp * math.pow(-1, i * (self.order-1))\n resp.append(int(round(_resp)))\n\n return resp","def appearance_space(state_data, C):\n return np.dot(C, state_data)","def MCtoStormpy(h):\n\tstate_labelling = _buildStateLabeling(h)\n\ttransition_matrix = h.matrix\n\ttransition_matrix = st.build_sparse_matrix(transition_matrix)\n\tcomponents = st.SparseModelComponents(transition_matrix=transition_matrix,\n\t\t\t\t\t\t\t\t\t\t state_labeling=state_labelling)\n\tmc = st.storage.SparseDtmc(components)\n\treturn mc","def _C(self):\n\n # Find the local x and y coordinates at each node\n xi = 0\n yi = 0\n xj = self.width()\n yj = 0\n xm = xj\n ym = self.height()\n xn = 0\n yn = ym\n\n # Calculate the [C] coefficient matrix\n C = array([[1, xi, yi, xi**2, xi*yi, yi**2, xi**3, xi**2*yi, xi*yi**2, yi**3, xi**3*yi, xi*yi**3],\n [0, 0, 1, 0, xi, 2*yi, 0, xi**2, 2*xi*yi, 3*yi**2, xi**3, 3*xi*yi**2],\n [0, -1, 0, -2*xi, -yi, 0, -3*xi**2, -2*xi*yi, -yi**2, 0, -3*xi**2*yi, -yi**3],\n \n [1, xj, yj, xj**2, xj*yj, yj**2, xj**3, xj**2*yj, xj*yj**2, yj**3, xj**3*yj, xj*yj**3],\n [0, 0, 1, 0, xj, 2*yj, 0, xj**2, 2*xj*yj, 3*yj**2, xj**3, 3*xj*yj**2],\n [0, -1, 0, -2*xj, -yj, 0, -3*xj**2, -2*xj*yj, -yj**2, 0, -3*xj**2*yj, -yj**3],\n\n [1, xm, ym, xm**2, xm*ym, ym**2, xm**3, xm**2*ym, xm*ym**2, ym**3, xm**3*ym, xm*ym**3],\n [0, 0, 1, 0, xm, 2*ym, 0, xm**2, 2*xm*ym, 3*ym**2, xm**3, 3*xm*ym**2],\n [0, -1, 0, -2*xm, -ym, 0, -3*xm**2, -2*xm*ym, -ym**2, 0, -3*xm**2*ym, -ym**3],\n\n [1, xn, yn, xn**2, xn*yn, yn**2, xn**3, xn**2*yn, xn*yn**2, yn**3, xn**3*yn, xn*yn**3],\n [0, 0, 1, 0, xn, 2*yn, 0, xn**2, 2*xn*yn, 3*yn**2, xn**3, 3*xn*yn**2],\n [0, -1, 0, -2*xn, -yn, 0, -3*xn**2, -2*xn*yn, -yn**2, 0, -3*xn**2*yn, -yn**3]])\n \n # Return the coefficient matrix\n return C","def get_co_occ_matrix(res, last_n, s_real, s_hc):\n\n s = np.array(res['s'])[-last_n:]\n hc_ml = np.array(res['hc_ml'])[-last_n:]\n co_occs = np.zeros((len(s_hc), len(s_real)))\n for si, hc_mli in zip(s, hc_ml):\n co_occs[s_hc.index(hc_mli), s_real.index(si)] += 1\n co_occs /= co_occs.sum()\n\n return co_occs","def state(self):\n state = np.array(self.get_state_arr())\n om = utils.build_occupancy_maps(utils.build_humans(state))\n # We only have a batch of one so just get the first element of\n # transform and rotate\n state = utils.transform_and_rotate(state.reshape((1, -1)))[0]\n return torch.cat((state, om), dim=1)","def ws(self, inhom_object):\n z = inhom_object.zeta\n # define bohr frequencies of eigenstates--currently a and b are \n # correlated \n wg = 0.0 + 0*z\n wa = z + self.wa_central\n wb = z + self.wb_central\n w2a = 2*wa - self.a_coupling\n w2b = 2*wb - self.b_coupling\n wc = wa + wb - self.ab_coupling\n # define coherence frequencies\n w_gg = wg - wg\n w_ag = wa - wg\n w_bg = wb - wg\n w_2ag = w2a - wg\n w_2bg = w2b - wg\n w_cg = wc - wg\n # array aggregates all frequencies to match state vectors\n w = np.array( [w_gg, \n w_ag, w_bg,\n w_2ag, w_2bg, w_cg\n ])\n return w","def get_input_state_matrix(self):\n\n # state matrix for control\n B = zeros((9,3))\n\n # control term impact on pendulum\n B[3:6,0:3] = matrix([\n [self.params['Km']/(self.params['Ix'])/self.params['Rm'], 0, 0],\n [0, self.params['Km']/(self.params['Iy'])/self.params['Rm'], 0],\n [0, 0, self.params['Km']/(self.params['Iz'])/self.params['Rm']]\n ])\n # control term impact on wheel\n B[6:9,0:3] = self.params['Km']/self.params['Iw']/self.params['Rm']*identity(3)\n\n return B","def covar(self) -> np.ndarray:\n if self._covar is None:\n self._covar = batched_inv_spd(batched_cholesky(self._inv_covar))\n return self._covar","def scc(self):\n return self.to_ddm().scc()","def conditional_component_covs(self):\n return np.array([d.conditional_cov() for d in self.conditionalMVNs])","def cudnn_lstm_state(lstm_cell_state):\n h = tf.stack([s.h for s in lstm_cell_state])\n c = tf.stack([s.c for s in lstm_cell_state])\n return (h, c)","def visualizeC(self, M=None):\n try:\n import seaborn as sns\n import matplotlib.pyplot as plt\n except:\n print(\"Seaborn or matplotlib not imported...can't build the heatmap\")\n if M is None:\n M = self.stateC\n a = torch.argmax(M, dim=0)\n print(self.find_TPname(a))\n M = pd.DataFrame(M.numpy(), index=list(\n self.filler2index.keys()), columns=list(self.role2index.keys()))\n sns.heatmap(M, annot=True, cmap=\"Blues\")\n plt.show()","def m_c(self) -> np.ndarray:\n assert self._k is not None, \"camera must be calibrated\"\n return forge_projective_matrix(self._k)","def get_constr_obs_states_high(self):\n constrHi = numpy.empty(self.get_num_variables())\n i = 0\n for v in self.variables:\n constrHi[i] = v.get_constraint_high()\n i += 1\n return constrHi","def get_state():\n global HMC_MOM\n return dict(randstate = np.random.get_state(),\n mom = HMC_MOM)","def get_cov_matrix_state_pars(self):\n cov = numpy.diag(numpy.zeros(self.get_num_variables() + self.get_num_parameters()))\n i = 0\n for v in self.variables:\n cov[i,i] = v.get_covariance()\n i += 1\n for p in self.parameters:\n cov[i,i] = p.get_covariance()\n i += 1\n return cov","def closeness_centrality(self):\n try:\n self.logger.info('正在计算网络的接近中心性 ...')\n return self.order_dict(nx.closeness_centrality(self.G), index=1)\n except Exception as e:\n self.logger.error(\"计算失败，原因：{0}\".format(e))","def to_coo_matrix(self):\n if self.E > 0:\n i, j = self.edges.T\n sm = coo_matrix((self.weights, (i, j)), shape=(self.V, self.V))\n else:\n sm = coo_matrix((self.V, self.V))\n return sm","def getstate(self):\n out = []\n for row in self.a:\n out.append([])\n for item in row:\n out[-1].append(itemstate(item))\n return (\"matrix\", out, self.y, self.x, self.converter, self.onlydiag())","def _get_state(self):\n\n # stack all variables and return state array\n state = np.hstack((self.sheep_com, self.farthest_sheep, \n self.target, self.dog_pose, self.radius_sheep, \n self.target_distance))\n return state","def sim_to_state(self):\n # As mentioned earlier, we only need z-axis variables.\n # This will keep our state nice and small.\n return [\n self.sim.pose[Z_AXIS],\n self.sim.v[Z_AXIS],\n self.sim.linear_accel[Z_AXIS]\n ]","def to_coo_matrix(self):\n if self.E > 0:\n i, j = self.edges.T\n sm = coo_matrix((np.ones(self.E), (i, j)),\n shape=(self.V, self.V))\n else:\n sm = coo_matrix((self.V, self.V))\n return sm","def get_state(self):\n return np.append(self.game.game_board.get_board(),\n [self.game.player_1.color, self.game.player_2.color])[None, :]","def get_matrix(self):\n matrix = np.zeros([len(self.states), len(self.states)])\n starting_states = []\n transitions = []\n\n for chords in self.training_data:\n states = []\n is_starting_state = True\n chunks = [chords[x:x+self.order] for x in range(0,\n len(chords), self.order)]\n for chunk in chunks:\n chunk_string = \" \".join(chunk)\n if is_starting_state:\n starting_states.append(chunk_string)\n is_starting_state = False\n states.append(chunk_string)\n\n for i in range(0, len(states)):\n if i < (len(states)) - 1:\n transitions.append([states[i], states[i + 1]])\n else:\n transitions.append([states[i]])\n\n self.starting_probabilities = np.zeros([len(self.states)])\n\n for transition in transitions:\n for row, row_contents in enumerate(self.transitions):\n for col, _ in enumerate(row_contents):\n if transition == self.transitions[row][col]:\n matrix[row][col] += 1\n\n for i, state in enumerate(self.states):\n for j, possible_state in enumerate(starting_states):\n if state == possible_state:\n self.starting_probabilities[j] += 1\n\n num = sum(self.starting_probabilities)\n for i, prob in enumerate(self.starting_probabilities):\n self.starting_probabilities[i] = prob / num\n\n for m in range(len(matrix)):\n num = sum(matrix[m])\n if int(num) is not 0:\n for i in range(len(matrix[m])):\n matrix[m][i] = (matrix[m][i] / num)\n else:\n matrix[m] = self.starting_probabilities\n return matrix","def complete_list_of_states():\n # funny way of getting all the states that are defined in ConcertClientState.msg\n return concert_msgs.ConductorGraph.__slots__","def get_transition_matrix(self):\n # Transition matrix\n P_trans = np.zeros((self.n_states, self.n_states))\n\n # Fill\n for s_num in range(2, (2**self.depth)):\n P_trans[(s_num - 1), (s_num//2 - 1)] = 1.0\n\n return P_trans","def covar(self):\n wwt = self.ww.copy()\n wwt[self.ww>0] = 1.0/self.ww[self.ww>0]\n covar = np.zeros((self.nstar,self.nstar),dtype=self.ww.dtype)\n for i in range(self.nstar):\n for j in range(i+1):\n covar[i,j] = np.sum(wwt * self.vv[:,i] * self.vv[:,j])\n covar[j,i] = covar[i,j]\n return covar","def armature_coor2world_coor():\n return armature.matrix_world @ bones_list[0].head","def coherency(self):\r\n data = self.input.data\r\n tseries_length = data.shape[0]\r\n spectrum_length = self.spectrum.shape[-1]\r\n\r\n coherency = np.zeros((tseries_length,\r\n tseries_length,\r\n spectrum_length), dtype=complex)\r\n\r\n for i in range(tseries_length):\r\n for j in range(i, tseries_length):\r\n coherency[i][j] = tsa.coherency_spec(self.spectrum[i][j],\r\n self.spectrum[i][i],\r\n self.spectrum[j][j])\r\n\r\n idx = tril_indices(tseries_length, -1)\r\n coherency[idx[0], idx[1], ...] = coherency[idx[1], idx[0], ...].conj()\r\n\r\n return coherency","def get_feature_matrix(self):\n phi_mat = np.empty((self.n_states, self.feature_dim))\n for s_idx in range(self.n_states):\n cur_phi = self.state_2_features((s_idx+1))\n phi_mat[s_idx, :] = cur_phi\n\n return phi_mat","def _compute_world_params(self) -> None:\n\n self.states = []\n for row in range(self.grid_height):\n for col in range(self.grid_width):\n cell = row * self.grid_width + col\n cell_type = self.grid[cell]\n\n possible_actions = {\n Action.up: self._get_action(max(row - 1, 0) * self.grid_width + col),\n Action.down: self._get_action(min(row + 1, self.grid_height - 1) * self.grid_width + col),\n Action.right: self._get_action(row * self.grid_width + min(col + 1, self.grid_width - 1)),\n Action.left: self._get_action(row * self.grid_width + max(col - 1, 0))\n }\n\n self.states.append(State(cell, possible_actions, cell_type))","def get_transition(self, row, col, action, tot_row, tot_col):\n\n '''\n Expand the grid of the environment to handle when the \n agent decides to move in the direction of a wall \n '''\n state_probabilities = np.zeros((int(np.sqrt(self.env.observation_space.n)) + 2, int(np.sqrt(self.env.observation_space.n)) + 2), dtype=float)\n\n if action == 'UP':\n row += 1\n col += 1\n state_probabilities[row - 1, col] = 0.33 #UP\n state_probabilities[row, col - 1 ] = 0.33 #LEFT\n state_probabilities[row, col + 1] = 0.33 # RIGHT\n state_probabilities[row + 1, col] = 0.0 #DOWN\n elif action == 'LEFT':\n row += 1\n col += 1\n state_probabilities[row - 1, col] = 0.33 #UP\n state_probabilities[row, col - 1 ] = 0.33 #LEFT\n state_probabilities[row, col + 1] = 0.0 # RIGHT\n state_probabilities[row + 1, col] = 0.33 #DOWN\n elif action == 'RIGHT':\n row += 1\n col += 1\n state_probabilities[row - 1, col] = 0.33 #UP\n state_probabilities[row, col - 1 ] = 0.0 #LEFT\n state_probabilities[row, col + 1] = 0.33 # RIGHT\n state_probabilities[row + 1, col] = 0.33 #DOWN\n elif action == 'DOWN':\n row += 1\n col += 1\n state_probabilities[row - 1, col] = 0.0 # UP\n state_probabilities[row, col - 1] = 0.33 # LEFT\n state_probabilities[row, col + 1] = 0.33 # RIGHT\n state_probabilities[row + 1, col] = 0.33 # DOWN\n\n for row in range (0, tot_row+1):\n if state_probabilities[row, 0] != 0:\n state_probabilities[row, 1] += state_probabilities[row, 0]\n elif state_probabilities[row, -1] != 0:\n state_probabilities[row, -2] += state_probabilities[row, -1]\n\n for col in range (0, tot_col+1):\n if state_probabilities[0, col] != 0:\n state_probabilities[1, col] += state_probabilities[0, col]\n elif state_probabilities[-1, col] != 0:\n state_probabilities[-2, col] += state_probabilities[-1, col]\n\n return state_probabilities[1: 1+tot_row, 1:1+tot_col]","def get_transition(self, row, col, action, tot_row, tot_col):\n\n '''\n Expand the grid of the environment to handle when the \n agent decides to move in the direction of a wall \n '''\n state_probabilities = np.zeros((int(np.sqrt(self.env.observation_space.n)) + 2, int(np.sqrt(self.env.observation_space.n)) + 2), dtype=float)\n\n if action == 'UP':\n row += 1\n col += 1\n state_probabilities[row - 1, col] = 0.33 #UP\n state_probabilities[row, col - 1 ] = 0.33 #LEFT\n state_probabilities[row, col + 1] = 0.33 # RIGHT\n state_probabilities[row + 1, col] = 0.0 #DOWN\n elif action == 'LEFT':\n row += 1\n col += 1\n state_probabilities[row - 1, col] = 0.33 #UP\n state_probabilities[row, col - 1 ] = 0.33 #LEFT\n state_probabilities[row, col + 1] = 0.0 # RIGHT\n state_probabilities[row + 1, col] = 0.33 #DOWN\n elif action == 'RIGHT':\n row += 1\n col += 1\n state_probabilities[row - 1, col] = 0.33 #UP\n state_probabilities[row, col - 1 ] = 0.0 #LEFT\n state_probabilities[row, col + 1] = 0.33 # RIGHT\n state_probabilities[row + 1, col] = 0.33 #DOWN\n elif action == 'DOWN':\n row += 1\n col += 1\n state_probabilities[row - 1, col] = 0.0 # UP\n state_probabilities[row, col - 1] = 0.33 # LEFT\n state_probabilities[row, col + 1] = 0.33 # RIGHT\n state_probabilities[row + 1, col] = 0.33 # DOWN\n\n for row in range (0, tot_row+1):\n if state_probabilities[row, 0] != 0:\n state_probabilities[row, 1] += state_probabilities[row, 0]\n elif state_probabilities[row, -1] != 0:\n state_probabilities[row, -2] += state_probabilities[row, -1]\n\n for col in range (0, tot_col+1):\n if state_probabilities[0, col] != 0:\n state_probabilities[1, col] += state_probabilities[0, col]\n elif state_probabilities[-1, col] != 0:\n state_probabilities[-2, col] += state_probabilities[-1, col]\n\n return state_probabilities[1: 1+tot_row, 1:1+tot_col]","def state_summary(self) -> np.ndarray:\n return np.array([len(self.current_clear_nodes), self.n_current_infected, len(self.current_isolated_nodes),\n len(self.current_immune_nodes), len(self.current_alive_nodes)])","def csi(self):\n return self.table[0, 0] / (self.table[0, 0] + self.table[0, 1] + self.table[1, 0])","def state_graph(self) -> np.ndarray:\n return nx.convert_matrix.to_numpy_array(self.g_,\n dtype=np.int16)","def get_initial_hx(self, input_seq, hidden_state):\n num_directions = 2 if self.lstm.bidirectional else 1\n # hidden state\n hidden = hidden_state.view(self.lstm.num_layers * num_directions, len(hidden_state), -1)\n # cell state\n c_zeros = torch.zeros(self.lstm.num_layers * num_directions,\n input_seq.size(0), self.lstm.hidden_size,\n dtype=input_seq.dtype, device=input_seq.device)\n return hidden, c_zeros","def _getState(self, board):\r\n mySide = board.mySide(self.id)\r\n oppSide = board.oppSide(self.id)\r\n myMancala = board.stonesInMyMancala(self.id)\r\n oppMancala = board.stonesInOppMancala(self.id)\r\n \r\n state = [] # size should be inputSize - 1\r\n state.append(float(myMancala))\r\n# for i in range(self.rowSize):\r\n# state.append(mySide[i])\r\n for my in mySide:\r\n state.append(float(my))\r\n state.append(float(oppMancala))\r\n# for i in range(self.rowSize):\r\n# state.append(oppSide[i])\r\n for op in oppSide:\r\n state.append(float(op))\r\n return state","def cost_matrix(self):\n return self.__cost_matrix","def cc(self):\n try:\n from scipy.sparse import cs_graph_components\n _, label = cs_graph_components(self.adjacency())\n except:\n pass\n lil = self.to_coo_matrix().tolil().rows.tolist()\n label = lil_cc(lil)\n return label","def modelCS(self,ht=None,hf=None):\n if ht is not None:\n hf = time2freq(ht)\n cs,a,b,c = make_model_cs(hf, self.s0, self.bw, self.ref_freq)\n \n return cs","def _compute_total_covariance_matrix_states_time(self) -> tf.Tensor:\n aux_matrix = tf.matmul(self.b_matrix, tf.matmul(self.omega_matrix,\n self.b_matrix,\n transpose_b=True))\\\n + self.t_matrix\n total_covariance_matrix = self.total_c_phi\\\n + tf.matmul(self.s_matrix_inv,\n tf.matmul(aux_matrix, self.s_matrix_inv))\n return total_covariance_matrix","def state_nodes(self) -> np.ndarray:\n return np.array([[nd[c] for c in [\"alive\", \"infected\", \"immune\", \"isolated\", \"masked\"]]\n for nv, nd in self.g_.nodes.data()])","def make_states(self) -> np.ndarray:\n state = []\n for agent in range(self.agents):\n r, c = self.game.get_agent_pos(agent)\n state.append(r)\n state.append(c)\n for field in constants.PAYOFF_LOCATIONS:\n r, c = field\n state.append(r)\n state.append(c)\n state.append(self.turns_count)\n for field in self.special_payoff_fields:\n r, c = field\n state.append(r)\n state.append(c)\n\n informed_state = np.array(state)\n uninformed_state = copy.deepcopy(informed_state)\n uninformed_state[-2:] = 0\n\n states = [\n informed_state if i < self.num_informed else uninformed_state\n for i in range(self.agents)\n ]\n states = np.stack(states, axis=0)\n\n return states","def EmptyStateMat(nX,nU,nY):\n Xx = np.zeros((nX,nX)) # Ac \n Yx = np.zeros((nY,nX)) # Gc \n Xu = np.zeros((nX,nU)) # Xu \n Yu = np.zeros((nY,nU)) # Jc \n return Xx,Xu,Yx,Yu","def total_electronic_hamiltonian(self):\n return block_diag(*[self.electronic_manifold(n) for n in range(3)])","def ground_state(self) -> numpy.ndarray:\n # Optimal cost\n cost_min = min(self.cost)\n # Find the args that corresponds to the optimal cost\n args = numpy.where(self.cost == cost_min)\n # Create the ideal state\n rho = numpy.zeros(2**self.num_nodes)\n for arg in args[0]:\n rho[arg] = 1\n return rho / numpy.sum(rho)","def co_vertexes(self):\n theta = self.orientation + np.pi / 2\n shifts = np.array([np.cos(theta), np.sin(theta)]) * self.b\n return self.coords + (shifts[:, None] * [-1, 1]).T","def clinopyroxene_92():\n\n rho = 3327.\n\n C = np.zeros((6,6), dtype=float)\n C[0,0] = 257.3; C[0,1] = 85.9; C[0,2] = 76.2; C[0,3] = 0.; C[0,4] = 7.1; C[0,5] = 0.\n C[1,0] = C[0,1]; C[1,1] = 216.2; C[1,2] = 71.8; C[1,3] = 0.; C[1,4] = 13.3; C[1,5] = 0.\n C[2,0] = C[0,2]; C[2,1] = C[1,2]; C[2,2] = 260.2; C[2,3] = 0.; C[2,4] = 33.7; C[2,5] = 0.\n C[3,0] = C[0,3]; C[3,1] = C[1,3]; C[3,2] = C[2,3]; C[3,3] = 80.2; C[3,4] = 0.; C[3,5] = 10.2\n C[4,0] = C[0,4]; C[4,1] = C[1,4]; C[4,2] = C[2,4]; C[4,3] = C[3,4]; C[4,4] = 70.6; C[4,5] = 0.\n C[5,0] = C[0,5]; C[5,1] = C[1,5]; C[5,2] = C[2,5]; C[5,3] = C[3,5]; C[5,4] = C[4,5]; C[5,5] = 85.8\n\n return C, rho","def gstate(N, periodic):\n\n # Create Hamiltonian matrix\n H = kronH(N, periodic)\n \n # Diagonalize\n print('Diagonalizing...', end=' ', flush=True)\n w, v = eigsh(H, k=1, which='SA')\n print('Done')\n\n return w[0]","def __getstate__(self):\n\n sparse_dict = self.__dict__.copy()\n\n # Store a sparse representation of this\n sparse_dict['transition_matrix'] = sparse.csr_matrix(self.__dict__['transition_matrix'])\n\n # Don't pickle this, because 1) it can be calculated and 2) it's large and not sparse\n sparse_dict.pop('cumulative_probabilities')\n\n return sparse_dict","def __build_state_index(self):\n\n # the index for the system state\n # [rho_i, q_in, q_out, r_i, f_i]\n x_index = {}\n\n # add the density index\n x_index['density'] = OrderedDict()\n for i in range(0, self.num_cells):\n x_index['density'][i] = i\n dim_state = self.num_cells\n\n # add the upstream boundary flow\n x_index['qin'] = dim_state\n x_index['qout'] = dim_state + 1\n dim_state += 2\n\n # add on ramp variables\n # x_index['onramp'] = OrderedDict()\n # if self.cell_onramp is not None:\n # # if onramp exist in the network, otherwise skip\n # for cell_id in self.cell_onramp:\n # # add the absolute index into the state index dictionary\n # # r_i index = self.x_index{'onramp'][cell_i]\n # x_index['onramp'][cell_id] = dim_state\n # dim_state += 1\n #\n #\n # # add off ramp state variables\n # x_index['offramp'] = OrderedDict()\n # if self.cell_offramp is not None:\n # for cell_id in self.cell_offramp:\n # # add the absolute index\n # x_index['offramp'][cell_id] = dim_state\n # dim_state += 1\n\n return x_index, dim_state","def calc_cmatrix(self):\n tw = self.twiss_df\n res = self._results_df\n\n LOG.debug(\"Calculating CMatrix.\")\n with timeit(lambda t:\n LOG.debug(\" CMatrix calculated in {:f}s\".format(t))):\n\n j = np.array([[0., 1.],\n [-1., 0.]])\n rs = np.reshape(tw.as_matrix(columns=[\"R11\", \"R12\",\n \"R21\", \"R22\"]),\n (len(tw), 2, 2))\n cs = np.einsum(\"ij,kjn,no->kio\",\n -j, np.transpose(rs, axes=(0, 2, 1)), j)\n cs = np.einsum(\"k,kij->kij\", (1 / np.sqrt(1 + np.linalg.det(rs))), cs)\n\n g11a = 1 / np.sqrt(tw.loc[:, \"BETX\"])\n g12a = np.zeros(len(tw))\n g21a = tw.loc[:, \"ALFX\"] / np.sqrt(tw.loc[:, \"BETX\"])\n g22a = np.sqrt(tw.loc[:, \"BETX\"])\n gas = np.reshape(np.array([g11a, g12a,\n g21a, g22a]).T,\n (len(tw), 2, 2))\n\n ig11b = np.sqrt(tw.loc[:, \"BETY\"])\n ig12b = np.zeros(len(tw))\n ig21b = -tw.loc[:, \"ALFY\"] / np.sqrt(tw.loc[:, \"BETY\"])\n ig22b = 1. / np.sqrt(tw.loc[:, \"BETY\"])\n igbs = np.reshape(np.array([ig11b, ig12b,\n ig21b, ig22b]).T,\n (len(tw), 2, 2))\n cs = np.einsum(\"kij,kjl,kln->kin\", gas, cs, igbs)\n gammas = np.sqrt(1 - np.linalg.det(cs))\n\n res.loc[:, \"GAMMA_C\"] = gammas\n\n res.loc[:, \"F1001_C\"] = ((cs[:, 0, 0] + cs[:, 1, 1]) * 1j +\n (cs[:, 0, 1] - cs[:, 1, 0])) / 4 / gammas\n res.loc[:, \"F1010_C\"] = ((cs[:, 0, 0] - cs[:, 1, 1]) * 1j +\n (-cs[:, 0, 1]) - cs[:, 1, 0]) / 4 / gammas\n\n res.loc[:, \"C11\"] = cs[:, 0, 0]\n res.loc[:, \"C12\"] = cs[:, 0, 1]\n res.loc[:, \"C21\"] = cs[:, 1, 0]\n res.loc[:, \"C22\"] = cs[:, 1, 1]\n\n LOG.debug(\" Average coupling amplitude |F1001|: {:g}\".format(np.mean(\n np.abs(res.loc[:, \"F1001_C\"]))))\n LOG.debug(\" Average coupling amplitude |F1010|: {:g}\".format(np.mean(\n np.abs(res.loc[:, \"F1010_C\"]))))\n LOG.debug(\" Average gamma: {:g}\".format(np.mean(\n np.abs(res.loc[:, \"GAMMA_C\"]))))\n\n self._log_added('GAMMA_C', 'F1001_C', 'F1010_C', 'C11', 'C12', 'C21', 'C22')","def incidence_matrix(self):\n try: \n return self._incidence_matrix\n except AttributeError:\n self._incidence_matrix = matrix(ZZ, len(self.Vrepresentation()), \n len(self.Hrepresentation()), 0)\n for V in self.Vrep_generator():\n for H in self.Hrep_generator():\n if self._is_zero(H*V):\n self._incidence_matrix[V.index(),H.index()] = 1\n\n return self._incidence_matrix","def to_coo_matrix(self):\n row_indices, column_indices, nonzero_elements = self.to_ijv()\n return coo_matrix((nonzero_elements, (row_indices, column_indices)),\n shape=(self.size, self.size))","def get_state(self):\n return self.fmu._get_continuous_states()","def return_state(board):\r\n state = np.array(INIT_ARRAY)\r\n for pos in BOARD_POSITIONS:\r\n state[pos[0]][pos[1]] = board[pos[0]][pos[1]].color\r\n\r\n return state","def covar(self):\n a, c, d, b = self.to_ccw()\n return a * d - b * c","def c( self , y , r , t = 0 ):\n \n u = np.zeros(self.m) # State derivative vector\n \n raise NotImplementedError\n \n return u","def connectivity_matrix(self):\n # TODO: make this more memory efficient by ordering i,j in code when needed.\n temp = []\n for i in range(self.n_atom):\n for j in range(i+1, self.n_atom):\n if self.bond(i, j):\n temp.append([i+1, j+1])\n self.connect = np.asarray(temp)","def _compute_grid_state(self, for_id):\n own = np.zeros_like(self._map, float)\n own_pos = self._id2pos[for_id]\n own[own_pos] = 1\n\n thieves = (self._map == THIEF ).astype(float)\n guardians = (self._map == GUARDIAN).astype(float)\n\n own_team = self.id2team[for_id]\n if own_team == THIEF:\n teammates = thieves\n opponents = guardians\n else:\n teammates = guardians\n opponents = thieves\n\n treasure_channel = (self._map == TREASURE).astype(float)\n\n # Channels first\n return np.stack([own, teammates, opponents, self._walls_channel, treasure_channel])","def _excitonic_coft_old(self,SS,AG,n):\n \n # FIXME: works only for 2 level molecules\n \n c0 = AG.monomers[0].get_egcf((0,1))\n Nt = len(c0)\n \n # SystemBathInteraction\n sbi = AG.get_SystemBathInteraction()\n # CorrelationFunctionMatrix\n cfm = sbi.CC\n \n # get number of monomeric basis states\n Na = 0\n for monomer in AG.monomers:\n Na += monomer.nel-1\n \n ct = numpy.zeros((Nt),dtype=numpy.complex128)\n #Na = AG.nmono\n for kk in range(Na):\n \n #nkk = AG.monomers[kk].egcf_mapping[0]\n \n for ll in range(Na):\n \n #nll = AG.monomers[ll].egcf_mapping[0]\n \n ct += ((SS[kk+1,n+1]**2)*(SS[ll+1,n+1]**2)*cfm.get_coft(kk,ll))\n #*AG.egcf_matrix.get_coft(nkk,nll))\n \n return ct","def get_meta_controller_state(self):\n state = np.zeros(self._num_primitive_actions)\n\n if len(self._selected_primitive_actions):\n selected_primitive_actions = np.array(self._selected_primitive_actions)\n max_primtive_action = np.max(selected_primitive_actions)\n state[max_primtive_action] = 1\n state = np.concatenate((state, np.copy(self._tried_constraints)), axis=0)\n\n return state","def _excitonic_coft(self,SS,AG,n):\n \n # SystemBathInteraction\n sbi = AG.get_SystemBathInteraction()\n # CorrelationFunctionMatrix\n cfm = sbi.CC\n \n c0 = AG.monomers[0].get_egcf((0,1))\n Nt = len(c0)\n \n ct = numpy.zeros((Nt),dtype=numpy.complex128)\n\n # electronic states corresponding to single excited states\n elst = numpy.where(AG.which_band == 1)[0]\n for el1 in elst:\n for el2 in elst:\n if cfm.cpointer[el1-1,el2-1] == 0:\n continue\n coft = cfm.get_coft(el1-1,el2-1) \n for kk in AG.vibindices[el1]:\n for ll in AG.vibindices[el2]:\n ct += ((SS[kk,n]**2)*(SS[ll,n]**2)*coft)\n return ct","def clinopyroxene_98():\n\n rho = 3190.\n\n C = np.zeros((6,6), dtype=float)\n C[0,0] = 237.8; C[0,1] = 83.5; C[0,2] = 80.; C[0,3] = 0.; C[0,4] = 9.; C[0,5] = 0.\n C[1,0] = C[0,1]; C[1,1] = 183.6; C[1,2] = 59.9; C[1,3] = 0.; C[1,4] = 9.5; C[1,5] = 0.\n C[2,0] = C[0,2]; C[2,1] = C[1,2]; C[2,2] = 229.5; C[2,3] = 0.; C[2,4] = 48.1; C[2,5] = 0.\n C[3,0] = C[0,3]; C[3,1] = C[1,3]; C[3,2] = C[2,3]; C[3,3] = 76.5; C[3,4] = 0.; C[3,5] = 8.4\n C[4,0] = C[0,4]; C[4,1] = C[1,4]; C[4,2] = C[2,4]; C[4,3] = C[3,4]; C[4,4] = 73.; C[4,5] = 0.\n C[5,0] = C[0,5]; C[5,1] = C[1,5]; C[5,2] = C[2,5]; C[5,3] = C[3,5]; C[5,4] = C[4,5]; C[5,5] = 81.6\n\n return C, rho","def covariates(self):\n return None","def cs(self):\n if hasattr(self, \"_cs_cache\"):\n return self._cs_cache\n return np.array([conf.cs for conf in self.configurations], dtype=int)","def x(self):\n # REPLACE THE FOLLOWING WITH THE LOGIC TO CONSTRUCT/RETURN THE STATE\n x = {key: 0.0 for key in self.model.states}\n\n return x","def get_state_arr(self):\n rpos = self.sim.getAgentPosition(self.robot_num)\n rvel = self.sim.getAgentVelocity(self.robot_num)\n rrad = self.sim.getAgentRadius(self.robot_num)\n v_pref = self.sim.getAgentMaxSpeed(self.robot_num)\n theta = math.atan2(rvel[1], rvel[0])\n # Robot's state entry. Note that goal is listed as the robot's current\n # position because we aren't using that goal as such, we are just\n # exploring.\n state = [\n rpos[0], rpos[1], rvel[0], rvel[1], rrad,\n self.headings[self.robot_num], rpos[0], rpos[1],\n v_pref, theta\n ]\n for agent in self.agents:\n if agent != self.robot_num: # We already accounted for the robot\n pos = self.sim.getAgentPosition(agent)\n vel = self.sim.getAgentVelocity(agent)\n rad = self.sim.getAgentRadius(agent)\n state.extend([pos[0], pos[1], vel[0], vel[1], rad,\n self.headings[agent]])\n for obs in self.obstacles:\n if len(obs) > 1:\n # Polygonal obstacle\n o = Polygon(obs)\n p = Point(rpos)\n p1, p2 = nearest_points(o, p)\n # Velocity is always 0 for obstacles\n # Heading is same as robot's\n state.extend([p1.x, p2.y, 0, 0, self.obs_width,\n self.headings[self.robot_num]])\n else:\n # Point obstacle\n state.extend([obs[0][0], obs[0][1], 0, 0, self.obs_width,\n self.headings[self.robot_num]])\n return state","def initialize_system(self):\n self.mfd.set_mesh(self.mesh)\n [[div_data, div_row, div_col], \n [div_t_data, div_t_row, div_t_col]] = self.mfd.build_div()\n [self.m_x_coo_data, \n m_x_coo_row, \n m_x_coo_col] = self.mfd.build_m(save_update_info=True)\n\n self.m_x_coo_length = len(self.m_x_coo_data)\n \n # The data for the bottom right should be zeros. \n [c_data, c_row, c_col] = self.mfd.build_bottom_right()\n \n [coupling_data, coupling_row, coupling_col] = self.mfd.build_coupling_terms()\n\n self.div = sparse.coo_matrix((np.array(div_data), \n (np.add(np.array(div_row), \n -self.mesh.get_number_of_faces()), \n np.array(div_col))))\n self.div = self.div.tocsr()\n\n lhs_data = self.m_x_coo_data\n lhs_row = m_x_coo_row\n lhs_col = m_x_coo_col\n \n lhs_data += div_data\n lhs_row += div_row\n lhs_col += div_col\n\n lhs_data += div_t_data\n lhs_row += div_t_row\n lhs_col += div_t_col \n \n self.c_start = len(lhs_data)\n \n lhs_data += c_data\n lhs_row += c_row\n lhs_col += c_col \n\n self.c_end = len(c_data)\n\n lhs_data += coupling_data\n lhs_row += coupling_row\n lhs_col += coupling_col\n\n # Convert m_x_coo_data to numpy array. \n self.m_x_coo_data = np.array(self.m_x_coo_data)\n\n self.lhs_coo = sparse.coo_matrix((np.array(lhs_data), \n (np.array(lhs_row), \n np.array(lhs_col))))\n\n # RHS construction is for Neumann and Dirichlet \n # boundaries specified by the mesh. \n self.rhs_mfd = self.mfd.build_rhs()","def get_feature_oriented_matrix(self):\n nbr_features = self.hyperparameters.time_series_depth\n matrix = np.ones(shape=(nbr_features, nbr_features), dtype=np.float)\n np.fill_diagonal(matrix, val=0)\n return matrix","def information_matrix(self):\n return self._cov.inv()","def chivecfn(theta, model):\n lnp_prior = model.prior_product(theta)\n if not np.isfinite(lnp_prior):\n return np.zeros(model.ndim) - np.infty\n\n # Generate mean model\n try:\n spec, phot, x = model.mean_model(theta, obs, sps=sps)\n except(ValueError):\n return np.zeros(model.ndim) - np.infty\n\n chispec = chi_spec(spec, obs)\n chiphot = chi_phot(phot, obs)\n return np.concatenate([chispec, chiphot])","def _get_current_game_state(board):\n return np.concatenate((_get_pieces_one_hot(board, color=False),\n _get_pieces_one_hot(board, color=True)),\n axis=-1)","def get_direction_matrix(self) -> int:","def line_vector_centrality(H):\n from ..algorithms import is_connected\n\n # If the hypergraph is empty, then return an empty dictionary\n if H.num_nodes == 0:\n return dict()\n\n if not is_connected(H):\n raise XGIError(\"This method is not defined for disconnected hypergraphs.\")\n\n LG = to_line_graph(H)\n LGcent = nx.eigenvector_centrality(LG)\n\n vc = {node: [] for node in H.nodes}\n\n edge_label_dict = {tuple(edge): index for index, edge in H._edge.items()}\n\n hyperedge_dims = {tuple(edge): len(edge) for edge in H.edges.members()}\n\n D = H.edges.size.max()\n\n for k in range(2, D + 1):\n c_i = np.zeros(len(H.nodes))\n\n for edge, _ in list(filter(lambda x: x[1] == k, hyperedge_dims.items())):\n for node in edge:\n try:\n c_i[node] += LGcent[edge_label_dict[edge]]\n except IndexError:\n raise Exception(\n \"Nodes must be written with the Pythonic indexing (0,1,2...)\"\n )\n\n c_i *= 1 / k\n\n for node in H.nodes:\n vc[node].append(c_i[node])\n\n return vc","def matrix(self):\n # group the observables based on what wires they act on\n U_list = []\n for _, g in itertools.groupby(self.obs, lambda x: x.wires):\n # extract the matrices of each diagonalizing gate\n mats = [i.matrix for i in g]\n\n if len(mats) > 1:\n # multiply all unitaries together before appending\n mats = [multi_dot(mats)]\n\n # append diagonalizing unitary for specific wire to U_list\n U_list.append(mats[0])\n\n # Return the Hermitian matrix representing the observable\n # over the defined wires.\n return functools.reduce(np.kron, U_list)","def controller_bookkeeping_vars(self):\n # Keeps track of all the controller states.\n controller_states = np.zeros(\n self._num_communication_turns + 1, self._num_controllers, self._controller_state_size)\n # Keeps track of all controllers' selected actions (communication + output).\n controller_actions = np.zeros(\n self._num_communication_turns, self._num_controllers, 1)\n # List that will contain the output actions.\n output_actions = []\n\n return controller_states, controller_actions, output_actions","def eclogite_foliated():\n\n rho = 3300.\n\n C = np.zeros((6,6), dtype=float)\n C[0,0] = 203.45; C[0,1] = 67.76; C[0,2] = 64.47; C[0,3] = 0.08; C[0,4] = 1.9; C[0,5] = -0.4\n C[1,0] = C[0,1]; C[1,1] = 220.58; C[1,2] = 63.65; C[1,3] = 0.46; C[1,4] = 0.59; C[1,5] = 0.06\n C[2,0] = C[0,2]; C[2,1] = C[1,2]; C[2,2] = 189.75; C[2,3] = 0.13; C[2,4] = 0.95; C[2,5] = -0.2\n C[3,0] = C[0,3]; C[3,1] = C[1,3]; C[3,2] = C[2,3]; C[3,3] = 66.32; C[3,4] = -0.27; C[3,5] = 0.73\n C[4,0] = C[0,4]; C[4,1] = C[1,4]; C[4,2] = C[2,4]; C[4,3] = C[3,4]; C[4,4] = 65.77; C[4,5] = -0.02\n C[5,0] = C[0,5]; C[5,1] = C[1,5]; C[5,2] = C[2,5]; C[5,3] = C[3,5]; C[5,4] = C[4,5]; C[5,5] = 70.75\n\n return C, rho","def head2eeg(self): \n LOG.info(\"Computing Head2EEGMat...\")\n h2s_mat = om.Head2EEGMat(self.om_head, self.om_sensors)\n LOG.info(\"head2eeg: %d x %d\" % (h2s_mat.nlin(), h2s_mat.ncol()))\n return h2s_mat","def get_state_observed_values(self):\n obsState = numpy.zeros(self.get_num_variables())\n i = 0\n for v in self.variables:\n obsState[i] = v.read_value_in_fmu(self.fmu)\n i += 1\n return obsState","def get_state(self,board):\n s = range(board.size())\n return [ board.getCell(x,y) for y in s for x in s]","def _compute_adjacency_matrix(self):\n\n # Set up a quick-reference index to map cells to indexes\n for i, cell in enumerate(self.sim.cells):\n self._cell_indexes[cell] = i\n\n if all([self.sim.hub.cells == [self.sim.damaged],\n self.sim.damaged not in self.sim.cells]):\n # Add the \"damaged\" virtual cell to the index if we need it\n self._cell_indexes[self.sim.damaged] = len(self.sim.cells)\n\n node_count = len(list(self._cell_indexes.keys()))\n g_sparse = np.zeros((node_count, node_count), dtype=float)\n g_sparse[:] = np.inf\n\n for cluster in self.sim.clusters + [self.sim.hub]:\n cluster_tour = cluster.tour\n i = len(cluster_tour.vertices) - 1\n j = 0\n while j < len(cluster_tour.vertices):\n start_vertex = cluster_tour.vertices[i]\n stop_vertex = cluster_tour.vertices[j]\n\n start_pt = cluster_tour.points[start_vertex]\n stop_pt = cluster_tour.points[stop_vertex]\n distance = np.linalg.norm(stop_pt - start_pt)\n\n start_seg = cluster_tour.objects[start_vertex]\n stop_seg = cluster_tour.objects[stop_vertex]\n\n start_index = self._cell_indexes[start_seg]\n stop_index = self._cell_indexes[stop_seg]\n\n g_sparse[start_index, stop_index] = distance\n\n i = j\n j += 1\n\n g_sparse = sp.csgraph_from_dense(g_sparse, null_value=np.inf)\n return g_sparse","def start_state(self):\r\n return np.zeros(self.board_size, dtype=np.int8)"],"string":"[\n \"def CTMCtoStormpy(h):\\n\\tstate_labelling = _buildStateLabeling(h)\\n\\ttransition_matrix = deepcopy(h.matrix)\\n\\te = array([h.e(s) for s in range(h.nb_states)])\\n\\ttransition_matrix /= e[:,newaxis]\\n\\ttransition_matrix = st.build_sparse_matrix(transition_matrix)\\n\\tcomponents = st.SparseModelComponents(transition_matrix=transition_matrix,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t state_labeling=state_labelling,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t rate_transitions=True)\\n\\tcomponents.exit_rates = e\\n\\tctmc = st.storage.SparseCtmc(components)\\n\\treturn ctmc\",\n \"def _get_obs(self):\\n # return np.concatenate((self.world.state[:6], self.world.state[7:13]))\\n return np.concatenate((self.world.state, np.zeros(7)))\\n # return self.world.state\",\n \"def get_cov_matrix_states(self):\\n cov = numpy.diag(numpy.zeros(self.get_num_variables()))\\n i = 0\\n for v in self.variables:\\n cov[i,i] = v.get_covariance()\\n i += 1\\n return cov\",\n \"def _excitonic_coft_all(self,SS,AG):\\n \\n # SystemBathInteraction\\n sbi = AG.get_SystemBathInteraction()\\n # CorrelationFunctionMatrix\\n cfm = sbi.CC\\n \\n c0 = AG.monomers[0].get_egcf((0,1))\\n Nt = len(c0)\\n \\n Nst = AG.HamOp.dim\\n ct = numpy.zeros((Nst,Nt),dtype=numpy.complex128)\\n\\n # electronic states corresponding to single excited states\\n import time\\n timecount = 0\\n elst = numpy.where(AG.which_band == 1)[0]\\n start = time.time()\\n for el1 in elst:\\n for el2 in elst:\\n coft = cfm.get_coft(el1-1,el2-1)\\n start2 = time.time()\\n for kk in AG.vibindices[el1]:\\n for ll in AG.vibindices[el2]:\\n ct[:,:] += numpy.dot(\\n numpy.expand_dims((SS[kk,:]**2)*(SS[ll,:]**2),axis=1),\\n numpy.expand_dims(coft,axis=0))\\n stop2 = time.time()\\n timecount += stop2 - start2\\n stop = time.time()\\n print(stop-start,stop-start - timecount)\\n return ct\",\n \"def c_():\\r\\n c = np.array([[0, 0], [0, 100], [100, 100], [100, 80], [20, 80],\\r\\n [20, 20], [100, 20], [100, 0], [0, 0]])\\r\\n return c\",\n \"def get_system_state_matrix(self):\\n\\n # Create empty matrix to describe system state\\n A = zeros((9,9))\\n\\n # Q-dot terms\\n A[0:3,3:6] = 0.5*identity(3)\\n\\n # X and Y gravity terms and Z rotation\\n A[3:6,0:3] = matrix([\\n [2*self.params['g']*self.params['lp']*self.params['Mp']/(self.params['Ix']), 0, 0],\\n [0, 2*self.params['g']*self.params['lp']*self.params['Mp']/(self.params['Iy']), 0],\\n [0, 0, 2*self.params['g']*self.params['Fspin_z']/(self.params['Iz'])]\\n ])\\n # Friction in pendulum rotation on pendulum\\n A[3:6,3:6] = matrix([\\n [self.params['Bx']/(self.params['Ix']), 0, 0],\\n [0, self.params['By']/(self.params['Iy']), 0],\\n [0, 0, self.params['Bz']/(self.params['Iz'])]\\n ])\\n # Friction in wheel rotation on pendulum\\n A[3:6,6:9] = matrix([\\n [-self.params['Bw']/(self.params['Ix']), 0, 0],\\n [0, -self.params['Bw']/(self.params['Iy']), 0],\\n [0, 0, -self.params['Bw']/(self.params['Iz'])]\\n ])\\n # Friction in wheel rotation on wheel\\n A[6:9,6:9] = -self.params['Bw']/self.params['Iw']*identity(3)\\n\\n return A\",\n \"def coherence(self):\\r\\n\\r\\n #XXX Calculate this from the standard output, instead of recalculating\\r\\n #the coherence:\\r\\n\\r\\n tseries_length = self.input.data.shape[0]\\r\\n spectrum_length = self.spectrum.shape[-1]\\r\\n coherence = np.zeros((tseries_length,\\r\\n tseries_length,\\r\\n spectrum_length))\\r\\n\\r\\n for i in range(tseries_length):\\r\\n for j in range(i, tseries_length):\\r\\n coherence[i][j] = tsa.coherence_spec(self.spectrum[i][j],\\r\\n self.spectrum[i][i],\\r\\n self.spectrum[j][j])\\r\\n\\r\\n idx = tril_indices(tseries_length, -1)\\r\\n coherence[idx[0], idx[1], ...] = coherence[idx[1], idx[0], ...].conj()\\r\\n\\r\\n return coherence\",\n \"def chol_covar(self) -> np.ndarray:\\n if self._chol_covar is None:\\n self._chol_covar = batched_cholesky(self.covar)\\n return self._chol_covar\",\n \"def get_global_stiffness_matrix(self) -> NDArray[np.float64]:\\n\\n s = self.sin()\\n c = self.cos()\\n matrix_helper = [\\n c ** 2,\\n c * s,\\n -(c ** 2),\\n -c * s,\\n c * s,\\n s ** 2,\\n -c * s,\\n -(s ** 2),\\n -(c ** 2),\\n -c * s,\\n c ** 2,\\n c * s,\\n -c * s,\\n -(s ** 2),\\n c * s,\\n s ** 2,\\n ]\\n return (\\n self.youngs_modulus\\n * self.area\\n / self.get_length()\\n * np.array(matrix_helper, dtype=np.float64).reshape(4, 4)\\n )\",\n \"def coaccessible_components(self):\\n DG = self.digraph().reverse()\\n coaccessible_states = DG.breadth_first_search(\\n [_.label() for _ in self.iter_final_states()])\\n return self.induced_sub_finite_state_machine(\\n [self.state(_) for _ in coaccessible_states])\",\n \"def get_features_critic(state):\\n # reshape to make it a matrix with one row (so we can transpose it later)\\n p, v = state\\n p_v = np.array([p, v]).reshape((1, -1)).T\\n X = np.array([p_v - c_entry.T for c_entry in C])\\n inv_cov = np.linalg.inv(np.diag([0.04, 0.0004]))\\n phi = np.array([np.exp(-(xi.T @ inv_cov @ xi) / 2) for xi in X])\\n\\n return np.squeeze(phi) # get rid of 2 unnecessary dimensions\",\n \"def cofactorMatrix(self):\\n returnvalue = Matrix()\\n for i in range(self._height):\\n newRow = list()\\n for j in range(self._width):\\n newRow.append(self.cofactor(i, j))\\n returnvalue.addRow(*newRow)\\n return returnvalue\",\n \"def scc(self):\\n if not self.is_square:\\n raise DMNonSquareMatrixError('Matrix must be square for scc')\\n\\n return self.rep.scc()\",\n \"def state_descriptor(self, state, player):\\n\\n\\t\\tboard = np.copy(state.board)\\n\\n\\t\\tnp.place(board, board == -1, 0)\\n\\t\\tnp.place(board, board == player, 3)\\n\\t\\tnp.place(board, board == 3 - player, -1)\\n\\t\\tnp.place(board, board == 3, 1)\\n\\n\\t\\tmy_cows = state.cows[player - 1]\\n\\t\\ten_cows = state.cows[2 - player]\\n\\n\\t\\treturn (self.totuple(board), my_cows, en_cows)\",\n \"def bc_outgoing_mat(n, h, k):\\n \\n d = [1.0, 2.0j*k*h]\\n i = [n-1, n-1]\\n j = [n-2, n-1]\\n return scipy.sparse.coo_matrix((d, (i, j)))\",\n \"def cofactor_matrix(self):\\n resp = []\\n len_b = len(self.take_vec())\\n for i in range(self.order):\\n _matrix = aux.cofactor(self.take_matrix(),\\n (i, self.order-1)\\n )\\n _resp = math.pow(-1, len_b-1)\\n _resp = _resp * np.linalg.det(_matrix)\\n _resp = _resp * math.pow(-1, i * (self.order-1))\\n resp.append(int(round(_resp)))\\n\\n return resp\",\n \"def appearance_space(state_data, C):\\n return np.dot(C, state_data)\",\n \"def MCtoStormpy(h):\\n\\tstate_labelling = _buildStateLabeling(h)\\n\\ttransition_matrix = h.matrix\\n\\ttransition_matrix = st.build_sparse_matrix(transition_matrix)\\n\\tcomponents = st.SparseModelComponents(transition_matrix=transition_matrix,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t state_labeling=state_labelling)\\n\\tmc = st.storage.SparseDtmc(components)\\n\\treturn mc\",\n \"def _C(self):\\n\\n # Find the local x and y coordinates at each node\\n xi = 0\\n yi = 0\\n xj = self.width()\\n yj = 0\\n xm = xj\\n ym = self.height()\\n xn = 0\\n yn = ym\\n\\n # Calculate the [C] coefficient matrix\\n C = array([[1, xi, yi, xi**2, xi*yi, yi**2, xi**3, xi**2*yi, xi*yi**2, yi**3, xi**3*yi, xi*yi**3],\\n [0, 0, 1, 0, xi, 2*yi, 0, xi**2, 2*xi*yi, 3*yi**2, xi**3, 3*xi*yi**2],\\n [0, -1, 0, -2*xi, -yi, 0, -3*xi**2, -2*xi*yi, -yi**2, 0, -3*xi**2*yi, -yi**3],\\n \\n [1, xj, yj, xj**2, xj*yj, yj**2, xj**3, xj**2*yj, xj*yj**2, yj**3, xj**3*yj, xj*yj**3],\\n [0, 0, 1, 0, xj, 2*yj, 0, xj**2, 2*xj*yj, 3*yj**2, xj**3, 3*xj*yj**2],\\n [0, -1, 0, -2*xj, -yj, 0, -3*xj**2, -2*xj*yj, -yj**2, 0, -3*xj**2*yj, -yj**3],\\n\\n [1, xm, ym, xm**2, xm*ym, ym**2, xm**3, xm**2*ym, xm*ym**2, ym**3, xm**3*ym, xm*ym**3],\\n [0, 0, 1, 0, xm, 2*ym, 0, xm**2, 2*xm*ym, 3*ym**2, xm**3, 3*xm*ym**2],\\n [0, -1, 0, -2*xm, -ym, 0, -3*xm**2, -2*xm*ym, -ym**2, 0, -3*xm**2*ym, -ym**3],\\n\\n [1, xn, yn, xn**2, xn*yn, yn**2, xn**3, xn**2*yn, xn*yn**2, yn**3, xn**3*yn, xn*yn**3],\\n [0, 0, 1, 0, xn, 2*yn, 0, xn**2, 2*xn*yn, 3*yn**2, xn**3, 3*xn*yn**2],\\n [0, -1, 0, -2*xn, -yn, 0, -3*xn**2, -2*xn*yn, -yn**2, 0, -3*xn**2*yn, -yn**3]])\\n \\n # Return the coefficient matrix\\n return C\",\n \"def get_co_occ_matrix(res, last_n, s_real, s_hc):\\n\\n s = np.array(res['s'])[-last_n:]\\n hc_ml = np.array(res['hc_ml'])[-last_n:]\\n co_occs = np.zeros((len(s_hc), len(s_real)))\\n for si, hc_mli in zip(s, hc_ml):\\n co_occs[s_hc.index(hc_mli), s_real.index(si)] += 1\\n co_occs /= co_occs.sum()\\n\\n return co_occs\",\n \"def state(self):\\n state = np.array(self.get_state_arr())\\n om = utils.build_occupancy_maps(utils.build_humans(state))\\n # We only have a batch of one so just get the first element of\\n # transform and rotate\\n state = utils.transform_and_rotate(state.reshape((1, -1)))[0]\\n return torch.cat((state, om), dim=1)\",\n \"def ws(self, inhom_object):\\n z = inhom_object.zeta\\n # define bohr frequencies of eigenstates--currently a and b are \\n # correlated \\n wg = 0.0 + 0*z\\n wa = z + self.wa_central\\n wb = z + self.wb_central\\n w2a = 2*wa - self.a_coupling\\n w2b = 2*wb - self.b_coupling\\n wc = wa + wb - self.ab_coupling\\n # define coherence frequencies\\n w_gg = wg - wg\\n w_ag = wa - wg\\n w_bg = wb - wg\\n w_2ag = w2a - wg\\n w_2bg = w2b - wg\\n w_cg = wc - wg\\n # array aggregates all frequencies to match state vectors\\n w = np.array( [w_gg, \\n w_ag, w_bg,\\n w_2ag, w_2bg, w_cg\\n ])\\n return w\",\n \"def get_input_state_matrix(self):\\n\\n # state matrix for control\\n B = zeros((9,3))\\n\\n # control term impact on pendulum\\n B[3:6,0:3] = matrix([\\n [self.params['Km']/(self.params['Ix'])/self.params['Rm'], 0, 0],\\n [0, self.params['Km']/(self.params['Iy'])/self.params['Rm'], 0],\\n [0, 0, self.params['Km']/(self.params['Iz'])/self.params['Rm']]\\n ])\\n # control term impact on wheel\\n B[6:9,0:3] = self.params['Km']/self.params['Iw']/self.params['Rm']*identity(3)\\n\\n return B\",\n \"def covar(self) -> np.ndarray:\\n if self._covar is None:\\n self._covar = batched_inv_spd(batched_cholesky(self._inv_covar))\\n return self._covar\",\n \"def scc(self):\\n return self.to_ddm().scc()\",\n \"def conditional_component_covs(self):\\n return np.array([d.conditional_cov() for d in self.conditionalMVNs])\",\n \"def cudnn_lstm_state(lstm_cell_state):\\n h = tf.stack([s.h for s in lstm_cell_state])\\n c = tf.stack([s.c for s in lstm_cell_state])\\n return (h, c)\",\n \"def visualizeC(self, M=None):\\n try:\\n import seaborn as sns\\n import matplotlib.pyplot as plt\\n except:\\n print(\\\"Seaborn or matplotlib not imported...can't build the heatmap\\\")\\n if M is None:\\n M = self.stateC\\n a = torch.argmax(M, dim=0)\\n print(self.find_TPname(a))\\n M = pd.DataFrame(M.numpy(), index=list(\\n self.filler2index.keys()), columns=list(self.role2index.keys()))\\n sns.heatmap(M, annot=True, cmap=\\\"Blues\\\")\\n plt.show()\",\n \"def m_c(self) -> np.ndarray:\\n assert self._k is not None, \\\"camera must be calibrated\\\"\\n return forge_projective_matrix(self._k)\",\n \"def get_constr_obs_states_high(self):\\n constrHi = numpy.empty(self.get_num_variables())\\n i = 0\\n for v in self.variables:\\n constrHi[i] = v.get_constraint_high()\\n i += 1\\n return constrHi\",\n \"def get_state():\\n global HMC_MOM\\n return dict(randstate = np.random.get_state(),\\n mom = HMC_MOM)\",\n \"def get_cov_matrix_state_pars(self):\\n cov = numpy.diag(numpy.zeros(self.get_num_variables() + self.get_num_parameters()))\\n i = 0\\n for v in self.variables:\\n cov[i,i] = v.get_covariance()\\n i += 1\\n for p in self.parameters:\\n cov[i,i] = p.get_covariance()\\n i += 1\\n return cov\",\n \"def closeness_centrality(self):\\n try:\\n self.logger.info('正在计算网络的接近中心性 ...')\\n return self.order_dict(nx.closeness_centrality(self.G), index=1)\\n except Exception as e:\\n self.logger.error(\\\"计算失败，原因：{0}\\\".format(e))\",\n \"def to_coo_matrix(self):\\n if self.E > 0:\\n i, j = self.edges.T\\n sm = coo_matrix((self.weights, (i, j)), shape=(self.V, self.V))\\n else:\\n sm = coo_matrix((self.V, self.V))\\n return sm\",\n \"def getstate(self):\\n out = []\\n for row in self.a:\\n out.append([])\\n for item in row:\\n out[-1].append(itemstate(item))\\n return (\\\"matrix\\\", out, self.y, self.x, self.converter, self.onlydiag())\",\n \"def _get_state(self):\\n\\n # stack all variables and return state array\\n state = np.hstack((self.sheep_com, self.farthest_sheep, \\n self.target, self.dog_pose, self.radius_sheep, \\n self.target_distance))\\n return state\",\n \"def sim_to_state(self):\\n # As mentioned earlier, we only need z-axis variables.\\n # This will keep our state nice and small.\\n return [\\n self.sim.pose[Z_AXIS],\\n self.sim.v[Z_AXIS],\\n self.sim.linear_accel[Z_AXIS]\\n ]\",\n \"def to_coo_matrix(self):\\n if self.E > 0:\\n i, j = self.edges.T\\n sm = coo_matrix((np.ones(self.E), (i, j)),\\n shape=(self.V, self.V))\\n else:\\n sm = coo_matrix((self.V, self.V))\\n return sm\",\n \"def get_state(self):\\n return np.append(self.game.game_board.get_board(),\\n [self.game.player_1.color, self.game.player_2.color])[None, :]\",\n \"def get_matrix(self):\\n matrix = np.zeros([len(self.states), len(self.states)])\\n starting_states = []\\n transitions = []\\n\\n for chords in self.training_data:\\n states = []\\n is_starting_state = True\\n chunks = [chords[x:x+self.order] for x in range(0,\\n len(chords), self.order)]\\n for chunk in chunks:\\n chunk_string = \\\" \\\".join(chunk)\\n if is_starting_state:\\n starting_states.append(chunk_string)\\n is_starting_state = False\\n states.append(chunk_string)\\n\\n for i in range(0, len(states)):\\n if i < (len(states)) - 1:\\n transitions.append([states[i], states[i + 1]])\\n else:\\n transitions.append([states[i]])\\n\\n self.starting_probabilities = np.zeros([len(self.states)])\\n\\n for transition in transitions:\\n for row, row_contents in enumerate(self.transitions):\\n for col, _ in enumerate(row_contents):\\n if transition == self.transitions[row][col]:\\n matrix[row][col] += 1\\n\\n for i, state in enumerate(self.states):\\n for j, possible_state in enumerate(starting_states):\\n if state == possible_state:\\n self.starting_probabilities[j] += 1\\n\\n num = sum(self.starting_probabilities)\\n for i, prob in enumerate(self.starting_probabilities):\\n self.starting_probabilities[i] = prob / num\\n\\n for m in range(len(matrix)):\\n num = sum(matrix[m])\\n if int(num) is not 0:\\n for i in range(len(matrix[m])):\\n matrix[m][i] = (matrix[m][i] / num)\\n else:\\n matrix[m] = self.starting_probabilities\\n return matrix\",\n \"def complete_list_of_states():\\n # funny way of getting all the states that are defined in ConcertClientState.msg\\n return concert_msgs.ConductorGraph.__slots__\",\n \"def get_transition_matrix(self):\\n # Transition matrix\\n P_trans = np.zeros((self.n_states, self.n_states))\\n\\n # Fill\\n for s_num in range(2, (2**self.depth)):\\n P_trans[(s_num - 1), (s_num//2 - 1)] = 1.0\\n\\n return P_trans\",\n \"def covar(self):\\n wwt = self.ww.copy()\\n wwt[self.ww>0] = 1.0/self.ww[self.ww>0]\\n covar = np.zeros((self.nstar,self.nstar),dtype=self.ww.dtype)\\n for i in range(self.nstar):\\n for j in range(i+1):\\n covar[i,j] = np.sum(wwt * self.vv[:,i] * self.vv[:,j])\\n covar[j,i] = covar[i,j]\\n return covar\",\n \"def armature_coor2world_coor():\\n return armature.matrix_world @ bones_list[0].head\",\n \"def coherency(self):\\r\\n data = self.input.data\\r\\n tseries_length = data.shape[0]\\r\\n spectrum_length = self.spectrum.shape[-1]\\r\\n\\r\\n coherency = np.zeros((tseries_length,\\r\\n tseries_length,\\r\\n spectrum_length), dtype=complex)\\r\\n\\r\\n for i in range(tseries_length):\\r\\n for j in range(i, tseries_length):\\r\\n coherency[i][j] = tsa.coherency_spec(self.spectrum[i][j],\\r\\n self.spectrum[i][i],\\r\\n self.spectrum[j][j])\\r\\n\\r\\n idx = tril_indices(tseries_length, -1)\\r\\n coherency[idx[0], idx[1], ...] = coherency[idx[1], idx[0], ...].conj()\\r\\n\\r\\n return coherency\",\n \"def get_feature_matrix(self):\\n phi_mat = np.empty((self.n_states, self.feature_dim))\\n for s_idx in range(self.n_states):\\n cur_phi = self.state_2_features((s_idx+1))\\n phi_mat[s_idx, :] = cur_phi\\n\\n return phi_mat\",\n \"def _compute_world_params(self) -> None:\\n\\n self.states = []\\n for row in range(self.grid_height):\\n for col in range(self.grid_width):\\n cell = row * self.grid_width + col\\n cell_type = self.grid[cell]\\n\\n possible_actions = {\\n Action.up: self._get_action(max(row - 1, 0) * self.grid_width + col),\\n Action.down: self._get_action(min(row + 1, self.grid_height - 1) * self.grid_width + col),\\n Action.right: self._get_action(row * self.grid_width + min(col + 1, self.grid_width - 1)),\\n Action.left: self._get_action(row * self.grid_width + max(col - 1, 0))\\n }\\n\\n self.states.append(State(cell, possible_actions, cell_type))\",\n \"def get_transition(self, row, col, action, tot_row, tot_col):\\n\\n '''\\n Expand the grid of the environment to handle when the \\n agent decides to move in the direction of a wall \\n '''\\n state_probabilities = np.zeros((int(np.sqrt(self.env.observation_space.n)) + 2, int(np.sqrt(self.env.observation_space.n)) + 2), dtype=float)\\n\\n if action == 'UP':\\n row += 1\\n col += 1\\n state_probabilities[row - 1, col] = 0.33 #UP\\n state_probabilities[row, col - 1 ] = 0.33 #LEFT\\n state_probabilities[row, col + 1] = 0.33 # RIGHT\\n state_probabilities[row + 1, col] = 0.0 #DOWN\\n elif action == 'LEFT':\\n row += 1\\n col += 1\\n state_probabilities[row - 1, col] = 0.33 #UP\\n state_probabilities[row, col - 1 ] = 0.33 #LEFT\\n state_probabilities[row, col + 1] = 0.0 # RIGHT\\n state_probabilities[row + 1, col] = 0.33 #DOWN\\n elif action == 'RIGHT':\\n row += 1\\n col += 1\\n state_probabilities[row - 1, col] = 0.33 #UP\\n state_probabilities[row, col - 1 ] = 0.0 #LEFT\\n state_probabilities[row, col + 1] = 0.33 # RIGHT\\n state_probabilities[row + 1, col] = 0.33 #DOWN\\n elif action == 'DOWN':\\n row += 1\\n col += 1\\n state_probabilities[row - 1, col] = 0.0 # UP\\n state_probabilities[row, col - 1] = 0.33 # LEFT\\n state_probabilities[row, col + 1] = 0.33 # RIGHT\\n state_probabilities[row + 1, col] = 0.33 # DOWN\\n\\n for row in range (0, tot_row+1):\\n if state_probabilities[row, 0] != 0:\\n state_probabilities[row, 1] += state_probabilities[row, 0]\\n elif state_probabilities[row, -1] != 0:\\n state_probabilities[row, -2] += state_probabilities[row, -1]\\n\\n for col in range (0, tot_col+1):\\n if state_probabilities[0, col] != 0:\\n state_probabilities[1, col] += state_probabilities[0, col]\\n elif state_probabilities[-1, col] != 0:\\n state_probabilities[-2, col] += state_probabilities[-1, col]\\n\\n return state_probabilities[1: 1+tot_row, 1:1+tot_col]\",\n \"def get_transition(self, row, col, action, tot_row, tot_col):\\n\\n '''\\n Expand the grid of the environment to handle when the \\n agent decides to move in the direction of a wall \\n '''\\n state_probabilities = np.zeros((int(np.sqrt(self.env.observation_space.n)) + 2, int(np.sqrt(self.env.observation_space.n)) + 2), dtype=float)\\n\\n if action == 'UP':\\n row += 1\\n col += 1\\n state_probabilities[row - 1, col] = 0.33 #UP\\n state_probabilities[row, col - 1 ] = 0.33 #LEFT\\n state_probabilities[row, col + 1] = 0.33 # RIGHT\\n state_probabilities[row + 1, col] = 0.0 #DOWN\\n elif action == 'LEFT':\\n row += 1\\n col += 1\\n state_probabilities[row - 1, col] = 0.33 #UP\\n state_probabilities[row, col - 1 ] = 0.33 #LEFT\\n state_probabilities[row, col + 1] = 0.0 # RIGHT\\n state_probabilities[row + 1, col] = 0.33 #DOWN\\n elif action == 'RIGHT':\\n row += 1\\n col += 1\\n state_probabilities[row - 1, col] = 0.33 #UP\\n state_probabilities[row, col - 1 ] = 0.0 #LEFT\\n state_probabilities[row, col + 1] = 0.33 # RIGHT\\n state_probabilities[row + 1, col] = 0.33 #DOWN\\n elif action == 'DOWN':\\n row += 1\\n col += 1\\n state_probabilities[row - 1, col] = 0.0 # UP\\n state_probabilities[row, col - 1] = 0.33 # LEFT\\n state_probabilities[row, col + 1] = 0.33 # RIGHT\\n state_probabilities[row + 1, col] = 0.33 # DOWN\\n\\n for row in range (0, tot_row+1):\\n if state_probabilities[row, 0] != 0:\\n state_probabilities[row, 1] += state_probabilities[row, 0]\\n elif state_probabilities[row, -1] != 0:\\n state_probabilities[row, -2] += state_probabilities[row, -1]\\n\\n for col in range (0, tot_col+1):\\n if state_probabilities[0, col] != 0:\\n state_probabilities[1, col] += state_probabilities[0, col]\\n elif state_probabilities[-1, col] != 0:\\n state_probabilities[-2, col] += state_probabilities[-1, col]\\n\\n return state_probabilities[1: 1+tot_row, 1:1+tot_col]\",\n \"def state_summary(self) -> np.ndarray:\\n return np.array([len(self.current_clear_nodes), self.n_current_infected, len(self.current_isolated_nodes),\\n len(self.current_immune_nodes), len(self.current_alive_nodes)])\",\n \"def csi(self):\\n return self.table[0, 0] / (self.table[0, 0] + self.table[0, 1] + self.table[1, 0])\",\n \"def state_graph(self) -> np.ndarray:\\n return nx.convert_matrix.to_numpy_array(self.g_,\\n dtype=np.int16)\",\n \"def get_initial_hx(self, input_seq, hidden_state):\\n num_directions = 2 if self.lstm.bidirectional else 1\\n # hidden state\\n hidden = hidden_state.view(self.lstm.num_layers * num_directions, len(hidden_state), -1)\\n # cell state\\n c_zeros = torch.zeros(self.lstm.num_layers * num_directions,\\n input_seq.size(0), self.lstm.hidden_size,\\n dtype=input_seq.dtype, device=input_seq.device)\\n return hidden, c_zeros\",\n \"def _getState(self, board):\\r\\n mySide = board.mySide(self.id)\\r\\n oppSide = board.oppSide(self.id)\\r\\n myMancala = board.stonesInMyMancala(self.id)\\r\\n oppMancala = board.stonesInOppMancala(self.id)\\r\\n \\r\\n state = [] # size should be inputSize - 1\\r\\n state.append(float(myMancala))\\r\\n# for i in range(self.rowSize):\\r\\n# state.append(mySide[i])\\r\\n for my in mySide:\\r\\n state.append(float(my))\\r\\n state.append(float(oppMancala))\\r\\n# for i in range(self.rowSize):\\r\\n# state.append(oppSide[i])\\r\\n for op in oppSide:\\r\\n state.append(float(op))\\r\\n return state\",\n \"def cost_matrix(self):\\n return self.__cost_matrix\",\n \"def cc(self):\\n try:\\n from scipy.sparse import cs_graph_components\\n _, label = cs_graph_components(self.adjacency())\\n except:\\n pass\\n lil = self.to_coo_matrix().tolil().rows.tolist()\\n label = lil_cc(lil)\\n return label\",\n \"def modelCS(self,ht=None,hf=None):\\n if ht is not None:\\n hf = time2freq(ht)\\n cs,a,b,c = make_model_cs(hf, self.s0, self.bw, self.ref_freq)\\n \\n return cs\",\n \"def _compute_total_covariance_matrix_states_time(self) -> tf.Tensor:\\n aux_matrix = tf.matmul(self.b_matrix, tf.matmul(self.omega_matrix,\\n self.b_matrix,\\n transpose_b=True))\\\\\\n + self.t_matrix\\n total_covariance_matrix = self.total_c_phi\\\\\\n + tf.matmul(self.s_matrix_inv,\\n tf.matmul(aux_matrix, self.s_matrix_inv))\\n return total_covariance_matrix\",\n \"def state_nodes(self) -> np.ndarray:\\n return np.array([[nd[c] for c in [\\\"alive\\\", \\\"infected\\\", \\\"immune\\\", \\\"isolated\\\", \\\"masked\\\"]]\\n for nv, nd in self.g_.nodes.data()])\",\n \"def make_states(self) -> np.ndarray:\\n state = []\\n for agent in range(self.agents):\\n r, c = self.game.get_agent_pos(agent)\\n state.append(r)\\n state.append(c)\\n for field in constants.PAYOFF_LOCATIONS:\\n r, c = field\\n state.append(r)\\n state.append(c)\\n state.append(self.turns_count)\\n for field in self.special_payoff_fields:\\n r, c = field\\n state.append(r)\\n state.append(c)\\n\\n informed_state = np.array(state)\\n uninformed_state = copy.deepcopy(informed_state)\\n uninformed_state[-2:] = 0\\n\\n states = [\\n informed_state if i < self.num_informed else uninformed_state\\n for i in range(self.agents)\\n ]\\n states = np.stack(states, axis=0)\\n\\n return states\",\n \"def EmptyStateMat(nX,nU,nY):\\n Xx = np.zeros((nX,nX)) # Ac \\n Yx = np.zeros((nY,nX)) # Gc \\n Xu = np.zeros((nX,nU)) # Xu \\n Yu = np.zeros((nY,nU)) # Jc \\n return Xx,Xu,Yx,Yu\",\n \"def total_electronic_hamiltonian(self):\\n return block_diag(*[self.electronic_manifold(n) for n in range(3)])\",\n \"def ground_state(self) -> numpy.ndarray:\\n # Optimal cost\\n cost_min = min(self.cost)\\n # Find the args that corresponds to the optimal cost\\n args = numpy.where(self.cost == cost_min)\\n # Create the ideal state\\n rho = numpy.zeros(2**self.num_nodes)\\n for arg in args[0]:\\n rho[arg] = 1\\n return rho / numpy.sum(rho)\",\n \"def co_vertexes(self):\\n theta = self.orientation + np.pi / 2\\n shifts = np.array([np.cos(theta), np.sin(theta)]) * self.b\\n return self.coords + (shifts[:, None] * [-1, 1]).T\",\n \"def clinopyroxene_92():\\n\\n rho = 3327.\\n\\n C = np.zeros((6,6), dtype=float)\\n C[0,0] = 257.3; C[0,1] = 85.9; C[0,2] = 76.2; C[0,3] = 0.; C[0,4] = 7.1; C[0,5] = 0.\\n C[1,0] = C[0,1]; C[1,1] = 216.2; C[1,2] = 71.8; C[1,3] = 0.; C[1,4] = 13.3; C[1,5] = 0.\\n C[2,0] = C[0,2]; C[2,1] = C[1,2]; C[2,2] = 260.2; C[2,3] = 0.; C[2,4] = 33.7; C[2,5] = 0.\\n C[3,0] = C[0,3]; C[3,1] = C[1,3]; C[3,2] = C[2,3]; C[3,3] = 80.2; C[3,4] = 0.; C[3,5] = 10.2\\n C[4,0] = C[0,4]; C[4,1] = C[1,4]; C[4,2] = C[2,4]; C[4,3] = C[3,4]; C[4,4] = 70.6; C[4,5] = 0.\\n C[5,0] = C[0,5]; C[5,1] = C[1,5]; C[5,2] = C[2,5]; C[5,3] = C[3,5]; C[5,4] = C[4,5]; C[5,5] = 85.8\\n\\n return C, rho\",\n \"def gstate(N, periodic):\\n\\n # Create Hamiltonian matrix\\n H = kronH(N, periodic)\\n \\n # Diagonalize\\n print('Diagonalizing...', end=' ', flush=True)\\n w, v = eigsh(H, k=1, which='SA')\\n print('Done')\\n\\n return w[0]\",\n \"def __getstate__(self):\\n\\n sparse_dict = self.__dict__.copy()\\n\\n # Store a sparse representation of this\\n sparse_dict['transition_matrix'] = sparse.csr_matrix(self.__dict__['transition_matrix'])\\n\\n # Don't pickle this, because 1) it can be calculated and 2) it's large and not sparse\\n sparse_dict.pop('cumulative_probabilities')\\n\\n return sparse_dict\",\n \"def __build_state_index(self):\\n\\n # the index for the system state\\n # [rho_i, q_in, q_out, r_i, f_i]\\n x_index = {}\\n\\n # add the density index\\n x_index['density'] = OrderedDict()\\n for i in range(0, self.num_cells):\\n x_index['density'][i] = i\\n dim_state = self.num_cells\\n\\n # add the upstream boundary flow\\n x_index['qin'] = dim_state\\n x_index['qout'] = dim_state + 1\\n dim_state += 2\\n\\n # add on ramp variables\\n # x_index['onramp'] = OrderedDict()\\n # if self.cell_onramp is not None:\\n # # if onramp exist in the network, otherwise skip\\n # for cell_id in self.cell_onramp:\\n # # add the absolute index into the state index dictionary\\n # # r_i index = self.x_index{'onramp'][cell_i]\\n # x_index['onramp'][cell_id] = dim_state\\n # dim_state += 1\\n #\\n #\\n # # add off ramp state variables\\n # x_index['offramp'] = OrderedDict()\\n # if self.cell_offramp is not None:\\n # for cell_id in self.cell_offramp:\\n # # add the absolute index\\n # x_index['offramp'][cell_id] = dim_state\\n # dim_state += 1\\n\\n return x_index, dim_state\",\n \"def calc_cmatrix(self):\\n tw = self.twiss_df\\n res = self._results_df\\n\\n LOG.debug(\\\"Calculating CMatrix.\\\")\\n with timeit(lambda t:\\n LOG.debug(\\\" CMatrix calculated in {:f}s\\\".format(t))):\\n\\n j = np.array([[0., 1.],\\n [-1., 0.]])\\n rs = np.reshape(tw.as_matrix(columns=[\\\"R11\\\", \\\"R12\\\",\\n \\\"R21\\\", \\\"R22\\\"]),\\n (len(tw), 2, 2))\\n cs = np.einsum(\\\"ij,kjn,no->kio\\\",\\n -j, np.transpose(rs, axes=(0, 2, 1)), j)\\n cs = np.einsum(\\\"k,kij->kij\\\", (1 / np.sqrt(1 + np.linalg.det(rs))), cs)\\n\\n g11a = 1 / np.sqrt(tw.loc[:, \\\"BETX\\\"])\\n g12a = np.zeros(len(tw))\\n g21a = tw.loc[:, \\\"ALFX\\\"] / np.sqrt(tw.loc[:, \\\"BETX\\\"])\\n g22a = np.sqrt(tw.loc[:, \\\"BETX\\\"])\\n gas = np.reshape(np.array([g11a, g12a,\\n g21a, g22a]).T,\\n (len(tw), 2, 2))\\n\\n ig11b = np.sqrt(tw.loc[:, \\\"BETY\\\"])\\n ig12b = np.zeros(len(tw))\\n ig21b = -tw.loc[:, \\\"ALFY\\\"] / np.sqrt(tw.loc[:, \\\"BETY\\\"])\\n ig22b = 1. / np.sqrt(tw.loc[:, \\\"BETY\\\"])\\n igbs = np.reshape(np.array([ig11b, ig12b,\\n ig21b, ig22b]).T,\\n (len(tw), 2, 2))\\n cs = np.einsum(\\\"kij,kjl,kln->kin\\\", gas, cs, igbs)\\n gammas = np.sqrt(1 - np.linalg.det(cs))\\n\\n res.loc[:, \\\"GAMMA_C\\\"] = gammas\\n\\n res.loc[:, \\\"F1001_C\\\"] = ((cs[:, 0, 0] + cs[:, 1, 1]) * 1j +\\n (cs[:, 0, 1] - cs[:, 1, 0])) / 4 / gammas\\n res.loc[:, \\\"F1010_C\\\"] = ((cs[:, 0, 0] - cs[:, 1, 1]) * 1j +\\n (-cs[:, 0, 1]) - cs[:, 1, 0]) / 4 / gammas\\n\\n res.loc[:, \\\"C11\\\"] = cs[:, 0, 0]\\n res.loc[:, \\\"C12\\\"] = cs[:, 0, 1]\\n res.loc[:, \\\"C21\\\"] = cs[:, 1, 0]\\n res.loc[:, \\\"C22\\\"] = cs[:, 1, 1]\\n\\n LOG.debug(\\\" Average coupling amplitude |F1001|: {:g}\\\".format(np.mean(\\n np.abs(res.loc[:, \\\"F1001_C\\\"]))))\\n LOG.debug(\\\" Average coupling amplitude |F1010|: {:g}\\\".format(np.mean(\\n np.abs(res.loc[:, \\\"F1010_C\\\"]))))\\n LOG.debug(\\\" Average gamma: {:g}\\\".format(np.mean(\\n np.abs(res.loc[:, \\\"GAMMA_C\\\"]))))\\n\\n self._log_added('GAMMA_C', 'F1001_C', 'F1010_C', 'C11', 'C12', 'C21', 'C22')\",\n \"def incidence_matrix(self):\\n try: \\n return self._incidence_matrix\\n except AttributeError:\\n self._incidence_matrix = matrix(ZZ, len(self.Vrepresentation()), \\n len(self.Hrepresentation()), 0)\\n for V in self.Vrep_generator():\\n for H in self.Hrep_generator():\\n if self._is_zero(H*V):\\n self._incidence_matrix[V.index(),H.index()] = 1\\n\\n return self._incidence_matrix\",\n \"def to_coo_matrix(self):\\n row_indices, column_indices, nonzero_elements = self.to_ijv()\\n return coo_matrix((nonzero_elements, (row_indices, column_indices)),\\n shape=(self.size, self.size))\",\n \"def get_state(self):\\n return self.fmu._get_continuous_states()\",\n \"def return_state(board):\\r\\n state = np.array(INIT_ARRAY)\\r\\n for pos in BOARD_POSITIONS:\\r\\n state[pos[0]][pos[1]] = board[pos[0]][pos[1]].color\\r\\n\\r\\n return state\",\n \"def covar(self):\\n a, c, d, b = self.to_ccw()\\n return a * d - b * c\",\n \"def c( self , y , r , t = 0 ):\\n \\n u = np.zeros(self.m) # State derivative vector\\n \\n raise NotImplementedError\\n \\n return u\",\n \"def connectivity_matrix(self):\\n # TODO: make this more memory efficient by ordering i,j in code when needed.\\n temp = []\\n for i in range(self.n_atom):\\n for j in range(i+1, self.n_atom):\\n if self.bond(i, j):\\n temp.append([i+1, j+1])\\n self.connect = np.asarray(temp)\",\n \"def _compute_grid_state(self, for_id):\\n own = np.zeros_like(self._map, float)\\n own_pos = self._id2pos[for_id]\\n own[own_pos] = 1\\n\\n thieves = (self._map == THIEF ).astype(float)\\n guardians = (self._map == GUARDIAN).astype(float)\\n\\n own_team = self.id2team[for_id]\\n if own_team == THIEF:\\n teammates = thieves\\n opponents = guardians\\n else:\\n teammates = guardians\\n opponents = thieves\\n\\n treasure_channel = (self._map == TREASURE).astype(float)\\n\\n # Channels first\\n return np.stack([own, teammates, opponents, self._walls_channel, treasure_channel])\",\n \"def _excitonic_coft_old(self,SS,AG,n):\\n \\n # FIXME: works only for 2 level molecules\\n \\n c0 = AG.monomers[0].get_egcf((0,1))\\n Nt = len(c0)\\n \\n # SystemBathInteraction\\n sbi = AG.get_SystemBathInteraction()\\n # CorrelationFunctionMatrix\\n cfm = sbi.CC\\n \\n # get number of monomeric basis states\\n Na = 0\\n for monomer in AG.monomers:\\n Na += monomer.nel-1\\n \\n ct = numpy.zeros((Nt),dtype=numpy.complex128)\\n #Na = AG.nmono\\n for kk in range(Na):\\n \\n #nkk = AG.monomers[kk].egcf_mapping[0]\\n \\n for ll in range(Na):\\n \\n #nll = AG.monomers[ll].egcf_mapping[0]\\n \\n ct += ((SS[kk+1,n+1]**2)*(SS[ll+1,n+1]**2)*cfm.get_coft(kk,ll))\\n #*AG.egcf_matrix.get_coft(nkk,nll))\\n \\n return ct\",\n \"def get_meta_controller_state(self):\\n state = np.zeros(self._num_primitive_actions)\\n\\n if len(self._selected_primitive_actions):\\n selected_primitive_actions = np.array(self._selected_primitive_actions)\\n max_primtive_action = np.max(selected_primitive_actions)\\n state[max_primtive_action] = 1\\n state = np.concatenate((state, np.copy(self._tried_constraints)), axis=0)\\n\\n return state\",\n \"def _excitonic_coft(self,SS,AG,n):\\n \\n # SystemBathInteraction\\n sbi = AG.get_SystemBathInteraction()\\n # CorrelationFunctionMatrix\\n cfm = sbi.CC\\n \\n c0 = AG.monomers[0].get_egcf((0,1))\\n Nt = len(c0)\\n \\n ct = numpy.zeros((Nt),dtype=numpy.complex128)\\n\\n # electronic states corresponding to single excited states\\n elst = numpy.where(AG.which_band == 1)[0]\\n for el1 in elst:\\n for el2 in elst:\\n if cfm.cpointer[el1-1,el2-1] == 0:\\n continue\\n coft = cfm.get_coft(el1-1,el2-1) \\n for kk in AG.vibindices[el1]:\\n for ll in AG.vibindices[el2]:\\n ct += ((SS[kk,n]**2)*(SS[ll,n]**2)*coft)\\n return ct\",\n \"def clinopyroxene_98():\\n\\n rho = 3190.\\n\\n C = np.zeros((6,6), dtype=float)\\n C[0,0] = 237.8; C[0,1] = 83.5; C[0,2] = 80.; C[0,3] = 0.; C[0,4] = 9.; C[0,5] = 0.\\n C[1,0] = C[0,1]; C[1,1] = 183.6; C[1,2] = 59.9; C[1,3] = 0.; C[1,4] = 9.5; C[1,5] = 0.\\n C[2,0] = C[0,2]; C[2,1] = C[1,2]; C[2,2] = 229.5; C[2,3] = 0.; C[2,4] = 48.1; C[2,5] = 0.\\n C[3,0] = C[0,3]; C[3,1] = C[1,3]; C[3,2] = C[2,3]; C[3,3] = 76.5; C[3,4] = 0.; C[3,5] = 8.4\\n C[4,0] = C[0,4]; C[4,1] = C[1,4]; C[4,2] = C[2,4]; C[4,3] = C[3,4]; C[4,4] = 73.; C[4,5] = 0.\\n C[5,0] = C[0,5]; C[5,1] = C[1,5]; C[5,2] = C[2,5]; C[5,3] = C[3,5]; C[5,4] = C[4,5]; C[5,5] = 81.6\\n\\n return C, rho\",\n \"def covariates(self):\\n return None\",\n \"def cs(self):\\n if hasattr(self, \\\"_cs_cache\\\"):\\n return self._cs_cache\\n return np.array([conf.cs for conf in self.configurations], dtype=int)\",\n \"def x(self):\\n # REPLACE THE FOLLOWING WITH THE LOGIC TO CONSTRUCT/RETURN THE STATE\\n x = {key: 0.0 for key in self.model.states}\\n\\n return x\",\n \"def get_state_arr(self):\\n rpos = self.sim.getAgentPosition(self.robot_num)\\n rvel = self.sim.getAgentVelocity(self.robot_num)\\n rrad = self.sim.getAgentRadius(self.robot_num)\\n v_pref = self.sim.getAgentMaxSpeed(self.robot_num)\\n theta = math.atan2(rvel[1], rvel[0])\\n # Robot's state entry. Note that goal is listed as the robot's current\\n # position because we aren't using that goal as such, we are just\\n # exploring.\\n state = [\\n rpos[0], rpos[1], rvel[0], rvel[1], rrad,\\n self.headings[self.robot_num], rpos[0], rpos[1],\\n v_pref, theta\\n ]\\n for agent in self.agents:\\n if agent != self.robot_num: # We already accounted for the robot\\n pos = self.sim.getAgentPosition(agent)\\n vel = self.sim.getAgentVelocity(agent)\\n rad = self.sim.getAgentRadius(agent)\\n state.extend([pos[0], pos[1], vel[0], vel[1], rad,\\n self.headings[agent]])\\n for obs in self.obstacles:\\n if len(obs) > 1:\\n # Polygonal obstacle\\n o = Polygon(obs)\\n p = Point(rpos)\\n p1, p2 = nearest_points(o, p)\\n # Velocity is always 0 for obstacles\\n # Heading is same as robot's\\n state.extend([p1.x, p2.y, 0, 0, self.obs_width,\\n self.headings[self.robot_num]])\\n else:\\n # Point obstacle\\n state.extend([obs[0][0], obs[0][1], 0, 0, self.obs_width,\\n self.headings[self.robot_num]])\\n return state\",\n \"def initialize_system(self):\\n self.mfd.set_mesh(self.mesh)\\n [[div_data, div_row, div_col], \\n [div_t_data, div_t_row, div_t_col]] = self.mfd.build_div()\\n [self.m_x_coo_data, \\n m_x_coo_row, \\n m_x_coo_col] = self.mfd.build_m(save_update_info=True)\\n\\n self.m_x_coo_length = len(self.m_x_coo_data)\\n \\n # The data for the bottom right should be zeros. \\n [c_data, c_row, c_col] = self.mfd.build_bottom_right()\\n \\n [coupling_data, coupling_row, coupling_col] = self.mfd.build_coupling_terms()\\n\\n self.div = sparse.coo_matrix((np.array(div_data), \\n (np.add(np.array(div_row), \\n -self.mesh.get_number_of_faces()), \\n np.array(div_col))))\\n self.div = self.div.tocsr()\\n\\n lhs_data = self.m_x_coo_data\\n lhs_row = m_x_coo_row\\n lhs_col = m_x_coo_col\\n \\n lhs_data += div_data\\n lhs_row += div_row\\n lhs_col += div_col\\n\\n lhs_data += div_t_data\\n lhs_row += div_t_row\\n lhs_col += div_t_col \\n \\n self.c_start = len(lhs_data)\\n \\n lhs_data += c_data\\n lhs_row += c_row\\n lhs_col += c_col \\n\\n self.c_end = len(c_data)\\n\\n lhs_data += coupling_data\\n lhs_row += coupling_row\\n lhs_col += coupling_col\\n\\n # Convert m_x_coo_data to numpy array. \\n self.m_x_coo_data = np.array(self.m_x_coo_data)\\n\\n self.lhs_coo = sparse.coo_matrix((np.array(lhs_data), \\n (np.array(lhs_row), \\n np.array(lhs_col))))\\n\\n # RHS construction is for Neumann and Dirichlet \\n # boundaries specified by the mesh. \\n self.rhs_mfd = self.mfd.build_rhs()\",\n \"def get_feature_oriented_matrix(self):\\n nbr_features = self.hyperparameters.time_series_depth\\n matrix = np.ones(shape=(nbr_features, nbr_features), dtype=np.float)\\n np.fill_diagonal(matrix, val=0)\\n return matrix\",\n \"def information_matrix(self):\\n return self._cov.inv()\",\n \"def chivecfn(theta, model):\\n lnp_prior = model.prior_product(theta)\\n if not np.isfinite(lnp_prior):\\n return np.zeros(model.ndim) - np.infty\\n\\n # Generate mean model\\n try:\\n spec, phot, x = model.mean_model(theta, obs, sps=sps)\\n except(ValueError):\\n return np.zeros(model.ndim) - np.infty\\n\\n chispec = chi_spec(spec, obs)\\n chiphot = chi_phot(phot, obs)\\n return np.concatenate([chispec, chiphot])\",\n \"def _get_current_game_state(board):\\n return np.concatenate((_get_pieces_one_hot(board, color=False),\\n _get_pieces_one_hot(board, color=True)),\\n axis=-1)\",\n \"def get_direction_matrix(self) -> int:\",\n \"def line_vector_centrality(H):\\n from ..algorithms import is_connected\\n\\n # If the hypergraph is empty, then return an empty dictionary\\n if H.num_nodes == 0:\\n return dict()\\n\\n if not is_connected(H):\\n raise XGIError(\\\"This method is not defined for disconnected hypergraphs.\\\")\\n\\n LG = to_line_graph(H)\\n LGcent = nx.eigenvector_centrality(LG)\\n\\n vc = {node: [] for node in H.nodes}\\n\\n edge_label_dict = {tuple(edge): index for index, edge in H._edge.items()}\\n\\n hyperedge_dims = {tuple(edge): len(edge) for edge in H.edges.members()}\\n\\n D = H.edges.size.max()\\n\\n for k in range(2, D + 1):\\n c_i = np.zeros(len(H.nodes))\\n\\n for edge, _ in list(filter(lambda x: x[1] == k, hyperedge_dims.items())):\\n for node in edge:\\n try:\\n c_i[node] += LGcent[edge_label_dict[edge]]\\n except IndexError:\\n raise Exception(\\n \\\"Nodes must be written with the Pythonic indexing (0,1,2...)\\\"\\n )\\n\\n c_i *= 1 / k\\n\\n for node in H.nodes:\\n vc[node].append(c_i[node])\\n\\n return vc\",\n \"def matrix(self):\\n # group the observables based on what wires they act on\\n U_list = []\\n for _, g in itertools.groupby(self.obs, lambda x: x.wires):\\n # extract the matrices of each diagonalizing gate\\n mats = [i.matrix for i in g]\\n\\n if len(mats) > 1:\\n # multiply all unitaries together before appending\\n mats = [multi_dot(mats)]\\n\\n # append diagonalizing unitary for specific wire to U_list\\n U_list.append(mats[0])\\n\\n # Return the Hermitian matrix representing the observable\\n # over the defined wires.\\n return functools.reduce(np.kron, U_list)\",\n \"def controller_bookkeeping_vars(self):\\n # Keeps track of all the controller states.\\n controller_states = np.zeros(\\n self._num_communication_turns + 1, self._num_controllers, self._controller_state_size)\\n # Keeps track of all controllers' selected actions (communication + output).\\n controller_actions = np.zeros(\\n self._num_communication_turns, self._num_controllers, 1)\\n # List that will contain the output actions.\\n output_actions = []\\n\\n return controller_states, controller_actions, output_actions\",\n \"def eclogite_foliated():\\n\\n rho = 3300.\\n\\n C = np.zeros((6,6), dtype=float)\\n C[0,0] = 203.45; C[0,1] = 67.76; C[0,2] = 64.47; C[0,3] = 0.08; C[0,4] = 1.9; C[0,5] = -0.4\\n C[1,0] = C[0,1]; C[1,1] = 220.58; C[1,2] = 63.65; C[1,3] = 0.46; C[1,4] = 0.59; C[1,5] = 0.06\\n C[2,0] = C[0,2]; C[2,1] = C[1,2]; C[2,2] = 189.75; C[2,3] = 0.13; C[2,4] = 0.95; C[2,5] = -0.2\\n C[3,0] = C[0,3]; C[3,1] = C[1,3]; C[3,2] = C[2,3]; C[3,3] = 66.32; C[3,4] = -0.27; C[3,5] = 0.73\\n C[4,0] = C[0,4]; C[4,1] = C[1,4]; C[4,2] = C[2,4]; C[4,3] = C[3,4]; C[4,4] = 65.77; C[4,5] = -0.02\\n C[5,0] = C[0,5]; C[5,1] = C[1,5]; C[5,2] = C[2,5]; C[5,3] = C[3,5]; C[5,4] = C[4,5]; C[5,5] = 70.75\\n\\n return C, rho\",\n \"def head2eeg(self): \\n LOG.info(\\\"Computing Head2EEGMat...\\\")\\n h2s_mat = om.Head2EEGMat(self.om_head, self.om_sensors)\\n LOG.info(\\\"head2eeg: %d x %d\\\" % (h2s_mat.nlin(), h2s_mat.ncol()))\\n return h2s_mat\",\n \"def get_state_observed_values(self):\\n obsState = numpy.zeros(self.get_num_variables())\\n i = 0\\n for v in self.variables:\\n obsState[i] = v.read_value_in_fmu(self.fmu)\\n i += 1\\n return obsState\",\n \"def get_state(self,board):\\n s = range(board.size())\\n return [ board.getCell(x,y) for y in s for x in s]\",\n \"def _compute_adjacency_matrix(self):\\n\\n # Set up a quick-reference index to map cells to indexes\\n for i, cell in enumerate(self.sim.cells):\\n self._cell_indexes[cell] = i\\n\\n if all([self.sim.hub.cells == [self.sim.damaged],\\n self.sim.damaged not in self.sim.cells]):\\n # Add the \\\"damaged\\\" virtual cell to the index if we need it\\n self._cell_indexes[self.sim.damaged] = len(self.sim.cells)\\n\\n node_count = len(list(self._cell_indexes.keys()))\\n g_sparse = np.zeros((node_count, node_count), dtype=float)\\n g_sparse[:] = np.inf\\n\\n for cluster in self.sim.clusters + [self.sim.hub]:\\n cluster_tour = cluster.tour\\n i = len(cluster_tour.vertices) - 1\\n j = 0\\n while j < len(cluster_tour.vertices):\\n start_vertex = cluster_tour.vertices[i]\\n stop_vertex = cluster_tour.vertices[j]\\n\\n start_pt = cluster_tour.points[start_vertex]\\n stop_pt = cluster_tour.points[stop_vertex]\\n distance = np.linalg.norm(stop_pt - start_pt)\\n\\n start_seg = cluster_tour.objects[start_vertex]\\n stop_seg = cluster_tour.objects[stop_vertex]\\n\\n start_index = self._cell_indexes[start_seg]\\n stop_index = self._cell_indexes[stop_seg]\\n\\n g_sparse[start_index, stop_index] = distance\\n\\n i = j\\n j += 1\\n\\n g_sparse = sp.csgraph_from_dense(g_sparse, null_value=np.inf)\\n return g_sparse\",\n \"def start_state(self):\\r\\n return np.zeros(self.board_size, dtype=np.int8)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.635167","0.59943235","0.58283806","0.5816899","0.5784517","0.57605594","0.5751287","0.5710274","0.57057977","0.56717366","0.5666582","0.56537753","0.562778","0.56184703","0.56027305","0.56016433","0.5601625","0.55812305","0.555667","0.5529863","0.55265343","0.54993033","0.549819","0.5483802","0.5482348","0.5452969","0.54493135","0.5430309","0.542297","0.5414111","0.5411937","0.5411675","0.5400565","0.53980577","0.5393366","0.53852403","0.535562","0.53408474","0.5335448","0.5316491","0.5312614","0.53111225","0.5311062","0.5297724","0.5280344","0.5278547","0.5262175","0.52472275","0.52472275","0.5240718","0.5234731","0.5230541","0.5219174","0.5214987","0.5213037","0.5211309","0.5200745","0.5200519","0.51964366","0.51939815","0.5193325","0.51869655","0.51842505","0.5179989","0.5171968","0.5171213","0.51710147","0.51669896","0.5159818","0.51527655","0.5150308","0.5148951","0.5144498","0.5142808","0.5141422","0.51406413","0.5140062","0.51363957","0.51316065","0.51272887","0.5122317","0.51183516","0.5118145","0.5116416","0.51112413","0.51107687","0.5099658","0.5098606","0.50979286","0.5093462","0.5093176","0.50924975","0.509001","0.50840926","0.5075531","0.5074492","0.50598466","0.5057407","0.505514","0.5050431"],"string":"[\n \"0.635167\",\n \"0.59943235\",\n \"0.58283806\",\n \"0.5816899\",\n \"0.5784517\",\n \"0.57605594\",\n \"0.5751287\",\n \"0.5710274\",\n \"0.57057977\",\n \"0.56717366\",\n \"0.5666582\",\n \"0.56537753\",\n \"0.562778\",\n \"0.56184703\",\n \"0.56027305\",\n \"0.56016433\",\n \"0.5601625\",\n \"0.55812305\",\n \"0.555667\",\n \"0.5529863\",\n \"0.55265343\",\n \"0.54993033\",\n \"0.549819\",\n \"0.5483802\",\n \"0.5482348\",\n \"0.5452969\",\n \"0.54493135\",\n \"0.5430309\",\n \"0.542297\",\n \"0.5414111\",\n \"0.5411937\",\n \"0.5411675\",\n \"0.5400565\",\n \"0.53980577\",\n \"0.5393366\",\n \"0.53852403\",\n \"0.535562\",\n \"0.53408474\",\n \"0.5335448\",\n \"0.5316491\",\n \"0.5312614\",\n \"0.53111225\",\n \"0.5311062\",\n \"0.5297724\",\n \"0.5280344\",\n \"0.5278547\",\n \"0.5262175\",\n \"0.52472275\",\n \"0.52472275\",\n \"0.5240718\",\n \"0.5234731\",\n \"0.5230541\",\n \"0.5219174\",\n \"0.5214987\",\n \"0.5213037\",\n \"0.5211309\",\n \"0.5200745\",\n \"0.5200519\",\n \"0.51964366\",\n \"0.51939815\",\n \"0.5193325\",\n \"0.51869655\",\n \"0.51842505\",\n \"0.5179989\",\n \"0.5171968\",\n \"0.5171213\",\n \"0.51710147\",\n \"0.51669896\",\n \"0.5159818\",\n \"0.51527655\",\n \"0.5150308\",\n \"0.5148951\",\n \"0.5144498\",\n \"0.5142808\",\n \"0.5141422\",\n \"0.51406413\",\n \"0.5140062\",\n \"0.51363957\",\n \"0.51316065\",\n \"0.51272887\",\n \"0.5122317\",\n \"0.51183516\",\n \"0.5118145\",\n \"0.5116416\",\n \"0.51112413\",\n \"0.51107687\",\n \"0.5099658\",\n \"0.5098606\",\n \"0.50979286\",\n \"0.5093462\",\n \"0.5093176\",\n \"0.50924975\",\n \"0.509001\",\n \"0.50840926\",\n \"0.5075531\",\n \"0.5074492\",\n \"0.50598466\",\n \"0.5057407\",\n \"0.505514\",\n \"0.5050431\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.63109434"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":22488,"cells":{"query":{"kind":"string","value":"Update world state HC state cooccurance matrix."},"document":{"kind":"string","value":"def update_co_occ_mat(hc_ro, i_s_real, co_occs, co_occ_list, idx, nsteps):\n\n # Go to next element in circular array.\n idx = (idx + 1) % nsteps\n\n # Remove estimate from left hand side of time window.\n if len(co_occ_list[idx]):\n i_s_real_left, hc_ro_left = co_occ_list[idx]\n co_occs[:, i_s_real_left] -= hc_ro_left\n\n # Add current estimate to matrix and list.\n co_occs[:, i_s_real] += hc_ro\n co_occ_list[idx] = [i_s_real, hc_ro]\n\n # Fix float imprecision.\n co_occs[(co_occs < 0) & (co_occs > -1e-10)] = 0\n\n return idx"},"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 update_world(self):\n pass","def update_H(self):\n self.grid.H[self.loc] -= (\n self.grid.courant_number\n * self.grid.inverse_permeability[self.loc]\n * self.phi_H\n )","def _update_loc(self) -> None:\n self.state[:, :, Boids.Attr.LOC] += self.state[:, :, Boids.Attr.VEL]\n # wrap-around the simulated environment\n self.state[:, :, Boids.Attr.LOC] %= np.expand_dims(self.env_bounds, axis=1)","def __updateMatC(self):\n\t\tif self.regScheme == 2:\n\t\t\tfor id1 in range(self.nTemplates):\n\t\t\t\tself.C[id1,id1] = 1.0 / self.w0[id1]**2","def update_state(self):\n self.reset_state()\n for piece in self.pieces:\n coordinates = piece.get_block_positions()\n for coor in coordinates:\n x, y = coor\n self.state[y][x] = piece","def get_transition(self, row, col, action, tot_row, tot_col):\n\n '''\n Expand the grid of the environment to handle when the \n agent decides to move in the direction of a wall \n '''\n state_probabilities = np.zeros((int(np.sqrt(self.env.observation_space.n)) + 2, int(np.sqrt(self.env.observation_space.n)) + 2), dtype=float)\n\n if action == 'UP':\n row += 1\n col += 1\n state_probabilities[row - 1, col] = 0.33 #UP\n state_probabilities[row, col - 1 ] = 0.33 #LEFT\n state_probabilities[row, col + 1] = 0.33 # RIGHT\n state_probabilities[row + 1, col] = 0.0 #DOWN\n elif action == 'LEFT':\n row += 1\n col += 1\n state_probabilities[row - 1, col] = 0.33 #UP\n state_probabilities[row, col - 1 ] = 0.33 #LEFT\n state_probabilities[row, col + 1] = 0.0 # RIGHT\n state_probabilities[row + 1, col] = 0.33 #DOWN\n elif action == 'RIGHT':\n row += 1\n col += 1\n state_probabilities[row - 1, col] = 0.33 #UP\n state_probabilities[row, col - 1 ] = 0.0 #LEFT\n state_probabilities[row, col + 1] = 0.33 # RIGHT\n state_probabilities[row + 1, col] = 0.33 #DOWN\n elif action == 'DOWN':\n row += 1\n col += 1\n state_probabilities[row - 1, col] = 0.0 # UP\n state_probabilities[row, col - 1] = 0.33 # LEFT\n state_probabilities[row, col + 1] = 0.33 # RIGHT\n state_probabilities[row + 1, col] = 0.33 # DOWN\n\n for row in range (0, tot_row+1):\n if state_probabilities[row, 0] != 0:\n state_probabilities[row, 1] += state_probabilities[row, 0]\n elif state_probabilities[row, -1] != 0:\n state_probabilities[row, -2] += state_probabilities[row, -1]\n\n for col in range (0, tot_col+1):\n if state_probabilities[0, col] != 0:\n state_probabilities[1, col] += state_probabilities[0, col]\n elif state_probabilities[-1, col] != 0:\n state_probabilities[-2, col] += state_probabilities[-1, col]\n\n return state_probabilities[1: 1+tot_row, 1:1+tot_col]","def get_transition(self, row, col, action, tot_row, tot_col):\n\n '''\n Expand the grid of the environment to handle when the \n agent decides to move in the direction of a wall \n '''\n state_probabilities = np.zeros((int(np.sqrt(self.env.observation_space.n)) + 2, int(np.sqrt(self.env.observation_space.n)) + 2), dtype=float)\n\n if action == 'UP':\n row += 1\n col += 1\n state_probabilities[row - 1, col] = 0.33 #UP\n state_probabilities[row, col - 1 ] = 0.33 #LEFT\n state_probabilities[row, col + 1] = 0.33 # RIGHT\n state_probabilities[row + 1, col] = 0.0 #DOWN\n elif action == 'LEFT':\n row += 1\n col += 1\n state_probabilities[row - 1, col] = 0.33 #UP\n state_probabilities[row, col - 1 ] = 0.33 #LEFT\n state_probabilities[row, col + 1] = 0.0 # RIGHT\n state_probabilities[row + 1, col] = 0.33 #DOWN\n elif action == 'RIGHT':\n row += 1\n col += 1\n state_probabilities[row - 1, col] = 0.33 #UP\n state_probabilities[row, col - 1 ] = 0.0 #LEFT\n state_probabilities[row, col + 1] = 0.33 # RIGHT\n state_probabilities[row + 1, col] = 0.33 #DOWN\n elif action == 'DOWN':\n row += 1\n col += 1\n state_probabilities[row - 1, col] = 0.0 # UP\n state_probabilities[row, col - 1] = 0.33 # LEFT\n state_probabilities[row, col + 1] = 0.33 # RIGHT\n state_probabilities[row + 1, col] = 0.33 # DOWN\n\n for row in range (0, tot_row+1):\n if state_probabilities[row, 0] != 0:\n state_probabilities[row, 1] += state_probabilities[row, 0]\n elif state_probabilities[row, -1] != 0:\n state_probabilities[row, -2] += state_probabilities[row, -1]\n\n for col in range (0, tot_col+1):\n if state_probabilities[0, col] != 0:\n state_probabilities[1, col] += state_probabilities[0, col]\n elif state_probabilities[-1, col] != 0:\n state_probabilities[-2, col] += state_probabilities[-1, col]\n\n return state_probabilities[1: 1+tot_row, 1:1+tot_col]","def updateSimState(self):\n self.sim_state = {k: v for k,v in self.state.iteritems()}","def HC_update(self, GS_HC, hc_ro):\n\n # Backward inference using GS - HC connectivity (generative model).\n # Weighted mean of GS - HC connectivity, with HC estimate as weights.\n hc_fb = np.average(GS_HC, 0, hc_ro)\n hc_fb = hc_fb / hc_fb.sum()\n\n self.P = hc_fb * self.P\n self.pass_through_lateral_conn()","def _compute_world_params(self) -> None:\n\n self.states = []\n for row in range(self.grid_height):\n for col in range(self.grid_width):\n cell = row * self.grid_width + col\n cell_type = self.grid[cell]\n\n possible_actions = {\n Action.up: self._get_action(max(row - 1, 0) * self.grid_width + col),\n Action.down: self._get_action(min(row + 1, self.grid_height - 1) * self.grid_width + col),\n Action.right: self._get_action(row * self.grid_width + min(col + 1, self.grid_width - 1)),\n Action.left: self._get_action(row * self.grid_width + max(col - 1, 0))\n }\n\n self.states.append(State(cell, possible_actions, cell_type))","def sense(self):\n self.robot.update_state_position()\n self.robot.update_state_proximity(self.world_props)\n self.robot.update_state_compass()\n self.robot.update_state_odometry()","def update_state_game_variables(self):\n self.model.numbers = [0, 1, 2, 3, 4, 5, 6, 7, 8]\n self.model.player_mark = \"\"\n self.model.player_move = 0\n self.model.boards = [\"board\"] * 9\n self.update_score_board()","def update_H(self):\n self.grid.H[:, -1, :, :] = self.grid.H[:, 0, :, :]","def update(self):\n self.active = False\n self.top.update(self.rgb,self.cmyk,self.hsv)\n self.bot.update(self.rgb,self.cmyk,self.hsv)\n self.active = True","def update_concentrations_batch(self): \n #--- Update the cell concentrations ---\n # dX_i/dt = mu_i*(1-rmp/100)*X_i*(1 - sum(i,(1-rmp/100)X(i))/carrying_capacity) or \n # (X_i(t+dt) - X_i(t))/dt = mu*(1-rmp/100)*X_i(t)*(1 - sum(i,(1-rmp/100)*X_i(t))/carrying_capacity)\n # where rmp is the random mortality percentage\n # If concentration is negative set it to zero\n members_gDW_per_ml_total = sum([(1 - self.random_mortality_percentage/100)*member.organism.gDW_per_ml[self._t] for member in self.community_members])\n self._logisticG_factor_gDW_per_ml = 1 - members_gDW_per_ml_total/self.carrying_capacity['gDW_per_ml']\n if len([member for member in self.community_members if member.organism.cells_per_ml != None]) == len(self.community_members):\n members_cells_per_ml_total = sum([(1 - self.random_mortality_percentage/100)*member.organism.cells_per_ml[self._t] for member in self.community_members])\n self._logisticG_factor_cells_per_ml = 1 - members_cells_per_ml_total/self.carrying_capacity['cells_per_ml']\n\n for member in self.community_members:\n # We always need gDW_per_ml to update compound concentrations but\n # providing cells_per_ml is optional\n member.organism.gDW_per_ml[self._t + self._dt] = max(member.organism.mu[self._t]*(1-self.random_mortality_percentage/100)*member.organism.gDW_per_ml[self._t]*self._logisticG_factor_gDW_per_ml*self._dt + member.organism.gDW_per_ml[self._t],0)\n\n if member.organism.cells_per_ml is not None:\n member.organism.cells_per_ml[self._t + self._dt] = max(member.organism.mu[self._t]*(1 - self.random_mortality_percentage/100)*member.organism.cells_per_ml[self._t]*self._logisticG_factor_cells_per_ml*self._dt + member.organism.cells_per_ml[self._t],0)\n\n # Total death rate (** newly added for DMMM_mortality **)\n if member.organism.mu[self._t] < 0:\n # In thise case random_mortality_rate has already been incorporated into mu\n # (see DMMM.py)\n member.organism.total_death_rate[self._t] = member.organism.mu[self._t] \n else:\n member.organism.total_death_rate[self._t] = member.organism.random_mortality_rate\n\n\n #--- Update shared compound concentrations ---\n # dC/dt = f where, f = sum(k,v_export_k*X_k) - sum(k,v_uptake_k*X_k) + dead_pool_rate\n # (C(t+dt) - c(t))/dt = sum(k,v_export_k*X_k) - sum(k,v_uptake_k*X_k) + dead_pool \n # where, dead_pool_rate = sum(k,-self.cell_pool_factor*cell_pool_concentration_k*total_death_rate_k*X_k)\n # Here, cell_pool_concentration is the concentration of the compound pool \n # per gDW or per cells, which should have already been assigned to each \n # shared compound. The minus sign is because total_death_rate is negative\n # while dead_pool_rate must be non-negative. Here, self.cell_pool_factor is\n # the factor that should be multiplied by concentration of that compound \n # (this is because sometimes we want to explore how the growth is affected if \n # the cell pool is higher than the ones reported experimentally)\n total_cmps_conc = sum([cmp.concentration[self._t] for cmp in self.shared_compounds])\n self._logisticG_factor_cmps = 1 - total_cmps_conc/self.carrying_capacity['compounds_mM']\n self._f = dict([(cmp,None) for cmp in self.shared_compounds])\n\n if not hasattr(self,'cell_pool_factor'):\n self.cell_pool_factor = 1\n\n for shared_cmp in self.shared_compounds:\n dead_pool_rate = -sum([self.cell_pool_factor*shared_cmp.cell_pool_concentration[member.organism.id]*member.organism.total_death_rate[self._t]*1000*member.organism.gDW_per_ml[self._t] for member in self.community_members])\n if dead_pool_rate < 0:\n raise userError('dead_pool_rate is negative')\n\n f = sum([r.flux[self._t]*1000*r.model.organism.gDW_per_ml[self._t] for r in shared_cmp.reactions]) + dead_pool_rate\n self._f[shared_cmp] = f\n\n conc = f*self._logisticG_factor_cmps*self._dt + shared_cmp.concentration[self._t]\n\n if conc >= 0 or (conc < 0 and abs(conc) <= 1e-9):\n conc = max(conc,0)\n\n shared_cmp.concentration[self._t + self._dt] = conc","def update_compartments(self, food_glucose):\n self.g_t, self.m_t = self.new_values(food_glucose, self.get_variables())","def update(self, world):\n self.input_comp.update(self)\n self._physics_comp.update(self, world)\n self._graphics_comp.update(self)","def update_H(self):\n self.grid.H[:, :, -1, :] = self.grid.H[:, :, 0, :]","def CTMCtoStormpy(h):\n\tstate_labelling = _buildStateLabeling(h)\n\ttransition_matrix = deepcopy(h.matrix)\n\te = array([h.e(s) for s in range(h.nb_states)])\n\ttransition_matrix /= e[:,newaxis]\n\ttransition_matrix = st.build_sparse_matrix(transition_matrix)\n\tcomponents = st.SparseModelComponents(transition_matrix=transition_matrix,\n\t\t\t\t\t\t\t\t\t\t state_labeling=state_labelling,\n\t\t\t\t\t\t\t\t\t\t rate_transitions=True)\n\tcomponents.exit_rates = e\n\tctmc = st.storage.SparseCtmc(components)\n\treturn ctmc","def update(self, z):\n\n Hjac = self._mat_desc[\"Hjac\"]\n H = self._mat_desc[\"H\"];\n zpred = self._measurement_model(self._state_vec_prev , H)\n\n innovation = z - zpred\n P = self._mat_desc[\"P\"]\n Hjac_T = Hjac.T\n R = self._mat_desc[\"R\"]\n M = self._mat_desc[\"M\"]\n M_T = M.T\n S = Hjac * P * Hjac_T + M * R * M_T\n S_inv = np.linalg.inv(S)\n self._mat_desc[\"K\"] = P * Hjac_T * S_inv\n self._state_vec += self._mat_desc[\"K\"] * innovation\n I = np.identity(self._state_vec.shape)\n\n # update the covariance matrix\n P = (I - self._mat_desc[\"K\"] * Hjac) * P\n self._mat_desc[\"P\"] = P","def updateState(self):\n self.state = self.microgridPolicy.computeState();","def update_states(self) -> None:\n self.set_states()\n self.async_write_ha_state()","def update_pop_matrix(self):\n for row in self.unique_rows[1:-1]: # First and last cell is water\n for col in self.unique_cols[1:-1]: # First and last cell is water\n cell = self.landscape[(row, col)]\n if cell.is_mainland:\n # print(cell)\n self.herb_pop_matrix[row - 1][col - 1] = cell.herb_count\n self.carn_pop_matrix[row - 1][col - 1] = cell.carn_count","def __update_visible(self) -> None:\n for i in range(0, 8):\n visible_row = self.__row_position + Labyrinth.ALL_ROW_MOVE[i]\n visible_col = self.__col_position + Labyrinth.ALL_COL_MOVE[i]\n if 0 <= visible_row < self.__labyrinth.labyrinth_height and \\\n 0 <= visible_col < self.__labyrinth.labyrinth_width:\n self.__labyrinth.visible_cells[visible_row][visible_col] = 1","def update_state(self):\n self.last_position = self.current_position\n self.last_distance = self.current_distance\n self.last_collision_time_stamp = self.current_collision_time_stamp\n self.current_kinematics = self.airsim_client.simGetGroundTruthKinematics(vehicle_name=self.drone_name)\n self.current_position = self.current_kinematics.position + self.base_offset\n self.current_collision_time_stamp = self.airsim_client.simGetCollisionInfo(vehicle_name=self.drone_name).time_stamp\n # print(\"DEBUG: simGetCollisionInfo:\", self.airsim_client.simGetCollisionInfo(vehicle_name=self.drone_name))\n # self.pending_death = self.airsim_client.simIsRacerDisqualified(vehicle_name=self.drone_name)\n self.objective_status = self.current_objective.next_gate_status(self.last_position, self.current_position)\n if self.objective_status == GateStatus.CROSSED or self.objective_status == GateStatus.PASSED:\n if self.switch_to_next_objective(): # if track is finished (changes self.last_distance)\n self.track_complete = True\n self.current_distance = self.current_position.distance_to(self.current_objective.gate_pose.position)","def updateWorld(self):\n\t self.screen.clear()\n self.update()\n self.screen.refresh()","def _update_grid_map_from_world_cord(self, world_cords_xy):\n # find occupancy map cords\n # self.logger.debug(f\"Updating Grid Map: {np.shape(world_cords_xy)}\")\n occu_cords = self.cord_translation_from_world(\n world_cords_xy=world_cords_xy)\n # self.logger.debug(f\"Occupancy Grid Map Cord shape = {np.shape(occu_cords)}\")\n occu_cords_x, occu_cords_y = occu_cords[:, 0], occu_cords[:, 1]\n min_occu_cords_x, max_occu_cords_x = np.min(occu_cords_x), np.max(occu_cords_x)\n min_occu_cords_y, max_occu_cords_y = np.min(occu_cords_y), np.max(occu_cords_y)\n self.map[min_occu_cords_y: max_occu_cords_y, min_occu_cords_x:max_occu_cords_x] -= 0.01\n self.map[occu_cords_y, occu_cords_x] += self.occu_prob\n self.map[min_occu_cords_y: max_occu_cords_y,\n min_occu_cords_x:max_occu_cords_x] = \\\n self.map[min_occu_cords_y: max_occu_cords_y, min_occu_cords_x:max_occu_cords_x].clip(0, 1)","def initialize_system(self):\n self.mfd.set_mesh(self.mesh)\n [[div_data, div_row, div_col], \n [div_t_data, div_t_row, div_t_col]] = self.mfd.build_div()\n [self.m_x_coo_data, \n m_x_coo_row, \n m_x_coo_col] = self.mfd.build_m(save_update_info=True)\n\n self.m_x_coo_length = len(self.m_x_coo_data)\n \n # The data for the bottom right should be zeros. \n [c_data, c_row, c_col] = self.mfd.build_bottom_right()\n \n [coupling_data, coupling_row, coupling_col] = self.mfd.build_coupling_terms()\n\n self.div = sparse.coo_matrix((np.array(div_data), \n (np.add(np.array(div_row), \n -self.mesh.get_number_of_faces()), \n np.array(div_col))))\n self.div = self.div.tocsr()\n\n lhs_data = self.m_x_coo_data\n lhs_row = m_x_coo_row\n lhs_col = m_x_coo_col\n \n lhs_data += div_data\n lhs_row += div_row\n lhs_col += div_col\n\n lhs_data += div_t_data\n lhs_row += div_t_row\n lhs_col += div_t_col \n \n self.c_start = len(lhs_data)\n \n lhs_data += c_data\n lhs_row += c_row\n lhs_col += c_col \n\n self.c_end = len(c_data)\n\n lhs_data += coupling_data\n lhs_row += coupling_row\n lhs_col += coupling_col\n\n # Convert m_x_coo_data to numpy array. \n self.m_x_coo_data = np.array(self.m_x_coo_data)\n\n self.lhs_coo = sparse.coo_matrix((np.array(lhs_data), \n (np.array(lhs_row), \n np.array(lhs_col))))\n\n # RHS construction is for Neumann and Dirichlet \n # boundaries specified by the mesh. \n self.rhs_mfd = self.mfd.build_rhs()","def update_H(self):","def update(world):\r\n infect = infection(world['SIR'], infection_rate, incubation_rate)\r\n disperse = dispersion(world['SIR'], dispersion_kernel, dispersion_rates)\r\n world['SIR'] += dt*( infect + disperse)\r\n world['t'] += dt","def apply(self, simulation):\n t = simulation.time\n dt = simulation.timeStep\n if main_rank == 0:\n simulation.printState()\n # OpenCL update\n self.numMethod(self.gpu_field.gpu_data[self.component],\n self.color)\n self.window.widget.updateGL()\n if simulation.currentIteration > 1:\n self.window.label.setText(\n self.labelText + \"t={0:6.2f}, fps={1:6.2f}\".format(\n t + dt,\n 1. / (self.timer.f_timers.values()[0].t - self.ctime)))\n self.ctime = self.timer.f_timers.values()[0].t","def update_H(self):\n self.grid.H[-1, :, :, :] = self.grid.H[0, :, :, :]","def _updateTransitionMatrix(self):\n N = self.N\n K = self.K\n T= self.T\n\n for i in range(1,self.K+1):\n den = 0\n for t in range(1,self.T):\n for n in range(1,N+1):\n den = den + self.posterior_state_trellis[n][(t,i)]\n \n for j in range(1,self.K+1): \n # For some state i,j\n s = 0\n for n in range(1,N+1): \n for t in range(1,self.T): \n cur_prob = self.posterior_transition_trellis[n][(t,t+1,j,i)]\n s = s+cur_prob\n\n # Compute total \n self.state_transition_mat[(j,i)] = (s/den)","def play_round_Conway_Cell(self):\n for x in self.board:\n for f in x:\n f.live_neighbors = 0\n\n for i in range(1, self.cols - 1):\n for j in range(1, self.rows - 1):\n status = self.board[i][j].status\n assert type(status)==int \n\n for m in range(i - 1, i + 2):\n for n in range(j - 1, j + 2):\n self.board[m][n].live_neighbors += status\n self.board[i][j].live_neighbors -= status","def update(self):\n if self.black + self.white == self.SIZE*self.SIZE:\n if self.black > self.white:\n self.gc.black_wins = True\n elif self.white > self.black:\n self.gc.white_wins = True\n else:\n self.gc.tie = True\n self.gc.black_num = self.black\n self.gc.white_num = self.white","def update(self):\n # GPS data\n self.model.GPS_latitude.set(self._kernel.data.lat)\n self.model.GPS_longitude.set(self._kernel.data.lon)\n \n self.model.GPS_heading.set(self._kernel.data.gps_heading)\n self.model.GPS_speed.set(self._kernel.data.speed)\n self.model.GPS_altitude.set(self._kernel.data.altitude)\n \n self.model.GPS_fix.set(self._kernel.data.fix)\n self.model.GPS_satellite_count.set(self._kernel.data.num_sat)\n \n # compass data\n self.model.compass_heading.set(self._kernel.data.compass_heading)\n \n # time data\n self.model.time.set(self._kernel.data.timestamp.isoformat())\n self.model.date.set(self._kernel.data.datestamp.isoformat())\n \n # other data\n self.model.temperature.set(self._kernel.data.temperature)","def _update(self, state_pred, covar_pred, sigma_points_pred, weights_c,\n z_pred, z_points_pred, H, z, R):\n # Compute the Kalman gains and the innovation\n K = self._KF_gain(state_pred, covar_pred, sigma_points_pred,\n weights_c, z_pred, z_points_pred, R)\n innovation = self.context.correct_observation_diff(z - z_pred)\n innovation = tf.expand_dims(innovation, -1)\n update = tf.squeeze(tf.matmul(K, innovation), -1)\n\n if self.debug:\n update = tf.Print(update, [z], summarize=10000, message='z \\n')\n update = tf.Print(update, [K], summarize=10000, message='K \\n')\n update = tf.Print(update, [update], summarize=10000,\n message='update \\n')\n\n # Compute the new state\n state_up = state_pred + update\n # correct the updated state, e.g. warping angles\n state_up = self.context.correct_state(state_up, diff=False)\n\n # Joseph form update: this is an alternative form of writing the update\n # of the covariance matrix that keeps the covariance matrix\n # positive semidefinite (i.e. it remains a correct covariance matrix)\n mult = tf.eye(self.dim_x) - tf.matmul(K, H)\n covar_up = \\\n tf.matmul(mult, tf.matmul(covar_pred,\n tf.linalg.matrix_transpose(mult)))\n covar_up += tf.matmul(K, tf.matmul(R,\n tf.linalg.matrix_transpose(K)))\n\n covar_up = self._make_valid(covar_up)\n return state_up, covar_up","def update_state(self):\n if self._coordinator.data:\n # get consumption value\n value_list = self._coordinator.data['values']\n values = [v['value'] for v in value_list]\n self._state = f\"{sum(values):.2f}\"","def act(self, world_state, agent_host, current_r):\n\n def world_observations():\n obs_text = world_state.observations[-1].text\n obs = json.loads(obs_text) # most recent observation\n return obs\n\n obs = world_observations()\n surrondings = obs['floor3x3']\n around_me = [surrondings[1], surrondings[7], surrondings[3], surrondings[5]] # FRONT LEFT RIGHT BACK -> BACK FRONT LEFT RIGHT\n\n if not u'XPos' in obs or not u'ZPos' in obs: # Incomplete observation received\n return 0\n\n def add_currentLoaction_to_qtable(): # q table dictionary with current position as key and list of actions as value, all set to 0\n current_s = \"%d:%d\" % (int(obs[u'XPos']), int(obs[u'ZPos'])) # string of current position\n\n if current_s not in self.q_table: # if the current postion is not in q table\n self.q_table[current_s] = ([0] * len(\n self.actions)) # Add that to q table dictionary with current position as value and set of actions as key, all set to 0\n for i in range(len(around_me)):\n if around_me[i] == 'lava' or around_me[i] == 'stone':\n self.q_table[current_s][i] = -100\n return current_s\n\n current_s = add_currentLoaction_to_qtable()\n\n def updateQtable(): # Update q table, Formula to update qtable in the function\n # update Q values \n if self.training and self.prev_s is not None and self.prev_a is not None: # we update q_table for previous action here\n old_q = self.q_table[self.prev_s][self.prev_a] # prev_a is previous action and prev_s is previous location\n self.q_table[self.prev_s][self.prev_a] = old_q + self.alpha * (current_r + self.gamma * max(self.q_table[current_s]) - old_q)\n\n updateQtable()\n\n def select_random_action():\n # select the next action\n rnd = random.random()\n if rnd < self.epsilon:\n\n m = self.q_table[current_s].copy()\n ll = list()\n for i in range(len(around_me)):\n if around_me[i] == 'lava' or around_me[i] == 'stone':\n ll.append(i)\n\n for i in sorted(ll, reverse=True):\n del m[i]\n\n m = max(m)\n l = list()\n for x in range(0, len(self.actions)):\n if self.q_table[current_s][x] == m:\n l.append(x)\n y = random.randint(0, len(l) - 1)\n a = l[y]\n\n\n #a = random.randint(0, len(self.actions) - 1)\n else: # Random selection between the max value of actions for current position\n m = self.q_table[current_s].copy()\n ll = list()\n for i in range(len(around_me)):\n if around_me[i] == 'lava' or around_me[i] == 'stone':\n ll.append(i)\n\n # movenorth 1\", \"movesouth 1\", \"movewest 1\", \"moveeast 1\n if self.prev_a == 0 and 1 not in ll and len(ll) != len(m) -1:\n ll.append(1)\n elif self.prev_a == 1 and 0 not in ll and len(ll) != len(m) -1:\n ll.append(0)\n elif self.prev_a == 2 and 3 not in ll and len(ll) != len(m) -1:\n ll.append(3)\n elif self.prev_a == 3 and 2 not in ll and len(ll) != len(m) -1:\n ll.append(2)\n\n for i in sorted(ll, reverse=True):\n del m[i]\n\n m = max(m)\n l = list()\n for x in range(0, len(self.actions)):\n if self.q_table[current_s][x] == m:\n l.append(x)\n y = random.randint(0, len(l) - 1)\n a = l[y]\n return a\n\n a = select_random_action()\n\n # send the selected action\n agent_host.sendCommand(self.actions[a])\n self.prev_s = current_s\n self.prev_a = a\n\n return current_r","def update_E(self):\n self.grid.E[self.loc] += (\n self.grid.courant_number\n * self.grid.inverse_permittivity[self.loc]\n * self.phi_E\n )","def update(self):\n self.board.update()","def updateBD(self):\r\n # itereigenupdated is always up-to-date in the diagonal case\r\n # just double check here\r\n if self.itereigenupdated == self.countiter:\r\n return\r\n\r\n if self.sp.neg.cmuexp: # cave:\r\n self.update_exponential(self.Zneg, -self.sp.neg.cmuexp)\r\n # self.C += self.Zpos # pos update after Zneg would be the correct update, overall:\r\n # self.C = self.Zpos + Cs * Mh.expms(-self.sp.neg.cmuexp*Csi*self.Zneg*Csi) * Cs\r\n self.Zneg = np.zeros((self.N, self.N))\r\n\r\n if self.sigma_vec is not 1 and not np.all(self.sigma_vec == 1):\r\n self.C = dot(dot(np.diag(self.sigma_vec), self.C), np.diag(self.sigma_vec))\r\n self.sigma_vec[:] = 1\r\n\r\n if self.opts['CMA_const_trace'] in (True, 1, 2): # normalize trace of C\r\n if self.opts['CMA_const_trace'] == 2:\r\n s = np.exp(np.mean(np.log(self.dC)))\r\n else:\r\n s = np.mean(self.dC)\r\n self.C /= s\r\n self.dC /= s\r\n self.C = (self.C + self.C.T) / 2\r\n # self.C = np.triu(self.C) + np.triu(self.C,1).T # should work as well\r\n # self.D, self.B = eigh(self.C) # hermitian, ie symmetric C is assumed\r\n\r\n if type(self.opts['CMA_eigenmethod']) == type(1):\r\n print('WARNING: option CMA_eigenmethod should be a function, not an integer')\r\n if self.opts['CMA_eigenmethod'] == -1:\r\n # pygsl\r\n # easy to install (well, in Windows install gsl binaries first,\r\n # set system path to respective libgsl-0.dll (or cp the dll to\r\n # python\\DLLS ?), in unzipped pygsl edit\r\n # gsl_dist/gsl_site_example.py into gsl_dist/gsl_site.py\r\n # and run \"python setup.py build\" and \"python setup.py install\"\r\n # in MINGW32)\r\n if 1 < 3: # import pygsl on the fly\r\n try:\r\n import pygsl.eigen.eigenvectors # TODO efficient enough?\r\n except ImportError:\r\n print('WARNING: could not find pygsl.eigen module, either install pygsl \\n' +\r\n ' or set option CMA_eigenmethod=1 (is much slower), option set to 1')\r\n self.opts['CMA_eigenmethod'] = 0 # use 0 if 1 is too slow\r\n\r\n self.D, self.B = pygsl.eigen.eigenvectors(self.C)\r\n\r\n elif self.opts['CMA_eigenmethod'] == 0:\r\n # TODO: thoroughly test np.linalg.eigh\r\n # numpy.linalg.eig crashes in 200-D\r\n # and EVecs with same EVals are not orthogonal\r\n self.D, self.B = np.linalg.eigh(self.C) # self.B[i] is a row and not an eigenvector\r\n else: # is overall two;ten times slower in 10;20-D\r\n self.D, self.B = Misc.eig(self.C) # def eig, see below\r\n else:\r\n self.D, self.B = self.opts['CMA_eigenmethod'](self.C)\r\n\r\n\r\n # assert(sum(self.D-DD) < 1e-6)\r\n # assert(sum(sum(np.dot(BB, BB.T)-np.eye(self.N))) < 1e-6)\r\n # assert(sum(sum(np.dot(BB * DD, BB.T) - self.C)) < 1e-6)\r\n idx = np.argsort(self.D)\r\n self.D = self.D[idx]\r\n self.B = self.B[:,idx] # self.B[i] is a row, columns self.B[:,i] are eigenvectors\r\n # assert(all(self.B[self.countiter % self.N] == self.B[self.countiter % self.N,:]))\r\n\r\n # qqqqqqqqqq\r\n if 11 < 3: # limit condition number to 1e13\r\n climit = 1e13 # cave: conditioncov termination is 1e14\r\n if self.D[-1] / self.D[0] > climit:\r\n self.D += self.D[-1] / climit\r\n for i in xrange(self.N):\r\n self.C[i][i] += self.D[-1] / climit\r\n\r\n if 11 < 3 and any(abs(sum(self.B[:,0:self.N-1] * self.B[:,1:], 0)) > 1e-6):\r\n print('B is not orthogonal')\r\n print(self.D)\r\n print(sum(self.B[:,0:self.N-1] * self.B[:,1:], 0))\r\n else:\r\n # is O(N^3)\r\n # assert(sum(abs(self.C - np.dot(self.D * self.B, self.B.T))) < N**2*1e-11)\r\n pass\r\n self.D **= 0.5\r\n self.itereigenupdated = self.countiter","def inc_rstate(self):\r\n #self.rstate = ff_2p134(self.rstate)\r\n self.rstate = multMatVect(self.rstate, A1p134, M1, A2p134, M2)\r\n assert self.rstate.dtype == numpy.int32","def init_status(self, init_cells):\n\n # get size of begining status set\n init_row = len(init_cells)\n if (init_row == 0):\n init_col = 0\n else:\n init_col = len(init_cells[0])\n # compute offset in row direction and column direction\n row_off = (self.row - init_row) / 2\n col_off = (self.col - init_col) / 2\n\n # check size\n if (row_off < 0 or col_off < 0):\n raise Exception(\"Size of begining status set is too large\")\n\n # create the world\n for crow in range(self.row):\n for ccol in range(self.col):\n srow = crow - row_off\n scol = ccol - col_off\n if (crow in range(row_off, row_off + init_row)\n and\n ccol in range(col_off, col_off + init_col)):\n self.now[crow, ccol] = init_cells[srow][scol]\n else:\n self.now[crow, ccol] = False;\n self.next[crow, ccol] = False;","def update_cov_chol(self):\n self.downscale.parameter_target.update_cov_chol()","def update_cov_chol(self):\n self.downscale.parameter_target.update_cov_chol()","def _handle_coordinator_update(self) -> None:\n _LOGGER.debug(f\"{self.name} updating\")\n self._value = self._data.wiserhub.system.away_mode_target_temperature\n # Support prior to 2022.7.0 Versions without deprecation warning\n if hasattr(self, \"_attr_value\"):\n self._attr_value = self._data.wiserhub.system.away_mode_target_temperature\n\n self.async_write_ha_state()","def synchronize_state(self):\n theta = self.unicycle_state[:,2]\n v = self.unicycle_state[:,3]\n self.position[:, :2] = self.unicycle_state[:,:2]\n self.orientation[:,2] = theta\n vx = v * np.cos(theta)\n vy = v * np.sin(theta)\n\n self.velocity[:, 0] = vx\n self.velocity[:, 1] = vy\n\n self.angular_velocity[:, 2] = self.unicycle_state[:, 4]","def state_to_features(self, game_state: dict) -> np.array:\n device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n # This is the dict before the game begins and after it ends\n if game_state is None:\n return None\n\n \n #get global information as a 17x17 channel\n x = game_state['field']\n x = np.swapaxes(x,0,1)\n for i in range(len(game_state['coins'])):\n a = game_state['coins'][i][1]\n b = game_state['coins'][i][0]\n x[a][b] = 4\n for i in range(len(game_state['bombs'])):\n a = game_state['bombs'][i][0][1]\n b = game_state['bombs'][i][0][0]\n x[a][b] = -(5+game_state['bombs'][i][1])\n for i in game_state['others']:\n if i[2]:\n x[i[3][1]][i[3][0]] = -10\n else:\n x[i[3][1]][i[3][0]] = -11\n if game_state['self'][2]:\n x[game_state['self'][3][1]][game_state['self'][3][0]] = 5\n else:\n x[game_state['self'][3][1]][game_state['self'][3][0]] = 6\n expl_List = np.argwhere(game_state['explosion_map'] != 0)\n for i in expl_List:\n x[i[1]][i[0]] = -4\n channel1 = x.copy()\n \n \n #prep local channel\n if self.modelToUse != 0:\n #get simpele direction to and aways from closest coin or crate if no coin on the field\n x_axis,y_axis,coin_creat_encoding = directionToNearestCoin_Crate(game_state['coins'], game_state['self'][3], game_state['field'])\n if x_axis == \"left\":\n if x[game_state['self'][3][1]][game_state['self'][3][0]-1] == 0:\n x[game_state['self'][3][1]][game_state['self'][3][0]-1] = coin_creat_encoding\n if x[game_state['self'][3][1]][game_state['self'][3][0]+1] == 0:\n x[game_state['self'][3][1]][game_state['self'][3][0]+1] = -2\n if x_axis == \"right\":\n if x[game_state['self'][3][1]][game_state['self'][3][0]-1] == 0:\n x[game_state['self'][3][1]][game_state['self'][3][0]-1] = -2\n if x[game_state['self'][3][1]][game_state['self'][3][0]+1] == 0:\n x[game_state['self'][3][1]][game_state['self'][3][0]+1] = coin_creat_encoding\n if y_axis == \"up\":\n if x[game_state['self'][3][1]-1][game_state['self'][3][0]] == 0:\n x[game_state['self'][3][1]-1][game_state['self'][3][0]] = coin_creat_encoding\n if x[game_state['self'][3][1]+1][game_state['self'][3][0]] == 0:\n x[game_state['self'][3][1]+1][game_state['self'][3][0]] = -2\n if y_axis == \"down\":\n if x[game_state['self'][3][1]-1][game_state['self'][3][0]] == 0:\n x[game_state['self'][3][1]-1][game_state['self'][3][0]] = -2\n if x[game_state['self'][3][1]+1][game_state['self'][3][0]] == 0:\n x[game_state['self'][3][1]+1][game_state['self'][3][0]] = coin_creat_encoding\n \n \n \n #get information of bombs: on which position the explotion will be and how far away the bomb is\n bombs = game_state['bombs']\n bombs.sort(key=lambda x: x[1],reverse=True)\n x = np.pad(x, (3,3), 'constant', constant_values=(-1))\n for i in (bombs):\n y_bomb = i[0][1] + 3\n x_bomb = i[0][0] + 3\n for j in range(4):\n if abs(x[y_bomb,x_bomb+j]) != 1 and x[y_bomb,x_bomb+j] != -4:\n blocked = False\n for l in range(j):\n if x[y_bomb,x_bomb+j-l] == -1:\n blocked = True\n if blocked == False:\n x[y_bomb,x_bomb+j] = -(9-j)\n #print(\"test1\")\n if abs(x[y_bomb,x_bomb-j]) != 1 and x[y_bomb,x_bomb-j] != -4:\n blocked = False\n for l in range(j):\n if x[y_bomb,x_bomb-j+l] == -1:\n blocked = True\n if blocked == False:\n x[y_bomb,x_bomb-j] = -(9-j)\n #print(\"test2\")\n if abs(x[y_bomb+j,x_bomb]) != 1 and x[y_bomb+j,x_bomb] != -4:\n blocked = False\n for l in range(j):\n if x[y_bomb+j-l,x_bomb] == -1:\n blocked = True\n if blocked == False:\n x[y_bomb+j,x_bomb] = -(9-j)\n #print(\"test3\")\n if abs(x[y_bomb-j,x_bomb]) != 1 and x[y_bomb-j,x_bomb] != -4:\n blocked = False\n for l in range(j):\n if x[y_bomb-j+l,x_bomb] == -1:\n blocked = True\n if blocked == False:\n x[y_bomb-j,x_bomb] = -(9-j)\n #print(\"test4\")\n x = x[3:-3,3:-3]\n \n \n #get local view and concatenate it with channel 1 (will be sliced apart in the model later)\n z = np.zeros(17)\n y = x[game_state['self'][3][1]-1:game_state['self'][3][1]+2,game_state['self'][3][0]-1:game_state['self'][3][0]+2]\n y = y.flatten()\n z[0:9] = y\n #get correct input for the model used\n if self.modelToUse == 2:\n z = Variable(torch.from_numpy(z)).to(device).to(torch.float)\n z = z.unsqueeze(0).unsqueeze(0).unsqueeze(0)\n channel1 = Variable(torch.from_numpy(channel1)).to(device).to(torch.float)\n channel1 = channel1.unsqueeze(0).unsqueeze(0)\n return torch.cat((channel1,z),2)\n elif self.modelToUse == 1:\n y = Variable(torch.from_numpy(y)).to(device).to(torch.float)\n y = y.unsqueeze(0)\n return y\n else:\n channel1 = Variable(torch.from_numpy(channel1)).to(device).to(torch.float)\n channel1 = channel1.unsqueeze(0).unsqueeze(0)\n return channel1\n return","def update(self):\n\t\t# If being controlled by COM\n\t\tif self.controled_by_com :\n\t\t\t# Substract 1 from the update counter\n\t\t\tself.update_counter -= 1\n\t\t\t# If the update counter reaches zero\n\t\t\tif self.update_counter == 0. :\n\t\t\t\t# then ask for an action \n\t\t\t\tif self.intermediate_phase is False :\n\t\t\t\t\tself.action_required = True \n\t\t\t\t\t\t\n\t\t\t\t# if during a change\n\t\t\t\t# then make the change\n\t\t\t\tif self.intermediate_phase is True : \n\t\t\t\t\tself.action_required = False\n\t\t\t\t\tself._color_changer() #Make the change in the Simulator\n\t\telse :\n\t\t\tpass","def _insert_CD_state(self, alpha, clear=True):\n # Defines a new thermodynamic state based on the neighboring state\n neighbor_ind = [alpha < p['alpha']\n for p in self.data['CD'].protocol].index(True) - 1\n params_n = self.system.paramsFromAlpha(\n alpha, params_o=self.data['CD'].protocol[neighbor_ind])\n\n # For sampling importance resampling,\n # prepare an augmented matrix for pymbar calculations\n # with a new thermodynamic state\n (u_kln_s, N_k) = self._u_kln(self.data['CD'].Es, self.data['CD'].protocol)\n (K, L, N) = u_kln_s.shape\n\n u_kln_n = self._u_kln(self.data['CD'].Es, [params_n])[0]\n L += 1\n N_k = np.append(N_k, [0])\n\n u_kln = np.zeros([K, L, N])\n u_kln[:, :-1, :] = u_kln_s\n for k in range(K):\n u_kln[k, -1, :] = u_kln_n[k, 0, :]\n\n # Determine SIR weights\n weights = self.run_MBAR(u_kln, N_k, augmented=True)[1][:, -1]\n weights = weights / sum(weights)\n\n # Resampling\n # Convert linear indices to 3 indicies: state, cycle, and snapshot\n cum_N_state = np.cumsum([0] + list(N_k))\n cum_N_cycle = [np.cumsum([0] + [self.data['CD'].Es[k][c]['MM'].shape[0] \\\n for c in range(len(self.data['CD'].Es[k]))]) for k in range(len(self.data['CD'].Es))]\n\n def linear_index_to_snapshot_index(ind):\n state_index = list(ind < cum_N_state).index(True) - 1\n nis_index = ind - cum_N_state[state_index]\n cycle_index = list(nis_index < cum_N_cycle[state_index]).index(True) - 1\n nic_index = nis_index - cum_N_cycle[state_index][cycle_index]\n return (state_index, cycle_index, nic_index)\n\n def snapshot_index_to_linear_index(state_index, cycle_index, nic_index):\n return cum_N_state[state_index] + cum_N_cycle[state_index][\n cycle_index] + nic_index\n\n # Terms to copy\n if self.args.params['CD']['pose'] > -1:\n # Pose BPMF\n terms = ['MM',\\\n 'k_angular_ext','k_spatial_ext','k_angular_int'] + scalables\n else:\n # BPMF\n terms = ['MM', 'site'] + scalables\n\n CD_Es_s = []\n confs_s = []\n for c in range(len(self.data['CD'].Es[0])):\n CD_Es_c = dict([(term, []) for term in terms])\n confs_c = []\n for n_in_c in range(len(self.data['CD'].Es[-1][c]['MM'])):\n if (cum_N_cycle[-1][c] == 0):\n (snapshot_s,snapshot_c,snapshot_n) = linear_index_to_snapshot_index(\\\n np.random.choice(range(len(weights)), size = 1, p = weights)[0])\n else:\n snapshot_c = np.inf\n while (snapshot_c > c):\n (snapshot_s,snapshot_c,snapshot_n) = linear_index_to_snapshot_index(\\\n np.random.choice(range(len(weights)), size = 1, p = weights)[0])\n for term in terms:\n CD_Es_c[term].append(\\\n np.copy(self.data['CD'].Es[snapshot_s][snapshot_c][term][snapshot_n]))\n if self.args.params['CD']['keep_intermediate']:\n # Has not been tested:\n confs_c.append(\\\n np.copy(self.data['CD'].confs['samples'][snapshot_s][snapshot_c]))\n for term in terms:\n CD_Es_c[term] = np.array(CD_Es_c[term])\n CD_Es_s.append(CD_Es_c)\n confs_s.append(confs_c)\n\n # Insert resampled values\n self.data['CD'].protocol.insert(neighbor_ind + 1, params_n)\n self.data['CD'].Es.insert(neighbor_ind + 1, CD_Es_s)\n self.data['CD'].confs['samples'].insert(neighbor_ind + 1, confs_s)\n self.data['CD'].confs['replicas'].insert(neighbor_ind+1, \\\n np.copy(self.data['CD'].confs['replicas'][neighbor_ind]))\n\n if clear:\n self._clear_f_RL()","def initialize_action_matrix(self):\n for start_i, cols in enumerate(self.A):\n for next_j, value in enumerate(cols):\n start_state = self.get_state_from_row(start_i)\n # (row_number, red loc, blue loc, green loc)\n next_state = self.get_state_from_row(next_j)\n\n # Valid states include moving exactly 1 dumbbell to a block that's\n # not already occupied, or to origin.\n num_moves, moves = count_moves_from_states(\n start_state, next_state)\n \n # key: location (where 0 = origin, 1/2/3 = block #), value: count\n next_state_loc_counts = Counter(next_state[1:])\n del next_state_loc_counts[0] # ignore origin location\n \n # There must not be more than 1 dumbbell at each block\n is_valid_next_state = all(\n map(lambda x: x <= 1, next_state_loc_counts.values()))\n \n if (num_moves == 1 and is_valid_next_state):\n move = moves[0] # Only 1 move\n # doing nothing or moving to origin is not valid\n if (start_state == next_state or move[1] == 0):\n continue\n self.A[start_i][next_j] = self.get_col_from_action(move)","def update_to_state(self, game_state):\n pass","def _update_cells(self):\n for row_number in range(self.number_cells_y):\n for col_number in range(self.number_cells_x):\n if self.to_be_updated[row_number][col_number]:\n self.cells[row_number][col_number].update()","def model_state_update(model, time_t, state_controller, input_f16):\n pass","def _get_obs(self):\n # return np.concatenate((self.world.state[:6], self.world.state[7:13]))\n return np.concatenate((self.world.state, np.zeros(7)))\n # return self.world.state","def update(self):\n self._state = 23","def update_board(self):\n for x in self.board:\n for f in x:\n if f.status == 0:\n if f.name == \"conway\":\n assert type(self.population)==int\n if f.live_neighbors == 3:\n f.symbol =\"*\"\n f.status = 1\n self.population += 1\n elif f.name == \"fredkin\":\n if f.live_neighbors == 1 or f.live_neighbors == 3 :\n f.status = 1\n f.symbol = str(f.age)\n self.population += 1\n else:\n f.status = 0\n\n elif f.status == 1:\n if f.name == \"conway\":\n assert type(self.population)==int\n #assert type(f.status)== 1\n if not((f.live_neighbors == 2 or f.live_neighbors == 3)):\n f.symbol = \".\"\n f.status = 0\n else:\n self.population += 1\n elif f.name == \"fredkin\":\n if f.live_neighbors == 1 or f.live_neighbors == 3:\n f.status = 1\n f.age += 1\n if f.age <= 2:\n f.symbol = str(f.age)\n self.population += 1\n else:\n self.board.replace(f, Conway_Cell(\"*\"))\n else:\n f.status = 0\n f.symbol = \"-\"","def update_nodes(self):\n\n L = self.level\n P = L.prob\n\n # only if the level has been touched before\n assert L.status.unlocked\n\n # get number of collocation nodes for easier access\n M = self.coll.num_nodes\n\n # gather all terms which are known already (e.g. from the previous iteration)\n # this corresponds to u0 + QF(u^k) - QdF(u^k) + tau\n\n # get QF(u^k)\n integral = self.integrate()\n for m in range(M):\n # get -QdF(u^k)_m\n for j in range(1, M + 1):\n integral[m] -= L.dt * self.QI[m + 1, j] * L.f[j]\n\n # add initial value\n integral[m] += L.u[0]\n # add tau if associated\n if L.tau[m] is not None:\n integral[m] += L.tau[m]\n\n # do the sweep\n for m in range(0, M):\n # build rhs, consisting of the known values from above and new values from previous nodes (at k+1)\n rhs = P.dtype_u(integral[m])\n for j in range(1, m + 1):\n rhs += L.dt * self.QI[m + 1, j] * L.f[j]\n\n # implicit solve with prefactor stemming from the diagonal of Qd\n L.u[m + 1] = P.solve_system(\n rhs, L.dt * self.QI[m + 1, m + 1], L.u[m + 1], L.time + L.dt * self.coll.nodes[m]\n )\n # update function values\n L.f[m + 1] = P.eval_f(L.u[m + 1], L.time + L.dt * self.coll.nodes[m])\n\n # indicate presence of new values at this level\n L.status.updated = True\n\n return None","def update_mmtr(self, now):\r\n if (self.position == SinglePhaseBreaker.CLOSED):\r\n # We only actually update the values if the position is closed\r\n super().update_mmtr(now)\r\n else:\r\n # Nevertheless, this is our last updated time, so that if we are\r\n # later closed, we update from the last updated time\r\n self.last_update = now","def update(self):\n # Update the weight matrix: \n self.W -= self.lr * self.grad_W \n \n # Update the bias matrices:\n self.b -= self.lr * np.array(self.grad_b) \n self.c -= self.lr * np.array(self.grad_c)","def update_H(self):\n gamma = self.get_gamma()\n delta = self.get_delta()\n summand2 = ((1 + (gamma.transpose().dot(self.H).dot(gamma) /\n delta.transpose().dot(gamma))) *\n delta.dot(delta.transpose()) / delta.transpose().dot(gamma)\n )\n summand3 = - ((delta.dot(gamma.transpose()).dot(self.H) +\n self.H.dot(gamma).dot(delta.transpose())) /\n delta.transpose().dot(gamma))\n self.H = self.H + summand2 + summand3","def update_cov_chol(self):\n log_precision_target = self.downscale.parameter_log_precision_target\n gp_target = self.downscale.parameter_gp_target\n # update the covariance matrix for each parameter\n for i in range(self.n_parameter):\n # calculate the cholesky of the gram matrix (sometimes referred to\n # as the kernel matrix)\n cov_chol = -self.downscale.square_error.copy()\n gp_scale = gp_target.scale_gp_state[i]\n cov_chol /= 2 * math.pow(gp_scale, 2)\n cov_chol = np.exp(cov_chol)\n cov_chol += np.identity(self.area_unmask) * self.small_covariance\n cov_chol = linalg.cholesky(cov_chol, True)\n # scale the cholesky matrix by a precision\n cov_chol *= math.exp(\n -0.5*log_precision_target.log_precision_state[i])\n self.cov_chol_array[i] = cov_chol","def _update_board(self, move: int) -> None:\n row = self._column_to_row[move] # Find what row to place the disk in\n if self._is_red_active:\n self.board_array[row][move] = 1\n self.hash = self.hash ^ int(self._red_hash_keys[row][move]) # Update hash\n else:\n self.board_array[row][move] = -1\n self.hash = self.hash ^ int(self._yellow_hash_keys[row][move]) # # Update hash\n\n self._column_to_row[move] += 1\n if self._column_to_row[move] == 6:\n self._valid_moves.remove(move)","def update(self):\n changes = {}\n for coord in INDICES: # the need for two for loops is necessary\n if self.chart[coord] == ALIVE and (\n self.number_of_neighbors(coord) < 2 or self.number_of_neighbors(coord) > 3):\n changes[coord] = KILL\n elif self.number_of_neighbors(coord) == 3:\n changes[coord] = REVIVE\n for coord in changes.keys(): # because the evolution is discrete\n if changes[coord] == KILL:\n self.kill(coord)\n elif changes[coord] == REVIVE:\n self.givebirth(coord)","def _update_hessian(self) -> None:\n assert self._species and self._coords is not None and self._method\n\n species = self._species.new_species(\n name=f\"{self._species.name}_opt_{self.iteration}\"\n )\n species.coordinates = self._coords.to(\"cartesian\")\n\n species.calc_hessian(\n method=self._method,\n keywords=self._method.keywords.hess,\n n_cores=self._n_cores,\n )\n assert species.hessian is not None, \"Failed to calculate H\"\n\n self._species.hessian = species.hessian.copy()\n self._coords.update_h_from_cart_h(self._species.hessian.to(\"Ha Å^-2\"))","def update(self, *args):\n return _osgAnimation.Channel_update(self, *args)","def update_cells(self, state):\n width = WIDTH / CELL_SIZE\n height = HEIGHT / CELL_SIZE\n\n for index in range(0, width * height):\n if state[index] != self.get_state(index):\n self.toggle_color(index)","def swarm(self) -> None:\n self.state[:, :, Boids.Attr.ACC] *= 0\n self.update_acc_by_rules()\n self._update_vel()\n self._update_loc()","def update_board(self, coordinate, hit):\n \n if hit:\n self.board_state[coordinate.row_idx][coordinate.col_idx] = \"H\"\n else:\n self.board_state[coordinate.row_idx][coordinate.col_idx] = \"M\"","def update_io(self, dt):\n self.light.change_color(traffic_lights_binary())\n self.seven_segment_display.activate_segments(seven_segment_binary())\n self.ascii.update_ascii_grid()","def update_z(self):\n w_actual = self.w_estimates[-1]\n # update the underlying linear model\n self.linear_model.w = w_actual\n gamma = np.divide(abs(w_actual), np.sqrt(self.z.value.T)).T\n Gamma_diag = np.zeros((gamma.shape[0], gamma.shape[0]), float)\n np.fill_diagonal(Gamma_diag, gamma)\n Sigma_y = self.lambda_param * np.eye(\n self.linear_model.data_num\n ) + self.linear_model.dict_mtx @ Gamma_diag @ np.transpose(\n self.linear_model.dict_mtx\n )\n\n self.z.value = np.diag(\n np.transpose(self.linear_model.dict_mtx)\n @ np.linalg.pinv(Sigma_y, hermitian=True)\n @ self.linear_model.dict_mtx\n )\n self.z_estimates.append(self.z.value)\n self.gamma_estimates.append(gamma)\n # print(f'state: {self.state_name} Sigma_y cond: {np.linalg.cond(np.linalg.pinv(Sigma_y, hermitian=True))} gamma: {np.divide(np.abs(w_actual),np.sqrt(self.z.value))}')","def _update_board(self):\n\n self.game_board.update_board(self.tetrino_set)","def update_state (self, ivec : np.ndarray) :\n self.xvec = RNN.sigmoid (self.Whid.dot (self.xvec) + self.Win.dot (ivec) + self.bvec)","def __setstate__(self, state):\n\n if sparse.issparse(state['transition_matrix']):\n state['transition_matrix'] = state['transition_matrix'].toarray()\n\n # Recalculate the cumulative probabilities\n state['cumulative_probabilities'] = np.cumsum(state['transition_matrix'], axis=1)\n\n self.__dict__ = state","def __update_state(self, x0, u):\n N = int(len(u) / 2)\n lower_triangular_ones_matrix = np.tril(np.ones((N, N)))\n kron = np.kron(lower_triangular_ones_matrix, np.eye(2))\n\n new_state = np.vstack([np.eye(2)] * int(N)) @ x0 + kron @ u * self.nmpc_timestep\n\n return new_state","def update(self, state, loss):\n # wana clip the losses so it doesn't go absolutely insane.\n jdecays = state[\"decays\"]\n cor_mean = state[\"means\"] / (1 - jdecays**(state[\"iteration\"] + 1))\n approx_max = jnp.max(cor_mean)\n approx_max = jnp.where(state[\"iteration\"] == 0, loss, approx_max)\n loss = jnp.minimum(jnp.abs(approx_max) * 2, loss)\n\n means = state[\"means\"] * jdecays + loss * (1. - jdecays)\n\n cor_mean = means / (1 - jdecays**(state[\"iteration\"] + 1))\n running_min = jnp.minimum(state[\"running_min\"], cor_mean)\n\n return {\n \"means\": means,\n \"iteration\": state[\"iteration\"] + 1,\n \"running_min\": running_min,\n \"decays\": state[\"decays\"],\n }","def updateGameState(self):\n boardArray = self._board.get_board()\n \n if self.state is self._BEGIN:\n\n for i in [1, self._board.get_board_size() - 2]:\n for j in range(1, self._board.get_board_size() - 1):\n\n if boardArray[i][j] != self._board._EMPTY:\n self.state = self._MIDDLE\n return\n\n if boardArray[j][i] != self._board._EMPTY:\n self.state = self._MIDDLE\n return\n\n\n elif self.state is self._MIDDLE:\n nbPieces = self._board.get_total_pieces()\n\n if nbPieces >= self._board.get_board_size()**2 - ENDGAME:\n self.state = self._END\n return","def update(self):\n self.wall_list.update()\n self.enemy_list.update()\n self.sludge.update()\n self.consumeable.update()\n self.can_climb.update()","def update_state(self):\n # return initial state if no observation was yet\n if len(self.obs_history) == 0:\n return self.kf.initial_state_mean, self.kf.initial_state_covariance\n\n hist = np.ma.masked_array(self.obs_history, mask=np.zeros((1,)))\n for i in range(len(hist)):\n if hist[i] == -1e8:\n hist[i] = np.ma.masked\n\n # print(hist, hist.shape)\n return self.kf.filter(hist)","def update_pressure(self):\n m_multipliers = np.ones(self.mesh.get_number_of_cells())\n\n\n rhs_current = np.zeros(self.mfd.get_number_of_dof()) \n rhs_current += self.rhs_mfd\n\n\n for cell_index in range(self.mesh.get_number_of_cells()):\n density = -self.ref_pressure\n density += self.current_pressure[cell_index]\n density *= self.compressibility\n density += 1.\n density *= self.ref_density\n\n # We multiply by the inverse of \\frac{\\rho}{\\mu}\n m_multipliers[cell_index] = self.viscosity/density\n\n c_entry = self.compressibility\n c_entry *= self.porosities[cell_index]\n c_entry /= self.delta_t\n c_entry *= self.mesh.get_cell_volume(cell_index)\n\n rhs_current[self.mesh.get_number_of_faces()+\n cell_index] += c_entry*self.current_pressure[cell_index]\n\n self.lhs_coo.data[self.c_start+cell_index] = c_entry\n\n for [index, cell_index] in enumerate(self.rate_wells):\n rhs_current[self.mesh.get_number_of_faces()+cell_index] += \\\n self.rate_wells_rate[index]\n\n self.mfd.update_m(self.lhs_coo.data[:self.m_x_coo_length], m_multipliers)\n\n solution = dsolve.spsolve(self.lhs_coo.tocsr(), rhs_current)\n self.prev_pressure = self.current_pressure\n self.current_pressure = solution[self.mesh.get_number_of_faces():]\n self.current_velocity = solution[:self.mesh.get_number_of_faces()]","def compute_controller(self, time):\n\n # here we implement an example for a consensus algorithm\n neig = self.get_neighbors()\n messages = self.get_messages()\n pos, rot = self.get_pos_and_orientation()\n\n # send message of positions to all neighbors indicating our position\n for n in neig:\n self.send_message(n, pos)\n\n # check if we received the position of our neighbors and compute desired change in position\n # as a function of the neighbors (message is composed of [neighbors id, position])\n dx = 0.\n dy = 0.\n if messages:\n\n if time < 5:\n # square Formation\n dx, dy = controller.formation_control(\"square\", messages, self.id, pos)\n\n elif time >= 5 and time < 30:\n # move out of room\n dx, dy = controller.follow_leader(3, 4, pos, messages,\n leader_alpha=30)\n\n elif time >= 30 and time < 65:\n # circle formation\n dx, dy = controller.formation_control(\"circle\", messages, self.id, pos)\n\n elif time >= 65 and time < 75:\n # surround purple ball\n dx, dy = controller.go_to_leader(self.purple_x, self.purple_y, pos,\n messages, 0, 0)\n self.init_push = True\n\n elif time >= 75 and time < 115:\n # push purple ball\n if self.init_push:\n output = controller.get_current_formation(messages, self.id, pos)\n self.des_xs, self.des_ys, self.cx, self.cy = output\n self.init_push = False\n\n dx, dy, self.cx, self.cy = controller.push_ball(\n messages, self.id, pos, self.des_xs, self.des_ys,\n self.cx, self.cy, 2.5, 5.5, K=3)\n\n elif time >= 115 and time < 130:\n # eject from purple ball\n dx, dy = controller.eject_from_ball(2.5, 5.5, messages, pos, d_0=1.5)\n\n elif time >= 130 and time < 155:\n # surround red ball\n dx, dy = controller.go_to_leader(self.red_x, self.red_y, pos,\n messages,\n self.purple_x, self.purple_y,\n obstacle=True, room_alpha=1)\n self.init_push = True\n\n elif time >= 155 and time < 205:\n # push red ball\n if self.init_push:\n output = controller.get_current_formation(messages, self.id, pos)\n self.des_xs, self.des_ys, self.cx, self.cy = output\n self.init_push = False\n\n dx, dy, self.cx, self.cy = controller.push_ball(\n messages, self.id, pos, self.des_xs, self.des_ys,\n self.cx, self.cy, 0.5, 5.5, K=3)\n\n elif time >= 205 and time < 220:\n # eject from red ball\n dx, dy = controller.eject_from_ball(0.5, 5.5, messages, pos)\n\n elif time >= 220 and time < 230:\n # return to room step 1\n dx, dy = controller.follow_leader(2, 3.5, pos, messages)\n\n elif time >= 230 and time < 270:\n # return to room step 2\n dx, dy = controller.follow_leader(2, 0, pos, messages,\n leader_alpha=30, agent_alpha=3)\n elif time >= 270 and time < 290:\n # return to room step 3\n dx, dy = controller.follow_leader(1.5, 0, pos, messages)\n\n elif time >= 290:\n # diamond formation\n dx, dy = controller.formation_control(\"diamond\", messages, self.id, pos)\n\n # compute velocity change for the wheels\n vel_norm = np.linalg.norm([dx, dy]) # norm of desired velocity\n if vel_norm < 0.01:\n vel_norm = 0.01\n des_theta = np.arctan2(dy/vel_norm, dx/vel_norm)\n right_wheel = np.sin(des_theta-rot)*vel_norm + np.cos(des_theta-rot)*vel_norm\n left_wheel = -np.sin(des_theta-rot)*vel_norm + np.cos(des_theta-rot)*vel_norm\n self.set_wheel_velocity([left_wheel, right_wheel])\n\n return dx, dy","def _update_state(self, currentPhase, phasetime, time):\n # compute new state without registered action\n tToNearGreenPhase = self._get_toNearGreenPhase(currentPhase, phasetime, self.extended)\n\n if self.numbus > 0:\n # last available checkout for this intersection\n if self.numbus > 1:\n # bunch, use current time as last checkout\n last_available_checkout_time = time\n elif self.last_checkout_bus is None:\n # no checked out bus, assume perfect headway\n last_available_checkout_time = time - self.CONFIG['target_headway']\n else:\n last_available_checkout_time = self.last_checkout_bus.check_out_time\n # check in time of the last bus checked in\n last_check_in_time = self.bus_list[-1].check_in_time\n check_in_hdy = self.bus_list[-1].check_in_headway\n new_state = [last_available_checkout_time, last_check_in_time, check_in_hdy, self.numbus, self.allnumvel,\n tToNearGreenPhase]\n else:\n if self.last_checkout_bus:\n last_available_checkout_time = self.last_checkout_bus.check_out_time\n check_in_hdy = self.last_checkout_bus.check_in_headway\n last_check_in_time = self.last_checkout_bus.check_in_time\n new_state = [last_available_checkout_time, last_check_in_time, check_in_hdy, 0, self.allnumvel, tToNearGreenPhase]\n else:\n new_state = [0, 0, 0, 0, self.allnumvel, tToNearGreenPhase]\n\n self.state = new_state\n return","def update_state(self):\n if not self.platforms:\n self.state = self.states['Win']\n elif self.player.y > 389:\n self.state = self.states['Lose'] \n else:\n self.state = self.states['Game']","def update(self) -> py_trees.common.Status:\n self.logger.debug(\"%s.update()\" % (self.__class__.__name__))\n self.blackboard.foo.bar.wow = \"colander\"\n\n return py_trees.common.Status.SUCCESS","def update(self):\n self._curses_window.clear()\n\n self._curses_window.addstr(0, 0,\n \"{:5s} {:5.1f}\".format(\n 'time:', round(time() - self.simulation_start, 1)\n )\n + \"\\t{:13s} {:4.1f}\".format(\n ' steps per s:', round(1 / global_vars.step_duration, 1)\n )\n + \"\\t{:4s} {:4d}\".format(' step:', global_vars.step)\n\t\t\t+ \"\\n{:4s} {:4d}{:1s}\".format('Death cause.. eaten:', global_vars.h_eaten, 'h')\n\t\t\t+ \"\\t{:4s} {:4d}{:1s}{:4d}{:1s}\".format(\n 'starved:', global_vars.h_starved, 'h/', global_vars.c_starved, 'c'\n )\n\t\t\t+ \"\\t{:4s} {:4d}{:1s}{:4d}{:1s}\".format(\n 'trampled:', global_vars.h_trampled, 'h/', global_vars.c_trampled, 'c'\n )\n + \"\\t{:4s} {:4d}{:1s}{:4d}{:1s}\".format(\n 'natural death:', global_vars.h_age, 'h/', global_vars.c_age, 'c'\n )\n )\n\n self._curses_window.noutrefresh()","def _update(self):\n self.cv.update()","def update(self):\n self._state = self._state","def update(self, new_state):\n\n if self.update_animation:\n self.canvas.delete(self.agent)\n row, col = new_state\n x1 = col * self.GRID_ROW_HEIGHT\n y1 = row * self.GRID_ROW_HEIGHT\n self.agent = self.canvas.create_image(x1 + self.GRID_ROW_HEIGHT / 2, y1 + self.GRID_ROW_HEIGHT / 2,\n image=self.penguin)","def create_gridworld(self):\n self.num_actions = 4\n self.num_states = self.num_cols * self.num_rows + 1\n self.start_state_seq = row_col_to_seq(self.start_state, self.num_cols)\n self.goal_states_seq = row_col_to_seq(self.goal_states, self.num_cols)\n\n # rewards structure\n self.R = self.r_step * np.ones((self.num_states, 1))\n self.R[self.num_states-1] = 0\n self.R[self.goal_states_seq] = self.r_goal\n for i in range(self.num_bad_states):\n if self.r_bad is None:\n raise Exception(\"Bad state specified but no reward is given\")\n bad_state = row_col_to_seq(self.bad_states[i,:].reshape(1,-1), self.num_cols)\n self.R[bad_state, :] = self.r_bad\n for i in range(self.num_restart_states):\n if self.r_restart is None:\n raise Exception(\"Restart state specified but no reward is given\")\n restart_state = row_col_to_seq(self.restart_states[i,:].reshape(1,-1), self.num_cols)\n self.R[restart_state, :] = self.r_restart\n\n # probability model\n if self.p_good_trans == None:\n raise Exception(\"Must assign probability and bias terms via the add_transition_probability method.\")\n\n self.P = np.zeros((self.num_states,self.num_states,self.num_actions))\n for action in range(self.num_actions):\n for state in range(self.num_states):\n\n # check if state is the fictional end state - self transition\n if state == self.num_states-1:\n self.P[state, state, action] = 1\n continue\n\n # check if the state is the goal state or an obstructed state - transition to end\n row_col = seq_to_col_row(state, self.num_cols)\n if self.obs_states is not None:\n end_states = np.vstack((self.obs_states, self.goal_states))\n else:\n end_states = self.goal_states\n\n if any(np.sum(np.abs(end_states-row_col), 1) == 0):\n self.P[state, self.num_states-1, action] = 1\n\n # else consider stochastic effects of action\n else:\n for dir in range(-1,2,1):\n direction = self._get_direction(action, dir)\n next_state = self._get_state(state, direction)\n if dir == 0:\n prob = self.p_good_trans\n elif dir == -1:\n prob = (1 - self.p_good_trans)*(self.bias)\n elif dir == 1:\n prob = (1 - self.p_good_trans)*(1-self.bias)\n\n self.P[state, next_state, action] += prob\n\n # make restart states transition back to the start state with\n # probability 1\n if self.restart_states is not None:\n if any(np.sum(np.abs(self.restart_states-row_col),1)==0):\n next_state = row_col_to_seq(self.start_state, self.num_cols)\n self.P[state,:,:] = 0\n self.P[state,next_state,:] = 1\n return self","def wfc_clear(parameters, state, output):\n # Crystal solving time matrix\n output.current_iteration_count_observation = 0\n output.current_iteration_count_propagation = 0\n output.current_iteration_count_last_touch = 0\n output.current_iteration_count_crystal = 0\n\n output.solving_time = np.full(\n (parameters.rows, parameters.columns), 0, dtype=np.int32\n )\n output.ones_time = np.full((parameters.rows, parameters.columns), 0, dtype=np.int32)\n output.propagation_time = np.full(\n (parameters.rows, parameters.columns), 0, dtype=np.int32\n )\n output.touch_time = np.full(\n (parameters.rows, parameters.columns), 0, dtype=np.int32\n )\n output.crystal_time = np.full(\n (parameters.rows, parameters.columns), 0, dtype=np.int32\n )\n output.method_time = np.full(\n (parameters.rows, parameters.columns), 0, dtype=np.int32\n )\n output.choices_recording = np.full(\n (parameters.rows, parameters.columns), 0, dtype=np.float32\n )\n output.stats_tracking[\"total_observations\"] = 0\n\n state.wave_table = np.full(parameters.wave_shape, True, dtype=np.bool_)\n\n compatible_shape = [\n parameters.rows,\n parameters.columns,\n parameters.number_of_patterns,\n parameters.number_of_directions,\n ]\n\n # Initialize the compatible count from the propagation matrix. This sets the\n # maximum domain of possible neighbors for each cell node.\n\n def prop_compat(p, d):\n return sum(parameters.propagator_matrix[(d + 2) % 4][p])\n\n def comp_count(r, c, p, d):\n return pattern_compatible_count[p][d]\n\n pcomp = np.vectorize(prop_compat)\n ccount = np.vectorize(comp_count)\n pattern_compatible_count = np.fromfunction(\n pcomp,\n (parameters.number_of_patterns, parameters.number_of_directions),\n dtype=np.int16,\n )\n state.compatible_count = np.fromfunction(\n ccount,\n (\n parameters.rows,\n parameters.columns,\n parameters.number_of_patterns,\n parameters.number_of_directions,\n ),\n dtype=np.int16,\n )\n\n # Likewise, set the weights to their maximum values\n state.sums_of_weights = np.full(\n [parameters.rows, parameters.columns], state.sum_of_weights, dtype=np.float64\n )\n state.sums_of_weight_log_weights = np.full(\n [parameters.rows, parameters.columns],\n state.sum_of_weight_log_weights,\n dtype=np.float64,\n )\n state.entropies = np.full(\n [parameters.rows, parameters.columns], state.starting_entropy, dtype=np.float64\n )\n\n state.recorded_steps = []\n state.observation_stack = collections.deque()\n\n # ground banning goes here\n if parameters.wfc_ns.ground != 0 and False:\n pass\n\n output.previous_decisions = []\n state.previous_decisions = []\n WFC_LOGGER.debug(\"clear complete\")\n\n return state, output","def update_params(self, mat):\n assert mat.shape == self.shape\n vec = mat.reshape(mat.size)\n self.alpha = np.linalg.solve(self.covs_mat.T.dot(self.covs_mat),\n self.covs_mat.T.dot(vec))","def _handle_coordinator_update(self) -> None:\n self._thermostat = self.coordinator.data[self._thermostat.serial_number]\n self.async_write_ha_state()","def compute_controller(self):\n \n # here we implement an example for a consensus algorithm\n neig = self.get_neighbors()\n messages = self.get_messages()\n pos, rot = self.get_pos_and_orientation()\n \n #send message of positions to all neighbors indicating our position\n for n in neig:\n self.send_message(n, pos)\n \n # check if we received the position of our neighbors and compute desired change in position\n # as a function of the neighbors (message is composed of [neighbors id, position])\n dx = 0.\n dy = 0.\n if messages:\n for m in messages:\n dx += m[1][0] - pos[0]\n dy += m[1][1] - pos[1]\n # integrate\n des_pos_x = pos[0] + self.dt * dx\n des_pos_y = pos[1] + self.dt * dy\n \n #compute velocity change for the wheels\n vel_norm = np.linalg.norm([dx, dy]) #norm of desired velocity\n if vel_norm < 0.01:\n vel_norm = 0.01\n des_theta = np.arctan2(dy/vel_norm, dx/vel_norm)\n right_wheel = np.sin(des_theta-rot)*vel_norm + np.cos(des_theta-rot)*vel_norm\n left_wheel = -np.sin(des_theta-rot)*vel_norm + np.cos(des_theta-rot)*vel_norm\n self.set_wheel_velocity([left_wheel, right_wheel])","def update_aco(self):\n if self.running:\n for x in range(self.columns):\n for y in range(self.rows):\n colour = get_colour((self.aco.pheromones[x][y]-1)/50, [(255, 255, 255), (0, 0, 0)])\n self.grid[x][y].config(bg=colour)\n for ant in self.aco.ants:\n ant_coors = ant.location\n self.grid[ant_coors[0]][ant_coors[1]].config(bg=ant.colour)\n\n home_coors = self.aco.home_coors\n self.grid[home_coors[0]][home_coors[1]].config(bg='blue')\n\n for food_key in self.aco.food.keys():\n food = self.aco.food[food_key].location\n self.grid[food[0]][food[1]].config(bg=get_colour(self.aco.food[food_key].food_val/self.aco.food[food_key].food_capacity, [(184, 232, 182), (55, 144, 52)]))\n \n total_food = self.aco.increment()\n brought_food = self.aco.brought_food\n if not self.aco.is_finished():\n if self.aco.food_deplete:\n self.status_text.set(\"Food left: \"+str(total_food)+\", food brought home: \"+str(brought_food))\n else: self.status_text.set(\"Food brought home: \"+str(brought_food))\n self.main_window.after(int(self.speed), self.update_aco)\n else:\n self.status.config(fg='green')\n self.status_text.set(\"Simulation complete!\")","def update(self):\n self.data.update()\n for sensor in self.data.daikinskyport.get_sensors(self._index):\n if sensor[\"type\"] == self._type and self._sensor_name == sensor[\"name\"]:\n self._state = sensor[\"value\"]","async def _async_force_refresh_state(self):\n await self._shade.refresh()\n self._async_update_current_cover_position()\n self.async_write_ha_state()","def update(self, state_sequence, reward_sequence):\n\n for i in range(reward_sequence.shape[0]):\n\n trajt_1 = state_sequence[:,i][:,np.newaxis] # No use of V_mu in computing distances!\n trajt = state_sequence[:,i+1][:,np.newaxis]\n # trajt_1 = np.concatenate((trajt_1, self.V_mu(trajt_1)), axis=0) # Use V_mu as well\n # trajt = np.concatenate((trajt, self.V_mu(trajt)), axis=0)\n k_t_1 = self.kernel(self.D, trajt_1)\n k_t = self.kernel(self.D, trajt)\n ktt = self.kernel(trajt, trajt)\n at = np.dot(self.K_inv, k_t)\n delk_t_1 = k_t_1 - self.gamma*k_t\n\n ct = np.dot(self.C_, delk_t_1) - (self.A - self.gamma*at)\n st = self.sigma0**2 - np.dot(ct.T, delk_t_1)\n\n diff_r = np.dot(delk_t_1.T, self.alpha_)[0,0] - reward_sequence[i]\n self.alpha_ = self.alpha_ + ct/st*diff_r\n\n self.C_ = self.C_ + np.dot(ct, ct.T)/st\n\n self.A = at\n\n assert (not np.isnan(self.alpha_).any()), \"Check alpha for NaN values\"\n\n self.diff_alpha_CV_D = self.alpha_ - np.dot(self.C_, self.V_D)","def propagate_state(self, msg):\r\n # Previous values\r\n x = self.state_vector[0, 0]\r\n y = self.state_vector[1, 0]\r\n theta = self.state_vector[2, 0]\r\n if theta < -pi:\r\n theta += 2 * pi\r\n elif theta > pi:\r\n theta -= 2 * pi\r\n\r\n # Current values\r\n vel = msg.twist.twist.linear.x\r\n ang = msg.twist.twist.angular.z\r\n nvel = 0\r\n nang = 0\r\n dt = msg.header.stamp.secs + msg.header.stamp.nsecs * 10 ** -9 - self.time_stamp\r\n self.time_stamp = msg.header.stamp.secs + msg.header.stamp.nsecs * 10 ** -9\r\n\r\n # Calculate Jacobians F and G\r\n self.motion_jacobian_state_vector(vel, ang, theta, 0, 0, dt)\r\n self.motion_jacobian_noise_components(vel, ang, theta, 0, 0, dt)\r\n\r\n # Choose motion model\r\n if ang == 0:\r\n # Propagate\r\n self.state_vector[0, 0] = x + (vel + nvel) * dt * cos(theta)\r\n self.state_vector[1, 0] = y + (vel + nvel) * dt * sin(theta)\r\n self.state_vector[2, 0] = theta\r\n else:\r\n # Propagate\r\n self.state_vector[0, 0] = x - ((vel + nvel) / (ang + nang)) * sin(theta) + (\r\n (vel + nvel) / (ang + nang)) * sin(theta + (ang + nang) * dt)\r\n self.state_vector[1, 0] = y + ((vel + nvel) / (ang + nang)) * cos(theta) - (\r\n (vel + nvel) / (ang + nang)) * cos(theta + (ang + nang) * dt)\r\n self.state_vector[2, 0] = theta + (ang + nang) * dt","def update_output(self, latent_mat, weight_mat, y_list):"],"string":"[\n \"def update_world(self):\\n pass\",\n \"def update_H(self):\\n self.grid.H[self.loc] -= (\\n self.grid.courant_number\\n * self.grid.inverse_permeability[self.loc]\\n * self.phi_H\\n )\",\n \"def _update_loc(self) -> None:\\n self.state[:, :, Boids.Attr.LOC] += self.state[:, :, Boids.Attr.VEL]\\n # wrap-around the simulated environment\\n self.state[:, :, Boids.Attr.LOC] %= np.expand_dims(self.env_bounds, axis=1)\",\n \"def __updateMatC(self):\\n\\t\\tif self.regScheme == 2:\\n\\t\\t\\tfor id1 in range(self.nTemplates):\\n\\t\\t\\t\\tself.C[id1,id1] = 1.0 / self.w0[id1]**2\",\n \"def update_state(self):\\n self.reset_state()\\n for piece in self.pieces:\\n coordinates = piece.get_block_positions()\\n for coor in coordinates:\\n x, y = coor\\n self.state[y][x] = piece\",\n \"def get_transition(self, row, col, action, tot_row, tot_col):\\n\\n '''\\n Expand the grid of the environment to handle when the \\n agent decides to move in the direction of a wall \\n '''\\n state_probabilities = np.zeros((int(np.sqrt(self.env.observation_space.n)) + 2, int(np.sqrt(self.env.observation_space.n)) + 2), dtype=float)\\n\\n if action == 'UP':\\n row += 1\\n col += 1\\n state_probabilities[row - 1, col] = 0.33 #UP\\n state_probabilities[row, col - 1 ] = 0.33 #LEFT\\n state_probabilities[row, col + 1] = 0.33 # RIGHT\\n state_probabilities[row + 1, col] = 0.0 #DOWN\\n elif action == 'LEFT':\\n row += 1\\n col += 1\\n state_probabilities[row - 1, col] = 0.33 #UP\\n state_probabilities[row, col - 1 ] = 0.33 #LEFT\\n state_probabilities[row, col + 1] = 0.0 # RIGHT\\n state_probabilities[row + 1, col] = 0.33 #DOWN\\n elif action == 'RIGHT':\\n row += 1\\n col += 1\\n state_probabilities[row - 1, col] = 0.33 #UP\\n state_probabilities[row, col - 1 ] = 0.0 #LEFT\\n state_probabilities[row, col + 1] = 0.33 # RIGHT\\n state_probabilities[row + 1, col] = 0.33 #DOWN\\n elif action == 'DOWN':\\n row += 1\\n col += 1\\n state_probabilities[row - 1, col] = 0.0 # UP\\n state_probabilities[row, col - 1] = 0.33 # LEFT\\n state_probabilities[row, col + 1] = 0.33 # RIGHT\\n state_probabilities[row + 1, col] = 0.33 # DOWN\\n\\n for row in range (0, tot_row+1):\\n if state_probabilities[row, 0] != 0:\\n state_probabilities[row, 1] += state_probabilities[row, 0]\\n elif state_probabilities[row, -1] != 0:\\n state_probabilities[row, -2] += state_probabilities[row, -1]\\n\\n for col in range (0, tot_col+1):\\n if state_probabilities[0, col] != 0:\\n state_probabilities[1, col] += state_probabilities[0, col]\\n elif state_probabilities[-1, col] != 0:\\n state_probabilities[-2, col] += state_probabilities[-1, col]\\n\\n return state_probabilities[1: 1+tot_row, 1:1+tot_col]\",\n \"def get_transition(self, row, col, action, tot_row, tot_col):\\n\\n '''\\n Expand the grid of the environment to handle when the \\n agent decides to move in the direction of a wall \\n '''\\n state_probabilities = np.zeros((int(np.sqrt(self.env.observation_space.n)) + 2, int(np.sqrt(self.env.observation_space.n)) + 2), dtype=float)\\n\\n if action == 'UP':\\n row += 1\\n col += 1\\n state_probabilities[row - 1, col] = 0.33 #UP\\n state_probabilities[row, col - 1 ] = 0.33 #LEFT\\n state_probabilities[row, col + 1] = 0.33 # RIGHT\\n state_probabilities[row + 1, col] = 0.0 #DOWN\\n elif action == 'LEFT':\\n row += 1\\n col += 1\\n state_probabilities[row - 1, col] = 0.33 #UP\\n state_probabilities[row, col - 1 ] = 0.33 #LEFT\\n state_probabilities[row, col + 1] = 0.0 # RIGHT\\n state_probabilities[row + 1, col] = 0.33 #DOWN\\n elif action == 'RIGHT':\\n row += 1\\n col += 1\\n state_probabilities[row - 1, col] = 0.33 #UP\\n state_probabilities[row, col - 1 ] = 0.0 #LEFT\\n state_probabilities[row, col + 1] = 0.33 # RIGHT\\n state_probabilities[row + 1, col] = 0.33 #DOWN\\n elif action == 'DOWN':\\n row += 1\\n col += 1\\n state_probabilities[row - 1, col] = 0.0 # UP\\n state_probabilities[row, col - 1] = 0.33 # LEFT\\n state_probabilities[row, col + 1] = 0.33 # RIGHT\\n state_probabilities[row + 1, col] = 0.33 # DOWN\\n\\n for row in range (0, tot_row+1):\\n if state_probabilities[row, 0] != 0:\\n state_probabilities[row, 1] += state_probabilities[row, 0]\\n elif state_probabilities[row, -1] != 0:\\n state_probabilities[row, -2] += state_probabilities[row, -1]\\n\\n for col in range (0, tot_col+1):\\n if state_probabilities[0, col] != 0:\\n state_probabilities[1, col] += state_probabilities[0, col]\\n elif state_probabilities[-1, col] != 0:\\n state_probabilities[-2, col] += state_probabilities[-1, col]\\n\\n return state_probabilities[1: 1+tot_row, 1:1+tot_col]\",\n \"def updateSimState(self):\\n self.sim_state = {k: v for k,v in self.state.iteritems()}\",\n \"def HC_update(self, GS_HC, hc_ro):\\n\\n # Backward inference using GS - HC connectivity (generative model).\\n # Weighted mean of GS - HC connectivity, with HC estimate as weights.\\n hc_fb = np.average(GS_HC, 0, hc_ro)\\n hc_fb = hc_fb / hc_fb.sum()\\n\\n self.P = hc_fb * self.P\\n self.pass_through_lateral_conn()\",\n \"def _compute_world_params(self) -> None:\\n\\n self.states = []\\n for row in range(self.grid_height):\\n for col in range(self.grid_width):\\n cell = row * self.grid_width + col\\n cell_type = self.grid[cell]\\n\\n possible_actions = {\\n Action.up: self._get_action(max(row - 1, 0) * self.grid_width + col),\\n Action.down: self._get_action(min(row + 1, self.grid_height - 1) * self.grid_width + col),\\n Action.right: self._get_action(row * self.grid_width + min(col + 1, self.grid_width - 1)),\\n Action.left: self._get_action(row * self.grid_width + max(col - 1, 0))\\n }\\n\\n self.states.append(State(cell, possible_actions, cell_type))\",\n \"def sense(self):\\n self.robot.update_state_position()\\n self.robot.update_state_proximity(self.world_props)\\n self.robot.update_state_compass()\\n self.robot.update_state_odometry()\",\n \"def update_state_game_variables(self):\\n self.model.numbers = [0, 1, 2, 3, 4, 5, 6, 7, 8]\\n self.model.player_mark = \\\"\\\"\\n self.model.player_move = 0\\n self.model.boards = [\\\"board\\\"] * 9\\n self.update_score_board()\",\n \"def update_H(self):\\n self.grid.H[:, -1, :, :] = self.grid.H[:, 0, :, :]\",\n \"def update(self):\\n self.active = False\\n self.top.update(self.rgb,self.cmyk,self.hsv)\\n self.bot.update(self.rgb,self.cmyk,self.hsv)\\n self.active = True\",\n \"def update_concentrations_batch(self): \\n #--- Update the cell concentrations ---\\n # dX_i/dt = mu_i*(1-rmp/100)*X_i*(1 - sum(i,(1-rmp/100)X(i))/carrying_capacity) or \\n # (X_i(t+dt) - X_i(t))/dt = mu*(1-rmp/100)*X_i(t)*(1 - sum(i,(1-rmp/100)*X_i(t))/carrying_capacity)\\n # where rmp is the random mortality percentage\\n # If concentration is negative set it to zero\\n members_gDW_per_ml_total = sum([(1 - self.random_mortality_percentage/100)*member.organism.gDW_per_ml[self._t] for member in self.community_members])\\n self._logisticG_factor_gDW_per_ml = 1 - members_gDW_per_ml_total/self.carrying_capacity['gDW_per_ml']\\n if len([member for member in self.community_members if member.organism.cells_per_ml != None]) == len(self.community_members):\\n members_cells_per_ml_total = sum([(1 - self.random_mortality_percentage/100)*member.organism.cells_per_ml[self._t] for member in self.community_members])\\n self._logisticG_factor_cells_per_ml = 1 - members_cells_per_ml_total/self.carrying_capacity['cells_per_ml']\\n\\n for member in self.community_members:\\n # We always need gDW_per_ml to update compound concentrations but\\n # providing cells_per_ml is optional\\n member.organism.gDW_per_ml[self._t + self._dt] = max(member.organism.mu[self._t]*(1-self.random_mortality_percentage/100)*member.organism.gDW_per_ml[self._t]*self._logisticG_factor_gDW_per_ml*self._dt + member.organism.gDW_per_ml[self._t],0)\\n\\n if member.organism.cells_per_ml is not None:\\n member.organism.cells_per_ml[self._t + self._dt] = max(member.organism.mu[self._t]*(1 - self.random_mortality_percentage/100)*member.organism.cells_per_ml[self._t]*self._logisticG_factor_cells_per_ml*self._dt + member.organism.cells_per_ml[self._t],0)\\n\\n # Total death rate (** newly added for DMMM_mortality **)\\n if member.organism.mu[self._t] < 0:\\n # In thise case random_mortality_rate has already been incorporated into mu\\n # (see DMMM.py)\\n member.organism.total_death_rate[self._t] = member.organism.mu[self._t] \\n else:\\n member.organism.total_death_rate[self._t] = member.organism.random_mortality_rate\\n\\n\\n #--- Update shared compound concentrations ---\\n # dC/dt = f where, f = sum(k,v_export_k*X_k) - sum(k,v_uptake_k*X_k) + dead_pool_rate\\n # (C(t+dt) - c(t))/dt = sum(k,v_export_k*X_k) - sum(k,v_uptake_k*X_k) + dead_pool \\n # where, dead_pool_rate = sum(k,-self.cell_pool_factor*cell_pool_concentration_k*total_death_rate_k*X_k)\\n # Here, cell_pool_concentration is the concentration of the compound pool \\n # per gDW or per cells, which should have already been assigned to each \\n # shared compound. The minus sign is because total_death_rate is negative\\n # while dead_pool_rate must be non-negative. Here, self.cell_pool_factor is\\n # the factor that should be multiplied by concentration of that compound \\n # (this is because sometimes we want to explore how the growth is affected if \\n # the cell pool is higher than the ones reported experimentally)\\n total_cmps_conc = sum([cmp.concentration[self._t] for cmp in self.shared_compounds])\\n self._logisticG_factor_cmps = 1 - total_cmps_conc/self.carrying_capacity['compounds_mM']\\n self._f = dict([(cmp,None) for cmp in self.shared_compounds])\\n\\n if not hasattr(self,'cell_pool_factor'):\\n self.cell_pool_factor = 1\\n\\n for shared_cmp in self.shared_compounds:\\n dead_pool_rate = -sum([self.cell_pool_factor*shared_cmp.cell_pool_concentration[member.organism.id]*member.organism.total_death_rate[self._t]*1000*member.organism.gDW_per_ml[self._t] for member in self.community_members])\\n if dead_pool_rate < 0:\\n raise userError('dead_pool_rate is negative')\\n\\n f = sum([r.flux[self._t]*1000*r.model.organism.gDW_per_ml[self._t] for r in shared_cmp.reactions]) + dead_pool_rate\\n self._f[shared_cmp] = f\\n\\n conc = f*self._logisticG_factor_cmps*self._dt + shared_cmp.concentration[self._t]\\n\\n if conc >= 0 or (conc < 0 and abs(conc) <= 1e-9):\\n conc = max(conc,0)\\n\\n shared_cmp.concentration[self._t + self._dt] = conc\",\n \"def update_compartments(self, food_glucose):\\n self.g_t, self.m_t = self.new_values(food_glucose, self.get_variables())\",\n \"def update(self, world):\\n self.input_comp.update(self)\\n self._physics_comp.update(self, world)\\n self._graphics_comp.update(self)\",\n \"def update_H(self):\\n self.grid.H[:, :, -1, :] = self.grid.H[:, :, 0, :]\",\n \"def CTMCtoStormpy(h):\\n\\tstate_labelling = _buildStateLabeling(h)\\n\\ttransition_matrix = deepcopy(h.matrix)\\n\\te = array([h.e(s) for s in range(h.nb_states)])\\n\\ttransition_matrix /= e[:,newaxis]\\n\\ttransition_matrix = st.build_sparse_matrix(transition_matrix)\\n\\tcomponents = st.SparseModelComponents(transition_matrix=transition_matrix,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t state_labeling=state_labelling,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t rate_transitions=True)\\n\\tcomponents.exit_rates = e\\n\\tctmc = st.storage.SparseCtmc(components)\\n\\treturn ctmc\",\n \"def update(self, z):\\n\\n Hjac = self._mat_desc[\\\"Hjac\\\"]\\n H = self._mat_desc[\\\"H\\\"];\\n zpred = self._measurement_model(self._state_vec_prev , H)\\n\\n innovation = z - zpred\\n P = self._mat_desc[\\\"P\\\"]\\n Hjac_T = Hjac.T\\n R = self._mat_desc[\\\"R\\\"]\\n M = self._mat_desc[\\\"M\\\"]\\n M_T = M.T\\n S = Hjac * P * Hjac_T + M * R * M_T\\n S_inv = np.linalg.inv(S)\\n self._mat_desc[\\\"K\\\"] = P * Hjac_T * S_inv\\n self._state_vec += self._mat_desc[\\\"K\\\"] * innovation\\n I = np.identity(self._state_vec.shape)\\n\\n # update the covariance matrix\\n P = (I - self._mat_desc[\\\"K\\\"] * Hjac) * P\\n self._mat_desc[\\\"P\\\"] = P\",\n \"def updateState(self):\\n self.state = self.microgridPolicy.computeState();\",\n \"def update_states(self) -> None:\\n self.set_states()\\n self.async_write_ha_state()\",\n \"def update_pop_matrix(self):\\n for row in self.unique_rows[1:-1]: # First and last cell is water\\n for col in self.unique_cols[1:-1]: # First and last cell is water\\n cell = self.landscape[(row, col)]\\n if cell.is_mainland:\\n # print(cell)\\n self.herb_pop_matrix[row - 1][col - 1] = cell.herb_count\\n self.carn_pop_matrix[row - 1][col - 1] = cell.carn_count\",\n \"def __update_visible(self) -> None:\\n for i in range(0, 8):\\n visible_row = self.__row_position + Labyrinth.ALL_ROW_MOVE[i]\\n visible_col = self.__col_position + Labyrinth.ALL_COL_MOVE[i]\\n if 0 <= visible_row < self.__labyrinth.labyrinth_height and \\\\\\n 0 <= visible_col < self.__labyrinth.labyrinth_width:\\n self.__labyrinth.visible_cells[visible_row][visible_col] = 1\",\n \"def update_state(self):\\n self.last_position = self.current_position\\n self.last_distance = self.current_distance\\n self.last_collision_time_stamp = self.current_collision_time_stamp\\n self.current_kinematics = self.airsim_client.simGetGroundTruthKinematics(vehicle_name=self.drone_name)\\n self.current_position = self.current_kinematics.position + self.base_offset\\n self.current_collision_time_stamp = self.airsim_client.simGetCollisionInfo(vehicle_name=self.drone_name).time_stamp\\n # print(\\\"DEBUG: simGetCollisionInfo:\\\", self.airsim_client.simGetCollisionInfo(vehicle_name=self.drone_name))\\n # self.pending_death = self.airsim_client.simIsRacerDisqualified(vehicle_name=self.drone_name)\\n self.objective_status = self.current_objective.next_gate_status(self.last_position, self.current_position)\\n if self.objective_status == GateStatus.CROSSED or self.objective_status == GateStatus.PASSED:\\n if self.switch_to_next_objective(): # if track is finished (changes self.last_distance)\\n self.track_complete = True\\n self.current_distance = self.current_position.distance_to(self.current_objective.gate_pose.position)\",\n \"def updateWorld(self):\\n\\t self.screen.clear()\\n self.update()\\n self.screen.refresh()\",\n \"def _update_grid_map_from_world_cord(self, world_cords_xy):\\n # find occupancy map cords\\n # self.logger.debug(f\\\"Updating Grid Map: {np.shape(world_cords_xy)}\\\")\\n occu_cords = self.cord_translation_from_world(\\n world_cords_xy=world_cords_xy)\\n # self.logger.debug(f\\\"Occupancy Grid Map Cord shape = {np.shape(occu_cords)}\\\")\\n occu_cords_x, occu_cords_y = occu_cords[:, 0], occu_cords[:, 1]\\n min_occu_cords_x, max_occu_cords_x = np.min(occu_cords_x), np.max(occu_cords_x)\\n min_occu_cords_y, max_occu_cords_y = np.min(occu_cords_y), np.max(occu_cords_y)\\n self.map[min_occu_cords_y: max_occu_cords_y, min_occu_cords_x:max_occu_cords_x] -= 0.01\\n self.map[occu_cords_y, occu_cords_x] += self.occu_prob\\n self.map[min_occu_cords_y: max_occu_cords_y,\\n min_occu_cords_x:max_occu_cords_x] = \\\\\\n self.map[min_occu_cords_y: max_occu_cords_y, min_occu_cords_x:max_occu_cords_x].clip(0, 1)\",\n \"def initialize_system(self):\\n self.mfd.set_mesh(self.mesh)\\n [[div_data, div_row, div_col], \\n [div_t_data, div_t_row, div_t_col]] = self.mfd.build_div()\\n [self.m_x_coo_data, \\n m_x_coo_row, \\n m_x_coo_col] = self.mfd.build_m(save_update_info=True)\\n\\n self.m_x_coo_length = len(self.m_x_coo_data)\\n \\n # The data for the bottom right should be zeros. \\n [c_data, c_row, c_col] = self.mfd.build_bottom_right()\\n \\n [coupling_data, coupling_row, coupling_col] = self.mfd.build_coupling_terms()\\n\\n self.div = sparse.coo_matrix((np.array(div_data), \\n (np.add(np.array(div_row), \\n -self.mesh.get_number_of_faces()), \\n np.array(div_col))))\\n self.div = self.div.tocsr()\\n\\n lhs_data = self.m_x_coo_data\\n lhs_row = m_x_coo_row\\n lhs_col = m_x_coo_col\\n \\n lhs_data += div_data\\n lhs_row += div_row\\n lhs_col += div_col\\n\\n lhs_data += div_t_data\\n lhs_row += div_t_row\\n lhs_col += div_t_col \\n \\n self.c_start = len(lhs_data)\\n \\n lhs_data += c_data\\n lhs_row += c_row\\n lhs_col += c_col \\n\\n self.c_end = len(c_data)\\n\\n lhs_data += coupling_data\\n lhs_row += coupling_row\\n lhs_col += coupling_col\\n\\n # Convert m_x_coo_data to numpy array. \\n self.m_x_coo_data = np.array(self.m_x_coo_data)\\n\\n self.lhs_coo = sparse.coo_matrix((np.array(lhs_data), \\n (np.array(lhs_row), \\n np.array(lhs_col))))\\n\\n # RHS construction is for Neumann and Dirichlet \\n # boundaries specified by the mesh. \\n self.rhs_mfd = self.mfd.build_rhs()\",\n \"def update_H(self):\",\n \"def update(world):\\r\\n infect = infection(world['SIR'], infection_rate, incubation_rate)\\r\\n disperse = dispersion(world['SIR'], dispersion_kernel, dispersion_rates)\\r\\n world['SIR'] += dt*( infect + disperse)\\r\\n world['t'] += dt\",\n \"def apply(self, simulation):\\n t = simulation.time\\n dt = simulation.timeStep\\n if main_rank == 0:\\n simulation.printState()\\n # OpenCL update\\n self.numMethod(self.gpu_field.gpu_data[self.component],\\n self.color)\\n self.window.widget.updateGL()\\n if simulation.currentIteration > 1:\\n self.window.label.setText(\\n self.labelText + \\\"t={0:6.2f}, fps={1:6.2f}\\\".format(\\n t + dt,\\n 1. / (self.timer.f_timers.values()[0].t - self.ctime)))\\n self.ctime = self.timer.f_timers.values()[0].t\",\n \"def update_H(self):\\n self.grid.H[-1, :, :, :] = self.grid.H[0, :, :, :]\",\n \"def _updateTransitionMatrix(self):\\n N = self.N\\n K = self.K\\n T= self.T\\n\\n for i in range(1,self.K+1):\\n den = 0\\n for t in range(1,self.T):\\n for n in range(1,N+1):\\n den = den + self.posterior_state_trellis[n][(t,i)]\\n \\n for j in range(1,self.K+1): \\n # For some state i,j\\n s = 0\\n for n in range(1,N+1): \\n for t in range(1,self.T): \\n cur_prob = self.posterior_transition_trellis[n][(t,t+1,j,i)]\\n s = s+cur_prob\\n\\n # Compute total \\n self.state_transition_mat[(j,i)] = (s/den)\",\n \"def play_round_Conway_Cell(self):\\n for x in self.board:\\n for f in x:\\n f.live_neighbors = 0\\n\\n for i in range(1, self.cols - 1):\\n for j in range(1, self.rows - 1):\\n status = self.board[i][j].status\\n assert type(status)==int \\n\\n for m in range(i - 1, i + 2):\\n for n in range(j - 1, j + 2):\\n self.board[m][n].live_neighbors += status\\n self.board[i][j].live_neighbors -= status\",\n \"def update(self):\\n if self.black + self.white == self.SIZE*self.SIZE:\\n if self.black > self.white:\\n self.gc.black_wins = True\\n elif self.white > self.black:\\n self.gc.white_wins = True\\n else:\\n self.gc.tie = True\\n self.gc.black_num = self.black\\n self.gc.white_num = self.white\",\n \"def update(self):\\n # GPS data\\n self.model.GPS_latitude.set(self._kernel.data.lat)\\n self.model.GPS_longitude.set(self._kernel.data.lon)\\n \\n self.model.GPS_heading.set(self._kernel.data.gps_heading)\\n self.model.GPS_speed.set(self._kernel.data.speed)\\n self.model.GPS_altitude.set(self._kernel.data.altitude)\\n \\n self.model.GPS_fix.set(self._kernel.data.fix)\\n self.model.GPS_satellite_count.set(self._kernel.data.num_sat)\\n \\n # compass data\\n self.model.compass_heading.set(self._kernel.data.compass_heading)\\n \\n # time data\\n self.model.time.set(self._kernel.data.timestamp.isoformat())\\n self.model.date.set(self._kernel.data.datestamp.isoformat())\\n \\n # other data\\n self.model.temperature.set(self._kernel.data.temperature)\",\n \"def _update(self, state_pred, covar_pred, sigma_points_pred, weights_c,\\n z_pred, z_points_pred, H, z, R):\\n # Compute the Kalman gains and the innovation\\n K = self._KF_gain(state_pred, covar_pred, sigma_points_pred,\\n weights_c, z_pred, z_points_pred, R)\\n innovation = self.context.correct_observation_diff(z - z_pred)\\n innovation = tf.expand_dims(innovation, -1)\\n update = tf.squeeze(tf.matmul(K, innovation), -1)\\n\\n if self.debug:\\n update = tf.Print(update, [z], summarize=10000, message='z \\\\n')\\n update = tf.Print(update, [K], summarize=10000, message='K \\\\n')\\n update = tf.Print(update, [update], summarize=10000,\\n message='update \\\\n')\\n\\n # Compute the new state\\n state_up = state_pred + update\\n # correct the updated state, e.g. warping angles\\n state_up = self.context.correct_state(state_up, diff=False)\\n\\n # Joseph form update: this is an alternative form of writing the update\\n # of the covariance matrix that keeps the covariance matrix\\n # positive semidefinite (i.e. it remains a correct covariance matrix)\\n mult = tf.eye(self.dim_x) - tf.matmul(K, H)\\n covar_up = \\\\\\n tf.matmul(mult, tf.matmul(covar_pred,\\n tf.linalg.matrix_transpose(mult)))\\n covar_up += tf.matmul(K, tf.matmul(R,\\n tf.linalg.matrix_transpose(K)))\\n\\n covar_up = self._make_valid(covar_up)\\n return state_up, covar_up\",\n \"def update_state(self):\\n if self._coordinator.data:\\n # get consumption value\\n value_list = self._coordinator.data['values']\\n values = [v['value'] for v in value_list]\\n self._state = f\\\"{sum(values):.2f}\\\"\",\n \"def act(self, world_state, agent_host, current_r):\\n\\n def world_observations():\\n obs_text = world_state.observations[-1].text\\n obs = json.loads(obs_text) # most recent observation\\n return obs\\n\\n obs = world_observations()\\n surrondings = obs['floor3x3']\\n around_me = [surrondings[1], surrondings[7], surrondings[3], surrondings[5]] # FRONT LEFT RIGHT BACK -> BACK FRONT LEFT RIGHT\\n\\n if not u'XPos' in obs or not u'ZPos' in obs: # Incomplete observation received\\n return 0\\n\\n def add_currentLoaction_to_qtable(): # q table dictionary with current position as key and list of actions as value, all set to 0\\n current_s = \\\"%d:%d\\\" % (int(obs[u'XPos']), int(obs[u'ZPos'])) # string of current position\\n\\n if current_s not in self.q_table: # if the current postion is not in q table\\n self.q_table[current_s] = ([0] * len(\\n self.actions)) # Add that to q table dictionary with current position as value and set of actions as key, all set to 0\\n for i in range(len(around_me)):\\n if around_me[i] == 'lava' or around_me[i] == 'stone':\\n self.q_table[current_s][i] = -100\\n return current_s\\n\\n current_s = add_currentLoaction_to_qtable()\\n\\n def updateQtable(): # Update q table, Formula to update qtable in the function\\n # update Q values \\n if self.training and self.prev_s is not None and self.prev_a is not None: # we update q_table for previous action here\\n old_q = self.q_table[self.prev_s][self.prev_a] # prev_a is previous action and prev_s is previous location\\n self.q_table[self.prev_s][self.prev_a] = old_q + self.alpha * (current_r + self.gamma * max(self.q_table[current_s]) - old_q)\\n\\n updateQtable()\\n\\n def select_random_action():\\n # select the next action\\n rnd = random.random()\\n if rnd < self.epsilon:\\n\\n m = self.q_table[current_s].copy()\\n ll = list()\\n for i in range(len(around_me)):\\n if around_me[i] == 'lava' or around_me[i] == 'stone':\\n ll.append(i)\\n\\n for i in sorted(ll, reverse=True):\\n del m[i]\\n\\n m = max(m)\\n l = list()\\n for x in range(0, len(self.actions)):\\n if self.q_table[current_s][x] == m:\\n l.append(x)\\n y = random.randint(0, len(l) - 1)\\n a = l[y]\\n\\n\\n #a = random.randint(0, len(self.actions) - 1)\\n else: # Random selection between the max value of actions for current position\\n m = self.q_table[current_s].copy()\\n ll = list()\\n for i in range(len(around_me)):\\n if around_me[i] == 'lava' or around_me[i] == 'stone':\\n ll.append(i)\\n\\n # movenorth 1\\\", \\\"movesouth 1\\\", \\\"movewest 1\\\", \\\"moveeast 1\\n if self.prev_a == 0 and 1 not in ll and len(ll) != len(m) -1:\\n ll.append(1)\\n elif self.prev_a == 1 and 0 not in ll and len(ll) != len(m) -1:\\n ll.append(0)\\n elif self.prev_a == 2 and 3 not in ll and len(ll) != len(m) -1:\\n ll.append(3)\\n elif self.prev_a == 3 and 2 not in ll and len(ll) != len(m) -1:\\n ll.append(2)\\n\\n for i in sorted(ll, reverse=True):\\n del m[i]\\n\\n m = max(m)\\n l = list()\\n for x in range(0, len(self.actions)):\\n if self.q_table[current_s][x] == m:\\n l.append(x)\\n y = random.randint(0, len(l) - 1)\\n a = l[y]\\n return a\\n\\n a = select_random_action()\\n\\n # send the selected action\\n agent_host.sendCommand(self.actions[a])\\n self.prev_s = current_s\\n self.prev_a = a\\n\\n return current_r\",\n \"def update_E(self):\\n self.grid.E[self.loc] += (\\n self.grid.courant_number\\n * self.grid.inverse_permittivity[self.loc]\\n * self.phi_E\\n )\",\n \"def update(self):\\n self.board.update()\",\n \"def updateBD(self):\\r\\n # itereigenupdated is always up-to-date in the diagonal case\\r\\n # just double check here\\r\\n if self.itereigenupdated == self.countiter:\\r\\n return\\r\\n\\r\\n if self.sp.neg.cmuexp: # cave:\\r\\n self.update_exponential(self.Zneg, -self.sp.neg.cmuexp)\\r\\n # self.C += self.Zpos # pos update after Zneg would be the correct update, overall:\\r\\n # self.C = self.Zpos + Cs * Mh.expms(-self.sp.neg.cmuexp*Csi*self.Zneg*Csi) * Cs\\r\\n self.Zneg = np.zeros((self.N, self.N))\\r\\n\\r\\n if self.sigma_vec is not 1 and not np.all(self.sigma_vec == 1):\\r\\n self.C = dot(dot(np.diag(self.sigma_vec), self.C), np.diag(self.sigma_vec))\\r\\n self.sigma_vec[:] = 1\\r\\n\\r\\n if self.opts['CMA_const_trace'] in (True, 1, 2): # normalize trace of C\\r\\n if self.opts['CMA_const_trace'] == 2:\\r\\n s = np.exp(np.mean(np.log(self.dC)))\\r\\n else:\\r\\n s = np.mean(self.dC)\\r\\n self.C /= s\\r\\n self.dC /= s\\r\\n self.C = (self.C + self.C.T) / 2\\r\\n # self.C = np.triu(self.C) + np.triu(self.C,1).T # should work as well\\r\\n # self.D, self.B = eigh(self.C) # hermitian, ie symmetric C is assumed\\r\\n\\r\\n if type(self.opts['CMA_eigenmethod']) == type(1):\\r\\n print('WARNING: option CMA_eigenmethod should be a function, not an integer')\\r\\n if self.opts['CMA_eigenmethod'] == -1:\\r\\n # pygsl\\r\\n # easy to install (well, in Windows install gsl binaries first,\\r\\n # set system path to respective libgsl-0.dll (or cp the dll to\\r\\n # python\\\\DLLS ?), in unzipped pygsl edit\\r\\n # gsl_dist/gsl_site_example.py into gsl_dist/gsl_site.py\\r\\n # and run \\\"python setup.py build\\\" and \\\"python setup.py install\\\"\\r\\n # in MINGW32)\\r\\n if 1 < 3: # import pygsl on the fly\\r\\n try:\\r\\n import pygsl.eigen.eigenvectors # TODO efficient enough?\\r\\n except ImportError:\\r\\n print('WARNING: could not find pygsl.eigen module, either install pygsl \\\\n' +\\r\\n ' or set option CMA_eigenmethod=1 (is much slower), option set to 1')\\r\\n self.opts['CMA_eigenmethod'] = 0 # use 0 if 1 is too slow\\r\\n\\r\\n self.D, self.B = pygsl.eigen.eigenvectors(self.C)\\r\\n\\r\\n elif self.opts['CMA_eigenmethod'] == 0:\\r\\n # TODO: thoroughly test np.linalg.eigh\\r\\n # numpy.linalg.eig crashes in 200-D\\r\\n # and EVecs with same EVals are not orthogonal\\r\\n self.D, self.B = np.linalg.eigh(self.C) # self.B[i] is a row and not an eigenvector\\r\\n else: # is overall two;ten times slower in 10;20-D\\r\\n self.D, self.B = Misc.eig(self.C) # def eig, see below\\r\\n else:\\r\\n self.D, self.B = self.opts['CMA_eigenmethod'](self.C)\\r\\n\\r\\n\\r\\n # assert(sum(self.D-DD) < 1e-6)\\r\\n # assert(sum(sum(np.dot(BB, BB.T)-np.eye(self.N))) < 1e-6)\\r\\n # assert(sum(sum(np.dot(BB * DD, BB.T) - self.C)) < 1e-6)\\r\\n idx = np.argsort(self.D)\\r\\n self.D = self.D[idx]\\r\\n self.B = self.B[:,idx] # self.B[i] is a row, columns self.B[:,i] are eigenvectors\\r\\n # assert(all(self.B[self.countiter % self.N] == self.B[self.countiter % self.N,:]))\\r\\n\\r\\n # qqqqqqqqqq\\r\\n if 11 < 3: # limit condition number to 1e13\\r\\n climit = 1e13 # cave: conditioncov termination is 1e14\\r\\n if self.D[-1] / self.D[0] > climit:\\r\\n self.D += self.D[-1] / climit\\r\\n for i in xrange(self.N):\\r\\n self.C[i][i] += self.D[-1] / climit\\r\\n\\r\\n if 11 < 3 and any(abs(sum(self.B[:,0:self.N-1] * self.B[:,1:], 0)) > 1e-6):\\r\\n print('B is not orthogonal')\\r\\n print(self.D)\\r\\n print(sum(self.B[:,0:self.N-1] * self.B[:,1:], 0))\\r\\n else:\\r\\n # is O(N^3)\\r\\n # assert(sum(abs(self.C - np.dot(self.D * self.B, self.B.T))) < N**2*1e-11)\\r\\n pass\\r\\n self.D **= 0.5\\r\\n self.itereigenupdated = self.countiter\",\n \"def inc_rstate(self):\\r\\n #self.rstate = ff_2p134(self.rstate)\\r\\n self.rstate = multMatVect(self.rstate, A1p134, M1, A2p134, M2)\\r\\n assert self.rstate.dtype == numpy.int32\",\n \"def init_status(self, init_cells):\\n\\n # get size of begining status set\\n init_row = len(init_cells)\\n if (init_row == 0):\\n init_col = 0\\n else:\\n init_col = len(init_cells[0])\\n # compute offset in row direction and column direction\\n row_off = (self.row - init_row) / 2\\n col_off = (self.col - init_col) / 2\\n\\n # check size\\n if (row_off < 0 or col_off < 0):\\n raise Exception(\\\"Size of begining status set is too large\\\")\\n\\n # create the world\\n for crow in range(self.row):\\n for ccol in range(self.col):\\n srow = crow - row_off\\n scol = ccol - col_off\\n if (crow in range(row_off, row_off + init_row)\\n and\\n ccol in range(col_off, col_off + init_col)):\\n self.now[crow, ccol] = init_cells[srow][scol]\\n else:\\n self.now[crow, ccol] = False;\\n self.next[crow, ccol] = False;\",\n \"def update_cov_chol(self):\\n self.downscale.parameter_target.update_cov_chol()\",\n \"def update_cov_chol(self):\\n self.downscale.parameter_target.update_cov_chol()\",\n \"def _handle_coordinator_update(self) -> None:\\n _LOGGER.debug(f\\\"{self.name} updating\\\")\\n self._value = self._data.wiserhub.system.away_mode_target_temperature\\n # Support prior to 2022.7.0 Versions without deprecation warning\\n if hasattr(self, \\\"_attr_value\\\"):\\n self._attr_value = self._data.wiserhub.system.away_mode_target_temperature\\n\\n self.async_write_ha_state()\",\n \"def synchronize_state(self):\\n theta = self.unicycle_state[:,2]\\n v = self.unicycle_state[:,3]\\n self.position[:, :2] = self.unicycle_state[:,:2]\\n self.orientation[:,2] = theta\\n vx = v * np.cos(theta)\\n vy = v * np.sin(theta)\\n\\n self.velocity[:, 0] = vx\\n self.velocity[:, 1] = vy\\n\\n self.angular_velocity[:, 2] = self.unicycle_state[:, 4]\",\n \"def state_to_features(self, game_state: dict) -> np.array:\\n device = torch.device(\\\"cuda\\\" if torch.cuda.is_available() else \\\"cpu\\\")\\n # This is the dict before the game begins and after it ends\\n if game_state is None:\\n return None\\n\\n \\n #get global information as a 17x17 channel\\n x = game_state['field']\\n x = np.swapaxes(x,0,1)\\n for i in range(len(game_state['coins'])):\\n a = game_state['coins'][i][1]\\n b = game_state['coins'][i][0]\\n x[a][b] = 4\\n for i in range(len(game_state['bombs'])):\\n a = game_state['bombs'][i][0][1]\\n b = game_state['bombs'][i][0][0]\\n x[a][b] = -(5+game_state['bombs'][i][1])\\n for i in game_state['others']:\\n if i[2]:\\n x[i[3][1]][i[3][0]] = -10\\n else:\\n x[i[3][1]][i[3][0]] = -11\\n if game_state['self'][2]:\\n x[game_state['self'][3][1]][game_state['self'][3][0]] = 5\\n else:\\n x[game_state['self'][3][1]][game_state['self'][3][0]] = 6\\n expl_List = np.argwhere(game_state['explosion_map'] != 0)\\n for i in expl_List:\\n x[i[1]][i[0]] = -4\\n channel1 = x.copy()\\n \\n \\n #prep local channel\\n if self.modelToUse != 0:\\n #get simpele direction to and aways from closest coin or crate if no coin on the field\\n x_axis,y_axis,coin_creat_encoding = directionToNearestCoin_Crate(game_state['coins'], game_state['self'][3], game_state['field'])\\n if x_axis == \\\"left\\\":\\n if x[game_state['self'][3][1]][game_state['self'][3][0]-1] == 0:\\n x[game_state['self'][3][1]][game_state['self'][3][0]-1] = coin_creat_encoding\\n if x[game_state['self'][3][1]][game_state['self'][3][0]+1] == 0:\\n x[game_state['self'][3][1]][game_state['self'][3][0]+1] = -2\\n if x_axis == \\\"right\\\":\\n if x[game_state['self'][3][1]][game_state['self'][3][0]-1] == 0:\\n x[game_state['self'][3][1]][game_state['self'][3][0]-1] = -2\\n if x[game_state['self'][3][1]][game_state['self'][3][0]+1] == 0:\\n x[game_state['self'][3][1]][game_state['self'][3][0]+1] = coin_creat_encoding\\n if y_axis == \\\"up\\\":\\n if x[game_state['self'][3][1]-1][game_state['self'][3][0]] == 0:\\n x[game_state['self'][3][1]-1][game_state['self'][3][0]] = coin_creat_encoding\\n if x[game_state['self'][3][1]+1][game_state['self'][3][0]] == 0:\\n x[game_state['self'][3][1]+1][game_state['self'][3][0]] = -2\\n if y_axis == \\\"down\\\":\\n if x[game_state['self'][3][1]-1][game_state['self'][3][0]] == 0:\\n x[game_state['self'][3][1]-1][game_state['self'][3][0]] = -2\\n if x[game_state['self'][3][1]+1][game_state['self'][3][0]] == 0:\\n x[game_state['self'][3][1]+1][game_state['self'][3][0]] = coin_creat_encoding\\n \\n \\n \\n #get information of bombs: on which position the explotion will be and how far away the bomb is\\n bombs = game_state['bombs']\\n bombs.sort(key=lambda x: x[1],reverse=True)\\n x = np.pad(x, (3,3), 'constant', constant_values=(-1))\\n for i in (bombs):\\n y_bomb = i[0][1] + 3\\n x_bomb = i[0][0] + 3\\n for j in range(4):\\n if abs(x[y_bomb,x_bomb+j]) != 1 and x[y_bomb,x_bomb+j] != -4:\\n blocked = False\\n for l in range(j):\\n if x[y_bomb,x_bomb+j-l] == -1:\\n blocked = True\\n if blocked == False:\\n x[y_bomb,x_bomb+j] = -(9-j)\\n #print(\\\"test1\\\")\\n if abs(x[y_bomb,x_bomb-j]) != 1 and x[y_bomb,x_bomb-j] != -4:\\n blocked = False\\n for l in range(j):\\n if x[y_bomb,x_bomb-j+l] == -1:\\n blocked = True\\n if blocked == False:\\n x[y_bomb,x_bomb-j] = -(9-j)\\n #print(\\\"test2\\\")\\n if abs(x[y_bomb+j,x_bomb]) != 1 and x[y_bomb+j,x_bomb] != -4:\\n blocked = False\\n for l in range(j):\\n if x[y_bomb+j-l,x_bomb] == -1:\\n blocked = True\\n if blocked == False:\\n x[y_bomb+j,x_bomb] = -(9-j)\\n #print(\\\"test3\\\")\\n if abs(x[y_bomb-j,x_bomb]) != 1 and x[y_bomb-j,x_bomb] != -4:\\n blocked = False\\n for l in range(j):\\n if x[y_bomb-j+l,x_bomb] == -1:\\n blocked = True\\n if blocked == False:\\n x[y_bomb-j,x_bomb] = -(9-j)\\n #print(\\\"test4\\\")\\n x = x[3:-3,3:-3]\\n \\n \\n #get local view and concatenate it with channel 1 (will be sliced apart in the model later)\\n z = np.zeros(17)\\n y = x[game_state['self'][3][1]-1:game_state['self'][3][1]+2,game_state['self'][3][0]-1:game_state['self'][3][0]+2]\\n y = y.flatten()\\n z[0:9] = y\\n #get correct input for the model used\\n if self.modelToUse == 2:\\n z = Variable(torch.from_numpy(z)).to(device).to(torch.float)\\n z = z.unsqueeze(0).unsqueeze(0).unsqueeze(0)\\n channel1 = Variable(torch.from_numpy(channel1)).to(device).to(torch.float)\\n channel1 = channel1.unsqueeze(0).unsqueeze(0)\\n return torch.cat((channel1,z),2)\\n elif self.modelToUse == 1:\\n y = Variable(torch.from_numpy(y)).to(device).to(torch.float)\\n y = y.unsqueeze(0)\\n return y\\n else:\\n channel1 = Variable(torch.from_numpy(channel1)).to(device).to(torch.float)\\n channel1 = channel1.unsqueeze(0).unsqueeze(0)\\n return channel1\\n return\",\n \"def update(self):\\n\\t\\t# If being controlled by COM\\n\\t\\tif self.controled_by_com :\\n\\t\\t\\t# Substract 1 from the update counter\\n\\t\\t\\tself.update_counter -= 1\\n\\t\\t\\t# If the update counter reaches zero\\n\\t\\t\\tif self.update_counter == 0. :\\n\\t\\t\\t\\t# then ask for an action \\n\\t\\t\\t\\tif self.intermediate_phase is False :\\n\\t\\t\\t\\t\\tself.action_required = True \\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t# if during a change\\n\\t\\t\\t\\t# then make the change\\n\\t\\t\\t\\tif self.intermediate_phase is True : \\n\\t\\t\\t\\t\\tself.action_required = False\\n\\t\\t\\t\\t\\tself._color_changer() #Make the change in the Simulator\\n\\t\\telse :\\n\\t\\t\\tpass\",\n \"def _insert_CD_state(self, alpha, clear=True):\\n # Defines a new thermodynamic state based on the neighboring state\\n neighbor_ind = [alpha < p['alpha']\\n for p in self.data['CD'].protocol].index(True) - 1\\n params_n = self.system.paramsFromAlpha(\\n alpha, params_o=self.data['CD'].protocol[neighbor_ind])\\n\\n # For sampling importance resampling,\\n # prepare an augmented matrix for pymbar calculations\\n # with a new thermodynamic state\\n (u_kln_s, N_k) = self._u_kln(self.data['CD'].Es, self.data['CD'].protocol)\\n (K, L, N) = u_kln_s.shape\\n\\n u_kln_n = self._u_kln(self.data['CD'].Es, [params_n])[0]\\n L += 1\\n N_k = np.append(N_k, [0])\\n\\n u_kln = np.zeros([K, L, N])\\n u_kln[:, :-1, :] = u_kln_s\\n for k in range(K):\\n u_kln[k, -1, :] = u_kln_n[k, 0, :]\\n\\n # Determine SIR weights\\n weights = self.run_MBAR(u_kln, N_k, augmented=True)[1][:, -1]\\n weights = weights / sum(weights)\\n\\n # Resampling\\n # Convert linear indices to 3 indicies: state, cycle, and snapshot\\n cum_N_state = np.cumsum([0] + list(N_k))\\n cum_N_cycle = [np.cumsum([0] + [self.data['CD'].Es[k][c]['MM'].shape[0] \\\\\\n for c in range(len(self.data['CD'].Es[k]))]) for k in range(len(self.data['CD'].Es))]\\n\\n def linear_index_to_snapshot_index(ind):\\n state_index = list(ind < cum_N_state).index(True) - 1\\n nis_index = ind - cum_N_state[state_index]\\n cycle_index = list(nis_index < cum_N_cycle[state_index]).index(True) - 1\\n nic_index = nis_index - cum_N_cycle[state_index][cycle_index]\\n return (state_index, cycle_index, nic_index)\\n\\n def snapshot_index_to_linear_index(state_index, cycle_index, nic_index):\\n return cum_N_state[state_index] + cum_N_cycle[state_index][\\n cycle_index] + nic_index\\n\\n # Terms to copy\\n if self.args.params['CD']['pose'] > -1:\\n # Pose BPMF\\n terms = ['MM',\\\\\\n 'k_angular_ext','k_spatial_ext','k_angular_int'] + scalables\\n else:\\n # BPMF\\n terms = ['MM', 'site'] + scalables\\n\\n CD_Es_s = []\\n confs_s = []\\n for c in range(len(self.data['CD'].Es[0])):\\n CD_Es_c = dict([(term, []) for term in terms])\\n confs_c = []\\n for n_in_c in range(len(self.data['CD'].Es[-1][c]['MM'])):\\n if (cum_N_cycle[-1][c] == 0):\\n (snapshot_s,snapshot_c,snapshot_n) = linear_index_to_snapshot_index(\\\\\\n np.random.choice(range(len(weights)), size = 1, p = weights)[0])\\n else:\\n snapshot_c = np.inf\\n while (snapshot_c > c):\\n (snapshot_s,snapshot_c,snapshot_n) = linear_index_to_snapshot_index(\\\\\\n np.random.choice(range(len(weights)), size = 1, p = weights)[0])\\n for term in terms:\\n CD_Es_c[term].append(\\\\\\n np.copy(self.data['CD'].Es[snapshot_s][snapshot_c][term][snapshot_n]))\\n if self.args.params['CD']['keep_intermediate']:\\n # Has not been tested:\\n confs_c.append(\\\\\\n np.copy(self.data['CD'].confs['samples'][snapshot_s][snapshot_c]))\\n for term in terms:\\n CD_Es_c[term] = np.array(CD_Es_c[term])\\n CD_Es_s.append(CD_Es_c)\\n confs_s.append(confs_c)\\n\\n # Insert resampled values\\n self.data['CD'].protocol.insert(neighbor_ind + 1, params_n)\\n self.data['CD'].Es.insert(neighbor_ind + 1, CD_Es_s)\\n self.data['CD'].confs['samples'].insert(neighbor_ind + 1, confs_s)\\n self.data['CD'].confs['replicas'].insert(neighbor_ind+1, \\\\\\n np.copy(self.data['CD'].confs['replicas'][neighbor_ind]))\\n\\n if clear:\\n self._clear_f_RL()\",\n \"def initialize_action_matrix(self):\\n for start_i, cols in enumerate(self.A):\\n for next_j, value in enumerate(cols):\\n start_state = self.get_state_from_row(start_i)\\n # (row_number, red loc, blue loc, green loc)\\n next_state = self.get_state_from_row(next_j)\\n\\n # Valid states include moving exactly 1 dumbbell to a block that's\\n # not already occupied, or to origin.\\n num_moves, moves = count_moves_from_states(\\n start_state, next_state)\\n \\n # key: location (where 0 = origin, 1/2/3 = block #), value: count\\n next_state_loc_counts = Counter(next_state[1:])\\n del next_state_loc_counts[0] # ignore origin location\\n \\n # There must not be more than 1 dumbbell at each block\\n is_valid_next_state = all(\\n map(lambda x: x <= 1, next_state_loc_counts.values()))\\n \\n if (num_moves == 1 and is_valid_next_state):\\n move = moves[0] # Only 1 move\\n # doing nothing or moving to origin is not valid\\n if (start_state == next_state or move[1] == 0):\\n continue\\n self.A[start_i][next_j] = self.get_col_from_action(move)\",\n \"def update_to_state(self, game_state):\\n pass\",\n \"def _update_cells(self):\\n for row_number in range(self.number_cells_y):\\n for col_number in range(self.number_cells_x):\\n if self.to_be_updated[row_number][col_number]:\\n self.cells[row_number][col_number].update()\",\n \"def model_state_update(model, time_t, state_controller, input_f16):\\n pass\",\n \"def _get_obs(self):\\n # return np.concatenate((self.world.state[:6], self.world.state[7:13]))\\n return np.concatenate((self.world.state, np.zeros(7)))\\n # return self.world.state\",\n \"def update(self):\\n self._state = 23\",\n \"def update_board(self):\\n for x in self.board:\\n for f in x:\\n if f.status == 0:\\n if f.name == \\\"conway\\\":\\n assert type(self.population)==int\\n if f.live_neighbors == 3:\\n f.symbol =\\\"*\\\"\\n f.status = 1\\n self.population += 1\\n elif f.name == \\\"fredkin\\\":\\n if f.live_neighbors == 1 or f.live_neighbors == 3 :\\n f.status = 1\\n f.symbol = str(f.age)\\n self.population += 1\\n else:\\n f.status = 0\\n\\n elif f.status == 1:\\n if f.name == \\\"conway\\\":\\n assert type(self.population)==int\\n #assert type(f.status)== 1\\n if not((f.live_neighbors == 2 or f.live_neighbors == 3)):\\n f.symbol = \\\".\\\"\\n f.status = 0\\n else:\\n self.population += 1\\n elif f.name == \\\"fredkin\\\":\\n if f.live_neighbors == 1 or f.live_neighbors == 3:\\n f.status = 1\\n f.age += 1\\n if f.age <= 2:\\n f.symbol = str(f.age)\\n self.population += 1\\n else:\\n self.board.replace(f, Conway_Cell(\\\"*\\\"))\\n else:\\n f.status = 0\\n f.symbol = \\\"-\\\"\",\n \"def update_nodes(self):\\n\\n L = self.level\\n P = L.prob\\n\\n # only if the level has been touched before\\n assert L.status.unlocked\\n\\n # get number of collocation nodes for easier access\\n M = self.coll.num_nodes\\n\\n # gather all terms which are known already (e.g. from the previous iteration)\\n # this corresponds to u0 + QF(u^k) - QdF(u^k) + tau\\n\\n # get QF(u^k)\\n integral = self.integrate()\\n for m in range(M):\\n # get -QdF(u^k)_m\\n for j in range(1, M + 1):\\n integral[m] -= L.dt * self.QI[m + 1, j] * L.f[j]\\n\\n # add initial value\\n integral[m] += L.u[0]\\n # add tau if associated\\n if L.tau[m] is not None:\\n integral[m] += L.tau[m]\\n\\n # do the sweep\\n for m in range(0, M):\\n # build rhs, consisting of the known values from above and new values from previous nodes (at k+1)\\n rhs = P.dtype_u(integral[m])\\n for j in range(1, m + 1):\\n rhs += L.dt * self.QI[m + 1, j] * L.f[j]\\n\\n # implicit solve with prefactor stemming from the diagonal of Qd\\n L.u[m + 1] = P.solve_system(\\n rhs, L.dt * self.QI[m + 1, m + 1], L.u[m + 1], L.time + L.dt * self.coll.nodes[m]\\n )\\n # update function values\\n L.f[m + 1] = P.eval_f(L.u[m + 1], L.time + L.dt * self.coll.nodes[m])\\n\\n # indicate presence of new values at this level\\n L.status.updated = True\\n\\n return None\",\n \"def update_mmtr(self, now):\\r\\n if (self.position == SinglePhaseBreaker.CLOSED):\\r\\n # We only actually update the values if the position is closed\\r\\n super().update_mmtr(now)\\r\\n else:\\r\\n # Nevertheless, this is our last updated time, so that if we are\\r\\n # later closed, we update from the last updated time\\r\\n self.last_update = now\",\n \"def update(self):\\n # Update the weight matrix: \\n self.W -= self.lr * self.grad_W \\n \\n # Update the bias matrices:\\n self.b -= self.lr * np.array(self.grad_b) \\n self.c -= self.lr * np.array(self.grad_c)\",\n \"def update_H(self):\\n gamma = self.get_gamma()\\n delta = self.get_delta()\\n summand2 = ((1 + (gamma.transpose().dot(self.H).dot(gamma) /\\n delta.transpose().dot(gamma))) *\\n delta.dot(delta.transpose()) / delta.transpose().dot(gamma)\\n )\\n summand3 = - ((delta.dot(gamma.transpose()).dot(self.H) +\\n self.H.dot(gamma).dot(delta.transpose())) /\\n delta.transpose().dot(gamma))\\n self.H = self.H + summand2 + summand3\",\n \"def update_cov_chol(self):\\n log_precision_target = self.downscale.parameter_log_precision_target\\n gp_target = self.downscale.parameter_gp_target\\n # update the covariance matrix for each parameter\\n for i in range(self.n_parameter):\\n # calculate the cholesky of the gram matrix (sometimes referred to\\n # as the kernel matrix)\\n cov_chol = -self.downscale.square_error.copy()\\n gp_scale = gp_target.scale_gp_state[i]\\n cov_chol /= 2 * math.pow(gp_scale, 2)\\n cov_chol = np.exp(cov_chol)\\n cov_chol += np.identity(self.area_unmask) * self.small_covariance\\n cov_chol = linalg.cholesky(cov_chol, True)\\n # scale the cholesky matrix by a precision\\n cov_chol *= math.exp(\\n -0.5*log_precision_target.log_precision_state[i])\\n self.cov_chol_array[i] = cov_chol\",\n \"def _update_board(self, move: int) -> None:\\n row = self._column_to_row[move] # Find what row to place the disk in\\n if self._is_red_active:\\n self.board_array[row][move] = 1\\n self.hash = self.hash ^ int(self._red_hash_keys[row][move]) # Update hash\\n else:\\n self.board_array[row][move] = -1\\n self.hash = self.hash ^ int(self._yellow_hash_keys[row][move]) # # Update hash\\n\\n self._column_to_row[move] += 1\\n if self._column_to_row[move] == 6:\\n self._valid_moves.remove(move)\",\n \"def update(self):\\n changes = {}\\n for coord in INDICES: # the need for two for loops is necessary\\n if self.chart[coord] == ALIVE and (\\n self.number_of_neighbors(coord) < 2 or self.number_of_neighbors(coord) > 3):\\n changes[coord] = KILL\\n elif self.number_of_neighbors(coord) == 3:\\n changes[coord] = REVIVE\\n for coord in changes.keys(): # because the evolution is discrete\\n if changes[coord] == KILL:\\n self.kill(coord)\\n elif changes[coord] == REVIVE:\\n self.givebirth(coord)\",\n \"def _update_hessian(self) -> None:\\n assert self._species and self._coords is not None and self._method\\n\\n species = self._species.new_species(\\n name=f\\\"{self._species.name}_opt_{self.iteration}\\\"\\n )\\n species.coordinates = self._coords.to(\\\"cartesian\\\")\\n\\n species.calc_hessian(\\n method=self._method,\\n keywords=self._method.keywords.hess,\\n n_cores=self._n_cores,\\n )\\n assert species.hessian is not None, \\\"Failed to calculate H\\\"\\n\\n self._species.hessian = species.hessian.copy()\\n self._coords.update_h_from_cart_h(self._species.hessian.to(\\\"Ha Å^-2\\\"))\",\n \"def update(self, *args):\\n return _osgAnimation.Channel_update(self, *args)\",\n \"def update_cells(self, state):\\n width = WIDTH / CELL_SIZE\\n height = HEIGHT / CELL_SIZE\\n\\n for index in range(0, width * height):\\n if state[index] != self.get_state(index):\\n self.toggle_color(index)\",\n \"def swarm(self) -> None:\\n self.state[:, :, Boids.Attr.ACC] *= 0\\n self.update_acc_by_rules()\\n self._update_vel()\\n self._update_loc()\",\n \"def update_board(self, coordinate, hit):\\n \\n if hit:\\n self.board_state[coordinate.row_idx][coordinate.col_idx] = \\\"H\\\"\\n else:\\n self.board_state[coordinate.row_idx][coordinate.col_idx] = \\\"M\\\"\",\n \"def update_io(self, dt):\\n self.light.change_color(traffic_lights_binary())\\n self.seven_segment_display.activate_segments(seven_segment_binary())\\n self.ascii.update_ascii_grid()\",\n \"def update_z(self):\\n w_actual = self.w_estimates[-1]\\n # update the underlying linear model\\n self.linear_model.w = w_actual\\n gamma = np.divide(abs(w_actual), np.sqrt(self.z.value.T)).T\\n Gamma_diag = np.zeros((gamma.shape[0], gamma.shape[0]), float)\\n np.fill_diagonal(Gamma_diag, gamma)\\n Sigma_y = self.lambda_param * np.eye(\\n self.linear_model.data_num\\n ) + self.linear_model.dict_mtx @ Gamma_diag @ np.transpose(\\n self.linear_model.dict_mtx\\n )\\n\\n self.z.value = np.diag(\\n np.transpose(self.linear_model.dict_mtx)\\n @ np.linalg.pinv(Sigma_y, hermitian=True)\\n @ self.linear_model.dict_mtx\\n )\\n self.z_estimates.append(self.z.value)\\n self.gamma_estimates.append(gamma)\\n # print(f'state: {self.state_name} Sigma_y cond: {np.linalg.cond(np.linalg.pinv(Sigma_y, hermitian=True))} gamma: {np.divide(np.abs(w_actual),np.sqrt(self.z.value))}')\",\n \"def _update_board(self):\\n\\n self.game_board.update_board(self.tetrino_set)\",\n \"def update_state (self, ivec : np.ndarray) :\\n self.xvec = RNN.sigmoid (self.Whid.dot (self.xvec) + self.Win.dot (ivec) + self.bvec)\",\n \"def __setstate__(self, state):\\n\\n if sparse.issparse(state['transition_matrix']):\\n state['transition_matrix'] = state['transition_matrix'].toarray()\\n\\n # Recalculate the cumulative probabilities\\n state['cumulative_probabilities'] = np.cumsum(state['transition_matrix'], axis=1)\\n\\n self.__dict__ = state\",\n \"def __update_state(self, x0, u):\\n N = int(len(u) / 2)\\n lower_triangular_ones_matrix = np.tril(np.ones((N, N)))\\n kron = np.kron(lower_triangular_ones_matrix, np.eye(2))\\n\\n new_state = np.vstack([np.eye(2)] * int(N)) @ x0 + kron @ u * self.nmpc_timestep\\n\\n return new_state\",\n \"def update(self, state, loss):\\n # wana clip the losses so it doesn't go absolutely insane.\\n jdecays = state[\\\"decays\\\"]\\n cor_mean = state[\\\"means\\\"] / (1 - jdecays**(state[\\\"iteration\\\"] + 1))\\n approx_max = jnp.max(cor_mean)\\n approx_max = jnp.where(state[\\\"iteration\\\"] == 0, loss, approx_max)\\n loss = jnp.minimum(jnp.abs(approx_max) * 2, loss)\\n\\n means = state[\\\"means\\\"] * jdecays + loss * (1. - jdecays)\\n\\n cor_mean = means / (1 - jdecays**(state[\\\"iteration\\\"] + 1))\\n running_min = jnp.minimum(state[\\\"running_min\\\"], cor_mean)\\n\\n return {\\n \\\"means\\\": means,\\n \\\"iteration\\\": state[\\\"iteration\\\"] + 1,\\n \\\"running_min\\\": running_min,\\n \\\"decays\\\": state[\\\"decays\\\"],\\n }\",\n \"def updateGameState(self):\\n boardArray = self._board.get_board()\\n \\n if self.state is self._BEGIN:\\n\\n for i in [1, self._board.get_board_size() - 2]:\\n for j in range(1, self._board.get_board_size() - 1):\\n\\n if boardArray[i][j] != self._board._EMPTY:\\n self.state = self._MIDDLE\\n return\\n\\n if boardArray[j][i] != self._board._EMPTY:\\n self.state = self._MIDDLE\\n return\\n\\n\\n elif self.state is self._MIDDLE:\\n nbPieces = self._board.get_total_pieces()\\n\\n if nbPieces >= self._board.get_board_size()**2 - ENDGAME:\\n self.state = self._END\\n return\",\n \"def update(self):\\n self.wall_list.update()\\n self.enemy_list.update()\\n self.sludge.update()\\n self.consumeable.update()\\n self.can_climb.update()\",\n \"def update_state(self):\\n # return initial state if no observation was yet\\n if len(self.obs_history) == 0:\\n return self.kf.initial_state_mean, self.kf.initial_state_covariance\\n\\n hist = np.ma.masked_array(self.obs_history, mask=np.zeros((1,)))\\n for i in range(len(hist)):\\n if hist[i] == -1e8:\\n hist[i] = np.ma.masked\\n\\n # print(hist, hist.shape)\\n return self.kf.filter(hist)\",\n \"def update_pressure(self):\\n m_multipliers = np.ones(self.mesh.get_number_of_cells())\\n\\n\\n rhs_current = np.zeros(self.mfd.get_number_of_dof()) \\n rhs_current += self.rhs_mfd\\n\\n\\n for cell_index in range(self.mesh.get_number_of_cells()):\\n density = -self.ref_pressure\\n density += self.current_pressure[cell_index]\\n density *= self.compressibility\\n density += 1.\\n density *= self.ref_density\\n\\n # We multiply by the inverse of \\\\frac{\\\\rho}{\\\\mu}\\n m_multipliers[cell_index] = self.viscosity/density\\n\\n c_entry = self.compressibility\\n c_entry *= self.porosities[cell_index]\\n c_entry /= self.delta_t\\n c_entry *= self.mesh.get_cell_volume(cell_index)\\n\\n rhs_current[self.mesh.get_number_of_faces()+\\n cell_index] += c_entry*self.current_pressure[cell_index]\\n\\n self.lhs_coo.data[self.c_start+cell_index] = c_entry\\n\\n for [index, cell_index] in enumerate(self.rate_wells):\\n rhs_current[self.mesh.get_number_of_faces()+cell_index] += \\\\\\n self.rate_wells_rate[index]\\n\\n self.mfd.update_m(self.lhs_coo.data[:self.m_x_coo_length], m_multipliers)\\n\\n solution = dsolve.spsolve(self.lhs_coo.tocsr(), rhs_current)\\n self.prev_pressure = self.current_pressure\\n self.current_pressure = solution[self.mesh.get_number_of_faces():]\\n self.current_velocity = solution[:self.mesh.get_number_of_faces()]\",\n \"def compute_controller(self, time):\\n\\n # here we implement an example for a consensus algorithm\\n neig = self.get_neighbors()\\n messages = self.get_messages()\\n pos, rot = self.get_pos_and_orientation()\\n\\n # send message of positions to all neighbors indicating our position\\n for n in neig:\\n self.send_message(n, pos)\\n\\n # check if we received the position of our neighbors and compute desired change in position\\n # as a function of the neighbors (message is composed of [neighbors id, position])\\n dx = 0.\\n dy = 0.\\n if messages:\\n\\n if time < 5:\\n # square Formation\\n dx, dy = controller.formation_control(\\\"square\\\", messages, self.id, pos)\\n\\n elif time >= 5 and time < 30:\\n # move out of room\\n dx, dy = controller.follow_leader(3, 4, pos, messages,\\n leader_alpha=30)\\n\\n elif time >= 30 and time < 65:\\n # circle formation\\n dx, dy = controller.formation_control(\\\"circle\\\", messages, self.id, pos)\\n\\n elif time >= 65 and time < 75:\\n # surround purple ball\\n dx, dy = controller.go_to_leader(self.purple_x, self.purple_y, pos,\\n messages, 0, 0)\\n self.init_push = True\\n\\n elif time >= 75 and time < 115:\\n # push purple ball\\n if self.init_push:\\n output = controller.get_current_formation(messages, self.id, pos)\\n self.des_xs, self.des_ys, self.cx, self.cy = output\\n self.init_push = False\\n\\n dx, dy, self.cx, self.cy = controller.push_ball(\\n messages, self.id, pos, self.des_xs, self.des_ys,\\n self.cx, self.cy, 2.5, 5.5, K=3)\\n\\n elif time >= 115 and time < 130:\\n # eject from purple ball\\n dx, dy = controller.eject_from_ball(2.5, 5.5, messages, pos, d_0=1.5)\\n\\n elif time >= 130 and time < 155:\\n # surround red ball\\n dx, dy = controller.go_to_leader(self.red_x, self.red_y, pos,\\n messages,\\n self.purple_x, self.purple_y,\\n obstacle=True, room_alpha=1)\\n self.init_push = True\\n\\n elif time >= 155 and time < 205:\\n # push red ball\\n if self.init_push:\\n output = controller.get_current_formation(messages, self.id, pos)\\n self.des_xs, self.des_ys, self.cx, self.cy = output\\n self.init_push = False\\n\\n dx, dy, self.cx, self.cy = controller.push_ball(\\n messages, self.id, pos, self.des_xs, self.des_ys,\\n self.cx, self.cy, 0.5, 5.5, K=3)\\n\\n elif time >= 205 and time < 220:\\n # eject from red ball\\n dx, dy = controller.eject_from_ball(0.5, 5.5, messages, pos)\\n\\n elif time >= 220 and time < 230:\\n # return to room step 1\\n dx, dy = controller.follow_leader(2, 3.5, pos, messages)\\n\\n elif time >= 230 and time < 270:\\n # return to room step 2\\n dx, dy = controller.follow_leader(2, 0, pos, messages,\\n leader_alpha=30, agent_alpha=3)\\n elif time >= 270 and time < 290:\\n # return to room step 3\\n dx, dy = controller.follow_leader(1.5, 0, pos, messages)\\n\\n elif time >= 290:\\n # diamond formation\\n dx, dy = controller.formation_control(\\\"diamond\\\", messages, self.id, pos)\\n\\n # compute velocity change for the wheels\\n vel_norm = np.linalg.norm([dx, dy]) # norm of desired velocity\\n if vel_norm < 0.01:\\n vel_norm = 0.01\\n des_theta = np.arctan2(dy/vel_norm, dx/vel_norm)\\n right_wheel = np.sin(des_theta-rot)*vel_norm + np.cos(des_theta-rot)*vel_norm\\n left_wheel = -np.sin(des_theta-rot)*vel_norm + np.cos(des_theta-rot)*vel_norm\\n self.set_wheel_velocity([left_wheel, right_wheel])\\n\\n return dx, dy\",\n \"def _update_state(self, currentPhase, phasetime, time):\\n # compute new state without registered action\\n tToNearGreenPhase = self._get_toNearGreenPhase(currentPhase, phasetime, self.extended)\\n\\n if self.numbus > 0:\\n # last available checkout for this intersection\\n if self.numbus > 1:\\n # bunch, use current time as last checkout\\n last_available_checkout_time = time\\n elif self.last_checkout_bus is None:\\n # no checked out bus, assume perfect headway\\n last_available_checkout_time = time - self.CONFIG['target_headway']\\n else:\\n last_available_checkout_time = self.last_checkout_bus.check_out_time\\n # check in time of the last bus checked in\\n last_check_in_time = self.bus_list[-1].check_in_time\\n check_in_hdy = self.bus_list[-1].check_in_headway\\n new_state = [last_available_checkout_time, last_check_in_time, check_in_hdy, self.numbus, self.allnumvel,\\n tToNearGreenPhase]\\n else:\\n if self.last_checkout_bus:\\n last_available_checkout_time = self.last_checkout_bus.check_out_time\\n check_in_hdy = self.last_checkout_bus.check_in_headway\\n last_check_in_time = self.last_checkout_bus.check_in_time\\n new_state = [last_available_checkout_time, last_check_in_time, check_in_hdy, 0, self.allnumvel, tToNearGreenPhase]\\n else:\\n new_state = [0, 0, 0, 0, self.allnumvel, tToNearGreenPhase]\\n\\n self.state = new_state\\n return\",\n \"def update_state(self):\\n if not self.platforms:\\n self.state = self.states['Win']\\n elif self.player.y > 389:\\n self.state = self.states['Lose'] \\n else:\\n self.state = self.states['Game']\",\n \"def update(self) -> py_trees.common.Status:\\n self.logger.debug(\\\"%s.update()\\\" % (self.__class__.__name__))\\n self.blackboard.foo.bar.wow = \\\"colander\\\"\\n\\n return py_trees.common.Status.SUCCESS\",\n \"def update(self):\\n self._curses_window.clear()\\n\\n self._curses_window.addstr(0, 0,\\n \\\"{:5s} {:5.1f}\\\".format(\\n 'time:', round(time() - self.simulation_start, 1)\\n )\\n + \\\"\\\\t{:13s} {:4.1f}\\\".format(\\n ' steps per s:', round(1 / global_vars.step_duration, 1)\\n )\\n + \\\"\\\\t{:4s} {:4d}\\\".format(' step:', global_vars.step)\\n\\t\\t\\t+ \\\"\\\\n{:4s} {:4d}{:1s}\\\".format('Death cause.. eaten:', global_vars.h_eaten, 'h')\\n\\t\\t\\t+ \\\"\\\\t{:4s} {:4d}{:1s}{:4d}{:1s}\\\".format(\\n 'starved:', global_vars.h_starved, 'h/', global_vars.c_starved, 'c'\\n )\\n\\t\\t\\t+ \\\"\\\\t{:4s} {:4d}{:1s}{:4d}{:1s}\\\".format(\\n 'trampled:', global_vars.h_trampled, 'h/', global_vars.c_trampled, 'c'\\n )\\n + \\\"\\\\t{:4s} {:4d}{:1s}{:4d}{:1s}\\\".format(\\n 'natural death:', global_vars.h_age, 'h/', global_vars.c_age, 'c'\\n )\\n )\\n\\n self._curses_window.noutrefresh()\",\n \"def _update(self):\\n self.cv.update()\",\n \"def update(self):\\n self._state = self._state\",\n \"def update(self, new_state):\\n\\n if self.update_animation:\\n self.canvas.delete(self.agent)\\n row, col = new_state\\n x1 = col * self.GRID_ROW_HEIGHT\\n y1 = row * self.GRID_ROW_HEIGHT\\n self.agent = self.canvas.create_image(x1 + self.GRID_ROW_HEIGHT / 2, y1 + self.GRID_ROW_HEIGHT / 2,\\n image=self.penguin)\",\n \"def create_gridworld(self):\\n self.num_actions = 4\\n self.num_states = self.num_cols * self.num_rows + 1\\n self.start_state_seq = row_col_to_seq(self.start_state, self.num_cols)\\n self.goal_states_seq = row_col_to_seq(self.goal_states, self.num_cols)\\n\\n # rewards structure\\n self.R = self.r_step * np.ones((self.num_states, 1))\\n self.R[self.num_states-1] = 0\\n self.R[self.goal_states_seq] = self.r_goal\\n for i in range(self.num_bad_states):\\n if self.r_bad is None:\\n raise Exception(\\\"Bad state specified but no reward is given\\\")\\n bad_state = row_col_to_seq(self.bad_states[i,:].reshape(1,-1), self.num_cols)\\n self.R[bad_state, :] = self.r_bad\\n for i in range(self.num_restart_states):\\n if self.r_restart is None:\\n raise Exception(\\\"Restart state specified but no reward is given\\\")\\n restart_state = row_col_to_seq(self.restart_states[i,:].reshape(1,-1), self.num_cols)\\n self.R[restart_state, :] = self.r_restart\\n\\n # probability model\\n if self.p_good_trans == None:\\n raise Exception(\\\"Must assign probability and bias terms via the add_transition_probability method.\\\")\\n\\n self.P = np.zeros((self.num_states,self.num_states,self.num_actions))\\n for action in range(self.num_actions):\\n for state in range(self.num_states):\\n\\n # check if state is the fictional end state - self transition\\n if state == self.num_states-1:\\n self.P[state, state, action] = 1\\n continue\\n\\n # check if the state is the goal state or an obstructed state - transition to end\\n row_col = seq_to_col_row(state, self.num_cols)\\n if self.obs_states is not None:\\n end_states = np.vstack((self.obs_states, self.goal_states))\\n else:\\n end_states = self.goal_states\\n\\n if any(np.sum(np.abs(end_states-row_col), 1) == 0):\\n self.P[state, self.num_states-1, action] = 1\\n\\n # else consider stochastic effects of action\\n else:\\n for dir in range(-1,2,1):\\n direction = self._get_direction(action, dir)\\n next_state = self._get_state(state, direction)\\n if dir == 0:\\n prob = self.p_good_trans\\n elif dir == -1:\\n prob = (1 - self.p_good_trans)*(self.bias)\\n elif dir == 1:\\n prob = (1 - self.p_good_trans)*(1-self.bias)\\n\\n self.P[state, next_state, action] += prob\\n\\n # make restart states transition back to the start state with\\n # probability 1\\n if self.restart_states is not None:\\n if any(np.sum(np.abs(self.restart_states-row_col),1)==0):\\n next_state = row_col_to_seq(self.start_state, self.num_cols)\\n self.P[state,:,:] = 0\\n self.P[state,next_state,:] = 1\\n return self\",\n \"def wfc_clear(parameters, state, output):\\n # Crystal solving time matrix\\n output.current_iteration_count_observation = 0\\n output.current_iteration_count_propagation = 0\\n output.current_iteration_count_last_touch = 0\\n output.current_iteration_count_crystal = 0\\n\\n output.solving_time = np.full(\\n (parameters.rows, parameters.columns), 0, dtype=np.int32\\n )\\n output.ones_time = np.full((parameters.rows, parameters.columns), 0, dtype=np.int32)\\n output.propagation_time = np.full(\\n (parameters.rows, parameters.columns), 0, dtype=np.int32\\n )\\n output.touch_time = np.full(\\n (parameters.rows, parameters.columns), 0, dtype=np.int32\\n )\\n output.crystal_time = np.full(\\n (parameters.rows, parameters.columns), 0, dtype=np.int32\\n )\\n output.method_time = np.full(\\n (parameters.rows, parameters.columns), 0, dtype=np.int32\\n )\\n output.choices_recording = np.full(\\n (parameters.rows, parameters.columns), 0, dtype=np.float32\\n )\\n output.stats_tracking[\\\"total_observations\\\"] = 0\\n\\n state.wave_table = np.full(parameters.wave_shape, True, dtype=np.bool_)\\n\\n compatible_shape = [\\n parameters.rows,\\n parameters.columns,\\n parameters.number_of_patterns,\\n parameters.number_of_directions,\\n ]\\n\\n # Initialize the compatible count from the propagation matrix. This sets the\\n # maximum domain of possible neighbors for each cell node.\\n\\n def prop_compat(p, d):\\n return sum(parameters.propagator_matrix[(d + 2) % 4][p])\\n\\n def comp_count(r, c, p, d):\\n return pattern_compatible_count[p][d]\\n\\n pcomp = np.vectorize(prop_compat)\\n ccount = np.vectorize(comp_count)\\n pattern_compatible_count = np.fromfunction(\\n pcomp,\\n (parameters.number_of_patterns, parameters.number_of_directions),\\n dtype=np.int16,\\n )\\n state.compatible_count = np.fromfunction(\\n ccount,\\n (\\n parameters.rows,\\n parameters.columns,\\n parameters.number_of_patterns,\\n parameters.number_of_directions,\\n ),\\n dtype=np.int16,\\n )\\n\\n # Likewise, set the weights to their maximum values\\n state.sums_of_weights = np.full(\\n [parameters.rows, parameters.columns], state.sum_of_weights, dtype=np.float64\\n )\\n state.sums_of_weight_log_weights = np.full(\\n [parameters.rows, parameters.columns],\\n state.sum_of_weight_log_weights,\\n dtype=np.float64,\\n )\\n state.entropies = np.full(\\n [parameters.rows, parameters.columns], state.starting_entropy, dtype=np.float64\\n )\\n\\n state.recorded_steps = []\\n state.observation_stack = collections.deque()\\n\\n # ground banning goes here\\n if parameters.wfc_ns.ground != 0 and False:\\n pass\\n\\n output.previous_decisions = []\\n state.previous_decisions = []\\n WFC_LOGGER.debug(\\\"clear complete\\\")\\n\\n return state, output\",\n \"def update_params(self, mat):\\n assert mat.shape == self.shape\\n vec = mat.reshape(mat.size)\\n self.alpha = np.linalg.solve(self.covs_mat.T.dot(self.covs_mat),\\n self.covs_mat.T.dot(vec))\",\n \"def _handle_coordinator_update(self) -> None:\\n self._thermostat = self.coordinator.data[self._thermostat.serial_number]\\n self.async_write_ha_state()\",\n \"def compute_controller(self):\\n \\n # here we implement an example for a consensus algorithm\\n neig = self.get_neighbors()\\n messages = self.get_messages()\\n pos, rot = self.get_pos_and_orientation()\\n \\n #send message of positions to all neighbors indicating our position\\n for n in neig:\\n self.send_message(n, pos)\\n \\n # check if we received the position of our neighbors and compute desired change in position\\n # as a function of the neighbors (message is composed of [neighbors id, position])\\n dx = 0.\\n dy = 0.\\n if messages:\\n for m in messages:\\n dx += m[1][0] - pos[0]\\n dy += m[1][1] - pos[1]\\n # integrate\\n des_pos_x = pos[0] + self.dt * dx\\n des_pos_y = pos[1] + self.dt * dy\\n \\n #compute velocity change for the wheels\\n vel_norm = np.linalg.norm([dx, dy]) #norm of desired velocity\\n if vel_norm < 0.01:\\n vel_norm = 0.01\\n des_theta = np.arctan2(dy/vel_norm, dx/vel_norm)\\n right_wheel = np.sin(des_theta-rot)*vel_norm + np.cos(des_theta-rot)*vel_norm\\n left_wheel = -np.sin(des_theta-rot)*vel_norm + np.cos(des_theta-rot)*vel_norm\\n self.set_wheel_velocity([left_wheel, right_wheel])\",\n \"def update_aco(self):\\n if self.running:\\n for x in range(self.columns):\\n for y in range(self.rows):\\n colour = get_colour((self.aco.pheromones[x][y]-1)/50, [(255, 255, 255), (0, 0, 0)])\\n self.grid[x][y].config(bg=colour)\\n for ant in self.aco.ants:\\n ant_coors = ant.location\\n self.grid[ant_coors[0]][ant_coors[1]].config(bg=ant.colour)\\n\\n home_coors = self.aco.home_coors\\n self.grid[home_coors[0]][home_coors[1]].config(bg='blue')\\n\\n for food_key in self.aco.food.keys():\\n food = self.aco.food[food_key].location\\n self.grid[food[0]][food[1]].config(bg=get_colour(self.aco.food[food_key].food_val/self.aco.food[food_key].food_capacity, [(184, 232, 182), (55, 144, 52)]))\\n \\n total_food = self.aco.increment()\\n brought_food = self.aco.brought_food\\n if not self.aco.is_finished():\\n if self.aco.food_deplete:\\n self.status_text.set(\\\"Food left: \\\"+str(total_food)+\\\", food brought home: \\\"+str(brought_food))\\n else: self.status_text.set(\\\"Food brought home: \\\"+str(brought_food))\\n self.main_window.after(int(self.speed), self.update_aco)\\n else:\\n self.status.config(fg='green')\\n self.status_text.set(\\\"Simulation complete!\\\")\",\n \"def update(self):\\n self.data.update()\\n for sensor in self.data.daikinskyport.get_sensors(self._index):\\n if sensor[\\\"type\\\"] == self._type and self._sensor_name == sensor[\\\"name\\\"]:\\n self._state = sensor[\\\"value\\\"]\",\n \"async def _async_force_refresh_state(self):\\n await self._shade.refresh()\\n self._async_update_current_cover_position()\\n self.async_write_ha_state()\",\n \"def update(self, state_sequence, reward_sequence):\\n\\n for i in range(reward_sequence.shape[0]):\\n\\n trajt_1 = state_sequence[:,i][:,np.newaxis] # No use of V_mu in computing distances!\\n trajt = state_sequence[:,i+1][:,np.newaxis]\\n # trajt_1 = np.concatenate((trajt_1, self.V_mu(trajt_1)), axis=0) # Use V_mu as well\\n # trajt = np.concatenate((trajt, self.V_mu(trajt)), axis=0)\\n k_t_1 = self.kernel(self.D, trajt_1)\\n k_t = self.kernel(self.D, trajt)\\n ktt = self.kernel(trajt, trajt)\\n at = np.dot(self.K_inv, k_t)\\n delk_t_1 = k_t_1 - self.gamma*k_t\\n\\n ct = np.dot(self.C_, delk_t_1) - (self.A - self.gamma*at)\\n st = self.sigma0**2 - np.dot(ct.T, delk_t_1)\\n\\n diff_r = np.dot(delk_t_1.T, self.alpha_)[0,0] - reward_sequence[i]\\n self.alpha_ = self.alpha_ + ct/st*diff_r\\n\\n self.C_ = self.C_ + np.dot(ct, ct.T)/st\\n\\n self.A = at\\n\\n assert (not np.isnan(self.alpha_).any()), \\\"Check alpha for NaN values\\\"\\n\\n self.diff_alpha_CV_D = self.alpha_ - np.dot(self.C_, self.V_D)\",\n \"def propagate_state(self, msg):\\r\\n # Previous values\\r\\n x = self.state_vector[0, 0]\\r\\n y = self.state_vector[1, 0]\\r\\n theta = self.state_vector[2, 0]\\r\\n if theta < -pi:\\r\\n theta += 2 * pi\\r\\n elif theta > pi:\\r\\n theta -= 2 * pi\\r\\n\\r\\n # Current values\\r\\n vel = msg.twist.twist.linear.x\\r\\n ang = msg.twist.twist.angular.z\\r\\n nvel = 0\\r\\n nang = 0\\r\\n dt = msg.header.stamp.secs + msg.header.stamp.nsecs * 10 ** -9 - self.time_stamp\\r\\n self.time_stamp = msg.header.stamp.secs + msg.header.stamp.nsecs * 10 ** -9\\r\\n\\r\\n # Calculate Jacobians F and G\\r\\n self.motion_jacobian_state_vector(vel, ang, theta, 0, 0, dt)\\r\\n self.motion_jacobian_noise_components(vel, ang, theta, 0, 0, dt)\\r\\n\\r\\n # Choose motion model\\r\\n if ang == 0:\\r\\n # Propagate\\r\\n self.state_vector[0, 0] = x + (vel + nvel) * dt * cos(theta)\\r\\n self.state_vector[1, 0] = y + (vel + nvel) * dt * sin(theta)\\r\\n self.state_vector[2, 0] = theta\\r\\n else:\\r\\n # Propagate\\r\\n self.state_vector[0, 0] = x - ((vel + nvel) / (ang + nang)) * sin(theta) + (\\r\\n (vel + nvel) / (ang + nang)) * sin(theta + (ang + nang) * dt)\\r\\n self.state_vector[1, 0] = y + ((vel + nvel) / (ang + nang)) * cos(theta) - (\\r\\n (vel + nvel) / (ang + nang)) * cos(theta + (ang + nang) * dt)\\r\\n self.state_vector[2, 0] = theta + (ang + nang) * dt\",\n \"def update_output(self, latent_mat, weight_mat, y_list):\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.59525","0.59345645","0.58975","0.57189","0.56345606","0.5619074","0.5619074","0.55726504","0.55646217","0.5538941","0.5527422","0.5505704","0.5498921","0.5483387","0.54796284","0.54754066","0.5472103","0.5468159","0.54635316","0.5440324","0.54234046","0.53653395","0.5363584","0.53521156","0.5351895","0.5343812","0.534128","0.5316739","0.53099597","0.5305542","0.5305218","0.5301019","0.52739507","0.5270612","0.52659756","0.5222188","0.5215186","0.52096254","0.5209192","0.520636","0.5199292","0.5190522","0.51743233","0.5168983","0.5164938","0.5164938","0.51648664","0.51644045","0.5163187","0.51608074","0.5157525","0.5154283","0.5153835","0.5153761","0.5142473","0.5140016","0.51366353","0.51274455","0.5121077","0.5117756","0.5114916","0.5111547","0.5108676","0.5105369","0.5104075","0.5097681","0.50966823","0.50884867","0.5088046","0.5087316","0.5070302","0.5066938","0.50652206","0.50482756","0.50402844","0.5035541","0.5026488","0.50260216","0.5023952","0.5020036","0.501833","0.50116676","0.5006094","0.50060105","0.50007856","0.49916643","0.49868956","0.49827003","0.49780747","0.49776247","0.4976188","0.4970407","0.49693066","0.49676487","0.49663973","0.49618793","0.49615553","0.49596277","0.4959179","0.4955751"],"string":"[\n \"0.59525\",\n \"0.59345645\",\n \"0.58975\",\n \"0.57189\",\n \"0.56345606\",\n \"0.5619074\",\n \"0.5619074\",\n \"0.55726504\",\n \"0.55646217\",\n \"0.5538941\",\n \"0.5527422\",\n \"0.5505704\",\n \"0.5498921\",\n \"0.5483387\",\n \"0.54796284\",\n \"0.54754066\",\n \"0.5472103\",\n \"0.5468159\",\n \"0.54635316\",\n \"0.5440324\",\n \"0.54234046\",\n \"0.53653395\",\n \"0.5363584\",\n \"0.53521156\",\n \"0.5351895\",\n \"0.5343812\",\n \"0.534128\",\n \"0.5316739\",\n \"0.53099597\",\n \"0.5305542\",\n \"0.5305218\",\n \"0.5301019\",\n \"0.52739507\",\n \"0.5270612\",\n \"0.52659756\",\n \"0.5222188\",\n \"0.5215186\",\n \"0.52096254\",\n \"0.5209192\",\n \"0.520636\",\n \"0.5199292\",\n \"0.5190522\",\n \"0.51743233\",\n \"0.5168983\",\n \"0.5164938\",\n \"0.5164938\",\n \"0.51648664\",\n \"0.51644045\",\n \"0.5163187\",\n \"0.51608074\",\n \"0.5157525\",\n \"0.5154283\",\n \"0.5153835\",\n \"0.5153761\",\n \"0.5142473\",\n \"0.5140016\",\n \"0.51366353\",\n \"0.51274455\",\n \"0.5121077\",\n \"0.5117756\",\n \"0.5114916\",\n \"0.5111547\",\n \"0.5108676\",\n \"0.5105369\",\n \"0.5104075\",\n \"0.5097681\",\n \"0.50966823\",\n \"0.50884867\",\n \"0.5088046\",\n \"0.5087316\",\n \"0.5070302\",\n \"0.5066938\",\n \"0.50652206\",\n \"0.50482756\",\n \"0.50402844\",\n \"0.5035541\",\n \"0.5026488\",\n \"0.50260216\",\n \"0.5023952\",\n \"0.5020036\",\n \"0.501833\",\n \"0.50116676\",\n \"0.5006094\",\n \"0.50060105\",\n \"0.50007856\",\n \"0.49916643\",\n \"0.49868956\",\n \"0.49827003\",\n \"0.49780747\",\n \"0.49776247\",\n \"0.4976188\",\n \"0.4970407\",\n \"0.49693066\",\n \"0.49676487\",\n \"0.49663973\",\n \"0.49618793\",\n \"0.49615553\",\n \"0.49596277\",\n \"0.4959179\",\n \"0.4955751\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5303621"},"document_rank":{"kind":"string","value":"31"}}},{"rowIdx":22489,"cells":{"query":{"kind":"string","value":"Collect data from a single step of connectivity learning."},"document":{"kind":"string","value":"def record_learning(village, s, GS, HC, hc_ro, dVC_HC, dGS_HC, co_occs, norm):\n\n # Animal's real and estimated position and location + uncertainty.\n x, y = village.animal_coords()\n gs_x, gs_y = analysis.GS_pos_mean(GS.P, GS.xvec, GS.yvec, GS.circular)\n gs_h = utils.entropy(GS.P.flatten())\n hc_ml = HC.s_names[hc_ro.argmax()]\n hc_h = utils.entropy(hc_ro)\n\n # Entropy between real state and HC state during last n steps.\n s_real_h = get_loc_co_occ_entropy(co_occs)\n s_hc_h = get_hc_co_occ_entropy(co_occs)\n\n # Connectivity stats.\n vc_hc_snr = np.mean(np.abs(HC.VC_HC), 1) / np.std(np.abs(HC.VC_HC), 1)\n gs_hc_pos = analysis.GS_HC_conn_mean(HC.GS_HC, GS.xvec, GS.yvec,\n GS.circular)\n # Entropy calculation below takes a lot of time --> approximated by max.\n # gs_hc_h = utils.entropy(HC.GS_HC.reshape((len(HC.s_names), -1)).T)\n gs_hc_max = HC.GS_HC.max(axis=(1, 2))\n\n # Connectivity change.\n dVC_HC_max = analysis.VC_HC_norm(dVC_HC, norm).max()\n dGS_HC_max = analysis.GS_HC_norm(dGS_HC, norm).max()\n\n # Compensate for the magnitude difference between VC and GS input (GS is a\n # PD, VC is not).\n dVC_HC_max /= dVC_HC.shape[1]\n\n res = {'s': s, 'x': x, 'y': y,\n 'gs_x': gs_x, 'gs_y': gs_y, 'gs_h': gs_h,\n 'hc_ro': hc_ro, 'hc_ml': hc_ml, 'hc_h': hc_h,\n 's_real_h': s_real_h, 's_hc_h': s_hc_h,\n 'vc_hc_snr': vc_hc_snr,\n 'gs_hc_pos': gs_hc_pos, 'gs_hc_max': gs_hc_max,\n 'dVC_HC_max': dVC_HC_max, 'dGS_HC_max': dGS_HC_max}\n\n return res"},"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 predict_collect(self, src, collector): # real signature unknown; restored from __doc__\n pass","def collect_data(endless):\r\n click.echo(\"start collecting data ...\")\r\n _collect_data(endless)","def collect_data(self,sensation,action,reward,next_sensation):\n pass","def run(self):\r\n self.collect_data()","def collect(self):\n pass","def step(self):\r\n self.datacollector.collect(self)\r\n self.datacollector2.collect(self)\r\n self.datacollector3.collect(self)\r\n self.datacollector4.collect(self)\r\n self.datacollector5.collect(self)\r\n self.datacollector6.collect(self)\r\n self.datacollector7.collect(self)\r\n self.datacollector8.collect(self)\r\n self.datacollector9.collect(self)\r\n self.datacollector10.collect(self)\r\n self.datacollector11.collect(self)\r\n self.datacollector12.collect(self)\r\n self.datacollector13.collect(self)\r\n\r\n self.datacollector14.collect(self)\r\n self.datacollector15.collect(self)\r\n self.datacollector16.collect(self)\r\n self.datacollector17.collect(self)\r\n self.datacollector18.collect(self)\r\n self.datacollector19.collect(self)\r\n self.datacollector20.collect(self)\r\n self.datacollector21.collect(self)\r\n self.datacollector22.collect(self)\r\n self.datacollector23.collect(self)\r\n self.datacollector24.collect(self)\r\n self.datacollector25.collect(self)\r\n self.datacollector26.collect(self)\r\n self.schedule.step()","def learn_connectome(self):\n episode_nodes = [node for node in self.container.nodes if node.is_episode]\n if len(episode_nodes) < 2:\n return\n connections_counter = {}\n for node in episode_nodes:\n self._collect_episode_callout_stats(node, connections_counter)\n\n pair_list = [(key, connections_counter[key]) for key in connections_counter]\n pair_list.sort(key=lambda item: item[1], reverse=True)\n top_count = pair_list[0][1]\n if top_count < 4:\n return\n # make connections for the top half of pairs\n for pair, cnt in pair_list:\n if cnt > top_count // 2:\n self._make_connection_for_pair(pair)","def train_step(self):\n pass","def TrainOneStep(self):\n pass","def _collect_data(self, current_generation):\n neighbours = self._Individuals()\n while True:\n\n if self._check_collected_data(neighbours):\n break\n if not self._data_consuming_queue.is_ready(current_generation):\n continue\n data = self._data_consuming_queue.consume_message(current_generation)\n if current_generation != data.generation:\n continue\n\n self._parse_received_data(neighbours, int(data.source), data.data)\n\n logger.info(\"RECEIVED data\" + str(data.data) + str(data.source))\n\n return neighbours","def _collect_data(self) -> None:\n self.set_websocket_data()\n self.set_stratum_data()\n self.set_cache_data()\n self.collect_peer_connection_metrics()\n self.set_tx_storage_data()","def learn(self):\n pass","def learn(self):\n pass","def step(self):\n # Pull data from the first available input channel.\n\n input_bag = self.get()\n\n # todo add timer\n self.handle_results(input_bag, input_bag.apply(self._stack))","def collect_data(self):\n self.lines = []\n\n while True:\n self._process_serial_data()","def _run_unlabelled_extraction(self, dataset_type: DatasetType, device: str) -> None:\n dataset = self.image_datasets.get_dataset(dataset_type)\n self.extractor_model = self.extractor_model.to(device)\n\n for image, file_id in tqdm(\n dataset, desc=\"Extracting features - competition\",\n ):\n feature_tensor = self.extractor_model(image.unsqueeze(0).to(device))\n self._save_tensor(DatasetType.Competition, feature_tensor, file_id)","def train(self):\n pass","def train(self):\n pass","def train(self):\n pass","def train(self):\n pass","def train(self):\n pass","def train(self, data):\n pass","def train_data() -> Iterator[Tuple[Label, ChanneledImage]]:\n return zip(*get_data(TRAIN_FILES, 60000))","def collect_samples(self):\n # TODO refactor this to not to duplicate collect from DDPG\n # - not so easy due to logger :(\n collected = 0\n while collected < self.acm_pre_train_samples:\n obs = self.env.reset()\n end = False\n obs = torch.tensor(obs, dtype=torch.float32, device=self.device)\n obs = obs.unsqueeze(0)\n\n prev_idx = self.replay_buffer.add_obs(obs)\n ep_len = 0\n\n while not end:\n acm_action = AcMTrainer.initial_act(self, obs)\n self.replay_buffer.add_acm_action(acm_action)\n obs, rew, done, _ = self.env.step(acm_action)\n ep_len += 1\n\n obs = torch.tensor(obs, dtype=torch.float32, device=self.device)\n obs = obs.unsqueeze(0)\n\n end = done\n done = False if ep_len == self.max_ep_len else done\n\n next_idx = self.replay_buffer.add_obs(obs)\n self.replay_buffer.add_timestep(prev_idx, next_idx, obs, rew, done, end)\n prev_idx = next_idx\n collected += 1","def setup(self):\n in_dataset, out_dataset = self.get_datasets()","def setup(self):\n in_dataset, out_dataset = self.get_datasets()","def train(self):\n\t\traise NotImplementedError","def prepare_cnn_entries(X,n_nodes=100):\n idx = X[:,:,0] > 0\n X_nozeros = X[idx]\n min_X = np.min(X_nozeros[:,0])\n max_X = np.max(X_nozeros[:,0])\n del X_nozeros\n gc.collect() \n\n \n \n _,step = np.linspace(min_X,max_X, num=n_nodes,retstep=True)\n #wavelength_edges = np.histogram_bin_edges(wavelength_all,bins=n_nodes)\n\n X_new = [[[] for i in range(n_nodes)] for j in range(X.shape[0])]\n \n \"\"\"\n #multiproc try\n varyingData = [X[i] for i in range(X.shape[0])]\n constantData = [min_X,step]\n\n with multiprocessing.Pool(processes=n_proc) as pool:\n X_new=pool.starmap(paralellised_append, zip(varyingData, itertools.repeat(constantData)))\n pool.close()\n \"\"\"\n \n print('Start converting to discrete wavelength inputs...') \n for i in range(X.shape[0]):\n if(i % int(X.shape[0] / 10) == 0):\n print('Part 1.... ' + str(round(100*i/X.shape[0],0)) + ' % completed')\n X_new[i] = paralellised_append(X[i],min_X,step)\n \n #del X\n gc.collect()\n \n X_new = np.array(X_new)\n print(np.count_nonzero(X_new))\n \n for i in range(X_new.shape[0]):\n if(i % int(X_new.shape[0] / 10) == 0):\n print('Part 2.... ' + str(round(100*i/X_new.shape[0])) + ' % completed')\n X_new[i] = paralellised_assign(X_new[i])\n \n print(np.count_nonzero(X_new))\n \n return X_new","def connect_data_and_network(self,\n outputs_collector=None,\n gradients_collector=None):\n raise NotImplementedError","def learn(self):\n raise NotImplementedError","def _process(self):\n export_collect_data(self.kwargs[\"collect\"])","def collect_batch(self, observation):\n observations, actions, rewards, dones = [observation], [], [], []\n for _ in range(self.rollout_len):\n action = self.act(observation)\n observation, reward, done = self.env_pool.step(action)\n observations.append(observation)\n actions.append(action)\n rewards.append(reward)\n dones.append(done)\n return observations, actions, rewards, dones","def collect_dataset():\n response = requests.get(\"https://raw.githubusercontent.com/yashLadha/\"+\n \"The_Math_of_Intelligence/master/Week1/ADRvs\" +\n \"Rating.csv\")\n lines = response.text.splitlines()\n data = []\n for item in lines:\n item = item.split(\",\")\n data.append(item)\n print(\"the raw data \", data)\n data.pop(0) #remove the label from the list\n data_set = np.array(data)\n print(\"data_set.shape\", data_set.shape)\n print(\"data after deal with: \", data_set)\n return data_set","def _fetch_data(self, samples):\n pass","def step(self, action):\n # Action indicates the position of a datapoint in self.indeces_unknown \n # that we want to sample in unknown_data\n # The index in train_data should be retrieved \n selection_absolute = self.indeces_unknown[action]\n # Label a datapoint: add its index to known samples and removes from unknown\n self.indeces_known = np.concatenate(([self.indeces_known, np.array([selection_absolute])]))\n self.indeces_unknown = np.delete(self.indeces_unknown, action) \n # Train a model with new labeled data\n known_data = self.dataset.train_data[self.indeces_known,:]\n known_labels = self.dataset.train_labels[self.indeces_known]\n known_labels = np.ravel(known_labels)\n self.model.fit(known_data, known_labels)\n # Get a new state \n classifier_state, next_action_state = self._get_state() \n # Update the number of available actions\n self.n_actions = np.size(self.indeces_unknown)\n # Compute the quality of the current classifier\n test_prediction = self.model.predict(self.dataset.test_data)\n new_score = self.quality_method(self.dataset.test_labels, test_prediction)\n self.episode_qualities.append(new_score)\n # Compute the reward\n reward = self._compute_reward()\n # Check if this episode terminated\n done = self._compute_is_terminal() \n return classifier_state, next_action_state, reward, done","def train(self)->None:","def train():\n pass","def collect(self):\n raise NotImplementedError(\"abstract\")","def train(self):\n raise NotImplementedError","def collect_incoming_data(self, data):\n self.__input.append(data)","def trainNet():","def _gather_data(self):\n for data in self._collection:\n label = data.label\n label = disambiguate(label, self._data)\n self._data[label] = data","def train(self):\n return","def _collect_data(endless=True):\r\n conf = Config.Config().get_config()\r\n\r\n Collector.DataCollector(conf.get('db', 'path'), 'config')\r\n\r\n if endless:\r\n try:\r\n while True:\r\n time.sleep(0.5)\r\n except KeyboardInterrupt:\r\n click.echo(\"Process terminated\")\r\n exit(0)","def learn(self, D, **kwargs):\n pass","def runconnectome(self, ):\n for ps in self.postSynaptic:\n if ps[:3] not in self.muscles and abs(self.postSynaptic[ps][self.thisState]) > self.threshold:\n self.fireNeuron(ps)\n self.motorcontrol()\n for ps in self.postSynaptic:\n # if self.postSynaptic[ps][thisState] != 0:\n # print ps\n # print \"Before Clone: \", self.postSynaptic[ps][thisState]\n\n # fired neurons keep getting reset to previous weight\n # wtf deepcopy -- So, the concern is that the deepcopy doesnt\n # scale up to larger neural networks?? \n self.postSynaptic[ps][self.thisState] = copy.deepcopy(self.postSynaptic[ps][self.nextState]) \n\n # this deep copy is not in the functioning version currently.\n # print \"After Clone: \", self.postSynaptic[ps][thisState]\n\n self.thisState, self.nextState = self.nextState, self.thisState","def collect_datset(self):\n response = requests.get(self.url)\n lines = response.text.splitlines()\n data = []\n for item in lines:\n item = item.split(\",\")\n data.append(item)\n data.pop(0) # to remove labels from list\n dataset = np.matrix(data)\n return dataset","def pipeline_test_data(self):\n if self.linearity:\n Detector1Pipeline.call(self.ramp_file, save_results=True, output_dir=self.output_dir, output_use_model=True,\n steps={'ipc': {'skip': True},\n 'rscd': {'skip': True},\n 'lastframe': {'save_results': True,\n 'output_dir': self.output_dir},\n 'dark_current': {'save_results': True,\n 'output_dir': self.output_dir},\n #'linearity': {'skip': True},\n 'jump': {'save_results': True,\n 'output_dir': self.output_dir}})\n else:\n Detector1Pipeline.call(self.ramp_file, save_results=True, output_dir=self.output_dir, output_use_model=True,\n steps={'ipc': {'skip': True},\n 'rscd': {'skip': True},\n 'lastframe': {'save_results': True,\n 'output_dir': self.output_dir},\n 'dark_current': {'save_results': True,\n 'output_dir': self.output_dir},\n 'linearity': {'skip': True},\n 'jump': {'save_results': True,\n 'output_dir': self.output_dir}})\n\n self.pre_dark_file = os.path.join(self.output_dir, 'step_lastframe.fits')\n self.post_dark_file = os.path.join(self.output_dir, 'step_dark_current.fits')\n self.jump_file = os.path.join(self.output_dir, 'step_jump.fits')\n self.rate_file = os.path.join(self.output_dir, 'step_rate.fits')","def train(self, ):\n raise NotImplementedError","def train(self, training_steps=10):","def _fetch_data(self):\n pass","def _collect_all(self):","def prepare_cnn_entries(X,n_nodes=100):\n idx = X[:,:,0] > 0\n X_nozeros = X[idx]\n min_X = np.min(X_nozeros[:,0])\n max_X = np.max(X_nozeros[:,0])\n del X_nozeros\n gc.collect() \n\n \n \n wavelength_all,step = np.linspace(min_X,max_X, num=n_nodes,retstep=True)\n #wavelength_edges = np.histogram_bin_edges(wavelength_all,bins=n_nodes)\n\n X_new = [[[] for i in range(n_nodes)] for j in range(X.shape[0])]\n\n \n print('Start converting to discrete wavelength inputs...')\n for i in range(X.shape[0]):\n if(i % int(X.shape[0] / 10) == 0):\n print('Part 1.... ' + str(round(100*i/X.shape[0],0)) + ' % completed')\n for j in range(X.shape[1]):\n if(X[i,j,0]>0):\n X_new[i][int((X[i,j,0] - min_X) / step)].append(X[i,j,1])\n #print(X_new[i][int((X[i,j,0] - min_X) / step)])\n \n #del X\n gc.collect()\n \n X_new = np.array(X_new)\n print(np.count_nonzero(X_new))\n \n for i in range(X_new.shape[0]):\n if(i % int(X_new.shape[0] / 10) == 0):\n print('Part 2.... ' + str(round(100*i/X_new.shape[0])) + ' % completed')\n for j in range(X_new.shape[1]):\n X_new[i,j] = np.array(X_new[i,j])\n if(X_new[i,j].size > 0):\n X_new[i,j] = np.mean(np.array(X_new[i,j]))\n else:\n X_new[i,j] = 0.\n \n print(np.count_nonzero(X_new))\n \n return X_new","def collectTargets(self, output):\n pass","def train(self) -> Any:\n pass","def learn(self):\n raise NotImplementedError()","def prep_data():\n loader = DLoader()\n cap = loader.visitor_cnt\n\n pass","def step(self, action):\n rich_obs, reward, done, info = super(ColoredCostmapRandomAisleTurnEnv, self).step(action)\n obs = rich_obs.costmap.get_data() # pylint: disable=no-member\n obs = np.expand_dims(obs, -1)\n return obs, reward, done, info","def collect_data(self, data_reader: CalibrationDataReader):\n while True:\n inputs = data_reader.get_next()\n if not inputs:\n break\n self.intermediate_outputs.append(self.infer_session.run(None, inputs))\n\n if len(self.intermediate_outputs) == 0:\n raise ValueError(\"No data is collected.\")\n\n output_names = [self.infer_session.get_outputs()[i].name for i in range(len(self.intermediate_outputs[0]))]\n output_dicts_list = [\n dict(zip(output_names, intermediate_output)) for intermediate_output in self.intermediate_outputs\n ]\n\n merged_dict = {}\n for d in output_dicts_list:\n for k, v in d.items():\n merged_dict.setdefault(k, []).append(v)\n\n clean_merged_dict = {i: merged_dict[i] for i in merged_dict if i in self.tensors_to_calibrate}\n\n if not self.collector:\n self.collector = HistogramCollector(\n method=self.method,\n symmetric=self.symmetric,\n num_bins=self.num_bins,\n num_quantized_bins=self.num_quantized_bins,\n percentile=self.percentile,\n scenario=self.scenario,\n )\n self.collector.collect(clean_merged_dict)\n\n self.clear_collected_data()","def fetch_batch(self, phase):\n pass","def forward(self, data, input):\n output = scatter_add(input, data.batch, dim=0, dim_size=data.num_graphs)\n return output","def start_traffic(self):\n raise NotImplementedError","def on_train_forward(self, runner):\n self.on_iter_forward(runner)","def feed(self) -> None:","def __init__(self):\n self.neurons = []\n self.connections = []\n self.t_history = []","def _collect(self, conll_directory) -> Iterator[Any]:\n logging.info(\"Reading .conll from %s\", conll_directory)\n return dataset_path_iterator(conll_directory, self.configs.file_ext)","def collect_samples(self):\n self.replay_buffer = self.collect_initial_batch(\n self.replay_buffer, self.acm_pre_train_samples\n )","def _load_data(self):\n\n from sklearn.datasets import fetch_openml\n mnist = fetch_openml('mnist_784', cache=True)\n # data_x = np.array(final_data_df)\n feat_data = np.array(mnist.data).astype('float32')\n target_data = mnist.target.astype('int64')\n shuffling_index = np.arange(feat_data.shape[0])\n np.random.shuffle(shuffling_index)\n feat_data = feat_data[shuffling_index]\n target_data = target_data[shuffling_index]\n\n cur_data_list = []\n cur_target_list = []\n for i in range(10):\n cur_mask = target_data == i\n cur_data_list.append(feat_data[cur_mask][:500])\n cur_target_list.append(target_data[cur_mask][:500])\n feat_data = np.concatenate(cur_data_list)\n target_data = np.concatenate(cur_target_list)\n\n self.data_x = feat_data\n self.data_y = self.to_one_hot_encoding(target_data)\n self.numerical_idx = np.arange(784)\n self.non_num_idx = None\n\n # Calculate adjacency matrix\n self.meta_inf = self.data_x.astype('float32')\n\n if self.args.graph_type:\n self.adj = self.get_adjacency()","def run(self, data):\n\t\treduced_data = PCA(n_components=2).fit_transform(data)\n\n\t\t# Run the algorithm\n\t\tself.estimator.fit_transform(reduced_data)\n\n\t\t# Save all relevent properties\n\t\tself.input_data = data\n\t\tself.centroids = self.estimator.cluster_centers_\n\t\tself.node_positions = reduced_data\n\t\tself.labels = self.estimator.labels_\n\n\t\t# Enable visualising when debugging\n\t\t# self.visualize(reduced_data)","def forward(self, x):\n out = self.features(x)\n out = out.view(out.size(0), -1)\n out = self.classifier(out)\n return out","def train(self):\n raise NotImplementedError()","def fetch_data(self):","def getTrainingData(self):\n raise NotImplementedError","def prepare_data():\n gennet.prepare_data('Resnet50')","def dist_collect(x):\n x = x.contiguous()\n out_list = [torch.zeros_like(x, device=x.device, dtype=x.dtype)\n for _ in range(dist.get_world_size())]\n dist.all_gather(out_list, x)\n return torch.stack(out_list)","def separate(self):\n print(\"start dataset separating\")\n sum = 0\n for i in tqdm(range(len(self.itemlen))):\n il = self.itemlen[i]\n if il < 3:\n sum += il\n continue\n rarr = list(range(sum, sum+il))\n random.shuffle(rarr)\n self.train.append({\n 'input': self.input[rarr[0]],\n 'label': self.label[i]\n })\n self.val.append({\n 'input': self.input[rarr[1]],\n 'label': self.label[i]\n })\n for j in range(2, len(rarr)):\n self.test.append({\n 'input': self.input[rarr[j]],\n 'label': self.label[i]\n })\n sum += il","def collect(self):\n if self._completion_status is None or not self._completion_status.done:\n raise RuntimeError(\"No reading until done!\")\n self._completion_status = None\n\n yield from self._data","def learn_online(self, dataset, method=\"Exp distance\", base_norm=\"Thresh\", \n noise_ratio=[0,0,1], sparsity_coeff=[0,0,1], sensitivity=[0,0,1],\n learning_rate=[0,0,1], base_norm_coeff=0):\n # The list of all data processed per layer devided for sublayer \n # input_data[sublayer][actual_recording][events, timestamp if 0 or xy coordinates if 1]\n input_data = []\n # In the first layer the input_data is the input dataset given by the user\n input_data.append(dataset)\n\n # Preallocating evolution methods, for plotting the network evolution.\n self.evolution_pre_allocation(len(dataset))\n \n for layer in range(self.layers):\n # now let's generate the surfaces and train the layers\n single_layer_surfaces = []\n single_layer_errors = []\n # The result of a single layer learning that will serve as input for the \n # next one\n outrecordings = []\n \n for sublayer in range(len(self.basis[layer])):\n \n # Create network evolution parameters, at the moment I'm using \n # an exponential decay kernel as a function, it can be changed in the code pretty\n # easly\n \n # end_time: how many time surfaces will be generated in the learing \n # process for this sublayer\n end_time = 0\n for i in range(len(input_data[sublayer])):\n end_time += len(input_data[sublayer][i][0])\n time = np.arange(end_time)\n \n noise_evolution = exp_decay(noise_ratio[0], noise_ratio[1], noise_ratio[2], time)\n learning_evolution = exp_decay(learning_rate[0], learning_rate[1], learning_rate[2], time)\n sparsity_evolution = exp_decay(sparsity_coeff[0], sparsity_coeff[1], sparsity_coeff[2], time) \n sensitivity_evolution = exp_decay(sensitivity[0], sensitivity[1], sensitivity[2], time)\n \n # Generate and allocate plots for online plotting\n if self.verbose is True and layer==1:\n # Building a list to contain the basis at each run and the original timesurface (the whole picture)\n # at the position 0\n timesurfaces_list = [Time_Surface_all(xdim=35, ydim=35, timestamp=dataset[0][0][-1], timecoeff=self.taus[0], dataset=dataset[0], num_polarities=1, minv=0.1, verbose=False)]\n plot_names = [\"Original Time Surface\"]\n for i in range(len(self.basis[layer][sublayer])):\n timesurfaces_list.append(self.basis[layer][sublayer][i])\n plot_names.append(\"Base N: \"+str(i))\n live_figures, live_axes = surface_live_plot(surfaces=timesurfaces_list, fig_names=plot_names)\n\n\n \n # Counter of surfaces that will be used to update evolution parameters\n time = 0\n sub_layer_surfaces = []\n sub_layer_reference_events = []\n sub_layer_errors = [] \n \n for recording in range(len(input_data[sublayer])):\n recording_surfaces = []\n recording_reference_events = []\n for k in range(len(input_data[sublayer][recording][0])):\n single_event = [input_data[sublayer][recording][0][k], input_data[sublayer][recording][1][k]]\n tsurface = Time_Surface_event(xdim = self.surfaces_dimensions[layer][0],\n ydim = self.surfaces_dimensions[layer][1],\n event=single_event,\n timecoeff=self.taus[layer],\n dataset=input_data[sublayer][recording],\n num_polarities=self.polarities[layer],\n verbose=False)\n if method==\"CG\" :\n sub_layer_errors.append(self.sublayer_response_CG(layer, sublayer,\n tsurface,\n sparsity_evolution[time], \n noise_evolution[time]))\n if method==\"Exp distance\" :\n sub_layer_errors.append(self.sublayer_response_exp_distance(layer, sublayer,\n tsurface,\n sparsity_evolution[time], \n noise_evolution[time], \n sensitivity_evolution[time]))\n if method==\"Dot product\":\n sub_layer_errors.append(self.sublayer_response_dot_product(layer, sublayer,\n tsurface,\n sparsity_evolution[time], \n noise_evolution[time], \n sensitivity_evolution[time]))\n recording_surfaces.append(tsurface)\n recording_reference_events.append(single_event)\n # Run a single step of the gradient descent\n if base_norm==\"L2\":\n update_basis_online(self.basis[layer][sublayer], self.activations[layer][sublayer],\n learning_evolution[time], tsurface, base_norm_coeff, 1, 0)\n if base_norm==\"Thresh\":\n update_basis_online_hard_treshold(self.basis[layer][sublayer], self.activations[layer][sublayer],\n learning_evolution[time], tsurface, 1, 0)\n \n time += 1\n if time!=0: \n self.evolution_save_point(layer, sublayer, recording, noise_evolution[time-1], sparsity_evolution[time-1], sensitivity_evolution[time-1], learning_evolution[time-1])\n # Update plots\n if self.verbose is True and layer==1: \n timesurfaces_list[0]=Time_Surface_all(xdim=35, ydim=35, timestamp=dataset[recording][0][-1], timecoeff=self.taus[0], dataset=dataset[recording], num_polarities=1, minv=0.1, verbose=False)\n for i in range(len(self.basis[layer][sublayer])):\n timesurfaces_list[i+1]=self.basis[layer][sublayer][i]\n live_figures, live_axes = surface_live_plot(surfaces=timesurfaces_list, figures=live_figures, axes=live_axes)\n print(\"Layer:\"+np.str(layer)+\" Sublayer:\"+np.str(sublayer)+\" Base optimization process per recording:\"+np.str(((recording+1)/len(input_data[sublayer]))*100)+\"%\")\n sub_layer_surfaces.append(recording_surfaces)\n sub_layer_reference_events.append(recording_reference_events)\n # Close plots before displaying next set of basis\n if self.verbose is True and layer==1:\n for i in range(len(live_figures)):\n plt.close(fig=live_figures[i])\n live_axes.clear()\n live_figures.clear()\n \n \n # Check if we are at the last layer or we need to recompute the activations\n # to train the next layer\n if layer+1 != self.layers:\n # Let's compute again the activations after defining the set of basis!\n \n # I use the evolution parameters at the steady state\n time = time - 1\n # Where I store all the events computed in a sublayer\n # on and off activations originate two batches out of a single one\n sub_layer_outevents_on = []\n sub_layer_outevents_off = []\n for recording in range(len(input_data[sublayer])):\n recording_activations = []\n for k in range(len(input_data[sublayer][recording][0])):\n if method==\"CG\" :\n self.sublayer_response_CG(layer, sublayer,\n sub_layer_surfaces[recording][k],\n sparsity_evolution[time], \n noise_evolution[time])\n if method==\"Exp distance\" :\n self.sublayer_response_exp_distance(layer, sublayer,\n sub_layer_surfaces[recording][k],\n sparsity_evolution[time], \n noise_evolution[time], \n sensitivity_evolution[time])\n if method==\"Dot product\":\n sub_layer_errors.append(self.sublayer_response_dot_product(layer, sublayer,\n sub_layer_surfaces[recording][k],\n sparsity_evolution[time], \n noise_evolution[time], \n sensitivity_evolution[time]))\n recording_activations.append(self.activations[layer][sublayer].copy())\n\n # Obtaining the events resulting from the activations in a single recording\n outevents = events_from_activations(recording_activations,\n sub_layer_reference_events[recording],\n self.delay_coeff)\n \n sub_layer_outevents_on.append(outevents[0])\n sub_layer_outevents_off.append(outevents[1])\n \n \n # They will be used to optimize the consequent of sublayers\n # in the next layer\n outrecordings.append(sub_layer_outevents_on) \n outrecordings.append(sub_layer_outevents_off) \n \n single_layer_surfaces.append(sub_layer_surfaces)\n single_layer_errors.append(sub_layer_errors)\n input_data = outrecordings.copy()\n self.surfaces.append(single_layer_surfaces)\n self.errors.append(single_layer_errors)","def learn(self):\n event_batch = self.memory.sample(self.batch_size)\n \n if event_batch is None:\n return\n\n event_batch = self.memory.deserialize(event_batch)\n self.update_critic(event_batch)\n self.update_actor(event_batch)\n self.update_target(self.local_actor, self.target_actor)\n self.update_target(self.local_critic, self.target_critic)","def train_network(self):\n batch = self.memory.sample(self.batch_size)\n inputs = np.array([b[\"state\"] for b in batch]) #####\n actions = np.array([b[\"action\"] for b in batch])\n rewards = np.array([b[\"reward\"] for b in batch])\n next_inputs = np.array([b[\"next_state\"] for b in batch])\n\n actions_one_hot = np.eye(self.action_space_size)[actions]\n\n next_qvalues = np.squeeze(self.target_network.model(next_inputs))\n targets = rewards + self.discount * np.amax(next_qvalues, axis=-1)\n\n self.online_network.train_step(inputs, targets, actions_one_hot)","def get_data(self):\n return self.train_edges, self.train_labels, self.test_edges, self.test_labels","def on_train_end(self):","def make_source_dataset(self, current_host_index, num_hosts):\n pass","def forward(self, input):\n if isinstance(input.data, torch.cuda.FloatTensor) and \\\n self.num_gpu > 1:\n out = nn.parallel.data_parallel(\n self.layer, input, range(self.num_gpu))\n else:\n out = self.layer(input)\n # flatten output\n return out","def forward(self, input):\n if isinstance(input.data, torch.cuda.FloatTensor) and \\\n self.num_gpu > 1:\n out = nn.parallel.data_parallel(\n self.layer, input, range(self.num_gpu))\n else:\n out = self.layer(input)\n # flatten output\n return out","def get_train_input(self, prev, i):\n pass","def simple_train(self, data_set, learning_rate=0.01, steps=100):\n for _ in range(steps):\n for feature, label in data_set:\n utop = self.network.forward(*[Unit(k) for k in feature])\n if label > 0 and utop.value < 1:\n pull = 1\n elif label < 0 and utop.value > -1:\n pull = -1\n else:\n pull = 0\n # Set the gradient of final unit and then backward to get the direction (gradient) of corresponding parameters\n # We can also set the pull (i.e. gradient) more/less than 1 to make the adjust more efficient\n self.network.set_utop_gradient(pull)\n self.network.backward()\n self.network.pull_weights(learning_rate)","def learn_offline(self, dataset, sparsity_coeff, learning_rate,\n max_steps, base_norm_coeff, precision):\n # The list of all data processed per layer devided for sublayer \n # input_data[sublayer][actual_recording][events, timestamp if 0 or xy coordinates if 1]\n input_data = []\n # In the first layer the input_data is the input dataset given by the user\n input_data.append(dataset)\n \n for layer in range(self.layers):\n # now let's generate the surfaces and train the layers\n single_layer_surfaces = []\n single_layer_errors = []\n # The result of a single layer learning that will serve as input for the \n # next one\n outrecordings = []\n for sublayer in range(len(self.basis[layer])):\n sub_layer_surfaces = []\n sub_layer_errors = []\n sub_layer_outevents_on = []\n sub_layer_outevents_off = []\n # A single array containing all the generated surfaces \n # for a single sublayer used for full offline learning \n all_surfaces = []\n all_reference_events = []\n # Generate and allocate plots for online plotting\n if self.verbose is True:\n plot_names = []\n # Building a list to contain the basis at each run\n for i in range(len(self.basis[layer][sublayer])):\n plot_names.append(\"Base N: \"+str(i))\n live_figures, live_axes = surface_live_plot(surfaces=self.basis[layer][sublayer], fig_names=plot_names)\n for recording in range(len(input_data[sublayer])):\n recording_surfaces = []\n for k in range(len(input_data[sublayer][recording][0])):\n single_event = [input_data[sublayer][recording][0][k], input_data[sublayer][recording][1][k]]\n tsurface = Time_Surface_event(xdim = self.surfaces_dimensions[layer][0],\n ydim = self.surfaces_dimensions[layer][1],\n event=single_event,\n timecoeff=self.taus[layer],\n dataset=input_data[sublayer][recording],\n num_polarities=self.polarities[layer],\n verbose=False)\n recording_surfaces.append(tsurface)\n # A single array containing all the generated surfaces \n # for a single sublayer used for full recording learning\n all_surfaces.append(tsurface) \n all_reference_events.append(single_event)\n sub_layer_surfaces.append(recording_surfaces)\n \n # Learning time\n n_steps = 0 \n previous_err = 0\n err = precision + 1\n while (abs(err-previous_err)>precision) & (n_steps<max_steps):\n #Let's compute the activations\n sub_layer_activations = []\n for i in range(len(all_surfaces)):\n self.sublayer_response_CG(layer, sublayer, all_surfaces[i], sparsity_coeff, 0)\n sub_layer_activations.append(self.activations[layer][sublayer].copy()) \n #Let's compute the basis\n opt_res = update_basis_offline_CG(self.basis[layer][sublayer],\n self.surfaces_dimensions[layer][0]*self.polarities[layer],\n self.surfaces_dimensions[layer][1], \n self.features_number[layer], sub_layer_activations,\n all_surfaces, base_norm_coeff)\n \n self.basis[layer][sublayer] = opt_res.x.reshape(self.features_number[layer],\n self.surfaces_dimensions[layer][1], self.surfaces_dimensions[layer][0]*self.polarities[layer])\n sub_layer_errors.append(opt_res.fun)\n n_steps += 1\n previous_err = err\n err = sub_layer_errors[-1]\n # Update plots\n if self.verbose is True: \n live_figures, live_axes = surface_live_plot(surfaces=self.basis[layer][sublayer], figures=live_figures, axes=live_axes)\n print(\"Layer:\"+np.str(layer)+\" Sublayer:\"+np.str(sublayer)+\" Base optimization process:\"+np.str((n_steps/max_steps)*100)+\"%\")\n if (n_steps<max_steps):\n print(\"Execution stopped, requested precision reached\")\n else:\n print(\"Maximum number of step reached, last delta error was :\" + np.str(abs(err-previous_err)))\n \n #Let's compute again the activations after defining the set of basis\n sub_layer_activations = []\n for i in range(len(all_surfaces)):\n self.sublayer_response_CG(layer, sublayer, all_surfaces[i], sparsity_coeff, 0)\n sub_layer_activations.append(self.activations[layer][sublayer].copy()) \n # sub_layer_activations contains all the activations to all the time surfaces\n # of different recordings, in order to build the next layer input data \n # I need to redefine the original ordering of the data\n current_position = 0\n for recording in range(len(input_data[sublayer])):\n current_recording_length = len(input_data[sublayer][recording][0]) \n # Obtaining the events resulting from the activations in a single recording\n outevents = events_from_activations(sub_layer_activations[current_position:(current_position+current_recording_length)],\n all_reference_events[current_position:(current_position+current_recording_length)],\n self.delay_coeff)\n sub_layer_outevents_on.append(outevents[0])\n sub_layer_outevents_off.append(outevents[1])\n current_position += current_recording_length\n \n single_layer_surfaces.append(sub_layer_surfaces)\n single_layer_errors.append(sub_layer_errors)\n # Saving each sub layer events deviding between negative and positive\n # activations, they will be used to optimize the consequent of \n # sublayers in the next layer\n outrecordings.append(sub_layer_outevents_on) \n outrecordings.append(sub_layer_outevents_off)\n # Close plots before displaying next set of basis\n if self.verbose is True:\n for i in range(len(live_figures)):\n plt.close(fig=live_figures[i])\n live_axes.clear()\n live_figures.clear()\n input_data = outrecordings.copy()\n self.surfaces.append(single_layer_surfaces)\n self.errors.append(single_layer_errors)","def run_pipeline(directory):\n\n # io = IO(path)\n # df = io.load_cleaned_file(download_always=False)\n # df = add_choke_events(df)\n\n # Add calls to features.Xxx here\n\n #directory = main_directory\n site=os.listdir(directory)\n site_dicom={}\n site_dicom_sub={}\n site_sub_files={}\n i,k,j=0,0,0\n for filename in site:\n site_dicom[i]=directory+'/'+filename+'/DICOM-raw'\n temporary_path=os.listdir(site_dicom[i])\n\n for another_file in temporary_path:\n site_dicom_sub[j]=site_dicom[i]+'/'+another_file+'/scans'\n temporary_path_1 = os.listdir(site_dicom_sub[j])\n for another_file_1 in temporary_path_1:\n site_sub_files[k]=site_dicom_sub[j]+'/'+another_file_1+'/'\n k=k+1\n j = j + 1\n i=i+1\n splitted={}\n output_mif={}\n for i in range (len(site_sub_files)):\n splitted[i]=site_sub_files[i].split('/')\n output_mif[i]=directory+'/'+splitted[i][5]+'/MIF-raw/'+splitted[i][5]+'_'+splitted[i][7]+'_'+splitted[i][9]+'.mif'\n\n\n # save (or return) dataframe here?\n return site_sub_files,output_mif","def collect_output(self):\n pass","def collect_output(self):\n pass","def _compute_single_source_data(self, start, end):\n single_source_data = {}\n dijkstra = Dijkstra(self.graph_provider)\n single_source_data['start'] = dijkstra.single_source(start)\n single_source_data['end'] = dijkstra.single_source(end)\n\n self._single_source_data = single_source_data","def _collect_and_train(self) -> None:\n self.info_process('\\n\\n')\n self.info_process('Performing daily data collection and model training...')\n\n for symbol in Settings.get_symbols(self):\n # Interrupt collection if the collection loop was stopped\n if not self._running:\n break\n\n # Revert data to last stable day.\n date_last_collected_for = self.time().now().date()\n # If it's past midnight, move back a day.\n if self.time().get_secs_to_open() < timedelta(hours=9, minutes=30).total_seconds():\n date_last_collected_for -= timedelta(days=1)\n # Move back two market days from the most recent market day.\n date_last_collected_for = self.time().get_prev_mkt_day(date_last_collected_for)\n date_last_collected_for = self.time().get_prev_mkt_day(date_last_collected_for)\n # Remove mongo price data after the stable day.\n self.mongo().remove_price_data_after(symbol, date_last_collected_for, today=self.time().now().today())\n date_rest_available_for = self.time().get_next_mkt_day(date_last_collected_for)\n\n # Collect yesterday's polygon-rest data and train on it.\n if self._train_on_rest_data(symbol, date_rest_available_for):\n self.info_process(f'Trained {symbol} on yesterday\\'s polygon rest data')\n else:\n self.warn_process(f'Invalid {symbol} rest data collected for {date_rest_available_for}. '\n f'Discarding them and attempting to use cached stream data instead')\n if self._train_on_stream_data(symbol, date_rest_available_for):\n self.info_process(f'Trained {symbol} on yesterday\\'s polygon stream data')\n else:\n self.warn_process(f'Invalid {symbol} candles cached for {date_rest_available_for}. '\n f'Could not find valid data to train on yesterday!')\n\n # Load today's polygon-stream data and train on it.\n date_cache_available_for = self.time().get_next_mkt_day(date_rest_available_for)\n if self._train_on_stream_data(symbol, date_cache_available_for):\n self.info_process(f'Trained {symbol} on today\\'s polygon stream data')\n else:\n self.warn_process(f'Invalid {symbol} candles cached for {date_rest_available_for}. '\n f'Could not find valid data to train on today!')","def feed_data(self):\n test = True\n\n sizex_pv = self.detector + ':image1:ArraySize0_RBV'\n sizey_pv = self.detector + ':image1:ArraySize1_RBV'\n acquire_pv_name = self.get_acquire_pv_name()\n while test:\n self.sizex = caget(sizex_pv)\n self.sizey = caget(sizey_pv)\n ack = caget(acquire_pv_name)\n if ack == 1:\n test = False\n self.start_processes()\n else:\n time.sleep(.005)\n\n # # start the infinit loop so the feed does not stop after this init\n # if True:\n # time.sleep(10)\n #\n return caget(acquire_pv_name)","def forward(self, data):\n template = data['template'].cuda()\n search = data['search'].cuda()\n label_cls = data['label_cls'].cuda()\n label_loc = data['label_loc'].cuda()\n\n # get feature\n zf = self.backbone(template)\n xf = self.backbone(search)\n\n if cfg.ADJUST.ADJUST:\n zf = self.neck(zf)\n xf = self.neck(xf)\n\n cls, loc = self.head(zf, xf)\n\n # get loss\n # cls loss with cross entropy loss\n cls_loss = self.select_cross_entropy_loss(cls, label_cls)\n\n # loc loss with iou loss\n loc_loss = self.select_iou_loss(loc, label_loc, label_cls)\n\n outputs = {\n 'total_loss':\n cfg.TRAIN.CLS_WEIGHT * cls_loss +\n cfg.TRAIN.LOC_WEIGHT * loc_loss,\n 'cls_loss': cls_loss,\n 'loc_loss': loc_loss\n }\n\n return outputs","def step(self):\n # Fast learning\n task_embedding = self._ilp.infer_task()\n\n # Posterior update\n #self._skip_flag = self._is_graph_same(task_embedding, self._prev_task_embedding)\n self._skip_flag = False # XXX do not skip test\n if not self._skip_flag:\n self._grprop.observe_task(task_embedding)\n self._prev_task_embedding = task_embedding\n else:\n print(\"skipping!\")","def dist_collect(x):\n x = x.contiguous()\n out_list = [torch.zeros_like(x, device=x.device, dtype=x.dtype).contiguous() for _ in range(dist.get_world_size())]\n out_list = diff_dist.all_gather(out_list, x)\n return torch.cat(out_list, dim=0).contiguous()","def train(self, trainData):\n pass","def forward(self, x):\n out = self.features(x)\n out = self.avgpool(out)\n out = torch.flatten(out, 1)\n out = self.classifier(out)\n return out","def forward(self, x):\n out = self.features(x)\n out = self.avgpool(out)\n out = torch.flatten(out, 1)\n out = self.classifier(out)\n return out","def collect_data(self):\n categories = []\n list_of_feature_lists = []\n feature_sets = set()\n with open(self.csv_path, \"r\") as f:\n reader = csv.DictReader(f)\n # collecting some stats for FDR calculation:\n self.PSM_count = 0\n self.decoy_count = 0\n\n if self[\"dump_svm_matrix\"]:\n self.init_svm_matrix_dump()\n additional_matrix_info = []\n\n for i, row in enumerate(\n sorted(\n reader,\n reverse=self[\"bigger_scores_better\"],\n key=lambda d: float(d[self.col_for_sorting]),\n )\n ):\n\n features = self.row_to_features(row)\n\n if tuple(features) in feature_sets:\n continue\n feature_sets.add(tuple(features))\n\n category, psm_FDR = self.get_psm_category(row)\n\n list_of_feature_lists.append(features)\n categories.append(category)\n\n if self[\"dump_svm_matrix\"]:\n label = -1 if row_is_decoy(row) else 1\n sequence = \"{0}.{1}#{2}.{3}\".format(\n row[\"Sequence Pre AA\"].strip(),\n row[\"Sequence\"].strip(),\n row[\"Modifications\"].strip(),\n row[\"Sequence Post AA\"].strip(),\n )\n additional_matrix_info.append(\n {\n \"psm_id\": row[\"Spectrum Title\"].strip(),\n \"label\": label,\n \"scannr\": row[\"Spectrum Title\"].strip().split(\".\")[-2],\n \"peptide\": sequence,\n \"proteins\": self.parse_protein_ids(row[\"Protein ID\"]),\n }\n )\n\n if i % 1000 == 0:\n score_val = float(row[self.col_for_sorting])\n msg = (\n \"Generating feature matrix from input csv \"\n \"(line ~{0}) with score {1} and FDR \"\n \"{2}\".format(i, score_val, psm_FDR)\n )\n print(msg, end=\"\\r\")\n\n # All data points are collected in one big matrix, to make standardization possible\n print(\"\\nConverting feature matrix to NumPy array...\")\n X_raw = np.array(list_of_feature_lists, dtype=float)\n\n print(\"Replacing empty/NaN values with the mean of each column...\")\n self.nan_replacer = Imputer()\n self.nan_replacer.fit(X_raw)\n X_raw = self.nan_replacer.transform(X_raw)\n # Standardize input matrix to ease machine learning! Scaled data has zero mean and unit variance\n print(\"Standardizing input matrix...\")\n self.scaler = SCALER.fit(X_raw)\n self.X = self.scaler.transform(X_raw)\n self.categories = np.array(categories)\n print()\n\n if self[\"dump_svm_matrix\"]:\n print(\"Dumping SVM matrix to\", self[\"dump_svm_matrix\"])\n\n for i, matrix_row in enumerate(self.X):\n matrix_row_info = additional_matrix_info[i]\n self.dump_svm_matrix_row(\n row=list(matrix_row),\n psm_id=matrix_row_info[\"psm_id\"],\n label=matrix_row_info[\"label\"],\n scannr=matrix_row_info[\"scannr\"],\n peptide=matrix_row_info[\"peptide\"],\n proteins=matrix_row_info[\"proteins\"],\n )\n\n print(\"Dumped SVM matrix to\", self[\"dump_svm_matrix\"])\n return"],"string":"[\n \"def predict_collect(self, src, collector): # real signature unknown; restored from __doc__\\n pass\",\n \"def collect_data(endless):\\r\\n click.echo(\\\"start collecting data ...\\\")\\r\\n _collect_data(endless)\",\n \"def collect_data(self,sensation,action,reward,next_sensation):\\n pass\",\n \"def run(self):\\r\\n self.collect_data()\",\n \"def collect(self):\\n pass\",\n \"def step(self):\\r\\n self.datacollector.collect(self)\\r\\n self.datacollector2.collect(self)\\r\\n self.datacollector3.collect(self)\\r\\n self.datacollector4.collect(self)\\r\\n self.datacollector5.collect(self)\\r\\n self.datacollector6.collect(self)\\r\\n self.datacollector7.collect(self)\\r\\n self.datacollector8.collect(self)\\r\\n self.datacollector9.collect(self)\\r\\n self.datacollector10.collect(self)\\r\\n self.datacollector11.collect(self)\\r\\n self.datacollector12.collect(self)\\r\\n self.datacollector13.collect(self)\\r\\n\\r\\n self.datacollector14.collect(self)\\r\\n self.datacollector15.collect(self)\\r\\n self.datacollector16.collect(self)\\r\\n self.datacollector17.collect(self)\\r\\n self.datacollector18.collect(self)\\r\\n self.datacollector19.collect(self)\\r\\n self.datacollector20.collect(self)\\r\\n self.datacollector21.collect(self)\\r\\n self.datacollector22.collect(self)\\r\\n self.datacollector23.collect(self)\\r\\n self.datacollector24.collect(self)\\r\\n self.datacollector25.collect(self)\\r\\n self.datacollector26.collect(self)\\r\\n self.schedule.step()\",\n \"def learn_connectome(self):\\n episode_nodes = [node for node in self.container.nodes if node.is_episode]\\n if len(episode_nodes) < 2:\\n return\\n connections_counter = {}\\n for node in episode_nodes:\\n self._collect_episode_callout_stats(node, connections_counter)\\n\\n pair_list = [(key, connections_counter[key]) for key in connections_counter]\\n pair_list.sort(key=lambda item: item[1], reverse=True)\\n top_count = pair_list[0][1]\\n if top_count < 4:\\n return\\n # make connections for the top half of pairs\\n for pair, cnt in pair_list:\\n if cnt > top_count // 2:\\n self._make_connection_for_pair(pair)\",\n \"def train_step(self):\\n pass\",\n \"def TrainOneStep(self):\\n pass\",\n \"def _collect_data(self, current_generation):\\n neighbours = self._Individuals()\\n while True:\\n\\n if self._check_collected_data(neighbours):\\n break\\n if not self._data_consuming_queue.is_ready(current_generation):\\n continue\\n data = self._data_consuming_queue.consume_message(current_generation)\\n if current_generation != data.generation:\\n continue\\n\\n self._parse_received_data(neighbours, int(data.source), data.data)\\n\\n logger.info(\\\"RECEIVED data\\\" + str(data.data) + str(data.source))\\n\\n return neighbours\",\n \"def _collect_data(self) -> None:\\n self.set_websocket_data()\\n self.set_stratum_data()\\n self.set_cache_data()\\n self.collect_peer_connection_metrics()\\n self.set_tx_storage_data()\",\n \"def learn(self):\\n pass\",\n \"def learn(self):\\n pass\",\n \"def step(self):\\n # Pull data from the first available input channel.\\n\\n input_bag = self.get()\\n\\n # todo add timer\\n self.handle_results(input_bag, input_bag.apply(self._stack))\",\n \"def collect_data(self):\\n self.lines = []\\n\\n while True:\\n self._process_serial_data()\",\n \"def _run_unlabelled_extraction(self, dataset_type: DatasetType, device: str) -> None:\\n dataset = self.image_datasets.get_dataset(dataset_type)\\n self.extractor_model = self.extractor_model.to(device)\\n\\n for image, file_id in tqdm(\\n dataset, desc=\\\"Extracting features - competition\\\",\\n ):\\n feature_tensor = self.extractor_model(image.unsqueeze(0).to(device))\\n self._save_tensor(DatasetType.Competition, feature_tensor, file_id)\",\n \"def train(self):\\n pass\",\n \"def train(self):\\n pass\",\n \"def train(self):\\n pass\",\n \"def train(self):\\n pass\",\n \"def train(self):\\n pass\",\n \"def train(self, data):\\n pass\",\n \"def train_data() -> Iterator[Tuple[Label, ChanneledImage]]:\\n return zip(*get_data(TRAIN_FILES, 60000))\",\n \"def collect_samples(self):\\n # TODO refactor this to not to duplicate collect from DDPG\\n # - not so easy due to logger :(\\n collected = 0\\n while collected < self.acm_pre_train_samples:\\n obs = self.env.reset()\\n end = False\\n obs = torch.tensor(obs, dtype=torch.float32, device=self.device)\\n obs = obs.unsqueeze(0)\\n\\n prev_idx = self.replay_buffer.add_obs(obs)\\n ep_len = 0\\n\\n while not end:\\n acm_action = AcMTrainer.initial_act(self, obs)\\n self.replay_buffer.add_acm_action(acm_action)\\n obs, rew, done, _ = self.env.step(acm_action)\\n ep_len += 1\\n\\n obs = torch.tensor(obs, dtype=torch.float32, device=self.device)\\n obs = obs.unsqueeze(0)\\n\\n end = done\\n done = False if ep_len == self.max_ep_len else done\\n\\n next_idx = self.replay_buffer.add_obs(obs)\\n self.replay_buffer.add_timestep(prev_idx, next_idx, obs, rew, done, end)\\n prev_idx = next_idx\\n collected += 1\",\n \"def setup(self):\\n in_dataset, out_dataset = self.get_datasets()\",\n \"def setup(self):\\n in_dataset, out_dataset = self.get_datasets()\",\n \"def train(self):\\n\\t\\traise NotImplementedError\",\n \"def prepare_cnn_entries(X,n_nodes=100):\\n idx = X[:,:,0] > 0\\n X_nozeros = X[idx]\\n min_X = np.min(X_nozeros[:,0])\\n max_X = np.max(X_nozeros[:,0])\\n del X_nozeros\\n gc.collect() \\n\\n \\n \\n _,step = np.linspace(min_X,max_X, num=n_nodes,retstep=True)\\n #wavelength_edges = np.histogram_bin_edges(wavelength_all,bins=n_nodes)\\n\\n X_new = [[[] for i in range(n_nodes)] for j in range(X.shape[0])]\\n \\n \\\"\\\"\\\"\\n #multiproc try\\n varyingData = [X[i] for i in range(X.shape[0])]\\n constantData = [min_X,step]\\n\\n with multiprocessing.Pool(processes=n_proc) as pool:\\n X_new=pool.starmap(paralellised_append, zip(varyingData, itertools.repeat(constantData)))\\n pool.close()\\n \\\"\\\"\\\"\\n \\n print('Start converting to discrete wavelength inputs...') \\n for i in range(X.shape[0]):\\n if(i % int(X.shape[0] / 10) == 0):\\n print('Part 1.... ' + str(round(100*i/X.shape[0],0)) + ' % completed')\\n X_new[i] = paralellised_append(X[i],min_X,step)\\n \\n #del X\\n gc.collect()\\n \\n X_new = np.array(X_new)\\n print(np.count_nonzero(X_new))\\n \\n for i in range(X_new.shape[0]):\\n if(i % int(X_new.shape[0] / 10) == 0):\\n print('Part 2.... ' + str(round(100*i/X_new.shape[0])) + ' % completed')\\n X_new[i] = paralellised_assign(X_new[i])\\n \\n print(np.count_nonzero(X_new))\\n \\n return X_new\",\n \"def connect_data_and_network(self,\\n outputs_collector=None,\\n gradients_collector=None):\\n raise NotImplementedError\",\n \"def learn(self):\\n raise NotImplementedError\",\n \"def _process(self):\\n export_collect_data(self.kwargs[\\\"collect\\\"])\",\n \"def collect_batch(self, observation):\\n observations, actions, rewards, dones = [observation], [], [], []\\n for _ in range(self.rollout_len):\\n action = self.act(observation)\\n observation, reward, done = self.env_pool.step(action)\\n observations.append(observation)\\n actions.append(action)\\n rewards.append(reward)\\n dones.append(done)\\n return observations, actions, rewards, dones\",\n \"def collect_dataset():\\n response = requests.get(\\\"https://raw.githubusercontent.com/yashLadha/\\\"+\\n \\\"The_Math_of_Intelligence/master/Week1/ADRvs\\\" +\\n \\\"Rating.csv\\\")\\n lines = response.text.splitlines()\\n data = []\\n for item in lines:\\n item = item.split(\\\",\\\")\\n data.append(item)\\n print(\\\"the raw data \\\", data)\\n data.pop(0) #remove the label from the list\\n data_set = np.array(data)\\n print(\\\"data_set.shape\\\", data_set.shape)\\n print(\\\"data after deal with: \\\", data_set)\\n return data_set\",\n \"def _fetch_data(self, samples):\\n pass\",\n \"def step(self, action):\\n # Action indicates the position of a datapoint in self.indeces_unknown \\n # that we want to sample in unknown_data\\n # The index in train_data should be retrieved \\n selection_absolute = self.indeces_unknown[action]\\n # Label a datapoint: add its index to known samples and removes from unknown\\n self.indeces_known = np.concatenate(([self.indeces_known, np.array([selection_absolute])]))\\n self.indeces_unknown = np.delete(self.indeces_unknown, action) \\n # Train a model with new labeled data\\n known_data = self.dataset.train_data[self.indeces_known,:]\\n known_labels = self.dataset.train_labels[self.indeces_known]\\n known_labels = np.ravel(known_labels)\\n self.model.fit(known_data, known_labels)\\n # Get a new state \\n classifier_state, next_action_state = self._get_state() \\n # Update the number of available actions\\n self.n_actions = np.size(self.indeces_unknown)\\n # Compute the quality of the current classifier\\n test_prediction = self.model.predict(self.dataset.test_data)\\n new_score = self.quality_method(self.dataset.test_labels, test_prediction)\\n self.episode_qualities.append(new_score)\\n # Compute the reward\\n reward = self._compute_reward()\\n # Check if this episode terminated\\n done = self._compute_is_terminal() \\n return classifier_state, next_action_state, reward, done\",\n \"def train(self)->None:\",\n \"def train():\\n pass\",\n \"def collect(self):\\n raise NotImplementedError(\\\"abstract\\\")\",\n \"def train(self):\\n raise NotImplementedError\",\n \"def collect_incoming_data(self, data):\\n self.__input.append(data)\",\n \"def trainNet():\",\n \"def _gather_data(self):\\n for data in self._collection:\\n label = data.label\\n label = disambiguate(label, self._data)\\n self._data[label] = data\",\n \"def train(self):\\n return\",\n \"def _collect_data(endless=True):\\r\\n conf = Config.Config().get_config()\\r\\n\\r\\n Collector.DataCollector(conf.get('db', 'path'), 'config')\\r\\n\\r\\n if endless:\\r\\n try:\\r\\n while True:\\r\\n time.sleep(0.5)\\r\\n except KeyboardInterrupt:\\r\\n click.echo(\\\"Process terminated\\\")\\r\\n exit(0)\",\n \"def learn(self, D, **kwargs):\\n pass\",\n \"def runconnectome(self, ):\\n for ps in self.postSynaptic:\\n if ps[:3] not in self.muscles and abs(self.postSynaptic[ps][self.thisState]) > self.threshold:\\n self.fireNeuron(ps)\\n self.motorcontrol()\\n for ps in self.postSynaptic:\\n # if self.postSynaptic[ps][thisState] != 0:\\n # print ps\\n # print \\\"Before Clone: \\\", self.postSynaptic[ps][thisState]\\n\\n # fired neurons keep getting reset to previous weight\\n # wtf deepcopy -- So, the concern is that the deepcopy doesnt\\n # scale up to larger neural networks?? \\n self.postSynaptic[ps][self.thisState] = copy.deepcopy(self.postSynaptic[ps][self.nextState]) \\n\\n # this deep copy is not in the functioning version currently.\\n # print \\\"After Clone: \\\", self.postSynaptic[ps][thisState]\\n\\n self.thisState, self.nextState = self.nextState, self.thisState\",\n \"def collect_datset(self):\\n response = requests.get(self.url)\\n lines = response.text.splitlines()\\n data = []\\n for item in lines:\\n item = item.split(\\\",\\\")\\n data.append(item)\\n data.pop(0) # to remove labels from list\\n dataset = np.matrix(data)\\n return dataset\",\n \"def pipeline_test_data(self):\\n if self.linearity:\\n Detector1Pipeline.call(self.ramp_file, save_results=True, output_dir=self.output_dir, output_use_model=True,\\n steps={'ipc': {'skip': True},\\n 'rscd': {'skip': True},\\n 'lastframe': {'save_results': True,\\n 'output_dir': self.output_dir},\\n 'dark_current': {'save_results': True,\\n 'output_dir': self.output_dir},\\n #'linearity': {'skip': True},\\n 'jump': {'save_results': True,\\n 'output_dir': self.output_dir}})\\n else:\\n Detector1Pipeline.call(self.ramp_file, save_results=True, output_dir=self.output_dir, output_use_model=True,\\n steps={'ipc': {'skip': True},\\n 'rscd': {'skip': True},\\n 'lastframe': {'save_results': True,\\n 'output_dir': self.output_dir},\\n 'dark_current': {'save_results': True,\\n 'output_dir': self.output_dir},\\n 'linearity': {'skip': True},\\n 'jump': {'save_results': True,\\n 'output_dir': self.output_dir}})\\n\\n self.pre_dark_file = os.path.join(self.output_dir, 'step_lastframe.fits')\\n self.post_dark_file = os.path.join(self.output_dir, 'step_dark_current.fits')\\n self.jump_file = os.path.join(self.output_dir, 'step_jump.fits')\\n self.rate_file = os.path.join(self.output_dir, 'step_rate.fits')\",\n \"def train(self, ):\\n raise NotImplementedError\",\n \"def train(self, training_steps=10):\",\n \"def _fetch_data(self):\\n pass\",\n \"def _collect_all(self):\",\n \"def prepare_cnn_entries(X,n_nodes=100):\\n idx = X[:,:,0] > 0\\n X_nozeros = X[idx]\\n min_X = np.min(X_nozeros[:,0])\\n max_X = np.max(X_nozeros[:,0])\\n del X_nozeros\\n gc.collect() \\n\\n \\n \\n wavelength_all,step = np.linspace(min_X,max_X, num=n_nodes,retstep=True)\\n #wavelength_edges = np.histogram_bin_edges(wavelength_all,bins=n_nodes)\\n\\n X_new = [[[] for i in range(n_nodes)] for j in range(X.shape[0])]\\n\\n \\n print('Start converting to discrete wavelength inputs...')\\n for i in range(X.shape[0]):\\n if(i % int(X.shape[0] / 10) == 0):\\n print('Part 1.... ' + str(round(100*i/X.shape[0],0)) + ' % completed')\\n for j in range(X.shape[1]):\\n if(X[i,j,0]>0):\\n X_new[i][int((X[i,j,0] - min_X) / step)].append(X[i,j,1])\\n #print(X_new[i][int((X[i,j,0] - min_X) / step)])\\n \\n #del X\\n gc.collect()\\n \\n X_new = np.array(X_new)\\n print(np.count_nonzero(X_new))\\n \\n for i in range(X_new.shape[0]):\\n if(i % int(X_new.shape[0] / 10) == 0):\\n print('Part 2.... ' + str(round(100*i/X_new.shape[0])) + ' % completed')\\n for j in range(X_new.shape[1]):\\n X_new[i,j] = np.array(X_new[i,j])\\n if(X_new[i,j].size > 0):\\n X_new[i,j] = np.mean(np.array(X_new[i,j]))\\n else:\\n X_new[i,j] = 0.\\n \\n print(np.count_nonzero(X_new))\\n \\n return X_new\",\n \"def collectTargets(self, output):\\n pass\",\n \"def train(self) -> Any:\\n pass\",\n \"def learn(self):\\n raise NotImplementedError()\",\n \"def prep_data():\\n loader = DLoader()\\n cap = loader.visitor_cnt\\n\\n pass\",\n \"def step(self, action):\\n rich_obs, reward, done, info = super(ColoredCostmapRandomAisleTurnEnv, self).step(action)\\n obs = rich_obs.costmap.get_data() # pylint: disable=no-member\\n obs = np.expand_dims(obs, -1)\\n return obs, reward, done, info\",\n \"def collect_data(self, data_reader: CalibrationDataReader):\\n while True:\\n inputs = data_reader.get_next()\\n if not inputs:\\n break\\n self.intermediate_outputs.append(self.infer_session.run(None, inputs))\\n\\n if len(self.intermediate_outputs) == 0:\\n raise ValueError(\\\"No data is collected.\\\")\\n\\n output_names = [self.infer_session.get_outputs()[i].name for i in range(len(self.intermediate_outputs[0]))]\\n output_dicts_list = [\\n dict(zip(output_names, intermediate_output)) for intermediate_output in self.intermediate_outputs\\n ]\\n\\n merged_dict = {}\\n for d in output_dicts_list:\\n for k, v in d.items():\\n merged_dict.setdefault(k, []).append(v)\\n\\n clean_merged_dict = {i: merged_dict[i] for i in merged_dict if i in self.tensors_to_calibrate}\\n\\n if not self.collector:\\n self.collector = HistogramCollector(\\n method=self.method,\\n symmetric=self.symmetric,\\n num_bins=self.num_bins,\\n num_quantized_bins=self.num_quantized_bins,\\n percentile=self.percentile,\\n scenario=self.scenario,\\n )\\n self.collector.collect(clean_merged_dict)\\n\\n self.clear_collected_data()\",\n \"def fetch_batch(self, phase):\\n pass\",\n \"def forward(self, data, input):\\n output = scatter_add(input, data.batch, dim=0, dim_size=data.num_graphs)\\n return output\",\n \"def start_traffic(self):\\n raise NotImplementedError\",\n \"def on_train_forward(self, runner):\\n self.on_iter_forward(runner)\",\n \"def feed(self) -> None:\",\n \"def __init__(self):\\n self.neurons = []\\n self.connections = []\\n self.t_history = []\",\n \"def _collect(self, conll_directory) -> Iterator[Any]:\\n logging.info(\\\"Reading .conll from %s\\\", conll_directory)\\n return dataset_path_iterator(conll_directory, self.configs.file_ext)\",\n \"def collect_samples(self):\\n self.replay_buffer = self.collect_initial_batch(\\n self.replay_buffer, self.acm_pre_train_samples\\n )\",\n \"def _load_data(self):\\n\\n from sklearn.datasets import fetch_openml\\n mnist = fetch_openml('mnist_784', cache=True)\\n # data_x = np.array(final_data_df)\\n feat_data = np.array(mnist.data).astype('float32')\\n target_data = mnist.target.astype('int64')\\n shuffling_index = np.arange(feat_data.shape[0])\\n np.random.shuffle(shuffling_index)\\n feat_data = feat_data[shuffling_index]\\n target_data = target_data[shuffling_index]\\n\\n cur_data_list = []\\n cur_target_list = []\\n for i in range(10):\\n cur_mask = target_data == i\\n cur_data_list.append(feat_data[cur_mask][:500])\\n cur_target_list.append(target_data[cur_mask][:500])\\n feat_data = np.concatenate(cur_data_list)\\n target_data = np.concatenate(cur_target_list)\\n\\n self.data_x = feat_data\\n self.data_y = self.to_one_hot_encoding(target_data)\\n self.numerical_idx = np.arange(784)\\n self.non_num_idx = None\\n\\n # Calculate adjacency matrix\\n self.meta_inf = self.data_x.astype('float32')\\n\\n if self.args.graph_type:\\n self.adj = self.get_adjacency()\",\n \"def run(self, data):\\n\\t\\treduced_data = PCA(n_components=2).fit_transform(data)\\n\\n\\t\\t# Run the algorithm\\n\\t\\tself.estimator.fit_transform(reduced_data)\\n\\n\\t\\t# Save all relevent properties\\n\\t\\tself.input_data = data\\n\\t\\tself.centroids = self.estimator.cluster_centers_\\n\\t\\tself.node_positions = reduced_data\\n\\t\\tself.labels = self.estimator.labels_\\n\\n\\t\\t# Enable visualising when debugging\\n\\t\\t# self.visualize(reduced_data)\",\n \"def forward(self, x):\\n out = self.features(x)\\n out = out.view(out.size(0), -1)\\n out = self.classifier(out)\\n return out\",\n \"def train(self):\\n raise NotImplementedError()\",\n \"def fetch_data(self):\",\n \"def getTrainingData(self):\\n raise NotImplementedError\",\n \"def prepare_data():\\n gennet.prepare_data('Resnet50')\",\n \"def dist_collect(x):\\n x = x.contiguous()\\n out_list = [torch.zeros_like(x, device=x.device, dtype=x.dtype)\\n for _ in range(dist.get_world_size())]\\n dist.all_gather(out_list, x)\\n return torch.stack(out_list)\",\n \"def separate(self):\\n print(\\\"start dataset separating\\\")\\n sum = 0\\n for i in tqdm(range(len(self.itemlen))):\\n il = self.itemlen[i]\\n if il < 3:\\n sum += il\\n continue\\n rarr = list(range(sum, sum+il))\\n random.shuffle(rarr)\\n self.train.append({\\n 'input': self.input[rarr[0]],\\n 'label': self.label[i]\\n })\\n self.val.append({\\n 'input': self.input[rarr[1]],\\n 'label': self.label[i]\\n })\\n for j in range(2, len(rarr)):\\n self.test.append({\\n 'input': self.input[rarr[j]],\\n 'label': self.label[i]\\n })\\n sum += il\",\n \"def collect(self):\\n if self._completion_status is None or not self._completion_status.done:\\n raise RuntimeError(\\\"No reading until done!\\\")\\n self._completion_status = None\\n\\n yield from self._data\",\n \"def learn_online(self, dataset, method=\\\"Exp distance\\\", base_norm=\\\"Thresh\\\", \\n noise_ratio=[0,0,1], sparsity_coeff=[0,0,1], sensitivity=[0,0,1],\\n learning_rate=[0,0,1], base_norm_coeff=0):\\n # The list of all data processed per layer devided for sublayer \\n # input_data[sublayer][actual_recording][events, timestamp if 0 or xy coordinates if 1]\\n input_data = []\\n # In the first layer the input_data is the input dataset given by the user\\n input_data.append(dataset)\\n\\n # Preallocating evolution methods, for plotting the network evolution.\\n self.evolution_pre_allocation(len(dataset))\\n \\n for layer in range(self.layers):\\n # now let's generate the surfaces and train the layers\\n single_layer_surfaces = []\\n single_layer_errors = []\\n # The result of a single layer learning that will serve as input for the \\n # next one\\n outrecordings = []\\n \\n for sublayer in range(len(self.basis[layer])):\\n \\n # Create network evolution parameters, at the moment I'm using \\n # an exponential decay kernel as a function, it can be changed in the code pretty\\n # easly\\n \\n # end_time: how many time surfaces will be generated in the learing \\n # process for this sublayer\\n end_time = 0\\n for i in range(len(input_data[sublayer])):\\n end_time += len(input_data[sublayer][i][0])\\n time = np.arange(end_time)\\n \\n noise_evolution = exp_decay(noise_ratio[0], noise_ratio[1], noise_ratio[2], time)\\n learning_evolution = exp_decay(learning_rate[0], learning_rate[1], learning_rate[2], time)\\n sparsity_evolution = exp_decay(sparsity_coeff[0], sparsity_coeff[1], sparsity_coeff[2], time) \\n sensitivity_evolution = exp_decay(sensitivity[0], sensitivity[1], sensitivity[2], time)\\n \\n # Generate and allocate plots for online plotting\\n if self.verbose is True and layer==1:\\n # Building a list to contain the basis at each run and the original timesurface (the whole picture)\\n # at the position 0\\n timesurfaces_list = [Time_Surface_all(xdim=35, ydim=35, timestamp=dataset[0][0][-1], timecoeff=self.taus[0], dataset=dataset[0], num_polarities=1, minv=0.1, verbose=False)]\\n plot_names = [\\\"Original Time Surface\\\"]\\n for i in range(len(self.basis[layer][sublayer])):\\n timesurfaces_list.append(self.basis[layer][sublayer][i])\\n plot_names.append(\\\"Base N: \\\"+str(i))\\n live_figures, live_axes = surface_live_plot(surfaces=timesurfaces_list, fig_names=plot_names)\\n\\n\\n \\n # Counter of surfaces that will be used to update evolution parameters\\n time = 0\\n sub_layer_surfaces = []\\n sub_layer_reference_events = []\\n sub_layer_errors = [] \\n \\n for recording in range(len(input_data[sublayer])):\\n recording_surfaces = []\\n recording_reference_events = []\\n for k in range(len(input_data[sublayer][recording][0])):\\n single_event = [input_data[sublayer][recording][0][k], input_data[sublayer][recording][1][k]]\\n tsurface = Time_Surface_event(xdim = self.surfaces_dimensions[layer][0],\\n ydim = self.surfaces_dimensions[layer][1],\\n event=single_event,\\n timecoeff=self.taus[layer],\\n dataset=input_data[sublayer][recording],\\n num_polarities=self.polarities[layer],\\n verbose=False)\\n if method==\\\"CG\\\" :\\n sub_layer_errors.append(self.sublayer_response_CG(layer, sublayer,\\n tsurface,\\n sparsity_evolution[time], \\n noise_evolution[time]))\\n if method==\\\"Exp distance\\\" :\\n sub_layer_errors.append(self.sublayer_response_exp_distance(layer, sublayer,\\n tsurface,\\n sparsity_evolution[time], \\n noise_evolution[time], \\n sensitivity_evolution[time]))\\n if method==\\\"Dot product\\\":\\n sub_layer_errors.append(self.sublayer_response_dot_product(layer, sublayer,\\n tsurface,\\n sparsity_evolution[time], \\n noise_evolution[time], \\n sensitivity_evolution[time]))\\n recording_surfaces.append(tsurface)\\n recording_reference_events.append(single_event)\\n # Run a single step of the gradient descent\\n if base_norm==\\\"L2\\\":\\n update_basis_online(self.basis[layer][sublayer], self.activations[layer][sublayer],\\n learning_evolution[time], tsurface, base_norm_coeff, 1, 0)\\n if base_norm==\\\"Thresh\\\":\\n update_basis_online_hard_treshold(self.basis[layer][sublayer], self.activations[layer][sublayer],\\n learning_evolution[time], tsurface, 1, 0)\\n \\n time += 1\\n if time!=0: \\n self.evolution_save_point(layer, sublayer, recording, noise_evolution[time-1], sparsity_evolution[time-1], sensitivity_evolution[time-1], learning_evolution[time-1])\\n # Update plots\\n if self.verbose is True and layer==1: \\n timesurfaces_list[0]=Time_Surface_all(xdim=35, ydim=35, timestamp=dataset[recording][0][-1], timecoeff=self.taus[0], dataset=dataset[recording], num_polarities=1, minv=0.1, verbose=False)\\n for i in range(len(self.basis[layer][sublayer])):\\n timesurfaces_list[i+1]=self.basis[layer][sublayer][i]\\n live_figures, live_axes = surface_live_plot(surfaces=timesurfaces_list, figures=live_figures, axes=live_axes)\\n print(\\\"Layer:\\\"+np.str(layer)+\\\" Sublayer:\\\"+np.str(sublayer)+\\\" Base optimization process per recording:\\\"+np.str(((recording+1)/len(input_data[sublayer]))*100)+\\\"%\\\")\\n sub_layer_surfaces.append(recording_surfaces)\\n sub_layer_reference_events.append(recording_reference_events)\\n # Close plots before displaying next set of basis\\n if self.verbose is True and layer==1:\\n for i in range(len(live_figures)):\\n plt.close(fig=live_figures[i])\\n live_axes.clear()\\n live_figures.clear()\\n \\n \\n # Check if we are at the last layer or we need to recompute the activations\\n # to train the next layer\\n if layer+1 != self.layers:\\n # Let's compute again the activations after defining the set of basis!\\n \\n # I use the evolution parameters at the steady state\\n time = time - 1\\n # Where I store all the events computed in a sublayer\\n # on and off activations originate two batches out of a single one\\n sub_layer_outevents_on = []\\n sub_layer_outevents_off = []\\n for recording in range(len(input_data[sublayer])):\\n recording_activations = []\\n for k in range(len(input_data[sublayer][recording][0])):\\n if method==\\\"CG\\\" :\\n self.sublayer_response_CG(layer, sublayer,\\n sub_layer_surfaces[recording][k],\\n sparsity_evolution[time], \\n noise_evolution[time])\\n if method==\\\"Exp distance\\\" :\\n self.sublayer_response_exp_distance(layer, sublayer,\\n sub_layer_surfaces[recording][k],\\n sparsity_evolution[time], \\n noise_evolution[time], \\n sensitivity_evolution[time])\\n if method==\\\"Dot product\\\":\\n sub_layer_errors.append(self.sublayer_response_dot_product(layer, sublayer,\\n sub_layer_surfaces[recording][k],\\n sparsity_evolution[time], \\n noise_evolution[time], \\n sensitivity_evolution[time]))\\n recording_activations.append(self.activations[layer][sublayer].copy())\\n\\n # Obtaining the events resulting from the activations in a single recording\\n outevents = events_from_activations(recording_activations,\\n sub_layer_reference_events[recording],\\n self.delay_coeff)\\n \\n sub_layer_outevents_on.append(outevents[0])\\n sub_layer_outevents_off.append(outevents[1])\\n \\n \\n # They will be used to optimize the consequent of sublayers\\n # in the next layer\\n outrecordings.append(sub_layer_outevents_on) \\n outrecordings.append(sub_layer_outevents_off) \\n \\n single_layer_surfaces.append(sub_layer_surfaces)\\n single_layer_errors.append(sub_layer_errors)\\n input_data = outrecordings.copy()\\n self.surfaces.append(single_layer_surfaces)\\n self.errors.append(single_layer_errors)\",\n \"def learn(self):\\n event_batch = self.memory.sample(self.batch_size)\\n \\n if event_batch is None:\\n return\\n\\n event_batch = self.memory.deserialize(event_batch)\\n self.update_critic(event_batch)\\n self.update_actor(event_batch)\\n self.update_target(self.local_actor, self.target_actor)\\n self.update_target(self.local_critic, self.target_critic)\",\n \"def train_network(self):\\n batch = self.memory.sample(self.batch_size)\\n inputs = np.array([b[\\\"state\\\"] for b in batch]) #####\\n actions = np.array([b[\\\"action\\\"] for b in batch])\\n rewards = np.array([b[\\\"reward\\\"] for b in batch])\\n next_inputs = np.array([b[\\\"next_state\\\"] for b in batch])\\n\\n actions_one_hot = np.eye(self.action_space_size)[actions]\\n\\n next_qvalues = np.squeeze(self.target_network.model(next_inputs))\\n targets = rewards + self.discount * np.amax(next_qvalues, axis=-1)\\n\\n self.online_network.train_step(inputs, targets, actions_one_hot)\",\n \"def get_data(self):\\n return self.train_edges, self.train_labels, self.test_edges, self.test_labels\",\n \"def on_train_end(self):\",\n \"def make_source_dataset(self, current_host_index, num_hosts):\\n pass\",\n \"def forward(self, input):\\n if isinstance(input.data, torch.cuda.FloatTensor) and \\\\\\n self.num_gpu > 1:\\n out = nn.parallel.data_parallel(\\n self.layer, input, range(self.num_gpu))\\n else:\\n out = self.layer(input)\\n # flatten output\\n return out\",\n \"def forward(self, input):\\n if isinstance(input.data, torch.cuda.FloatTensor) and \\\\\\n self.num_gpu > 1:\\n out = nn.parallel.data_parallel(\\n self.layer, input, range(self.num_gpu))\\n else:\\n out = self.layer(input)\\n # flatten output\\n return out\",\n \"def get_train_input(self, prev, i):\\n pass\",\n \"def simple_train(self, data_set, learning_rate=0.01, steps=100):\\n for _ in range(steps):\\n for feature, label in data_set:\\n utop = self.network.forward(*[Unit(k) for k in feature])\\n if label > 0 and utop.value < 1:\\n pull = 1\\n elif label < 0 and utop.value > -1:\\n pull = -1\\n else:\\n pull = 0\\n # Set the gradient of final unit and then backward to get the direction (gradient) of corresponding parameters\\n # We can also set the pull (i.e. gradient) more/less than 1 to make the adjust more efficient\\n self.network.set_utop_gradient(pull)\\n self.network.backward()\\n self.network.pull_weights(learning_rate)\",\n \"def learn_offline(self, dataset, sparsity_coeff, learning_rate,\\n max_steps, base_norm_coeff, precision):\\n # The list of all data processed per layer devided for sublayer \\n # input_data[sublayer][actual_recording][events, timestamp if 0 or xy coordinates if 1]\\n input_data = []\\n # In the first layer the input_data is the input dataset given by the user\\n input_data.append(dataset)\\n \\n for layer in range(self.layers):\\n # now let's generate the surfaces and train the layers\\n single_layer_surfaces = []\\n single_layer_errors = []\\n # The result of a single layer learning that will serve as input for the \\n # next one\\n outrecordings = []\\n for sublayer in range(len(self.basis[layer])):\\n sub_layer_surfaces = []\\n sub_layer_errors = []\\n sub_layer_outevents_on = []\\n sub_layer_outevents_off = []\\n # A single array containing all the generated surfaces \\n # for a single sublayer used for full offline learning \\n all_surfaces = []\\n all_reference_events = []\\n # Generate and allocate plots for online plotting\\n if self.verbose is True:\\n plot_names = []\\n # Building a list to contain the basis at each run\\n for i in range(len(self.basis[layer][sublayer])):\\n plot_names.append(\\\"Base N: \\\"+str(i))\\n live_figures, live_axes = surface_live_plot(surfaces=self.basis[layer][sublayer], fig_names=plot_names)\\n for recording in range(len(input_data[sublayer])):\\n recording_surfaces = []\\n for k in range(len(input_data[sublayer][recording][0])):\\n single_event = [input_data[sublayer][recording][0][k], input_data[sublayer][recording][1][k]]\\n tsurface = Time_Surface_event(xdim = self.surfaces_dimensions[layer][0],\\n ydim = self.surfaces_dimensions[layer][1],\\n event=single_event,\\n timecoeff=self.taus[layer],\\n dataset=input_data[sublayer][recording],\\n num_polarities=self.polarities[layer],\\n verbose=False)\\n recording_surfaces.append(tsurface)\\n # A single array containing all the generated surfaces \\n # for a single sublayer used for full recording learning\\n all_surfaces.append(tsurface) \\n all_reference_events.append(single_event)\\n sub_layer_surfaces.append(recording_surfaces)\\n \\n # Learning time\\n n_steps = 0 \\n previous_err = 0\\n err = precision + 1\\n while (abs(err-previous_err)>precision) & (n_steps<max_steps):\\n #Let's compute the activations\\n sub_layer_activations = []\\n for i in range(len(all_surfaces)):\\n self.sublayer_response_CG(layer, sublayer, all_surfaces[i], sparsity_coeff, 0)\\n sub_layer_activations.append(self.activations[layer][sublayer].copy()) \\n #Let's compute the basis\\n opt_res = update_basis_offline_CG(self.basis[layer][sublayer],\\n self.surfaces_dimensions[layer][0]*self.polarities[layer],\\n self.surfaces_dimensions[layer][1], \\n self.features_number[layer], sub_layer_activations,\\n all_surfaces, base_norm_coeff)\\n \\n self.basis[layer][sublayer] = opt_res.x.reshape(self.features_number[layer],\\n self.surfaces_dimensions[layer][1], self.surfaces_dimensions[layer][0]*self.polarities[layer])\\n sub_layer_errors.append(opt_res.fun)\\n n_steps += 1\\n previous_err = err\\n err = sub_layer_errors[-1]\\n # Update plots\\n if self.verbose is True: \\n live_figures, live_axes = surface_live_plot(surfaces=self.basis[layer][sublayer], figures=live_figures, axes=live_axes)\\n print(\\\"Layer:\\\"+np.str(layer)+\\\" Sublayer:\\\"+np.str(sublayer)+\\\" Base optimization process:\\\"+np.str((n_steps/max_steps)*100)+\\\"%\\\")\\n if (n_steps<max_steps):\\n print(\\\"Execution stopped, requested precision reached\\\")\\n else:\\n print(\\\"Maximum number of step reached, last delta error was :\\\" + np.str(abs(err-previous_err)))\\n \\n #Let's compute again the activations after defining the set of basis\\n sub_layer_activations = []\\n for i in range(len(all_surfaces)):\\n self.sublayer_response_CG(layer, sublayer, all_surfaces[i], sparsity_coeff, 0)\\n sub_layer_activations.append(self.activations[layer][sublayer].copy()) \\n # sub_layer_activations contains all the activations to all the time surfaces\\n # of different recordings, in order to build the next layer input data \\n # I need to redefine the original ordering of the data\\n current_position = 0\\n for recording in range(len(input_data[sublayer])):\\n current_recording_length = len(input_data[sublayer][recording][0]) \\n # Obtaining the events resulting from the activations in a single recording\\n outevents = events_from_activations(sub_layer_activations[current_position:(current_position+current_recording_length)],\\n all_reference_events[current_position:(current_position+current_recording_length)],\\n self.delay_coeff)\\n sub_layer_outevents_on.append(outevents[0])\\n sub_layer_outevents_off.append(outevents[1])\\n current_position += current_recording_length\\n \\n single_layer_surfaces.append(sub_layer_surfaces)\\n single_layer_errors.append(sub_layer_errors)\\n # Saving each sub layer events deviding between negative and positive\\n # activations, they will be used to optimize the consequent of \\n # sublayers in the next layer\\n outrecordings.append(sub_layer_outevents_on) \\n outrecordings.append(sub_layer_outevents_off)\\n # Close plots before displaying next set of basis\\n if self.verbose is True:\\n for i in range(len(live_figures)):\\n plt.close(fig=live_figures[i])\\n live_axes.clear()\\n live_figures.clear()\\n input_data = outrecordings.copy()\\n self.surfaces.append(single_layer_surfaces)\\n self.errors.append(single_layer_errors)\",\n \"def run_pipeline(directory):\\n\\n # io = IO(path)\\n # df = io.load_cleaned_file(download_always=False)\\n # df = add_choke_events(df)\\n\\n # Add calls to features.Xxx here\\n\\n #directory = main_directory\\n site=os.listdir(directory)\\n site_dicom={}\\n site_dicom_sub={}\\n site_sub_files={}\\n i,k,j=0,0,0\\n for filename in site:\\n site_dicom[i]=directory+'/'+filename+'/DICOM-raw'\\n temporary_path=os.listdir(site_dicom[i])\\n\\n for another_file in temporary_path:\\n site_dicom_sub[j]=site_dicom[i]+'/'+another_file+'/scans'\\n temporary_path_1 = os.listdir(site_dicom_sub[j])\\n for another_file_1 in temporary_path_1:\\n site_sub_files[k]=site_dicom_sub[j]+'/'+another_file_1+'/'\\n k=k+1\\n j = j + 1\\n i=i+1\\n splitted={}\\n output_mif={}\\n for i in range (len(site_sub_files)):\\n splitted[i]=site_sub_files[i].split('/')\\n output_mif[i]=directory+'/'+splitted[i][5]+'/MIF-raw/'+splitted[i][5]+'_'+splitted[i][7]+'_'+splitted[i][9]+'.mif'\\n\\n\\n # save (or return) dataframe here?\\n return site_sub_files,output_mif\",\n \"def collect_output(self):\\n pass\",\n \"def collect_output(self):\\n pass\",\n \"def _compute_single_source_data(self, start, end):\\n single_source_data = {}\\n dijkstra = Dijkstra(self.graph_provider)\\n single_source_data['start'] = dijkstra.single_source(start)\\n single_source_data['end'] = dijkstra.single_source(end)\\n\\n self._single_source_data = single_source_data\",\n \"def _collect_and_train(self) -> None:\\n self.info_process('\\\\n\\\\n')\\n self.info_process('Performing daily data collection and model training...')\\n\\n for symbol in Settings.get_symbols(self):\\n # Interrupt collection if the collection loop was stopped\\n if not self._running:\\n break\\n\\n # Revert data to last stable day.\\n date_last_collected_for = self.time().now().date()\\n # If it's past midnight, move back a day.\\n if self.time().get_secs_to_open() < timedelta(hours=9, minutes=30).total_seconds():\\n date_last_collected_for -= timedelta(days=1)\\n # Move back two market days from the most recent market day.\\n date_last_collected_for = self.time().get_prev_mkt_day(date_last_collected_for)\\n date_last_collected_for = self.time().get_prev_mkt_day(date_last_collected_for)\\n # Remove mongo price data after the stable day.\\n self.mongo().remove_price_data_after(symbol, date_last_collected_for, today=self.time().now().today())\\n date_rest_available_for = self.time().get_next_mkt_day(date_last_collected_for)\\n\\n # Collect yesterday's polygon-rest data and train on it.\\n if self._train_on_rest_data(symbol, date_rest_available_for):\\n self.info_process(f'Trained {symbol} on yesterday\\\\'s polygon rest data')\\n else:\\n self.warn_process(f'Invalid {symbol} rest data collected for {date_rest_available_for}. '\\n f'Discarding them and attempting to use cached stream data instead')\\n if self._train_on_stream_data(symbol, date_rest_available_for):\\n self.info_process(f'Trained {symbol} on yesterday\\\\'s polygon stream data')\\n else:\\n self.warn_process(f'Invalid {symbol} candles cached for {date_rest_available_for}. '\\n f'Could not find valid data to train on yesterday!')\\n\\n # Load today's polygon-stream data and train on it.\\n date_cache_available_for = self.time().get_next_mkt_day(date_rest_available_for)\\n if self._train_on_stream_data(symbol, date_cache_available_for):\\n self.info_process(f'Trained {symbol} on today\\\\'s polygon stream data')\\n else:\\n self.warn_process(f'Invalid {symbol} candles cached for {date_rest_available_for}. '\\n f'Could not find valid data to train on today!')\",\n \"def feed_data(self):\\n test = True\\n\\n sizex_pv = self.detector + ':image1:ArraySize0_RBV'\\n sizey_pv = self.detector + ':image1:ArraySize1_RBV'\\n acquire_pv_name = self.get_acquire_pv_name()\\n while test:\\n self.sizex = caget(sizex_pv)\\n self.sizey = caget(sizey_pv)\\n ack = caget(acquire_pv_name)\\n if ack == 1:\\n test = False\\n self.start_processes()\\n else:\\n time.sleep(.005)\\n\\n # # start the infinit loop so the feed does not stop after this init\\n # if True:\\n # time.sleep(10)\\n #\\n return caget(acquire_pv_name)\",\n \"def forward(self, data):\\n template = data['template'].cuda()\\n search = data['search'].cuda()\\n label_cls = data['label_cls'].cuda()\\n label_loc = data['label_loc'].cuda()\\n\\n # get feature\\n zf = self.backbone(template)\\n xf = self.backbone(search)\\n\\n if cfg.ADJUST.ADJUST:\\n zf = self.neck(zf)\\n xf = self.neck(xf)\\n\\n cls, loc = self.head(zf, xf)\\n\\n # get loss\\n # cls loss with cross entropy loss\\n cls_loss = self.select_cross_entropy_loss(cls, label_cls)\\n\\n # loc loss with iou loss\\n loc_loss = self.select_iou_loss(loc, label_loc, label_cls)\\n\\n outputs = {\\n 'total_loss':\\n cfg.TRAIN.CLS_WEIGHT * cls_loss +\\n cfg.TRAIN.LOC_WEIGHT * loc_loss,\\n 'cls_loss': cls_loss,\\n 'loc_loss': loc_loss\\n }\\n\\n return outputs\",\n \"def step(self):\\n # Fast learning\\n task_embedding = self._ilp.infer_task()\\n\\n # Posterior update\\n #self._skip_flag = self._is_graph_same(task_embedding, self._prev_task_embedding)\\n self._skip_flag = False # XXX do not skip test\\n if not self._skip_flag:\\n self._grprop.observe_task(task_embedding)\\n self._prev_task_embedding = task_embedding\\n else:\\n print(\\\"skipping!\\\")\",\n \"def dist_collect(x):\\n x = x.contiguous()\\n out_list = [torch.zeros_like(x, device=x.device, dtype=x.dtype).contiguous() for _ in range(dist.get_world_size())]\\n out_list = diff_dist.all_gather(out_list, x)\\n return torch.cat(out_list, dim=0).contiguous()\",\n \"def train(self, trainData):\\n pass\",\n \"def forward(self, x):\\n out = self.features(x)\\n out = self.avgpool(out)\\n out = torch.flatten(out, 1)\\n out = self.classifier(out)\\n return out\",\n \"def forward(self, x):\\n out = self.features(x)\\n out = self.avgpool(out)\\n out = torch.flatten(out, 1)\\n out = self.classifier(out)\\n return out\",\n \"def collect_data(self):\\n categories = []\\n list_of_feature_lists = []\\n feature_sets = set()\\n with open(self.csv_path, \\\"r\\\") as f:\\n reader = csv.DictReader(f)\\n # collecting some stats for FDR calculation:\\n self.PSM_count = 0\\n self.decoy_count = 0\\n\\n if self[\\\"dump_svm_matrix\\\"]:\\n self.init_svm_matrix_dump()\\n additional_matrix_info = []\\n\\n for i, row in enumerate(\\n sorted(\\n reader,\\n reverse=self[\\\"bigger_scores_better\\\"],\\n key=lambda d: float(d[self.col_for_sorting]),\\n )\\n ):\\n\\n features = self.row_to_features(row)\\n\\n if tuple(features) in feature_sets:\\n continue\\n feature_sets.add(tuple(features))\\n\\n category, psm_FDR = self.get_psm_category(row)\\n\\n list_of_feature_lists.append(features)\\n categories.append(category)\\n\\n if self[\\\"dump_svm_matrix\\\"]:\\n label = -1 if row_is_decoy(row) else 1\\n sequence = \\\"{0}.{1}#{2}.{3}\\\".format(\\n row[\\\"Sequence Pre AA\\\"].strip(),\\n row[\\\"Sequence\\\"].strip(),\\n row[\\\"Modifications\\\"].strip(),\\n row[\\\"Sequence Post AA\\\"].strip(),\\n )\\n additional_matrix_info.append(\\n {\\n \\\"psm_id\\\": row[\\\"Spectrum Title\\\"].strip(),\\n \\\"label\\\": label,\\n \\\"scannr\\\": row[\\\"Spectrum Title\\\"].strip().split(\\\".\\\")[-2],\\n \\\"peptide\\\": sequence,\\n \\\"proteins\\\": self.parse_protein_ids(row[\\\"Protein ID\\\"]),\\n }\\n )\\n\\n if i % 1000 == 0:\\n score_val = float(row[self.col_for_sorting])\\n msg = (\\n \\\"Generating feature matrix from input csv \\\"\\n \\\"(line ~{0}) with score {1} and FDR \\\"\\n \\\"{2}\\\".format(i, score_val, psm_FDR)\\n )\\n print(msg, end=\\\"\\\\r\\\")\\n\\n # All data points are collected in one big matrix, to make standardization possible\\n print(\\\"\\\\nConverting feature matrix to NumPy array...\\\")\\n X_raw = np.array(list_of_feature_lists, dtype=float)\\n\\n print(\\\"Replacing empty/NaN values with the mean of each column...\\\")\\n self.nan_replacer = Imputer()\\n self.nan_replacer.fit(X_raw)\\n X_raw = self.nan_replacer.transform(X_raw)\\n # Standardize input matrix to ease machine learning! Scaled data has zero mean and unit variance\\n print(\\\"Standardizing input matrix...\\\")\\n self.scaler = SCALER.fit(X_raw)\\n self.X = self.scaler.transform(X_raw)\\n self.categories = np.array(categories)\\n print()\\n\\n if self[\\\"dump_svm_matrix\\\"]:\\n print(\\\"Dumping SVM matrix to\\\", self[\\\"dump_svm_matrix\\\"])\\n\\n for i, matrix_row in enumerate(self.X):\\n matrix_row_info = additional_matrix_info[i]\\n self.dump_svm_matrix_row(\\n row=list(matrix_row),\\n psm_id=matrix_row_info[\\\"psm_id\\\"],\\n label=matrix_row_info[\\\"label\\\"],\\n scannr=matrix_row_info[\\\"scannr\\\"],\\n peptide=matrix_row_info[\\\"peptide\\\"],\\n proteins=matrix_row_info[\\\"proteins\\\"],\\n )\\n\\n print(\\\"Dumped SVM matrix to\\\", self[\\\"dump_svm_matrix\\\"])\\n return\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.58938706","0.5764855","0.5659415","0.555809","0.54953295","0.54437625","0.5437285","0.54306674","0.542526","0.53984416","0.5388055","0.5355798","0.5355798","0.5281315","0.52788657","0.5276297","0.5263628","0.5263628","0.5263628","0.5263628","0.5263628","0.52610576","0.5255361","0.52548444","0.5222754","0.5222754","0.51641154","0.5162901","0.5150727","0.5130981","0.51298195","0.51294076","0.5124334","0.5118463","0.5099626","0.50976866","0.5085604","0.50595057","0.50498307","0.5035881","0.5029372","0.502452","0.50232023","0.50199044","0.5010849","0.49924675","0.49912164","0.49843988","0.49842328","0.4982627","0.49808267","0.49798146","0.49760428","0.4975581","0.49724334","0.49660206","0.49494547","0.49488354","0.49445826","0.49415582","0.49331722","0.49270764","0.49171737","0.49170506","0.4914676","0.49112022","0.49019313","0.48996672","0.4890734","0.48878288","0.48769474","0.48696962","0.48696315","0.48695672","0.48650092","0.48646107","0.48604906","0.48573166","0.4853555","0.4851237","0.4850915","0.48440942","0.48436022","0.48419732","0.48419732","0.4839946","0.48362762","0.48354313","0.48306075","0.48227954","0.48227954","0.48188087","0.48165402","0.48114306","0.48099843","0.4802123","0.4802055","0.48020035","0.48016086","0.48016086","0.48010588"],"string":"[\n \"0.58938706\",\n \"0.5764855\",\n \"0.5659415\",\n \"0.555809\",\n \"0.54953295\",\n \"0.54437625\",\n \"0.5437285\",\n \"0.54306674\",\n \"0.542526\",\n \"0.53984416\",\n \"0.5388055\",\n \"0.5355798\",\n \"0.5355798\",\n \"0.5281315\",\n \"0.52788657\",\n \"0.5276297\",\n \"0.5263628\",\n \"0.5263628\",\n \"0.5263628\",\n \"0.5263628\",\n \"0.5263628\",\n \"0.52610576\",\n \"0.5255361\",\n \"0.52548444\",\n \"0.5222754\",\n \"0.5222754\",\n \"0.51641154\",\n \"0.5162901\",\n \"0.5150727\",\n \"0.5130981\",\n \"0.51298195\",\n \"0.51294076\",\n \"0.5124334\",\n \"0.5118463\",\n \"0.5099626\",\n \"0.50976866\",\n \"0.5085604\",\n \"0.50595057\",\n \"0.50498307\",\n \"0.5035881\",\n \"0.5029372\",\n \"0.502452\",\n \"0.50232023\",\n \"0.50199044\",\n \"0.5010849\",\n \"0.49924675\",\n \"0.49912164\",\n \"0.49843988\",\n \"0.49842328\",\n \"0.4982627\",\n \"0.49808267\",\n \"0.49798146\",\n \"0.49760428\",\n \"0.4975581\",\n \"0.49724334\",\n \"0.49660206\",\n \"0.49494547\",\n \"0.49488354\",\n \"0.49445826\",\n \"0.49415582\",\n \"0.49331722\",\n \"0.49270764\",\n \"0.49171737\",\n \"0.49170506\",\n \"0.4914676\",\n \"0.49112022\",\n \"0.49019313\",\n \"0.48996672\",\n \"0.4890734\",\n \"0.48878288\",\n \"0.48769474\",\n \"0.48696962\",\n \"0.48696315\",\n \"0.48695672\",\n \"0.48650092\",\n \"0.48646107\",\n \"0.48604906\",\n \"0.48573166\",\n \"0.4853555\",\n \"0.4851237\",\n \"0.4850915\",\n \"0.48440942\",\n \"0.48436022\",\n \"0.48419732\",\n \"0.48419732\",\n \"0.4839946\",\n \"0.48362762\",\n \"0.48354313\",\n \"0.48306075\",\n \"0.48227954\",\n \"0.48227954\",\n \"0.48188087\",\n \"0.48165402\",\n \"0.48114306\",\n \"0.48099843\",\n \"0.4802123\",\n \"0.4802055\",\n \"0.48020035\",\n \"0.48016086\",\n \"0.48016086\",\n \"0.48010588\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":22490,"cells":{"query":{"kind":"string","value":"Get number of real and estimated state cooccurances."},"document":{"kind":"string","value":"def get_co_occ_matrix(res, last_n, s_real, s_hc):\n\n s = np.array(res['s'])[-last_n:]\n hc_ml = np.array(res['hc_ml'])[-last_n:]\n co_occs = np.zeros((len(s_hc), len(s_real)))\n for si, hc_mli in zip(s, hc_ml):\n co_occs[s_hc.index(hc_mli), s_real.index(si)] += 1\n co_occs /= co_occs.sum()\n\n return co_occs"},"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 constituent_count(self):\n return self._constituent_count","def num_conll(self):\n pass","def StateCounts(self):\r\n\t\treturn self._get_attribute('stateCounts')","def num_carns(self):\n return self._num_carns","def number_of_constituents(bc_class):\n num_trn = 0\n cn = bc_class.constituent_properties\n if cn.salinity:\n num_trn += 1\n if cn.temperature:\n num_trn += 1\n if cn.vorticity:\n num_trn += 1\n if not cn.general_constituents.empty:\n num_trn += len(cn.general_constituents.index)\n if not cn.sand.empty:\n num_trn += len(cn.sand.index)\n if not cn.clay.empty:\n num_trn += len(cn.clay.index)\n return num_trn","def n_cs(self):\n return np.size(self._cs, 0)","def get_num_states(self):\n return self.N_STATES","def state_summary(self) -> np.ndarray:\n return np.array([len(self.current_clear_nodes), self.n_current_infected, len(self.current_isolated_nodes),\n len(self.current_immune_nodes), len(self.current_alive_nodes)])","def get_num_states(self):\n return self.n_states","def state_count(self):\n\t\tcstate = []\n\t\tfor i in self.state:\n\t\t\tcstate.append(len(i))\n\t\treturn cstate","def count(self):\n nreq, nres = 0, 0\n for entry in self.__history:\n if entry.oreq is not None:\n nreq += 1\n if entry.ores is not None:\n nres += 1\n return nreq, nres","def test_getObservationCount(self):\r\n # Verified with iNEXT.\r\n self.assertEqual(self.est1.getObservationCount(), 5)\r\n\r\n # Verified against results in Colwell 2012 paper.\r\n self.assertEqual(self.est2.getObservationCount(), 140)\r\n self.assertEqual(self.est3.getObservationCount(), 112)","def numStates(self):\n return len(self._mdp.S)","def getObservationCount(self):\r\n return self._s_obs","def __len__(self):\n i = 0\n for S in self.states():\n i += 1\n return i","def resultCounter(detections):\n counter = 0\n for attribute, value in classIterator(detections):\n if 'crease' in attribute:\n counter += len(value)\n return counter","def n_cf(self):\n return self.meta.n_caps - self.n_cs","def num_arcs(self):\n return sum(len(state) for state in self)","def count_compounds(self) -> int:\n return self._count_model(Compound)","def _get_observation_count(self):\n observation_count = 0\n for sequence in self.seq_list:\n observation_count += sequence.shape[0] \n \n return observation_count","def StateCounts(self):\n return self._get_attribute('stateCounts')","def getTotalIndividualCount(self):\r\n return self._n","def current_nbc_coverage():\n covered = 0\n total = 0\n for layer in layer_to_compute:\n covered = covered + np.count_nonzero(nbc_cov_dict[layer.name])\n total = total + np.size(nbc_cov_dict[layer.name])\n return covered / float(total)","def coherence(self):\r\n return np.abs(self.coherency) ** 2","def counts(self):\n return sum(self.counter.values()), len(self.visited)","def counts(self):\n return sum(self.counter.values()), len(self.visited)","def coherence(self):\r\n coherence = np.abs(self.coherency ** 2)\r\n\r\n return coherence","def count_neighbourhood_states(self,state):\n\t\tassert len(state) > 0\n\t\tenergies = [float(\"inf\") for i in range (self._size)]\n\t\ttaxes = [float(\"inf\") for i in range (self._size)]\n\n\t\tfor i in range (0,self._size):\n\t\t\tnewState = copy(state)\n\t\t\tnewState[i] = 1 - newState[i]\n\t\t\tenergies[i] = self.count_energy(newState)\n\t\t\ttaxes[i] = self.count_tax(newState)\n\n\t\treturn energies,taxes","def n_good_features_(self):\n return np.sum(self.important_features_)","def get_number_of_atoms_to_optimize(self):\n v = self.c.get(simulation_cell=True)\n return len(v.data.stoichiometry)","def get_active_coeff(self):\r\n num_active_coeff = 0\r\n for coefficient in self.model_.coef_:\r\n if abs(coefficient) > 0:\r\n num_active_coeff += 1\r\n return num_active_coeff","def n_states(self):\n if self._thermodynamic_states is None:\n return 0\n else:\n return len(self._thermodynamic_states)","def get_num_of_states_and_facts(self):\n\n public_predicates = {}\n private_predicates = {}\n sum_of_facts = 0\n\n for domain in self._domains:\n\n problem = list(filter(lambda prob: prob.domain == domain.name, self._problems))[0]\n last_index = problem.name.rfind('-')\n agent_name = problem.name[last_index + 1:]\n\n for predicate in domain.predicates:\n\n predicate_has_private_arg = False\n arg_types = list(map(lambda arg: Agent.get_all_subtypes(arg.type, domain.type_hierarchy),\n predicate.args))\n ordered_objs = []\n\n # go over each arg type (type of first param, second param ...)\n for possible_types in arg_types:\n objs_of_subtype = []\n\n # for each arg add all the types it can be as a list of types\n for type in possible_types:\n if problem.objects.__contains__(type):\n if problem.objects[type][0].private:\n predicate_has_private_arg = True\n objs_of_subtype += problem.objects[type]\n\n ordered_objs.append(objs_of_subtype)\n\n param_combinations = list(itertools.product(*ordered_objs))\n\n if (predicate.is_private or predicate_has_private_arg) and predicate.name not in private_predicates:\n private_predicates[predicate.name + '-' + agent_name] = len(param_combinations)\n sum_of_facts += len(param_combinations)\n elif predicate.name not in public_predicates:\n public_predicates[predicate.name] = len(param_combinations)\n sum_of_facts += len(param_combinations)\n\n total_assignments = list(public_predicates.values()) + list(private_predicates.values())\n num_of_states = reduce(lambda a, b: a * b, total_assignments, 1)\n return num_of_states, sum_of_facts","def _get_concentration(self, state):\n return self.fc(state.float_features).exp() + self.EPSILON","def n_cf(self):\n return np.size(self._ref_ii, 0)","def estimateUnobservedObservationCount(self):\r\n return self._f_hat","def count_indications(self) -> int:\n return self._count_model(Indication)","def state_size(self):\n # estimated state, its covariance, and the step number\n return [[self.dim_x], [self.dim_x * self.dim_x], [1]]","def num_cones(self):\n return self._shape_count(_sff.cone)","def getNumberOfEvents(self):\n whereClause = \"ecc_id = 1 and r_power = 0 and n = 2\"\n Nevent = self.db.selectFromTable(\"eccentricities\", \"count()\", whereClause)\n return Nevent[0][0]","def count(self):\n # TODO not implemented yet\n return 0","def n(self):\n return sum(self._comp.values())","def count_codon_all(self):\n return Counter(list(self))","def _numconditions(self, node_p):\n node_p = self.getnodenamed(node_p) # Verify pointer.\n parents = self.getnodeparents(node_p) # nl_p\n nparents = self.lengthnodelist(parents) # integer\n # Get the number of states in each parent node, multiply them\n npstates = 1\n for idx in range(nparents):\n node_i = self.nthnode(parents, idx)\n nstates_i = self.getnodenumberstates(node_i)\n npstates *= nstates_i\n\n return npstates","def carn_count(self):\n return len(self.carnivores)","def count_standard_residues(self):\n n = 0\n for na in self.iter_standard_residues():\n n += 1\n return n","def cumulativeCadenceRevolutionCount(self):\n return (self.raw[4] << 8) | self.raw[3]","def get_num_us_states(self):\n return len(self.us_state_city_mapper.keys())","def N(self):\n return len(self.cavity_grid.cavities) + 1","def tally(self):\n return self.count","def cardinality(self):\n estimate = self._alpha * math.pow(self._m, 2) / sum(math.pow(2, -x) for x in self._registers)\n\n if estimate <= 2.5 * self._m:\n # get number of registers equal to zero\n empty_registers = self._registers.count(0)\n if empty_registers != 0:\n return self._linear_count(empty_registers)\n else:\n return estimate\n elif estimate <= ((1 << 32) / 30):\n return estimate\n else:\n return self._large_range_correction(estimate)","def n_evidence(self):\n return self.state.get('n_evidence', 0)","def _n_spin_states(self) -> int:\n if self.spin is None:\n raise Exception(\n \"cannot request number of spin states for spinless fermions\"\n )\n return round(2 * self.spin + 1)","def aquire_cnts(self,n):\n return self.cnts.get_n(n)","def num_energy_states(self) -> int:\n return self._num_energy_states","def count(self):\n return sum([self.bits[x][y] for x in range(self.n_rows)\n for y in range(self.n_columns)])","def NumCoefficients(self):\n return nchoosek(self.degree + self.dimension, self.degree, exact=True)","def estimateFullRichness(self):\r\n # S_est = S_obs + f_hat\r\n return self.getObservationCount() + \\\r\n self.estimateUnobservedObservationCount()","def total_count(self):\n return self.applied_count + self.error_count","def count(self):\n return self._reduce_for_stat_function(F.count, only_numeric=False)","def number_of_carnivores_island(self):\n return np.sum(self.carnivores_on_island)","def get_count(self):\n\n\t\treturn self.__count","def getEmpiricalCounts(self):\n return self.empirical_counts","def get_state_num(self):\n robot_state = self.get_state('turtlebot3_waffle_pi','world')\n ball_state = self.get_state('soccer_ball','world')\n # each object is in a \"box\" that is RESOLUTION meters wide.\n robot_xbox = np.ceil((robot_state.pose.position.x-Learn.FIELD_XLEFT)/Learn.RESOLUTION)\n robot_ybox = np.ceil(robot_state.pose.position.y/Learn.RESOLUTION)\n ball_xbox = np.ceil((ball_state.pose.position.x-Learn.FIELD_XLEFT)/Learn.RESOLUTION)\n ball_ybox = np.ceil(ball_state.pose.position.y/Learn.RESOLUTION)\n # the state is the combination of dx and dy.\n dx = int(ball_xbox - robot_xbox)\n dy = int(ball_ybox - robot_ybox)\n # adjusting to remove negative values for states\n dx += Learn.BOXES_X-1\n dy += Learn.BOXES_Y-1\n # converting to unique number between 0 and NSTATES-1:\n return (2*Learn.BOXES_X-1)*dy+dx","def coherency(self):\r\n coherency = tsa.cache_to_coherency(self.cache, self.ij)\r\n\r\n return coherency","def num_considered(self):\n return self._current","def count(self):\r\n return self.count_helper(self.top_node)","def test_getTotalIndividualCount(self):\r\n # Verified with iNEXT.\r\n self.assertEqual(self.est1.getTotalIndividualCount(), 15)\r\n\r\n # Verified against results in Colwell 2012 paper.\r\n self.assertEqual(self.est2.getTotalIndividualCount(), 976)\r\n self.assertEqual(self.est3.getTotalIndividualCount(), 237)","def number_of_iterations(self):\n return self._solution.nit","def count(self):\n return self.get_count()","def get_num_arcs(self):\n num_arcs = 0\n for node in self._nodes.values(): num_arcs += node._deg\n return (num_arcs / 2) + 1","def get_num_arcs(self):\n num_arcs = 0\n for node in self._nodes.values(): num_arcs += node._deg\n return (num_arcs / 2) + 1","def getdecisionCounts(decision):\r\n countY = 0; countN = 0\r\n for row in decision:\r\n # in our dataset format, the label is always the last column\r\n if row == 'No':\r\n countN += 1\r\n else :\r\n countY += 1\r\n return countY, countN","def current_knc_coverage():\n covered = 0\n total = 0\n for layer in layer_to_compute:\n covered = covered + np.count_nonzero(knc_cov_dict[layer.name])\n total = total + np.size(knc_cov_dict[layer.name])\n return covered / float(total)","def count_energy(self,state):\n\t\tassert len(state) == self._size\n\n\t\ttmp = 0\n\t\tfor i in range(self._size):\n\t\t\tfor j in range(self._size):\n\t\t\t\ttmp += self.myWeights[i][j]* state [i] * state [j]\n\t\treturn tmp - self.myB * sum(state)","def get_count(self):\r\n return self.count","def observation_count(self):\n if not self.can_update():\n self._handle_error(910, [self.type])\n return self.tc_requests.observation_count(\n self.api_type, self.api_branch, self.unique_id, owner=self.owner\n )","def count(self) -> float:\n return pulumi.get(self, \"count\")","def numnems(self):\n count = 0\n for o in self._objs.values():\n count += len(o.netifs())\n return count","def valency(self):\n return len(self.neighbors())","def count(self):\n return sum(1 for _ in self)","def count(self):\n return len(self._components)","def get_count(self):\n return self.count","def get_count(self):\n return self.count","def Count(self) -> int:","def Count(self) -> int:","def Count(self) -> int:","def Count(self) -> int:","def get_number_of_cows(self):\n bulls = self.get_number_of_bulls()\n list_of_cows = set(self.puzzle) & set(self.guess)\n cows = (len(list_of_cows) - bulls)\n return cows","def get_counts(self):\n counts = [0, 0]\n for i in range(self._num_rows):\n for j in range(self._num_cols):\n if self._board[i][j] == \"B\":\n counts[0] += 1\n elif self._board[i][j] == \"W\":\n counts[1] += 1\n return counts","def count(self):\n return self._count","def count(self):\n return self._count","def count(self):\n return self._count","def count(self):\n return self._count","def count(self):\n return self._count","def count(self):\n return self._count","def count(self):\n return self._count","def count(self):\n return self._count","def count(self):\n return self._count","def count(self):\n return self._count","def occurs(self) -> int:\n return self._occurs"],"string":"[\n \"def constituent_count(self):\\n return self._constituent_count\",\n \"def num_conll(self):\\n pass\",\n \"def StateCounts(self):\\r\\n\\t\\treturn self._get_attribute('stateCounts')\",\n \"def num_carns(self):\\n return self._num_carns\",\n \"def number_of_constituents(bc_class):\\n num_trn = 0\\n cn = bc_class.constituent_properties\\n if cn.salinity:\\n num_trn += 1\\n if cn.temperature:\\n num_trn += 1\\n if cn.vorticity:\\n num_trn += 1\\n if not cn.general_constituents.empty:\\n num_trn += len(cn.general_constituents.index)\\n if not cn.sand.empty:\\n num_trn += len(cn.sand.index)\\n if not cn.clay.empty:\\n num_trn += len(cn.clay.index)\\n return num_trn\",\n \"def n_cs(self):\\n return np.size(self._cs, 0)\",\n \"def get_num_states(self):\\n return self.N_STATES\",\n \"def state_summary(self) -> np.ndarray:\\n return np.array([len(self.current_clear_nodes), self.n_current_infected, len(self.current_isolated_nodes),\\n len(self.current_immune_nodes), len(self.current_alive_nodes)])\",\n \"def get_num_states(self):\\n return self.n_states\",\n \"def state_count(self):\\n\\t\\tcstate = []\\n\\t\\tfor i in self.state:\\n\\t\\t\\tcstate.append(len(i))\\n\\t\\treturn cstate\",\n \"def count(self):\\n nreq, nres = 0, 0\\n for entry in self.__history:\\n if entry.oreq is not None:\\n nreq += 1\\n if entry.ores is not None:\\n nres += 1\\n return nreq, nres\",\n \"def test_getObservationCount(self):\\r\\n # Verified with iNEXT.\\r\\n self.assertEqual(self.est1.getObservationCount(), 5)\\r\\n\\r\\n # Verified against results in Colwell 2012 paper.\\r\\n self.assertEqual(self.est2.getObservationCount(), 140)\\r\\n self.assertEqual(self.est3.getObservationCount(), 112)\",\n \"def numStates(self):\\n return len(self._mdp.S)\",\n \"def getObservationCount(self):\\r\\n return self._s_obs\",\n \"def __len__(self):\\n i = 0\\n for S in self.states():\\n i += 1\\n return i\",\n \"def resultCounter(detections):\\n counter = 0\\n for attribute, value in classIterator(detections):\\n if 'crease' in attribute:\\n counter += len(value)\\n return counter\",\n \"def n_cf(self):\\n return self.meta.n_caps - self.n_cs\",\n \"def num_arcs(self):\\n return sum(len(state) for state in self)\",\n \"def count_compounds(self) -> int:\\n return self._count_model(Compound)\",\n \"def _get_observation_count(self):\\n observation_count = 0\\n for sequence in self.seq_list:\\n observation_count += sequence.shape[0] \\n \\n return observation_count\",\n \"def StateCounts(self):\\n return self._get_attribute('stateCounts')\",\n \"def getTotalIndividualCount(self):\\r\\n return self._n\",\n \"def current_nbc_coverage():\\n covered = 0\\n total = 0\\n for layer in layer_to_compute:\\n covered = covered + np.count_nonzero(nbc_cov_dict[layer.name])\\n total = total + np.size(nbc_cov_dict[layer.name])\\n return covered / float(total)\",\n \"def coherence(self):\\r\\n return np.abs(self.coherency) ** 2\",\n \"def counts(self):\\n return sum(self.counter.values()), len(self.visited)\",\n \"def counts(self):\\n return sum(self.counter.values()), len(self.visited)\",\n \"def coherence(self):\\r\\n coherence = np.abs(self.coherency ** 2)\\r\\n\\r\\n return coherence\",\n \"def count_neighbourhood_states(self,state):\\n\\t\\tassert len(state) > 0\\n\\t\\tenergies = [float(\\\"inf\\\") for i in range (self._size)]\\n\\t\\ttaxes = [float(\\\"inf\\\") for i in range (self._size)]\\n\\n\\t\\tfor i in range (0,self._size):\\n\\t\\t\\tnewState = copy(state)\\n\\t\\t\\tnewState[i] = 1 - newState[i]\\n\\t\\t\\tenergies[i] = self.count_energy(newState)\\n\\t\\t\\ttaxes[i] = self.count_tax(newState)\\n\\n\\t\\treturn energies,taxes\",\n \"def n_good_features_(self):\\n return np.sum(self.important_features_)\",\n \"def get_number_of_atoms_to_optimize(self):\\n v = self.c.get(simulation_cell=True)\\n return len(v.data.stoichiometry)\",\n \"def get_active_coeff(self):\\r\\n num_active_coeff = 0\\r\\n for coefficient in self.model_.coef_:\\r\\n if abs(coefficient) > 0:\\r\\n num_active_coeff += 1\\r\\n return num_active_coeff\",\n \"def n_states(self):\\n if self._thermodynamic_states is None:\\n return 0\\n else:\\n return len(self._thermodynamic_states)\",\n \"def get_num_of_states_and_facts(self):\\n\\n public_predicates = {}\\n private_predicates = {}\\n sum_of_facts = 0\\n\\n for domain in self._domains:\\n\\n problem = list(filter(lambda prob: prob.domain == domain.name, self._problems))[0]\\n last_index = problem.name.rfind('-')\\n agent_name = problem.name[last_index + 1:]\\n\\n for predicate in domain.predicates:\\n\\n predicate_has_private_arg = False\\n arg_types = list(map(lambda arg: Agent.get_all_subtypes(arg.type, domain.type_hierarchy),\\n predicate.args))\\n ordered_objs = []\\n\\n # go over each arg type (type of first param, second param ...)\\n for possible_types in arg_types:\\n objs_of_subtype = []\\n\\n # for each arg add all the types it can be as a list of types\\n for type in possible_types:\\n if problem.objects.__contains__(type):\\n if problem.objects[type][0].private:\\n predicate_has_private_arg = True\\n objs_of_subtype += problem.objects[type]\\n\\n ordered_objs.append(objs_of_subtype)\\n\\n param_combinations = list(itertools.product(*ordered_objs))\\n\\n if (predicate.is_private or predicate_has_private_arg) and predicate.name not in private_predicates:\\n private_predicates[predicate.name + '-' + agent_name] = len(param_combinations)\\n sum_of_facts += len(param_combinations)\\n elif predicate.name not in public_predicates:\\n public_predicates[predicate.name] = len(param_combinations)\\n sum_of_facts += len(param_combinations)\\n\\n total_assignments = list(public_predicates.values()) + list(private_predicates.values())\\n num_of_states = reduce(lambda a, b: a * b, total_assignments, 1)\\n return num_of_states, sum_of_facts\",\n \"def _get_concentration(self, state):\\n return self.fc(state.float_features).exp() + self.EPSILON\",\n \"def n_cf(self):\\n return np.size(self._ref_ii, 0)\",\n \"def estimateUnobservedObservationCount(self):\\r\\n return self._f_hat\",\n \"def count_indications(self) -> int:\\n return self._count_model(Indication)\",\n \"def state_size(self):\\n # estimated state, its covariance, and the step number\\n return [[self.dim_x], [self.dim_x * self.dim_x], [1]]\",\n \"def num_cones(self):\\n return self._shape_count(_sff.cone)\",\n \"def getNumberOfEvents(self):\\n whereClause = \\\"ecc_id = 1 and r_power = 0 and n = 2\\\"\\n Nevent = self.db.selectFromTable(\\\"eccentricities\\\", \\\"count()\\\", whereClause)\\n return Nevent[0][0]\",\n \"def count(self):\\n # TODO not implemented yet\\n return 0\",\n \"def n(self):\\n return sum(self._comp.values())\",\n \"def count_codon_all(self):\\n return Counter(list(self))\",\n \"def _numconditions(self, node_p):\\n node_p = self.getnodenamed(node_p) # Verify pointer.\\n parents = self.getnodeparents(node_p) # nl_p\\n nparents = self.lengthnodelist(parents) # integer\\n # Get the number of states in each parent node, multiply them\\n npstates = 1\\n for idx in range(nparents):\\n node_i = self.nthnode(parents, idx)\\n nstates_i = self.getnodenumberstates(node_i)\\n npstates *= nstates_i\\n\\n return npstates\",\n \"def carn_count(self):\\n return len(self.carnivores)\",\n \"def count_standard_residues(self):\\n n = 0\\n for na in self.iter_standard_residues():\\n n += 1\\n return n\",\n \"def cumulativeCadenceRevolutionCount(self):\\n return (self.raw[4] << 8) | self.raw[3]\",\n \"def get_num_us_states(self):\\n return len(self.us_state_city_mapper.keys())\",\n \"def N(self):\\n return len(self.cavity_grid.cavities) + 1\",\n \"def tally(self):\\n return self.count\",\n \"def cardinality(self):\\n estimate = self._alpha * math.pow(self._m, 2) / sum(math.pow(2, -x) for x in self._registers)\\n\\n if estimate <= 2.5 * self._m:\\n # get number of registers equal to zero\\n empty_registers = self._registers.count(0)\\n if empty_registers != 0:\\n return self._linear_count(empty_registers)\\n else:\\n return estimate\\n elif estimate <= ((1 << 32) / 30):\\n return estimate\\n else:\\n return self._large_range_correction(estimate)\",\n \"def n_evidence(self):\\n return self.state.get('n_evidence', 0)\",\n \"def _n_spin_states(self) -> int:\\n if self.spin is None:\\n raise Exception(\\n \\\"cannot request number of spin states for spinless fermions\\\"\\n )\\n return round(2 * self.spin + 1)\",\n \"def aquire_cnts(self,n):\\n return self.cnts.get_n(n)\",\n \"def num_energy_states(self) -> int:\\n return self._num_energy_states\",\n \"def count(self):\\n return sum([self.bits[x][y] for x in range(self.n_rows)\\n for y in range(self.n_columns)])\",\n \"def NumCoefficients(self):\\n return nchoosek(self.degree + self.dimension, self.degree, exact=True)\",\n \"def estimateFullRichness(self):\\r\\n # S_est = S_obs + f_hat\\r\\n return self.getObservationCount() + \\\\\\r\\n self.estimateUnobservedObservationCount()\",\n \"def total_count(self):\\n return self.applied_count + self.error_count\",\n \"def count(self):\\n return self._reduce_for_stat_function(F.count, only_numeric=False)\",\n \"def number_of_carnivores_island(self):\\n return np.sum(self.carnivores_on_island)\",\n \"def get_count(self):\\n\\n\\t\\treturn self.__count\",\n \"def getEmpiricalCounts(self):\\n return self.empirical_counts\",\n \"def get_state_num(self):\\n robot_state = self.get_state('turtlebot3_waffle_pi','world')\\n ball_state = self.get_state('soccer_ball','world')\\n # each object is in a \\\"box\\\" that is RESOLUTION meters wide.\\n robot_xbox = np.ceil((robot_state.pose.position.x-Learn.FIELD_XLEFT)/Learn.RESOLUTION)\\n robot_ybox = np.ceil(robot_state.pose.position.y/Learn.RESOLUTION)\\n ball_xbox = np.ceil((ball_state.pose.position.x-Learn.FIELD_XLEFT)/Learn.RESOLUTION)\\n ball_ybox = np.ceil(ball_state.pose.position.y/Learn.RESOLUTION)\\n # the state is the combination of dx and dy.\\n dx = int(ball_xbox - robot_xbox)\\n dy = int(ball_ybox - robot_ybox)\\n # adjusting to remove negative values for states\\n dx += Learn.BOXES_X-1\\n dy += Learn.BOXES_Y-1\\n # converting to unique number between 0 and NSTATES-1:\\n return (2*Learn.BOXES_X-1)*dy+dx\",\n \"def coherency(self):\\r\\n coherency = tsa.cache_to_coherency(self.cache, self.ij)\\r\\n\\r\\n return coherency\",\n \"def num_considered(self):\\n return self._current\",\n \"def count(self):\\r\\n return self.count_helper(self.top_node)\",\n \"def test_getTotalIndividualCount(self):\\r\\n # Verified with iNEXT.\\r\\n self.assertEqual(self.est1.getTotalIndividualCount(), 15)\\r\\n\\r\\n # Verified against results in Colwell 2012 paper.\\r\\n self.assertEqual(self.est2.getTotalIndividualCount(), 976)\\r\\n self.assertEqual(self.est3.getTotalIndividualCount(), 237)\",\n \"def number_of_iterations(self):\\n return self._solution.nit\",\n \"def count(self):\\n return self.get_count()\",\n \"def get_num_arcs(self):\\n num_arcs = 0\\n for node in self._nodes.values(): num_arcs += node._deg\\n return (num_arcs / 2) + 1\",\n \"def get_num_arcs(self):\\n num_arcs = 0\\n for node in self._nodes.values(): num_arcs += node._deg\\n return (num_arcs / 2) + 1\",\n \"def getdecisionCounts(decision):\\r\\n countY = 0; countN = 0\\r\\n for row in decision:\\r\\n # in our dataset format, the label is always the last column\\r\\n if row == 'No':\\r\\n countN += 1\\r\\n else :\\r\\n countY += 1\\r\\n return countY, countN\",\n \"def current_knc_coverage():\\n covered = 0\\n total = 0\\n for layer in layer_to_compute:\\n covered = covered + np.count_nonzero(knc_cov_dict[layer.name])\\n total = total + np.size(knc_cov_dict[layer.name])\\n return covered / float(total)\",\n \"def count_energy(self,state):\\n\\t\\tassert len(state) == self._size\\n\\n\\t\\ttmp = 0\\n\\t\\tfor i in range(self._size):\\n\\t\\t\\tfor j in range(self._size):\\n\\t\\t\\t\\ttmp += self.myWeights[i][j]* state [i] * state [j]\\n\\t\\treturn tmp - self.myB * sum(state)\",\n \"def get_count(self):\\r\\n return self.count\",\n \"def observation_count(self):\\n if not self.can_update():\\n self._handle_error(910, [self.type])\\n return self.tc_requests.observation_count(\\n self.api_type, self.api_branch, self.unique_id, owner=self.owner\\n )\",\n \"def count(self) -> float:\\n return pulumi.get(self, \\\"count\\\")\",\n \"def numnems(self):\\n count = 0\\n for o in self._objs.values():\\n count += len(o.netifs())\\n return count\",\n \"def valency(self):\\n return len(self.neighbors())\",\n \"def count(self):\\n return sum(1 for _ in self)\",\n \"def count(self):\\n return len(self._components)\",\n \"def get_count(self):\\n return self.count\",\n \"def get_count(self):\\n return self.count\",\n \"def Count(self) -> int:\",\n \"def Count(self) -> int:\",\n \"def Count(self) -> int:\",\n \"def Count(self) -> int:\",\n \"def get_number_of_cows(self):\\n bulls = self.get_number_of_bulls()\\n list_of_cows = set(self.puzzle) & set(self.guess)\\n cows = (len(list_of_cows) - bulls)\\n return cows\",\n \"def get_counts(self):\\n counts = [0, 0]\\n for i in range(self._num_rows):\\n for j in range(self._num_cols):\\n if self._board[i][j] == \\\"B\\\":\\n counts[0] += 1\\n elif self._board[i][j] == \\\"W\\\":\\n counts[1] += 1\\n return counts\",\n \"def count(self):\\n return self._count\",\n \"def count(self):\\n return self._count\",\n \"def count(self):\\n return self._count\",\n \"def count(self):\\n return self._count\",\n \"def count(self):\\n return self._count\",\n \"def count(self):\\n return self._count\",\n \"def count(self):\\n return self._count\",\n \"def count(self):\\n return self._count\",\n \"def count(self):\\n return self._count\",\n \"def count(self):\\n return self._count\",\n \"def occurs(self) -> int:\\n return self._occurs\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.72566885","0.6694087","0.6514297","0.65112036","0.64747995","0.6469168","0.64518386","0.64387137","0.64271784","0.64253205","0.6421117","0.64210594","0.6419776","0.64074904","0.6388321","0.63843083","0.6365295","0.63594586","0.63399863","0.63103896","0.63025224","0.62935895","0.62818754","0.6271979","0.6271652","0.6271652","0.6269048","0.626581","0.626356","0.6261705","0.6258155","0.62464595","0.6244597","0.62379515","0.6222799","0.6215282","0.62096006","0.6205041","0.6203388","0.61766285","0.6170001","0.61598736","0.61489964","0.6147514","0.6143505","0.61356205","0.61148125","0.61103815","0.6109255","0.6106708","0.60967225","0.6085998","0.6023931","0.60228115","0.6016199","0.6016099","0.60052896","0.60006016","0.59955317","0.59894806","0.59872097","0.5981282","0.5968982","0.59680086","0.5965927","0.593175","0.5927636","0.5923799","0.59231675","0.59201264","0.5916465","0.5916465","0.591295","0.59037524","0.5893247","0.5891538","0.5891329","0.5880272","0.5864273","0.58581847","0.5857997","0.5854582","0.5851879","0.5851879","0.58490676","0.58490676","0.58490676","0.58490676","0.58459175","0.584267","0.5841645","0.5841645","0.5841645","0.5841645","0.5841645","0.5841645","0.5841645","0.5841645","0.5841645","0.5841645","0.5841217"],"string":"[\n \"0.72566885\",\n \"0.6694087\",\n \"0.6514297\",\n \"0.65112036\",\n \"0.64747995\",\n \"0.6469168\",\n \"0.64518386\",\n \"0.64387137\",\n \"0.64271784\",\n \"0.64253205\",\n \"0.6421117\",\n \"0.64210594\",\n \"0.6419776\",\n \"0.64074904\",\n \"0.6388321\",\n \"0.63843083\",\n \"0.6365295\",\n \"0.63594586\",\n \"0.63399863\",\n \"0.63103896\",\n \"0.63025224\",\n \"0.62935895\",\n \"0.62818754\",\n \"0.6271979\",\n \"0.6271652\",\n \"0.6271652\",\n \"0.6269048\",\n \"0.626581\",\n \"0.626356\",\n \"0.6261705\",\n \"0.6258155\",\n \"0.62464595\",\n \"0.6244597\",\n \"0.62379515\",\n \"0.6222799\",\n \"0.6215282\",\n \"0.62096006\",\n \"0.6205041\",\n \"0.6203388\",\n \"0.61766285\",\n \"0.6170001\",\n \"0.61598736\",\n \"0.61489964\",\n \"0.6147514\",\n \"0.6143505\",\n \"0.61356205\",\n \"0.61148125\",\n \"0.61103815\",\n \"0.6109255\",\n \"0.6106708\",\n \"0.60967225\",\n \"0.6085998\",\n \"0.6023931\",\n \"0.60228115\",\n \"0.6016199\",\n \"0.6016099\",\n \"0.60052896\",\n \"0.60006016\",\n \"0.59955317\",\n \"0.59894806\",\n \"0.59872097\",\n \"0.5981282\",\n \"0.5968982\",\n \"0.59680086\",\n \"0.5965927\",\n \"0.593175\",\n \"0.5927636\",\n \"0.5923799\",\n \"0.59231675\",\n \"0.59201264\",\n \"0.5916465\",\n \"0.5916465\",\n \"0.591295\",\n \"0.59037524\",\n \"0.5893247\",\n \"0.5891538\",\n \"0.5891329\",\n \"0.5880272\",\n \"0.5864273\",\n \"0.58581847\",\n \"0.5857997\",\n \"0.5854582\",\n \"0.5851879\",\n \"0.5851879\",\n \"0.58490676\",\n \"0.58490676\",\n \"0.58490676\",\n \"0.58490676\",\n \"0.58459175\",\n \"0.584267\",\n \"0.5841645\",\n \"0.5841645\",\n \"0.5841645\",\n \"0.5841645\",\n \"0.5841645\",\n \"0.5841645\",\n \"0.5841645\",\n \"0.5841645\",\n \"0.5841645\",\n \"0.5841645\",\n \"0.5841217\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":22491,"cells":{"query":{"kind":"string","value":"Return learned HC state order from cooccurance matrix by taking best matching pairs world location HC state pairs."},"document":{"kind":"string","value":"def get_s_order(co_occs, s_hc=None):\n\n # Greedy approach: just go through items from max to min.\n free_rows, free_cols = [list(range(n)) for n in co_occs.shape]\n s_ord = -np.ones(co_occs.shape[0], dtype=int)\n\n co_normed = norm_co_occ_matrix(co_occs)\n isrtd = np.unravel_index(co_normed.argsort(axis=None)[::-1], co_occs.shape)\n for irow, icol in zip(isrtd[0], isrtd[1]):\n # If neither row nor column has been taken yet, it's a match!\n if irow in free_rows and icol in free_cols:\n s_ord[icol] = irow\n free_rows.remove(irow)\n free_cols.remove(icol)\n if not len(free_rows) or not len(free_cols):\n break\n\n # Unmatched HC states go to the end.\n s_ord[s_ord == -1] = free_rows\n\n # Also sort state name list, if provided.\n s_name_srtd = np.array(s_hc)[s_ord] if s_hc is not None else None\n\n return s_ord, s_name_srtd"},"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 calc_nearest_state(self): # TODO: Check if we need here state, instead of self.state\n self.stateC = self.toConceptual(self.state)\n CTP, winners = self.find_winner()\n\n state_name = self.find_TPname(filleridx=winners)\n binding = self.find_symBinding(filleridx=winners)\n state_num = self.find_TPnum(stateName=state_name)\n TP_state = self.TP.matmul(fortran_reshape(CTP, (torch.numel(CTP), 1)))\n Cdist = torch.norm(CTP - self.stateC, p='fro') # Frobenius Norm\n Sdist = self.L2norm(TP_state - self.state)\n TP_h = self.calc_harmony(state=TP_state)\n\n return TP_state, winners, state_name, binding, Cdist, Sdist, state_num, TP_h","def find_winner(self):\n\n # Extract the max value of each col in the conceptual state matrix\n winners_idx = torch.argmax(self.stateC, dim=0)\n\n M = torch.zeros_like(self.stateC)\n # Populate the matrix\n for r in range(M.shape[1]):\n M[winners_idx[r].item(), r] = 1\n return M, winners_idx","def computer_driver_heuristic(self, pc):\n if pc == self.departure_location:\n return self.nearest_neigbor(pc)\n else:\n # encode state: State -> Generalized One hot vector\n # print(len(self.idx_to_pc)+1)\n encoded_vector = np.zeros(len(self.idx_to_pc)+1)\n\n # indices of locations FOR ENCODING\n pickup_jobs_idx = [self.pc_to_idx[p]+1 for p in list(self.state.P_k.keys())] # +1 is to make room for the time dim\n deliv_jobs_idx = [self.pc_to_idx[p]+1 for p in list(self.state.D_k.keys())]\n\n # indices of locations FOR PC READING\n pickup_jobs_idx_read = [self.pc_to_idx[p] for p in list(self.state.P_k.keys())]\n deliv_jobs_idx_read = [self.pc_to_idx[p] for p in list(self.state.D_k.keys())]\n tasks = set(pickup_jobs_idx_read + deliv_jobs_idx_read)\n\n if len(tasks) > 0:\n # set appropriate values at the index corresponding to the location\n encoded_vector[pickup_jobs_idx] = -0.5\n encoded_vector[deliv_jobs_idx] = 0.5\n encoded_vector[self.pc_to_idx[pc]+1] = 1\n\n # # current time encoded as nb of seconds between 12pm and now/nb seconds between 12pm and 12am\n total_nb_seconds = datetime.timedelta(hours=12, minutes=0)\n cur_time = self.state.t_k.time()\n cur_time = datetime.timedelta(hours=cur_time.hour, minutes=cur_time.minute) # nb of seconds from 12am\n # # TODO this can further be noramlized as most values will be >0 (>6am)\n cur_time = 2 * cur_time.seconds / total_nb_seconds.seconds - 1 # normalized time in [-1,1]\n encoded_vector[0] = cur_time\n\n # predict decision\n pred = self.heuristic_model.predict_proba(encoded_vector.reshape(1,-1))\n\n # take the most probable location among the remaining jobs\n # # set proba to 0 if location not among remaining jobs\n # print(\"##############\")\n # print(\"shape of pred \", pred.shape)\n # print(\"Number of locations considered : \", len(self.idx_to_pc))\n print(\"Possible indices to choose from : \", tasks)\n pred[0, list(set(range(0, len(self.idx_to_pc))) - set(pickup_jobs_idx_read + deliv_jobs_idx_read))] = 0\n\n idx_opt = np.argsort(pred[0,:])[-1] # most probable location (by its index) among remaining jobs\n print(\"Index chosen : \", idx_opt )\n return self.idx_to_pc[idx_opt]\n\n elif len(tasks) == 0:\n return 0\n\n else:\n raise ValueError('Problem with tasks, which has negative length...')","def _get_state(self):\n # COMPUTE CLASSIFIER_STATE\n predictions = self.model.predict_proba(self.dataset.state_data)[:,0]\n predictions = np.array(predictions)\n idx = np.argsort(predictions)\n # the state representation is the *sorted* list of scores \n classifier_state = predictions[idx]\n \n # COMPUTE ACTION_STATE\n unknown_data = self.dataset.train_data[self.indeces_unknown,:]\n # prediction (score) of classifier on each unlabelled sample\n a1 = self.model.predict_proba(unknown_data)[:,0]\n # average distance to every unlabelled datapoint\n a2 = np.mean(self.dataset.distances[self.indeces_unknown,:][:,self.indeces_unknown],axis=0)\n # average distance to every labelled datapoint\n a3 = np.mean(self.dataset.distances[self.indeces_known,:][:,self.indeces_unknown],axis=0)\n next_action_state = np.concatenate(([a1], [a2], [a3]), axis=0)\n return classifier_state, next_action_state","def search(world_state, robot_pose, goal_pose):\n if world_state.shape[0] == 0 or world_state.shape[1] == 0:\n print(\"Error, empty world_state!!!\")\n return None\n if not is_pos_valid(robot_pose, world_state.shape):\n print(\"Error, invalid robot_pose!!!\", robot_pose)\n return None\n if not is_pos_valid(goal_pose, world_state.shape):\n print(\"Error, invalid goal_pose!!!\", goal_pose)\n return None\n\n directions = [(-1, 0), (1, 0), (0, -1), (0, 1)] # orthogonal directions\n found = False\n\n x, y = robot_pose\n g = 0\n h = heuristic(robot_pose, goal_pose)\n f = g + h\n open = [[f, x, y]]\n came_from = {}\n came_from[robot_pose] = None\n cost_so_far = {}\n cost_so_far[robot_pose] = 0\n\n while open:\n open.sort() # sort based on f value\n current = open.pop(0)\n\n x, y = current[1:]\n g = cost_so_far[(x, y)]\n\n if (x, y) == goal_pose:\n found = True\n break\n else:\n # find available next positions\n for direction in directions:\n x2 = x + direction[0]\n y2 = y + direction[1]\n\n # check whether x2 and y2 are valid\n if not is_pos_valid((x2, y2), world_state.shape):\n continue\n\n g2 = g + 1\n if world_state[x2, y2] == 0 and ((x2, y2) not in cost_so_far or g2 < cost_so_far[(x2, y2)]):\n\n h2 = heuristic((x2, y2), goal_pose)\n f2 = g2 + h2\n open.append([f2, x2, y2])\n came_from[(x2, y2)] = (x, y)\n cost_so_far[(x2, y2)] = g2\n if found:\n path = [goal_pose]\n current = goal_pose\n while came_from[current]:\n current = came_from[current]\n path.append(current)\n\n path.reverse()\n return path\n\n else:\n return None","def find_topo_order(s,graph):\n\n ## initialization\n matrix = graph.get_adjacency()\n n, c = matrix.shape\n sym_matrix = np.empty((n,c), dtype=object)\n # cost_matrix = np.zeros((n,c))\n cache = {}\n\n def symbolize(i,j):\n \"given two indices, create a symbolic variable\"\n s = z.Int('edge_{0}{1}'.format(i,j))\n return s\n\n\n def value_of(i,j):\n \"given two indices, return the (i,j)th value in the adjacency matrix\"\n return sym_matrix[i][j]\n\n\n def constraint_1(n,i,j,k):\n y_ij = value_of(i,j)\n y_jk = value_of(j,k)\n y_ik = value_of(i,k)\n\n name = \"c1\" + str((n,i,j,k))\n constraint = (y_ij + y_jk - y_ik) <= 1\n\n # if name not in cache:\n # cache[name] = constraint\n s.assert_and_track(constraint, name)\n\n\n def constraint_2(n,i,j,k):\n y_ij = value_of(i,j)\n y_jk = value_of(j,k)\n y_ik = value_of(i,k)\n\n name = \"c2\" + str((n,i,j,k))\n constraint = (-y_ij - y_jk + y_ik) <= 0\n\n # if name not in cache:\n # cache[name] = constraint\n s.assert_and_track(constraint, name)\n\n\n def constraint_3(symbolic):\n s.add(z.Or([symbolic == 0, symbolic == 1]))\n\n\n def int_formulation(j):\n left = z.Sum([matrix[k][j] * sym_matrix[k][j] for k in range(j)])\n right = z.Sum([matrix[l][j] * (1 - sym_matrix[j][l]) for l in range(j+1, n)])\n\n return [left, right]\n\n\n ## constraint 3, every edge must be a 0 or a 1, we get the 0 or 1 directly\n ## from the adjacency matrix\n ## we do this first so that the sym_matrix is populated\n for n_iter in range(n):\n for j in range(n_iter+1):\n for i in range(j):\n s_edge = symbolize(i,j)\n sym_matrix[i][j] = s_edge\n constraint_3(s_edge)\n\n ## Iteration for triangle inequalities\n for n_iter in range(n):\n for k in range(n_iter+1):\n for j in range(k):\n for i in range(j):\n constraint_1(n_iter,i,j,k)\n constraint_2(n_iter,i,j,k)\n\n\n ## minimization\n o = z.Optimize()\n y = z.Int('y')\n\n y = z.Sum(u.flatten([int_formulation(j) for j in range(n)]))\n o.minimize(y)\n\n result = []\n\n if s.check() == z.sat:\n result = s.model()\n\n return result","def _get2dCostMatrix(c_hat, time_step_indices: TimeStepIndices, track_count, measurement_count):\n\n # Compute track-measurement cost for single scan problem from hypothesis\n # cost, including null assignment costs\n # Construct track to measurement assignment matrix at scan k\n cost = np.full((track_count, measurement_count), np.inf)\n nullCost = np.full((track_count,), np.inf)\n # store index of the single target hypothesis with the minimum cost for\n # each track and measurement\n idxCost = np.full((track_count, measurement_count), -1, dtype=np.int32)\n idxNullCost = np.full((track_count,), -1, dtype=np.int32)\n for track_id, trackNull_index_for_track in enumerate(time_step_indices.trackNull_index):\n if len(trackNull_index_for_track):\n min_index = c_hat[trackNull_index_for_track].argmin()\n min_index_all_c_hat = trackNull_index_for_track[min_index]\n nullCost[track_id] = c_hat[min_index_all_c_hat]\n idxNullCost[track_id] = min_index_all_c_hat\n # find single target hypotheses in track i that use this\n # measurement if found, find the single target hypothesis with\n # the minimum cost, and record its index\n for measurement in range(measurement_count):\n measTrack_index_for_meas = time_step_indices.measTrack_index[measurement, track_id]\n if len(measTrack_index_for_meas):\n min_index = c_hat[measTrack_index_for_meas].argmin()\n min_index_all_c_hat = measTrack_index_for_meas[min_index]\n cost[track_id, measurement] = c_hat[min_index_all_c_hat]\n idxCost[track_id, measurement] = min_index_all_c_hat\n\n # Create cost matrix for nulls with null costs on diagonal and inf\n # elsewhere (so we can have any number of null assignments)\n nullCostMatrix = np.full((track_count, track_count), np.inf, dtype=np.float64)\n # Pick out diagonal entries (like np.diagonal, but this is a writable view)\n nullCostMatrix.ravel()[::track_count + 1] = nullCost\n\n fullCostMatrix = np.concatenate((cost, nullCostMatrix), axis=1)\n return fullCostMatrix, idxCost, idxNullCost","def greedy_selector(self):\n r_k = 0 \n best_route = []\n cities_to_visit = [i for i in range(1, self.city_count)]\n for _ in range(1, self.city_count):\n s_ind = np.argmax([self.tau[(r_k, u)] for u in cities_to_visit])\n s_k = cities_to_visit.pop(s_ind)\n best_route.append((r_k, s_k))\n r_k = s_k\n best_route.append((r_k, 0))\n \n shortest_path = np.sum([self.phi[(p)] for p in best_route])\n return best_route, shortest_path","def h(self, state):\n loc = dict((val, (i, j)) for i, row in enumerate(self.goal_state)\n for j, val in enumerate(row))\n\n def calculate_cost(i, j):\n val = state[i][j]\n goal_i, goal_j = loc[val]\n return abs(goal_i - i) + abs(goal_j - j)\n\n return sum(\n calculate_cost(i, j) for i in xrange(self.size)\n for j in xrange(self.size) if state[i][j])","def misplaced_heuristic(state):\n msp_h = 0\n size = len(state)\n for i in range (size):\n for j in range (size):\n if state[i][j] == 0:\n continue\n elif state[i][j] != i*size + j:\n msp_h += 1\n return msp_h","def sort_state(self, best_hyp_indices: mx.nd.NDArray, best_word_indices: mx.nd.NDArray):\n self.prev_target_word_id = best_word_indices\n self.decoder_states = [mx.nd.take(ds, best_hyp_indices) for ds in self.decoder_states]","def compute_lookuptable(self):\n\n if self.uselookuptable:\n # Evaluation lookup tables \n self.action_isok = np.zeros( ( self.nodes_n , self.actions_n ) , dtype = bool )\n self.x_next = np.zeros( ( self.nodes_n , self.actions_n , self.DS.n ) , dtype = float ) # lookup table for dynamic\n \n # For all state nodes \n for node in range( self.nodes_n ): \n \n x = self.nodes_state[ node , : ]\n \n # For all control actions\n for action in range( self.actions_n ):\n \n u = self.actions_input[ action , : ]\n \n # Compute next state for all inputs\n x_next = self.DS.fc( x , u ) * self.dt + x\n \n # validity of the options\n x_ok = self.DS.isavalidstate(x_next)\n u_ok = self.DS.isavalidinput(x,u)\n \n self.x_next[ node, action, : ] = x_next\n self.action_isok[ node, action] = ( u_ok & x_ok )","def top_k_betweenness_centrality(self):\n d={}\n l=[]\n for v in vertices:\n a=self.betweenness_centrality(v)\n d[v]=a\n l.append(a)\n m=max(l)\n l1=[]\n for key in d:\n if d[key]==m:\n l1.append(key)\n\n return l1","def csuccessors(state):\n M1, C1, B1, M2, C2, B2 = state\n assert B1 is 1 or B2 is 1\n pairs = {} \n moves = [(0,1), (1,0), (1,1), (2,0), (0,2)]\n if C1 > M1 or C2 > M2: \n return pairs # more cannibals than missionaries\n if B1 is 1:\n B1, B2 = 0, 1\n for m, c in moves:\n if M1 - m >= 0 and C1 - c >= 0:\n state = (M1-m, C1-c, B1, M2+m, C2+c, B2)\n action = 'M' * m + 'C' * c + '->'\n pairs[state] = action\n else:\n B1, B2 = 1, 0\n for m, c in moves:\n if M2 - m >= 0 and C2 - c >= 0:\n state = (M1+m, C1+c, B1, M2-m, C2-c, B2)\n action = '<-' + 'M' * m + 'C' * c\n pairs[state] = action\n return pairs","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 greedy_match(cost_matrix):\n num_detections, num_tracks = cost_matrix.shape\n distance_1d = cost_matrix.reshape(-1)\n index_1d = np.argsort(distance_1d)\n index_2d = np.stack([index_1d // num_tracks, index_1d % num_tracks], axis=1).astype(int, copy=False)\n\n matched_indices = []\n matched_firsts = set()\n matched_seconds = set()\n\n for (first_id, second_id) in index_2d:\n if first_id not in matched_firsts and second_id not in matched_seconds:\n matched_seconds.add(second_id)\n matched_firsts.add(first_id)\n matched_indices.append([first_id, second_id])\n return np.array(matched_indices)","def heuristic(state, depth):\n if state.has_tic_tac_toe(COMP):\n score = depth + 1\n elif state.has_tic_tac_toe(HUMAN):\n score = -(depth + 1)\n else: # draw/undetermined outcome\n score = 0\n return score","def get_constr_obs_states_high(self):\n constrHi = numpy.empty(self.get_num_variables())\n i = 0\n for v in self.variables:\n constrHi[i] = v.get_constraint_high()\n i += 1\n return constrHi","def score(self, candidate_holder, new_scores):\n cand_seqs = candidate_holder.cand_seqs\n cand_states = candidate_holder.cand_states\n cand_syms = cand_seqs[:, -1]\n\n cand_state_value = []\n cand_score_value = []\n for j in range(cand_states[self.state_index].shape[0]):\n cand_state_value.append(cand_states[self.state_index][j][cand_syms[j]])\n cand_score_value.append(cand_states[self.score_index][j][cand_syms[j]])\n ctc_score_result = []\n ctc_score_total = []\n new_states = []\n for i in tf.range(new_scores.shape[0]):\n num_sym_state = np.array([self.init_state] * self.num_classes)\n num_sym_score = np.array([0.0] * self.num_classes, dtype=np.float32)\n num_sym_score_minus = np.array([0.0] * self.num_classes, dtype=np.float32)\n cand_seq = cand_seqs[i]\n ctc_pre_state = cand_state_value[i]\n top_ctc_candidates = np.argsort(new_scores[i, :])\n top_ctc_candidates = sorted(top_ctc_candidates[-self.ctc_beam :].tolist())\n cand_seq = np.array(cand_seq)\n top_ctc_candidates = np.array(top_ctc_candidates)\n ctc_pre_state = ctc_pre_state.numpy()\n ctc_score, new_state = self.cand_score(\n cand_seq, top_ctc_candidates, ctc_pre_state\n )\n ctc_pre_score = tf.cast(cand_score_value[i], tf.float32)\n ctc_score_minus = self.ctc_weight * (ctc_score - ctc_pre_score) + 500\n\n for k in range(len(top_ctc_candidates)):\n num_sym_score[top_ctc_candidates[k]] = ctc_score[k]\n num_sym_score_minus[top_ctc_candidates[k]] = ctc_score_minus[k]\n num_sym_state[top_ctc_candidates[k]] = new_state[k]\n num_sym_score_minus -= 500\n ctc_score_result.append(num_sym_score_minus)\n ctc_score_total.append(num_sym_score)\n new_states.append(num_sym_state)\n cand_states[self.state_index] = tf.convert_to_tensor(np.array(new_states))\n ctc_score_result = tf.convert_to_tensor(np.array(ctc_score_result))\n ctc_score_total = tf.convert_to_tensor(np.array(ctc_score_total))\n cand_states[self.score_index] = ctc_score_total\n return ctc_score_result, cand_states","def costMatrix(row_feats, col_feats, row_labels, col_labels, metric=\"Pearson\"):\n\n # Get unique label values in non-moving and moving brain\n row_labs = np.asarray(list(set(row_labels).difference({-1, 0})))\n col_labs = np.asarray(list(set(col_labels).difference({-1, 0})))\n\n # Initialize cost matrix\n costMatrix = np.zeros((len(row_labs), len(col_labs)))\n print(costMatrix.shape)\n\n # Compute pairwise costs between all label sets\n for i, r in enumerate(row_labs):\n indr = np.where(row_labels == r)[0]\n lr = len(indr)\n\n if metric in [\"Spearman\",\"Euclidean\",\"Pearson\"]:\n featr = row_feats[indr, :]\n\n for j, c in enumerate(col_labs):\n indc = np.where(col_labels == c)[0]\n \n if metric in [\"Spearman\",\"Euclidean\",\"Pearson\"]:\n featc = col_feats[indc, :]\n\n if metric == \"Spearman\":\n [rVal, _] = spearmanr(featr, featc, axis=1)\n rVal = 1-rVal[lr:, 0:lr]\n\n elif metric == \"Pearson\":\n rVal = cdist(featr, featc, metric='Correlation').mean()\n\n elif metric == \"Euclidean\":\n rVal = cdist(featr, featc).mean()\n\n elif metric == \"Dice\":\n rVal = 1-hmg.dice(indr, indc)\n\n elif metric == \"EMD\":\n rmu = row_feats[indr, :].mean(0)\n rmu = rmu/rmu.sum()\n\n cmu = col_feats[indc, :].mean(0)\n cmu = cmu/cmu.sum()\n\n rVal = emd(rmu, cmu)\n\n\n costMatrix[i, j] = rVal\n\n return [row_labs, col_labs, costMatrix]","def get_observation_neighbour(self):\n state = {}\n for grid_id, grid in self.grids.items():\n drivers = list(grid.get_idle_drivers().values())\n orders = self.get_active_order_neighbour(grid)\n neighbour_drivers = self.get_active_driver_neighbour(grid)\n for driver in drivers:\n driver_id = driver.get_driver_id()\n loc = driver.get_position()\n time = driver.city_time\n neighbour_drivers.remove(driver) # pop itself\n assert driver_id not in state\n state[driver_id] = [(loc, time), orders, neighbour_drivers]\n neighbour_drivers.append(driver) # insert back\n return state","def state_descriptor(self, state, player):\n\n\t\tboard = np.copy(state.board)\n\n\t\tnp.place(board, board == -1, 0)\n\t\tnp.place(board, board == player, 3)\n\t\tnp.place(board, board == 3 - player, -1)\n\t\tnp.place(board, board == 3, 1)\n\n\t\tmy_cows = state.cows[player - 1]\n\t\ten_cows = state.cows[2 - player]\n\n\t\treturn (self.totuple(board), my_cows, en_cows)","def solve(list_of_locations, list_of_homes, starting_car_location, adjacency_matrix, params=[]):\n\n loc_map = {}\n drop_off_dict = {}\n num_home_visited = 0\n\n \"\"\"\n for i in range(len(list_of_locations)):\n loc_map[i] = list_of_locations[0]\n \"\"\"\n\n home_indexes = convert_locations_to_indices(list_of_homes, list_of_locations)\n start = list_of_locations.index(starting_car_location)\n graph, msg = adjacency_matrix_to_graph(adjacency_matrix)\n num_homes = len(list_of_homes)\n\n car_path = []\n all_paths = dict(nx.all_pairs_dijkstra(graph))\n visited = set()\n\n #print(start)\n car_path.append(start)\n current_node = start\n\n if start in home_indexes:\n visited.add(start)\n drop_off_dict[start] = [start]\n num_home_visited += 1\n\n while num_home_visited < num_homes:\n dist_dict = all_paths.get(current_node)[0]\n paths_dict = all_paths.get(current_node)[1]\n\n dist_dict = {k:v for (k,v) in dist_dict.items() if k not in visited and k in home_indexes}\n min_dist = min(dist_dict.values())\n min_list = [k for k in dist_dict.keys() if dist_dict[k] <= min_dist]\n #print(dist_dict.values())\n target = min_list[0]\n drop_off_dict[target] = [target]\n #print(target+1)\n #print(target)\n car_path.pop()\n car_path.extend(paths_dict[target])\n\n visited.add(target)\n current_node = target\n num_home_visited += 1\n\n paths_dict = all_paths.get(current_node)[1]\n car_path.pop()\n car_path.extend(paths_dict[start])\n #print((drop_off_dict.keys()))\n #car_path = [start, ...., start]\n #drop_off_dict = {drop_off_loc: [home1, home2, ...] }\n\n return car_path, drop_off_dict","def evaluateBoardState(self, board):\n\n \"\"\"\n These are the variables and functions for board objects which may be helpful when creating your Agent.\n Look into board.py for more information/descriptions of each, or to look for any other definitions which may help you.\n\n Board Variables:\n board.width \n board.height\n board.last_move\n board.num_to_connect\n board.winning_zones\n board.score_array \n board.current_player_score\n\n Board Functions:\n get_cell_value(row, col)\n try_move(col)\n valid_move(row, col)\n valid_moves()\n terminal(self)\n legal_moves()\n next_state(turn)\n winner()\n \"\"\"\n if self.id == 1:\n opponent_id = 2\n else:\n opponent_id = 1\n\n maxvalue = 100000\n minvalue = -maxvalue\n winner = board.winner()\n if winner == self.id:\n return maxvalue\n elif winner == opponent_id:\n return minvalue\n size_y = board.height\n size_x = board.width\n map_ = []\n num_to_connect = board.num_to_connect\n total_points = 0\n\n multiply_reachable = 1\n multiply_oddeven = 1\n # basically this function is calculating all the possible win positions\n # more pieces in a possible win position will be counted with more weights\n # a win position with X pieces in it will be counted as X^2 points\n # initialise the zones maps\n for i in range(size_y):\n map_.append([])\n for j in range(size_x):\n map_[i].append([])\n\n # Fill in the horizontal win positions\n for i in range(size_y):\n for j in range(size_x - num_to_connect + 1):\n points = 0\n self_pieces_count = 0\n opponent_pieces_count = 0\n for k in range(num_to_connect):\n if board.board[i][j + k] == opponent_id:\n opponent_pieces_count += 1\n elif board.board[i][j + k] == self.id:\n points += len(board.winning_zones[j+k][i])\n if (self.id == 1 and i % 2 == 1) or (self.id == 2 and i%2 == 0):\n points *= multiply_oddeven\n self_pieces_count += 1\n if self_pieces_count == 3 and opponent_pieces_count == 0:\n if j - 1 >= 0 and board.board[i][j + 3] == 0 and board.board[i][j - 1] == 0 \\\n and board.try_move(j + 3) == i and board.try_move(j - 1) == i:\n return maxvalue\n elif j + 4 < size_y and board.board[i][j + 4] == 0 and board.board[i][j] == 0 \\\n and board.try_move(j + 4) == i and board.try_move(j) == i:\n return maxvalue\n else:\n for k in range(num_to_connect):\n if board.board[i][j + k] == 0 and board.try_move(j + k) == i:\n points *= multiply_reachable\n elif opponent_pieces_count == 3 and self_pieces_count == 0:\n if j - 1 >= 0 and board.board[i][j + 3] == 0 and board.board[i][j - 1] == 0 \\\n and board.try_move(j + 3) == i and board.try_move(j - 1) == i:\n return minvalue\n elif j + 4 < size_y and board.board[i][j + 4] == 0 and board.board[i][j] == 0 \\\n and board.try_move(j + 4) == i and board.try_move(j) == i:\n return minvalue\n # else:\n # for k in range(num_to_connect):\n # if board.board[i][j + k] == 0 and board.try_move(j + k) == i:\n # points *= -multiply_reachable\n if (opponent_pieces_count == 3 and self_pieces_count == 0) or opponent_pieces_count == 0:\n total_points += points\n\n # Fill in the vertical win positions\n for i in range(size_x):\n for j in range(size_y - num_to_connect + 1):\n points = 0\n self_pieces_count = 0\n for k in range(num_to_connect):\n if board.board[j + k][i] == opponent_id:\n opponent_pieces_count += 1\n elif board.board[j + k][i] == self.id:\n points += len(board.winning_zones[i][j+k])\n if (self.id == 1 and (j+k) % 2 == 1) or (self.id == 2 and (j+k)%2 == 0):\n points *= multiply_oddeven\n self_pieces_count += 1\n points *= multiply_reachable\n # if opponent_pieces_count == 3 and self_pieces_count == 0:\n # points *= -1\n if (opponent_pieces_count == 3 and self_pieces_count == 0) or opponent_pieces_count == 0:\n total_points += points\n\n # Fill in the forward diagonal win positions\n for i in range(size_y - num_to_connect + 1):\n for j in range(size_x - num_to_connect + 1):\n points = 0\n self_pieces_count = 0\n opponent_pieces_count = 0\n for k in range(num_to_connect):\n if board.board[i + k][j + k] == opponent_id:\n opponent_pieces_count += 1\n elif board.board[i + k][j + k] == self.id:\n points += len(board.winning_zones[j+k][i+k])\n if (self.id == 1 and (i+k) % 2 == 1) or (self.id == 2 and (i+k)%2 == 0):\n points *= multiply_oddeven\n self_pieces_count += 1\n if self_pieces_count == 3 and opponent_pieces_count == 0:\n if i - 1 >= 0 and j - 1 >= 0 and board.board[i + 3][j + 3] == 0 and board.board[i - 1][j - 1] == 0 \\\n and board.try_move(j + 3) == i + 3 and board.try_move(j - 1) == i - 1:\n return maxvalue\n elif i + 4 < size_y and j + 4 < size_x and board.board[i + 4][j + 4] == 0 and board.board[i][j] == 0 \\\n and board.try_move(j + 4) == i + 4 and board.try_move(j) == i:\n return maxvalue\n else:\n for k in range(num_to_connect):\n if board.board[i + k][j + k] == 0 and board.try_move(j + k) == i + k:\n points *= multiply_reachable\n elif opponent_pieces_count == 3 and self_pieces_count == 0:\n if i - 1 >= 0 and j - 1 >= 0 and board.board[i + 3][j + 3] == 0 and board.board[i - 1][j - 1] == 0 \\\n and board.try_move(j + 3) == i + 3 and board.try_move(j - 1) == i - 1:\n return minvalue\n elif i + 4 < size_y and j + 4 < size_x and board.board[i + 4][j + 4] == 0 and board.board[i][j] == 0 \\\n and board.try_move(j + 4) == i + 4 and board.try_move(j) == i:\n return minvalue\n # else:\n # for k in range(num_to_connect):\n # if board.board[i + k][j + k] == 0 and board.try_move(j + k) == i + k:\n # points *= -multiply_reachable\n if (opponent_pieces_count == 3 and self_pieces_count == 0) or opponent_pieces_count == 0:\n total_points += points\n\n # Fill in the backward diagonal win positions\n for i in range(size_y - num_to_connect + 1):\n for j in range(size_x - 1, num_to_connect - 1 - 1, -1):\n points = 0\n self_pieces_count = 0\n opponent_pieces_count = 0\n for k in range(num_to_connect):\n if board.board[i + k][j - k] == opponent_id:\n opponent_pieces_count += 1\n elif board.board[i + k][j - k] == self.id:\n points += len(board.winning_zones[j-k][i+k])\n if (self.id == 1 and (i+k) % 2 == 1) or (self.id == 2 and (i+k)%2 == 0):\n points *= multiply_oddeven\n self_pieces_count += 1\n if self_pieces_count == 3 and self_pieces_count == 0:\n if board.board[i + 3][j - 3] == 0 and board.board[i - 1][j + 1] == 0 \\\n and board.try_move(j - 3) == i + 3 and board.try_move(j + 1) == i - 1:\n return maxvalue\n elif i + 4 < size_y and j - 4 >= 0 and board.board[i + 4][j - 4] == 0 and board.board[i][j] == 0 \\\n and board.try_move(j - 4) == i + 4 and board.try_move(j) == i:\n return maxvalue\n else:\n for k in range(num_to_connect):\n if board.board[i + k][j - k] == 0 and board.try_move(j - k) == i + k:\n points *= multiply_reachable\n\n elif opponent_pieces_count == 3 and self_pieces_count == 0:\n if board.board[i + 3][j - 3] == 0 and board.board[i - 1][j + 1] == 0 \\\n and board.try_move(j - 3) == i + 3 and board.try_move(j + 1) == i - 1:\n return minvalue\n elif i + 4 < size_y and j - 4 >= 0 and board.board[i + 4][j - 4] == 0 and board.board[i][j] == 0 \\\n and board.try_move(j - 4) == i + 4 and board.try_move(j) == i:\n return minvalue\n # else:\n # for k in range(num_to_connect):\n # if board.board[i + k][j - k] == 0 and board.try_move(j - k) == i + k:\n # points *= -multiply_reachable\n if (opponent_pieces_count == 3 and self_pieces_count == 0) or opponent_pieces_count == 0:\n total_points += points\n return total_points","def get_pathologic_covering_routes(n_pl, n_target, attacker_strategy, target_values):\n # computes the coefficient used by the greedy oracle to choose routes\n targets_coeff = np.transpose(np.multiply(attacker_strategy, target_values))\n\n # randomly selects the player for which the non optimal choice will be made\n wrong_pl = randint(1, n_pl)\n\n # generate the non optimal route randomly\n n_covered_targets = randint(n_pl,n_target-1)\n non_opt_action = np.zeros(n_target)\n for i in range(0, n_covered_targets):\n random_covered_target = randint(0, n_target-1)\n non_opt_action[random_covered_target] = 1\n\n # compute the value of the non optimal route\n non_opt_val = get_value_single_route(non_opt_action, targets_coeff)\n\n # generate routes that have, as a single, values smaller than the best greedy route but taken togher perform\n # at least as well. [[0,1,...],[...],...] a[r][t]=1 iff t is covered by r.\n # The returned list should have n_pl - 1 routes\n opt_routes = get_opt_routes(n_pl, non_opt_action)\n\n I={}\n for pl in range(1, n_pl+1):\n\n n_r = randint(0, MAX_ROUTES)\n temp = lil_matrix((n_r+1, n_target), dtype='int8')\n\n if pl == wrong_pl:\n # put the non opt route in the bucket\n for t in non_opt_action.nonzero():\n temp[0,t] = 1\n else:\n for t in opt_routes.pop().nonzero():\n temp[0,t] = 1\n\n # generate other random routes with single value less than the non_opt_value\n for r in range(1, n_r):\n new_route = get_r_limited_val(non_opt_val, targets_coeff)\n\n for t in new_route.nonzero():\n temp[r,t] = 1\n\n I[pl] = temp.tocsr()\n\n return I","def next_state_rowcol(self):\n # state before and after propagation\n self.state_before = self.state_after\n transition_probs = list(Customer.TPM.loc[Customer.TPM.index==self.state_before].values[0])\n self.state_after = random.choices(Customer.STATES, weights=transition_probs)[0]\n # row and col before and after propagation\n self.row_before = self.row_after\n self.col_before = self.col_after\n\n # randomly chose row_after, col_after depending on the state_after\n if self.state_after == 'fruit':\n self.row_after = random.choice(Customer.STATE_ROW_COL['fruit'][0])\n self.col_after = random.choice(Customer.STATE_ROW_COL['fruit'][1])\n elif self.state_after == 'spices':\n self.row_after = random.choice(Customer.STATE_ROW_COL['spices'][0])\n self.col_after = random.choice(Customer.STATE_ROW_COL['spices'][1])\n elif self.state_after == 'dairy':\n self.row_after = random.choice(Customer.STATE_ROW_COL['dairy'][0])\n self.col_after = random.choice(Customer.STATE_ROW_COL['dairy'][1])\n elif self.state_after == 'drinks':\n self.row_after = random.choice(Customer.STATE_ROW_COL['drinks'][0])\n self.col_after= random.choice(Customer.STATE_ROW_COL['drinks'][1])\n elif self.state_after == 'checkout':\n self.row_after = random.choice(Customer.STATE_ROW_COL['checkout'][0])\n self.col_after = random.choice(Customer.STATE_ROW_COL['checkout'][1])","def get_states(self):\n states = []\n for chords in self.training_data:\n chunks = [chords[x:x+self.order] for x in range(0,\n len(chords), self.order)]\n for chunk in chunks:\n chunk_string = \" \".join(chunk)\n if chunk_string not in states:\n states.append(chunk_string)\n return sorted(states)","def select_observation(self):\n\n # New board to collect the states sampled by the MC agent\n score_board = np.zeros_like(self.observed_board)\n n_samples = self.nb_samples\n\n # +----------+\n # | Task 1 |\n # +----------+\n # Check if there is already an \"open\" hit, i.e. a ship that has been hit but not sunk\n # These locations are handled by the observation_board as 1\n # If there is already a hit, choose a random one to deal with next.\n # Create a score board including that hit, and reduce the number of samples to 1/10\n hit = None\n if 1 in self.observed_board:\n hits = np.where(self.observed_board == 1)\n i = random.choice(range(len(hits[0])))\n hit = (hits[0][i], hits[1][i])\n n_samples /= 10\n\n # +----------+\n # | Task 2 |\n # +----------+\n # Populate the score_board with possible boat placements\n i = 0\n while i < n_samples:\n board = np.zeros_like(self.observed_board)\n for boat_size in self.remaining_ships:\n while True:\n o = random.choice(['h', 'v'])\n x = random.choice(range(self.size - (boat_size - 1 if o == 'h' else 0)))\n y = random.choice(range(self.size - (boat_size - 1 if o == 'v' else 0)))\n\n d = np.hstack((np.zeros((boat_size, 1)), np.arange(boat_size)[:, None])[::(-1 if o == 'h' else 1)])\n boat = tuple((np.array([[x, y]]) + d).astype(int).T)\n\n # Place boats non-overlapping and not on misses or sunken ships.\n if 1 in board[boat] or -1 in self.observed_board[boat]:\n continue\n\n board[boat] = 1\n break\n\n # Sanity check\n assert board.sum() == sum(self.remaining_ships)\n\n if not hit or board[hit] == 1:\n i += 1\n score_board += board\n\n # +----------+\n # | Task 3 |\n # +----------+\n # Having populated the score board, select a new position by choosing the location with the highest score.\n score_board *= np.where(self.observed_board == 1, 0, 1)\n plt.imshow(score_board, cmap='hot')\n plt.show()\n time.sleep(1)\n i_new, j_new = np.unravel_index(np.argmax(score_board), score_board.shape)\n\n # return the next location to query, i_new: int, j_new: int\n return i_new, j_new, score_board","def bridge_problem3(here):\r\n\r\n def all_over(state):\r\n here, _ = state\r\n return not here or here == set([\"light\"])\r\n\r\n start = (frozenset(here) | frozenset([\"light\"]), frozenset())\r\n return lowest_cost_search(start, bsuccessors2, all_over, bcost)","def calc_ply_order(constraints, targets):\r\n if constraints.sym:\r\n ply_order = np.arange(targets.n_plies // 2 + targets.n_plies % 2)\r\n return ply_order\r\n\r\n order_before_sorting = np.arange(targets.n_plies)\r\n ply_order = np.zeros((targets.n_plies,), int)\r\n ply_order[0::2] = order_before_sorting[\r\n :targets.n_plies // 2 + targets.n_plies % 2]\r\n ply_order[1::2] = order_before_sorting[\r\n targets.n_plies // 2 + targets.n_plies % 2:][::-1]\r\n return ply_order","def _get_next_position(x: float, y: float, heading: float, state: str, hp_info:\n pd.DataFrame, rw_info: pd.DataFrame, ac: int,\n CURR_LANDING_AC) -> Tuple[float, float, float, str]:\n\n if state == \"A\":\n\n radius = np.sqrt(x ** 2 + y ** 2)\n\n min_R = CONTROL_ZONE_RADIUS - MIN_SEPARATION - POSIITION_TOLERANCE\n max_R = CONTROL_ZONE_RADIUS - MIN_SEPARATION + POSIITION_TOLERANCE\n\n if (min_R < radius) | (radius < max_R):\n\n hp_ind = _get_closest_control_zone(x, y, hp_info)\n\n if hp_info[2][hp_ind] == 0:\n\n state_new = \"C\"\n heading_new = _get_ac_heading(hp_info[0][hp_ind] - x, hp_info[1][hp_ind] - y)\n\n else:\n\n state_new = \"B\"\n heading_new = (hp_info[2][hp_ind] + np.pi / 2) % (2 * np.pi)\n\n else:\n\n state_new = \"A\"\n heading_new = heading\n\n x_new = x + MAX_SPEED * np.sin(heading_new) / TIME_STEP_FREQUENCY\n y_new = y + MAX_SPEED * np.cos(heading_new) / TIME_STEP_FREQUENCY\n\n elif state == \"B\":\n\n hp_ind = _get_closest_control_zone(x, y, hp_info)\n\n if hp_info[2][hp_ind] == 0:\n\n state_new = \"C\"\n heading_new = _get_ac_heading(hp_info[0][hp_ind] - x, hp_info[1][hp_ind] - y)\n\n else:\n\n state_new = \"B\"\n heading_new = heading - MAX_SPEED / (TIME_STEP_FREQUENCY * (CONTROL_ZONE_RADIUS - MIN_SEPARATION))\n\n x_new = x + MAX_SPEED * np.sin(heading_new) / TIME_STEP_FREQUENCY\n y_new = y + MAX_SPEED * np.cos(heading_new) / TIME_STEP_FREQUENCY\n\n elif state == \"C\":\n\n hp_ind = _get_closest_control_zone(x, y, hp_info)\n dist = np.sqrt((hp_info[0][hp_ind] - x) ** 2 + (hp_info[1][hp_ind] - y) ** 2)\n\n if dist < POSIITION_TOLERANCE + 1:\n\n state_new = \"D\"\n heading_new = heading\n\n x_new = x\n y_new = y\n\n else:\n\n state_new = \"C\"\n heading_new = heading\n\n x_new = x + MAX_SPEED * np.sin(heading_new) / TIME_STEP_FREQUENCY\n y_new = y + MAX_SPEED * np.cos(heading_new) / TIME_STEP_FREQUENCY\n\n elif state == \"D\":\n\n if ac == CURR_LANDING_AC:\n\n row_ind, x_ind, y_ind = _get_closest_threshold(x, y, rw_info)\n\n state_new = \"E\"\n heading_new = _get_ac_heading(rw_info[x_ind][row_ind] - x, rw_info[y_ind][row_ind] - y)\n\n x_new = x + MAX_SPEED * np.sin(heading_new) / TIME_STEP_FREQUENCY\n y_new = y + MAX_SPEED * np.cos(heading_new) / TIME_STEP_FREQUENCY\n\n else:\n\n state_new = \"D\"\n heading_new = heading\n\n x_new = x\n y_new = y\n\n elif state == \"E\":\n\n row_ind, x_ind, y_ind = _get_closest_threshold(x, y, rw_info)\n dist = np.sqrt((rw_info[x_ind][row_ind] - x) ** 2 + (rw_info[y_ind][row_ind] - y) ** 2)\n\n if (dist < MIN_SEPARATION) | (CURR_LANDING_AC == ac):\n\n x_ind = 0 if x_ind == 2 else 2\n y_ind = 1 if y_ind == 3 else 3\n\n CURR_LANDING_AC += 1\n\n state_new = \"F\"\n heading_new = _get_ac_heading(rw_info[x_ind][row_ind] - x, rw_info[y_ind][row_ind] - y)\n\n x_new = x + MAX_SPEED * np.sin(heading_new) / TIME_STEP_FREQUENCY\n y_new = y + MAX_SPEED * np.cos(heading_new) / TIME_STEP_FREQUENCY\n\n else:\n\n state_new = \"E\"\n heading_new = heading\n\n x_new = x + MAX_SPEED * np.sin(heading_new) / TIME_STEP_FREQUENCY\n y_new = y + MAX_SPEED * np.cos(heading_new) / TIME_STEP_FREQUENCY\n\n elif state == \"F\":\n\n row_ind, x_ind, y_ind = _get_closest_threshold(x, y, rw_info)\n dist = np.sqrt((rw_info[x_ind][row_ind] - x) ** 2 + (rw_info[y_ind][row_ind] - y) ** 2)\n\n if abs(dist - RUNWAY_LENGTH / 2) < POSIITION_TOLERANCE:\n x_new, y_new, heading_new, state_new = -1, -1, -1, \"END\"\n\n else:\n\n state_new = \"F\"\n heading_new = heading\n\n x_new = x + MAX_SPEED * np.sin(heading_new) / TIME_STEP_FREQUENCY\n y_new = y + MAX_SPEED * np.cos(heading_new) / TIME_STEP_FREQUENCY\n\n else:\n\n x_new, y_new, heading_new, state_new = -1, -1, -1, \"END\"\n\n return x_new, y_new, heading_new, state_new","def test_location_to_state_and_state_to_location_match():\n env = Four_Rooms_Environment(stochastic_actions_probability=0.0)\n env.reset()\n for row in range(env.grid_height):\n for col in range(env.grid_width):\n assert env.location_to_state((row, col)) == env.location_to_state(env.state_to_location(env.location_to_state((row, col))))","def getNewRYGState(self, tl_combinations):\n highScore = 0\n new_ryg_state = None # The new traffic light combination\n for combination in tl_combinations:\n if combination.score > highScore:\n highScore = combination.score\n new_ryg_state = combination.ryg_state\n return new_ryg_state","def determine_move_position(self):\n green_probs = []\n net_size = len(self.net)\n adjacents = self.net[self.current_pos].adjacents\n #Belief propagation:\n #Analyzes each position's probability of obtaining\n #green when measuring at a time t+1.\n for i in adjacents:\n accum = 0\n for j in range(0, net_size):\n distance = self.__get_distance(i-1, j)\n if distance == 0: #Probability of measure green at distance 0 from 'i'.\n accum += self.enemy_net[i-1].value * self.ct[0][0]\n elif distance == 1: #Probability of measure green at distance 1 from 'i'.\n accum += self.enemy_net[i-1].value * self.ct[1][0]\n elif distance == 2: #Probability of measure green at distance 2 from 'i'.\n accum += self.enemy_net[i-1].value * self.ct[2][0]\n elif distance == 3: #Probability of measure green at distance 3 from 'i'.\n accum += self.enemy_net[i-1].value * self.ct[3][0]\n else: #Probability of measure green at a distance >= 4 from 'i'.\n accum += self.enemy_net[i-1].value * self.ct[4][0]\n green_probs.append((i, accum))\n #Returns the position in which the probability of\n #obtaining green when measuring is the lowest.\n return min(green_probs, key=itemgetter(1))[0]","def compute_map(self):\n number_of_orders = 0\n orders = []\n for i, line in enumerate(self.__grid):\n for j, column in enumerate(line):\n if self.__grid[i][j][\"humans\"] != 0:\n number_of_orders += 1\n orders.append(i)\n orders.append(j)\n orders.append(self.__grid[i][j][\"humans\"])\n orders.append(0)\n orders.append(0)\n if self.__grid[i][j][\"vampires\"] != 0:\n number_of_orders += 1\n orders.append(i)\n orders.append(j)\n orders.append(0)\n orders.append(self.__grid[i][j][\"vampires\"])\n orders.append(0)\n if self.__grid[i][j][\"werewolves\"] != 0:\n number_of_orders += 1\n orders.append(i)\n orders.append(j)\n orders.append(0)\n orders.append(0)\n orders.append(self.__grid[i][j][\"werewolves\"])\n return number_of_orders, orders","def best_routes_matrix(self) -> np.array:\n x = np.empty((0, 4))\n y = np.empty((0, 4))\n for k, v in self.routes.items():\n x_row = np.zeros((1, 4))\n y_row = np.zeros((1, 4))\n\n # Player start position\n player = Pt(k[0][0], k[0][1])\n x_row[0, 0] = player.x\n x_row[0, 1] = player.y\n\n # Goal start position\n goal = Pt(k[1][0], k[1][1])\n x_row[0, 2] = goal.x\n x_row[0, 3] = goal.y\n x = np.vstack((x, x_row))\n\n # Outputs\n best_route = self.best_route(player, goal)\n y_row[0, 0] = best_route[\"UP\"]\n y_row[0, 1] = best_route[\"DOWN\"]\n y_row[0, 2] = best_route[\"LEFT\"]\n y_row[0, 3] = best_route[\"RIGHT\"]\n\n y = np.vstack((y, y_row))\n\n return x, y","def branch_precursor(state, time, d):\n assert d[\"alpha_IL2\"] < d[\"alpha1\"] and d[\"alpha_IL2\"] < d[\"alpha2\"]\n \n th0 = state[0]\n \n th1 = state[1:(d[\"alpha1\"]+d[\"alpha1_p\"]+1)]\n th2 = state[(d[\"alpha1\"]+d[\"alpha1_p\"]+1):]\n #print(len(state), len(th1))\n ### get all cytokine secreting cells \n th1_all = np.sum(th1[-d[\"alpha1_p\"]:])\n th2_all = np.sum(th2[-d[\"alpha2_p\"]:])\n \n t_eff = th1_all+th2_all\n t_il2 = np.sum(th1[:d[\"alpha_IL2\"]]) + np.sum(th2[:d[\"alpha_IL2\"]])\n\n ### calculate cytokine concentrations\n cyto_1 = d[\"beta_cyto_1\"]*th1_all + d[\"ifn_ext\"]\n cyto_2 = d[\"beta_cyto_2\"]*th2_all + d[\"il21_ext\"]\n \n conc_il2 = d[\"rate_il2\"]*t_il2/(d[\"K_il2\"]+t_eff)\n\n # compute feedbacks\n fb1 = d[\"fb_rate1\"]*cyto_1**3/(cyto_1**3+d[\"K_1\"]**3)\n fb2 = d[\"fb_rate2\"]*cyto_2**3/(cyto_2**3+d[\"K_2\"]**3)\n ### update differantiation rate\n beta1 = d[\"beta1\"]*(1+fb1)\n beta2 = d[\"beta2\"]*(1+fb2) \n \n ### calculate probability, note that these are adjusted to beta1 beta2 so that\n # they are not necessarily \\in (0,1)\n p1, p2 = get_prob(d, beta1, beta2, cyto_1, cyto_2)\n \n #print(beta1*p1_adj/(beta1*p1_adj+beta2))\n beta1_p = d[\"beta1_p\"]\n beta2_p = d[\"beta2_p\"]\n rate_death = d[\"d_eff\"] \n \n # check for homeostasis regulation\n if d[\"crit\"] == False:\n update_t0(d, time, conc_il2, t_eff)\n elif d[\"death_mode\"] == False:\n assert d[\"crit\"] == True \n beta1_p = beta1_p*np.exp(-d[\"decay_p\"]*(time-d[\"t0\"]))\n beta2_p = beta2_p*np.exp(-d[\"decay_p\"]*(time-d[\"t0\"]))\n\n else:\n rate_death = rate_death*np.exp(time-d[\"t0\"])\n\n # this is the actual differentiation where odes are computed \n dt_th1 = diff_precursor(th1, th0, d[\"alpha1\"], beta1, beta1_p, p1, rate_death, d)\n dt_th2 = diff_precursor(th2, th0, d[\"alpha2\"], beta2, beta2_p, p2, rate_death, d)\n dt_th0 = -(beta1*p1+beta2)*th0 \n dt_state = np.concatenate(([dt_th0], dt_th1, dt_th2))\n\n return dt_state","def get_nearest_component(action_dct, init_component):\n components = action_dct['context']['direct_object'] + action_dct['context']['direct_object'] + \\\n action_dct['context']['auxiliar_object'] + action_dct['context']['attributes']\n components.sort(key=lambda obj_dict: obj_dict['indexes'][1])\n nearest_comp = None\n for obj_dict in components:\n if obj_dict['indexes'][1] > init_component['indexes'][1]:\n break\n nearest_comp = obj_dict\n return nearest_comp","def get_matrix(self):\n matrix = np.zeros([len(self.states), len(self.states)])\n starting_states = []\n transitions = []\n\n for chords in self.training_data:\n states = []\n is_starting_state = True\n chunks = [chords[x:x+self.order] for x in range(0,\n len(chords), self.order)]\n for chunk in chunks:\n chunk_string = \" \".join(chunk)\n if is_starting_state:\n starting_states.append(chunk_string)\n is_starting_state = False\n states.append(chunk_string)\n\n for i in range(0, len(states)):\n if i < (len(states)) - 1:\n transitions.append([states[i], states[i + 1]])\n else:\n transitions.append([states[i]])\n\n self.starting_probabilities = np.zeros([len(self.states)])\n\n for transition in transitions:\n for row, row_contents in enumerate(self.transitions):\n for col, _ in enumerate(row_contents):\n if transition == self.transitions[row][col]:\n matrix[row][col] += 1\n\n for i, state in enumerate(self.states):\n for j, possible_state in enumerate(starting_states):\n if state == possible_state:\n self.starting_probabilities[j] += 1\n\n num = sum(self.starting_probabilities)\n for i, prob in enumerate(self.starting_probabilities):\n self.starting_probabilities[i] = prob / num\n\n for m in range(len(matrix)):\n num = sum(matrix[m])\n if int(num) is not 0:\n for i in range(len(matrix[m])):\n matrix[m][i] = (matrix[m][i] / num)\n else:\n matrix[m] = self.starting_probabilities\n return matrix","def get_switchy_score_order(x):\n switchy_scores = np.apply_along_axis(switchy_score, axis=0, arr=x)\n return np.argsort(switchy_scores)","def generateState(self, gameState):\n state = [None, None, None, None, None, None]\n\n #Calculamos la distancia al fantasma mas cercano\n distGhosts = gameState.data.ghostDistances\n nearest = 100000\n for i in distGhosts:\n if i < nearest and i is not None:\n nearest = i\n if nearest <= 3:\n state[0] = 0\n elif nearest > 3 and nearest <= 7:\n state[0] = 1\n else:\n state[0] = 2\n\n legalActions = gameState.getLegalActions()\n #Calculamos los isPared\n #isParedEast\n if \"East\" in legalActions:\n state[1] = 0\n else:\n state[1] = 1\n\n #isParedWest\n if \"West\" in legalActions:\n state[2] = 0\n else:\n state[2] = 1\n\n #isParedNorth\n if \"North\" in legalActions:\n state[3] = 0\n else:\n state[3] = 1\n\n #isParedShouth\n if \"South\" in legalActions:\n state[4] = 0\n else:\n state[4] = 1\n\n #Bloque que controla si el pacman ha pasado por el hueco encontrado\n controlador =0\n x1,y1 = gameState.getPacmanPosition()\n if len(self.huecos)==2:\n\t\t\tif x1 ==self.huecos[0]:\n\t\t\t\tcontrolador=controlador+1\n\t\t\tif y1 ==self.huecos[1]:\n\t\t\t\tcontrolador=controlador+1\n\t\t\tif controlador == 2:\n\t\t\t\tself.huecos=[]\n\n posGhosts = gameState.getGhostPositions()\n nearestGhostIndex = distGhosts.index(nearest)\n nearestGhostPos = posGhosts[nearestGhostIndex]\n pacmanPosition = gameState.getPacmanPosition()\n\n print(self.huecos)\n if(len(self.huecos) == 2):\n goalPosition = self.huecos\n else:\n goalPosition = nearestGhostPos\n\n legales = []\n for a in self.goodActions(pacmanPosition,goalPosition): #Metodo goodActions devuelve las mejores acciones que puede realizar el pacman para llegar a su objetivo\n if a in legalActions:\n legales.append(a) #Buenas acciones legales\n if (len(legales) < 1):\n if (len(self.goodActions(pacmanPosition,goalPosition))): #Si existen buenas acciones pero no son legales es porque un muro esta impidiendo su ejecucion\n self.huecos = []\n rd= random.randint(0, len(self.goodActions(pacmanPosition,goalPosition))-1)\n self.buscaHueco(gameState,self.goodActions(pacmanPosition,goalPosition)[rd],pacmanPosition[0],pacmanPosition[1]) #Se busca hueco en una de las direcciones de las buenas acciones\n\n #Calculamos la direccion en la que se encuentra el fantasma o el hueco mas cercano\n # esto lo hacemos calculando la distancia del fantasma al pacman y viendo cual es la componente mayor.\n # Si es la X, la direccion sera o derecha o izquierda y dependera del sentido de esta. Con la Y parasria lo mismo\n relX = gameState.getPacmanPosition()[0] - goalPosition[0]\n relY = gameState.getPacmanPosition()[1] - goalPosition[1]\n\n if abs(relX) >= abs(relY):\n if relX > 0:\n state[5] = \"West\"\n elif relX < 0:\n state[5] = \"East\"\n elif abs(relY) > abs(relX):\n if relY > 0:\n state[5] = \"South\"\n elif relY < 0:\n state[5] = \"North\"\n\n\n return state","def make_alternative_states(self) -> np.ndarray:\n states = []\n for agent in range(self.agents):\n agent_state = []\n\n # Own distance\n r, c = self.game.get_agent_pos(agent)\n agent_state.append(r / 6)\n agent_state.append(c / 6)\n\n # Distances to others\n distances_r = [\n (r - pos[0]) / 12\n for key, pos in self.game.agent_positions.items()\n if key != agent\n ]\n distances_c = [\n (c - pos[1]) / 12\n for key, pos in self.game.agent_positions.items()\n if key != agent\n ]\n agent_state += distances_r\n agent_state += distances_c\n\n # Goal distances\n distances_goal_r = [(r - pos[0]) / 12 for pos in self.payoff_fields]\n distances_goal_c = [(c - pos[1]) / 12 for pos in self.payoff_fields]\n agent_state += distances_goal_r\n agent_state += distances_goal_c\n\n if agent < self.num_informed:\n agent_state.append((r - self.special_payoff_fields[0][0]) / 12)\n agent_state.append((c - self.special_payoff_fields[0][1]) / 12)\n else:\n agent_state += [0, 0]\n agent_state.append(self.max_turns - self.turns_count)\n states.append(np.array(agent_state))\n\n states = np.stack(states, axis=0)\n return states","def get_state_arr(self):\n rpos = self.sim.getAgentPosition(self.robot_num)\n rvel = self.sim.getAgentVelocity(self.robot_num)\n rrad = self.sim.getAgentRadius(self.robot_num)\n v_pref = self.sim.getAgentMaxSpeed(self.robot_num)\n theta = math.atan2(rvel[1], rvel[0])\n # Robot's state entry. Note that goal is listed as the robot's current\n # position because we aren't using that goal as such, we are just\n # exploring.\n state = [\n rpos[0], rpos[1], rvel[0], rvel[1], rrad,\n self.headings[self.robot_num], rpos[0], rpos[1],\n v_pref, theta\n ]\n for agent in self.agents:\n if agent != self.robot_num: # We already accounted for the robot\n pos = self.sim.getAgentPosition(agent)\n vel = self.sim.getAgentVelocity(agent)\n rad = self.sim.getAgentRadius(agent)\n state.extend([pos[0], pos[1], vel[0], vel[1], rad,\n self.headings[agent]])\n for obs in self.obstacles:\n if len(obs) > 1:\n # Polygonal obstacle\n o = Polygon(obs)\n p = Point(rpos)\n p1, p2 = nearest_points(o, p)\n # Velocity is always 0 for obstacles\n # Heading is same as robot's\n state.extend([p1.x, p2.y, 0, 0, self.obs_width,\n self.headings[self.robot_num]])\n else:\n # Point obstacle\n state.extend([obs[0][0], obs[0][1], 0, 0, self.obs_width,\n self.headings[self.robot_num]])\n return state","def h2d_level_to_order_lookup(tree, helmholtz_k, epsilon):\n\n if tree.dimensions != 2:\n raise ValueError(\"tree must be 2d\")\n\n orders = np.empty(tree.nlevels, dtype=int)\n bbox_area = np.max(\n tree.bounding_box[1] - tree.bounding_box[0]) ** 2\n\n from pyfmmlib import h2dterms\n for level in range(tree.nlevels):\n nterms, ier = h2dterms(bbox_area / 2 ** level, helmholtz_k, epsilon)\n if ier != 0:\n raise RuntimeError(\n \"h2dterms returned error code {ier}\".format(ier=ier))\n orders[level] = nterms\n\n return orders","def tracking_rafts(prev_rafts_locations, detected_centers):\n cost_matrix = scipy_distance.cdist(prev_rafts_locations, detected_centers, 'euclidean')\n row_ind, col_ind = linear_sum_assignment(cost_matrix)\n\n return col_ind","def get_heurisitc_cost_stacks(element, stack_index, goal_state, unused_stacks, unused_index):\n for k,v in goal_state.items():\n if element in v:\n return (abs(stack_index - k), v.index(element))\n if stack_index in unused_stacks:\n return (0, -1)\n else:\n return (abs(unused_index - stack_index), -1)","def get_board_positions_pieces_OHE(board):\n information = str(board).replace(\"\\n\", \" \")\n one_hot_encoded_board_positions = np.zeros(shape = (8,8,12))\n x = 0\n y = 0\n for piece in information:\n if(piece == \" \"):\n continue\n one_hot_encoded_board_positions[y, x, :] = get_piece_OHE(piece)\n if(x <= 7):\n x += 1 \n if(x > 7):\n x = 0\n y += 1\n return one_hot_encoded_board_positions","def get_co_occ_mat(s_hc_ml, n_s_real, n_s_hc):\n\n co_occs = np.zeros((n_s_hc, n_s_real))\n for idx, n in s_hc_ml.items():\n co_occs[idx] = n\n\n return co_occs","def getGameState(self):\n ### Student code goes here\n row1 = ()\n row2 = ()\n row3 = ()\n for currRow in range(1,4):\n for currCol in range(1,4):\n tileFound = False\n for fact in self.kb.facts:\n if fact.statement.predicate == \"located\":\n tile = fact.statement.terms[0].term.element\n column = fact.statement.terms[1].term.element\n row = fact.statement.terms[2].term.element\n\n tileNumber = int(tile[-1])\n columnNumber = int(column[-1])\n rowNumber = int(row[-1])\n\n if rowNumber == currRow and columnNumber == currCol:\n tileFound = True\n if rowNumber == 1:\n row1 += tuple([tileNumber])\n elif rowNumber == 2:\n row2 += tuple([tileNumber])\n elif rowNumber == 3:\n row3 += tuple([tileNumber])\n \n break\n\n if not tileFound:\n if currRow == 1:\n row1 += tuple([-1])\n elif currRow == 2:\n row2 += tuple([-1])\n elif currRow == 3:\n row3 += tuple([-1])\n\n\n return (row1, row2, row3)","def get_observation_driver_state(self):\n next_state = np.zeros(self.n_grids)\n grids = list(self.grids.values())\n for idx, grid in enumerate(grids):\n if grid is not None:\n next_state[idx] = grid.get_idle_driver_numbers_loop()\n return next_state","def branch_competetive(state, time, d):\n\n th0 = state[0] \n th1 = state[1:(d[\"alpha1\"]+d[\"alpha1_p\"])]\n th2 = state[(d[\"alpha1\"]+d[\"alpha1_p\"]):]\n \n #print(len(state), len(th1))\n ### get all cytokine secreting cells \n th1_all = np.sum(th1[-d[\"alpha1_p\"]:])\n th2_all = np.sum(th2[-d[\"alpha2_p\"]:])\n ### calculate cytokine concentrations\n cyto_1 = d[\"beta_cyto_1\"]*th1_all + d[\"ifn_ext\"]\n cyto_2 = d[\"beta_cyto_2\"]*th2_all + d[\"il21_ext\"]\n ### calculate cytokine effect on rate\n fb1 = d[\"fb_rate1\"]*cyto_1**3/(cyto_1**3+d[\"K_1\"]**3)\n fb2 = d[\"fb_rate2\"]*cyto_2**3/(cyto_2**3+d[\"K_2\"]**3)\n ### update differantiation rate\n beta1 = d[\"beta1\"]*(1+fb1)\n beta2 = d[\"beta2\"]*(1+fb2)\n \n ### differentiate effectors th1 \n alpha = d[\"alpha1\"]\n p = 1.\n dt_th1 = diff_effector2(th1, th0, alpha, beta1, d[\"beta1_p\"], p, d)\n ### differentiate effectors th2\n alpha = d[\"alpha2\"]\n p = 1.\n dt_th2 = diff_effector2(th2, th0, alpha, beta2, d[\"beta2_p\"], p, d)\n \n ### combine all cells\n dt_th0 = -(beta1+beta2)*th0\n dt_state = np.concatenate(([dt_th0], dt_th1, dt_th2))\n\n return dt_state","def successorStates(self, state):\r\n\r\n successors = []\r\n\r\n for action in Directions.CARDINAL:\r\n x, y = state\r\n dx, dy = Actions.directionToVector(action)\r\n nextx, nexty = int(x + dx), int(y + dy)\r\n\r\n if (not self.walls[nextx][nexty]):\r\n nextState = (nextx, nexty)\r\n cost = self.costFn(nextState)\r\n\r\n successors.append((nextState, action, cost))\r\n\r\n # Bookkeeping for display purposes (the highlight in the GUI).\r\n self._numExpanded += 1\r\n if (state not in self._visitedLocations):\r\n self._visitedLocations.add(state)\r\n self._visitHistory.append(state)\r\n\r\n return successors","def scc_order(self):\n if not self.__scc_order:\n self._tarjan()\n return self.__scc_order","def spotlessroomba_second_heuristic(state : SpotlessRoombaState) -> float:\n # TODO a nontrivial consistent heuristic\n \n if not state.dirty_locations:\n return 0\n \n best_start = 0 # best dirty tile to start from\n best_cost = INF # cost of the path from the above start tile\n\n for i in range(len(state.dirty_locations)):\n estimate_cost = 0\n lowest_cost = INF\n closest_dirty = 0\n dirty_locations = list(state.dirty_locations)\n current_pos = dirty_locations.pop(i)\n\n # find the shortest cost solution path from this starting tile\n while dirty_locations:\n for j in range(len(dirty_locations)):\n manhattan = abs(current_pos.row - dirty_locations[j].row) + abs(current_pos.col - dirty_locations[j].col)\n if manhattan < lowest_cost:\n lowest_cost = manhattan\n closest_dirty = j\n estimate_cost += lowest_cost\n current_pos = dirty_locations.pop(closest_dirty)\n lowest_cost = INF\n # if estimated path cost is cheaper than best path cost so far, replace best_cost and best_start\n if estimate_cost < best_cost:\n best_cost = estimate_cost\n best_start = i\n # if estimated path cost and best path cost so far are equal, tiebreak with proximity to start tile\n if estimate_cost == best_cost:\n current_pos = state.position\n dist_to_prev_best = abs(current_pos.row - state.dirty_locations[best_start].row) + abs(current_pos.col - state.dirty_locations[best_start].col)\n dist_to_i = abs(current_pos.row - state.dirty_locations[i].row) + abs(current_pos.col - state.dirty_locations[i].col)\n if dist_to_i < dist_to_prev_best:\n best_start = i\n \n\n current_pos = state.position\n # Calculate distance to the best start tile\n dist_to_start = abs(current_pos.row - state.dirty_locations[best_start].row) + abs(current_pos.col - state.dirty_locations[best_start].col)\n # Returned heuristic is the sum of distance to the start tile and estimated cost from said tile\n return dist_to_start + best_cost","def get_possible_actions(self, world_state,agent_host):\n action_list = []\n possibilities = {'movenorth 1': -3,'movesouth 1': 3,'moveeast 1': 1,'movewest 1': -1}\n #check walls to see whether can move left,right,back,forward\n #check floor beneath to see whether should do anything at all, or just nothing and sink\n obs_text = world_state.observations[-1].text\n obs = json.loads(obs_text)\n grid = load_grid(world_state)\n print 'GRID SIZE: ', len(grid)\n for k,v in possibilities.items():\n #with current grid, index 31 will always be our agent's current location\n #check walls to see whether can move left,right,back,forward\n if grid[31+v+9] == 'water' or grid[31+v+9] == 'wooden_door': #+9 because we want to check\n action_list.append(k) #where our feet are located\n #check if you can teleport down a level\n if grid[31-27] == 'water' or grid[31-27] == 'wooden_door':\n action_list.append(self.teleport(agent_host,False))\n #check if you can teleport up a level\n if grid[31+45] == 'water' or grid[31+45] == 'wooden_door':\n action_list.append(self.teleport(agent_host,True))\n\n print(\"ACTION LIST: {}\".format(action_list))\n return action_list","def minimax(state, depth, player):\n if depth == 9:\n row = choice([0, 1, 2])\n col = choice([0, 1, 2])\n return row, col, ''\n\n if player == COMP:\n best = [-1, -1, float(\"-inf\")]\n else:\n best = [-1, -1, float(\"inf\")]\n\n if depth == 0 or state.has_tic_tac_toe(COMP) or state.has_tic_tac_toe(HUMAN):\n score = heuristic(state, depth)\n return [-1, -1, score]\n \"\"\"\n Checks if any of the player is one away from winning in any board and make the appropriate move.\n \"\"\"\n if player==COMP:\n empty_cells=get_empty_cells(state)\n dangerous_cells=state.is_one_away_from_tic_tac_toe((player%2)+1)\n if dangerous_cells:\n found_dangerous_cells=True\n else:\n found_dangerous_cells=False\n print \"no dangerous local boards\"\n favoring_cells=state.is_one_away_from_tic_tac_toe(player)\n if favoring_cells:\n found_favoring_cells=True\n else:\n found_favoring_cells=False\n print \"no favoring local boards\"\n if found_dangerous_cells==False and found_favoring_cells==False:\n pass\n if found_dangerous_cells==False and found_favoring_cells==True:\n empty_cells[:]=[]\n for cell in favoring_cells:\n empty_cells.append(cell)\n if found_dangerous_cells==True and found_favoring_cells==False:\n empty_cells[:]=[]\n for cell in dangerous_cells:\n empty_cells.append(cell)\n if found_dangerous_cells==True and found_favoring_cells==True:\n empty_cells[:]=[]\n for cell in dangerous_cells:\n empty_cells.append(cell)\n else:\n empty_cells=get_empty_cells(state)\n for cell in empty_cells:\n row, col = cell[0], cell[1]\n state.board[row][col] = player\n score = minimax(state, depth - 1, (player % 2) + 1)\n state.board[row][col] = 0\n score[0], score[1] = row, col\n if player == COMP:\n if score[2] >= best[2]:\n if score[2]==best[2]:\n \"\"\"\n Favors middle positions over sides or corners\n MIDDLE > CORNERS > SIDES\n \"\"\"\n if (best[0]==0 and best[1]==0) or (best[0]==0 and best[1]==2) or (best[0]==2 and best[1]==0) or (best[0]==2 and best[1]==2):\n if score[0]==0 and score[1]==0: #favoring centre position over diagonal position\n best=score\n print(\"centre position chosen over diagonal positions\")\n else:\n if ((score[0]==0 and score[1]==1) or (score[0]==1 and score[1]==0) or (score[0]==1 and score[1]==2) or (score[0]==2 and score[1]==1))==0:\n best=score #favoring any position over side position as long as the new position is not a side position too\n print(\"diagonal and centre positions chosen over side positions\")\n else:\n best = score\n else:\n bestMoves=[]\n if score[2] < best[2]:\n best=score\n return best","def coranking_matrix(high_data, low_data):\n n, m = high_data.shape\n high_distance = distance.squareform(distance.pdist(high_data))\n low_distance = distance.squareform(distance.pdist(low_data))\n\n high_ranking = high_distance.argsort(axis=1).argsort(axis=1)\n low_ranking = low_distance.argsort(axis=1).argsort(axis=1)\n\n Q, xedges, yedges = np.histogram2d(high_ranking.flatten(),\n low_ranking.flatten(),\n bins=n)\n\n Q = Q[1:, 1:] # remove rankings which correspond to themselves\n return Q","def _get_maze_transition_probabilities(locs, movedir, exits):\n num_states = len(locs) + 1 # including final \"game over\" state\n num_actions = len(movedir)\n\n p = np.zeros((num_states, num_states, num_actions))\n for s, c in enumerate(locs):\n if c in exits:\n # transition to \"game over\" state (assumed to be last state)\n p[-1, s, :] = 1\n else:\n for a in range(num_actions):\n for j in [-1, 0, 1]:\n # next location\n cnext = [c[0] + movedir[(a + j) % num_actions][0],\n c[1] + movedir[(a + j) % num_actions][1]]\n if cnext in locs:\n snext = locs.index(cnext)\n else:\n # remain at current field\n snext = s\n # update probability table\n p[snext, s, a] += (0.8 if j == 0 else 0.1)\n # \"game over\" state transitions to itself\n p[-1, -1, :] = 1\n\n return p","def successorStates(self, state):\n currentState = state[1]\n successors = []\n for action in Directions.CARDINAL:\n x, y = state[0] # currentPosition\n print(\"State: {}\".format(state[0]))\n dx, dy = Actions.directionToVector(action)\n nextx, nexty = int(x + dx), int(y + dy)\n hitsWall = self.walls[nextx][nexty]\n\n # Implement a successor discovery, check if any corners are satisfied\n # and update values as they are satisfied\n if (not hitsWall):\n successorsState = []\n nextxy = (nextx, nexty)\n if nextxy == self.corners[0]:\n successorsState.append(True)\n else:\n successorsState.append(currentState[0])\n if nextxy == self.corners[1]:\n successorsState.append(True)\n else:\n successorsState.append(currentState[1])\n if nextxy == self.corners[2]:\n successorsState.append(True)\n else:\n successorsState.append(currentState[2])\n if nextxy == self.corners[3]:\n successorsState.append(True)\n else:\n successorsState.append(currentState[3])\n # Put all updated values of 4 corners to a variable\n successorPost = (successorsState[0], successorsState[1],\n successorsState[2], successorsState[3])\n # Append to go to the next move\n successors.append(((nextxy, successorPost), action, 1))\n\n self._numExpanded += 1 # Count the number of nodes expanded\n return successors","def minconflict(dict_prefs, initial_guess):\n ordering=deepcopy(initial_guess)\n while True:\n best=0\n best_swap=None\n pairs_indices=_get_pairs_indices(len(ordering))\n for pair_ind in pairs_indices:\n score_as_is,score_rev=_pair_net_effect(ordering,pair_ind,dict_prefs)\n improvement=(score_rev-score_as_is)\n if improvement>best:\n best=improvement\n best_swap=pair_ind\n if best_swap is None:\n break\n else:\n _swap(ordering,best_swap)\n return ordering","def get_successors(state): \n \n child_states = []\n \n size = len(state)\n i = 0\n j = 0\n for i in range (size):\n if 0 in state[i]:\n for j in range (size):\n if state[i][j] == 0:\n break \n break\n\n if j != size-1:\n child_states.append ((\"Left\", swap_cells(state, i, j, i, j+1)))\n if j != 0:\n child_states.append ((\"Right\", swap_cells(state, i, j, i, j-1)))\n if i != size-1:\n child_states.append ((\"Up\", swap_cells(state, i, j, i+1, j)))\n if i != 0:\n child_states.append ((\"Down\", swap_cells(state, i, j, i-1, j)))\n \n return child_states","def counter_opponent_adv(self):\n\n # get essential values\n board = self.get_game_space()\n affinity = self.get_affinity()\n opaffinity = self.get_opponent().get_affinity()\n\n # pick the right check for the game we are playing\n if isinstance(board, Gomoku):\n\n # get advantageous blocks\n blocks_advn = board.get_adv_blocks(opaffinity)\n best_moves = []\n best_move = None\n\n # sort the blocks which may be countered\n for block in blocks_advn:\n if block.direction == 'horizontal':\n x1 = block.tiles[0][0] - 1\n y1 = block.tiles[0][1] \n x2 = block.tiles[2][0] + 1\n y2 = block.tiles[2][1] \n if x1 < 0 or x2 >= 7: return None\n if board.get_tile(x1,y1) == BLANK_TILE() and \\\n board.get_tile(x2,y2) == BLANK_TILE():\n best_moves.append((x1,y1))\n elif block.direction == 'vertical':\n x1 = block.tiles[0][0] \n y1 = block.tiles[0][1] - 1 \n x2 = block.tiles[2][0]\n y2 = block.tiles[2][1] + 1\n if y1 < 0 or y2 >= 7: return None\n if board.get_tile(x1,y1) == BLANK_TILE() and \\\n board.get_tile(x2,y2) == BLANK_TILE():\n best_moves.append((x2,y2))\n elif block.direction == 'diagonal(\\)':\n x1 = block.tiles[0][0] - 1\n y1 = block.tiles[0][1] - 1\n x2 = block.tiles[2][0] + 1\n y2 = block.tiles[2][1] + 1\n if x1 < 0 or y1 < 0 or x2 >= 7 or y2 >= 7: return None\n if board.get_tile(x1,y1) == BLANK_TILE() and \\\n board.get_tile(x2,y2) == BLANK_TILE():\n best_moves.append((x1,y1))\n elif block.direction == 'diagonal(/)':\n x1 = block.tiles[0][0] - 1\n y1 = block.tiles[0][1] + 1\n x2 = block.tiles[2][0] + 1\n y2 = block.tiles[2][1] - 1\n if x1 < 0 or y1 >= 7 or x2 >= 7 or y2 < 0: return None\n if board.get_tile(x1,y1) == BLANK_TILE() and \\\n board.get_tile(x2,y2) == BLANK_TILE():\n best_moves.append((x1,y1))\n\n # pick the best move in the best block to counter\n for move in best_moves:\n print('considered advantageous move:'+str(move))\n if best_move is None: best_move = move \n elif move[0] < best_move[0] and move[1] == best_move[1]:\n best_move = move\n elif move[0] == best_move[0] and move[1] > best_move[1]:\n best_move = move\n elif move[0] < best_move[0] and move[1] > best_move[1]:\n best_move = move\n\n return best_move","def getRowHeuristics(matrix):\n row, col = matrix.shape\n rHeuristic = np.zeros((row,2)) # Dos columnas. La primera para indicar la columna la segunda para la Heuristica\n for i in range(0,row):\n rHeuristic[i,0] = int(i)\n #print (i,sum(matrix[:,i]), pesos[i], float(pesos[i]/sum(matrix[:,i])))\n rHeuristic[i,1] = 1/sum(matrix[i,:])\n return rHeuristic[rHeuristic[:,1].argsort()]","def get_transition(self, row, col, action, tot_row, tot_col):\n\n '''\n Expand the grid of the environment to handle when the \n agent decides to move in the direction of a wall \n '''\n state_probabilities = np.zeros((int(np.sqrt(self.env.observation_space.n)) + 2, int(np.sqrt(self.env.observation_space.n)) + 2), dtype=float)\n\n if action == 'UP':\n row += 1\n col += 1\n state_probabilities[row - 1, col] = 0.33 #UP\n state_probabilities[row, col - 1 ] = 0.33 #LEFT\n state_probabilities[row, col + 1] = 0.33 # RIGHT\n state_probabilities[row + 1, col] = 0.0 #DOWN\n elif action == 'LEFT':\n row += 1\n col += 1\n state_probabilities[row - 1, col] = 0.33 #UP\n state_probabilities[row, col - 1 ] = 0.33 #LEFT\n state_probabilities[row, col + 1] = 0.0 # RIGHT\n state_probabilities[row + 1, col] = 0.33 #DOWN\n elif action == 'RIGHT':\n row += 1\n col += 1\n state_probabilities[row - 1, col] = 0.33 #UP\n state_probabilities[row, col - 1 ] = 0.0 #LEFT\n state_probabilities[row, col + 1] = 0.33 # RIGHT\n state_probabilities[row + 1, col] = 0.33 #DOWN\n elif action == 'DOWN':\n row += 1\n col += 1\n state_probabilities[row - 1, col] = 0.0 # UP\n state_probabilities[row, col - 1] = 0.33 # LEFT\n state_probabilities[row, col + 1] = 0.33 # RIGHT\n state_probabilities[row + 1, col] = 0.33 # DOWN\n\n for row in range (0, tot_row+1):\n if state_probabilities[row, 0] != 0:\n state_probabilities[row, 1] += state_probabilities[row, 0]\n elif state_probabilities[row, -1] != 0:\n state_probabilities[row, -2] += state_probabilities[row, -1]\n\n for col in range (0, tot_col+1):\n if state_probabilities[0, col] != 0:\n state_probabilities[1, col] += state_probabilities[0, col]\n elif state_probabilities[-1, col] != 0:\n state_probabilities[-2, col] += state_probabilities[-1, col]\n\n return state_probabilities[1: 1+tot_row, 1:1+tot_col]","def get_transition(self, row, col, action, tot_row, tot_col):\n\n '''\n Expand the grid of the environment to handle when the \n agent decides to move in the direction of a wall \n '''\n state_probabilities = np.zeros((int(np.sqrt(self.env.observation_space.n)) + 2, int(np.sqrt(self.env.observation_space.n)) + 2), dtype=float)\n\n if action == 'UP':\n row += 1\n col += 1\n state_probabilities[row - 1, col] = 0.33 #UP\n state_probabilities[row, col - 1 ] = 0.33 #LEFT\n state_probabilities[row, col + 1] = 0.33 # RIGHT\n state_probabilities[row + 1, col] = 0.0 #DOWN\n elif action == 'LEFT':\n row += 1\n col += 1\n state_probabilities[row - 1, col] = 0.33 #UP\n state_probabilities[row, col - 1 ] = 0.33 #LEFT\n state_probabilities[row, col + 1] = 0.0 # RIGHT\n state_probabilities[row + 1, col] = 0.33 #DOWN\n elif action == 'RIGHT':\n row += 1\n col += 1\n state_probabilities[row - 1, col] = 0.33 #UP\n state_probabilities[row, col - 1 ] = 0.0 #LEFT\n state_probabilities[row, col + 1] = 0.33 # RIGHT\n state_probabilities[row + 1, col] = 0.33 #DOWN\n elif action == 'DOWN':\n row += 1\n col += 1\n state_probabilities[row - 1, col] = 0.0 # UP\n state_probabilities[row, col - 1] = 0.33 # LEFT\n state_probabilities[row, col + 1] = 0.33 # RIGHT\n state_probabilities[row + 1, col] = 0.33 # DOWN\n\n for row in range (0, tot_row+1):\n if state_probabilities[row, 0] != 0:\n state_probabilities[row, 1] += state_probabilities[row, 0]\n elif state_probabilities[row, -1] != 0:\n state_probabilities[row, -2] += state_probabilities[row, -1]\n\n for col in range (0, tot_col+1):\n if state_probabilities[0, col] != 0:\n state_probabilities[1, col] += state_probabilities[0, col]\n elif state_probabilities[-1, col] != 0:\n state_probabilities[-2, col] += state_probabilities[-1, col]\n\n return state_probabilities[1: 1+tot_row, 1:1+tot_col]","def getCapsuleGoal(self, gameState):\n capsules = self.getCapsules(gameState)\n myPos = self.getCurrentObservation().getAgentState(self.index).getPosition()\n if len(capsules) > 0:\n dis = 9999\n nearestCapsule = capsules[0]\n for c in capsules:\n temp = self.getMazeDistance(myPos, c)\n if temp < dis:\n dis = temp\n nearestCapsule = c\n return nearestCapsule, dis\n else:\n return None, None","def get_others(map_, r, c):\n nums = 0\n # your code here\n if r == 0 and c == 0: #top left corder\n nums += 2\n if len(map_[0]) > 1:\n if map_[r][c+1] == 0:\n nums += 1\n if map_[r+1][c] == 0:\n nums += 1\n elif r == 0 and c == len(map_[0])-1: #top right corner\n nums += 2\n if len(map_[0]) > 1:\n if map_[r][c-1] == 0:\n nums += 1\n if map_[r+1][c] == 0:\n nums += 1\n elif r == len(map_)-1 and c == 0: #bottom left corder\n nums += 2\n if len(map_[0]) > 1:\n if map_[r][c+1] == 0:\n nums += 1\n if map_[r-1][c] == 0:\n nums += 1\n elif r == len(map_)-1 and c == len(map_[0])-1: #bottom right corner\n nums += 2\n if map_[r][c-1] == 0:\n nums += 1\n if map_[r-1][c] == 0:\n nums += 1\n elif r == 0: # top edge, excluding corner\n nums += 1\n if map_[r][c-1] == 0:\n nums += 1\n if map_[r][c+1] == 0:\n nums += 1\n if len(map_) > r and map_[r+1][c] == 0:\n nums += 1\n elif r == len(map_)-1: # bottom edge, excluding corner\n nums += 1\n if map_[r][c-1] == 0:\n nums += 1\n if map_[r][c+1] == 0:\n nums += 1\n if map_[r-1][c] == 0:\n nums += 1\n elif c == 0: # left edge, excluding corner\n nums += 1\n if map_[r-1][c] == 0:\n nums += 1\n if map_[r+1][c] == 0:\n nums += 1\n if len(map_[0]) > c and map_[r][c+1] == 0:\n nums += 1\n elif c == len(map_[0])-1: # right edge. excluding corner\n nums += 1\n if map_[r-1][c] == 0:\n nums += 1\n if map_[r+1][c] == 0:\n nums += 1\n if map_[r][c-1] == 0:\n nums += 1\n else: # the rest, excluding edge and corner\n if map_[r-1][c] == 0:\n nums += 1\n if map_[r+1][c] == 0:\n nums += 1\n if map_[r][c-1] == 0:\n nums += 1\n if map_[r][c+1] == 0:\n nums += 1\n return nums","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 cudnn_lstm_state(lstm_cell_state):\n h = tf.stack([s.h for s in lstm_cell_state])\n c = tf.stack([s.c for s in lstm_cell_state])\n return (h, c)","def solve():\n\n s, g, e = make_lattice(21)\n stack = deque([[e]])\n vals = {s: 1}\n max_n = 0\n\n while stack:\n max_n = max(max_n, len(stack))\n n, *p = stack.pop()\n for c in g.get_connected(n):\n if c > n:\n continue\n if c in vals:\n propagate(c, [n] + p, vals)\n else:\n stack.append([c, n] + p)\n return vals[e]","def _get_state(self, obs_env):\n state = []\n obs_env = obs_env.reshape(self.n_agent, 2)\n for i in range(self.n_agent):\n local_obs = obs_env[i]\n if self.agent.startswith('ia2c'):\n imgs = [local_obs]\n\n if not self.agent == 'ia2c_fp': # ia2c\n for j in np.where(self.neighbor_mask[i] == 1)[0]:\n imgs.append(obs_env[j])\n imgs = np.array(imgs, dtype=np.float32)\n fps = np.array([], dtype=np.float32)\n\n else: # ia2c_fp\n fps = []\n for j in np.where(self.neighbor_mask[i] == 1)[0]:\n imgs.append(obs_env[j])\n fps.append(self.fp[j])\n imgs = np.array(imgs, dtype=np.float32)\n fps = np.concatenate(fps).astype(np.float32)\n\n agent_obs = [imgs, fps]\n\n else: # ma2c\n agent_obs = local_obs.astype(np.float32)\n\n state.append(agent_obs)\n\n return state\n # return [[obs_env, np.array([], dtype=np.float32)] for _ in range(self.n_agent)]","def coaccessible_components(self):\n DG = self.digraph().reverse()\n coaccessible_states = DG.breadth_first_search(\n [_.label() for _ in self.iter_final_states()])\n return self.induced_sub_finite_state_machine(\n [self.state(_) for _ in coaccessible_states])","def find_best_match_jobs(self,candidate_matrix):\n\n \"\"\"\n Example format for input:\n candidate_matrix =\n {\n 0: {\"This is a skill description\": 10, \"This is another skill description\": 7},\n 1: {\"This is a knowledge description\" : 8, \"This is another knowledge description\": 6}\n }\n \"\"\"\n\n\n\n\n\n\n # Get a list of all job codes to begin importing details for\n cursor = self.conn.execute(\"SELECT job_code FROM job\")\n\n # Get python list of all job codes\n job_codes = cursor.fetchall()\n\n\n job_score = {}\n\n for job_code in job_codes:\n job_code = job_code[0]\n job_importance_matrix = self.generate_job_importance_matrix(job_code)\n\n\n job_score[job_code] = self.calculate_job_score(candidate_matrix, job_importance_matrix)\n\n\n # Sort by job scores!\n sorted_jobs = sorted(job_score.iteritems(), key=operator.itemgetter(1))\n\n # Get in descending order\n sorted_jobs.reverse()\n\n # Return the jobs with the top 5 job scores for the candidate\n return sorted_jobs[:5]","def change_way(coins, opponentLocation, player_location):\n global best_weight, best_path\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(5, coins, player_location, dists_matrix)\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 best_weight = float(\"inf\")\n best_path = []\n api.debug(coins_to_search)\n exhaustive(coins_to_search, player_location, [], 0, dist_matrix)\n meta_route = [player_location] + best_path\n api.debug(meta_route)\n route = u.location_list_to_route(meta_route, route_matrix)\n \n return coins_to_search, meta_route, route, dist_matrix[player_location][meta_route[1]]","def initialize_action_matrix(self):\n for start_i, cols in enumerate(self.A):\n for next_j, value in enumerate(cols):\n start_state = self.get_state_from_row(start_i)\n # (row_number, red loc, blue loc, green loc)\n next_state = self.get_state_from_row(next_j)\n\n # Valid states include moving exactly 1 dumbbell to a block that's\n # not already occupied, or to origin.\n num_moves, moves = count_moves_from_states(\n start_state, next_state)\n \n # key: location (where 0 = origin, 1/2/3 = block #), value: count\n next_state_loc_counts = Counter(next_state[1:])\n del next_state_loc_counts[0] # ignore origin location\n \n # There must not be more than 1 dumbbell at each block\n is_valid_next_state = all(\n map(lambda x: x <= 1, next_state_loc_counts.values()))\n \n if (num_moves == 1 and is_valid_next_state):\n move = moves[0] # Only 1 move\n # doing nothing or moving to origin is not valid\n if (start_state == next_state or move[1] == 0):\n continue\n self.A[start_i][next_j] = self.get_col_from_action(move)","def compute_order(h: list[permutation]) -> int:\n\n # If only the trivial permutation is present in the generating set, the order is 1.\n if len(h) == 1 and h[0].istrivial():\n return 1\n\n # Choose a non-trivial orbit\n alpha = -1\n orbit = [0]\n while len(orbit) < 2:\n alpha += 1\n orbit = Orbit(h, alpha)\n\n # Calculate the order of the stabilizer\n stabilizer = Stabilizer(h, alpha)\n if len(stabilizer) == 0:\n stab_order = 1\n else:\n stab_order = compute_order(stabilizer)\n\n return len(orbit) * stab_order","def get_pair_lookup(fold, threshold=1, force=False):\n try:\n assert not force # this is some hacky control flow right here\n M = load_cooc_matrix(fold)\n logging.info('Loaded cooc matrix')\n except (AssertionError, IOError):\n logging.info('Building cooc matrix')\n users = iterate_wrapped_users(fold)\n with time_me('Built cooc matrix', mode='print'):\n M = build_cooc_matrix(users)\n M = M.tocoo()\n # converting to coo at this point might provide some memory relief\n save_cooc_matrix(fold, M)\n\n logging.info('Loaded cooc matrix with {:,} nonzero entries'.format(M.nnz))\n next_ix = 0\n lookup = {}\n # Turns out using scipy.sparse.find is slow af\n #with time_me('finded'):\n # focals, others, counts = scipy.sparse.find(M)\n assert isinstance(M, scipy.sparse.coo_matrix)\n focals, others, counts = M.row, M.col, M.data\n #for i in xrange(len(counts)):\n # focal_pidx = focals[i]\n # other_pidx = others[i]\n # count = counts[i]\n for (focal_pidx, other_pidx, count) in itertools.izip(focals, others, counts):\n\n if count >= threshold:\n k = (focal_pidx+1, other_pidx+1)\n lookup[k] = next_ix\n next_ix += 1\n if next_ix % 10**6 == 0:\n print '{:,}... '.format(next_ix)\n\n logging.info('Went from {:,} non-zero entries to {:,} pairs after thresholding by {}'.format(\n M.nnz, next_ix, threshold))\n if next_ix + constants.N_PRODUCTS > 2**31:\n logging.warn(\"Too many features even after thresholding. You're gonna have a bad time.\")\n return lookup","def calc_heuristic(self, state):\n h = 0\n board = state.board.array\n\n for i in range(self._n):\n for j in range(self._n):\n\n if board[i][j] != space_rep:\n tile_as_number = board[i][j]\n correct_x = (tile_as_number - 1) // self._n\n correct_y = (tile_as_number - 1) % self._n\n else:\n continue\n h += calc_diffs(i, j, correct_x, correct_y)\n return h","def persistent_homology(self):\n\n def low(j, R):\n \"\"\"\n :return: maximum line index of the column j in the matrix R with a 1 in it\n \"\"\"\n if R[j] == []:\n return (-1)\n else:\n return (sorted(R[j])[-1])\n\n # low_j = 0\n # for k in range(j):\n # if R[k, j] == 1:\n # low_j = k\n # return (low_j)\n\n N = self.nbr_splxs\n self.homology_matrix = self.neighbours_matrix[:]\n n = self.nbr_0_splxs\n # initilize the low_j matrix\n self.low_j_to_j_list = N * [-1]\n # Apply the persistence algorithm\n j = 0\n while low(j, self.homology_matrix) == -1:\n j += 1\n self.low_j_to_j_list[low(j, self.homology_matrix)] = j\n j += 1\n while j < N:\n low_j = low(j, self.homology_matrix)\n j0 = self.low_j_to_j_list[low_j]\n while j0 != -1:\n self.homology_matrix[j] = self.sum_column(j, j0, self.homology_matrix)\n # self.homology_matrix[:j, j] = (self.homology_matrix[:j, j0] + self.homology_matrix[:j, j]) % 2\n low_j = low(j, self.homology_matrix)\n j0 = self.low_j_to_j_list[low_j]\n if low_j != -1:\n self.low_j_to_j_list[low_j] = j\n j += 1\n if j % 10 == 0:\n print(j / N)\n # for j in range(1, N):\n # test = True\n # while test:\n # test = False\n # for j0 in range(j):\n # if low(j0, self.homology_matrix) == low(j, self.homology_matrix) \\\n # and low(j0, self.homology_matrix) != 0:\n # self.homology_matrix[:j, j] = (self.homology_matrix[:j, j0] + self.homology_matrix[:j, j]) % 2\n # test = True\n # if j % 10 == 0:\n # print(np.log(j + 1) / np.log(N))\n\n for j in range(N):\n low_j = low(j, self.homology_matrix)\n if low_j != -1:\n # print(low_j,j)\n # self.pers_pairs_birth.append(self.dist_appearance[low_j])\n # self.pers_pairs_death.append(self.dist_appearance[j])\n if self.splxs[low_j][0] == 0:\n self.h0_birth.append(self.dist_appearance[low_j])\n self.h0_death.append(self.dist_appearance[j])\n print(low_j)\n else:\n self.h1_birth.append(self.dist_appearance[low_j])\n self.h1_death.append(self.dist_appearance[j])\n print(\"persistant homology achieved\")\n return ()","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 next_possible_states(path, check_dict, check):\r\n \r\n current_state_tuple = path[-1]\r\n state_container = []\r\n x = current_state_tuple[1][0]\r\n y = current_state_tuple[1][1]\r\n current_state = current_state_tuple[0]\r\n\r\n # Down\r\n if y < 3:\r\n new_state = copy.deepcopy(current_state)\r\n new_state[y][x] = new_state[y + 1][x]\r\n new_state[y + 1][x] = 0\r\n if not been_there(new_state, check_dict, check):\r\n new_index = (x, y + 1)\r\n h1 = euclidean_dist(new_state, path)\r\n new_state_tuple = (new_state, new_index, h1)\r\n state_container.append(new_state_tuple)\r\n\r\n # Up\r\n if y > 0:\r\n new_state = copy.deepcopy(current_state)\r\n if y == 1 and x == 0:\r\n new_state[y][x] = new_state[y - 1][x]\r\n new_state[y - 1][x] = 0\r\n if is_goal(new_state):\r\n new_index = (x, y - 1)\r\n h1 = euclidean_dist(new_state, path)\r\n new_state_tuple = (new_state, new_index, h1)\r\n state_container.append(new_state_tuple)\r\n elif y > 1:\r\n new_state[y][x] = new_state[y - 1][x]\r\n new_state[y - 1][x] = 0\r\n if not been_there(new_state, check_dict, check):\r\n new_index = (x, y - 1)\r\n h1 = euclidean_dist(new_state, path)\r\n new_state_tuple = (new_state, new_index, h1)\r\n state_container.append(new_state_tuple)\r\n\r\n # Left\r\n if x > 0 and y > 0:\r\n new_state = copy.deepcopy(current_state)\r\n new_state[y][x] = new_state[y][x - 1]\r\n new_state[y][x - 1] = 0\r\n if not been_there(new_state, check_dict, check):\r\n new_index = (x - 1, y)\r\n h1 = euclidean_dist(new_state, path)\r\n new_state_tuple = (new_state, new_index, h1)\r\n state_container.append(new_state_tuple)\r\n\r\n # Right\r\n if x < 2 and y > 0:\r\n new_state = copy.deepcopy(current_state)\r\n new_state[y][x] = new_state[y][x + 1]\r\n new_state[y][x + 1] = 0\r\n if not been_there(new_state, check_dict, check):\r\n new_index = (x + 1, y)\r\n h1 = euclidean_dist(new_state, path)\r\n new_state_tuple = (new_state, new_index, h1)\r\n state_container.append(new_state_tuple)\r\n\r\n return state_container","def get_reverse_state_arr(self):\n rpos = self.sim.getAgentPosition(self.robot_num)\n rvel = self.sim.getAgentVelocity(self.robot_num)\n rrad = self.sim.getAgentRadius(self.robot_num)\n v_pref = self.sim.getAgentMaxSpeed(self.robot_num)\n theta = math.atan2(rvel[1], rvel[0])\n # Robot's state entry.\n state = [\n rpos[0], rpos[1], rvel[0], rvel[1], rrad,\n self.headings[self.robot_num], self.overall_robot_goal[0],\n self.overall_robot_goal[1], v_pref, theta\n ]\n for obs in self.obstacles:\n if len(obs) > 1:\n # Polygonal obstacle\n o = Polygon(obs)\n p = Point(rpos)\n p1, p2 = nearest_points(o, p)\n # Velocity is always 0 for obstacles\n # Heading is same as robot's\n state.extend([p1.x, p2.y, 0, 0, self.obs_width,\n self.headings[self.robot_num]])\n else:\n # Point obstacle\n state.extend([obs[0][0], obs[0][1], 0, 0, self.obs_width,\n self.headings[self.robot_num]])\n return state","def next_state(s_curr, action, params):\n P_dist = params['P_dist']\n R = params['R']\n n_rows = params['n_rows']\n n_cols = params['n_cols']\n occ_grid = params['occ_grid']\n\n rnd = np.random.uniform()\n\n s_next = s_curr\n\n # Actions - ['left','right','up','down']\n\n if rnd <= P_dist:\n if action == 0:\n move = 2\n elif action == 1:\n move = 2\n elif action == 2:\n move = 1\n else:\n move = 0\n elif rnd < 2*P_dist:\n if action == 0:\n move = 3\n elif action == 1:\n move = 3\n elif action == 2:\n move = 1\n else:\n move = 1\n else:\n move = action\n\n # Move left\n if move == 0:\n row_next = s_curr[0]\n col_next = s_curr[1] - 1\n if col_next >= 0 and occ_grid[row_next, col_next] == 0:\n s_next = [row_next, col_next]\n\n # Move right\n if move == 1:\n row_next = s_curr[0]\n col_next = s_curr[1] + 1\n if col_next < n_cols and occ_grid[row_next, col_next] == 0:\n s_next = [row_next, col_next]\n\n # Move up\n if move == 2:\n row_next = s_curr[0] - 1\n col_next = s_curr[1]\n if row_next >= 0 and occ_grid[row_next, col_next] == 0:\n s_next = [row_next, col_next]\n\n # Move down\n if move == 3:\n row_next = s_curr[0] + 1\n col_next = s_curr[1]\n if row_next < n_rows and occ_grid[row_next, col_next] == 0:\n s_next = [row_next, col_next]\n\n r = R[s_next[0], s_next[1]]\n return s_next, r","def compute_map(current_agent_id,agent_order,number_of_timestep,state_schedules, conv :StateConverter):\r\n #Find the agent has the highest number of time steps\r\n highest_timestep = 0\r\n # Find the highest time step\r\n if len(number_of_timestep) >0:\r\n highest_timestep = np.max(number_of_timestep)\r\n occupancy_map = []\r\n # Since we don't know yet how many time step of the current id so\r\n # the number of time steps of the occupancy map == highest number of time step\r\n # of the current schedule\r\n for time_step in range(int(highest_timestep)):\r\n # Initialize the occupancy for current time step\r\n current_occupancy_map = np.zeros(conv.num_tiles)\r\n # We loop through schedule of each agent at current time step\r\n for i in range(len(state_schedules)):\r\n # Get the agent id of current schedule\r\n agent_of_schedule = agent_order[i]\r\n if time_step < len(state_schedules[i]):\r\n # The first case when the agent of current schedule is executed after the current agent\r\n if agent_of_schedule > current_agent_id:\r\n # Get the current state\r\n current_state = state_schedules[i][time_step]\r\n # Convert the current state to tile index\r\n current_tile = conv.state_to_tile(current_state)\r\n # Occupied the current tile in the occupancy map\r\n current_occupancy_map[current_tile] = 1\r\n if time_step + 1 < len(state_schedules[i]):\r\n # Get the next state\r\n next_state = state_schedules[i][time_step + 1]\r\n # Convert next state to next tile will be occupied\r\n next_tile_index = conv.state_to_tile(next_state)\r\n # Occupied the next tile in the occupancy map\r\n current_occupancy_map[next_tile_index] = 1\r\n # The second case when the agent of current schedule is executed before the current agent\r\n else:\r\n if time_step + 1 < len(state_schedules[i]):\r\n # Get the next state\r\n next_state = state_schedules[i][time_step + 1]\r\n # Convert next state to next tile will be occupied\r\n next_tile_index = conv.state_to_tile(next_state)\r\n # Occupied the next tile in the occupancy map\r\n current_occupancy_map[next_tile_index] = 1\r\n if time_step + 2 < len(state_schedules[i]):\r\n # Get the next 2 state\r\n next_2state = state_schedules[i][time_step+2]\r\n # Convert the current state to tile index\r\n next_2tile = conv.state_to_tile(next_2state)\r\n # Occupied the current tile in the occupancy map\r\n current_occupancy_map[next_2tile] = 1\r\n occupancy_map.append(current_occupancy_map)\r\n return occupancy_map","def state_rep (self):\n\n # Computing dealer_card\n dealer_card = self.dealer[0]\n\n # Compute player_max\n player_max = self.max_safe_sum()\n\n # State should not be bust\n assert (1 <= dealer_card <= 10)\n assert (0 <= player_max <= 31)\n\n # Compute table number\n possibilities = get_full_state (self.me)\n # possibilities = [p for p in possibilities if 0 <= p <= 31]\n\n table_no = 0\n for idx, p in enumerate(possibilities):\n if 0 <= p <= 31:\n table_no = idx\n assert 0 <= table_no <= 3\n\n # print (possibilities)\n return (table_no, dealer_card, player_max)","def getNextState(self, board, player, action):\n b = self._base_board.with_np_pieces(np_pieces=np.copy(board))\n b.add_stone(action, player)\n return b.np_pieces, -player","def get_success_state_arr(self):\n rpos = self.sim.getAgentPosition(self.robot_num)\n rvel = self.sim.getAgentVelocity(self.robot_num)\n rrad = self.sim.getAgentRadius(self.robot_num)\n v_pref = self.sim.getAgentMaxSpeed(self.robot_num)\n theta = math.atan2(rvel[1], rvel[0])\n # Robot's state entry.\n state = [\n rpos[0], rpos[1], rvel[0], rvel[1], rrad,\n self.headings[self.robot_num], self.overall_robot_goal[0],\n self.overall_robot_goal[1], v_pref, theta\n ]\n for agent in self.agents:\n if agent != self.robot_num: # We already accounted for the robot\n pos = self.sim.getAgentPosition(agent)\n vel = self.sim.getAgentVelocity(agent)\n rad = self.sim.getAgentRadius(agent)\n state.extend([pos[0], pos[1], vel[0], vel[1], rad,\n self.headings[agent]])\n for obs in self.obstacles:\n if len(obs) > 1:\n # Polygonal obstacle\n o = Polygon(obs)\n p = Point(rpos)\n p1, p2 = nearest_points(o, p)\n # Velocity is always 0 for obstacles\n # Heading is same as robot's\n state.extend([p1.x, p2.y, 0, 0, self.obs_width,\n self.headings[self.robot_num]])\n else:\n # Point obstacle\n state.extend([obs[0][0], obs[0][1], 0, 0, self.obs_width,\n self.headings[self.robot_num]])\n return state","def calculate_next_board_state(self):\n new_board_state = np.zeros_like(self.board_state)\n\n for x in range(self.board_size[0]):\n for y in range(self.board_size[0]):\n new_board_state[x][y] = self.next_state_of_cell(x,y)\n \n self.set_state(new_board_state)","def calc_neck_score(self):\n ordered_c, edgelen = self.coords, self.edgelen\n nc = np.vstack((ordered_c, ordered_c[:edgelen, :]))\n score = []\n for n, ci in enumerate(nc[:-edgelen, :]):\n try:\n score.append(calc_clockwise_degree(ordered_c[n-edgelen, :], nc[n, :], nc[n+edgelen, :]))\n except:\n pass\n idx = np.flipud(np.argsort(score))\n return np.array(score)[idx], ordered_c[idx]","def close_to_opponent_high_pass(obs, player_x, player_y):\n def environment_fits(obs, player_x, player_y):\n \"\"\" environment fits constraints \"\"\"\n for i in range(1, len(obs[\"right_team\"])):\n distance_to_opponent = get_distance(player_x, player_y, obs[\"right_team\"][i][0], obs[\"right_team\"][i][1])\n if distance_to_opponent < 0.06:\n for j in range(1, len(obs[\"left_team\"])):\n player_to_opponents = get_average_distance_to_opponents(obs, player_x, player_y)\n teammate_to_opponents = get_average_distance_to_opponents(obs, player_x, player_y)\n distance_to_teammate = get_distance(player_x, player_y, obs[\"left_team\"][j][0], obs[\"left_team\"][j][1])\n if distance_to_teammate < 0.6 and distance_to_teammate > 0.4 and player_to_opponents > teammate_to_opponents:\n teammate_distance_to_goal = get_distance(obs[\"left_team\"][j][0], obs[\"left_team\"][j][1], 1, 0)\n player_distance_to_goal = get_distance(player_x, player_y, 1, 0)\n if teammate_distance_to_goal < player_distance_to_goal - 0.2:\n return True\n break\n return False\n \n def get_action(obs, player_x, player_y):\n \"\"\" get action of this memory pattern \"\"\"\n if Action.Sprint in obs[\"sticky_actions\"]:\n return Action.ReleaseSprint\n return Action.HighPass\n\n return {\"environment_fits\": environment_fits, \"get_action\": get_action}","def search(self, canonicalBoard, this_player):\n\n def player_net(player):\n return self.white_nnet if player == Player.white else self.black_nnet\n\n value_stack = []\n next_player = this_player\n iteration = 0\n\n # build stack\n while True:\n # workaround because in some cases this function doesn't stop (bug might exist in the original version also)\n # print(\" iteration \" + str(iteration))\n iteration += 1\n if iteration > 1000:\n print(\"more MCTS search iterations than the maximum, breaking out of possibly infinite loop!\")\n return\n\n # workaround end\n\n # bytes are much faster\n s = self.game.stringRepresentation(canonicalBoard) + next_player.to_bytes(1, byteorder='big', signed=True)\n # s = self.game.stringRepresentation(canonicalBoard) + str(next_player) # this addition is needed so that\n # the search algorithm doesn't get confused when the same board state as before is reached, but it's the\n # other player's turn\n\n if s not in self.Es:\n self.Es[s] = self.game.getGameEnded(canonicalBoard, next_player)\n if self.Es[s] != 0:\n # terminal node\n last_iteration_v = -self.Es[s]\n break\n\n if s not in self.Ps:\n # leaf node\n valids = self.game.getValidMoves(canonicalBoard, next_player)\n\n # occurrences = np.zeros(self.size * self.size * self.size * 2)\n # for index, action in enumerate(valids):\n # if action == 1 and index != self.size * self.size * self.size * 2:\n # explicit = action_conversion__index_to_explicit(index, self.size)\n # occurrences[index] = 1 if canonicalBoard.would_next_board_be_second_third(2, explicit) else 0\n\n self.Ps[s], v = player_net(next_player).predict(canonicalBoard, np.array([canonicalBoard.king_position[0], canonicalBoard.king_position[1]]))\n # valids = self.game.getValidMoves(canonicalBoard, next_player)\n self.Ps[s] = self.Ps[s] * valids # masking invalid moves\n sum_Ps_s = np.sum(self.Ps[s])\n if sum_Ps_s > 0:\n self.Ps[s] /= sum_Ps_s # renormalize\n else:\n # if all valid moves were masked make all valid moves equally probable\n\n # NB! All valid moves may be masked if either your NNet architecture is insufficient or you've get overfitting or something else.\n # If you have got dozens or hundreds of these messages you should pay attention to your NNet and/or training process.\n print(\"All valid moves were masked, do workaround.\")\n print(valids)\n self.Ps[s] = self.Ps[s] + valids\n self.Ps[s] /= np.sum(self.Ps[s])\n\n self.Vs[s] = valids\n self.Ns[s] = 0\n last_iteration_v = -v\n break\n\n valids = self.Vs[s]\n cur_best = -float('inf')\n best_act = -1\n\n # pick the action with the highest upper confidence bound\n for a in range(self.game.getActionSize()):\n if valids[a]:\n if (s, a) in self.Qsa:\n u = self.Qsa[(s, a)] + self.args.cpuct * self.Ps[s][a] * math.sqrt(self.Ns[s]) / (\n 1 + self.Nsa[(s, a)])\n else:\n u = self.args.cpuct * self.Ps[s][a] * math.sqrt(self.Ns[s] + EPS) # Q = 0 ?\n\n if u > cur_best:\n cur_best = u\n best_act = a\n\n a = best_act\n\n next_s, next_player = self.game.getNextState(canonicalBoard, next_player, a)\n canonicalBoard = self.game.getCanonicalForm(next_s, next_player)\n\n value_stack.append((s, a))\n\n # take from stack\n while len(value_stack) > 0:\n s, a = value_stack.pop()\n\n if (s, a) in self.Qsa:\n self.Qsa[(s, a)] = (self.Nsa[(s, a)] * self.Qsa[(s, a)] + last_iteration_v) / (self.Nsa[(s, a)] + 1)\n self.Nsa[(s, a)] += 1\n\n else:\n self.Qsa[(s, a)] = last_iteration_v\n self.Nsa[(s, a)] = 1\n\n self.Ns[s] += 1\n last_iteration_v = -last_iteration_v\n\n return -last_iteration_v","def orderMetric(y_true, y_pred, weights):\n ok = weights > 0.0\n return spearmanr(y_true[ok], y_pred[ok])[0]","def _calculate_sentence_level_chrf_score(targets: List[str], pred_char_n_grams_counts: Dict[int, Dict[Tuple[str, ...], Tensor]], pred_word_n_grams_counts: Dict[int, Dict[Tuple[str, ...], Tensor]], preds_char_n_grams: Dict[int, Tensor], preds_word_n_grams: Dict[int, Tensor], n_char_order: int, n_word_order: int, n_order: float, beta: float, lowercase: bool, whitespace: bool) ->Tuple[Tensor, Dict[int, Tensor], Dict[int, Tensor], Dict[int, Tensor], Dict[int, Tensor]]:\n best_f_score = tensor(0.0)\n best_matching_char_n_grams: Dict[int, Tensor] = defaultdict(lambda : tensor(0.0))\n best_matching_word_n_grams: Dict[int, Tensor] = defaultdict(lambda : tensor(0.0))\n best_target_char_n_grams: Dict[int, Tensor] = defaultdict(lambda : tensor(0.0))\n best_target_word_n_grams: Dict[int, Tensor] = defaultdict(lambda : tensor(0.0))\n for target in targets:\n target_char_n_grams_counts, target_word_n_grams_counts, target_char_n_grams, target_word_n_grams = _get_n_grams_counts_and_total_ngrams(target, n_char_order, n_word_order, lowercase, whitespace)\n matching_char_n_grams = _get_ngram_matches(target_char_n_grams_counts, pred_char_n_grams_counts)\n matching_word_n_grams = _get_ngram_matches(target_word_n_grams_counts, pred_word_n_grams_counts)\n f_score = _calculate_fscore(matching_char_n_grams, matching_word_n_grams, preds_char_n_grams, preds_word_n_grams, target_char_n_grams, target_word_n_grams, n_order, beta)\n if f_score > best_f_score:\n best_f_score = f_score\n best_matching_char_n_grams = matching_char_n_grams\n best_matching_word_n_grams = matching_word_n_grams\n best_target_char_n_grams = target_char_n_grams\n best_target_word_n_grams = target_word_n_grams\n return best_f_score, best_matching_char_n_grams, best_matching_word_n_grams, best_target_char_n_grams, best_target_word_n_grams","def solve_tsp_grouped(list_of_locations, list_of_homes, starting_car_location, adjacency_matrix, params=[]):\n drop_off_dict = {}\n car_path = []\n home_map = {}\n group_score = {}\n center_map = {}\n epsilon = 1.2\n home_indexes = convert_locations_to_indices(list_of_homes, list_of_locations)\n orig_home_indexes = home_indexes[:]\n cluster_map = {}\n\n start = list_of_locations.index(starting_car_location)\n graph, msg = adjacency_matrix_to_graph(adjacency_matrix)\n all_paths = dict(nx.all_pairs_dijkstra(graph))\n\n for v in range(len(list_of_locations)):\n group_score[v], cluster_map[v] = compute_group(v, home_indexes[:], all_paths, epsilon)\n\n sorted_v = sorted([k for k in group_score.keys() if group_score[k] > 0], key = lambda x: group_score[x])\n min_group_score = group_score[sorted_v[0]]\n #print(min_group_score)\n delta = 6\n\n high_centrality_homes = set() #LOW GROUP SCORE VERTICES (CLUSTER)\n used_homes = set()\n newHome = home_indexes[:]\n\n while newHome and group_score[sorted_v[0]] < delta * min_group_score:\n\n v = sorted_v[0]\n high_centrality_homes.add(v)\n usedList = cluster_map[v]\n #print(usedList)\n if v in newHome and v not in usedList:\n usedList.append(v)\n if v in center_map.keys():\n center_map[v].extend(usedList)\n else:\n center_map[v] = usedList\n #if v in home_indexes:\n #center_map[v].append(v)\n for vert in usedList:\n used_homes.add(vert)\n if vert in newHome:\n newHome.remove(vert)\n used_homes.add(v)\n while v in newHome:\n newHome.remove(v)\n for x in range(len(list_of_locations)):\n group_score[x], cluster_map[x] = compute_group(x, newHome[:], all_paths, epsilon)\n sorted_v = sorted([k for k in group_score.keys() if group_score[k] > 0], key = lambda x: group_score[x])\n\n for home in newHome:\n if home not in center_map.keys():\n center_map[home] = [home]\n \"\"\"START TIM\n\n for home in high_centrality_homes:\n center_map[home] = [home]\n dist_dict = all_paths.get(home)[0]\n paths_dict = all_paths.get(home)[1]\n dist_dict = {k:v for (k,v) in dist_dict.items() if k not in used_homes and k in home_indexes} #distance dict of all remaing homes\n\n min_dist = min(dist_dict.values()) #closest home to high centrality home\n dist_dict = {k:v for (k,v) in dist_dict.items() if dist_dict[k] <= min_dist*epsilon}\n\n for cluster_home in dist_dict.keys():\n center_map[home].append(cluster_home)\n home_indexes.remove(cluster_home)\n used_homes.add(cluster_home)\n\n start_in_home = start in home_indexes\n if start in home_indexes:\n home_indexes.remove(start)\n home_indexes.insert(0, start)\n home_count = 0;\n\n for home in home_indexes:\n #print(home, end = \" \")\n home_map[home_count] = home\n home_count += 1\n # Instantiate the data problem.\n #print(len(home_map))\n END TIM \"\"\"\n tspInput = list(high_centrality_homes)\n tspInput.extend(newHome)\n if start in tspInput:\n tspInput.remove(start)\n tspInput.insert(0, start)\n home_map.clear()\n for i in range(len(tspInput)):\n home_map[i] = tspInput[i]\n data = create_data_model(tspInput, 0)\n\n # Create the routing index manager.\n manager = pywrapcp.RoutingIndexManager(len(data['locations']),\n data['num_vehicles'], data['depot'])\n\n #print(manager.NodeToIndex(15))\n # Create Routing Model.\n routing = pywrapcp.RoutingModel(manager)\n\n def distance_callback(from_index, to_index):\n \"\"\"Returns the distance between the two nodes.\"\"\"\n # Convert from routing variable Index to distance matrix NodeIndex.\n #print(home_map[to_index], end = \" \")\n from_index = manager.IndexToNode(from_index)\n to_index = manager.IndexToNode(to_index)\n dist_to = all_paths.get(home_map[from_index])[0][home_map[to_index]]\n #if from_index >= 25 or to_index >= 25:\n # print(\"from\" if from_index >= 25 else \"to\", end = \" \")\n #dist_to = all_paths[from_index][0][to_index]\n return dist_to\n\n transit_callback_index = routing.RegisterTransitCallback(distance_callback)\n\n # Define cost of each arc.\n routing.SetArcCostEvaluatorOfAllVehicles(transit_callback_index)\n\n # Setting first solution heuristic.\n \"\"\"\n search_parameters = pywrapcp.DefaultRoutingSearchParameters()\n search_parameters.first_solution_strategy = (\n routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC)\n \"\"\"\n\n search_parameters = pywrapcp.DefaultRoutingSearchParameters()\n search_parameters.local_search_metaheuristic = (\n routing_enums_pb2.LocalSearchMetaheuristic.GUIDED_LOCAL_SEARCH)\n search_parameters.time_limit.seconds = 3\n #search_parameters.log_search = True\n\n # Solve the problem.\n assignment = routing.SolveWithParameters(search_parameters)\n\n # if assignment:\n # print_solution(manager, routing, assignment)\n # Print solution on console.\n\n #if start in home_indexes:\n # drop_off_dict[start] = [start]\n\n\n index = routing.Start(0)\n car_path.append(start)\n\n while not routing.IsEnd(index):\n previous_index = manager.IndexToNode(index)\n index = assignment.Value(routing.NextVar(index))\n to_index = manager.IndexToNode(index)\n car_path.append(home_map[to_index])\n #path_to = all_paths.get(home_map[previous_index])[1][home_map[to_index]]\n #drop_off_dict[home_map[to_index]] = [home_map[to_index]]\n #print(to_index, end = ' ')\n #car_path.extend(path_to)\n #route_distance += routing.GetArcCostForVehicle(previous_index, index, 0)\n # for i in car_path:\n # print(i)\n\n #print(car_path)\n drop_off_dict = center_map\n new_path = [start]\n previous_index = start\n\n\n #print(center_map)\n for to_index in car_path[1:]:\n new_path.pop()\n path_to = all_paths.get(previous_index)[1][to_index]\n new_path.extend(path_to)\n previous_index = to_index\n keys = drop_off_dict.keys()\n singleadd = []\n for i in keys:\n if i not in orig_home_indexes:\n vals = drop_off_dict[i]\n for v in vals:\n if v in new_path:\n if v in keys:\n drop_off_dict[v].append(v)\n drop_off_dict[i].remove(v)\n else:\n singleadd.append(v)\n drop_off_dict[i].remove(v)\n for item in singleadd:\n drop_off_dict[item] = [item]\n\n for i in drop_off_dict.keys():\n for k in drop_off_dict.keys():\n if i != k:\n if i in drop_off_dict[k]:\n drop_off_dict[k].remove(i)\n drop_off_dict[i].append(i)\n\n removeIt = set()\n for i in drop_off_dict.keys():\n if not drop_off_dict[i]:\n removeIt.add(i)\n\n for i in removeIt:\n drop_off_dict.pop(i, None)\n\n car_path = new_path\n return car_path, drop_off_dict","def find_closest_state(H, reference_state, QN):\n \n #Make a state vector for reference state\n reference_state_vec = reference_state.state_vector(QN)\n \n #Find eigenvectors of the given Hamiltonian\n E, V = np.linalg.eigh(H)\n \n #Find out which of the eigenstates of H corresponds to reference state\n state_index = find_state_idx(reference_state_vec,V,n=1)\n \n #Find state vector of state corresponding to reference\n state_vec = V[:,state_index:state_index+1]\n \n #return the state\n state = matrix_to_states(state_vec,QN)[0]\n \n return state","def determine_most_stable_conformer(self, label):\n if self.species_dict[label].is_ts:\n raise SchedulerError('The determine_most_stable_conformer() method does not deal with transition '\n 'state guesses.')\n if 'conformers' in self.job_dict[label].keys() and all(e is None for e in self.species_dict[label].conformer_energies):\n logger.error(f'No conformer converged for species {label}! Trying to troubleshoot conformer jobs...')\n for i, job in self.job_dict[label]['conformers'].items():\n self.troubleshoot_ess(label, job, level_of_theory=job.level, conformer=job.conformer)\n else:\n conformer_xyz = None\n xyzs = list()\n if self.species_dict[label].conformer_energies:\n xyzs = self.species_dict[label].conformers\n else:\n for job in self.job_dict[label]['conformers'].values():\n xyzs.append(parser.parse_xyz_from_file(path=job.local_path_to_output_file))\n xyzs_in_original_order = xyzs\n energies, xyzs = sort_two_lists_by_the_first(self.species_dict[label].conformer_energies, xyzs)\n plotter.save_conformers_file(project_directory=self.project_directory,\n label=label,\n xyzs=self.species_dict[label].conformers,\n level_of_theory=self.conformer_level,\n multiplicity=self.species_dict[label].multiplicity,\n charge=self.species_dict[label].charge,\n is_ts=False,\n energies=self.species_dict[label].conformer_energies, # after optimization\n )\n # Run isomorphism checks if a 2D representation is available\n if self.species_dict[label].mol is not None:\n for i, xyz in enumerate(xyzs):\n try:\n b_mol = molecules_from_xyz(xyz,\n multiplicity=self.species_dict[label].multiplicity,\n charge=self.species_dict[label].charge)[1]\n except SanitizationError:\n b_mol = None\n if b_mol is not None:\n try:\n is_isomorphic = check_isomorphism(self.species_dict[label].mol, b_mol)\n except ValueError as e:\n if self.allow_nonisomorphic_2d or \\\n are_coords_compliant_with_graph(xyz=xyz, mol=self.species_dict[label].mol):\n logger.error(f'Could not determine isomorphism for charged species {label}. '\n f'Optimizing the most stable conformer anyway. Got the '\n f'following error:\\n{e}')\n conformer_xyz = xyz\n break\n else:\n logger.error(f'Could not determine isomorphism for species {label}. Got the '\n f'following error:\\n{e}')\n break\n if is_isomorphic or are_coords_compliant_with_graph(xyz=xyz, mol=self.species_dict[label].mol):\n if i == 0:\n b_mol_smiles = b_mol.copy(deep=True).to_smiles() \\\n if b_mol is not None else '<no 2D structure available>'\n logger.info(f'Most stable conformer for species {label} was found to be isomorphic '\n f'with the 2D graph representation {b_mol_smiles}\\n')\n conformer_xyz = xyz\n if 'passed isomorphism check' not in self.output[label]['conformers']:\n self.output[label]['conformers'] += f'most stable conformer ({i}) passed ' \\\n f'isomorphism check; '\n self.species_dict[label].conf_is_isomorphic = True\n else:\n if energies[i] is not None:\n mol = molecules_from_xyz(xyzs[0],\n multiplicity=self.species_dict[label].multiplicity,\n charge=self.species_dict[label].charge)[1]\n smiles_1 = self.species_dict[label].mol.copy(deep=True).to_smiles() \\\n if self.species_dict[label].mol is not None else '<no 2D structure available>'\n smiles_2 = mol.copy(deep=True).to_smiles() \\\n if mol is not None else '<no 2D structure available>'\n logger.info(f'A conformer for species {label} was found to be isomorphic with the '\n f'2D graph representation {smiles_1}.\\n'\n f'This conformer is {energies[i] - energies[0]:.2f} kJ/mol above the '\n f'most stable one corresponding to {smiles_2} (and is not isomorphic). '\n f'Using the isomorphic conformer for further geometry optimization.')\n self.output[label]['conformers'] += f'Conformer {i} was found to be the lowest ' \\\n f'energy isomorphic conformer; '\n conformer_xyz = xyz\n if 'Conformers optimized and compared' not in self.output[label]['conformers']:\n self.output[label]['conformers'] += \\\n f'Conformers optimized and compared at {self.conformer_level.simple()}; '\n break\n else:\n if i == 0:\n self.output[label]['conformers'] += f'most stable conformer ({i}) did not ' \\\n f'pass isomorphism check; '\n self.species_dict[label].conf_is_isomorphic = False\n smiles_1 = b_mol.copy(deep=True).to_smiles() \\\n if b_mol is not None else '<no 2D structure available>'\n smiles_2 = self.species_dict[label].mol.copy(deep=True).to_smiles() \\\n if self.species_dict[label].mol is not None else '<no 2D structure available>'\n logger.warning(f'Most stable conformer for species {label} with structure '\n f'{smiles_1} was found to be NON-isomorphic '\n f'with the 2D graph representation {smiles_2}. '\n f'Searching for a different conformer that is isomorphic...')\n else:\n # all conformers for the species failed isomorphism test\n smiles_list = list()\n for xyz in xyzs:\n try:\n b_mol = molecules_from_xyz(xyz,\n multiplicity=self.species_dict[label].multiplicity,\n charge=self.species_dict[label].charge)[1]\n smiles_list.append(b_mol.copy(deep=True).to_smiles())\n except (SanitizationError, AttributeError):\n smiles_list.append('Could not perceive molecule')\n if self.allow_nonisomorphic_2d or self.species_dict[label].charge:\n # we'll optimize the most stable conformer even if it is not isomorphic to the 2D graph\n logger.error(f'No conformer for {label} was found to be isomorphic with the 2D graph '\n f'representation {self.species_dict[label].mol.copy(deep=True).to_smiles()} '\n f'(got: {smiles_list}). Optimizing the most stable conformer anyway.')\n self.output[label]['conformers'] += 'No conformer was found to be isomorphic with ' \\\n 'the 2D graph representation; '\n if self.species_dict[label].charge:\n logger.warning(f'Isomorphism check cannot be done for charged species {label}')\n conformer_xyz = xyzs[0]\n else:\n # troubleshoot when all conformers of a species failed isomorphic test\n logger.warning(f'Isomorphism check for all conformers of species {label} failed at '\n f'{self.conformer_level.simple()}. '\n f'Attempting to troubleshoot using a different level.')\n self.output[label]['conformers'] += \\\n f'Error: No conformer was found to be isomorphic with the 2D graph representation at ' \\\n f'{self.conformer_level.simple()}; '\n self.troubleshoot_conformer_isomorphism(label=label)\n else:\n logger.warning(f'Could not run isomorphism check for species {label} due to missing 2D graph '\n f'representation. Using the most stable conformer for further geometry optimization.')\n conformer_xyz = xyzs[0]\n if conformer_xyz is not None:\n self.species_dict[label].initial_xyz = conformer_xyz\n self.species_dict[label].most_stable_conformer = xyzs_in_original_order.index(conformer_xyz)\n logger.info(f'Conformer number {xyzs_in_original_order.index(conformer_xyz)} for species {label} is '\n f'used for geometry optimization.')\n self.output[label]['job_types']['conformers'] = True","def next(self):\n self.current_state = self.next_state\n self.next_state = self.clear_screen() # set values to 0\n for x in range(1, 101):\n for y in range(1, 101):\n # calculate the number of alive neighbours at given coordinates\n self.neighbours_alive = self.check_neighbours_alive(x, y)\n\n # assign the result value from rule sets\n self.next_state[x][y] = self.rule_sets[self.selected_rule][ # selected rule name\n str(self.current_state[x][y])][ # 0 or 1 (dead or alive)\n self.neighbours_alive] # number between 0 to 8\n return self.next_state","def metropolis_hastings(dict_prefs, initial_guess, iterations=50000, explore_fact=2, random_seed=None):\n ordering=deepcopy(initial_guess)\n best=0\n current_score=0\n if random_seed is not None:\n random.seed(random_seed)\n np.random.seed(random_seed)\n for it in range(iterations):\n candidates_ind=random.sample(range(len(ordering)), 2)\n score_as_is,score_rev=_pair_net_effect(ordering,candidates_ind,dict_prefs)\n if score_rev>score_as_is:\n _swap(ordering,candidates_ind)\n current_score+=(score_rev-score_as_is)\n if current_score>best:\n best=current_score\n best_ordering=deepcopy(ordering)\n else:\n criterion=(explore_fact)**(score_rev-score_as_is)\n if np.random.random()<=criterion:\n _swap(ordering,candidates_ind)\n current_score+=(score_rev-score_as_is)\n return best_ordering","def find_next_moves(self):\n # iterate through all cells, and group them with upper cells and left\n # cells\n\n # generate separated cells then merge the them with same neighbours\n matrix_rows = len(self.status)\n if matrix_rows == 0:\n matrix_cols = 0\n else:\n matrix_cols = len(self.status[0])\n matrix = []\n for i in range(matrix_rows):\n matrix.append([[(i, j)] for j in range(matrix_cols)])\n # merge coordinations\n for i in range(matrix_rows):\n for j in range(matrix_cols):\n if self.status[i][j] != '':\n # is same with right cell?\n if j < matrix_cols - 1 and self.status[i][j] == self.status[i][j + 1]:\n new_item = matrix[i][j] + matrix[i][j + 1]\n matrix[i][j] = matrix[i][j + 1] = new_item\n # is same with down cell?\n if i < matrix_rows - 1 and self.status[i][j] == self.status[i + 1][j]:\n new_item = matrix[i][j] + matrix[i + 1][j]\n matrix[i][j] = matrix[i + 1][j] = new_item\n\n # filter out all unvalid results\n result = []\n # filter out all single-cell groups\n for i in range(matrix_rows):\n for j in range(matrix_cols):\n if (len(matrix[i][j]) > 1 and\n matrix[i][j] not in result):\n result.append(matrix[i][j])\n\n # filter sublists\n result = sorted(result, key=len, reverse=True)\n changed = True\n while changed:\n changed = False\n for i in range(len(result)):\n for j in range(i + 1, len(result)):\n if set(result[i]).issuperset(set(result[j])):\n result.remove(result[j])\n changed = True\n break\n if changed:\n break\n\n if result:\n for i in result:\n yield (self.convert_coordinations(i),\n len(i) * len(i) * 5,\n self.calc_new_status(i))\n else:\n left_cells = sum([len(i) - i.count('') for i in self.status])\n left_cells_score = 2000 - 20 * left_cells * left_cells\n if left_cells_score < 0:\n left_cells_score = 0\n for i in self.parents:\n i.children[self] = [(i.children[self][0][0] + left_cells_score,\n i.children[self][0][1],\n i.children[self][0][2])]","def solve(list_of_locations, list_of_homes, starting_car_location, adjacency_matrix, params=[]):\n\n\n\n\n path = [starting_car_location]\n dict = {}\n index = 0\n for i in range(len(list_of_locations)):\n if list_of_locations[i] == starting_car_location:\n index = i\n\n path = [index]\n\n G, m = adjacency_matrix_to_graph(adjacency_matrix)\n\n home_indexes = []\n\n for home in list_of_homes:\n for i in range(len(list_of_locations)):\n if list_of_locations[i] == home:\n home_indexes.append(i)\n break\n\n new_adjacency = [[\"x\" for i in range(len(list_of_locations))] for j in range(len(list_of_locations))]\n\n # for sake of figuring out where to walk\n for home in home_indexes:\n di_path = nx.dijkstra_path(G, index, home)\n for i in range(len(di_path) - 1):\n new_adjacency[di_path[i]][di_path[i + 1]] = adjacency_matrix[di_path[i]][di_path[i + 1]]\n new_adjacency[di_path[i + 1]][di_path[i]] = adjacency_matrix[di_path[i]][di_path[i + 1]]\n\n\n for home1 in home_indexes:\n for home2 in home_indexes:\n if not home1 == home2:\n di_path = nx.dijkstra_path(G, home1, home2)\n for i in range(len(di_path) - 1):\n new_adjacency[di_path[i]][di_path[i + 1]] = adjacency_matrix[di_path[i]][di_path[i + 1]]\n new_adjacency[di_path[i + 1]][di_path[i]] = adjacency_matrix[di_path[i]][di_path[i + 1]]\n\n\n\n\n G2, m = adjacency_matrix_to_graph(new_adjacency)\n\n all_driving_path = list(nx.dfs_edges(G2))\n\n\n\n\n walking_to = []\n walking_from = {}\n\n for i in range(len(new_adjacency)):\n if i in home_indexes:\n count = 0\n edge_to = 0\n for j in range(len(new_adjacency)):\n if new_adjacency[i][j] != \"x\":\n count += 1\n edge_to = j\n\n #must ensure that this is not a home that we are already dropping someone off at, otherwise it will cut off a line of two homes\n if count == 1 and i != index and i not in walking_from.keys():\n new_adjacency[i][edge_to] = \"x\"\n new_adjacency[edge_to][i] = \"x\"\n walking_to.append(i)\n if edge_to in walking_from:\n walking_from[edge_to] = walking_from[edge_to] + [i]\n else:\n walking_from[edge_to] = [i]\n\n #\n # for i in range(len(all_driving_path) - 1):\n # #if first vertex in edge is the same, we should walk\n # if all_driving_path[i][0] == all_driving_path[i + 1][0]:\n # print(all_driving_path[i][0])\n # print(all_driving_path[i][1])\n # #get rid of only edge connected to this home\n # new_adjacency[all_driving_path[i][0]][all_driving_path[i][1]] = \"x\"\n # new_adjacency[all_driving_path[i][1]][all_driving_path[i][0]] = \"x\"\n # walking_to.append(all_driving_path[i][1])\n # if all_driving_path[i][0] in walking_from:\n # walking_from[all_driving_path[i][0]] = walking_from[all_driving_path[i][0]] + [all_driving_path[i][1]]\n # else:\n # walking_from[all_driving_path[i][0]] = [all_driving_path[i][1]]\n\n\n\n dropoff_locations = list(walking_from.keys())\n for loc in dropoff_locations:\n if loc in home_indexes:\n dropoff_locations.remove(loc)\n\n\n for loc in dropoff_locations:\n di_path = nx.dijkstra_path(G, loc, home)\n for i in range(len(di_path) - 1):\n new_adjacency[di_path[i]][di_path[i + 1]] = adjacency_matrix[di_path[i]][di_path[i + 1]]\n new_adjacency[di_path[i + 1]][di_path[i]] = adjacency_matrix[di_path[i]][di_path[i + 1]]\n\n for loc in dropoff_locations:\n for home in home_indexes:\n di_path = nx.dijkstra_path(G, loc, home)\n for i in range(len(di_path) - 1):\n new_adjacency[di_path[i]][di_path[i + 1]] = adjacency_matrix[di_path[i]][di_path[i + 1]]\n new_adjacency[di_path[i + 1]][di_path[i]] = adjacency_matrix[di_path[i]][di_path[i + 1]]\n\n\n G2, m = adjacency_matrix_to_graph(new_adjacency)\n # G = G2\n # pos=nx.spring_layout(G2)\n # nx.draw_networkx_nodes(G2,pos)\n # nx.draw_networkx_labels(G2, pos)\n # nx.draw_networkx_edges(G2,pos,width=1.0,alpha=0.5)\n #\n # plt.draw()\n # plt.show()\n\n # condensed shortest paths to edges - use G3 for real\n\n new_adjacency2 = [[\"x\" for i in range(len(list_of_locations))] for j in range(len(list_of_locations))]\n\n for home in home_indexes:\n if home not in walking_to:\n di_path = nx.dijkstra_path(G2, index, home)\n start = di_path[0]\n end = di_path[len(di_path) - 1]\n new_adjacency2[start][end] = 0\n new_adjacency2[end][start] = 0\n for i in range(len(di_path) - 1):\n new_adjacency2[start][end] += new_adjacency[di_path[i]][di_path[i + 1]]\n new_adjacency2[end][start] += new_adjacency[di_path[i]][di_path[i + 1]]\n\n\n for home1 in home_indexes:\n for home2 in home_indexes:\n if not home1 == home2 and home1 not in walking_to and home2 not in walking_to:\n di_path = nx.dijkstra_path(G2, home1, home2)\n start = di_path[0]\n end = di_path[len(di_path) - 1]\n new_adjacency2[start][end] = 0\n new_adjacency2[end][start] = 0\n for i in range(len(di_path) - 1):\n new_adjacency2[start][end] += new_adjacency[di_path[i]][di_path[i + 1]]\n new_adjacency2[end][start] += new_adjacency[di_path[i]][di_path[i + 1]]\n\n for loc in dropoff_locations:\n di_path = nx.dijkstra_path(G2, index, loc)\n start = di_path[0]\n end = di_path[len(di_path) - 1]\n new_adjacency2[start][end] = 0\n new_adjacency2[end][start] = 0\n for i in range(len(di_path) - 1):\n new_adjacency2[start][end] += new_adjacency[di_path[i]][di_path[i + 1]]\n new_adjacency2[end][start] += new_adjacency[di_path[i]][di_path[i + 1]]\n\n for loc in dropoff_locations:\n for home in home_indexes:\n di_path = nx.dijkstra_path(G2, loc, home)\n start = di_path[0]\n end = di_path[len(di_path) - 1]\n new_adjacency2[start][end] = 0\n new_adjacency2[end][start] = 0\n for i in range(len(di_path) - 1):\n new_adjacency2[start][end] += new_adjacency[di_path[i]][di_path[i + 1]]\n new_adjacency2[end][start] += new_adjacency[di_path[i]][di_path[i + 1]]\n\n\n\n\n final_G, m = adjacency_matrix_to_graph(new_adjacency2)\n drive_path = list(nx.dfs_edges(final_G, source=index))\n drive_path.append(index)\n\n mst = nx.minimum_spanning_tree(final_G)\n\n\n\n new_mst = nx.MultiGraph(mst)\n for edge in mst.edges():\n new_mst.add_edge(edge[0], edge[1])\n\n\n if new_mst.degree[index] != 0:\n to_remove = []\n for node in new_mst:\n if (new_mst.degree[node] == 0):\n to_remove.append(node)\n new_mst.remove_nodes_from(to_remove)\n\n eulerian = list(nx.eulerian_circuit(new_mst, index))\n\n path = []\n for edge in eulerian:\n path.append(edge[0])\n\n path.append(eulerian[len(eulerian) - 1][1])\n\n already_seen = []\n to_remove = []\n for i in range(len(path) - 1):\n if path[i] in already_seen:\n to_remove.append(i)\n else:\n already_seen.append(path[i])\n\n new_path = []\n for i in range(len(path) - 1):\n if i not in to_remove:\n new_path.append(path[i])\n path = new_path\n print(eulerian)\n else:\n path = [index]\n print(path)\n\n\n\n\n\n\n\n # print(path)\n final_path = []\n for node in path:\n if node == index:\n final_path.append(node)\n # print(\"Index: \", node)\n elif node in home_indexes and node not in walking_to:\n final_path.append(node)\n # print(\"Home but not walking: \", node)\n elif node in dropoff_locations:\n final_path.append(node)\n # print(\"Dropoff loc: \", node)\n final_path.append(index)\n #print(walking_from)\n # print(final_path)\n # nx.draw(mst)\n # plt.draw()\n # plt.show()\n for node in final_path:\n if node in walking_from and node in home_indexes:\n dict[node] = [node] + walking_from[node]\n elif node in home_indexes:\n dict[node] = [node]\n elif node in walking_from:\n dict[node] = walking_from[node]\n\n very_final_path = []\n for i in range(len(final_path) - 1):\n condensed_path = nx.dijkstra_path(G2, final_path[i], final_path[i+1])\n for j in range(len(condensed_path) - 1):\n if condensed_path[j] != condensed_path[j + 1]:\n very_final_path.append(condensed_path[j])\n\n if len(very_final_path) >= 1 and [len(very_final_path) - 1] != index:\n very_final_path.append(index)\n\n if len(very_final_path) == 0:\n very_final_path = [index]\n\n print(very_final_path)\n print(dict)\n\n\n path2 = list(nx.dfs_preorder_nodes(mst, index))\n\n final_path2 = []\n for node in path2:\n if node == index:\n final_path2.append(node)\n # print(\"Index: \", node)\n elif node in home_indexes and node not in walking_to:\n final_path2.append(node)\n # print(\"Home but not walking: \", node)\n elif node in dropoff_locations:\n final_path2.append(node)\n # print(\"Dropoff loc: \", node)\n final_path2.append(index)\n\n\n for node in final_path2:\n if node in walking_from and node in home_indexes:\n dict[node] = [node] + walking_from[node]\n elif node in home_indexes:\n dict[node] = [node]\n elif node in walking_from:\n dict[node] = walking_from[node]\n\n very_final_path2 = []\n for i in range(len(final_path2) - 1):\n condensed_path = nx.dijkstra_path(G2, final_path2[i], final_path2[i+1])\n for j in range(len(condensed_path) - 1):\n if condensed_path[j] != condensed_path[j + 1]:\n very_final_path2.append(condensed_path[j])\n\n if len(very_final_path2) >= 1 and [len(very_final_path2) - 1] != index:\n very_final_path2.append(index)\n\n if len(very_final_path2) == 0:\n very_final_path2 = [index]\n\n opt1 = cost_of_solution(G, very_final_path, dict)\n opt2 = cost_of_solution(G, very_final_path2, dict)\n\n ultra_final_path = []\n if (opt1 <= opt2):\n ultra_final_path = very_final_path\n else:\n ultra_final_path = very_final_path2\n\n return ultra_final_path, dict\n\n pass"],"string":"[\n \"def calc_nearest_state(self): # TODO: Check if we need here state, instead of self.state\\n self.stateC = self.toConceptual(self.state)\\n CTP, winners = self.find_winner()\\n\\n state_name = self.find_TPname(filleridx=winners)\\n binding = self.find_symBinding(filleridx=winners)\\n state_num = self.find_TPnum(stateName=state_name)\\n TP_state = self.TP.matmul(fortran_reshape(CTP, (torch.numel(CTP), 1)))\\n Cdist = torch.norm(CTP - self.stateC, p='fro') # Frobenius Norm\\n Sdist = self.L2norm(TP_state - self.state)\\n TP_h = self.calc_harmony(state=TP_state)\\n\\n return TP_state, winners, state_name, binding, Cdist, Sdist, state_num, TP_h\",\n \"def find_winner(self):\\n\\n # Extract the max value of each col in the conceptual state matrix\\n winners_idx = torch.argmax(self.stateC, dim=0)\\n\\n M = torch.zeros_like(self.stateC)\\n # Populate the matrix\\n for r in range(M.shape[1]):\\n M[winners_idx[r].item(), r] = 1\\n return M, winners_idx\",\n \"def computer_driver_heuristic(self, pc):\\n if pc == self.departure_location:\\n return self.nearest_neigbor(pc)\\n else:\\n # encode state: State -> Generalized One hot vector\\n # print(len(self.idx_to_pc)+1)\\n encoded_vector = np.zeros(len(self.idx_to_pc)+1)\\n\\n # indices of locations FOR ENCODING\\n pickup_jobs_idx = [self.pc_to_idx[p]+1 for p in list(self.state.P_k.keys())] # +1 is to make room for the time dim\\n deliv_jobs_idx = [self.pc_to_idx[p]+1 for p in list(self.state.D_k.keys())]\\n\\n # indices of locations FOR PC READING\\n pickup_jobs_idx_read = [self.pc_to_idx[p] for p in list(self.state.P_k.keys())]\\n deliv_jobs_idx_read = [self.pc_to_idx[p] for p in list(self.state.D_k.keys())]\\n tasks = set(pickup_jobs_idx_read + deliv_jobs_idx_read)\\n\\n if len(tasks) > 0:\\n # set appropriate values at the index corresponding to the location\\n encoded_vector[pickup_jobs_idx] = -0.5\\n encoded_vector[deliv_jobs_idx] = 0.5\\n encoded_vector[self.pc_to_idx[pc]+1] = 1\\n\\n # # current time encoded as nb of seconds between 12pm and now/nb seconds between 12pm and 12am\\n total_nb_seconds = datetime.timedelta(hours=12, minutes=0)\\n cur_time = self.state.t_k.time()\\n cur_time = datetime.timedelta(hours=cur_time.hour, minutes=cur_time.minute) # nb of seconds from 12am\\n # # TODO this can further be noramlized as most values will be >0 (>6am)\\n cur_time = 2 * cur_time.seconds / total_nb_seconds.seconds - 1 # normalized time in [-1,1]\\n encoded_vector[0] = cur_time\\n\\n # predict decision\\n pred = self.heuristic_model.predict_proba(encoded_vector.reshape(1,-1))\\n\\n # take the most probable location among the remaining jobs\\n # # set proba to 0 if location not among remaining jobs\\n # print(\\\"##############\\\")\\n # print(\\\"shape of pred \\\", pred.shape)\\n # print(\\\"Number of locations considered : \\\", len(self.idx_to_pc))\\n print(\\\"Possible indices to choose from : \\\", tasks)\\n pred[0, list(set(range(0, len(self.idx_to_pc))) - set(pickup_jobs_idx_read + deliv_jobs_idx_read))] = 0\\n\\n idx_opt = np.argsort(pred[0,:])[-1] # most probable location (by its index) among remaining jobs\\n print(\\\"Index chosen : \\\", idx_opt )\\n return self.idx_to_pc[idx_opt]\\n\\n elif len(tasks) == 0:\\n return 0\\n\\n else:\\n raise ValueError('Problem with tasks, which has negative length...')\",\n \"def _get_state(self):\\n # COMPUTE CLASSIFIER_STATE\\n predictions = self.model.predict_proba(self.dataset.state_data)[:,0]\\n predictions = np.array(predictions)\\n idx = np.argsort(predictions)\\n # the state representation is the *sorted* list of scores \\n classifier_state = predictions[idx]\\n \\n # COMPUTE ACTION_STATE\\n unknown_data = self.dataset.train_data[self.indeces_unknown,:]\\n # prediction (score) of classifier on each unlabelled sample\\n a1 = self.model.predict_proba(unknown_data)[:,0]\\n # average distance to every unlabelled datapoint\\n a2 = np.mean(self.dataset.distances[self.indeces_unknown,:][:,self.indeces_unknown],axis=0)\\n # average distance to every labelled datapoint\\n a3 = np.mean(self.dataset.distances[self.indeces_known,:][:,self.indeces_unknown],axis=0)\\n next_action_state = np.concatenate(([a1], [a2], [a3]), axis=0)\\n return classifier_state, next_action_state\",\n \"def search(world_state, robot_pose, goal_pose):\\n if world_state.shape[0] == 0 or world_state.shape[1] == 0:\\n print(\\\"Error, empty world_state!!!\\\")\\n return None\\n if not is_pos_valid(robot_pose, world_state.shape):\\n print(\\\"Error, invalid robot_pose!!!\\\", robot_pose)\\n return None\\n if not is_pos_valid(goal_pose, world_state.shape):\\n print(\\\"Error, invalid goal_pose!!!\\\", goal_pose)\\n return None\\n\\n directions = [(-1, 0), (1, 0), (0, -1), (0, 1)] # orthogonal directions\\n found = False\\n\\n x, y = robot_pose\\n g = 0\\n h = heuristic(robot_pose, goal_pose)\\n f = g + h\\n open = [[f, x, y]]\\n came_from = {}\\n came_from[robot_pose] = None\\n cost_so_far = {}\\n cost_so_far[robot_pose] = 0\\n\\n while open:\\n open.sort() # sort based on f value\\n current = open.pop(0)\\n\\n x, y = current[1:]\\n g = cost_so_far[(x, y)]\\n\\n if (x, y) == goal_pose:\\n found = True\\n break\\n else:\\n # find available next positions\\n for direction in directions:\\n x2 = x + direction[0]\\n y2 = y + direction[1]\\n\\n # check whether x2 and y2 are valid\\n if not is_pos_valid((x2, y2), world_state.shape):\\n continue\\n\\n g2 = g + 1\\n if world_state[x2, y2] == 0 and ((x2, y2) not in cost_so_far or g2 < cost_so_far[(x2, y2)]):\\n\\n h2 = heuristic((x2, y2), goal_pose)\\n f2 = g2 + h2\\n open.append([f2, x2, y2])\\n came_from[(x2, y2)] = (x, y)\\n cost_so_far[(x2, y2)] = g2\\n if found:\\n path = [goal_pose]\\n current = goal_pose\\n while came_from[current]:\\n current = came_from[current]\\n path.append(current)\\n\\n path.reverse()\\n return path\\n\\n else:\\n return None\",\n \"def find_topo_order(s,graph):\\n\\n ## initialization\\n matrix = graph.get_adjacency()\\n n, c = matrix.shape\\n sym_matrix = np.empty((n,c), dtype=object)\\n # cost_matrix = np.zeros((n,c))\\n cache = {}\\n\\n def symbolize(i,j):\\n \\\"given two indices, create a symbolic variable\\\"\\n s = z.Int('edge_{0}{1}'.format(i,j))\\n return s\\n\\n\\n def value_of(i,j):\\n \\\"given two indices, return the (i,j)th value in the adjacency matrix\\\"\\n return sym_matrix[i][j]\\n\\n\\n def constraint_1(n,i,j,k):\\n y_ij = value_of(i,j)\\n y_jk = value_of(j,k)\\n y_ik = value_of(i,k)\\n\\n name = \\\"c1\\\" + str((n,i,j,k))\\n constraint = (y_ij + y_jk - y_ik) <= 1\\n\\n # if name not in cache:\\n # cache[name] = constraint\\n s.assert_and_track(constraint, name)\\n\\n\\n def constraint_2(n,i,j,k):\\n y_ij = value_of(i,j)\\n y_jk = value_of(j,k)\\n y_ik = value_of(i,k)\\n\\n name = \\\"c2\\\" + str((n,i,j,k))\\n constraint = (-y_ij - y_jk + y_ik) <= 0\\n\\n # if name not in cache:\\n # cache[name] = constraint\\n s.assert_and_track(constraint, name)\\n\\n\\n def constraint_3(symbolic):\\n s.add(z.Or([symbolic == 0, symbolic == 1]))\\n\\n\\n def int_formulation(j):\\n left = z.Sum([matrix[k][j] * sym_matrix[k][j] for k in range(j)])\\n right = z.Sum([matrix[l][j] * (1 - sym_matrix[j][l]) for l in range(j+1, n)])\\n\\n return [left, right]\\n\\n\\n ## constraint 3, every edge must be a 0 or a 1, we get the 0 or 1 directly\\n ## from the adjacency matrix\\n ## we do this first so that the sym_matrix is populated\\n for n_iter in range(n):\\n for j in range(n_iter+1):\\n for i in range(j):\\n s_edge = symbolize(i,j)\\n sym_matrix[i][j] = s_edge\\n constraint_3(s_edge)\\n\\n ## Iteration for triangle inequalities\\n for n_iter in range(n):\\n for k in range(n_iter+1):\\n for j in range(k):\\n for i in range(j):\\n constraint_1(n_iter,i,j,k)\\n constraint_2(n_iter,i,j,k)\\n\\n\\n ## minimization\\n o = z.Optimize()\\n y = z.Int('y')\\n\\n y = z.Sum(u.flatten([int_formulation(j) for j in range(n)]))\\n o.minimize(y)\\n\\n result = []\\n\\n if s.check() == z.sat:\\n result = s.model()\\n\\n return result\",\n \"def _get2dCostMatrix(c_hat, time_step_indices: TimeStepIndices, track_count, measurement_count):\\n\\n # Compute track-measurement cost for single scan problem from hypothesis\\n # cost, including null assignment costs\\n # Construct track to measurement assignment matrix at scan k\\n cost = np.full((track_count, measurement_count), np.inf)\\n nullCost = np.full((track_count,), np.inf)\\n # store index of the single target hypothesis with the minimum cost for\\n # each track and measurement\\n idxCost = np.full((track_count, measurement_count), -1, dtype=np.int32)\\n idxNullCost = np.full((track_count,), -1, dtype=np.int32)\\n for track_id, trackNull_index_for_track in enumerate(time_step_indices.trackNull_index):\\n if len(trackNull_index_for_track):\\n min_index = c_hat[trackNull_index_for_track].argmin()\\n min_index_all_c_hat = trackNull_index_for_track[min_index]\\n nullCost[track_id] = c_hat[min_index_all_c_hat]\\n idxNullCost[track_id] = min_index_all_c_hat\\n # find single target hypotheses in track i that use this\\n # measurement if found, find the single target hypothesis with\\n # the minimum cost, and record its index\\n for measurement in range(measurement_count):\\n measTrack_index_for_meas = time_step_indices.measTrack_index[measurement, track_id]\\n if len(measTrack_index_for_meas):\\n min_index = c_hat[measTrack_index_for_meas].argmin()\\n min_index_all_c_hat = measTrack_index_for_meas[min_index]\\n cost[track_id, measurement] = c_hat[min_index_all_c_hat]\\n idxCost[track_id, measurement] = min_index_all_c_hat\\n\\n # Create cost matrix for nulls with null costs on diagonal and inf\\n # elsewhere (so we can have any number of null assignments)\\n nullCostMatrix = np.full((track_count, track_count), np.inf, dtype=np.float64)\\n # Pick out diagonal entries (like np.diagonal, but this is a writable view)\\n nullCostMatrix.ravel()[::track_count + 1] = nullCost\\n\\n fullCostMatrix = np.concatenate((cost, nullCostMatrix), axis=1)\\n return fullCostMatrix, idxCost, idxNullCost\",\n \"def greedy_selector(self):\\n r_k = 0 \\n best_route = []\\n cities_to_visit = [i for i in range(1, self.city_count)]\\n for _ in range(1, self.city_count):\\n s_ind = np.argmax([self.tau[(r_k, u)] for u in cities_to_visit])\\n s_k = cities_to_visit.pop(s_ind)\\n best_route.append((r_k, s_k))\\n r_k = s_k\\n best_route.append((r_k, 0))\\n \\n shortest_path = np.sum([self.phi[(p)] for p in best_route])\\n return best_route, shortest_path\",\n \"def h(self, state):\\n loc = dict((val, (i, j)) for i, row in enumerate(self.goal_state)\\n for j, val in enumerate(row))\\n\\n def calculate_cost(i, j):\\n val = state[i][j]\\n goal_i, goal_j = loc[val]\\n return abs(goal_i - i) + abs(goal_j - j)\\n\\n return sum(\\n calculate_cost(i, j) for i in xrange(self.size)\\n for j in xrange(self.size) if state[i][j])\",\n \"def misplaced_heuristic(state):\\n msp_h = 0\\n size = len(state)\\n for i in range (size):\\n for j in range (size):\\n if state[i][j] == 0:\\n continue\\n elif state[i][j] != i*size + j:\\n msp_h += 1\\n return msp_h\",\n \"def sort_state(self, best_hyp_indices: mx.nd.NDArray, best_word_indices: mx.nd.NDArray):\\n self.prev_target_word_id = best_word_indices\\n self.decoder_states = [mx.nd.take(ds, best_hyp_indices) for ds in self.decoder_states]\",\n \"def compute_lookuptable(self):\\n\\n if self.uselookuptable:\\n # Evaluation lookup tables \\n self.action_isok = np.zeros( ( self.nodes_n , self.actions_n ) , dtype = bool )\\n self.x_next = np.zeros( ( self.nodes_n , self.actions_n , self.DS.n ) , dtype = float ) # lookup table for dynamic\\n \\n # For all state nodes \\n for node in range( self.nodes_n ): \\n \\n x = self.nodes_state[ node , : ]\\n \\n # For all control actions\\n for action in range( self.actions_n ):\\n \\n u = self.actions_input[ action , : ]\\n \\n # Compute next state for all inputs\\n x_next = self.DS.fc( x , u ) * self.dt + x\\n \\n # validity of the options\\n x_ok = self.DS.isavalidstate(x_next)\\n u_ok = self.DS.isavalidinput(x,u)\\n \\n self.x_next[ node, action, : ] = x_next\\n self.action_isok[ node, action] = ( u_ok & x_ok )\",\n \"def top_k_betweenness_centrality(self):\\n d={}\\n l=[]\\n for v in vertices:\\n a=self.betweenness_centrality(v)\\n d[v]=a\\n l.append(a)\\n m=max(l)\\n l1=[]\\n for key in d:\\n if d[key]==m:\\n l1.append(key)\\n\\n return l1\",\n \"def csuccessors(state):\\n M1, C1, B1, M2, C2, B2 = state\\n assert B1 is 1 or B2 is 1\\n pairs = {} \\n moves = [(0,1), (1,0), (1,1), (2,0), (0,2)]\\n if C1 > M1 or C2 > M2: \\n return pairs # more cannibals than missionaries\\n if B1 is 1:\\n B1, B2 = 0, 1\\n for m, c in moves:\\n if M1 - m >= 0 and C1 - c >= 0:\\n state = (M1-m, C1-c, B1, M2+m, C2+c, B2)\\n action = 'M' * m + 'C' * c + '->'\\n pairs[state] = action\\n else:\\n B1, B2 = 1, 0\\n for m, c in moves:\\n if M2 - m >= 0 and C2 - c >= 0:\\n state = (M1+m, C1+c, B1, M2-m, C2-c, B2)\\n action = '<-' + 'M' * m + 'C' * c\\n pairs[state] = action\\n return pairs\",\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 greedy_match(cost_matrix):\\n num_detections, num_tracks = cost_matrix.shape\\n distance_1d = cost_matrix.reshape(-1)\\n index_1d = np.argsort(distance_1d)\\n index_2d = np.stack([index_1d // num_tracks, index_1d % num_tracks], axis=1).astype(int, copy=False)\\n\\n matched_indices = []\\n matched_firsts = set()\\n matched_seconds = set()\\n\\n for (first_id, second_id) in index_2d:\\n if first_id not in matched_firsts and second_id not in matched_seconds:\\n matched_seconds.add(second_id)\\n matched_firsts.add(first_id)\\n matched_indices.append([first_id, second_id])\\n return np.array(matched_indices)\",\n \"def heuristic(state, depth):\\n if state.has_tic_tac_toe(COMP):\\n score = depth + 1\\n elif state.has_tic_tac_toe(HUMAN):\\n score = -(depth + 1)\\n else: # draw/undetermined outcome\\n score = 0\\n return score\",\n \"def get_constr_obs_states_high(self):\\n constrHi = numpy.empty(self.get_num_variables())\\n i = 0\\n for v in self.variables:\\n constrHi[i] = v.get_constraint_high()\\n i += 1\\n return constrHi\",\n \"def score(self, candidate_holder, new_scores):\\n cand_seqs = candidate_holder.cand_seqs\\n cand_states = candidate_holder.cand_states\\n cand_syms = cand_seqs[:, -1]\\n\\n cand_state_value = []\\n cand_score_value = []\\n for j in range(cand_states[self.state_index].shape[0]):\\n cand_state_value.append(cand_states[self.state_index][j][cand_syms[j]])\\n cand_score_value.append(cand_states[self.score_index][j][cand_syms[j]])\\n ctc_score_result = []\\n ctc_score_total = []\\n new_states = []\\n for i in tf.range(new_scores.shape[0]):\\n num_sym_state = np.array([self.init_state] * self.num_classes)\\n num_sym_score = np.array([0.0] * self.num_classes, dtype=np.float32)\\n num_sym_score_minus = np.array([0.0] * self.num_classes, dtype=np.float32)\\n cand_seq = cand_seqs[i]\\n ctc_pre_state = cand_state_value[i]\\n top_ctc_candidates = np.argsort(new_scores[i, :])\\n top_ctc_candidates = sorted(top_ctc_candidates[-self.ctc_beam :].tolist())\\n cand_seq = np.array(cand_seq)\\n top_ctc_candidates = np.array(top_ctc_candidates)\\n ctc_pre_state = ctc_pre_state.numpy()\\n ctc_score, new_state = self.cand_score(\\n cand_seq, top_ctc_candidates, ctc_pre_state\\n )\\n ctc_pre_score = tf.cast(cand_score_value[i], tf.float32)\\n ctc_score_minus = self.ctc_weight * (ctc_score - ctc_pre_score) + 500\\n\\n for k in range(len(top_ctc_candidates)):\\n num_sym_score[top_ctc_candidates[k]] = ctc_score[k]\\n num_sym_score_minus[top_ctc_candidates[k]] = ctc_score_minus[k]\\n num_sym_state[top_ctc_candidates[k]] = new_state[k]\\n num_sym_score_minus -= 500\\n ctc_score_result.append(num_sym_score_minus)\\n ctc_score_total.append(num_sym_score)\\n new_states.append(num_sym_state)\\n cand_states[self.state_index] = tf.convert_to_tensor(np.array(new_states))\\n ctc_score_result = tf.convert_to_tensor(np.array(ctc_score_result))\\n ctc_score_total = tf.convert_to_tensor(np.array(ctc_score_total))\\n cand_states[self.score_index] = ctc_score_total\\n return ctc_score_result, cand_states\",\n \"def costMatrix(row_feats, col_feats, row_labels, col_labels, metric=\\\"Pearson\\\"):\\n\\n # Get unique label values in non-moving and moving brain\\n row_labs = np.asarray(list(set(row_labels).difference({-1, 0})))\\n col_labs = np.asarray(list(set(col_labels).difference({-1, 0})))\\n\\n # Initialize cost matrix\\n costMatrix = np.zeros((len(row_labs), len(col_labs)))\\n print(costMatrix.shape)\\n\\n # Compute pairwise costs between all label sets\\n for i, r in enumerate(row_labs):\\n indr = np.where(row_labels == r)[0]\\n lr = len(indr)\\n\\n if metric in [\\\"Spearman\\\",\\\"Euclidean\\\",\\\"Pearson\\\"]:\\n featr = row_feats[indr, :]\\n\\n for j, c in enumerate(col_labs):\\n indc = np.where(col_labels == c)[0]\\n \\n if metric in [\\\"Spearman\\\",\\\"Euclidean\\\",\\\"Pearson\\\"]:\\n featc = col_feats[indc, :]\\n\\n if metric == \\\"Spearman\\\":\\n [rVal, _] = spearmanr(featr, featc, axis=1)\\n rVal = 1-rVal[lr:, 0:lr]\\n\\n elif metric == \\\"Pearson\\\":\\n rVal = cdist(featr, featc, metric='Correlation').mean()\\n\\n elif metric == \\\"Euclidean\\\":\\n rVal = cdist(featr, featc).mean()\\n\\n elif metric == \\\"Dice\\\":\\n rVal = 1-hmg.dice(indr, indc)\\n\\n elif metric == \\\"EMD\\\":\\n rmu = row_feats[indr, :].mean(0)\\n rmu = rmu/rmu.sum()\\n\\n cmu = col_feats[indc, :].mean(0)\\n cmu = cmu/cmu.sum()\\n\\n rVal = emd(rmu, cmu)\\n\\n\\n costMatrix[i, j] = rVal\\n\\n return [row_labs, col_labs, costMatrix]\",\n \"def get_observation_neighbour(self):\\n state = {}\\n for grid_id, grid in self.grids.items():\\n drivers = list(grid.get_idle_drivers().values())\\n orders = self.get_active_order_neighbour(grid)\\n neighbour_drivers = self.get_active_driver_neighbour(grid)\\n for driver in drivers:\\n driver_id = driver.get_driver_id()\\n loc = driver.get_position()\\n time = driver.city_time\\n neighbour_drivers.remove(driver) # pop itself\\n assert driver_id not in state\\n state[driver_id] = [(loc, time), orders, neighbour_drivers]\\n neighbour_drivers.append(driver) # insert back\\n return state\",\n \"def state_descriptor(self, state, player):\\n\\n\\t\\tboard = np.copy(state.board)\\n\\n\\t\\tnp.place(board, board == -1, 0)\\n\\t\\tnp.place(board, board == player, 3)\\n\\t\\tnp.place(board, board == 3 - player, -1)\\n\\t\\tnp.place(board, board == 3, 1)\\n\\n\\t\\tmy_cows = state.cows[player - 1]\\n\\t\\ten_cows = state.cows[2 - player]\\n\\n\\t\\treturn (self.totuple(board), my_cows, en_cows)\",\n \"def solve(list_of_locations, list_of_homes, starting_car_location, adjacency_matrix, params=[]):\\n\\n loc_map = {}\\n drop_off_dict = {}\\n num_home_visited = 0\\n\\n \\\"\\\"\\\"\\n for i in range(len(list_of_locations)):\\n loc_map[i] = list_of_locations[0]\\n \\\"\\\"\\\"\\n\\n home_indexes = convert_locations_to_indices(list_of_homes, list_of_locations)\\n start = list_of_locations.index(starting_car_location)\\n graph, msg = adjacency_matrix_to_graph(adjacency_matrix)\\n num_homes = len(list_of_homes)\\n\\n car_path = []\\n all_paths = dict(nx.all_pairs_dijkstra(graph))\\n visited = set()\\n\\n #print(start)\\n car_path.append(start)\\n current_node = start\\n\\n if start in home_indexes:\\n visited.add(start)\\n drop_off_dict[start] = [start]\\n num_home_visited += 1\\n\\n while num_home_visited < num_homes:\\n dist_dict = all_paths.get(current_node)[0]\\n paths_dict = all_paths.get(current_node)[1]\\n\\n dist_dict = {k:v for (k,v) in dist_dict.items() if k not in visited and k in home_indexes}\\n min_dist = min(dist_dict.values())\\n min_list = [k for k in dist_dict.keys() if dist_dict[k] <= min_dist]\\n #print(dist_dict.values())\\n target = min_list[0]\\n drop_off_dict[target] = [target]\\n #print(target+1)\\n #print(target)\\n car_path.pop()\\n car_path.extend(paths_dict[target])\\n\\n visited.add(target)\\n current_node = target\\n num_home_visited += 1\\n\\n paths_dict = all_paths.get(current_node)[1]\\n car_path.pop()\\n car_path.extend(paths_dict[start])\\n #print((drop_off_dict.keys()))\\n #car_path = [start, ...., start]\\n #drop_off_dict = {drop_off_loc: [home1, home2, ...] }\\n\\n return car_path, drop_off_dict\",\n \"def evaluateBoardState(self, board):\\n\\n \\\"\\\"\\\"\\n These are the variables and functions for board objects which may be helpful when creating your Agent.\\n Look into board.py for more information/descriptions of each, or to look for any other definitions which may help you.\\n\\n Board Variables:\\n board.width \\n board.height\\n board.last_move\\n board.num_to_connect\\n board.winning_zones\\n board.score_array \\n board.current_player_score\\n\\n Board Functions:\\n get_cell_value(row, col)\\n try_move(col)\\n valid_move(row, col)\\n valid_moves()\\n terminal(self)\\n legal_moves()\\n next_state(turn)\\n winner()\\n \\\"\\\"\\\"\\n if self.id == 1:\\n opponent_id = 2\\n else:\\n opponent_id = 1\\n\\n maxvalue = 100000\\n minvalue = -maxvalue\\n winner = board.winner()\\n if winner == self.id:\\n return maxvalue\\n elif winner == opponent_id:\\n return minvalue\\n size_y = board.height\\n size_x = board.width\\n map_ = []\\n num_to_connect = board.num_to_connect\\n total_points = 0\\n\\n multiply_reachable = 1\\n multiply_oddeven = 1\\n # basically this function is calculating all the possible win positions\\n # more pieces in a possible win position will be counted with more weights\\n # a win position with X pieces in it will be counted as X^2 points\\n # initialise the zones maps\\n for i in range(size_y):\\n map_.append([])\\n for j in range(size_x):\\n map_[i].append([])\\n\\n # Fill in the horizontal win positions\\n for i in range(size_y):\\n for j in range(size_x - num_to_connect + 1):\\n points = 0\\n self_pieces_count = 0\\n opponent_pieces_count = 0\\n for k in range(num_to_connect):\\n if board.board[i][j + k] == opponent_id:\\n opponent_pieces_count += 1\\n elif board.board[i][j + k] == self.id:\\n points += len(board.winning_zones[j+k][i])\\n if (self.id == 1 and i % 2 == 1) or (self.id == 2 and i%2 == 0):\\n points *= multiply_oddeven\\n self_pieces_count += 1\\n if self_pieces_count == 3 and opponent_pieces_count == 0:\\n if j - 1 >= 0 and board.board[i][j + 3] == 0 and board.board[i][j - 1] == 0 \\\\\\n and board.try_move(j + 3) == i and board.try_move(j - 1) == i:\\n return maxvalue\\n elif j + 4 < size_y and board.board[i][j + 4] == 0 and board.board[i][j] == 0 \\\\\\n and board.try_move(j + 4) == i and board.try_move(j) == i:\\n return maxvalue\\n else:\\n for k in range(num_to_connect):\\n if board.board[i][j + k] == 0 and board.try_move(j + k) == i:\\n points *= multiply_reachable\\n elif opponent_pieces_count == 3 and self_pieces_count == 0:\\n if j - 1 >= 0 and board.board[i][j + 3] == 0 and board.board[i][j - 1] == 0 \\\\\\n and board.try_move(j + 3) == i and board.try_move(j - 1) == i:\\n return minvalue\\n elif j + 4 < size_y and board.board[i][j + 4] == 0 and board.board[i][j] == 0 \\\\\\n and board.try_move(j + 4) == i and board.try_move(j) == i:\\n return minvalue\\n # else:\\n # for k in range(num_to_connect):\\n # if board.board[i][j + k] == 0 and board.try_move(j + k) == i:\\n # points *= -multiply_reachable\\n if (opponent_pieces_count == 3 and self_pieces_count == 0) or opponent_pieces_count == 0:\\n total_points += points\\n\\n # Fill in the vertical win positions\\n for i in range(size_x):\\n for j in range(size_y - num_to_connect + 1):\\n points = 0\\n self_pieces_count = 0\\n for k in range(num_to_connect):\\n if board.board[j + k][i] == opponent_id:\\n opponent_pieces_count += 1\\n elif board.board[j + k][i] == self.id:\\n points += len(board.winning_zones[i][j+k])\\n if (self.id == 1 and (j+k) % 2 == 1) or (self.id == 2 and (j+k)%2 == 0):\\n points *= multiply_oddeven\\n self_pieces_count += 1\\n points *= multiply_reachable\\n # if opponent_pieces_count == 3 and self_pieces_count == 0:\\n # points *= -1\\n if (opponent_pieces_count == 3 and self_pieces_count == 0) or opponent_pieces_count == 0:\\n total_points += points\\n\\n # Fill in the forward diagonal win positions\\n for i in range(size_y - num_to_connect + 1):\\n for j in range(size_x - num_to_connect + 1):\\n points = 0\\n self_pieces_count = 0\\n opponent_pieces_count = 0\\n for k in range(num_to_connect):\\n if board.board[i + k][j + k] == opponent_id:\\n opponent_pieces_count += 1\\n elif board.board[i + k][j + k] == self.id:\\n points += len(board.winning_zones[j+k][i+k])\\n if (self.id == 1 and (i+k) % 2 == 1) or (self.id == 2 and (i+k)%2 == 0):\\n points *= multiply_oddeven\\n self_pieces_count += 1\\n if self_pieces_count == 3 and opponent_pieces_count == 0:\\n if i - 1 >= 0 and j - 1 >= 0 and board.board[i + 3][j + 3] == 0 and board.board[i - 1][j - 1] == 0 \\\\\\n and board.try_move(j + 3) == i + 3 and board.try_move(j - 1) == i - 1:\\n return maxvalue\\n elif i + 4 < size_y and j + 4 < size_x and board.board[i + 4][j + 4] == 0 and board.board[i][j] == 0 \\\\\\n and board.try_move(j + 4) == i + 4 and board.try_move(j) == i:\\n return maxvalue\\n else:\\n for k in range(num_to_connect):\\n if board.board[i + k][j + k] == 0 and board.try_move(j + k) == i + k:\\n points *= multiply_reachable\\n elif opponent_pieces_count == 3 and self_pieces_count == 0:\\n if i - 1 >= 0 and j - 1 >= 0 and board.board[i + 3][j + 3] == 0 and board.board[i - 1][j - 1] == 0 \\\\\\n and board.try_move(j + 3) == i + 3 and board.try_move(j - 1) == i - 1:\\n return minvalue\\n elif i + 4 < size_y and j + 4 < size_x and board.board[i + 4][j + 4] == 0 and board.board[i][j] == 0 \\\\\\n and board.try_move(j + 4) == i + 4 and board.try_move(j) == i:\\n return minvalue\\n # else:\\n # for k in range(num_to_connect):\\n # if board.board[i + k][j + k] == 0 and board.try_move(j + k) == i + k:\\n # points *= -multiply_reachable\\n if (opponent_pieces_count == 3 and self_pieces_count == 0) or opponent_pieces_count == 0:\\n total_points += points\\n\\n # Fill in the backward diagonal win positions\\n for i in range(size_y - num_to_connect + 1):\\n for j in range(size_x - 1, num_to_connect - 1 - 1, -1):\\n points = 0\\n self_pieces_count = 0\\n opponent_pieces_count = 0\\n for k in range(num_to_connect):\\n if board.board[i + k][j - k] == opponent_id:\\n opponent_pieces_count += 1\\n elif board.board[i + k][j - k] == self.id:\\n points += len(board.winning_zones[j-k][i+k])\\n if (self.id == 1 and (i+k) % 2 == 1) or (self.id == 2 and (i+k)%2 == 0):\\n points *= multiply_oddeven\\n self_pieces_count += 1\\n if self_pieces_count == 3 and self_pieces_count == 0:\\n if board.board[i + 3][j - 3] == 0 and board.board[i - 1][j + 1] == 0 \\\\\\n and board.try_move(j - 3) == i + 3 and board.try_move(j + 1) == i - 1:\\n return maxvalue\\n elif i + 4 < size_y and j - 4 >= 0 and board.board[i + 4][j - 4] == 0 and board.board[i][j] == 0 \\\\\\n and board.try_move(j - 4) == i + 4 and board.try_move(j) == i:\\n return maxvalue\\n else:\\n for k in range(num_to_connect):\\n if board.board[i + k][j - k] == 0 and board.try_move(j - k) == i + k:\\n points *= multiply_reachable\\n\\n elif opponent_pieces_count == 3 and self_pieces_count == 0:\\n if board.board[i + 3][j - 3] == 0 and board.board[i - 1][j + 1] == 0 \\\\\\n and board.try_move(j - 3) == i + 3 and board.try_move(j + 1) == i - 1:\\n return minvalue\\n elif i + 4 < size_y and j - 4 >= 0 and board.board[i + 4][j - 4] == 0 and board.board[i][j] == 0 \\\\\\n and board.try_move(j - 4) == i + 4 and board.try_move(j) == i:\\n return minvalue\\n # else:\\n # for k in range(num_to_connect):\\n # if board.board[i + k][j - k] == 0 and board.try_move(j - k) == i + k:\\n # points *= -multiply_reachable\\n if (opponent_pieces_count == 3 and self_pieces_count == 0) or opponent_pieces_count == 0:\\n total_points += points\\n return total_points\",\n \"def get_pathologic_covering_routes(n_pl, n_target, attacker_strategy, target_values):\\n # computes the coefficient used by the greedy oracle to choose routes\\n targets_coeff = np.transpose(np.multiply(attacker_strategy, target_values))\\n\\n # randomly selects the player for which the non optimal choice will be made\\n wrong_pl = randint(1, n_pl)\\n\\n # generate the non optimal route randomly\\n n_covered_targets = randint(n_pl,n_target-1)\\n non_opt_action = np.zeros(n_target)\\n for i in range(0, n_covered_targets):\\n random_covered_target = randint(0, n_target-1)\\n non_opt_action[random_covered_target] = 1\\n\\n # compute the value of the non optimal route\\n non_opt_val = get_value_single_route(non_opt_action, targets_coeff)\\n\\n # generate routes that have, as a single, values smaller than the best greedy route but taken togher perform\\n # at least as well. [[0,1,...],[...],...] a[r][t]=1 iff t is covered by r.\\n # The returned list should have n_pl - 1 routes\\n opt_routes = get_opt_routes(n_pl, non_opt_action)\\n\\n I={}\\n for pl in range(1, n_pl+1):\\n\\n n_r = randint(0, MAX_ROUTES)\\n temp = lil_matrix((n_r+1, n_target), dtype='int8')\\n\\n if pl == wrong_pl:\\n # put the non opt route in the bucket\\n for t in non_opt_action.nonzero():\\n temp[0,t] = 1\\n else:\\n for t in opt_routes.pop().nonzero():\\n temp[0,t] = 1\\n\\n # generate other random routes with single value less than the non_opt_value\\n for r in range(1, n_r):\\n new_route = get_r_limited_val(non_opt_val, targets_coeff)\\n\\n for t in new_route.nonzero():\\n temp[r,t] = 1\\n\\n I[pl] = temp.tocsr()\\n\\n return I\",\n \"def next_state_rowcol(self):\\n # state before and after propagation\\n self.state_before = self.state_after\\n transition_probs = list(Customer.TPM.loc[Customer.TPM.index==self.state_before].values[0])\\n self.state_after = random.choices(Customer.STATES, weights=transition_probs)[0]\\n # row and col before and after propagation\\n self.row_before = self.row_after\\n self.col_before = self.col_after\\n\\n # randomly chose row_after, col_after depending on the state_after\\n if self.state_after == 'fruit':\\n self.row_after = random.choice(Customer.STATE_ROW_COL['fruit'][0])\\n self.col_after = random.choice(Customer.STATE_ROW_COL['fruit'][1])\\n elif self.state_after == 'spices':\\n self.row_after = random.choice(Customer.STATE_ROW_COL['spices'][0])\\n self.col_after = random.choice(Customer.STATE_ROW_COL['spices'][1])\\n elif self.state_after == 'dairy':\\n self.row_after = random.choice(Customer.STATE_ROW_COL['dairy'][0])\\n self.col_after = random.choice(Customer.STATE_ROW_COL['dairy'][1])\\n elif self.state_after == 'drinks':\\n self.row_after = random.choice(Customer.STATE_ROW_COL['drinks'][0])\\n self.col_after= random.choice(Customer.STATE_ROW_COL['drinks'][1])\\n elif self.state_after == 'checkout':\\n self.row_after = random.choice(Customer.STATE_ROW_COL['checkout'][0])\\n self.col_after = random.choice(Customer.STATE_ROW_COL['checkout'][1])\",\n \"def get_states(self):\\n states = []\\n for chords in self.training_data:\\n chunks = [chords[x:x+self.order] for x in range(0,\\n len(chords), self.order)]\\n for chunk in chunks:\\n chunk_string = \\\" \\\".join(chunk)\\n if chunk_string not in states:\\n states.append(chunk_string)\\n return sorted(states)\",\n \"def select_observation(self):\\n\\n # New board to collect the states sampled by the MC agent\\n score_board = np.zeros_like(self.observed_board)\\n n_samples = self.nb_samples\\n\\n # +----------+\\n # | Task 1 |\\n # +----------+\\n # Check if there is already an \\\"open\\\" hit, i.e. a ship that has been hit but not sunk\\n # These locations are handled by the observation_board as 1\\n # If there is already a hit, choose a random one to deal with next.\\n # Create a score board including that hit, and reduce the number of samples to 1/10\\n hit = None\\n if 1 in self.observed_board:\\n hits = np.where(self.observed_board == 1)\\n i = random.choice(range(len(hits[0])))\\n hit = (hits[0][i], hits[1][i])\\n n_samples /= 10\\n\\n # +----------+\\n # | Task 2 |\\n # +----------+\\n # Populate the score_board with possible boat placements\\n i = 0\\n while i < n_samples:\\n board = np.zeros_like(self.observed_board)\\n for boat_size in self.remaining_ships:\\n while True:\\n o = random.choice(['h', 'v'])\\n x = random.choice(range(self.size - (boat_size - 1 if o == 'h' else 0)))\\n y = random.choice(range(self.size - (boat_size - 1 if o == 'v' else 0)))\\n\\n d = np.hstack((np.zeros((boat_size, 1)), np.arange(boat_size)[:, None])[::(-1 if o == 'h' else 1)])\\n boat = tuple((np.array([[x, y]]) + d).astype(int).T)\\n\\n # Place boats non-overlapping and not on misses or sunken ships.\\n if 1 in board[boat] or -1 in self.observed_board[boat]:\\n continue\\n\\n board[boat] = 1\\n break\\n\\n # Sanity check\\n assert board.sum() == sum(self.remaining_ships)\\n\\n if not hit or board[hit] == 1:\\n i += 1\\n score_board += board\\n\\n # +----------+\\n # | Task 3 |\\n # +----------+\\n # Having populated the score board, select a new position by choosing the location with the highest score.\\n score_board *= np.where(self.observed_board == 1, 0, 1)\\n plt.imshow(score_board, cmap='hot')\\n plt.show()\\n time.sleep(1)\\n i_new, j_new = np.unravel_index(np.argmax(score_board), score_board.shape)\\n\\n # return the next location to query, i_new: int, j_new: int\\n return i_new, j_new, score_board\",\n \"def bridge_problem3(here):\\r\\n\\r\\n def all_over(state):\\r\\n here, _ = state\\r\\n return not here or here == set([\\\"light\\\"])\\r\\n\\r\\n start = (frozenset(here) | frozenset([\\\"light\\\"]), frozenset())\\r\\n return lowest_cost_search(start, bsuccessors2, all_over, bcost)\",\n \"def calc_ply_order(constraints, targets):\\r\\n if constraints.sym:\\r\\n ply_order = np.arange(targets.n_plies // 2 + targets.n_plies % 2)\\r\\n return ply_order\\r\\n\\r\\n order_before_sorting = np.arange(targets.n_plies)\\r\\n ply_order = np.zeros((targets.n_plies,), int)\\r\\n ply_order[0::2] = order_before_sorting[\\r\\n :targets.n_plies // 2 + targets.n_plies % 2]\\r\\n ply_order[1::2] = order_before_sorting[\\r\\n targets.n_plies // 2 + targets.n_plies % 2:][::-1]\\r\\n return ply_order\",\n \"def _get_next_position(x: float, y: float, heading: float, state: str, hp_info:\\n pd.DataFrame, rw_info: pd.DataFrame, ac: int,\\n CURR_LANDING_AC) -> Tuple[float, float, float, str]:\\n\\n if state == \\\"A\\\":\\n\\n radius = np.sqrt(x ** 2 + y ** 2)\\n\\n min_R = CONTROL_ZONE_RADIUS - MIN_SEPARATION - POSIITION_TOLERANCE\\n max_R = CONTROL_ZONE_RADIUS - MIN_SEPARATION + POSIITION_TOLERANCE\\n\\n if (min_R < radius) | (radius < max_R):\\n\\n hp_ind = _get_closest_control_zone(x, y, hp_info)\\n\\n if hp_info[2][hp_ind] == 0:\\n\\n state_new = \\\"C\\\"\\n heading_new = _get_ac_heading(hp_info[0][hp_ind] - x, hp_info[1][hp_ind] - y)\\n\\n else:\\n\\n state_new = \\\"B\\\"\\n heading_new = (hp_info[2][hp_ind] + np.pi / 2) % (2 * np.pi)\\n\\n else:\\n\\n state_new = \\\"A\\\"\\n heading_new = heading\\n\\n x_new = x + MAX_SPEED * np.sin(heading_new) / TIME_STEP_FREQUENCY\\n y_new = y + MAX_SPEED * np.cos(heading_new) / TIME_STEP_FREQUENCY\\n\\n elif state == \\\"B\\\":\\n\\n hp_ind = _get_closest_control_zone(x, y, hp_info)\\n\\n if hp_info[2][hp_ind] == 0:\\n\\n state_new = \\\"C\\\"\\n heading_new = _get_ac_heading(hp_info[0][hp_ind] - x, hp_info[1][hp_ind] - y)\\n\\n else:\\n\\n state_new = \\\"B\\\"\\n heading_new = heading - MAX_SPEED / (TIME_STEP_FREQUENCY * (CONTROL_ZONE_RADIUS - MIN_SEPARATION))\\n\\n x_new = x + MAX_SPEED * np.sin(heading_new) / TIME_STEP_FREQUENCY\\n y_new = y + MAX_SPEED * np.cos(heading_new) / TIME_STEP_FREQUENCY\\n\\n elif state == \\\"C\\\":\\n\\n hp_ind = _get_closest_control_zone(x, y, hp_info)\\n dist = np.sqrt((hp_info[0][hp_ind] - x) ** 2 + (hp_info[1][hp_ind] - y) ** 2)\\n\\n if dist < POSIITION_TOLERANCE + 1:\\n\\n state_new = \\\"D\\\"\\n heading_new = heading\\n\\n x_new = x\\n y_new = y\\n\\n else:\\n\\n state_new = \\\"C\\\"\\n heading_new = heading\\n\\n x_new = x + MAX_SPEED * np.sin(heading_new) / TIME_STEP_FREQUENCY\\n y_new = y + MAX_SPEED * np.cos(heading_new) / TIME_STEP_FREQUENCY\\n\\n elif state == \\\"D\\\":\\n\\n if ac == CURR_LANDING_AC:\\n\\n row_ind, x_ind, y_ind = _get_closest_threshold(x, y, rw_info)\\n\\n state_new = \\\"E\\\"\\n heading_new = _get_ac_heading(rw_info[x_ind][row_ind] - x, rw_info[y_ind][row_ind] - y)\\n\\n x_new = x + MAX_SPEED * np.sin(heading_new) / TIME_STEP_FREQUENCY\\n y_new = y + MAX_SPEED * np.cos(heading_new) / TIME_STEP_FREQUENCY\\n\\n else:\\n\\n state_new = \\\"D\\\"\\n heading_new = heading\\n\\n x_new = x\\n y_new = y\\n\\n elif state == \\\"E\\\":\\n\\n row_ind, x_ind, y_ind = _get_closest_threshold(x, y, rw_info)\\n dist = np.sqrt((rw_info[x_ind][row_ind] - x) ** 2 + (rw_info[y_ind][row_ind] - y) ** 2)\\n\\n if (dist < MIN_SEPARATION) | (CURR_LANDING_AC == ac):\\n\\n x_ind = 0 if x_ind == 2 else 2\\n y_ind = 1 if y_ind == 3 else 3\\n\\n CURR_LANDING_AC += 1\\n\\n state_new = \\\"F\\\"\\n heading_new = _get_ac_heading(rw_info[x_ind][row_ind] - x, rw_info[y_ind][row_ind] - y)\\n\\n x_new = x + MAX_SPEED * np.sin(heading_new) / TIME_STEP_FREQUENCY\\n y_new = y + MAX_SPEED * np.cos(heading_new) / TIME_STEP_FREQUENCY\\n\\n else:\\n\\n state_new = \\\"E\\\"\\n heading_new = heading\\n\\n x_new = x + MAX_SPEED * np.sin(heading_new) / TIME_STEP_FREQUENCY\\n y_new = y + MAX_SPEED * np.cos(heading_new) / TIME_STEP_FREQUENCY\\n\\n elif state == \\\"F\\\":\\n\\n row_ind, x_ind, y_ind = _get_closest_threshold(x, y, rw_info)\\n dist = np.sqrt((rw_info[x_ind][row_ind] - x) ** 2 + (rw_info[y_ind][row_ind] - y) ** 2)\\n\\n if abs(dist - RUNWAY_LENGTH / 2) < POSIITION_TOLERANCE:\\n x_new, y_new, heading_new, state_new = -1, -1, -1, \\\"END\\\"\\n\\n else:\\n\\n state_new = \\\"F\\\"\\n heading_new = heading\\n\\n x_new = x + MAX_SPEED * np.sin(heading_new) / TIME_STEP_FREQUENCY\\n y_new = y + MAX_SPEED * np.cos(heading_new) / TIME_STEP_FREQUENCY\\n\\n else:\\n\\n x_new, y_new, heading_new, state_new = -1, -1, -1, \\\"END\\\"\\n\\n return x_new, y_new, heading_new, state_new\",\n \"def test_location_to_state_and_state_to_location_match():\\n env = Four_Rooms_Environment(stochastic_actions_probability=0.0)\\n env.reset()\\n for row in range(env.grid_height):\\n for col in range(env.grid_width):\\n assert env.location_to_state((row, col)) == env.location_to_state(env.state_to_location(env.location_to_state((row, col))))\",\n \"def getNewRYGState(self, tl_combinations):\\n highScore = 0\\n new_ryg_state = None # The new traffic light combination\\n for combination in tl_combinations:\\n if combination.score > highScore:\\n highScore = combination.score\\n new_ryg_state = combination.ryg_state\\n return new_ryg_state\",\n \"def determine_move_position(self):\\n green_probs = []\\n net_size = len(self.net)\\n adjacents = self.net[self.current_pos].adjacents\\n #Belief propagation:\\n #Analyzes each position's probability of obtaining\\n #green when measuring at a time t+1.\\n for i in adjacents:\\n accum = 0\\n for j in range(0, net_size):\\n distance = self.__get_distance(i-1, j)\\n if distance == 0: #Probability of measure green at distance 0 from 'i'.\\n accum += self.enemy_net[i-1].value * self.ct[0][0]\\n elif distance == 1: #Probability of measure green at distance 1 from 'i'.\\n accum += self.enemy_net[i-1].value * self.ct[1][0]\\n elif distance == 2: #Probability of measure green at distance 2 from 'i'.\\n accum += self.enemy_net[i-1].value * self.ct[2][0]\\n elif distance == 3: #Probability of measure green at distance 3 from 'i'.\\n accum += self.enemy_net[i-1].value * self.ct[3][0]\\n else: #Probability of measure green at a distance >= 4 from 'i'.\\n accum += self.enemy_net[i-1].value * self.ct[4][0]\\n green_probs.append((i, accum))\\n #Returns the position in which the probability of\\n #obtaining green when measuring is the lowest.\\n return min(green_probs, key=itemgetter(1))[0]\",\n \"def compute_map(self):\\n number_of_orders = 0\\n orders = []\\n for i, line in enumerate(self.__grid):\\n for j, column in enumerate(line):\\n if self.__grid[i][j][\\\"humans\\\"] != 0:\\n number_of_orders += 1\\n orders.append(i)\\n orders.append(j)\\n orders.append(self.__grid[i][j][\\\"humans\\\"])\\n orders.append(0)\\n orders.append(0)\\n if self.__grid[i][j][\\\"vampires\\\"] != 0:\\n number_of_orders += 1\\n orders.append(i)\\n orders.append(j)\\n orders.append(0)\\n orders.append(self.__grid[i][j][\\\"vampires\\\"])\\n orders.append(0)\\n if self.__grid[i][j][\\\"werewolves\\\"] != 0:\\n number_of_orders += 1\\n orders.append(i)\\n orders.append(j)\\n orders.append(0)\\n orders.append(0)\\n orders.append(self.__grid[i][j][\\\"werewolves\\\"])\\n return number_of_orders, orders\",\n \"def best_routes_matrix(self) -> np.array:\\n x = np.empty((0, 4))\\n y = np.empty((0, 4))\\n for k, v in self.routes.items():\\n x_row = np.zeros((1, 4))\\n y_row = np.zeros((1, 4))\\n\\n # Player start position\\n player = Pt(k[0][0], k[0][1])\\n x_row[0, 0] = player.x\\n x_row[0, 1] = player.y\\n\\n # Goal start position\\n goal = Pt(k[1][0], k[1][1])\\n x_row[0, 2] = goal.x\\n x_row[0, 3] = goal.y\\n x = np.vstack((x, x_row))\\n\\n # Outputs\\n best_route = self.best_route(player, goal)\\n y_row[0, 0] = best_route[\\\"UP\\\"]\\n y_row[0, 1] = best_route[\\\"DOWN\\\"]\\n y_row[0, 2] = best_route[\\\"LEFT\\\"]\\n y_row[0, 3] = best_route[\\\"RIGHT\\\"]\\n\\n y = np.vstack((y, y_row))\\n\\n return x, y\",\n \"def branch_precursor(state, time, d):\\n assert d[\\\"alpha_IL2\\\"] < d[\\\"alpha1\\\"] and d[\\\"alpha_IL2\\\"] < d[\\\"alpha2\\\"]\\n \\n th0 = state[0]\\n \\n th1 = state[1:(d[\\\"alpha1\\\"]+d[\\\"alpha1_p\\\"]+1)]\\n th2 = state[(d[\\\"alpha1\\\"]+d[\\\"alpha1_p\\\"]+1):]\\n #print(len(state), len(th1))\\n ### get all cytokine secreting cells \\n th1_all = np.sum(th1[-d[\\\"alpha1_p\\\"]:])\\n th2_all = np.sum(th2[-d[\\\"alpha2_p\\\"]:])\\n \\n t_eff = th1_all+th2_all\\n t_il2 = np.sum(th1[:d[\\\"alpha_IL2\\\"]]) + np.sum(th2[:d[\\\"alpha_IL2\\\"]])\\n\\n ### calculate cytokine concentrations\\n cyto_1 = d[\\\"beta_cyto_1\\\"]*th1_all + d[\\\"ifn_ext\\\"]\\n cyto_2 = d[\\\"beta_cyto_2\\\"]*th2_all + d[\\\"il21_ext\\\"]\\n \\n conc_il2 = d[\\\"rate_il2\\\"]*t_il2/(d[\\\"K_il2\\\"]+t_eff)\\n\\n # compute feedbacks\\n fb1 = d[\\\"fb_rate1\\\"]*cyto_1**3/(cyto_1**3+d[\\\"K_1\\\"]**3)\\n fb2 = d[\\\"fb_rate2\\\"]*cyto_2**3/(cyto_2**3+d[\\\"K_2\\\"]**3)\\n ### update differantiation rate\\n beta1 = d[\\\"beta1\\\"]*(1+fb1)\\n beta2 = d[\\\"beta2\\\"]*(1+fb2) \\n \\n ### calculate probability, note that these are adjusted to beta1 beta2 so that\\n # they are not necessarily \\\\in (0,1)\\n p1, p2 = get_prob(d, beta1, beta2, cyto_1, cyto_2)\\n \\n #print(beta1*p1_adj/(beta1*p1_adj+beta2))\\n beta1_p = d[\\\"beta1_p\\\"]\\n beta2_p = d[\\\"beta2_p\\\"]\\n rate_death = d[\\\"d_eff\\\"] \\n \\n # check for homeostasis regulation\\n if d[\\\"crit\\\"] == False:\\n update_t0(d, time, conc_il2, t_eff)\\n elif d[\\\"death_mode\\\"] == False:\\n assert d[\\\"crit\\\"] == True \\n beta1_p = beta1_p*np.exp(-d[\\\"decay_p\\\"]*(time-d[\\\"t0\\\"]))\\n beta2_p = beta2_p*np.exp(-d[\\\"decay_p\\\"]*(time-d[\\\"t0\\\"]))\\n\\n else:\\n rate_death = rate_death*np.exp(time-d[\\\"t0\\\"])\\n\\n # this is the actual differentiation where odes are computed \\n dt_th1 = diff_precursor(th1, th0, d[\\\"alpha1\\\"], beta1, beta1_p, p1, rate_death, d)\\n dt_th2 = diff_precursor(th2, th0, d[\\\"alpha2\\\"], beta2, beta2_p, p2, rate_death, d)\\n dt_th0 = -(beta1*p1+beta2)*th0 \\n dt_state = np.concatenate(([dt_th0], dt_th1, dt_th2))\\n\\n return dt_state\",\n \"def get_nearest_component(action_dct, init_component):\\n components = action_dct['context']['direct_object'] + action_dct['context']['direct_object'] + \\\\\\n action_dct['context']['auxiliar_object'] + action_dct['context']['attributes']\\n components.sort(key=lambda obj_dict: obj_dict['indexes'][1])\\n nearest_comp = None\\n for obj_dict in components:\\n if obj_dict['indexes'][1] > init_component['indexes'][1]:\\n break\\n nearest_comp = obj_dict\\n return nearest_comp\",\n \"def get_matrix(self):\\n matrix = np.zeros([len(self.states), len(self.states)])\\n starting_states = []\\n transitions = []\\n\\n for chords in self.training_data:\\n states = []\\n is_starting_state = True\\n chunks = [chords[x:x+self.order] for x in range(0,\\n len(chords), self.order)]\\n for chunk in chunks:\\n chunk_string = \\\" \\\".join(chunk)\\n if is_starting_state:\\n starting_states.append(chunk_string)\\n is_starting_state = False\\n states.append(chunk_string)\\n\\n for i in range(0, len(states)):\\n if i < (len(states)) - 1:\\n transitions.append([states[i], states[i + 1]])\\n else:\\n transitions.append([states[i]])\\n\\n self.starting_probabilities = np.zeros([len(self.states)])\\n\\n for transition in transitions:\\n for row, row_contents in enumerate(self.transitions):\\n for col, _ in enumerate(row_contents):\\n if transition == self.transitions[row][col]:\\n matrix[row][col] += 1\\n\\n for i, state in enumerate(self.states):\\n for j, possible_state in enumerate(starting_states):\\n if state == possible_state:\\n self.starting_probabilities[j] += 1\\n\\n num = sum(self.starting_probabilities)\\n for i, prob in enumerate(self.starting_probabilities):\\n self.starting_probabilities[i] = prob / num\\n\\n for m in range(len(matrix)):\\n num = sum(matrix[m])\\n if int(num) is not 0:\\n for i in range(len(matrix[m])):\\n matrix[m][i] = (matrix[m][i] / num)\\n else:\\n matrix[m] = self.starting_probabilities\\n return matrix\",\n \"def get_switchy_score_order(x):\\n switchy_scores = np.apply_along_axis(switchy_score, axis=0, arr=x)\\n return np.argsort(switchy_scores)\",\n \"def generateState(self, gameState):\\n state = [None, None, None, None, None, None]\\n\\n #Calculamos la distancia al fantasma mas cercano\\n distGhosts = gameState.data.ghostDistances\\n nearest = 100000\\n for i in distGhosts:\\n if i < nearest and i is not None:\\n nearest = i\\n if nearest <= 3:\\n state[0] = 0\\n elif nearest > 3 and nearest <= 7:\\n state[0] = 1\\n else:\\n state[0] = 2\\n\\n legalActions = gameState.getLegalActions()\\n #Calculamos los isPared\\n #isParedEast\\n if \\\"East\\\" in legalActions:\\n state[1] = 0\\n else:\\n state[1] = 1\\n\\n #isParedWest\\n if \\\"West\\\" in legalActions:\\n state[2] = 0\\n else:\\n state[2] = 1\\n\\n #isParedNorth\\n if \\\"North\\\" in legalActions:\\n state[3] = 0\\n else:\\n state[3] = 1\\n\\n #isParedShouth\\n if \\\"South\\\" in legalActions:\\n state[4] = 0\\n else:\\n state[4] = 1\\n\\n #Bloque que controla si el pacman ha pasado por el hueco encontrado\\n controlador =0\\n x1,y1 = gameState.getPacmanPosition()\\n if len(self.huecos)==2:\\n\\t\\t\\tif x1 ==self.huecos[0]:\\n\\t\\t\\t\\tcontrolador=controlador+1\\n\\t\\t\\tif y1 ==self.huecos[1]:\\n\\t\\t\\t\\tcontrolador=controlador+1\\n\\t\\t\\tif controlador == 2:\\n\\t\\t\\t\\tself.huecos=[]\\n\\n posGhosts = gameState.getGhostPositions()\\n nearestGhostIndex = distGhosts.index(nearest)\\n nearestGhostPos = posGhosts[nearestGhostIndex]\\n pacmanPosition = gameState.getPacmanPosition()\\n\\n print(self.huecos)\\n if(len(self.huecos) == 2):\\n goalPosition = self.huecos\\n else:\\n goalPosition = nearestGhostPos\\n\\n legales = []\\n for a in self.goodActions(pacmanPosition,goalPosition): #Metodo goodActions devuelve las mejores acciones que puede realizar el pacman para llegar a su objetivo\\n if a in legalActions:\\n legales.append(a) #Buenas acciones legales\\n if (len(legales) < 1):\\n if (len(self.goodActions(pacmanPosition,goalPosition))): #Si existen buenas acciones pero no son legales es porque un muro esta impidiendo su ejecucion\\n self.huecos = []\\n rd= random.randint(0, len(self.goodActions(pacmanPosition,goalPosition))-1)\\n self.buscaHueco(gameState,self.goodActions(pacmanPosition,goalPosition)[rd],pacmanPosition[0],pacmanPosition[1]) #Se busca hueco en una de las direcciones de las buenas acciones\\n\\n #Calculamos la direccion en la que se encuentra el fantasma o el hueco mas cercano\\n # esto lo hacemos calculando la distancia del fantasma al pacman y viendo cual es la componente mayor.\\n # Si es la X, la direccion sera o derecha o izquierda y dependera del sentido de esta. Con la Y parasria lo mismo\\n relX = gameState.getPacmanPosition()[0] - goalPosition[0]\\n relY = gameState.getPacmanPosition()[1] - goalPosition[1]\\n\\n if abs(relX) >= abs(relY):\\n if relX > 0:\\n state[5] = \\\"West\\\"\\n elif relX < 0:\\n state[5] = \\\"East\\\"\\n elif abs(relY) > abs(relX):\\n if relY > 0:\\n state[5] = \\\"South\\\"\\n elif relY < 0:\\n state[5] = \\\"North\\\"\\n\\n\\n return state\",\n \"def make_alternative_states(self) -> np.ndarray:\\n states = []\\n for agent in range(self.agents):\\n agent_state = []\\n\\n # Own distance\\n r, c = self.game.get_agent_pos(agent)\\n agent_state.append(r / 6)\\n agent_state.append(c / 6)\\n\\n # Distances to others\\n distances_r = [\\n (r - pos[0]) / 12\\n for key, pos in self.game.agent_positions.items()\\n if key != agent\\n ]\\n distances_c = [\\n (c - pos[1]) / 12\\n for key, pos in self.game.agent_positions.items()\\n if key != agent\\n ]\\n agent_state += distances_r\\n agent_state += distances_c\\n\\n # Goal distances\\n distances_goal_r = [(r - pos[0]) / 12 for pos in self.payoff_fields]\\n distances_goal_c = [(c - pos[1]) / 12 for pos in self.payoff_fields]\\n agent_state += distances_goal_r\\n agent_state += distances_goal_c\\n\\n if agent < self.num_informed:\\n agent_state.append((r - self.special_payoff_fields[0][0]) / 12)\\n agent_state.append((c - self.special_payoff_fields[0][1]) / 12)\\n else:\\n agent_state += [0, 0]\\n agent_state.append(self.max_turns - self.turns_count)\\n states.append(np.array(agent_state))\\n\\n states = np.stack(states, axis=0)\\n return states\",\n \"def get_state_arr(self):\\n rpos = self.sim.getAgentPosition(self.robot_num)\\n rvel = self.sim.getAgentVelocity(self.robot_num)\\n rrad = self.sim.getAgentRadius(self.robot_num)\\n v_pref = self.sim.getAgentMaxSpeed(self.robot_num)\\n theta = math.atan2(rvel[1], rvel[0])\\n # Robot's state entry. Note that goal is listed as the robot's current\\n # position because we aren't using that goal as such, we are just\\n # exploring.\\n state = [\\n rpos[0], rpos[1], rvel[0], rvel[1], rrad,\\n self.headings[self.robot_num], rpos[0], rpos[1],\\n v_pref, theta\\n ]\\n for agent in self.agents:\\n if agent != self.robot_num: # We already accounted for the robot\\n pos = self.sim.getAgentPosition(agent)\\n vel = self.sim.getAgentVelocity(agent)\\n rad = self.sim.getAgentRadius(agent)\\n state.extend([pos[0], pos[1], vel[0], vel[1], rad,\\n self.headings[agent]])\\n for obs in self.obstacles:\\n if len(obs) > 1:\\n # Polygonal obstacle\\n o = Polygon(obs)\\n p = Point(rpos)\\n p1, p2 = nearest_points(o, p)\\n # Velocity is always 0 for obstacles\\n # Heading is same as robot's\\n state.extend([p1.x, p2.y, 0, 0, self.obs_width,\\n self.headings[self.robot_num]])\\n else:\\n # Point obstacle\\n state.extend([obs[0][0], obs[0][1], 0, 0, self.obs_width,\\n self.headings[self.robot_num]])\\n return state\",\n \"def h2d_level_to_order_lookup(tree, helmholtz_k, epsilon):\\n\\n if tree.dimensions != 2:\\n raise ValueError(\\\"tree must be 2d\\\")\\n\\n orders = np.empty(tree.nlevels, dtype=int)\\n bbox_area = np.max(\\n tree.bounding_box[1] - tree.bounding_box[0]) ** 2\\n\\n from pyfmmlib import h2dterms\\n for level in range(tree.nlevels):\\n nterms, ier = h2dterms(bbox_area / 2 ** level, helmholtz_k, epsilon)\\n if ier != 0:\\n raise RuntimeError(\\n \\\"h2dterms returned error code {ier}\\\".format(ier=ier))\\n orders[level] = nterms\\n\\n return orders\",\n \"def tracking_rafts(prev_rafts_locations, detected_centers):\\n cost_matrix = scipy_distance.cdist(prev_rafts_locations, detected_centers, 'euclidean')\\n row_ind, col_ind = linear_sum_assignment(cost_matrix)\\n\\n return col_ind\",\n \"def get_heurisitc_cost_stacks(element, stack_index, goal_state, unused_stacks, unused_index):\\n for k,v in goal_state.items():\\n if element in v:\\n return (abs(stack_index - k), v.index(element))\\n if stack_index in unused_stacks:\\n return (0, -1)\\n else:\\n return (abs(unused_index - stack_index), -1)\",\n \"def get_board_positions_pieces_OHE(board):\\n information = str(board).replace(\\\"\\\\n\\\", \\\" \\\")\\n one_hot_encoded_board_positions = np.zeros(shape = (8,8,12))\\n x = 0\\n y = 0\\n for piece in information:\\n if(piece == \\\" \\\"):\\n continue\\n one_hot_encoded_board_positions[y, x, :] = get_piece_OHE(piece)\\n if(x <= 7):\\n x += 1 \\n if(x > 7):\\n x = 0\\n y += 1\\n return one_hot_encoded_board_positions\",\n \"def get_co_occ_mat(s_hc_ml, n_s_real, n_s_hc):\\n\\n co_occs = np.zeros((n_s_hc, n_s_real))\\n for idx, n in s_hc_ml.items():\\n co_occs[idx] = n\\n\\n return co_occs\",\n \"def getGameState(self):\\n ### Student code goes here\\n row1 = ()\\n row2 = ()\\n row3 = ()\\n for currRow in range(1,4):\\n for currCol in range(1,4):\\n tileFound = False\\n for fact in self.kb.facts:\\n if fact.statement.predicate == \\\"located\\\":\\n tile = fact.statement.terms[0].term.element\\n column = fact.statement.terms[1].term.element\\n row = fact.statement.terms[2].term.element\\n\\n tileNumber = int(tile[-1])\\n columnNumber = int(column[-1])\\n rowNumber = int(row[-1])\\n\\n if rowNumber == currRow and columnNumber == currCol:\\n tileFound = True\\n if rowNumber == 1:\\n row1 += tuple([tileNumber])\\n elif rowNumber == 2:\\n row2 += tuple([tileNumber])\\n elif rowNumber == 3:\\n row3 += tuple([tileNumber])\\n \\n break\\n\\n if not tileFound:\\n if currRow == 1:\\n row1 += tuple([-1])\\n elif currRow == 2:\\n row2 += tuple([-1])\\n elif currRow == 3:\\n row3 += tuple([-1])\\n\\n\\n return (row1, row2, row3)\",\n \"def get_observation_driver_state(self):\\n next_state = np.zeros(self.n_grids)\\n grids = list(self.grids.values())\\n for idx, grid in enumerate(grids):\\n if grid is not None:\\n next_state[idx] = grid.get_idle_driver_numbers_loop()\\n return next_state\",\n \"def branch_competetive(state, time, d):\\n\\n th0 = state[0] \\n th1 = state[1:(d[\\\"alpha1\\\"]+d[\\\"alpha1_p\\\"])]\\n th2 = state[(d[\\\"alpha1\\\"]+d[\\\"alpha1_p\\\"]):]\\n \\n #print(len(state), len(th1))\\n ### get all cytokine secreting cells \\n th1_all = np.sum(th1[-d[\\\"alpha1_p\\\"]:])\\n th2_all = np.sum(th2[-d[\\\"alpha2_p\\\"]:])\\n ### calculate cytokine concentrations\\n cyto_1 = d[\\\"beta_cyto_1\\\"]*th1_all + d[\\\"ifn_ext\\\"]\\n cyto_2 = d[\\\"beta_cyto_2\\\"]*th2_all + d[\\\"il21_ext\\\"]\\n ### calculate cytokine effect on rate\\n fb1 = d[\\\"fb_rate1\\\"]*cyto_1**3/(cyto_1**3+d[\\\"K_1\\\"]**3)\\n fb2 = d[\\\"fb_rate2\\\"]*cyto_2**3/(cyto_2**3+d[\\\"K_2\\\"]**3)\\n ### update differantiation rate\\n beta1 = d[\\\"beta1\\\"]*(1+fb1)\\n beta2 = d[\\\"beta2\\\"]*(1+fb2)\\n \\n ### differentiate effectors th1 \\n alpha = d[\\\"alpha1\\\"]\\n p = 1.\\n dt_th1 = diff_effector2(th1, th0, alpha, beta1, d[\\\"beta1_p\\\"], p, d)\\n ### differentiate effectors th2\\n alpha = d[\\\"alpha2\\\"]\\n p = 1.\\n dt_th2 = diff_effector2(th2, th0, alpha, beta2, d[\\\"beta2_p\\\"], p, d)\\n \\n ### combine all cells\\n dt_th0 = -(beta1+beta2)*th0\\n dt_state = np.concatenate(([dt_th0], dt_th1, dt_th2))\\n\\n return dt_state\",\n \"def successorStates(self, state):\\r\\n\\r\\n successors = []\\r\\n\\r\\n for action in Directions.CARDINAL:\\r\\n x, y = state\\r\\n dx, dy = Actions.directionToVector(action)\\r\\n nextx, nexty = int(x + dx), int(y + dy)\\r\\n\\r\\n if (not self.walls[nextx][nexty]):\\r\\n nextState = (nextx, nexty)\\r\\n cost = self.costFn(nextState)\\r\\n\\r\\n successors.append((nextState, action, cost))\\r\\n\\r\\n # Bookkeeping for display purposes (the highlight in the GUI).\\r\\n self._numExpanded += 1\\r\\n if (state not in self._visitedLocations):\\r\\n self._visitedLocations.add(state)\\r\\n self._visitHistory.append(state)\\r\\n\\r\\n return successors\",\n \"def scc_order(self):\\n if not self.__scc_order:\\n self._tarjan()\\n return self.__scc_order\",\n \"def spotlessroomba_second_heuristic(state : SpotlessRoombaState) -> float:\\n # TODO a nontrivial consistent heuristic\\n \\n if not state.dirty_locations:\\n return 0\\n \\n best_start = 0 # best dirty tile to start from\\n best_cost = INF # cost of the path from the above start tile\\n\\n for i in range(len(state.dirty_locations)):\\n estimate_cost = 0\\n lowest_cost = INF\\n closest_dirty = 0\\n dirty_locations = list(state.dirty_locations)\\n current_pos = dirty_locations.pop(i)\\n\\n # find the shortest cost solution path from this starting tile\\n while dirty_locations:\\n for j in range(len(dirty_locations)):\\n manhattan = abs(current_pos.row - dirty_locations[j].row) + abs(current_pos.col - dirty_locations[j].col)\\n if manhattan < lowest_cost:\\n lowest_cost = manhattan\\n closest_dirty = j\\n estimate_cost += lowest_cost\\n current_pos = dirty_locations.pop(closest_dirty)\\n lowest_cost = INF\\n # if estimated path cost is cheaper than best path cost so far, replace best_cost and best_start\\n if estimate_cost < best_cost:\\n best_cost = estimate_cost\\n best_start = i\\n # if estimated path cost and best path cost so far are equal, tiebreak with proximity to start tile\\n if estimate_cost == best_cost:\\n current_pos = state.position\\n dist_to_prev_best = abs(current_pos.row - state.dirty_locations[best_start].row) + abs(current_pos.col - state.dirty_locations[best_start].col)\\n dist_to_i = abs(current_pos.row - state.dirty_locations[i].row) + abs(current_pos.col - state.dirty_locations[i].col)\\n if dist_to_i < dist_to_prev_best:\\n best_start = i\\n \\n\\n current_pos = state.position\\n # Calculate distance to the best start tile\\n dist_to_start = abs(current_pos.row - state.dirty_locations[best_start].row) + abs(current_pos.col - state.dirty_locations[best_start].col)\\n # Returned heuristic is the sum of distance to the start tile and estimated cost from said tile\\n return dist_to_start + best_cost\",\n \"def get_possible_actions(self, world_state,agent_host):\\n action_list = []\\n possibilities = {'movenorth 1': -3,'movesouth 1': 3,'moveeast 1': 1,'movewest 1': -1}\\n #check walls to see whether can move left,right,back,forward\\n #check floor beneath to see whether should do anything at all, or just nothing and sink\\n obs_text = world_state.observations[-1].text\\n obs = json.loads(obs_text)\\n grid = load_grid(world_state)\\n print 'GRID SIZE: ', len(grid)\\n for k,v in possibilities.items():\\n #with current grid, index 31 will always be our agent's current location\\n #check walls to see whether can move left,right,back,forward\\n if grid[31+v+9] == 'water' or grid[31+v+9] == 'wooden_door': #+9 because we want to check\\n action_list.append(k) #where our feet are located\\n #check if you can teleport down a level\\n if grid[31-27] == 'water' or grid[31-27] == 'wooden_door':\\n action_list.append(self.teleport(agent_host,False))\\n #check if you can teleport up a level\\n if grid[31+45] == 'water' or grid[31+45] == 'wooden_door':\\n action_list.append(self.teleport(agent_host,True))\\n\\n print(\\\"ACTION LIST: {}\\\".format(action_list))\\n return action_list\",\n \"def minimax(state, depth, player):\\n if depth == 9:\\n row = choice([0, 1, 2])\\n col = choice([0, 1, 2])\\n return row, col, ''\\n\\n if player == COMP:\\n best = [-1, -1, float(\\\"-inf\\\")]\\n else:\\n best = [-1, -1, float(\\\"inf\\\")]\\n\\n if depth == 0 or state.has_tic_tac_toe(COMP) or state.has_tic_tac_toe(HUMAN):\\n score = heuristic(state, depth)\\n return [-1, -1, score]\\n \\\"\\\"\\\"\\n Checks if any of the player is one away from winning in any board and make the appropriate move.\\n \\\"\\\"\\\"\\n if player==COMP:\\n empty_cells=get_empty_cells(state)\\n dangerous_cells=state.is_one_away_from_tic_tac_toe((player%2)+1)\\n if dangerous_cells:\\n found_dangerous_cells=True\\n else:\\n found_dangerous_cells=False\\n print \\\"no dangerous local boards\\\"\\n favoring_cells=state.is_one_away_from_tic_tac_toe(player)\\n if favoring_cells:\\n found_favoring_cells=True\\n else:\\n found_favoring_cells=False\\n print \\\"no favoring local boards\\\"\\n if found_dangerous_cells==False and found_favoring_cells==False:\\n pass\\n if found_dangerous_cells==False and found_favoring_cells==True:\\n empty_cells[:]=[]\\n for cell in favoring_cells:\\n empty_cells.append(cell)\\n if found_dangerous_cells==True and found_favoring_cells==False:\\n empty_cells[:]=[]\\n for cell in dangerous_cells:\\n empty_cells.append(cell)\\n if found_dangerous_cells==True and found_favoring_cells==True:\\n empty_cells[:]=[]\\n for cell in dangerous_cells:\\n empty_cells.append(cell)\\n else:\\n empty_cells=get_empty_cells(state)\\n for cell in empty_cells:\\n row, col = cell[0], cell[1]\\n state.board[row][col] = player\\n score = minimax(state, depth - 1, (player % 2) + 1)\\n state.board[row][col] = 0\\n score[0], score[1] = row, col\\n if player == COMP:\\n if score[2] >= best[2]:\\n if score[2]==best[2]:\\n \\\"\\\"\\\"\\n Favors middle positions over sides or corners\\n MIDDLE > CORNERS > SIDES\\n \\\"\\\"\\\"\\n if (best[0]==0 and best[1]==0) or (best[0]==0 and best[1]==2) or (best[0]==2 and best[1]==0) or (best[0]==2 and best[1]==2):\\n if score[0]==0 and score[1]==0: #favoring centre position over diagonal position\\n best=score\\n print(\\\"centre position chosen over diagonal positions\\\")\\n else:\\n if ((score[0]==0 and score[1]==1) or (score[0]==1 and score[1]==0) or (score[0]==1 and score[1]==2) or (score[0]==2 and score[1]==1))==0:\\n best=score #favoring any position over side position as long as the new position is not a side position too\\n print(\\\"diagonal and centre positions chosen over side positions\\\")\\n else:\\n best = score\\n else:\\n bestMoves=[]\\n if score[2] < best[2]:\\n best=score\\n return best\",\n \"def coranking_matrix(high_data, low_data):\\n n, m = high_data.shape\\n high_distance = distance.squareform(distance.pdist(high_data))\\n low_distance = distance.squareform(distance.pdist(low_data))\\n\\n high_ranking = high_distance.argsort(axis=1).argsort(axis=1)\\n low_ranking = low_distance.argsort(axis=1).argsort(axis=1)\\n\\n Q, xedges, yedges = np.histogram2d(high_ranking.flatten(),\\n low_ranking.flatten(),\\n bins=n)\\n\\n Q = Q[1:, 1:] # remove rankings which correspond to themselves\\n return Q\",\n \"def _get_maze_transition_probabilities(locs, movedir, exits):\\n num_states = len(locs) + 1 # including final \\\"game over\\\" state\\n num_actions = len(movedir)\\n\\n p = np.zeros((num_states, num_states, num_actions))\\n for s, c in enumerate(locs):\\n if c in exits:\\n # transition to \\\"game over\\\" state (assumed to be last state)\\n p[-1, s, :] = 1\\n else:\\n for a in range(num_actions):\\n for j in [-1, 0, 1]:\\n # next location\\n cnext = [c[0] + movedir[(a + j) % num_actions][0],\\n c[1] + movedir[(a + j) % num_actions][1]]\\n if cnext in locs:\\n snext = locs.index(cnext)\\n else:\\n # remain at current field\\n snext = s\\n # update probability table\\n p[snext, s, a] += (0.8 if j == 0 else 0.1)\\n # \\\"game over\\\" state transitions to itself\\n p[-1, -1, :] = 1\\n\\n return p\",\n \"def successorStates(self, state):\\n currentState = state[1]\\n successors = []\\n for action in Directions.CARDINAL:\\n x, y = state[0] # currentPosition\\n print(\\\"State: {}\\\".format(state[0]))\\n dx, dy = Actions.directionToVector(action)\\n nextx, nexty = int(x + dx), int(y + dy)\\n hitsWall = self.walls[nextx][nexty]\\n\\n # Implement a successor discovery, check if any corners are satisfied\\n # and update values as they are satisfied\\n if (not hitsWall):\\n successorsState = []\\n nextxy = (nextx, nexty)\\n if nextxy == self.corners[0]:\\n successorsState.append(True)\\n else:\\n successorsState.append(currentState[0])\\n if nextxy == self.corners[1]:\\n successorsState.append(True)\\n else:\\n successorsState.append(currentState[1])\\n if nextxy == self.corners[2]:\\n successorsState.append(True)\\n else:\\n successorsState.append(currentState[2])\\n if nextxy == self.corners[3]:\\n successorsState.append(True)\\n else:\\n successorsState.append(currentState[3])\\n # Put all updated values of 4 corners to a variable\\n successorPost = (successorsState[0], successorsState[1],\\n successorsState[2], successorsState[3])\\n # Append to go to the next move\\n successors.append(((nextxy, successorPost), action, 1))\\n\\n self._numExpanded += 1 # Count the number of nodes expanded\\n return successors\",\n \"def minconflict(dict_prefs, initial_guess):\\n ordering=deepcopy(initial_guess)\\n while True:\\n best=0\\n best_swap=None\\n pairs_indices=_get_pairs_indices(len(ordering))\\n for pair_ind in pairs_indices:\\n score_as_is,score_rev=_pair_net_effect(ordering,pair_ind,dict_prefs)\\n improvement=(score_rev-score_as_is)\\n if improvement>best:\\n best=improvement\\n best_swap=pair_ind\\n if best_swap is None:\\n break\\n else:\\n _swap(ordering,best_swap)\\n return ordering\",\n \"def get_successors(state): \\n \\n child_states = []\\n \\n size = len(state)\\n i = 0\\n j = 0\\n for i in range (size):\\n if 0 in state[i]:\\n for j in range (size):\\n if state[i][j] == 0:\\n break \\n break\\n\\n if j != size-1:\\n child_states.append ((\\\"Left\\\", swap_cells(state, i, j, i, j+1)))\\n if j != 0:\\n child_states.append ((\\\"Right\\\", swap_cells(state, i, j, i, j-1)))\\n if i != size-1:\\n child_states.append ((\\\"Up\\\", swap_cells(state, i, j, i+1, j)))\\n if i != 0:\\n child_states.append ((\\\"Down\\\", swap_cells(state, i, j, i-1, j)))\\n \\n return child_states\",\n \"def counter_opponent_adv(self):\\n\\n # get essential values\\n board = self.get_game_space()\\n affinity = self.get_affinity()\\n opaffinity = self.get_opponent().get_affinity()\\n\\n # pick the right check for the game we are playing\\n if isinstance(board, Gomoku):\\n\\n # get advantageous blocks\\n blocks_advn = board.get_adv_blocks(opaffinity)\\n best_moves = []\\n best_move = None\\n\\n # sort the blocks which may be countered\\n for block in blocks_advn:\\n if block.direction == 'horizontal':\\n x1 = block.tiles[0][0] - 1\\n y1 = block.tiles[0][1] \\n x2 = block.tiles[2][0] + 1\\n y2 = block.tiles[2][1] \\n if x1 < 0 or x2 >= 7: return None\\n if board.get_tile(x1,y1) == BLANK_TILE() and \\\\\\n board.get_tile(x2,y2) == BLANK_TILE():\\n best_moves.append((x1,y1))\\n elif block.direction == 'vertical':\\n x1 = block.tiles[0][0] \\n y1 = block.tiles[0][1] - 1 \\n x2 = block.tiles[2][0]\\n y2 = block.tiles[2][1] + 1\\n if y1 < 0 or y2 >= 7: return None\\n if board.get_tile(x1,y1) == BLANK_TILE() and \\\\\\n board.get_tile(x2,y2) == BLANK_TILE():\\n best_moves.append((x2,y2))\\n elif block.direction == 'diagonal(\\\\)':\\n x1 = block.tiles[0][0] - 1\\n y1 = block.tiles[0][1] - 1\\n x2 = block.tiles[2][0] + 1\\n y2 = block.tiles[2][1] + 1\\n if x1 < 0 or y1 < 0 or x2 >= 7 or y2 >= 7: return None\\n if board.get_tile(x1,y1) == BLANK_TILE() and \\\\\\n board.get_tile(x2,y2) == BLANK_TILE():\\n best_moves.append((x1,y1))\\n elif block.direction == 'diagonal(/)':\\n x1 = block.tiles[0][0] - 1\\n y1 = block.tiles[0][1] + 1\\n x2 = block.tiles[2][0] + 1\\n y2 = block.tiles[2][1] - 1\\n if x1 < 0 or y1 >= 7 or x2 >= 7 or y2 < 0: return None\\n if board.get_tile(x1,y1) == BLANK_TILE() and \\\\\\n board.get_tile(x2,y2) == BLANK_TILE():\\n best_moves.append((x1,y1))\\n\\n # pick the best move in the best block to counter\\n for move in best_moves:\\n print('considered advantageous move:'+str(move))\\n if best_move is None: best_move = move \\n elif move[0] < best_move[0] and move[1] == best_move[1]:\\n best_move = move\\n elif move[0] == best_move[0] and move[1] > best_move[1]:\\n best_move = move\\n elif move[0] < best_move[0] and move[1] > best_move[1]:\\n best_move = move\\n\\n return best_move\",\n \"def getRowHeuristics(matrix):\\n row, col = matrix.shape\\n rHeuristic = np.zeros((row,2)) # Dos columnas. La primera para indicar la columna la segunda para la Heuristica\\n for i in range(0,row):\\n rHeuristic[i,0] = int(i)\\n #print (i,sum(matrix[:,i]), pesos[i], float(pesos[i]/sum(matrix[:,i])))\\n rHeuristic[i,1] = 1/sum(matrix[i,:])\\n return rHeuristic[rHeuristic[:,1].argsort()]\",\n \"def get_transition(self, row, col, action, tot_row, tot_col):\\n\\n '''\\n Expand the grid of the environment to handle when the \\n agent decides to move in the direction of a wall \\n '''\\n state_probabilities = np.zeros((int(np.sqrt(self.env.observation_space.n)) + 2, int(np.sqrt(self.env.observation_space.n)) + 2), dtype=float)\\n\\n if action == 'UP':\\n row += 1\\n col += 1\\n state_probabilities[row - 1, col] = 0.33 #UP\\n state_probabilities[row, col - 1 ] = 0.33 #LEFT\\n state_probabilities[row, col + 1] = 0.33 # RIGHT\\n state_probabilities[row + 1, col] = 0.0 #DOWN\\n elif action == 'LEFT':\\n row += 1\\n col += 1\\n state_probabilities[row - 1, col] = 0.33 #UP\\n state_probabilities[row, col - 1 ] = 0.33 #LEFT\\n state_probabilities[row, col + 1] = 0.0 # RIGHT\\n state_probabilities[row + 1, col] = 0.33 #DOWN\\n elif action == 'RIGHT':\\n row += 1\\n col += 1\\n state_probabilities[row - 1, col] = 0.33 #UP\\n state_probabilities[row, col - 1 ] = 0.0 #LEFT\\n state_probabilities[row, col + 1] = 0.33 # RIGHT\\n state_probabilities[row + 1, col] = 0.33 #DOWN\\n elif action == 'DOWN':\\n row += 1\\n col += 1\\n state_probabilities[row - 1, col] = 0.0 # UP\\n state_probabilities[row, col - 1] = 0.33 # LEFT\\n state_probabilities[row, col + 1] = 0.33 # RIGHT\\n state_probabilities[row + 1, col] = 0.33 # DOWN\\n\\n for row in range (0, tot_row+1):\\n if state_probabilities[row, 0] != 0:\\n state_probabilities[row, 1] += state_probabilities[row, 0]\\n elif state_probabilities[row, -1] != 0:\\n state_probabilities[row, -2] += state_probabilities[row, -1]\\n\\n for col in range (0, tot_col+1):\\n if state_probabilities[0, col] != 0:\\n state_probabilities[1, col] += state_probabilities[0, col]\\n elif state_probabilities[-1, col] != 0:\\n state_probabilities[-2, col] += state_probabilities[-1, col]\\n\\n return state_probabilities[1: 1+tot_row, 1:1+tot_col]\",\n \"def get_transition(self, row, col, action, tot_row, tot_col):\\n\\n '''\\n Expand the grid of the environment to handle when the \\n agent decides to move in the direction of a wall \\n '''\\n state_probabilities = np.zeros((int(np.sqrt(self.env.observation_space.n)) + 2, int(np.sqrt(self.env.observation_space.n)) + 2), dtype=float)\\n\\n if action == 'UP':\\n row += 1\\n col += 1\\n state_probabilities[row - 1, col] = 0.33 #UP\\n state_probabilities[row, col - 1 ] = 0.33 #LEFT\\n state_probabilities[row, col + 1] = 0.33 # RIGHT\\n state_probabilities[row + 1, col] = 0.0 #DOWN\\n elif action == 'LEFT':\\n row += 1\\n col += 1\\n state_probabilities[row - 1, col] = 0.33 #UP\\n state_probabilities[row, col - 1 ] = 0.33 #LEFT\\n state_probabilities[row, col + 1] = 0.0 # RIGHT\\n state_probabilities[row + 1, col] = 0.33 #DOWN\\n elif action == 'RIGHT':\\n row += 1\\n col += 1\\n state_probabilities[row - 1, col] = 0.33 #UP\\n state_probabilities[row, col - 1 ] = 0.0 #LEFT\\n state_probabilities[row, col + 1] = 0.33 # RIGHT\\n state_probabilities[row + 1, col] = 0.33 #DOWN\\n elif action == 'DOWN':\\n row += 1\\n col += 1\\n state_probabilities[row - 1, col] = 0.0 # UP\\n state_probabilities[row, col - 1] = 0.33 # LEFT\\n state_probabilities[row, col + 1] = 0.33 # RIGHT\\n state_probabilities[row + 1, col] = 0.33 # DOWN\\n\\n for row in range (0, tot_row+1):\\n if state_probabilities[row, 0] != 0:\\n state_probabilities[row, 1] += state_probabilities[row, 0]\\n elif state_probabilities[row, -1] != 0:\\n state_probabilities[row, -2] += state_probabilities[row, -1]\\n\\n for col in range (0, tot_col+1):\\n if state_probabilities[0, col] != 0:\\n state_probabilities[1, col] += state_probabilities[0, col]\\n elif state_probabilities[-1, col] != 0:\\n state_probabilities[-2, col] += state_probabilities[-1, col]\\n\\n return state_probabilities[1: 1+tot_row, 1:1+tot_col]\",\n \"def getCapsuleGoal(self, gameState):\\n capsules = self.getCapsules(gameState)\\n myPos = self.getCurrentObservation().getAgentState(self.index).getPosition()\\n if len(capsules) > 0:\\n dis = 9999\\n nearestCapsule = capsules[0]\\n for c in capsules:\\n temp = self.getMazeDistance(myPos, c)\\n if temp < dis:\\n dis = temp\\n nearestCapsule = c\\n return nearestCapsule, dis\\n else:\\n return None, None\",\n \"def get_others(map_, r, c):\\n nums = 0\\n # your code here\\n if r == 0 and c == 0: #top left corder\\n nums += 2\\n if len(map_[0]) > 1:\\n if map_[r][c+1] == 0:\\n nums += 1\\n if map_[r+1][c] == 0:\\n nums += 1\\n elif r == 0 and c == len(map_[0])-1: #top right corner\\n nums += 2\\n if len(map_[0]) > 1:\\n if map_[r][c-1] == 0:\\n nums += 1\\n if map_[r+1][c] == 0:\\n nums += 1\\n elif r == len(map_)-1 and c == 0: #bottom left corder\\n nums += 2\\n if len(map_[0]) > 1:\\n if map_[r][c+1] == 0:\\n nums += 1\\n if map_[r-1][c] == 0:\\n nums += 1\\n elif r == len(map_)-1 and c == len(map_[0])-1: #bottom right corner\\n nums += 2\\n if map_[r][c-1] == 0:\\n nums += 1\\n if map_[r-1][c] == 0:\\n nums += 1\\n elif r == 0: # top edge, excluding corner\\n nums += 1\\n if map_[r][c-1] == 0:\\n nums += 1\\n if map_[r][c+1] == 0:\\n nums += 1\\n if len(map_) > r and map_[r+1][c] == 0:\\n nums += 1\\n elif r == len(map_)-1: # bottom edge, excluding corner\\n nums += 1\\n if map_[r][c-1] == 0:\\n nums += 1\\n if map_[r][c+1] == 0:\\n nums += 1\\n if map_[r-1][c] == 0:\\n nums += 1\\n elif c == 0: # left edge, excluding corner\\n nums += 1\\n if map_[r-1][c] == 0:\\n nums += 1\\n if map_[r+1][c] == 0:\\n nums += 1\\n if len(map_[0]) > c and map_[r][c+1] == 0:\\n nums += 1\\n elif c == len(map_[0])-1: # right edge. excluding corner\\n nums += 1\\n if map_[r-1][c] == 0:\\n nums += 1\\n if map_[r+1][c] == 0:\\n nums += 1\\n if map_[r][c-1] == 0:\\n nums += 1\\n else: # the rest, excluding edge and corner\\n if map_[r-1][c] == 0:\\n nums += 1\\n if map_[r+1][c] == 0:\\n nums += 1\\n if map_[r][c-1] == 0:\\n nums += 1\\n if map_[r][c+1] == 0:\\n nums += 1\\n return nums\",\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 cudnn_lstm_state(lstm_cell_state):\\n h = tf.stack([s.h for s in lstm_cell_state])\\n c = tf.stack([s.c for s in lstm_cell_state])\\n return (h, c)\",\n \"def solve():\\n\\n s, g, e = make_lattice(21)\\n stack = deque([[e]])\\n vals = {s: 1}\\n max_n = 0\\n\\n while stack:\\n max_n = max(max_n, len(stack))\\n n, *p = stack.pop()\\n for c in g.get_connected(n):\\n if c > n:\\n continue\\n if c in vals:\\n propagate(c, [n] + p, vals)\\n else:\\n stack.append([c, n] + p)\\n return vals[e]\",\n \"def _get_state(self, obs_env):\\n state = []\\n obs_env = obs_env.reshape(self.n_agent, 2)\\n for i in range(self.n_agent):\\n local_obs = obs_env[i]\\n if self.agent.startswith('ia2c'):\\n imgs = [local_obs]\\n\\n if not self.agent == 'ia2c_fp': # ia2c\\n for j in np.where(self.neighbor_mask[i] == 1)[0]:\\n imgs.append(obs_env[j])\\n imgs = np.array(imgs, dtype=np.float32)\\n fps = np.array([], dtype=np.float32)\\n\\n else: # ia2c_fp\\n fps = []\\n for j in np.where(self.neighbor_mask[i] == 1)[0]:\\n imgs.append(obs_env[j])\\n fps.append(self.fp[j])\\n imgs = np.array(imgs, dtype=np.float32)\\n fps = np.concatenate(fps).astype(np.float32)\\n\\n agent_obs = [imgs, fps]\\n\\n else: # ma2c\\n agent_obs = local_obs.astype(np.float32)\\n\\n state.append(agent_obs)\\n\\n return state\\n # return [[obs_env, np.array([], dtype=np.float32)] for _ in range(self.n_agent)]\",\n \"def coaccessible_components(self):\\n DG = self.digraph().reverse()\\n coaccessible_states = DG.breadth_first_search(\\n [_.label() for _ in self.iter_final_states()])\\n return self.induced_sub_finite_state_machine(\\n [self.state(_) for _ in coaccessible_states])\",\n \"def find_best_match_jobs(self,candidate_matrix):\\n\\n \\\"\\\"\\\"\\n Example format for input:\\n candidate_matrix =\\n {\\n 0: {\\\"This is a skill description\\\": 10, \\\"This is another skill description\\\": 7},\\n 1: {\\\"This is a knowledge description\\\" : 8, \\\"This is another knowledge description\\\": 6}\\n }\\n \\\"\\\"\\\"\\n\\n\\n\\n\\n\\n\\n # Get a list of all job codes to begin importing details for\\n cursor = self.conn.execute(\\\"SELECT job_code FROM job\\\")\\n\\n # Get python list of all job codes\\n job_codes = cursor.fetchall()\\n\\n\\n job_score = {}\\n\\n for job_code in job_codes:\\n job_code = job_code[0]\\n job_importance_matrix = self.generate_job_importance_matrix(job_code)\\n\\n\\n job_score[job_code] = self.calculate_job_score(candidate_matrix, job_importance_matrix)\\n\\n\\n # Sort by job scores!\\n sorted_jobs = sorted(job_score.iteritems(), key=operator.itemgetter(1))\\n\\n # Get in descending order\\n sorted_jobs.reverse()\\n\\n # Return the jobs with the top 5 job scores for the candidate\\n return sorted_jobs[:5]\",\n \"def change_way(coins, opponentLocation, player_location):\\n global best_weight, best_path\\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(5, coins, player_location, dists_matrix)\\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 best_weight = float(\\\"inf\\\")\\n best_path = []\\n api.debug(coins_to_search)\\n exhaustive(coins_to_search, player_location, [], 0, dist_matrix)\\n meta_route = [player_location] + best_path\\n api.debug(meta_route)\\n route = u.location_list_to_route(meta_route, route_matrix)\\n \\n return coins_to_search, meta_route, route, dist_matrix[player_location][meta_route[1]]\",\n \"def initialize_action_matrix(self):\\n for start_i, cols in enumerate(self.A):\\n for next_j, value in enumerate(cols):\\n start_state = self.get_state_from_row(start_i)\\n # (row_number, red loc, blue loc, green loc)\\n next_state = self.get_state_from_row(next_j)\\n\\n # Valid states include moving exactly 1 dumbbell to a block that's\\n # not already occupied, or to origin.\\n num_moves, moves = count_moves_from_states(\\n start_state, next_state)\\n \\n # key: location (where 0 = origin, 1/2/3 = block #), value: count\\n next_state_loc_counts = Counter(next_state[1:])\\n del next_state_loc_counts[0] # ignore origin location\\n \\n # There must not be more than 1 dumbbell at each block\\n is_valid_next_state = all(\\n map(lambda x: x <= 1, next_state_loc_counts.values()))\\n \\n if (num_moves == 1 and is_valid_next_state):\\n move = moves[0] # Only 1 move\\n # doing nothing or moving to origin is not valid\\n if (start_state == next_state or move[1] == 0):\\n continue\\n self.A[start_i][next_j] = self.get_col_from_action(move)\",\n \"def compute_order(h: list[permutation]) -> int:\\n\\n # If only the trivial permutation is present in the generating set, the order is 1.\\n if len(h) == 1 and h[0].istrivial():\\n return 1\\n\\n # Choose a non-trivial orbit\\n alpha = -1\\n orbit = [0]\\n while len(orbit) < 2:\\n alpha += 1\\n orbit = Orbit(h, alpha)\\n\\n # Calculate the order of the stabilizer\\n stabilizer = Stabilizer(h, alpha)\\n if len(stabilizer) == 0:\\n stab_order = 1\\n else:\\n stab_order = compute_order(stabilizer)\\n\\n return len(orbit) * stab_order\",\n \"def get_pair_lookup(fold, threshold=1, force=False):\\n try:\\n assert not force # this is some hacky control flow right here\\n M = load_cooc_matrix(fold)\\n logging.info('Loaded cooc matrix')\\n except (AssertionError, IOError):\\n logging.info('Building cooc matrix')\\n users = iterate_wrapped_users(fold)\\n with time_me('Built cooc matrix', mode='print'):\\n M = build_cooc_matrix(users)\\n M = M.tocoo()\\n # converting to coo at this point might provide some memory relief\\n save_cooc_matrix(fold, M)\\n\\n logging.info('Loaded cooc matrix with {:,} nonzero entries'.format(M.nnz))\\n next_ix = 0\\n lookup = {}\\n # Turns out using scipy.sparse.find is slow af\\n #with time_me('finded'):\\n # focals, others, counts = scipy.sparse.find(M)\\n assert isinstance(M, scipy.sparse.coo_matrix)\\n focals, others, counts = M.row, M.col, M.data\\n #for i in xrange(len(counts)):\\n # focal_pidx = focals[i]\\n # other_pidx = others[i]\\n # count = counts[i]\\n for (focal_pidx, other_pidx, count) in itertools.izip(focals, others, counts):\\n\\n if count >= threshold:\\n k = (focal_pidx+1, other_pidx+1)\\n lookup[k] = next_ix\\n next_ix += 1\\n if next_ix % 10**6 == 0:\\n print '{:,}... '.format(next_ix)\\n\\n logging.info('Went from {:,} non-zero entries to {:,} pairs after thresholding by {}'.format(\\n M.nnz, next_ix, threshold))\\n if next_ix + constants.N_PRODUCTS > 2**31:\\n logging.warn(\\\"Too many features even after thresholding. You're gonna have a bad time.\\\")\\n return lookup\",\n \"def calc_heuristic(self, state):\\n h = 0\\n board = state.board.array\\n\\n for i in range(self._n):\\n for j in range(self._n):\\n\\n if board[i][j] != space_rep:\\n tile_as_number = board[i][j]\\n correct_x = (tile_as_number - 1) // self._n\\n correct_y = (tile_as_number - 1) % self._n\\n else:\\n continue\\n h += calc_diffs(i, j, correct_x, correct_y)\\n return h\",\n \"def persistent_homology(self):\\n\\n def low(j, R):\\n \\\"\\\"\\\"\\n :return: maximum line index of the column j in the matrix R with a 1 in it\\n \\\"\\\"\\\"\\n if R[j] == []:\\n return (-1)\\n else:\\n return (sorted(R[j])[-1])\\n\\n # low_j = 0\\n # for k in range(j):\\n # if R[k, j] == 1:\\n # low_j = k\\n # return (low_j)\\n\\n N = self.nbr_splxs\\n self.homology_matrix = self.neighbours_matrix[:]\\n n = self.nbr_0_splxs\\n # initilize the low_j matrix\\n self.low_j_to_j_list = N * [-1]\\n # Apply the persistence algorithm\\n j = 0\\n while low(j, self.homology_matrix) == -1:\\n j += 1\\n self.low_j_to_j_list[low(j, self.homology_matrix)] = j\\n j += 1\\n while j < N:\\n low_j = low(j, self.homology_matrix)\\n j0 = self.low_j_to_j_list[low_j]\\n while j0 != -1:\\n self.homology_matrix[j] = self.sum_column(j, j0, self.homology_matrix)\\n # self.homology_matrix[:j, j] = (self.homology_matrix[:j, j0] + self.homology_matrix[:j, j]) % 2\\n low_j = low(j, self.homology_matrix)\\n j0 = self.low_j_to_j_list[low_j]\\n if low_j != -1:\\n self.low_j_to_j_list[low_j] = j\\n j += 1\\n if j % 10 == 0:\\n print(j / N)\\n # for j in range(1, N):\\n # test = True\\n # while test:\\n # test = False\\n # for j0 in range(j):\\n # if low(j0, self.homology_matrix) == low(j, self.homology_matrix) \\\\\\n # and low(j0, self.homology_matrix) != 0:\\n # self.homology_matrix[:j, j] = (self.homology_matrix[:j, j0] + self.homology_matrix[:j, j]) % 2\\n # test = True\\n # if j % 10 == 0:\\n # print(np.log(j + 1) / np.log(N))\\n\\n for j in range(N):\\n low_j = low(j, self.homology_matrix)\\n if low_j != -1:\\n # print(low_j,j)\\n # self.pers_pairs_birth.append(self.dist_appearance[low_j])\\n # self.pers_pairs_death.append(self.dist_appearance[j])\\n if self.splxs[low_j][0] == 0:\\n self.h0_birth.append(self.dist_appearance[low_j])\\n self.h0_death.append(self.dist_appearance[j])\\n print(low_j)\\n else:\\n self.h1_birth.append(self.dist_appearance[low_j])\\n self.h1_death.append(self.dist_appearance[j])\\n print(\\\"persistant homology achieved\\\")\\n return ()\",\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 next_possible_states(path, check_dict, check):\\r\\n \\r\\n current_state_tuple = path[-1]\\r\\n state_container = []\\r\\n x = current_state_tuple[1][0]\\r\\n y = current_state_tuple[1][1]\\r\\n current_state = current_state_tuple[0]\\r\\n\\r\\n # Down\\r\\n if y < 3:\\r\\n new_state = copy.deepcopy(current_state)\\r\\n new_state[y][x] = new_state[y + 1][x]\\r\\n new_state[y + 1][x] = 0\\r\\n if not been_there(new_state, check_dict, check):\\r\\n new_index = (x, y + 1)\\r\\n h1 = euclidean_dist(new_state, path)\\r\\n new_state_tuple = (new_state, new_index, h1)\\r\\n state_container.append(new_state_tuple)\\r\\n\\r\\n # Up\\r\\n if y > 0:\\r\\n new_state = copy.deepcopy(current_state)\\r\\n if y == 1 and x == 0:\\r\\n new_state[y][x] = new_state[y - 1][x]\\r\\n new_state[y - 1][x] = 0\\r\\n if is_goal(new_state):\\r\\n new_index = (x, y - 1)\\r\\n h1 = euclidean_dist(new_state, path)\\r\\n new_state_tuple = (new_state, new_index, h1)\\r\\n state_container.append(new_state_tuple)\\r\\n elif y > 1:\\r\\n new_state[y][x] = new_state[y - 1][x]\\r\\n new_state[y - 1][x] = 0\\r\\n if not been_there(new_state, check_dict, check):\\r\\n new_index = (x, y - 1)\\r\\n h1 = euclidean_dist(new_state, path)\\r\\n new_state_tuple = (new_state, new_index, h1)\\r\\n state_container.append(new_state_tuple)\\r\\n\\r\\n # Left\\r\\n if x > 0 and y > 0:\\r\\n new_state = copy.deepcopy(current_state)\\r\\n new_state[y][x] = new_state[y][x - 1]\\r\\n new_state[y][x - 1] = 0\\r\\n if not been_there(new_state, check_dict, check):\\r\\n new_index = (x - 1, y)\\r\\n h1 = euclidean_dist(new_state, path)\\r\\n new_state_tuple = (new_state, new_index, h1)\\r\\n state_container.append(new_state_tuple)\\r\\n\\r\\n # Right\\r\\n if x < 2 and y > 0:\\r\\n new_state = copy.deepcopy(current_state)\\r\\n new_state[y][x] = new_state[y][x + 1]\\r\\n new_state[y][x + 1] = 0\\r\\n if not been_there(new_state, check_dict, check):\\r\\n new_index = (x + 1, y)\\r\\n h1 = euclidean_dist(new_state, path)\\r\\n new_state_tuple = (new_state, new_index, h1)\\r\\n state_container.append(new_state_tuple)\\r\\n\\r\\n return state_container\",\n \"def get_reverse_state_arr(self):\\n rpos = self.sim.getAgentPosition(self.robot_num)\\n rvel = self.sim.getAgentVelocity(self.robot_num)\\n rrad = self.sim.getAgentRadius(self.robot_num)\\n v_pref = self.sim.getAgentMaxSpeed(self.robot_num)\\n theta = math.atan2(rvel[1], rvel[0])\\n # Robot's state entry.\\n state = [\\n rpos[0], rpos[1], rvel[0], rvel[1], rrad,\\n self.headings[self.robot_num], self.overall_robot_goal[0],\\n self.overall_robot_goal[1], v_pref, theta\\n ]\\n for obs in self.obstacles:\\n if len(obs) > 1:\\n # Polygonal obstacle\\n o = Polygon(obs)\\n p = Point(rpos)\\n p1, p2 = nearest_points(o, p)\\n # Velocity is always 0 for obstacles\\n # Heading is same as robot's\\n state.extend([p1.x, p2.y, 0, 0, self.obs_width,\\n self.headings[self.robot_num]])\\n else:\\n # Point obstacle\\n state.extend([obs[0][0], obs[0][1], 0, 0, self.obs_width,\\n self.headings[self.robot_num]])\\n return state\",\n \"def next_state(s_curr, action, params):\\n P_dist = params['P_dist']\\n R = params['R']\\n n_rows = params['n_rows']\\n n_cols = params['n_cols']\\n occ_grid = params['occ_grid']\\n\\n rnd = np.random.uniform()\\n\\n s_next = s_curr\\n\\n # Actions - ['left','right','up','down']\\n\\n if rnd <= P_dist:\\n if action == 0:\\n move = 2\\n elif action == 1:\\n move = 2\\n elif action == 2:\\n move = 1\\n else:\\n move = 0\\n elif rnd < 2*P_dist:\\n if action == 0:\\n move = 3\\n elif action == 1:\\n move = 3\\n elif action == 2:\\n move = 1\\n else:\\n move = 1\\n else:\\n move = action\\n\\n # Move left\\n if move == 0:\\n row_next = s_curr[0]\\n col_next = s_curr[1] - 1\\n if col_next >= 0 and occ_grid[row_next, col_next] == 0:\\n s_next = [row_next, col_next]\\n\\n # Move right\\n if move == 1:\\n row_next = s_curr[0]\\n col_next = s_curr[1] + 1\\n if col_next < n_cols and occ_grid[row_next, col_next] == 0:\\n s_next = [row_next, col_next]\\n\\n # Move up\\n if move == 2:\\n row_next = s_curr[0] - 1\\n col_next = s_curr[1]\\n if row_next >= 0 and occ_grid[row_next, col_next] == 0:\\n s_next = [row_next, col_next]\\n\\n # Move down\\n if move == 3:\\n row_next = s_curr[0] + 1\\n col_next = s_curr[1]\\n if row_next < n_rows and occ_grid[row_next, col_next] == 0:\\n s_next = [row_next, col_next]\\n\\n r = R[s_next[0], s_next[1]]\\n return s_next, r\",\n \"def compute_map(current_agent_id,agent_order,number_of_timestep,state_schedules, conv :StateConverter):\\r\\n #Find the agent has the highest number of time steps\\r\\n highest_timestep = 0\\r\\n # Find the highest time step\\r\\n if len(number_of_timestep) >0:\\r\\n highest_timestep = np.max(number_of_timestep)\\r\\n occupancy_map = []\\r\\n # Since we don't know yet how many time step of the current id so\\r\\n # the number of time steps of the occupancy map == highest number of time step\\r\\n # of the current schedule\\r\\n for time_step in range(int(highest_timestep)):\\r\\n # Initialize the occupancy for current time step\\r\\n current_occupancy_map = np.zeros(conv.num_tiles)\\r\\n # We loop through schedule of each agent at current time step\\r\\n for i in range(len(state_schedules)):\\r\\n # Get the agent id of current schedule\\r\\n agent_of_schedule = agent_order[i]\\r\\n if time_step < len(state_schedules[i]):\\r\\n # The first case when the agent of current schedule is executed after the current agent\\r\\n if agent_of_schedule > current_agent_id:\\r\\n # Get the current state\\r\\n current_state = state_schedules[i][time_step]\\r\\n # Convert the current state to tile index\\r\\n current_tile = conv.state_to_tile(current_state)\\r\\n # Occupied the current tile in the occupancy map\\r\\n current_occupancy_map[current_tile] = 1\\r\\n if time_step + 1 < len(state_schedules[i]):\\r\\n # Get the next state\\r\\n next_state = state_schedules[i][time_step + 1]\\r\\n # Convert next state to next tile will be occupied\\r\\n next_tile_index = conv.state_to_tile(next_state)\\r\\n # Occupied the next tile in the occupancy map\\r\\n current_occupancy_map[next_tile_index] = 1\\r\\n # The second case when the agent of current schedule is executed before the current agent\\r\\n else:\\r\\n if time_step + 1 < len(state_schedules[i]):\\r\\n # Get the next state\\r\\n next_state = state_schedules[i][time_step + 1]\\r\\n # Convert next state to next tile will be occupied\\r\\n next_tile_index = conv.state_to_tile(next_state)\\r\\n # Occupied the next tile in the occupancy map\\r\\n current_occupancy_map[next_tile_index] = 1\\r\\n if time_step + 2 < len(state_schedules[i]):\\r\\n # Get the next 2 state\\r\\n next_2state = state_schedules[i][time_step+2]\\r\\n # Convert the current state to tile index\\r\\n next_2tile = conv.state_to_tile(next_2state)\\r\\n # Occupied the current tile in the occupancy map\\r\\n current_occupancy_map[next_2tile] = 1\\r\\n occupancy_map.append(current_occupancy_map)\\r\\n return occupancy_map\",\n \"def state_rep (self):\\n\\n # Computing dealer_card\\n dealer_card = self.dealer[0]\\n\\n # Compute player_max\\n player_max = self.max_safe_sum()\\n\\n # State should not be bust\\n assert (1 <= dealer_card <= 10)\\n assert (0 <= player_max <= 31)\\n\\n # Compute table number\\n possibilities = get_full_state (self.me)\\n # possibilities = [p for p in possibilities if 0 <= p <= 31]\\n\\n table_no = 0\\n for idx, p in enumerate(possibilities):\\n if 0 <= p <= 31:\\n table_no = idx\\n assert 0 <= table_no <= 3\\n\\n # print (possibilities)\\n return (table_no, dealer_card, player_max)\",\n \"def getNextState(self, board, player, action):\\n b = self._base_board.with_np_pieces(np_pieces=np.copy(board))\\n b.add_stone(action, player)\\n return b.np_pieces, -player\",\n \"def get_success_state_arr(self):\\n rpos = self.sim.getAgentPosition(self.robot_num)\\n rvel = self.sim.getAgentVelocity(self.robot_num)\\n rrad = self.sim.getAgentRadius(self.robot_num)\\n v_pref = self.sim.getAgentMaxSpeed(self.robot_num)\\n theta = math.atan2(rvel[1], rvel[0])\\n # Robot's state entry.\\n state = [\\n rpos[0], rpos[1], rvel[0], rvel[1], rrad,\\n self.headings[self.robot_num], self.overall_robot_goal[0],\\n self.overall_robot_goal[1], v_pref, theta\\n ]\\n for agent in self.agents:\\n if agent != self.robot_num: # We already accounted for the robot\\n pos = self.sim.getAgentPosition(agent)\\n vel = self.sim.getAgentVelocity(agent)\\n rad = self.sim.getAgentRadius(agent)\\n state.extend([pos[0], pos[1], vel[0], vel[1], rad,\\n self.headings[agent]])\\n for obs in self.obstacles:\\n if len(obs) > 1:\\n # Polygonal obstacle\\n o = Polygon(obs)\\n p = Point(rpos)\\n p1, p2 = nearest_points(o, p)\\n # Velocity is always 0 for obstacles\\n # Heading is same as robot's\\n state.extend([p1.x, p2.y, 0, 0, self.obs_width,\\n self.headings[self.robot_num]])\\n else:\\n # Point obstacle\\n state.extend([obs[0][0], obs[0][1], 0, 0, self.obs_width,\\n self.headings[self.robot_num]])\\n return state\",\n \"def calculate_next_board_state(self):\\n new_board_state = np.zeros_like(self.board_state)\\n\\n for x in range(self.board_size[0]):\\n for y in range(self.board_size[0]):\\n new_board_state[x][y] = self.next_state_of_cell(x,y)\\n \\n self.set_state(new_board_state)\",\n \"def calc_neck_score(self):\\n ordered_c, edgelen = self.coords, self.edgelen\\n nc = np.vstack((ordered_c, ordered_c[:edgelen, :]))\\n score = []\\n for n, ci in enumerate(nc[:-edgelen, :]):\\n try:\\n score.append(calc_clockwise_degree(ordered_c[n-edgelen, :], nc[n, :], nc[n+edgelen, :]))\\n except:\\n pass\\n idx = np.flipud(np.argsort(score))\\n return np.array(score)[idx], ordered_c[idx]\",\n \"def close_to_opponent_high_pass(obs, player_x, player_y):\\n def environment_fits(obs, player_x, player_y):\\n \\\"\\\"\\\" environment fits constraints \\\"\\\"\\\"\\n for i in range(1, len(obs[\\\"right_team\\\"])):\\n distance_to_opponent = get_distance(player_x, player_y, obs[\\\"right_team\\\"][i][0], obs[\\\"right_team\\\"][i][1])\\n if distance_to_opponent < 0.06:\\n for j in range(1, len(obs[\\\"left_team\\\"])):\\n player_to_opponents = get_average_distance_to_opponents(obs, player_x, player_y)\\n teammate_to_opponents = get_average_distance_to_opponents(obs, player_x, player_y)\\n distance_to_teammate = get_distance(player_x, player_y, obs[\\\"left_team\\\"][j][0], obs[\\\"left_team\\\"][j][1])\\n if distance_to_teammate < 0.6 and distance_to_teammate > 0.4 and player_to_opponents > teammate_to_opponents:\\n teammate_distance_to_goal = get_distance(obs[\\\"left_team\\\"][j][0], obs[\\\"left_team\\\"][j][1], 1, 0)\\n player_distance_to_goal = get_distance(player_x, player_y, 1, 0)\\n if teammate_distance_to_goal < player_distance_to_goal - 0.2:\\n return True\\n break\\n return False\\n \\n def get_action(obs, player_x, player_y):\\n \\\"\\\"\\\" get action of this memory pattern \\\"\\\"\\\"\\n if Action.Sprint in obs[\\\"sticky_actions\\\"]:\\n return Action.ReleaseSprint\\n return Action.HighPass\\n\\n return {\\\"environment_fits\\\": environment_fits, \\\"get_action\\\": get_action}\",\n \"def search(self, canonicalBoard, this_player):\\n\\n def player_net(player):\\n return self.white_nnet if player == Player.white else self.black_nnet\\n\\n value_stack = []\\n next_player = this_player\\n iteration = 0\\n\\n # build stack\\n while True:\\n # workaround because in some cases this function doesn't stop (bug might exist in the original version also)\\n # print(\\\" iteration \\\" + str(iteration))\\n iteration += 1\\n if iteration > 1000:\\n print(\\\"more MCTS search iterations than the maximum, breaking out of possibly infinite loop!\\\")\\n return\\n\\n # workaround end\\n\\n # bytes are much faster\\n s = self.game.stringRepresentation(canonicalBoard) + next_player.to_bytes(1, byteorder='big', signed=True)\\n # s = self.game.stringRepresentation(canonicalBoard) + str(next_player) # this addition is needed so that\\n # the search algorithm doesn't get confused when the same board state as before is reached, but it's the\\n # other player's turn\\n\\n if s not in self.Es:\\n self.Es[s] = self.game.getGameEnded(canonicalBoard, next_player)\\n if self.Es[s] != 0:\\n # terminal node\\n last_iteration_v = -self.Es[s]\\n break\\n\\n if s not in self.Ps:\\n # leaf node\\n valids = self.game.getValidMoves(canonicalBoard, next_player)\\n\\n # occurrences = np.zeros(self.size * self.size * self.size * 2)\\n # for index, action in enumerate(valids):\\n # if action == 1 and index != self.size * self.size * self.size * 2:\\n # explicit = action_conversion__index_to_explicit(index, self.size)\\n # occurrences[index] = 1 if canonicalBoard.would_next_board_be_second_third(2, explicit) else 0\\n\\n self.Ps[s], v = player_net(next_player).predict(canonicalBoard, np.array([canonicalBoard.king_position[0], canonicalBoard.king_position[1]]))\\n # valids = self.game.getValidMoves(canonicalBoard, next_player)\\n self.Ps[s] = self.Ps[s] * valids # masking invalid moves\\n sum_Ps_s = np.sum(self.Ps[s])\\n if sum_Ps_s > 0:\\n self.Ps[s] /= sum_Ps_s # renormalize\\n else:\\n # if all valid moves were masked make all valid moves equally probable\\n\\n # NB! All valid moves may be masked if either your NNet architecture is insufficient or you've get overfitting or something else.\\n # If you have got dozens or hundreds of these messages you should pay attention to your NNet and/or training process.\\n print(\\\"All valid moves were masked, do workaround.\\\")\\n print(valids)\\n self.Ps[s] = self.Ps[s] + valids\\n self.Ps[s] /= np.sum(self.Ps[s])\\n\\n self.Vs[s] = valids\\n self.Ns[s] = 0\\n last_iteration_v = -v\\n break\\n\\n valids = self.Vs[s]\\n cur_best = -float('inf')\\n best_act = -1\\n\\n # pick the action with the highest upper confidence bound\\n for a in range(self.game.getActionSize()):\\n if valids[a]:\\n if (s, a) in self.Qsa:\\n u = self.Qsa[(s, a)] + self.args.cpuct * self.Ps[s][a] * math.sqrt(self.Ns[s]) / (\\n 1 + self.Nsa[(s, a)])\\n else:\\n u = self.args.cpuct * self.Ps[s][a] * math.sqrt(self.Ns[s] + EPS) # Q = 0 ?\\n\\n if u > cur_best:\\n cur_best = u\\n best_act = a\\n\\n a = best_act\\n\\n next_s, next_player = self.game.getNextState(canonicalBoard, next_player, a)\\n canonicalBoard = self.game.getCanonicalForm(next_s, next_player)\\n\\n value_stack.append((s, a))\\n\\n # take from stack\\n while len(value_stack) > 0:\\n s, a = value_stack.pop()\\n\\n if (s, a) in self.Qsa:\\n self.Qsa[(s, a)] = (self.Nsa[(s, a)] * self.Qsa[(s, a)] + last_iteration_v) / (self.Nsa[(s, a)] + 1)\\n self.Nsa[(s, a)] += 1\\n\\n else:\\n self.Qsa[(s, a)] = last_iteration_v\\n self.Nsa[(s, a)] = 1\\n\\n self.Ns[s] += 1\\n last_iteration_v = -last_iteration_v\\n\\n return -last_iteration_v\",\n \"def orderMetric(y_true, y_pred, weights):\\n ok = weights > 0.0\\n return spearmanr(y_true[ok], y_pred[ok])[0]\",\n \"def _calculate_sentence_level_chrf_score(targets: List[str], pred_char_n_grams_counts: Dict[int, Dict[Tuple[str, ...], Tensor]], pred_word_n_grams_counts: Dict[int, Dict[Tuple[str, ...], Tensor]], preds_char_n_grams: Dict[int, Tensor], preds_word_n_grams: Dict[int, Tensor], n_char_order: int, n_word_order: int, n_order: float, beta: float, lowercase: bool, whitespace: bool) ->Tuple[Tensor, Dict[int, Tensor], Dict[int, Tensor], Dict[int, Tensor], Dict[int, Tensor]]:\\n best_f_score = tensor(0.0)\\n best_matching_char_n_grams: Dict[int, Tensor] = defaultdict(lambda : tensor(0.0))\\n best_matching_word_n_grams: Dict[int, Tensor] = defaultdict(lambda : tensor(0.0))\\n best_target_char_n_grams: Dict[int, Tensor] = defaultdict(lambda : tensor(0.0))\\n best_target_word_n_grams: Dict[int, Tensor] = defaultdict(lambda : tensor(0.0))\\n for target in targets:\\n target_char_n_grams_counts, target_word_n_grams_counts, target_char_n_grams, target_word_n_grams = _get_n_grams_counts_and_total_ngrams(target, n_char_order, n_word_order, lowercase, whitespace)\\n matching_char_n_grams = _get_ngram_matches(target_char_n_grams_counts, pred_char_n_grams_counts)\\n matching_word_n_grams = _get_ngram_matches(target_word_n_grams_counts, pred_word_n_grams_counts)\\n f_score = _calculate_fscore(matching_char_n_grams, matching_word_n_grams, preds_char_n_grams, preds_word_n_grams, target_char_n_grams, target_word_n_grams, n_order, beta)\\n if f_score > best_f_score:\\n best_f_score = f_score\\n best_matching_char_n_grams = matching_char_n_grams\\n best_matching_word_n_grams = matching_word_n_grams\\n best_target_char_n_grams = target_char_n_grams\\n best_target_word_n_grams = target_word_n_grams\\n return best_f_score, best_matching_char_n_grams, best_matching_word_n_grams, best_target_char_n_grams, best_target_word_n_grams\",\n \"def solve_tsp_grouped(list_of_locations, list_of_homes, starting_car_location, adjacency_matrix, params=[]):\\n drop_off_dict = {}\\n car_path = []\\n home_map = {}\\n group_score = {}\\n center_map = {}\\n epsilon = 1.2\\n home_indexes = convert_locations_to_indices(list_of_homes, list_of_locations)\\n orig_home_indexes = home_indexes[:]\\n cluster_map = {}\\n\\n start = list_of_locations.index(starting_car_location)\\n graph, msg = adjacency_matrix_to_graph(adjacency_matrix)\\n all_paths = dict(nx.all_pairs_dijkstra(graph))\\n\\n for v in range(len(list_of_locations)):\\n group_score[v], cluster_map[v] = compute_group(v, home_indexes[:], all_paths, epsilon)\\n\\n sorted_v = sorted([k for k in group_score.keys() if group_score[k] > 0], key = lambda x: group_score[x])\\n min_group_score = group_score[sorted_v[0]]\\n #print(min_group_score)\\n delta = 6\\n\\n high_centrality_homes = set() #LOW GROUP SCORE VERTICES (CLUSTER)\\n used_homes = set()\\n newHome = home_indexes[:]\\n\\n while newHome and group_score[sorted_v[0]] < delta * min_group_score:\\n\\n v = sorted_v[0]\\n high_centrality_homes.add(v)\\n usedList = cluster_map[v]\\n #print(usedList)\\n if v in newHome and v not in usedList:\\n usedList.append(v)\\n if v in center_map.keys():\\n center_map[v].extend(usedList)\\n else:\\n center_map[v] = usedList\\n #if v in home_indexes:\\n #center_map[v].append(v)\\n for vert in usedList:\\n used_homes.add(vert)\\n if vert in newHome:\\n newHome.remove(vert)\\n used_homes.add(v)\\n while v in newHome:\\n newHome.remove(v)\\n for x in range(len(list_of_locations)):\\n group_score[x], cluster_map[x] = compute_group(x, newHome[:], all_paths, epsilon)\\n sorted_v = sorted([k for k in group_score.keys() if group_score[k] > 0], key = lambda x: group_score[x])\\n\\n for home in newHome:\\n if home not in center_map.keys():\\n center_map[home] = [home]\\n \\\"\\\"\\\"START TIM\\n\\n for home in high_centrality_homes:\\n center_map[home] = [home]\\n dist_dict = all_paths.get(home)[0]\\n paths_dict = all_paths.get(home)[1]\\n dist_dict = {k:v for (k,v) in dist_dict.items() if k not in used_homes and k in home_indexes} #distance dict of all remaing homes\\n\\n min_dist = min(dist_dict.values()) #closest home to high centrality home\\n dist_dict = {k:v for (k,v) in dist_dict.items() if dist_dict[k] <= min_dist*epsilon}\\n\\n for cluster_home in dist_dict.keys():\\n center_map[home].append(cluster_home)\\n home_indexes.remove(cluster_home)\\n used_homes.add(cluster_home)\\n\\n start_in_home = start in home_indexes\\n if start in home_indexes:\\n home_indexes.remove(start)\\n home_indexes.insert(0, start)\\n home_count = 0;\\n\\n for home in home_indexes:\\n #print(home, end = \\\" \\\")\\n home_map[home_count] = home\\n home_count += 1\\n # Instantiate the data problem.\\n #print(len(home_map))\\n END TIM \\\"\\\"\\\"\\n tspInput = list(high_centrality_homes)\\n tspInput.extend(newHome)\\n if start in tspInput:\\n tspInput.remove(start)\\n tspInput.insert(0, start)\\n home_map.clear()\\n for i in range(len(tspInput)):\\n home_map[i] = tspInput[i]\\n data = create_data_model(tspInput, 0)\\n\\n # Create the routing index manager.\\n manager = pywrapcp.RoutingIndexManager(len(data['locations']),\\n data['num_vehicles'], data['depot'])\\n\\n #print(manager.NodeToIndex(15))\\n # Create Routing Model.\\n routing = pywrapcp.RoutingModel(manager)\\n\\n def distance_callback(from_index, to_index):\\n \\\"\\\"\\\"Returns the distance between the two nodes.\\\"\\\"\\\"\\n # Convert from routing variable Index to distance matrix NodeIndex.\\n #print(home_map[to_index], end = \\\" \\\")\\n from_index = manager.IndexToNode(from_index)\\n to_index = manager.IndexToNode(to_index)\\n dist_to = all_paths.get(home_map[from_index])[0][home_map[to_index]]\\n #if from_index >= 25 or to_index >= 25:\\n # print(\\\"from\\\" if from_index >= 25 else \\\"to\\\", end = \\\" \\\")\\n #dist_to = all_paths[from_index][0][to_index]\\n return dist_to\\n\\n transit_callback_index = routing.RegisterTransitCallback(distance_callback)\\n\\n # Define cost of each arc.\\n routing.SetArcCostEvaluatorOfAllVehicles(transit_callback_index)\\n\\n # Setting first solution heuristic.\\n \\\"\\\"\\\"\\n search_parameters = pywrapcp.DefaultRoutingSearchParameters()\\n search_parameters.first_solution_strategy = (\\n routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC)\\n \\\"\\\"\\\"\\n\\n search_parameters = pywrapcp.DefaultRoutingSearchParameters()\\n search_parameters.local_search_metaheuristic = (\\n routing_enums_pb2.LocalSearchMetaheuristic.GUIDED_LOCAL_SEARCH)\\n search_parameters.time_limit.seconds = 3\\n #search_parameters.log_search = True\\n\\n # Solve the problem.\\n assignment = routing.SolveWithParameters(search_parameters)\\n\\n # if assignment:\\n # print_solution(manager, routing, assignment)\\n # Print solution on console.\\n\\n #if start in home_indexes:\\n # drop_off_dict[start] = [start]\\n\\n\\n index = routing.Start(0)\\n car_path.append(start)\\n\\n while not routing.IsEnd(index):\\n previous_index = manager.IndexToNode(index)\\n index = assignment.Value(routing.NextVar(index))\\n to_index = manager.IndexToNode(index)\\n car_path.append(home_map[to_index])\\n #path_to = all_paths.get(home_map[previous_index])[1][home_map[to_index]]\\n #drop_off_dict[home_map[to_index]] = [home_map[to_index]]\\n #print(to_index, end = ' ')\\n #car_path.extend(path_to)\\n #route_distance += routing.GetArcCostForVehicle(previous_index, index, 0)\\n # for i in car_path:\\n # print(i)\\n\\n #print(car_path)\\n drop_off_dict = center_map\\n new_path = [start]\\n previous_index = start\\n\\n\\n #print(center_map)\\n for to_index in car_path[1:]:\\n new_path.pop()\\n path_to = all_paths.get(previous_index)[1][to_index]\\n new_path.extend(path_to)\\n previous_index = to_index\\n keys = drop_off_dict.keys()\\n singleadd = []\\n for i in keys:\\n if i not in orig_home_indexes:\\n vals = drop_off_dict[i]\\n for v in vals:\\n if v in new_path:\\n if v in keys:\\n drop_off_dict[v].append(v)\\n drop_off_dict[i].remove(v)\\n else:\\n singleadd.append(v)\\n drop_off_dict[i].remove(v)\\n for item in singleadd:\\n drop_off_dict[item] = [item]\\n\\n for i in drop_off_dict.keys():\\n for k in drop_off_dict.keys():\\n if i != k:\\n if i in drop_off_dict[k]:\\n drop_off_dict[k].remove(i)\\n drop_off_dict[i].append(i)\\n\\n removeIt = set()\\n for i in drop_off_dict.keys():\\n if not drop_off_dict[i]:\\n removeIt.add(i)\\n\\n for i in removeIt:\\n drop_off_dict.pop(i, None)\\n\\n car_path = new_path\\n return car_path, drop_off_dict\",\n \"def find_closest_state(H, reference_state, QN):\\n \\n #Make a state vector for reference state\\n reference_state_vec = reference_state.state_vector(QN)\\n \\n #Find eigenvectors of the given Hamiltonian\\n E, V = np.linalg.eigh(H)\\n \\n #Find out which of the eigenstates of H corresponds to reference state\\n state_index = find_state_idx(reference_state_vec,V,n=1)\\n \\n #Find state vector of state corresponding to reference\\n state_vec = V[:,state_index:state_index+1]\\n \\n #return the state\\n state = matrix_to_states(state_vec,QN)[0]\\n \\n return state\",\n \"def determine_most_stable_conformer(self, label):\\n if self.species_dict[label].is_ts:\\n raise SchedulerError('The determine_most_stable_conformer() method does not deal with transition '\\n 'state guesses.')\\n if 'conformers' in self.job_dict[label].keys() and all(e is None for e in self.species_dict[label].conformer_energies):\\n logger.error(f'No conformer converged for species {label}! Trying to troubleshoot conformer jobs...')\\n for i, job in self.job_dict[label]['conformers'].items():\\n self.troubleshoot_ess(label, job, level_of_theory=job.level, conformer=job.conformer)\\n else:\\n conformer_xyz = None\\n xyzs = list()\\n if self.species_dict[label].conformer_energies:\\n xyzs = self.species_dict[label].conformers\\n else:\\n for job in self.job_dict[label]['conformers'].values():\\n xyzs.append(parser.parse_xyz_from_file(path=job.local_path_to_output_file))\\n xyzs_in_original_order = xyzs\\n energies, xyzs = sort_two_lists_by_the_first(self.species_dict[label].conformer_energies, xyzs)\\n plotter.save_conformers_file(project_directory=self.project_directory,\\n label=label,\\n xyzs=self.species_dict[label].conformers,\\n level_of_theory=self.conformer_level,\\n multiplicity=self.species_dict[label].multiplicity,\\n charge=self.species_dict[label].charge,\\n is_ts=False,\\n energies=self.species_dict[label].conformer_energies, # after optimization\\n )\\n # Run isomorphism checks if a 2D representation is available\\n if self.species_dict[label].mol is not None:\\n for i, xyz in enumerate(xyzs):\\n try:\\n b_mol = molecules_from_xyz(xyz,\\n multiplicity=self.species_dict[label].multiplicity,\\n charge=self.species_dict[label].charge)[1]\\n except SanitizationError:\\n b_mol = None\\n if b_mol is not None:\\n try:\\n is_isomorphic = check_isomorphism(self.species_dict[label].mol, b_mol)\\n except ValueError as e:\\n if self.allow_nonisomorphic_2d or \\\\\\n are_coords_compliant_with_graph(xyz=xyz, mol=self.species_dict[label].mol):\\n logger.error(f'Could not determine isomorphism for charged species {label}. '\\n f'Optimizing the most stable conformer anyway. Got the '\\n f'following error:\\\\n{e}')\\n conformer_xyz = xyz\\n break\\n else:\\n logger.error(f'Could not determine isomorphism for species {label}. Got the '\\n f'following error:\\\\n{e}')\\n break\\n if is_isomorphic or are_coords_compliant_with_graph(xyz=xyz, mol=self.species_dict[label].mol):\\n if i == 0:\\n b_mol_smiles = b_mol.copy(deep=True).to_smiles() \\\\\\n if b_mol is not None else '<no 2D structure available>'\\n logger.info(f'Most stable conformer for species {label} was found to be isomorphic '\\n f'with the 2D graph representation {b_mol_smiles}\\\\n')\\n conformer_xyz = xyz\\n if 'passed isomorphism check' not in self.output[label]['conformers']:\\n self.output[label]['conformers'] += f'most stable conformer ({i}) passed ' \\\\\\n f'isomorphism check; '\\n self.species_dict[label].conf_is_isomorphic = True\\n else:\\n if energies[i] is not None:\\n mol = molecules_from_xyz(xyzs[0],\\n multiplicity=self.species_dict[label].multiplicity,\\n charge=self.species_dict[label].charge)[1]\\n smiles_1 = self.species_dict[label].mol.copy(deep=True).to_smiles() \\\\\\n if self.species_dict[label].mol is not None else '<no 2D structure available>'\\n smiles_2 = mol.copy(deep=True).to_smiles() \\\\\\n if mol is not None else '<no 2D structure available>'\\n logger.info(f'A conformer for species {label} was found to be isomorphic with the '\\n f'2D graph representation {smiles_1}.\\\\n'\\n f'This conformer is {energies[i] - energies[0]:.2f} kJ/mol above the '\\n f'most stable one corresponding to {smiles_2} (and is not isomorphic). '\\n f'Using the isomorphic conformer for further geometry optimization.')\\n self.output[label]['conformers'] += f'Conformer {i} was found to be the lowest ' \\\\\\n f'energy isomorphic conformer; '\\n conformer_xyz = xyz\\n if 'Conformers optimized and compared' not in self.output[label]['conformers']:\\n self.output[label]['conformers'] += \\\\\\n f'Conformers optimized and compared at {self.conformer_level.simple()}; '\\n break\\n else:\\n if i == 0:\\n self.output[label]['conformers'] += f'most stable conformer ({i}) did not ' \\\\\\n f'pass isomorphism check; '\\n self.species_dict[label].conf_is_isomorphic = False\\n smiles_1 = b_mol.copy(deep=True).to_smiles() \\\\\\n if b_mol is not None else '<no 2D structure available>'\\n smiles_2 = self.species_dict[label].mol.copy(deep=True).to_smiles() \\\\\\n if self.species_dict[label].mol is not None else '<no 2D structure available>'\\n logger.warning(f'Most stable conformer for species {label} with structure '\\n f'{smiles_1} was found to be NON-isomorphic '\\n f'with the 2D graph representation {smiles_2}. '\\n f'Searching for a different conformer that is isomorphic...')\\n else:\\n # all conformers for the species failed isomorphism test\\n smiles_list = list()\\n for xyz in xyzs:\\n try:\\n b_mol = molecules_from_xyz(xyz,\\n multiplicity=self.species_dict[label].multiplicity,\\n charge=self.species_dict[label].charge)[1]\\n smiles_list.append(b_mol.copy(deep=True).to_smiles())\\n except (SanitizationError, AttributeError):\\n smiles_list.append('Could not perceive molecule')\\n if self.allow_nonisomorphic_2d or self.species_dict[label].charge:\\n # we'll optimize the most stable conformer even if it is not isomorphic to the 2D graph\\n logger.error(f'No conformer for {label} was found to be isomorphic with the 2D graph '\\n f'representation {self.species_dict[label].mol.copy(deep=True).to_smiles()} '\\n f'(got: {smiles_list}). Optimizing the most stable conformer anyway.')\\n self.output[label]['conformers'] += 'No conformer was found to be isomorphic with ' \\\\\\n 'the 2D graph representation; '\\n if self.species_dict[label].charge:\\n logger.warning(f'Isomorphism check cannot be done for charged species {label}')\\n conformer_xyz = xyzs[0]\\n else:\\n # troubleshoot when all conformers of a species failed isomorphic test\\n logger.warning(f'Isomorphism check for all conformers of species {label} failed at '\\n f'{self.conformer_level.simple()}. '\\n f'Attempting to troubleshoot using a different level.')\\n self.output[label]['conformers'] += \\\\\\n f'Error: No conformer was found to be isomorphic with the 2D graph representation at ' \\\\\\n f'{self.conformer_level.simple()}; '\\n self.troubleshoot_conformer_isomorphism(label=label)\\n else:\\n logger.warning(f'Could not run isomorphism check for species {label} due to missing 2D graph '\\n f'representation. Using the most stable conformer for further geometry optimization.')\\n conformer_xyz = xyzs[0]\\n if conformer_xyz is not None:\\n self.species_dict[label].initial_xyz = conformer_xyz\\n self.species_dict[label].most_stable_conformer = xyzs_in_original_order.index(conformer_xyz)\\n logger.info(f'Conformer number {xyzs_in_original_order.index(conformer_xyz)} for species {label} is '\\n f'used for geometry optimization.')\\n self.output[label]['job_types']['conformers'] = True\",\n \"def next(self):\\n self.current_state = self.next_state\\n self.next_state = self.clear_screen() # set values to 0\\n for x in range(1, 101):\\n for y in range(1, 101):\\n # calculate the number of alive neighbours at given coordinates\\n self.neighbours_alive = self.check_neighbours_alive(x, y)\\n\\n # assign the result value from rule sets\\n self.next_state[x][y] = self.rule_sets[self.selected_rule][ # selected rule name\\n str(self.current_state[x][y])][ # 0 or 1 (dead or alive)\\n self.neighbours_alive] # number between 0 to 8\\n return self.next_state\",\n \"def metropolis_hastings(dict_prefs, initial_guess, iterations=50000, explore_fact=2, random_seed=None):\\n ordering=deepcopy(initial_guess)\\n best=0\\n current_score=0\\n if random_seed is not None:\\n random.seed(random_seed)\\n np.random.seed(random_seed)\\n for it in range(iterations):\\n candidates_ind=random.sample(range(len(ordering)), 2)\\n score_as_is,score_rev=_pair_net_effect(ordering,candidates_ind,dict_prefs)\\n if score_rev>score_as_is:\\n _swap(ordering,candidates_ind)\\n current_score+=(score_rev-score_as_is)\\n if current_score>best:\\n best=current_score\\n best_ordering=deepcopy(ordering)\\n else:\\n criterion=(explore_fact)**(score_rev-score_as_is)\\n if np.random.random()<=criterion:\\n _swap(ordering,candidates_ind)\\n current_score+=(score_rev-score_as_is)\\n return best_ordering\",\n \"def find_next_moves(self):\\n # iterate through all cells, and group them with upper cells and left\\n # cells\\n\\n # generate separated cells then merge the them with same neighbours\\n matrix_rows = len(self.status)\\n if matrix_rows == 0:\\n matrix_cols = 0\\n else:\\n matrix_cols = len(self.status[0])\\n matrix = []\\n for i in range(matrix_rows):\\n matrix.append([[(i, j)] for j in range(matrix_cols)])\\n # merge coordinations\\n for i in range(matrix_rows):\\n for j in range(matrix_cols):\\n if self.status[i][j] != '':\\n # is same with right cell?\\n if j < matrix_cols - 1 and self.status[i][j] == self.status[i][j + 1]:\\n new_item = matrix[i][j] + matrix[i][j + 1]\\n matrix[i][j] = matrix[i][j + 1] = new_item\\n # is same with down cell?\\n if i < matrix_rows - 1 and self.status[i][j] == self.status[i + 1][j]:\\n new_item = matrix[i][j] + matrix[i + 1][j]\\n matrix[i][j] = matrix[i + 1][j] = new_item\\n\\n # filter out all unvalid results\\n result = []\\n # filter out all single-cell groups\\n for i in range(matrix_rows):\\n for j in range(matrix_cols):\\n if (len(matrix[i][j]) > 1 and\\n matrix[i][j] not in result):\\n result.append(matrix[i][j])\\n\\n # filter sublists\\n result = sorted(result, key=len, reverse=True)\\n changed = True\\n while changed:\\n changed = False\\n for i in range(len(result)):\\n for j in range(i + 1, len(result)):\\n if set(result[i]).issuperset(set(result[j])):\\n result.remove(result[j])\\n changed = True\\n break\\n if changed:\\n break\\n\\n if result:\\n for i in result:\\n yield (self.convert_coordinations(i),\\n len(i) * len(i) * 5,\\n self.calc_new_status(i))\\n else:\\n left_cells = sum([len(i) - i.count('') for i in self.status])\\n left_cells_score = 2000 - 20 * left_cells * left_cells\\n if left_cells_score < 0:\\n left_cells_score = 0\\n for i in self.parents:\\n i.children[self] = [(i.children[self][0][0] + left_cells_score,\\n i.children[self][0][1],\\n i.children[self][0][2])]\",\n \"def solve(list_of_locations, list_of_homes, starting_car_location, adjacency_matrix, params=[]):\\n\\n\\n\\n\\n path = [starting_car_location]\\n dict = {}\\n index = 0\\n for i in range(len(list_of_locations)):\\n if list_of_locations[i] == starting_car_location:\\n index = i\\n\\n path = [index]\\n\\n G, m = adjacency_matrix_to_graph(adjacency_matrix)\\n\\n home_indexes = []\\n\\n for home in list_of_homes:\\n for i in range(len(list_of_locations)):\\n if list_of_locations[i] == home:\\n home_indexes.append(i)\\n break\\n\\n new_adjacency = [[\\\"x\\\" for i in range(len(list_of_locations))] for j in range(len(list_of_locations))]\\n\\n # for sake of figuring out where to walk\\n for home in home_indexes:\\n di_path = nx.dijkstra_path(G, index, home)\\n for i in range(len(di_path) - 1):\\n new_adjacency[di_path[i]][di_path[i + 1]] = adjacency_matrix[di_path[i]][di_path[i + 1]]\\n new_adjacency[di_path[i + 1]][di_path[i]] = adjacency_matrix[di_path[i]][di_path[i + 1]]\\n\\n\\n for home1 in home_indexes:\\n for home2 in home_indexes:\\n if not home1 == home2:\\n di_path = nx.dijkstra_path(G, home1, home2)\\n for i in range(len(di_path) - 1):\\n new_adjacency[di_path[i]][di_path[i + 1]] = adjacency_matrix[di_path[i]][di_path[i + 1]]\\n new_adjacency[di_path[i + 1]][di_path[i]] = adjacency_matrix[di_path[i]][di_path[i + 1]]\\n\\n\\n\\n\\n G2, m = adjacency_matrix_to_graph(new_adjacency)\\n\\n all_driving_path = list(nx.dfs_edges(G2))\\n\\n\\n\\n\\n walking_to = []\\n walking_from = {}\\n\\n for i in range(len(new_adjacency)):\\n if i in home_indexes:\\n count = 0\\n edge_to = 0\\n for j in range(len(new_adjacency)):\\n if new_adjacency[i][j] != \\\"x\\\":\\n count += 1\\n edge_to = j\\n\\n #must ensure that this is not a home that we are already dropping someone off at, otherwise it will cut off a line of two homes\\n if count == 1 and i != index and i not in walking_from.keys():\\n new_adjacency[i][edge_to] = \\\"x\\\"\\n new_adjacency[edge_to][i] = \\\"x\\\"\\n walking_to.append(i)\\n if edge_to in walking_from:\\n walking_from[edge_to] = walking_from[edge_to] + [i]\\n else:\\n walking_from[edge_to] = [i]\\n\\n #\\n # for i in range(len(all_driving_path) - 1):\\n # #if first vertex in edge is the same, we should walk\\n # if all_driving_path[i][0] == all_driving_path[i + 1][0]:\\n # print(all_driving_path[i][0])\\n # print(all_driving_path[i][1])\\n # #get rid of only edge connected to this home\\n # new_adjacency[all_driving_path[i][0]][all_driving_path[i][1]] = \\\"x\\\"\\n # new_adjacency[all_driving_path[i][1]][all_driving_path[i][0]] = \\\"x\\\"\\n # walking_to.append(all_driving_path[i][1])\\n # if all_driving_path[i][0] in walking_from:\\n # walking_from[all_driving_path[i][0]] = walking_from[all_driving_path[i][0]] + [all_driving_path[i][1]]\\n # else:\\n # walking_from[all_driving_path[i][0]] = [all_driving_path[i][1]]\\n\\n\\n\\n dropoff_locations = list(walking_from.keys())\\n for loc in dropoff_locations:\\n if loc in home_indexes:\\n dropoff_locations.remove(loc)\\n\\n\\n for loc in dropoff_locations:\\n di_path = nx.dijkstra_path(G, loc, home)\\n for i in range(len(di_path) - 1):\\n new_adjacency[di_path[i]][di_path[i + 1]] = adjacency_matrix[di_path[i]][di_path[i + 1]]\\n new_adjacency[di_path[i + 1]][di_path[i]] = adjacency_matrix[di_path[i]][di_path[i + 1]]\\n\\n for loc in dropoff_locations:\\n for home in home_indexes:\\n di_path = nx.dijkstra_path(G, loc, home)\\n for i in range(len(di_path) - 1):\\n new_adjacency[di_path[i]][di_path[i + 1]] = adjacency_matrix[di_path[i]][di_path[i + 1]]\\n new_adjacency[di_path[i + 1]][di_path[i]] = adjacency_matrix[di_path[i]][di_path[i + 1]]\\n\\n\\n G2, m = adjacency_matrix_to_graph(new_adjacency)\\n # G = G2\\n # pos=nx.spring_layout(G2)\\n # nx.draw_networkx_nodes(G2,pos)\\n # nx.draw_networkx_labels(G2, pos)\\n # nx.draw_networkx_edges(G2,pos,width=1.0,alpha=0.5)\\n #\\n # plt.draw()\\n # plt.show()\\n\\n # condensed shortest paths to edges - use G3 for real\\n\\n new_adjacency2 = [[\\\"x\\\" for i in range(len(list_of_locations))] for j in range(len(list_of_locations))]\\n\\n for home in home_indexes:\\n if home not in walking_to:\\n di_path = nx.dijkstra_path(G2, index, home)\\n start = di_path[0]\\n end = di_path[len(di_path) - 1]\\n new_adjacency2[start][end] = 0\\n new_adjacency2[end][start] = 0\\n for i in range(len(di_path) - 1):\\n new_adjacency2[start][end] += new_adjacency[di_path[i]][di_path[i + 1]]\\n new_adjacency2[end][start] += new_adjacency[di_path[i]][di_path[i + 1]]\\n\\n\\n for home1 in home_indexes:\\n for home2 in home_indexes:\\n if not home1 == home2 and home1 not in walking_to and home2 not in walking_to:\\n di_path = nx.dijkstra_path(G2, home1, home2)\\n start = di_path[0]\\n end = di_path[len(di_path) - 1]\\n new_adjacency2[start][end] = 0\\n new_adjacency2[end][start] = 0\\n for i in range(len(di_path) - 1):\\n new_adjacency2[start][end] += new_adjacency[di_path[i]][di_path[i + 1]]\\n new_adjacency2[end][start] += new_adjacency[di_path[i]][di_path[i + 1]]\\n\\n for loc in dropoff_locations:\\n di_path = nx.dijkstra_path(G2, index, loc)\\n start = di_path[0]\\n end = di_path[len(di_path) - 1]\\n new_adjacency2[start][end] = 0\\n new_adjacency2[end][start] = 0\\n for i in range(len(di_path) - 1):\\n new_adjacency2[start][end] += new_adjacency[di_path[i]][di_path[i + 1]]\\n new_adjacency2[end][start] += new_adjacency[di_path[i]][di_path[i + 1]]\\n\\n for loc in dropoff_locations:\\n for home in home_indexes:\\n di_path = nx.dijkstra_path(G2, loc, home)\\n start = di_path[0]\\n end = di_path[len(di_path) - 1]\\n new_adjacency2[start][end] = 0\\n new_adjacency2[end][start] = 0\\n for i in range(len(di_path) - 1):\\n new_adjacency2[start][end] += new_adjacency[di_path[i]][di_path[i + 1]]\\n new_adjacency2[end][start] += new_adjacency[di_path[i]][di_path[i + 1]]\\n\\n\\n\\n\\n final_G, m = adjacency_matrix_to_graph(new_adjacency2)\\n drive_path = list(nx.dfs_edges(final_G, source=index))\\n drive_path.append(index)\\n\\n mst = nx.minimum_spanning_tree(final_G)\\n\\n\\n\\n new_mst = nx.MultiGraph(mst)\\n for edge in mst.edges():\\n new_mst.add_edge(edge[0], edge[1])\\n\\n\\n if new_mst.degree[index] != 0:\\n to_remove = []\\n for node in new_mst:\\n if (new_mst.degree[node] == 0):\\n to_remove.append(node)\\n new_mst.remove_nodes_from(to_remove)\\n\\n eulerian = list(nx.eulerian_circuit(new_mst, index))\\n\\n path = []\\n for edge in eulerian:\\n path.append(edge[0])\\n\\n path.append(eulerian[len(eulerian) - 1][1])\\n\\n already_seen = []\\n to_remove = []\\n for i in range(len(path) - 1):\\n if path[i] in already_seen:\\n to_remove.append(i)\\n else:\\n already_seen.append(path[i])\\n\\n new_path = []\\n for i in range(len(path) - 1):\\n if i not in to_remove:\\n new_path.append(path[i])\\n path = new_path\\n print(eulerian)\\n else:\\n path = [index]\\n print(path)\\n\\n\\n\\n\\n\\n\\n\\n # print(path)\\n final_path = []\\n for node in path:\\n if node == index:\\n final_path.append(node)\\n # print(\\\"Index: \\\", node)\\n elif node in home_indexes and node not in walking_to:\\n final_path.append(node)\\n # print(\\\"Home but not walking: \\\", node)\\n elif node in dropoff_locations:\\n final_path.append(node)\\n # print(\\\"Dropoff loc: \\\", node)\\n final_path.append(index)\\n #print(walking_from)\\n # print(final_path)\\n # nx.draw(mst)\\n # plt.draw()\\n # plt.show()\\n for node in final_path:\\n if node in walking_from and node in home_indexes:\\n dict[node] = [node] + walking_from[node]\\n elif node in home_indexes:\\n dict[node] = [node]\\n elif node in walking_from:\\n dict[node] = walking_from[node]\\n\\n very_final_path = []\\n for i in range(len(final_path) - 1):\\n condensed_path = nx.dijkstra_path(G2, final_path[i], final_path[i+1])\\n for j in range(len(condensed_path) - 1):\\n if condensed_path[j] != condensed_path[j + 1]:\\n very_final_path.append(condensed_path[j])\\n\\n if len(very_final_path) >= 1 and [len(very_final_path) - 1] != index:\\n very_final_path.append(index)\\n\\n if len(very_final_path) == 0:\\n very_final_path = [index]\\n\\n print(very_final_path)\\n print(dict)\\n\\n\\n path2 = list(nx.dfs_preorder_nodes(mst, index))\\n\\n final_path2 = []\\n for node in path2:\\n if node == index:\\n final_path2.append(node)\\n # print(\\\"Index: \\\", node)\\n elif node in home_indexes and node not in walking_to:\\n final_path2.append(node)\\n # print(\\\"Home but not walking: \\\", node)\\n elif node in dropoff_locations:\\n final_path2.append(node)\\n # print(\\\"Dropoff loc: \\\", node)\\n final_path2.append(index)\\n\\n\\n for node in final_path2:\\n if node in walking_from and node in home_indexes:\\n dict[node] = [node] + walking_from[node]\\n elif node in home_indexes:\\n dict[node] = [node]\\n elif node in walking_from:\\n dict[node] = walking_from[node]\\n\\n very_final_path2 = []\\n for i in range(len(final_path2) - 1):\\n condensed_path = nx.dijkstra_path(G2, final_path2[i], final_path2[i+1])\\n for j in range(len(condensed_path) - 1):\\n if condensed_path[j] != condensed_path[j + 1]:\\n very_final_path2.append(condensed_path[j])\\n\\n if len(very_final_path2) >= 1 and [len(very_final_path2) - 1] != index:\\n very_final_path2.append(index)\\n\\n if len(very_final_path2) == 0:\\n very_final_path2 = [index]\\n\\n opt1 = cost_of_solution(G, very_final_path, dict)\\n opt2 = cost_of_solution(G, very_final_path2, dict)\\n\\n ultra_final_path = []\\n if (opt1 <= opt2):\\n ultra_final_path = very_final_path\\n else:\\n ultra_final_path = very_final_path2\\n\\n return ultra_final_path, dict\\n\\n pass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5657669","0.5559561","0.5372872","0.53349","0.5301023","0.5282986","0.52419263","0.5127219","0.5086206","0.50741035","0.5065264","0.50308555","0.5025374","0.50204796","0.50141317","0.5002204","0.5002081","0.4969301","0.49646002","0.4962375","0.4949405","0.4942153","0.49354175","0.4929495","0.49136695","0.48923928","0.48881328","0.48833677","0.48670334","0.48476198","0.4841689","0.48311627","0.48250306","0.48217928","0.4808214","0.48052076","0.48051602","0.4804722","0.4794197","0.47930613","0.4790471","0.47847357","0.47836587","0.47815976","0.47814187","0.47799754","0.4772598","0.47721386","0.47637227","0.47541085","0.47489193","0.47417074","0.47354496","0.47280502","0.4723759","0.47231123","0.47228578","0.47127962","0.471181","0.47091076","0.4698813","0.46931496","0.4691813","0.4691069","0.4691069","0.4685527","0.46846715","0.46834844","0.46822003","0.46821794","0.46810776","0.46803883","0.46789646","0.46735373","0.46706438","0.46684995","0.46671033","0.46647397","0.46592727","0.46589932","0.46575376","0.46521994","0.46515307","0.46497008","0.4649125","0.46487743","0.4647636","0.4646938","0.46442837","0.46370307","0.46350592","0.4634445","0.46321544","0.4631656","0.4630982","0.46288285","0.46270153","0.4625995","0.46250448","0.46227792"],"string":"[\n \"0.5657669\",\n \"0.5559561\",\n \"0.5372872\",\n \"0.53349\",\n \"0.5301023\",\n \"0.5282986\",\n \"0.52419263\",\n \"0.5127219\",\n \"0.5086206\",\n \"0.50741035\",\n \"0.5065264\",\n \"0.50308555\",\n \"0.5025374\",\n \"0.50204796\",\n \"0.50141317\",\n \"0.5002204\",\n \"0.5002081\",\n \"0.4969301\",\n \"0.49646002\",\n \"0.4962375\",\n \"0.4949405\",\n \"0.4942153\",\n \"0.49354175\",\n \"0.4929495\",\n \"0.49136695\",\n \"0.48923928\",\n \"0.48881328\",\n \"0.48833677\",\n \"0.48670334\",\n \"0.48476198\",\n \"0.4841689\",\n \"0.48311627\",\n \"0.48250306\",\n \"0.48217928\",\n \"0.4808214\",\n \"0.48052076\",\n \"0.48051602\",\n \"0.4804722\",\n \"0.4794197\",\n \"0.47930613\",\n \"0.4790471\",\n \"0.47847357\",\n \"0.47836587\",\n \"0.47815976\",\n \"0.47814187\",\n \"0.47799754\",\n \"0.4772598\",\n \"0.47721386\",\n \"0.47637227\",\n \"0.47541085\",\n \"0.47489193\",\n \"0.47417074\",\n \"0.47354496\",\n \"0.47280502\",\n \"0.4723759\",\n \"0.47231123\",\n \"0.47228578\",\n \"0.47127962\",\n \"0.471181\",\n \"0.47091076\",\n \"0.4698813\",\n \"0.46931496\",\n \"0.4691813\",\n \"0.4691069\",\n \"0.4691069\",\n \"0.4685527\",\n \"0.46846715\",\n \"0.46834844\",\n \"0.46822003\",\n \"0.46821794\",\n \"0.46810776\",\n \"0.46803883\",\n \"0.46789646\",\n \"0.46735373\",\n \"0.46706438\",\n \"0.46684995\",\n \"0.46671033\",\n \"0.46647397\",\n \"0.46592727\",\n \"0.46589932\",\n \"0.46575376\",\n \"0.46521994\",\n \"0.46515307\",\n \"0.46497008\",\n \"0.4649125\",\n \"0.46487743\",\n \"0.4647636\",\n \"0.4646938\",\n \"0.46442837\",\n \"0.46370307\",\n \"0.46350592\",\n \"0.4634445\",\n \"0.46321544\",\n \"0.4631656\",\n \"0.4630982\",\n \"0.46288285\",\n \"0.46270153\",\n \"0.4625995\",\n \"0.46250448\",\n \"0.46227792\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.62907976"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":22492,"cells":{"query":{"kind":"string","value":"Return entropy of each village location state across HC activations."},"document":{"kind":"string","value":"def get_loc_co_occ_entropy(co_occs, v_repl_nan=None):\n\n return utils.get_row_entropy(co_occs, v_repl_nan)"},"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 entropy(self):\n raise NotImplementedError","def entropy(self):\n\n \"\"\"Gets the first neighbours, which are the first 2*r+1 cells.\"\"\"\n current_neighbours = []\n amount = [0] * self.k ** (2 * self.r + 1)\n for i in range(2 * self.r + 1):\n current_neighbours.append(self.config[self.t, i % self.width])\n\n \"\"\"Calculates the rule and adds one to it's amount. It then removes the\n leftmost cell and adds a cell to the right.\"\"\"\n for i in range(len(self.config[self.t]) - 1):\n rule = 0\n for j in range(len(current_neighbours)):\n rule += int(current_neighbours[j] *\n self.k ** ((2 * self.r + 1) - j - 1))\n amount[len(amount) - 1 - rule] += 1\n current_neighbours.pop(0)\n current_neighbours.append(\n self.config[self.t, (2 * self.r + 1 + i) % self.width])\n\n \"\"\"Calculates the rule for the last neighbourhood.\"\"\"\n rule = 0\n for j in range(len(current_neighbours)):\n rule += int(current_neighbours[j] *\n self.k ** ((2 * self.r + 1) - j - 1))\n amount[len(amount)-1 - rule] += 1\n\n \"\"\"Calculates the Shannon entropy and the the average entropy so far.\"\"\"\n shannon = 0\n for i in range(len(amount)):\n if(amount[i] != 0):\n probability = amount[i] / self.width\n shannon -= probability * np.log2(probability)\n self.average_entropy = (self.average_entropy *\n self.t + shannon) / (self.t + 1)","def entropy(self, unlabeled_sequence):\n unlabeled_sequence = self._transform(unlabeled_sequence)\n\n T = len(unlabeled_sequence)\n N = len(self._states)\n\n alpha = self._forward_probability(unlabeled_sequence)\n beta = self._backward_probability(unlabeled_sequence)\n normalisation = logsumexp2(alpha[T - 1])\n\n entropy = normalisation\n\n # starting state, t = 0\n for i, state in enumerate(self._states):\n p = 2 ** (alpha[0, i] + beta[0, i] - normalisation)\n entropy -= p * self._priors.logprob(state)\n # print('p(s_0 = %s) =' % state, p)\n\n # state transitions\n for t0 in range(T - 1):\n t1 = t0 + 1\n for i0, s0 in enumerate(self._states):\n for i1, s1 in enumerate(self._states):\n p = 2 ** (\n alpha[t0, i0]\n + self._transitions[s0].logprob(s1)\n + self._outputs[s1].logprob(unlabeled_sequence[t1][_TEXT])\n + beta[t1, i1]\n - normalisation\n )\n entropy -= p * self._transitions[s0].logprob(s1)\n # print('p(s_%d = %s, s_%d = %s) =' % (t0, s0, t1, s1), p)\n\n # symbol emissions\n for t in range(T):\n for i, state in enumerate(self._states):\n p = 2 ** (alpha[t, i] + beta[t, i] - normalisation)\n entropy -= p * self._outputs[state].logprob(\n unlabeled_sequence[t][_TEXT]\n )\n # print('p(s_%d = %s) =' % (t, state), p)\n\n return entropy","def cal_localEntropy(self):\n if len(self.locality) == 0:\n proportion = np.asarray(self.pop / self.pop_sum)\n\n else:\n polygon_sum = np.sum(self.locality, axis=1).reshape(self.n_location, 1)\n proportion = np.asarray(self.locality / polygon_sum)\n\n entropy = proportion * np.log(1 / proportion)\n\n entropy[np.isnan(entropy)] = 0\n entropy[np.isinf(entropy)] = 0\n entropy = np.sum(entropy, axis=1)\n entropy = entropy.reshape((self.n_location, 1))\n\n return entropy","def _entropies(self):\n H_C = fentropy(self.row_totals)\n H_K = fentropy(self.col_totals)\n H_actual = fentropy(self.itervalues())\n H_expected = H_C + H_K\n I_CK = H_expected - H_actual\n return H_C, H_K, I_CK","def entropy(self):\n return -np.sum(self.log_likelihoods * np.exp(self.log_likelihoods))","def entropycell(self):\n cells = [0] * self.k\n for i in range(self.width):\n cells[int(self.config[self.t, i])] += 1\n\n \"\"\"Calculates the Shannon entropy and the the average entropy so far.\"\"\"\n shannon = 0\n for i in range(self.k):\n if(cells[i] != 0):\n probability = cells[i] / self.width\n shannon -= probability * np.log2(probability)\n self.average_cell = (self.average_cell * self.t + shannon) / (self.t + 1)","def entropy_root(self):\n\t\tif self.size() == 0:\n\t\t\treturn 0\n\n\t\tcounter = collections.Counter(self.y)\n\t\treturn entropy([x[1] for x in counter.items()], self.size())","def entropy(data):\n e = 0\n\n counter = collections.Counter(data)\n l = len(data)\n for count in counter.values():\n p_x = count / l\n e += - p_x * math.log2(p_x)\n\n return e","def get_entropy(self, rows):\n label_count = defaultdict(int)\n total_count = 0\n for row in rows:\n label = row[self.target_attribute]\n label_count[label] += 1\n total_count += 1\n return sum([-(float(label_count[label]) /\n total_count) * np.log2(float(label_count[label]) / total_count)\n for label in label_count.keys()])","def entropy(self):\n return self._normal.entropy()","def entropy(x):\n nz = np.nonzero(x)[0]\n return -np.sum(x[nz]*np.log2(x[nz]))","def get_entropy(self, state):\n entropy = self.entropy_model.predict(state)\n return entropy[0]","def entropy(self):\n n = len(self.y)\n sum_ = 0\n for i in np.unique(self.y):\n v = len(self.y[self.y == i])\n sum_ += -((v/n) * log2(v/n))\n return sum_","def ensemble_entropy(self):\n return transition_matrices.entropy_measure(self.ensemble_transition_matrix)","def entropy(self):\n Z = self.sum()\n assert (Z > 0), 'Non-normalizable factor (perhaps log factor?)' # also check for positivity?\n H = 0.0\n for x in np.nditer(self.t, op_flags=['readonly']):\n p = x/Z\n H += 0.0 if p==0 else -p*np.log(p)\n return H","def entropy(group_counts):\n total = sum(group_counts)\n entro = 0\n for item_count in group_counts:\n entro += item_entropy(item_count, total)\n return entro","def entropy(l):\n\n probabilities = np.bincount(l) / len(l)\n with np.errstate(divide='ignore'): # ignore log(0) errors, we'll handle\n log_probabilities = np.log2(probabilities)\n log_probabilities[~np.isfinite(log_probabilities)] = 0\n return -np.sum(probabilities * log_probabilities)","def cal_globalEntropy(self):\n group_score = []\n pop_total = np.sum(self.pop_sum)\n prop = np.asarray(np.sum(self.pop, axis=0))[0]\n\n # loop at sum of each population groups\n for group in prop:\n group_idx = group / pop_total * np.log(1 / (group / pop_total))\n group_score.append(group_idx)\n entropy = np.sum(group_score)\n\n return entropy","def point_entropy(self, unlabeled_sequence):\n unlabeled_sequence = self._transform(unlabeled_sequence)\n\n T = len(unlabeled_sequence)\n N = len(self._states)\n\n alpha = self._forward_probability(unlabeled_sequence)\n beta = self._backward_probability(unlabeled_sequence)\n normalisation = logsumexp2(alpha[T - 1])\n\n entropies = np.zeros(T, np.float64)\n probs = np.zeros(N, np.float64)\n for t in range(T):\n for s in range(N):\n probs[s] = alpha[t, s] + beta[t, s] - normalisation\n\n for s in range(N):\n entropies[t] -= 2 ** (probs[s]) * probs[s]\n\n return entropies","def entropy(self):\n return self._entropy_func","def entropy(y):\r\n\r\n # INSERT YOUR CODE HERE\r\n value, count = np.unique(y,return_counts = True)\r\n Hy = 0.0\r\n prob = count.astype(float)/len(y)\r\n for p in prob:\r\n Hy += -(p)*(np.log2(p))\r\n return Hy\r\n raise Exception('Function not yet implemented!')","def entropy_balance(self):\n return","def entropy_balance(self):\n return","def entropy(self):\n ent = 0.0\n for f in self.byte_freq:\n if f > 0:\n freq = float(f) / self.byte_total\n ent = ent + freq * math.log(freq, 2)\n return -ent","def stateOccupationProbabilityGeneration(self):\n self.L = zeros((self.noOfEmmittingStates, self.T))\n\n for j in range(self.noOfEmmittingStates):\n for t in range(self.T):\n self.L[j,t] = (self.alpha[j+1, t+1] * self.beta[j+1, t+1]) / self.observationLikelihood","def shannon_entropy(c):\n\n c_normalized = c / float(np.sum(c))\n c_normalized_nonzero = c_normalized[np.nonzero(c_normalized)] # gives 1D array\n entropy = -sum(c_normalized_nonzero * np.log2(c_normalized_nonzero)) # unit in bits\n return entropy","def entropy(y):\n p = _proba(y)\n return (-p * np.log2(p)).sum()","def cal_localIndexH(self):\n local_entropy = self.cal_localEntropy()\n global_entropy = self.cal_globalEntropy()\n\n et = global_entropy * np.sum(self.pop_sum)\n eei = np.asarray(global_entropy - local_entropy)\n h_local = np.asarray(self.pop_sum) * eei / et\n\n return h_local","def calc_entropy_for_states(states,n,T):\n\n D = int(1e7)\n n_s = len(states)\n\n data = sample_states_all(exampleRatios, T, D)\n CDataObj = CompressionData(data, len(exampleRatios), 1,\n 0, len(exampleRatios)-1, np.uint8)\n # print(\"Compressed size is \" + str(CDataObj.size_data()))\n # print(\"Zero size is \" + str(CDataObj.size_zeros()))\n # print(\"Random size is \" + str(CDataObj.size_random()))\n # print(\"Compression ratio is \" + str(CDataObj.get_compression_ratios()))\n\n eta = CDataObj.get_compression_ratios()\n entropy_estimate = eta * D * np.log(n_s)\n\n entropy_real = calc_entropy_one(exampleRatios, T) * D\n \n return (entropy_real, entropy_estimate)","def entropy(img):\n # by calculating\n histogram = img.histogram()\n histogram_size = sum(histogram)\n histogram = [float(h) / histogram_size for h in histogram]\n\n return -sum([p * math.log(p, 2) for p in histogram if p != 0])","def _entropy(self):\n return self.rv.entropy(*self._pymc_dists_to_value(self.args), **self.kwds)","def entropy(message):\n n = len(message)\n message = letter_freq(message)\n h = 0\n for n_i in message.values():\n p_i = n_i/n\n h += -p_i*(log2(p_i))\n return h","def H(self, data):\n entropy = 0\n\n if not data:\n return entropy\n\n for x in range(256):\n p_x = float(data.count(chr(x))) / len(data)\n if p_x > 0:\n entropy -= p_x * math.log(p_x, 2)\n\n return entropy","def entropy(y):\n return -1 * sum(\n [\n pipe(np.sum(y == value) / len(y), lambda ratio: ratio * np.log(ratio))\n for value in set(y)\n ]\n )","def entropy(message):\n message = letter_freq(message)\n n = sum(message.values())\n h = 0\n for n_i in message.values():\n p_i = n_i / n\n h += -p_i * log2(p_i)\n return h","def entropy(Y):\n\n temp = np.unique(Y, return_counts=True)\n uniq_Y = list(temp[0])\n Y_count = list(temp[1])\n \n total = sum(Y_count)\n\n ent = 0\n for elem in uniq_Y:\n prob = Y_count[uniq_Y.index(elem)] / total\n # print(\"prob:\", prob)\n ent -= (prob * (math.log2(prob)))\n # print(\"ent:\",ent)\n\n return ent","def entropy(data):\n strings, lens = Counter(data), np.float(len(data))\n return -sum(count / lens * np.log2(count / lens) for count in strings.values())","def _entropy(data):\n hist = np.array(PIL.Image.fromarray(data).histogram())\n hist = hist / hist.sum()\n hist = hist[hist != 0]\n return -np.sum(hist * np.log2(hist))","def calc_entropy(frequency_wn, temperature):\r\n\tentropy = 0\r\n\tfrequency = [entry * SPEED_OF_LIGHT for entry in frequency_wn]\r\n\tfor entry in frequency:\r\n\t\tfactor = ((PLANCK_CONSTANT*entry)/(BOLTZMANN_CONSTANT*temperature))\r\n\t\ttemp = factor*(1/(math.exp(factor)-1)) - math.log(1-math.exp(-factor))\r\n\t\ttemp = temp*GAS_CONSTANT/4.184\r\n\t\tentropy = entropy + temp \r\n\treturn entropy","def entropy(y):\n EPS = 0.0005\n\n # YOUR CODE HERE\n if len(y) == 0:\n return 0.\n \n pk = np.mean(y, axis=0)\n \n return - np.sum(pk * np.log(pk + EPS))","def entropy(*args):\n\n\n values = []\n leaf = -1\n\n for i, val in enumerate(args):\n if(val != 0):\n values.append(val * math.log(val, len(args)))\n if(val == 1):\n leaf = i\n \n return -sum(values), leaf","def entropy ( target_array ):\n return -1 * sum (\n [\n pipe ( np.sum ( target_array == value ) / len ( target_array ), lambda ratio: ratio * np.log ( ratio ) )\n for value in set ( target_array )\n ]\n ) # End entropy()","def entropy(self, y):\n n = y.size\n if n <= 1:\n return 0\n\n labels, counts = unique(y, return_counts=True)\n\n if counts.size <= 1:\n return 0\n\n probs = counts / n\n entropy = -sum([p * log(p, 2) for p in probs])\n return entropy","def entropy(counts):\n assert (counts >= 0).all()\n probs = counts / counts.sum()\n probs = probs[probs > 0] # Avoid log(0)\n return - np.sum(probs * np.log2(probs))","def entropy(s):\n p, lns = Counter(s), float(len(s))\n return -sum( count/lns * math.log(count/lns, 2) for count in p.values())","def entropy(self,classData):\n\n\t\t###### your implementation below ######\n\t\ttotalEntropy = 0\n\t\ttempSet = {}\n\t\tfor i in range(len(classData)):\n\t\t\ttempSet[classData[i]] = (classData.count(classData[i]))\n\t\ttotal = sum(tempSet.values())\n\t\tfor x in tempSet:\n\t\t\ttotalEntropy += -(tempSet[x]/total * math.log(tempSet[x]/total, 2))\n\t\treturn totalEntropy","def entropy(data, idxList):\n df = data.loc[idxList]\n counts = df.value_counts().to_numpy()\n counts = counts.reshape(1, -1).astype(np.float32)\n counts /= np.sum(counts)\n log_sum = counts @ np.log2(counts.T)\n return -log_sum[0, 0]","def entropy(target_col):\n elements,counts = np.unique(target_col,return_counts = True)\n entropy = np.sum([(-counts[i]/np.sum(counts))*np.log2(counts[i]/np.sum(counts)) for i in range(len(elements))])\n return entropy","def entropy(x):\n x_max, x_min = x.max(), x.min()\n assert (x_min >= 0) and (x_max <= 1)\n if x_min == x_max == 0:\n return np.float32(0.)\n # Take only non-zero values as log(0) = 0 :\n nnz_x = x[np.nonzero(x)]\n entr = -np.sum(nnz_x * np.log2(nnz_x))\n\n return entr","def __compute_entropy(row:pd.Series) -> float:\n probability = row.value_counts(normalize=True).values\n entropy = EntropicSolver.__compute_entropy_probability(probability)\n return entropy","def c_entropy(state,target=None,log_base=2):\n if target!=None:\n state = state.ptrace(target)\n return entropy(com_measure(state),base=log_base)","def entropy(dist):\n #dist = array([max(d,1e-100) for d in dist])\n dist = dist + 1e-20\n return dot(dist,(log(1.0/dist) * (1.0/log(2.0))).T)","def entropy( freq ):\n N = 0.0\n entropy = 0.0\n for x, v in freq.items( ):\n N += v\n entropy -= v * math.log( v, 2 )\n return (N * math.log( N, 2 ) + entropy) / N","def exploreEntropy(self, position):\n pass","def entropy(class_probabilities):\n return sum(-p * math.log(p, 2)\n for p in class_probabilities\n if p) #ignore 0's","def entropy_analysis(data_df):\n seq_list = data_df['sequence'].values.tolist()\n base_set = set([])\n for seq in seq_list:\n base_set.update(set(seq))\n H_list = []\n null_freq_list = []\n STEP = ceil(len(seq_list[0]) / 10)\n for base_idx in range(len(seq_list[0])):\n if base_idx % STEP == 0:\n logging.info('Entropy analysis in progress: {}% completed.'.format(10 * base_idx // STEP))\n H, null_freq = base_entropy_masked(seq_list, base_set, base_idx)\n H_list.append(H,)\n null_freq_list.append(null_freq)\n logging.info('Entropy analysis in progress: DONE.')\n return H_list, null_freq_list","def entropy(self, priors=None):\n def entropy_f(x):\n x[x != 0] *= np.log(x[x != 0])\n return -x.sum(axis=0)\n return self.utility(entropy_f, priors)","def entropy(probabilities):\n return -(sum([p * log(p, 2) if p > 0 else 0 for p in probabilities]))","def compute_entropy_all(self) -> pd.Series:\n if self.game_matrix is None:\n return self.word_matrix.apply(self.__compute_entropy, axis=1)\n return self.game_matrix.apply(self.__compute_entropy, axis=1)","def entropy(self):\r\n return 1/2 * (self.dim * (_LOG_2PI + 1) + self._log_det_cov)","def spatial_entropy(map_):\n map_ = map_ / np.sum(map_)\n return -1 * np.sum(map_ * np.log(map_))","def h(self, state):\n loc = dict((val, (i, j)) for i, row in enumerate(self.goal_state)\n for j, val in enumerate(row))\n\n def calculate_cost(i, j):\n val = state[i][j]\n goal_i, goal_j = loc[val]\n return abs(goal_i - i) + abs(goal_j - j)\n\n return sum(\n calculate_cost(i, j) for i in xrange(self.size)\n for j in xrange(self.size) if state[i][j])","def entropy(data):\n\n freqs = {}\n suma = len(data)\n\n for i in range(0, len(data)):\n freqs[data[i]] = 1.0 + freqs.get(data[i], 0)\n\n res = 0.0\n for i in freqs:\n res += (freqs[i] / suma) * log((freqs[i] / suma), 2)\n return -res","def get_entropy(distribution, samples):\n entropy = -tf.reduce_sum(distribution.log_prob(samples), axis=1)\n return entropy","def entropy(y):\n total = y.size\n value_counts = np.bincount(y).astype(\"float\")\n proportions = value_counts / y.size\n\n return sum(-i * np.log(i) for i in proportions if i)","def entropy(p):\n ent = tf.where(p > np.finfo(np.float32).eps, -p * tf.log(p), tf.zeros_like(p))\n ent = tf.reduce_sum(ent, axis=1)\n return ent","def GetEntropy(flanks):\n countA = 0\n countT = 0\n countG = 0\n countC = 0\n for i in flanks:\n if i == \"A\":\n countA += 1\n elif i == \"T\":\n countT += 1\n elif i == \"C\":\n countC += 1\n elif i == \"G\":\n countG += 1\n else: pass\n total = countA+countT+countG+countC\n fractions = [item*1.0/total for item in [countA,countT,countG,countC]]\n entropy = sum([-1.0*item*math.log(item,2) for item in fractions if item != 0])\n return entropy","def entropy(self, policy_params):\n return self.head.entropy(policy_params)","def entropy(class_probabilities):\n return sum(-p * math.log(p,2)\n for p in class_probabilities\n if p)","def _entropy(self, y):\n # Get size\n n = y.shape[0]\n summation = 0\n\n # Summatory\n for c_i in np.unique(y):\n prob = sum(y == c_i) / float(n)\n summation += prob * np.log2(prob)\n\n return -summation","def entropy(Y):\n unique, count = np.unique(Y, return_counts=True, axis=0)\n prob = count/len(Y)\n en = np.sum((-1)*prob*np.log2(prob))\n return en","def div(self):\n freqList = [i / sum(self.has.values()) for i in self.has.values()]\n entropies = [i * math.log(i, 2) for i in freqList]\n entropy = -sum(entropies)\n return entropy","def count_neighbourhood_states(self,state):\n\t\tassert len(state) > 0\n\t\tenergies = [float(\"inf\") for i in range (self._size)]\n\t\ttaxes = [float(\"inf\") for i in range (self._size)]\n\n\t\tfor i in range (0,self._size):\n\t\t\tnewState = copy(state)\n\t\t\tnewState[i] = 1 - newState[i]\n\t\t\tenergies[i] = self.count_energy(newState)\n\t\t\ttaxes[i] = self.count_tax(newState)\n\n\t\treturn energies,taxes","def entropyDistributed(distribution):\n return -sum(map(lambda p : p * log(p, 2), distribution))","def _entropy(self, feature, node):\n entropy = 0\n categories = np.unique(feature)\n num_point = len(feature)\n for category in categories:\n # for each category in that feature\n num_category = len(feature[feature == category])\n for c in self.num_class:\n # count the number of each class\n num_category_class = len(feature[np.logical_and(feature == category, node.y == c)])\n if num_category_class == 0:\n continue\n # compute entropy/information gain or classification error\n entropy += num_category / num_point * (\n -num_category_class / num_category * log2(num_category_class / num_category))\n return entropy","def conditional_entropy_hyper(self) -> float:\n pass","def entropy(self) -> float:\n probabilities = np.array([len(players) for players in self.answers.values()])\n probabilities = probabilities / sum(probabilities)\n return sc.stats.entropy(probabilities)","def compute_entropy(bincounts, include_zeros):\n num_total = tf.cast(tf.reduce_sum(bincounts), tf.float64)\n if not include_zeros:\n bincounts = bincounts[1:]\n mask = tf.greater(bincounts, 0)\n nonzero_bincounts = tf.cast(\n tf.boolean_mask(bincounts, mask), tf.float64)\n num_nonzero = tf.cast(tf.reduce_sum(nonzero_bincounts), tf.float64)\n log_nonzero_bincounts = tf.math.log(nonzero_bincounts)\n log_prob = log_nonzero_bincounts - tf.reduce_logsumexp(\n log_nonzero_bincounts)\n entropy = tf.math.reduce_sum(\n log_prob * tf.exp(log_prob)) / -tf.math.log(tf.cast(2, tf.float64))\n return entropy * num_nonzero / num_total","def entropy(P):\n P_nan = P.copy()\n P_nan[P_nan == 0] = np.nan\n return np.nansum(np.multiply(P_nan, np.log2(1 / P_nan)))","def entropy(n_bits):\n return n_bits and random.getrandbits(n_bits)","def entropy(d, total, word_count):\n\t# Entropie je - Sum_morf p(morf) * log_2 p(morf)\n\t# p(morf) = c(morf) / c(all)\n\te = 0\n\tfor count in d.values():\n\t\tp = count/total\n\t\ttype_e = - p * log2(p)\n\t\te += type_e * count\n\treturn e / word_count","def conditional_entropy(self) -> float:\n pass","def calculate_entropy(dataset) :\n\n num_entries = len(dataset)\n label_counts = {}\n for vector in dataset :\n # the label is at the last index of the data set\n current_label = vector[-1]\n if current_label not in label_counts :\n label_counts[current_label] = 0\n label_counts[current_label] += 1\n # Calculate the entropy\n entropy = 0.0\n for label in label_counts :\n # Calculate probability of each label within the dataset\n prob_of_label = label_counts[label]/num_entries\n # Since the entropy is the negative of the sum of all probability,\n # simply subtract it\n entropy -= prob_of_label * log(prob_of_label, 2)\n return entropy","def transfer_entropy(X, Y):\n coords = Counter(zip(Y[1:], X[:-1], Y[:-1]))\n\n p_dist = np.zeros((config.NUM_STATES, config.NUM_STATES, config.NUM_STATES))\n for y_f, x_p, y_p in coords.keys():\n p_dist[y_p, y_f, x_p] = coords[(y_f, x_p, y_p)] / (len(X) - 1)\n\n p_yp = p_dist.sum(axis=2).sum(axis=1)\n p_joint_cond_yp = p_dist / p_yp[:, None, None]\n p_yf_cond_yp = p_dist.sum(axis=2) / p_yp[:, None]\n p_xp_cond_yp = p_dist.sum(axis=1) / p_yp[:, None]\n\n denominator = np.multiply(p_yf_cond_yp, p_xp_cond_yp)\n denominator[denominator == 0] = np.nan\n\n division = np.divide(p_joint_cond_yp, denominator[:, :, None])\n division[division == 0] = np.nan\n\n log = np.log2(division)\n\n return np.nansum(np.multiply(p_dist, log))","def get_entropy_df(self, df=None):\n if df is None:\n df = self.df\n target = self.target\n\n entropy = 0\n values = df[target].unique()\n\n for value in values:\n # Fraction of values of 'value' in target feature\n fraction = df[target].value_counts()[value]/len(df[target])\n entropy += -fraction*np.log2(fraction)\n\n return entropy","def __entropy(self, labels):\n class_probs = np.unique(labels, return_counts=True)[1] / labels.size\n class_prob_logs = np.log2(class_probs)\n entropy = -np.sum(class_probs * class_prob_logs)\n return entropy","def entropy(rows, resCol = None):\n from math import log\n if not resCol: #create the dictionary of counts for each class using pure python\n total = len(rows)\n counts = __uniqueCounts(rows)\n else: #Create the dictionary of counts for each class using pandas.\n assert 'index' in dir(rows)\n total = len(rows.index)\n counts = __uniqueCountsPandas(rows, resCol)\n log2 = lambda x:log(x)/log(2) #Create a function to take log-base 2 of a number\n ent = 0 #Initialize the entropy at zero\n #Implement the formula for entropy, using log-base2\n fracs = [float(x)/total for x in counts.values()]\n for x in fracs:\n ent += -x*log2(x)\n return ent","def energy_states(self) -> List[int]:\n return self._energy_states","def entropy(self, **kwargs) -> TensorType:","def entropy(self, **kwargs) -> TensorType:","def get_entropy(df: pd.DataFrame) -> int:\r\n\r\n # get unique values and their counts for the last column of df\r\n values, val_freq = np.unique(df[df.columns[-1]], return_counts=True)\r\n val_freq = val_freq / val_freq.sum()\r\n\r\n entropy = 0\r\n for freq in val_freq:\r\n entropy -= freq * np.log2(freq)\r\n return entropy","def calculate_entropy():\n\tstat = {} # dictionary - chars and number of repetitions\n\tallchar = 0.0 # total number of characters\n\tentropy = 0.0 # initial entropy\n\n\tfor line in sys.stdin.readlines():\n\t\tline = re.sub(r'\\s', '', line)\n\t\tfor znak in line:\n\t\t\tif znak in stat:\n\t\t\t\tstat[znak] += 1\n\t\t\telse:\n\t\t\t\tstat[znak] = 1\n\t\t\tallchar += 1\n\n\tfor znak in stat:\n\t\tstat[znak] = stat[znak]/allchar\n\t\tentropy += stat[znak] * log(stat[znak], 2)\n\n\tentropy *= -1\n\treturn entropy","def nats(self) -> float:\n return self.entropy()","def entropy(self, f):\n f_log = -torch.log(self.einsum(\"q,q->q\", [f, 1 / self.w]))\n return self.einsum(\"q,q->\", [f, f_log])","def entropy_fn(args: StepFunctionArgs) -> SingleScorePerStepTensor:\n logits = args.attribution_model.output2logits(args.forward_output)\n out = torch.distributions.Categorical(logits=logits).entropy()\n if out.ndim > 1:\n out = out.squeeze(-1)\n return out","def entropy(a):\n a = a.upper()\n\n freq = collections.defaultdict(int) # int() is the default constructor for non existent item, and returns 0\n for c in a:\n freq[c] = freq[c] + 1\n\n e = 0.0\n for f in freq.values():\n if f:\n p = f / len(a)\n e += p * math.log(p)\n\n return -e","def calc_entropy(data_set): #calculates total entropy of the dataset\r\n republicans = 0\r\n democrats = 0\r\n total = 0\r\n for data_point in data_set:\r\n party = data_point.dat_party\r\n if party == \"R\":\r\n republicans+=1\r\n elif party == \"D\":\r\n democrats+=1\r\n total+=1\r\n\r\n if total == 0: return 0\r\n prob_dem = democrats/total\r\n prob_rep = republicans/total\r\n if prob_dem == 0: return -(prob_rep * math.log(prob_rep, 2))\r\n if prob_rep == 0: return -(prob_dem * math.log(prob_dem, 2))\r\n\r\n entropy = (-prob_dem * math.log(prob_dem, 2)) -(prob_rep * math.log(prob_rep, 2))\r\n return entropy","def entropy_function(c, n):\n return -(c*1.0/n)*math.log(c*1.0/n,2)","def get_entropy(*labels):\n entropies = [] #list of entropy values from each subset\n total = 0 #total number of datapoints\n for subset in labels:\n n = len(subset)\n total += n\n counts = np.unique(subset, return_counts=True)[1] #frequency of unique values\n entropy = np.sum([-(i/n) * np.log2(i/n) for i in counts]) #subset entropy calcuation\n entropies.append((entropy, n))\n return np.sum([(n/total) * ent for n, ent in iter(entropies)])","def entropy(predictions, number):\n val = []\n for i in range(0, predictions.shape[0]):\n tmp = 0\n for j in range(0, len(predictions[i])):\n if predictions[i][j] > 0:\n tmp -= predictions[i][j] * np.log(predictions[i][j])\n else:\n tmp -= 0.000001 * np.log(0.000001)\n val.append(tmp)\n\n return __get_max_indexes(val, number)"],"string":"[\n \"def entropy(self):\\n raise NotImplementedError\",\n \"def entropy(self):\\n\\n \\\"\\\"\\\"Gets the first neighbours, which are the first 2*r+1 cells.\\\"\\\"\\\"\\n current_neighbours = []\\n amount = [0] * self.k ** (2 * self.r + 1)\\n for i in range(2 * self.r + 1):\\n current_neighbours.append(self.config[self.t, i % self.width])\\n\\n \\\"\\\"\\\"Calculates the rule and adds one to it's amount. It then removes the\\n leftmost cell and adds a cell to the right.\\\"\\\"\\\"\\n for i in range(len(self.config[self.t]) - 1):\\n rule = 0\\n for j in range(len(current_neighbours)):\\n rule += int(current_neighbours[j] *\\n self.k ** ((2 * self.r + 1) - j - 1))\\n amount[len(amount) - 1 - rule] += 1\\n current_neighbours.pop(0)\\n current_neighbours.append(\\n self.config[self.t, (2 * self.r + 1 + i) % self.width])\\n\\n \\\"\\\"\\\"Calculates the rule for the last neighbourhood.\\\"\\\"\\\"\\n rule = 0\\n for j in range(len(current_neighbours)):\\n rule += int(current_neighbours[j] *\\n self.k ** ((2 * self.r + 1) - j - 1))\\n amount[len(amount)-1 - rule] += 1\\n\\n \\\"\\\"\\\"Calculates the Shannon entropy and the the average entropy so far.\\\"\\\"\\\"\\n shannon = 0\\n for i in range(len(amount)):\\n if(amount[i] != 0):\\n probability = amount[i] / self.width\\n shannon -= probability * np.log2(probability)\\n self.average_entropy = (self.average_entropy *\\n self.t + shannon) / (self.t + 1)\",\n \"def entropy(self, unlabeled_sequence):\\n unlabeled_sequence = self._transform(unlabeled_sequence)\\n\\n T = len(unlabeled_sequence)\\n N = len(self._states)\\n\\n alpha = self._forward_probability(unlabeled_sequence)\\n beta = self._backward_probability(unlabeled_sequence)\\n normalisation = logsumexp2(alpha[T - 1])\\n\\n entropy = normalisation\\n\\n # starting state, t = 0\\n for i, state in enumerate(self._states):\\n p = 2 ** (alpha[0, i] + beta[0, i] - normalisation)\\n entropy -= p * self._priors.logprob(state)\\n # print('p(s_0 = %s) =' % state, p)\\n\\n # state transitions\\n for t0 in range(T - 1):\\n t1 = t0 + 1\\n for i0, s0 in enumerate(self._states):\\n for i1, s1 in enumerate(self._states):\\n p = 2 ** (\\n alpha[t0, i0]\\n + self._transitions[s0].logprob(s1)\\n + self._outputs[s1].logprob(unlabeled_sequence[t1][_TEXT])\\n + beta[t1, i1]\\n - normalisation\\n )\\n entropy -= p * self._transitions[s0].logprob(s1)\\n # print('p(s_%d = %s, s_%d = %s) =' % (t0, s0, t1, s1), p)\\n\\n # symbol emissions\\n for t in range(T):\\n for i, state in enumerate(self._states):\\n p = 2 ** (alpha[t, i] + beta[t, i] - normalisation)\\n entropy -= p * self._outputs[state].logprob(\\n unlabeled_sequence[t][_TEXT]\\n )\\n # print('p(s_%d = %s) =' % (t, state), p)\\n\\n return entropy\",\n \"def cal_localEntropy(self):\\n if len(self.locality) == 0:\\n proportion = np.asarray(self.pop / self.pop_sum)\\n\\n else:\\n polygon_sum = np.sum(self.locality, axis=1).reshape(self.n_location, 1)\\n proportion = np.asarray(self.locality / polygon_sum)\\n\\n entropy = proportion * np.log(1 / proportion)\\n\\n entropy[np.isnan(entropy)] = 0\\n entropy[np.isinf(entropy)] = 0\\n entropy = np.sum(entropy, axis=1)\\n entropy = entropy.reshape((self.n_location, 1))\\n\\n return entropy\",\n \"def _entropies(self):\\n H_C = fentropy(self.row_totals)\\n H_K = fentropy(self.col_totals)\\n H_actual = fentropy(self.itervalues())\\n H_expected = H_C + H_K\\n I_CK = H_expected - H_actual\\n return H_C, H_K, I_CK\",\n \"def entropy(self):\\n return -np.sum(self.log_likelihoods * np.exp(self.log_likelihoods))\",\n \"def entropycell(self):\\n cells = [0] * self.k\\n for i in range(self.width):\\n cells[int(self.config[self.t, i])] += 1\\n\\n \\\"\\\"\\\"Calculates the Shannon entropy and the the average entropy so far.\\\"\\\"\\\"\\n shannon = 0\\n for i in range(self.k):\\n if(cells[i] != 0):\\n probability = cells[i] / self.width\\n shannon -= probability * np.log2(probability)\\n self.average_cell = (self.average_cell * self.t + shannon) / (self.t + 1)\",\n \"def entropy_root(self):\\n\\t\\tif self.size() == 0:\\n\\t\\t\\treturn 0\\n\\n\\t\\tcounter = collections.Counter(self.y)\\n\\t\\treturn entropy([x[1] for x in counter.items()], self.size())\",\n \"def entropy(data):\\n e = 0\\n\\n counter = collections.Counter(data)\\n l = len(data)\\n for count in counter.values():\\n p_x = count / l\\n e += - p_x * math.log2(p_x)\\n\\n return e\",\n \"def get_entropy(self, rows):\\n label_count = defaultdict(int)\\n total_count = 0\\n for row in rows:\\n label = row[self.target_attribute]\\n label_count[label] += 1\\n total_count += 1\\n return sum([-(float(label_count[label]) /\\n total_count) * np.log2(float(label_count[label]) / total_count)\\n for label in label_count.keys()])\",\n \"def entropy(self):\\n return self._normal.entropy()\",\n \"def entropy(x):\\n nz = np.nonzero(x)[0]\\n return -np.sum(x[nz]*np.log2(x[nz]))\",\n \"def get_entropy(self, state):\\n entropy = self.entropy_model.predict(state)\\n return entropy[0]\",\n \"def entropy(self):\\n n = len(self.y)\\n sum_ = 0\\n for i in np.unique(self.y):\\n v = len(self.y[self.y == i])\\n sum_ += -((v/n) * log2(v/n))\\n return sum_\",\n \"def ensemble_entropy(self):\\n return transition_matrices.entropy_measure(self.ensemble_transition_matrix)\",\n \"def entropy(self):\\n Z = self.sum()\\n assert (Z > 0), 'Non-normalizable factor (perhaps log factor?)' # also check for positivity?\\n H = 0.0\\n for x in np.nditer(self.t, op_flags=['readonly']):\\n p = x/Z\\n H += 0.0 if p==0 else -p*np.log(p)\\n return H\",\n \"def entropy(group_counts):\\n total = sum(group_counts)\\n entro = 0\\n for item_count in group_counts:\\n entro += item_entropy(item_count, total)\\n return entro\",\n \"def entropy(l):\\n\\n probabilities = np.bincount(l) / len(l)\\n with np.errstate(divide='ignore'): # ignore log(0) errors, we'll handle\\n log_probabilities = np.log2(probabilities)\\n log_probabilities[~np.isfinite(log_probabilities)] = 0\\n return -np.sum(probabilities * log_probabilities)\",\n \"def cal_globalEntropy(self):\\n group_score = []\\n pop_total = np.sum(self.pop_sum)\\n prop = np.asarray(np.sum(self.pop, axis=0))[0]\\n\\n # loop at sum of each population groups\\n for group in prop:\\n group_idx = group / pop_total * np.log(1 / (group / pop_total))\\n group_score.append(group_idx)\\n entropy = np.sum(group_score)\\n\\n return entropy\",\n \"def point_entropy(self, unlabeled_sequence):\\n unlabeled_sequence = self._transform(unlabeled_sequence)\\n\\n T = len(unlabeled_sequence)\\n N = len(self._states)\\n\\n alpha = self._forward_probability(unlabeled_sequence)\\n beta = self._backward_probability(unlabeled_sequence)\\n normalisation = logsumexp2(alpha[T - 1])\\n\\n entropies = np.zeros(T, np.float64)\\n probs = np.zeros(N, np.float64)\\n for t in range(T):\\n for s in range(N):\\n probs[s] = alpha[t, s] + beta[t, s] - normalisation\\n\\n for s in range(N):\\n entropies[t] -= 2 ** (probs[s]) * probs[s]\\n\\n return entropies\",\n \"def entropy(self):\\n return self._entropy_func\",\n \"def entropy(y):\\r\\n\\r\\n # INSERT YOUR CODE HERE\\r\\n value, count = np.unique(y,return_counts = True)\\r\\n Hy = 0.0\\r\\n prob = count.astype(float)/len(y)\\r\\n for p in prob:\\r\\n Hy += -(p)*(np.log2(p))\\r\\n return Hy\\r\\n raise Exception('Function not yet implemented!')\",\n \"def entropy_balance(self):\\n return\",\n \"def entropy_balance(self):\\n return\",\n \"def entropy(self):\\n ent = 0.0\\n for f in self.byte_freq:\\n if f > 0:\\n freq = float(f) / self.byte_total\\n ent = ent + freq * math.log(freq, 2)\\n return -ent\",\n \"def stateOccupationProbabilityGeneration(self):\\n self.L = zeros((self.noOfEmmittingStates, self.T))\\n\\n for j in range(self.noOfEmmittingStates):\\n for t in range(self.T):\\n self.L[j,t] = (self.alpha[j+1, t+1] * self.beta[j+1, t+1]) / self.observationLikelihood\",\n \"def shannon_entropy(c):\\n\\n c_normalized = c / float(np.sum(c))\\n c_normalized_nonzero = c_normalized[np.nonzero(c_normalized)] # gives 1D array\\n entropy = -sum(c_normalized_nonzero * np.log2(c_normalized_nonzero)) # unit in bits\\n return entropy\",\n \"def entropy(y):\\n p = _proba(y)\\n return (-p * np.log2(p)).sum()\",\n \"def cal_localIndexH(self):\\n local_entropy = self.cal_localEntropy()\\n global_entropy = self.cal_globalEntropy()\\n\\n et = global_entropy * np.sum(self.pop_sum)\\n eei = np.asarray(global_entropy - local_entropy)\\n h_local = np.asarray(self.pop_sum) * eei / et\\n\\n return h_local\",\n \"def calc_entropy_for_states(states,n,T):\\n\\n D = int(1e7)\\n n_s = len(states)\\n\\n data = sample_states_all(exampleRatios, T, D)\\n CDataObj = CompressionData(data, len(exampleRatios), 1,\\n 0, len(exampleRatios)-1, np.uint8)\\n # print(\\\"Compressed size is \\\" + str(CDataObj.size_data()))\\n # print(\\\"Zero size is \\\" + str(CDataObj.size_zeros()))\\n # print(\\\"Random size is \\\" + str(CDataObj.size_random()))\\n # print(\\\"Compression ratio is \\\" + str(CDataObj.get_compression_ratios()))\\n\\n eta = CDataObj.get_compression_ratios()\\n entropy_estimate = eta * D * np.log(n_s)\\n\\n entropy_real = calc_entropy_one(exampleRatios, T) * D\\n \\n return (entropy_real, entropy_estimate)\",\n \"def entropy(img):\\n # by calculating\\n histogram = img.histogram()\\n histogram_size = sum(histogram)\\n histogram = [float(h) / histogram_size for h in histogram]\\n\\n return -sum([p * math.log(p, 2) for p in histogram if p != 0])\",\n \"def _entropy(self):\\n return self.rv.entropy(*self._pymc_dists_to_value(self.args), **self.kwds)\",\n \"def entropy(message):\\n n = len(message)\\n message = letter_freq(message)\\n h = 0\\n for n_i in message.values():\\n p_i = n_i/n\\n h += -p_i*(log2(p_i))\\n return h\",\n \"def H(self, data):\\n entropy = 0\\n\\n if not data:\\n return entropy\\n\\n for x in range(256):\\n p_x = float(data.count(chr(x))) / len(data)\\n if p_x > 0:\\n entropy -= p_x * math.log(p_x, 2)\\n\\n return entropy\",\n \"def entropy(y):\\n return -1 * sum(\\n [\\n pipe(np.sum(y == value) / len(y), lambda ratio: ratio * np.log(ratio))\\n for value in set(y)\\n ]\\n )\",\n \"def entropy(message):\\n message = letter_freq(message)\\n n = sum(message.values())\\n h = 0\\n for n_i in message.values():\\n p_i = n_i / n\\n h += -p_i * log2(p_i)\\n return h\",\n \"def entropy(Y):\\n\\n temp = np.unique(Y, return_counts=True)\\n uniq_Y = list(temp[0])\\n Y_count = list(temp[1])\\n \\n total = sum(Y_count)\\n\\n ent = 0\\n for elem in uniq_Y:\\n prob = Y_count[uniq_Y.index(elem)] / total\\n # print(\\\"prob:\\\", prob)\\n ent -= (prob * (math.log2(prob)))\\n # print(\\\"ent:\\\",ent)\\n\\n return ent\",\n \"def entropy(data):\\n strings, lens = Counter(data), np.float(len(data))\\n return -sum(count / lens * np.log2(count / lens) for count in strings.values())\",\n \"def _entropy(data):\\n hist = np.array(PIL.Image.fromarray(data).histogram())\\n hist = hist / hist.sum()\\n hist = hist[hist != 0]\\n return -np.sum(hist * np.log2(hist))\",\n \"def calc_entropy(frequency_wn, temperature):\\r\\n\\tentropy = 0\\r\\n\\tfrequency = [entry * SPEED_OF_LIGHT for entry in frequency_wn]\\r\\n\\tfor entry in frequency:\\r\\n\\t\\tfactor = ((PLANCK_CONSTANT*entry)/(BOLTZMANN_CONSTANT*temperature))\\r\\n\\t\\ttemp = factor*(1/(math.exp(factor)-1)) - math.log(1-math.exp(-factor))\\r\\n\\t\\ttemp = temp*GAS_CONSTANT/4.184\\r\\n\\t\\tentropy = entropy + temp \\r\\n\\treturn entropy\",\n \"def entropy(y):\\n EPS = 0.0005\\n\\n # YOUR CODE HERE\\n if len(y) == 0:\\n return 0.\\n \\n pk = np.mean(y, axis=0)\\n \\n return - np.sum(pk * np.log(pk + EPS))\",\n \"def entropy(*args):\\n\\n\\n values = []\\n leaf = -1\\n\\n for i, val in enumerate(args):\\n if(val != 0):\\n values.append(val * math.log(val, len(args)))\\n if(val == 1):\\n leaf = i\\n \\n return -sum(values), leaf\",\n \"def entropy ( target_array ):\\n return -1 * sum (\\n [\\n pipe ( np.sum ( target_array == value ) / len ( target_array ), lambda ratio: ratio * np.log ( ratio ) )\\n for value in set ( target_array )\\n ]\\n ) # End entropy()\",\n \"def entropy(self, y):\\n n = y.size\\n if n <= 1:\\n return 0\\n\\n labels, counts = unique(y, return_counts=True)\\n\\n if counts.size <= 1:\\n return 0\\n\\n probs = counts / n\\n entropy = -sum([p * log(p, 2) for p in probs])\\n return entropy\",\n \"def entropy(counts):\\n assert (counts >= 0).all()\\n probs = counts / counts.sum()\\n probs = probs[probs > 0] # Avoid log(0)\\n return - np.sum(probs * np.log2(probs))\",\n \"def entropy(s):\\n p, lns = Counter(s), float(len(s))\\n return -sum( count/lns * math.log(count/lns, 2) for count in p.values())\",\n \"def entropy(self,classData):\\n\\n\\t\\t###### your implementation below ######\\n\\t\\ttotalEntropy = 0\\n\\t\\ttempSet = {}\\n\\t\\tfor i in range(len(classData)):\\n\\t\\t\\ttempSet[classData[i]] = (classData.count(classData[i]))\\n\\t\\ttotal = sum(tempSet.values())\\n\\t\\tfor x in tempSet:\\n\\t\\t\\ttotalEntropy += -(tempSet[x]/total * math.log(tempSet[x]/total, 2))\\n\\t\\treturn totalEntropy\",\n \"def entropy(data, idxList):\\n df = data.loc[idxList]\\n counts = df.value_counts().to_numpy()\\n counts = counts.reshape(1, -1).astype(np.float32)\\n counts /= np.sum(counts)\\n log_sum = counts @ np.log2(counts.T)\\n return -log_sum[0, 0]\",\n \"def entropy(target_col):\\n elements,counts = np.unique(target_col,return_counts = True)\\n entropy = np.sum([(-counts[i]/np.sum(counts))*np.log2(counts[i]/np.sum(counts)) for i in range(len(elements))])\\n return entropy\",\n \"def entropy(x):\\n x_max, x_min = x.max(), x.min()\\n assert (x_min >= 0) and (x_max <= 1)\\n if x_min == x_max == 0:\\n return np.float32(0.)\\n # Take only non-zero values as log(0) = 0 :\\n nnz_x = x[np.nonzero(x)]\\n entr = -np.sum(nnz_x * np.log2(nnz_x))\\n\\n return entr\",\n \"def __compute_entropy(row:pd.Series) -> float:\\n probability = row.value_counts(normalize=True).values\\n entropy = EntropicSolver.__compute_entropy_probability(probability)\\n return entropy\",\n \"def c_entropy(state,target=None,log_base=2):\\n if target!=None:\\n state = state.ptrace(target)\\n return entropy(com_measure(state),base=log_base)\",\n \"def entropy(dist):\\n #dist = array([max(d,1e-100) for d in dist])\\n dist = dist + 1e-20\\n return dot(dist,(log(1.0/dist) * (1.0/log(2.0))).T)\",\n \"def entropy( freq ):\\n N = 0.0\\n entropy = 0.0\\n for x, v in freq.items( ):\\n N += v\\n entropy -= v * math.log( v, 2 )\\n return (N * math.log( N, 2 ) + entropy) / N\",\n \"def exploreEntropy(self, position):\\n pass\",\n \"def entropy(class_probabilities):\\n return sum(-p * math.log(p, 2)\\n for p in class_probabilities\\n if p) #ignore 0's\",\n \"def entropy_analysis(data_df):\\n seq_list = data_df['sequence'].values.tolist()\\n base_set = set([])\\n for seq in seq_list:\\n base_set.update(set(seq))\\n H_list = []\\n null_freq_list = []\\n STEP = ceil(len(seq_list[0]) / 10)\\n for base_idx in range(len(seq_list[0])):\\n if base_idx % STEP == 0:\\n logging.info('Entropy analysis in progress: {}% completed.'.format(10 * base_idx // STEP))\\n H, null_freq = base_entropy_masked(seq_list, base_set, base_idx)\\n H_list.append(H,)\\n null_freq_list.append(null_freq)\\n logging.info('Entropy analysis in progress: DONE.')\\n return H_list, null_freq_list\",\n \"def entropy(self, priors=None):\\n def entropy_f(x):\\n x[x != 0] *= np.log(x[x != 0])\\n return -x.sum(axis=0)\\n return self.utility(entropy_f, priors)\",\n \"def entropy(probabilities):\\n return -(sum([p * log(p, 2) if p > 0 else 0 for p in probabilities]))\",\n \"def compute_entropy_all(self) -> pd.Series:\\n if self.game_matrix is None:\\n return self.word_matrix.apply(self.__compute_entropy, axis=1)\\n return self.game_matrix.apply(self.__compute_entropy, axis=1)\",\n \"def entropy(self):\\r\\n return 1/2 * (self.dim * (_LOG_2PI + 1) + self._log_det_cov)\",\n \"def spatial_entropy(map_):\\n map_ = map_ / np.sum(map_)\\n return -1 * np.sum(map_ * np.log(map_))\",\n \"def h(self, state):\\n loc = dict((val, (i, j)) for i, row in enumerate(self.goal_state)\\n for j, val in enumerate(row))\\n\\n def calculate_cost(i, j):\\n val = state[i][j]\\n goal_i, goal_j = loc[val]\\n return abs(goal_i - i) + abs(goal_j - j)\\n\\n return sum(\\n calculate_cost(i, j) for i in xrange(self.size)\\n for j in xrange(self.size) if state[i][j])\",\n \"def entropy(data):\\n\\n freqs = {}\\n suma = len(data)\\n\\n for i in range(0, len(data)):\\n freqs[data[i]] = 1.0 + freqs.get(data[i], 0)\\n\\n res = 0.0\\n for i in freqs:\\n res += (freqs[i] / suma) * log((freqs[i] / suma), 2)\\n return -res\",\n \"def get_entropy(distribution, samples):\\n entropy = -tf.reduce_sum(distribution.log_prob(samples), axis=1)\\n return entropy\",\n \"def entropy(y):\\n total = y.size\\n value_counts = np.bincount(y).astype(\\\"float\\\")\\n proportions = value_counts / y.size\\n\\n return sum(-i * np.log(i) for i in proportions if i)\",\n \"def entropy(p):\\n ent = tf.where(p > np.finfo(np.float32).eps, -p * tf.log(p), tf.zeros_like(p))\\n ent = tf.reduce_sum(ent, axis=1)\\n return ent\",\n \"def GetEntropy(flanks):\\n countA = 0\\n countT = 0\\n countG = 0\\n countC = 0\\n for i in flanks:\\n if i == \\\"A\\\":\\n countA += 1\\n elif i == \\\"T\\\":\\n countT += 1\\n elif i == \\\"C\\\":\\n countC += 1\\n elif i == \\\"G\\\":\\n countG += 1\\n else: pass\\n total = countA+countT+countG+countC\\n fractions = [item*1.0/total for item in [countA,countT,countG,countC]]\\n entropy = sum([-1.0*item*math.log(item,2) for item in fractions if item != 0])\\n return entropy\",\n \"def entropy(self, policy_params):\\n return self.head.entropy(policy_params)\",\n \"def entropy(class_probabilities):\\n return sum(-p * math.log(p,2)\\n for p in class_probabilities\\n if p)\",\n \"def _entropy(self, y):\\n # Get size\\n n = y.shape[0]\\n summation = 0\\n\\n # Summatory\\n for c_i in np.unique(y):\\n prob = sum(y == c_i) / float(n)\\n summation += prob * np.log2(prob)\\n\\n return -summation\",\n \"def entropy(Y):\\n unique, count = np.unique(Y, return_counts=True, axis=0)\\n prob = count/len(Y)\\n en = np.sum((-1)*prob*np.log2(prob))\\n return en\",\n \"def div(self):\\n freqList = [i / sum(self.has.values()) for i in self.has.values()]\\n entropies = [i * math.log(i, 2) for i in freqList]\\n entropy = -sum(entropies)\\n return entropy\",\n \"def count_neighbourhood_states(self,state):\\n\\t\\tassert len(state) > 0\\n\\t\\tenergies = [float(\\\"inf\\\") for i in range (self._size)]\\n\\t\\ttaxes = [float(\\\"inf\\\") for i in range (self._size)]\\n\\n\\t\\tfor i in range (0,self._size):\\n\\t\\t\\tnewState = copy(state)\\n\\t\\t\\tnewState[i] = 1 - newState[i]\\n\\t\\t\\tenergies[i] = self.count_energy(newState)\\n\\t\\t\\ttaxes[i] = self.count_tax(newState)\\n\\n\\t\\treturn energies,taxes\",\n \"def entropyDistributed(distribution):\\n return -sum(map(lambda p : p * log(p, 2), distribution))\",\n \"def _entropy(self, feature, node):\\n entropy = 0\\n categories = np.unique(feature)\\n num_point = len(feature)\\n for category in categories:\\n # for each category in that feature\\n num_category = len(feature[feature == category])\\n for c in self.num_class:\\n # count the number of each class\\n num_category_class = len(feature[np.logical_and(feature == category, node.y == c)])\\n if num_category_class == 0:\\n continue\\n # compute entropy/information gain or classification error\\n entropy += num_category / num_point * (\\n -num_category_class / num_category * log2(num_category_class / num_category))\\n return entropy\",\n \"def conditional_entropy_hyper(self) -> float:\\n pass\",\n \"def entropy(self) -> float:\\n probabilities = np.array([len(players) for players in self.answers.values()])\\n probabilities = probabilities / sum(probabilities)\\n return sc.stats.entropy(probabilities)\",\n \"def compute_entropy(bincounts, include_zeros):\\n num_total = tf.cast(tf.reduce_sum(bincounts), tf.float64)\\n if not include_zeros:\\n bincounts = bincounts[1:]\\n mask = tf.greater(bincounts, 0)\\n nonzero_bincounts = tf.cast(\\n tf.boolean_mask(bincounts, mask), tf.float64)\\n num_nonzero = tf.cast(tf.reduce_sum(nonzero_bincounts), tf.float64)\\n log_nonzero_bincounts = tf.math.log(nonzero_bincounts)\\n log_prob = log_nonzero_bincounts - tf.reduce_logsumexp(\\n log_nonzero_bincounts)\\n entropy = tf.math.reduce_sum(\\n log_prob * tf.exp(log_prob)) / -tf.math.log(tf.cast(2, tf.float64))\\n return entropy * num_nonzero / num_total\",\n \"def entropy(P):\\n P_nan = P.copy()\\n P_nan[P_nan == 0] = np.nan\\n return np.nansum(np.multiply(P_nan, np.log2(1 / P_nan)))\",\n \"def entropy(n_bits):\\n return n_bits and random.getrandbits(n_bits)\",\n \"def entropy(d, total, word_count):\\n\\t# Entropie je - Sum_morf p(morf) * log_2 p(morf)\\n\\t# p(morf) = c(morf) / c(all)\\n\\te = 0\\n\\tfor count in d.values():\\n\\t\\tp = count/total\\n\\t\\ttype_e = - p * log2(p)\\n\\t\\te += type_e * count\\n\\treturn e / word_count\",\n \"def conditional_entropy(self) -> float:\\n pass\",\n \"def calculate_entropy(dataset) :\\n\\n num_entries = len(dataset)\\n label_counts = {}\\n for vector in dataset :\\n # the label is at the last index of the data set\\n current_label = vector[-1]\\n if current_label not in label_counts :\\n label_counts[current_label] = 0\\n label_counts[current_label] += 1\\n # Calculate the entropy\\n entropy = 0.0\\n for label in label_counts :\\n # Calculate probability of each label within the dataset\\n prob_of_label = label_counts[label]/num_entries\\n # Since the entropy is the negative of the sum of all probability,\\n # simply subtract it\\n entropy -= prob_of_label * log(prob_of_label, 2)\\n return entropy\",\n \"def transfer_entropy(X, Y):\\n coords = Counter(zip(Y[1:], X[:-1], Y[:-1]))\\n\\n p_dist = np.zeros((config.NUM_STATES, config.NUM_STATES, config.NUM_STATES))\\n for y_f, x_p, y_p in coords.keys():\\n p_dist[y_p, y_f, x_p] = coords[(y_f, x_p, y_p)] / (len(X) - 1)\\n\\n p_yp = p_dist.sum(axis=2).sum(axis=1)\\n p_joint_cond_yp = p_dist / p_yp[:, None, None]\\n p_yf_cond_yp = p_dist.sum(axis=2) / p_yp[:, None]\\n p_xp_cond_yp = p_dist.sum(axis=1) / p_yp[:, None]\\n\\n denominator = np.multiply(p_yf_cond_yp, p_xp_cond_yp)\\n denominator[denominator == 0] = np.nan\\n\\n division = np.divide(p_joint_cond_yp, denominator[:, :, None])\\n division[division == 0] = np.nan\\n\\n log = np.log2(division)\\n\\n return np.nansum(np.multiply(p_dist, log))\",\n \"def get_entropy_df(self, df=None):\\n if df is None:\\n df = self.df\\n target = self.target\\n\\n entropy = 0\\n values = df[target].unique()\\n\\n for value in values:\\n # Fraction of values of 'value' in target feature\\n fraction = df[target].value_counts()[value]/len(df[target])\\n entropy += -fraction*np.log2(fraction)\\n\\n return entropy\",\n \"def __entropy(self, labels):\\n class_probs = np.unique(labels, return_counts=True)[1] / labels.size\\n class_prob_logs = np.log2(class_probs)\\n entropy = -np.sum(class_probs * class_prob_logs)\\n return entropy\",\n \"def entropy(rows, resCol = None):\\n from math import log\\n if not resCol: #create the dictionary of counts for each class using pure python\\n total = len(rows)\\n counts = __uniqueCounts(rows)\\n else: #Create the dictionary of counts for each class using pandas.\\n assert 'index' in dir(rows)\\n total = len(rows.index)\\n counts = __uniqueCountsPandas(rows, resCol)\\n log2 = lambda x:log(x)/log(2) #Create a function to take log-base 2 of a number\\n ent = 0 #Initialize the entropy at zero\\n #Implement the formula for entropy, using log-base2\\n fracs = [float(x)/total for x in counts.values()]\\n for x in fracs:\\n ent += -x*log2(x)\\n return ent\",\n \"def energy_states(self) -> List[int]:\\n return self._energy_states\",\n \"def entropy(self, **kwargs) -> TensorType:\",\n \"def entropy(self, **kwargs) -> TensorType:\",\n \"def get_entropy(df: pd.DataFrame) -> int:\\r\\n\\r\\n # get unique values and their counts for the last column of df\\r\\n values, val_freq = np.unique(df[df.columns[-1]], return_counts=True)\\r\\n val_freq = val_freq / val_freq.sum()\\r\\n\\r\\n entropy = 0\\r\\n for freq in val_freq:\\r\\n entropy -= freq * np.log2(freq)\\r\\n return entropy\",\n \"def calculate_entropy():\\n\\tstat = {} # dictionary - chars and number of repetitions\\n\\tallchar = 0.0 # total number of characters\\n\\tentropy = 0.0 # initial entropy\\n\\n\\tfor line in sys.stdin.readlines():\\n\\t\\tline = re.sub(r'\\\\s', '', line)\\n\\t\\tfor znak in line:\\n\\t\\t\\tif znak in stat:\\n\\t\\t\\t\\tstat[znak] += 1\\n\\t\\t\\telse:\\n\\t\\t\\t\\tstat[znak] = 1\\n\\t\\t\\tallchar += 1\\n\\n\\tfor znak in stat:\\n\\t\\tstat[znak] = stat[znak]/allchar\\n\\t\\tentropy += stat[znak] * log(stat[znak], 2)\\n\\n\\tentropy *= -1\\n\\treturn entropy\",\n \"def nats(self) -> float:\\n return self.entropy()\",\n \"def entropy(self, f):\\n f_log = -torch.log(self.einsum(\\\"q,q->q\\\", [f, 1 / self.w]))\\n return self.einsum(\\\"q,q->\\\", [f, f_log])\",\n \"def entropy_fn(args: StepFunctionArgs) -> SingleScorePerStepTensor:\\n logits = args.attribution_model.output2logits(args.forward_output)\\n out = torch.distributions.Categorical(logits=logits).entropy()\\n if out.ndim > 1:\\n out = out.squeeze(-1)\\n return out\",\n \"def entropy(a):\\n a = a.upper()\\n\\n freq = collections.defaultdict(int) # int() is the default constructor for non existent item, and returns 0\\n for c in a:\\n freq[c] = freq[c] + 1\\n\\n e = 0.0\\n for f in freq.values():\\n if f:\\n p = f / len(a)\\n e += p * math.log(p)\\n\\n return -e\",\n \"def calc_entropy(data_set): #calculates total entropy of the dataset\\r\\n republicans = 0\\r\\n democrats = 0\\r\\n total = 0\\r\\n for data_point in data_set:\\r\\n party = data_point.dat_party\\r\\n if party == \\\"R\\\":\\r\\n republicans+=1\\r\\n elif party == \\\"D\\\":\\r\\n democrats+=1\\r\\n total+=1\\r\\n\\r\\n if total == 0: return 0\\r\\n prob_dem = democrats/total\\r\\n prob_rep = republicans/total\\r\\n if prob_dem == 0: return -(prob_rep * math.log(prob_rep, 2))\\r\\n if prob_rep == 0: return -(prob_dem * math.log(prob_dem, 2))\\r\\n\\r\\n entropy = (-prob_dem * math.log(prob_dem, 2)) -(prob_rep * math.log(prob_rep, 2))\\r\\n return entropy\",\n \"def entropy_function(c, n):\\n return -(c*1.0/n)*math.log(c*1.0/n,2)\",\n \"def get_entropy(*labels):\\n entropies = [] #list of entropy values from each subset\\n total = 0 #total number of datapoints\\n for subset in labels:\\n n = len(subset)\\n total += n\\n counts = np.unique(subset, return_counts=True)[1] #frequency of unique values\\n entropy = np.sum([-(i/n) * np.log2(i/n) for i in counts]) #subset entropy calcuation\\n entropies.append((entropy, n))\\n return np.sum([(n/total) * ent for n, ent in iter(entropies)])\",\n \"def entropy(predictions, number):\\n val = []\\n for i in range(0, predictions.shape[0]):\\n tmp = 0\\n for j in range(0, len(predictions[i])):\\n if predictions[i][j] > 0:\\n tmp -= predictions[i][j] * np.log(predictions[i][j])\\n else:\\n tmp -= 0.000001 * np.log(0.000001)\\n val.append(tmp)\\n\\n return __get_max_indexes(val, number)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.64681673","0.64361626","0.6397468","0.63540465","0.6292579","0.6235017","0.6153285","0.6081148","0.60573524","0.6038061","0.60269433","0.60266095","0.60248816","0.59867597","0.5969544","0.59659535","0.5924177","0.5898555","0.58884066","0.583654","0.581489","0.57974917","0.57879364","0.57879364","0.57717746","0.57615256","0.5748876","0.57275224","0.5719267","0.56995016","0.56877476","0.5680003","0.56742805","0.5671283","0.56549245","0.5648068","0.5638661","0.5637223","0.56354165","0.5626828","0.56139404","0.560771","0.55995387","0.5598465","0.5598156","0.55838364","0.5569013","0.5564825","0.5559233","0.5548665","0.5528521","0.55268073","0.55259424","0.5521822","0.55203193","0.5513598","0.55082667","0.5506506","0.55047417","0.5502495","0.55019975","0.5499382","0.54962784","0.54937774","0.5482782","0.54824305","0.54768115","0.54713726","0.54692054","0.5467757","0.5467547","0.54672426","0.54652005","0.5452193","0.545089","0.54421264","0.5440813","0.5434153","0.5431662","0.54234135","0.54210365","0.54206276","0.53911215","0.5389895","0.5375319","0.5366252","0.5365179","0.5359494","0.535579","0.5353554","0.5353554","0.5352998","0.532653","0.531255","0.5307436","0.5306273","0.5306131","0.53011817","0.5300345","0.5286691","0.52849114"],"string":"[\n \"0.64681673\",\n \"0.64361626\",\n \"0.6397468\",\n \"0.63540465\",\n \"0.6292579\",\n \"0.6235017\",\n \"0.6153285\",\n \"0.6081148\",\n \"0.60573524\",\n \"0.6038061\",\n \"0.60269433\",\n \"0.60266095\",\n \"0.60248816\",\n \"0.59867597\",\n \"0.5969544\",\n \"0.59659535\",\n \"0.5924177\",\n \"0.5898555\",\n \"0.58884066\",\n \"0.583654\",\n \"0.581489\",\n \"0.57974917\",\n \"0.57879364\",\n \"0.57879364\",\n \"0.57717746\",\n \"0.57615256\",\n \"0.5748876\",\n \"0.57275224\",\n \"0.5719267\",\n \"0.56995016\",\n \"0.56877476\",\n \"0.5680003\",\n \"0.56742805\",\n \"0.5671283\",\n \"0.56549245\",\n \"0.5648068\",\n \"0.5638661\",\n \"0.5637223\",\n \"0.56354165\",\n \"0.5626828\",\n \"0.56139404\",\n \"0.560771\",\n \"0.55995387\",\n \"0.5598465\",\n \"0.5598156\",\n \"0.55838364\",\n \"0.5569013\",\n \"0.5564825\",\n \"0.5559233\",\n \"0.5548665\",\n \"0.5528521\",\n \"0.55268073\",\n \"0.55259424\",\n \"0.5521822\",\n \"0.55203193\",\n \"0.5513598\",\n \"0.55082667\",\n \"0.5506506\",\n \"0.55047417\",\n \"0.5502495\",\n \"0.55019975\",\n \"0.5499382\",\n \"0.54962784\",\n \"0.54937774\",\n \"0.5482782\",\n \"0.54824305\",\n \"0.54768115\",\n \"0.54713726\",\n \"0.54692054\",\n \"0.5467757\",\n \"0.5467547\",\n \"0.54672426\",\n \"0.54652005\",\n \"0.5452193\",\n \"0.545089\",\n \"0.54421264\",\n \"0.5440813\",\n \"0.5434153\",\n \"0.5431662\",\n \"0.54234135\",\n \"0.54210365\",\n \"0.54206276\",\n \"0.53911215\",\n \"0.5389895\",\n \"0.5375319\",\n \"0.5366252\",\n \"0.5365179\",\n \"0.5359494\",\n \"0.535579\",\n \"0.5353554\",\n \"0.5353554\",\n \"0.5352998\",\n \"0.532653\",\n \"0.531255\",\n \"0.5307436\",\n \"0.5306273\",\n \"0.5306131\",\n \"0.53011817\",\n \"0.5300345\",\n \"0.5286691\",\n \"0.52849114\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":22493,"cells":{"query":{"kind":"string","value":"Return entropy of each HC state across village locations."},"document":{"kind":"string","value":"def get_hc_co_occ_entropy(co_occs, v_repl_nan=None):\n\n return utils.get_row_entropy(co_occs.T, v_repl_nan)"},"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 entropy(self):\n\n \"\"\"Gets the first neighbours, which are the first 2*r+1 cells.\"\"\"\n current_neighbours = []\n amount = [0] * self.k ** (2 * self.r + 1)\n for i in range(2 * self.r + 1):\n current_neighbours.append(self.config[self.t, i % self.width])\n\n \"\"\"Calculates the rule and adds one to it's amount. It then removes the\n leftmost cell and adds a cell to the right.\"\"\"\n for i in range(len(self.config[self.t]) - 1):\n rule = 0\n for j in range(len(current_neighbours)):\n rule += int(current_neighbours[j] *\n self.k ** ((2 * self.r + 1) - j - 1))\n amount[len(amount) - 1 - rule] += 1\n current_neighbours.pop(0)\n current_neighbours.append(\n self.config[self.t, (2 * self.r + 1 + i) % self.width])\n\n \"\"\"Calculates the rule for the last neighbourhood.\"\"\"\n rule = 0\n for j in range(len(current_neighbours)):\n rule += int(current_neighbours[j] *\n self.k ** ((2 * self.r + 1) - j - 1))\n amount[len(amount)-1 - rule] += 1\n\n \"\"\"Calculates the Shannon entropy and the the average entropy so far.\"\"\"\n shannon = 0\n for i in range(len(amount)):\n if(amount[i] != 0):\n probability = amount[i] / self.width\n shannon -= probability * np.log2(probability)\n self.average_entropy = (self.average_entropy *\n self.t + shannon) / (self.t + 1)","def entropy(self):\n raise NotImplementedError","def entropy(self):\n Z = self.sum()\n assert (Z > 0), 'Non-normalizable factor (perhaps log factor?)' # also check for positivity?\n H = 0.0\n for x in np.nditer(self.t, op_flags=['readonly']):\n p = x/Z\n H += 0.0 if p==0 else -p*np.log(p)\n return H","def _entropies(self):\n H_C = fentropy(self.row_totals)\n H_K = fentropy(self.col_totals)\n H_actual = fentropy(self.itervalues())\n H_expected = H_C + H_K\n I_CK = H_expected - H_actual\n return H_C, H_K, I_CK","def entropycell(self):\n cells = [0] * self.k\n for i in range(self.width):\n cells[int(self.config[self.t, i])] += 1\n\n \"\"\"Calculates the Shannon entropy and the the average entropy so far.\"\"\"\n shannon = 0\n for i in range(self.k):\n if(cells[i] != 0):\n probability = cells[i] / self.width\n shannon -= probability * np.log2(probability)\n self.average_cell = (self.average_cell * self.t + shannon) / (self.t + 1)","def entropy(self, unlabeled_sequence):\n unlabeled_sequence = self._transform(unlabeled_sequence)\n\n T = len(unlabeled_sequence)\n N = len(self._states)\n\n alpha = self._forward_probability(unlabeled_sequence)\n beta = self._backward_probability(unlabeled_sequence)\n normalisation = logsumexp2(alpha[T - 1])\n\n entropy = normalisation\n\n # starting state, t = 0\n for i, state in enumerate(self._states):\n p = 2 ** (alpha[0, i] + beta[0, i] - normalisation)\n entropy -= p * self._priors.logprob(state)\n # print('p(s_0 = %s) =' % state, p)\n\n # state transitions\n for t0 in range(T - 1):\n t1 = t0 + 1\n for i0, s0 in enumerate(self._states):\n for i1, s1 in enumerate(self._states):\n p = 2 ** (\n alpha[t0, i0]\n + self._transitions[s0].logprob(s1)\n + self._outputs[s1].logprob(unlabeled_sequence[t1][_TEXT])\n + beta[t1, i1]\n - normalisation\n )\n entropy -= p * self._transitions[s0].logprob(s1)\n # print('p(s_%d = %s, s_%d = %s) =' % (t0, s0, t1, s1), p)\n\n # symbol emissions\n for t in range(T):\n for i, state in enumerate(self._states):\n p = 2 ** (alpha[t, i] + beta[t, i] - normalisation)\n entropy -= p * self._outputs[state].logprob(\n unlabeled_sequence[t][_TEXT]\n )\n # print('p(s_%d = %s) =' % (t, state), p)\n\n return entropy","def entropy(data):\n e = 0\n\n counter = collections.Counter(data)\n l = len(data)\n for count in counter.values():\n p_x = count / l\n e += - p_x * math.log2(p_x)\n\n return e","def entropy_root(self):\n\t\tif self.size() == 0:\n\t\t\treturn 0\n\n\t\tcounter = collections.Counter(self.y)\n\t\treturn entropy([x[1] for x in counter.items()], self.size())","def entropy(x):\n nz = np.nonzero(x)[0]\n return -np.sum(x[nz]*np.log2(x[nz]))","def entropy(self):\n n = len(self.y)\n sum_ = 0\n for i in np.unique(self.y):\n v = len(self.y[self.y == i])\n sum_ += -((v/n) * log2(v/n))\n return sum_","def entropy(self):\n return -np.sum(self.log_likelihoods * np.exp(self.log_likelihoods))","def get_entropy(self, state):\n entropy = self.entropy_model.predict(state)\n return entropy[0]","def entropy(group_counts):\n total = sum(group_counts)\n entro = 0\n for item_count in group_counts:\n entro += item_entropy(item_count, total)\n return entro","def calc_entropy_for_states(states,n,T):\n\n D = int(1e7)\n n_s = len(states)\n\n data = sample_states_all(exampleRatios, T, D)\n CDataObj = CompressionData(data, len(exampleRatios), 1,\n 0, len(exampleRatios)-1, np.uint8)\n # print(\"Compressed size is \" + str(CDataObj.size_data()))\n # print(\"Zero size is \" + str(CDataObj.size_zeros()))\n # print(\"Random size is \" + str(CDataObj.size_random()))\n # print(\"Compression ratio is \" + str(CDataObj.get_compression_ratios()))\n\n eta = CDataObj.get_compression_ratios()\n entropy_estimate = eta * D * np.log(n_s)\n\n entropy_real = calc_entropy_one(exampleRatios, T) * D\n \n return (entropy_real, entropy_estimate)","def ensemble_entropy(self):\n return transition_matrices.entropy_measure(self.ensemble_transition_matrix)","def entropy(self):\n return self._normal.entropy()","def entropy(message):\n n = len(message)\n message = letter_freq(message)\n h = 0\n for n_i in message.values():\n p_i = n_i/n\n h += -p_i*(log2(p_i))\n return h","def H(self, data):\n entropy = 0\n\n if not data:\n return entropy\n\n for x in range(256):\n p_x = float(data.count(chr(x))) / len(data)\n if p_x > 0:\n entropy -= p_x * math.log(p_x, 2)\n\n return entropy","def entropy(l):\n\n probabilities = np.bincount(l) / len(l)\n with np.errstate(divide='ignore'): # ignore log(0) errors, we'll handle\n log_probabilities = np.log2(probabilities)\n log_probabilities[~np.isfinite(log_probabilities)] = 0\n return -np.sum(probabilities * log_probabilities)","def entropy(self):\n ent = 0.0\n for f in self.byte_freq:\n if f > 0:\n freq = float(f) / self.byte_total\n ent = ent + freq * math.log(freq, 2)\n return -ent","def entropy(message):\n message = letter_freq(message)\n n = sum(message.values())\n h = 0\n for n_i in message.values():\n p_i = n_i / n\n h += -p_i * log2(p_i)\n return h","def get_entropy(self, rows):\n label_count = defaultdict(int)\n total_count = 0\n for row in rows:\n label = row[self.target_attribute]\n label_count[label] += 1\n total_count += 1\n return sum([-(float(label_count[label]) /\n total_count) * np.log2(float(label_count[label]) / total_count)\n for label in label_count.keys()])","def entropy(y):\r\n\r\n # INSERT YOUR CODE HERE\r\n value, count = np.unique(y,return_counts = True)\r\n Hy = 0.0\r\n prob = count.astype(float)/len(y)\r\n for p in prob:\r\n Hy += -(p)*(np.log2(p))\r\n return Hy\r\n raise Exception('Function not yet implemented!')","def cal_localEntropy(self):\n if len(self.locality) == 0:\n proportion = np.asarray(self.pop / self.pop_sum)\n\n else:\n polygon_sum = np.sum(self.locality, axis=1).reshape(self.n_location, 1)\n proportion = np.asarray(self.locality / polygon_sum)\n\n entropy = proportion * np.log(1 / proportion)\n\n entropy[np.isnan(entropy)] = 0\n entropy[np.isinf(entropy)] = 0\n entropy = np.sum(entropy, axis=1)\n entropy = entropy.reshape((self.n_location, 1))\n\n return entropy","def shannon_entropy(c):\n\n c_normalized = c / float(np.sum(c))\n c_normalized_nonzero = c_normalized[np.nonzero(c_normalized)] # gives 1D array\n entropy = -sum(c_normalized_nonzero * np.log2(c_normalized_nonzero)) # unit in bits\n return entropy","def entropy(y):\n return -1 * sum(\n [\n pipe(np.sum(y == value) / len(y), lambda ratio: ratio * np.log(ratio))\n for value in set(y)\n ]\n )","def entropy( freq ):\n N = 0.0\n entropy = 0.0\n for x, v in freq.items( ):\n N += v\n entropy -= v * math.log( v, 2 )\n return (N * math.log( N, 2 ) + entropy) / N","def GetEntropy(flanks):\n countA = 0\n countT = 0\n countG = 0\n countC = 0\n for i in flanks:\n if i == \"A\":\n countA += 1\n elif i == \"T\":\n countT += 1\n elif i == \"C\":\n countC += 1\n elif i == \"G\":\n countG += 1\n else: pass\n total = countA+countT+countG+countC\n fractions = [item*1.0/total for item in [countA,countT,countG,countC]]\n entropy = sum([-1.0*item*math.log(item,2) for item in fractions if item != 0])\n return entropy","def entropy(img):\n # by calculating\n histogram = img.histogram()\n histogram_size = sum(histogram)\n histogram = [float(h) / histogram_size for h in histogram]\n\n return -sum([p * math.log(p, 2) for p in histogram if p != 0])","def div(self):\n freqList = [i / sum(self.has.values()) for i in self.has.values()]\n entropies = [i * math.log(i, 2) for i in freqList]\n entropy = -sum(entropies)\n return entropy","def entropy(s):\n p, lns = Counter(s), float(len(s))\n return -sum( count/lns * math.log(count/lns, 2) for count in p.values())","def cal_globalEntropy(self):\n group_score = []\n pop_total = np.sum(self.pop_sum)\n prop = np.asarray(np.sum(self.pop, axis=0))[0]\n\n # loop at sum of each population groups\n for group in prop:\n group_idx = group / pop_total * np.log(1 / (group / pop_total))\n group_score.append(group_idx)\n entropy = np.sum(group_score)\n\n return entropy","def entropy(self):\n return self._entropy_func","def entropy(self, y):\n n = y.size\n if n <= 1:\n return 0\n\n labels, counts = unique(y, return_counts=True)\n\n if counts.size <= 1:\n return 0\n\n probs = counts / n\n entropy = -sum([p * log(p, 2) for p in probs])\n return entropy","def entropy(self,classData):\n\n\t\t###### your implementation below ######\n\t\ttotalEntropy = 0\n\t\ttempSet = {}\n\t\tfor i in range(len(classData)):\n\t\t\ttempSet[classData[i]] = (classData.count(classData[i]))\n\t\ttotal = sum(tempSet.values())\n\t\tfor x in tempSet:\n\t\t\ttotalEntropy += -(tempSet[x]/total * math.log(tempSet[x]/total, 2))\n\t\treturn totalEntropy","def entropy(data):\n strings, lens = Counter(data), np.float(len(data))\n return -sum(count / lens * np.log2(count / lens) for count in strings.values())","def entropy(y):\n p = _proba(y)\n return (-p * np.log2(p)).sum()","def entropy(Y):\n\n temp = np.unique(Y, return_counts=True)\n uniq_Y = list(temp[0])\n Y_count = list(temp[1])\n \n total = sum(Y_count)\n\n ent = 0\n for elem in uniq_Y:\n prob = Y_count[uniq_Y.index(elem)] / total\n # print(\"prob:\", prob)\n ent -= (prob * (math.log2(prob)))\n # print(\"ent:\",ent)\n\n return ent","def entropy(counts):\n assert (counts >= 0).all()\n probs = counts / counts.sum()\n probs = probs[probs > 0] # Avoid log(0)\n return - np.sum(probs * np.log2(probs))","def _entropy(self):\n return self.rv.entropy(*self._pymc_dists_to_value(self.args), **self.kwds)","def calc_entropy(frequency_wn, temperature):\r\n\tentropy = 0\r\n\tfrequency = [entry * SPEED_OF_LIGHT for entry in frequency_wn]\r\n\tfor entry in frequency:\r\n\t\tfactor = ((PLANCK_CONSTANT*entry)/(BOLTZMANN_CONSTANT*temperature))\r\n\t\ttemp = factor*(1/(math.exp(factor)-1)) - math.log(1-math.exp(-factor))\r\n\t\ttemp = temp*GAS_CONSTANT/4.184\r\n\t\tentropy = entropy + temp \r\n\treturn entropy","def entropy_analysis(data_df):\n seq_list = data_df['sequence'].values.tolist()\n base_set = set([])\n for seq in seq_list:\n base_set.update(set(seq))\n H_list = []\n null_freq_list = []\n STEP = ceil(len(seq_list[0]) / 10)\n for base_idx in range(len(seq_list[0])):\n if base_idx % STEP == 0:\n logging.info('Entropy analysis in progress: {}% completed.'.format(10 * base_idx // STEP))\n H, null_freq = base_entropy_masked(seq_list, base_set, base_idx)\n H_list.append(H,)\n null_freq_list.append(null_freq)\n logging.info('Entropy analysis in progress: DONE.')\n return H_list, null_freq_list","def entropy(data, idxList):\n df = data.loc[idxList]\n counts = df.value_counts().to_numpy()\n counts = counts.reshape(1, -1).astype(np.float32)\n counts /= np.sum(counts)\n log_sum = counts @ np.log2(counts.T)\n return -log_sum[0, 0]","def entropy(*args):\n\n\n values = []\n leaf = -1\n\n for i, val in enumerate(args):\n if(val != 0):\n values.append(val * math.log(val, len(args)))\n if(val == 1):\n leaf = i\n \n return -sum(values), leaf","def _entropy(data):\n hist = np.array(PIL.Image.fromarray(data).histogram())\n hist = hist / hist.sum()\n hist = hist[hist != 0]\n return -np.sum(hist * np.log2(hist))","def entropy(data):\n\n freqs = {}\n suma = len(data)\n\n for i in range(0, len(data)):\n freqs[data[i]] = 1.0 + freqs.get(data[i], 0)\n\n res = 0.0\n for i in freqs:\n res += (freqs[i] / suma) * log((freqs[i] / suma), 2)\n return -res","def entropy(dist):\n #dist = array([max(d,1e-100) for d in dist])\n dist = dist + 1e-20\n return dot(dist,(log(1.0/dist) * (1.0/log(2.0))).T)","def cal_localIndexH(self):\n local_entropy = self.cal_localEntropy()\n global_entropy = self.cal_globalEntropy()\n\n et = global_entropy * np.sum(self.pop_sum)\n eei = np.asarray(global_entropy - local_entropy)\n h_local = np.asarray(self.pop_sum) * eei / et\n\n return h_local","def entropy(d, total, word_count):\n\t# Entropie je - Sum_morf p(morf) * log_2 p(morf)\n\t# p(morf) = c(morf) / c(all)\n\te = 0\n\tfor count in d.values():\n\t\tp = count/total\n\t\ttype_e = - p * log2(p)\n\t\te += type_e * count\n\treturn e / word_count","def entropy_balance(self):\n return","def entropy_balance(self):\n return","def entropy(y):\n EPS = 0.0005\n\n # YOUR CODE HERE\n if len(y) == 0:\n return 0.\n \n pk = np.mean(y, axis=0)\n \n return - np.sum(pk * np.log(pk + EPS))","def entropy(self):\r\n return 1/2 * (self.dim * (_LOG_2PI + 1) + self._log_det_cov)","def entropy ( target_array ):\n return -1 * sum (\n [\n pipe ( np.sum ( target_array == value ) / len ( target_array ), lambda ratio: ratio * np.log ( ratio ) )\n for value in set ( target_array )\n ]\n ) # End entropy()","def entropy(y):\n total = y.size\n value_counts = np.bincount(y).astype(\"float\")\n proportions = value_counts / y.size\n\n return sum(-i * np.log(i) for i in proportions if i)","def c_entropy(state,target=None,log_base=2):\n if target!=None:\n state = state.ptrace(target)\n return entropy(com_measure(state),base=log_base)","def entropy(self, priors=None):\n def entropy_f(x):\n x[x != 0] *= np.log(x[x != 0])\n return -x.sum(axis=0)\n return self.utility(entropy_f, priors)","def entropy(self, policy_params):\n return self.head.entropy(policy_params)","def _entropy(self, y):\n # Get size\n n = y.shape[0]\n summation = 0\n\n # Summatory\n for c_i in np.unique(y):\n prob = sum(y == c_i) / float(n)\n summation += prob * np.log2(prob)\n\n return -summation","def entropy(class_probabilities):\n return sum(-p * math.log(p, 2)\n for p in class_probabilities\n if p) #ignore 0's","def entropy(x):\n x_max, x_min = x.max(), x.min()\n assert (x_min >= 0) and (x_max <= 1)\n if x_min == x_max == 0:\n return np.float32(0.)\n # Take only non-zero values as log(0) = 0 :\n nnz_x = x[np.nonzero(x)]\n entr = -np.sum(nnz_x * np.log2(nnz_x))\n\n return entr","def conditional_entropy_hyper(self) -> float:\n pass","def entropy(n_bits):\n return n_bits and random.getrandbits(n_bits)","def entropyDistributed(distribution):\n return -sum(map(lambda p : p * log(p, 2), distribution))","def entropy(self, complete=True, dl=False, partition_dl=True, edges_dl=True,\n degree_dl=True, dense=False, multigraph=True, norm=False,\n dl_ent=False, **kwargs):\n\n S = 0\n if self.layers:\n if dl and partition_dl:\n S += self.total_state.entropy(dl=True, partition_dl=True,\n degree_dl=False, edges_dl=False,\n norm=False) - \\\n self.total_state.entropy(dl=False, norm=False)\n for state in self.states[1:]:\n S += state.entropy(complete=complete, dl=dl, partition_dl=False,\n edges_dl=False, degree_dl=degree_dl,\n dense=dense, multigraph=multigraph,\n norm=False, dl_ent=dl_ent, **kwargs)\n else:\n for l, state in enumerate(self.states):\n if l == 0:\n S += state.entropy(complete=complete, dl=dl,\n partition_dl=partition_dl,\n edges_dl=False, degree_dl=degree_dl,\n dense=dense, multigraph=multigraph,\n norm=False, dl_ent=dl_ent, **kwargs)\n S -= libcommunity.covariate_entropy(state.bg._Graph__graph,\n _prop(\"e\", state.bg,\n state.mrs))\n if multigraph:\n S -= libcommunity.entropy_parallel(state.g._Graph__graph,\n _prop(\"e\", state.g,\n state.eweight))\n else:\n S += libcommunity.covariate_entropy(state.bg._Graph__graph,\n _prop(\"e\", state.bg,\n state.mrs))\n if multigraph:\n S += libcommunity.entropy_parallel(state.g._Graph__graph,\n _prop(\"e\", state.g,\n state.eweight))\n\n if dl and edges_dl:\n bstate = self.get_block_state(b=zeros(self.B), layers=True)[0]\n S += bstate.entropy(dense=True, multigraph=True, dl=False,\n norm=False)\n\n if dl:\n S += self.__get_layer_entropy()\n\n if norm:\n S /= self.E\n return S","def calculate_entropy():\n\tstat = {} # dictionary - chars and number of repetitions\n\tallchar = 0.0 # total number of characters\n\tentropy = 0.0 # initial entropy\n\n\tfor line in sys.stdin.readlines():\n\t\tline = re.sub(r'\\s', '', line)\n\t\tfor znak in line:\n\t\t\tif znak in stat:\n\t\t\t\tstat[znak] += 1\n\t\t\telse:\n\t\t\t\tstat[znak] = 1\n\t\t\tallchar += 1\n\n\tfor znak in stat:\n\t\tstat[znak] = stat[znak]/allchar\n\t\tentropy += stat[znak] * log(stat[znak], 2)\n\n\tentropy *= -1\n\treturn entropy","def calculate_entropy(dataset) :\n\n num_entries = len(dataset)\n label_counts = {}\n for vector in dataset :\n # the label is at the last index of the data set\n current_label = vector[-1]\n if current_label not in label_counts :\n label_counts[current_label] = 0\n label_counts[current_label] += 1\n # Calculate the entropy\n entropy = 0.0\n for label in label_counts :\n # Calculate probability of each label within the dataset\n prob_of_label = label_counts[label]/num_entries\n # Since the entropy is the negative of the sum of all probability,\n # simply subtract it\n entropy -= prob_of_label * log(prob_of_label, 2)\n return entropy","def entropy(class_probabilities):\n return sum(-p * math.log(p,2)\n for p in class_probabilities\n if p)","def __compute_entropy(row:pd.Series) -> float:\n probability = row.value_counts(normalize=True).values\n entropy = EntropicSolver.__compute_entropy_probability(probability)\n return entropy","def entropy(probabilities):\n return -(sum([p * log(p, 2) if p > 0 else 0 for p in probabilities]))","def entropy(Y):\n unique, count = np.unique(Y, return_counts=True, axis=0)\n prob = count/len(Y)\n en = np.sum((-1)*prob*np.log2(prob))\n return en","def compute_entropy_all(self) -> pd.Series:\n if self.game_matrix is None:\n return self.word_matrix.apply(self.__compute_entropy, axis=1)\n return self.game_matrix.apply(self.__compute_entropy, axis=1)","def entropy(target_col):\n elements,counts = np.unique(target_col,return_counts = True)\n entropy = np.sum([(-counts[i]/np.sum(counts))*np.log2(counts[i]/np.sum(counts)) for i in range(len(elements))])\n return entropy","def get_entropy(df: pd.DataFrame) -> int:\r\n\r\n # get unique values and their counts for the last column of df\r\n values, val_freq = np.unique(df[df.columns[-1]], return_counts=True)\r\n val_freq = val_freq / val_freq.sum()\r\n\r\n entropy = 0\r\n for freq in val_freq:\r\n entropy -= freq * np.log2(freq)\r\n return entropy","def entropy(self) -> float:\n probabilities = np.array([len(players) for players in self.answers.values()])\n probabilities = probabilities / sum(probabilities)\n return sc.stats.entropy(probabilities)","def calEntropy(vList):\n from collections import Counter\n counter = Counter(vList)\n entropy, N = 0, len(vList)\n for v in counter:\n p = counter[v] / N\n entropy += - p * np.log(p)\n return entropy","def entropy(P):\n P_nan = P.copy()\n P_nan[P_nan == 0] = np.nan\n return np.nansum(np.multiply(P_nan, np.log2(1 / P_nan)))","def entropy_function(c, n):\n return -(c*1.0/n)*math.log(c*1.0/n,2)","def calc_entropy(data_set): #calculates total entropy of the dataset\r\n republicans = 0\r\n democrats = 0\r\n total = 0\r\n for data_point in data_set:\r\n party = data_point.dat_party\r\n if party == \"R\":\r\n republicans+=1\r\n elif party == \"D\":\r\n democrats+=1\r\n total+=1\r\n\r\n if total == 0: return 0\r\n prob_dem = democrats/total\r\n prob_rep = republicans/total\r\n if prob_dem == 0: return -(prob_rep * math.log(prob_rep, 2))\r\n if prob_rep == 0: return -(prob_dem * math.log(prob_dem, 2))\r\n\r\n entropy = (-prob_dem * math.log(prob_dem, 2)) -(prob_rep * math.log(prob_rep, 2))\r\n return entropy","def h(self, state):\n loc = dict((val, (i, j)) for i, row in enumerate(self.goal_state)\n for j, val in enumerate(row))\n\n def calculate_cost(i, j):\n val = state[i][j]\n goal_i, goal_j = loc[val]\n return abs(goal_i - i) + abs(goal_j - j)\n\n return sum(\n calculate_cost(i, j) for i in xrange(self.size)\n for j in xrange(self.size) if state[i][j])","def get_entropy_df(self, df=None):\n if df is None:\n df = self.df\n target = self.target\n\n entropy = 0\n values = df[target].unique()\n\n for value in values:\n # Fraction of values of 'value' in target feature\n fraction = df[target].value_counts()[value]/len(df[target])\n entropy += -fraction*np.log2(fraction)\n\n return entropy","def stateOccupationProbabilityGeneration(self):\n self.L = zeros((self.noOfEmmittingStates, self.T))\n\n for j in range(self.noOfEmmittingStates):\n for t in range(self.T):\n self.L[j,t] = (self.alpha[j+1, t+1] * self.beta[j+1, t+1]) / self.observationLikelihood","def computeEntropy(self, img):\n hist, bins = np.histogram(img.ravel(), bins=256, density=True)\n return scipy.stats.entropy(hist)","def conditional_entropy(self) -> float:\n pass","def entropy(a):\n a = a.upper()\n\n freq = collections.defaultdict(int) # int() is the default constructor for non existent item, and returns 0\n for c in a:\n freq[c] = freq[c] + 1\n\n e = 0.0\n for f in freq.values():\n if f:\n p = f / len(a)\n e += p * math.log(p)\n\n return -e","def point_entropy(self, unlabeled_sequence):\n unlabeled_sequence = self._transform(unlabeled_sequence)\n\n T = len(unlabeled_sequence)\n N = len(self._states)\n\n alpha = self._forward_probability(unlabeled_sequence)\n beta = self._backward_probability(unlabeled_sequence)\n normalisation = logsumexp2(alpha[T - 1])\n\n entropies = np.zeros(T, np.float64)\n probs = np.zeros(N, np.float64)\n for t in range(T):\n for s in range(N):\n probs[s] = alpha[t, s] + beta[t, s] - normalisation\n\n for s in range(N):\n entropies[t] -= 2 ** (probs[s]) * probs[s]\n\n return entropies","def entropy(rows, resCol = None):\n from math import log\n if not resCol: #create the dictionary of counts for each class using pure python\n total = len(rows)\n counts = __uniqueCounts(rows)\n else: #Create the dictionary of counts for each class using pandas.\n assert 'index' in dir(rows)\n total = len(rows.index)\n counts = __uniqueCountsPandas(rows, resCol)\n log2 = lambda x:log(x)/log(2) #Create a function to take log-base 2 of a number\n ent = 0 #Initialize the entropy at zero\n #Implement the formula for entropy, using log-base2\n fracs = [float(x)/total for x in counts.values()]\n for x in fracs:\n ent += -x*log2(x)\n return ent","def entropy(data):\n n, m = np.shape(data)\n data = np.tanh(data)\n data = data / np.sum(data, axis=0)\n a = data * 1.0\n a[np.where(data == 0)] = 0.000001\n\n e = (-1.0 / np.log(n)) * np.sum(data * np.log(a), axis=0)\n w = (1 - e) / np.sum(1 - e)\n return w","def compute_entropy(bincounts, include_zeros):\n num_total = tf.cast(tf.reduce_sum(bincounts), tf.float64)\n if not include_zeros:\n bincounts = bincounts[1:]\n mask = tf.greater(bincounts, 0)\n nonzero_bincounts = tf.cast(\n tf.boolean_mask(bincounts, mask), tf.float64)\n num_nonzero = tf.cast(tf.reduce_sum(nonzero_bincounts), tf.float64)\n log_nonzero_bincounts = tf.math.log(nonzero_bincounts)\n log_prob = log_nonzero_bincounts - tf.reduce_logsumexp(\n log_nonzero_bincounts)\n entropy = tf.math.reduce_sum(\n log_prob * tf.exp(log_prob)) / -tf.math.log(tf.cast(2, tf.float64))\n return entropy * num_nonzero / num_total","def _entropy(self, feature, node):\n entropy = 0\n categories = np.unique(feature)\n num_point = len(feature)\n for category in categories:\n # for each category in that feature\n num_category = len(feature[feature == category])\n for c in self.num_class:\n # count the number of each class\n num_category_class = len(feature[np.logical_and(feature == category, node.y == c)])\n if num_category_class == 0:\n continue\n # compute entropy/information gain or classification error\n entropy += num_category / num_point * (\n -num_category_class / num_category * log2(num_category_class / num_category))\n return entropy","def image_entropy(img):\r\n hist = img.histogram()\r\n hist_size = sum(hist)\r\n hist = [float(h) / hist_size for h in hist]\r\n\r\n return -sum([p * math.log(p, 2) for p in hist if p != 0])","def exploreEntropy(self, position):\n pass","def get_entropy(*labels):\n entropies = [] #list of entropy values from each subset\n total = 0 #total number of datapoints\n for subset in labels:\n n = len(subset)\n total += n\n counts = np.unique(subset, return_counts=True)[1] #frequency of unique values\n entropy = np.sum([-(i/n) * np.log2(i/n) for i in counts]) #subset entropy calcuation\n entropies.append((entropy, n))\n return np.sum([(n/total) * ent for n, ent in iter(entropies)])","def shannon_entropy(counts):\n freq = np.array(counts) * 1.0 / np.sum(counts)\n return -np.sum([f * np.log2(f) for f in freq if f != 0])","def calculate_entropy(y):\n\tlog2 = lambda x: math.log(x) / math.log(2)\n\tunique_labels = np.unique(y)\n\tentropy = 0\n\tfor label in unique_labels:\n\t\tcount = len(y[y == label])\n\t\tp = count / len(y)\n\t\tentropy += -p * log2(p)\n\treturn entropy","def calc_entropy_one(states, T):\n\n # S = - kB \\sum_i p_i \\log p_i = - k_B <\\log p_i>\n P = calc_probabilities_one(states, T)\n return -kB * np.sum(P * np.log(P))","def get_entropy(distribution, samples):\n entropy = -tf.reduce_sum(distribution.log_prob(samples), axis=1)\n return entropy","def __entropy(self, labels):\n class_probs = np.unique(labels, return_counts=True)[1] / labels.size\n class_prob_logs = np.log2(class_probs)\n entropy = -np.sum(class_probs * class_prob_logs)\n return entropy","def calc_entropy(data_set):\n size = len(data_set)\n label_counts = {}\n for feat_vector in data_set:\n label = feat_vector[-1]\n label_counts.setdefault(label, 0)\n label_counts[label] += 1\n\n entropy = 0.0\n for key, count in label_counts.iteritems():\n prob = float(count) / size\n entropy -= prob * log(prob, 2)\n\n return entropy","def transfer_entropy(X, Y):\n coords = Counter(zip(Y[1:], X[:-1], Y[:-1]))\n\n p_dist = np.zeros((config.NUM_STATES, config.NUM_STATES, config.NUM_STATES))\n for y_f, x_p, y_p in coords.keys():\n p_dist[y_p, y_f, x_p] = coords[(y_f, x_p, y_p)] / (len(X) - 1)\n\n p_yp = p_dist.sum(axis=2).sum(axis=1)\n p_joint_cond_yp = p_dist / p_yp[:, None, None]\n p_yf_cond_yp = p_dist.sum(axis=2) / p_yp[:, None]\n p_xp_cond_yp = p_dist.sum(axis=1) / p_yp[:, None]\n\n denominator = np.multiply(p_yf_cond_yp, p_xp_cond_yp)\n denominator[denominator == 0] = np.nan\n\n division = np.divide(p_joint_cond_yp, denominator[:, :, None])\n division[division == 0] = np.nan\n\n log = np.log2(division)\n\n return np.nansum(np.multiply(p_dist, log))","def entropy(p):\n ent = tf.where(p > np.finfo(np.float32).eps, -p * tf.log(p), tf.zeros_like(p))\n ent = tf.reduce_sum(ent, axis=1)\n return ent"],"string":"[\n \"def entropy(self):\\n\\n \\\"\\\"\\\"Gets the first neighbours, which are the first 2*r+1 cells.\\\"\\\"\\\"\\n current_neighbours = []\\n amount = [0] * self.k ** (2 * self.r + 1)\\n for i in range(2 * self.r + 1):\\n current_neighbours.append(self.config[self.t, i % self.width])\\n\\n \\\"\\\"\\\"Calculates the rule and adds one to it's amount. It then removes the\\n leftmost cell and adds a cell to the right.\\\"\\\"\\\"\\n for i in range(len(self.config[self.t]) - 1):\\n rule = 0\\n for j in range(len(current_neighbours)):\\n rule += int(current_neighbours[j] *\\n self.k ** ((2 * self.r + 1) - j - 1))\\n amount[len(amount) - 1 - rule] += 1\\n current_neighbours.pop(0)\\n current_neighbours.append(\\n self.config[self.t, (2 * self.r + 1 + i) % self.width])\\n\\n \\\"\\\"\\\"Calculates the rule for the last neighbourhood.\\\"\\\"\\\"\\n rule = 0\\n for j in range(len(current_neighbours)):\\n rule += int(current_neighbours[j] *\\n self.k ** ((2 * self.r + 1) - j - 1))\\n amount[len(amount)-1 - rule] += 1\\n\\n \\\"\\\"\\\"Calculates the Shannon entropy and the the average entropy so far.\\\"\\\"\\\"\\n shannon = 0\\n for i in range(len(amount)):\\n if(amount[i] != 0):\\n probability = amount[i] / self.width\\n shannon -= probability * np.log2(probability)\\n self.average_entropy = (self.average_entropy *\\n self.t + shannon) / (self.t + 1)\",\n \"def entropy(self):\\n raise NotImplementedError\",\n \"def entropy(self):\\n Z = self.sum()\\n assert (Z > 0), 'Non-normalizable factor (perhaps log factor?)' # also check for positivity?\\n H = 0.0\\n for x in np.nditer(self.t, op_flags=['readonly']):\\n p = x/Z\\n H += 0.0 if p==0 else -p*np.log(p)\\n return H\",\n \"def _entropies(self):\\n H_C = fentropy(self.row_totals)\\n H_K = fentropy(self.col_totals)\\n H_actual = fentropy(self.itervalues())\\n H_expected = H_C + H_K\\n I_CK = H_expected - H_actual\\n return H_C, H_K, I_CK\",\n \"def entropycell(self):\\n cells = [0] * self.k\\n for i in range(self.width):\\n cells[int(self.config[self.t, i])] += 1\\n\\n \\\"\\\"\\\"Calculates the Shannon entropy and the the average entropy so far.\\\"\\\"\\\"\\n shannon = 0\\n for i in range(self.k):\\n if(cells[i] != 0):\\n probability = cells[i] / self.width\\n shannon -= probability * np.log2(probability)\\n self.average_cell = (self.average_cell * self.t + shannon) / (self.t + 1)\",\n \"def entropy(self, unlabeled_sequence):\\n unlabeled_sequence = self._transform(unlabeled_sequence)\\n\\n T = len(unlabeled_sequence)\\n N = len(self._states)\\n\\n alpha = self._forward_probability(unlabeled_sequence)\\n beta = self._backward_probability(unlabeled_sequence)\\n normalisation = logsumexp2(alpha[T - 1])\\n\\n entropy = normalisation\\n\\n # starting state, t = 0\\n for i, state in enumerate(self._states):\\n p = 2 ** (alpha[0, i] + beta[0, i] - normalisation)\\n entropy -= p * self._priors.logprob(state)\\n # print('p(s_0 = %s) =' % state, p)\\n\\n # state transitions\\n for t0 in range(T - 1):\\n t1 = t0 + 1\\n for i0, s0 in enumerate(self._states):\\n for i1, s1 in enumerate(self._states):\\n p = 2 ** (\\n alpha[t0, i0]\\n + self._transitions[s0].logprob(s1)\\n + self._outputs[s1].logprob(unlabeled_sequence[t1][_TEXT])\\n + beta[t1, i1]\\n - normalisation\\n )\\n entropy -= p * self._transitions[s0].logprob(s1)\\n # print('p(s_%d = %s, s_%d = %s) =' % (t0, s0, t1, s1), p)\\n\\n # symbol emissions\\n for t in range(T):\\n for i, state in enumerate(self._states):\\n p = 2 ** (alpha[t, i] + beta[t, i] - normalisation)\\n entropy -= p * self._outputs[state].logprob(\\n unlabeled_sequence[t][_TEXT]\\n )\\n # print('p(s_%d = %s) =' % (t, state), p)\\n\\n return entropy\",\n \"def entropy(data):\\n e = 0\\n\\n counter = collections.Counter(data)\\n l = len(data)\\n for count in counter.values():\\n p_x = count / l\\n e += - p_x * math.log2(p_x)\\n\\n return e\",\n \"def entropy_root(self):\\n\\t\\tif self.size() == 0:\\n\\t\\t\\treturn 0\\n\\n\\t\\tcounter = collections.Counter(self.y)\\n\\t\\treturn entropy([x[1] for x in counter.items()], self.size())\",\n \"def entropy(x):\\n nz = np.nonzero(x)[0]\\n return -np.sum(x[nz]*np.log2(x[nz]))\",\n \"def entropy(self):\\n n = len(self.y)\\n sum_ = 0\\n for i in np.unique(self.y):\\n v = len(self.y[self.y == i])\\n sum_ += -((v/n) * log2(v/n))\\n return sum_\",\n \"def entropy(self):\\n return -np.sum(self.log_likelihoods * np.exp(self.log_likelihoods))\",\n \"def get_entropy(self, state):\\n entropy = self.entropy_model.predict(state)\\n return entropy[0]\",\n \"def entropy(group_counts):\\n total = sum(group_counts)\\n entro = 0\\n for item_count in group_counts:\\n entro += item_entropy(item_count, total)\\n return entro\",\n \"def calc_entropy_for_states(states,n,T):\\n\\n D = int(1e7)\\n n_s = len(states)\\n\\n data = sample_states_all(exampleRatios, T, D)\\n CDataObj = CompressionData(data, len(exampleRatios), 1,\\n 0, len(exampleRatios)-1, np.uint8)\\n # print(\\\"Compressed size is \\\" + str(CDataObj.size_data()))\\n # print(\\\"Zero size is \\\" + str(CDataObj.size_zeros()))\\n # print(\\\"Random size is \\\" + str(CDataObj.size_random()))\\n # print(\\\"Compression ratio is \\\" + str(CDataObj.get_compression_ratios()))\\n\\n eta = CDataObj.get_compression_ratios()\\n entropy_estimate = eta * D * np.log(n_s)\\n\\n entropy_real = calc_entropy_one(exampleRatios, T) * D\\n \\n return (entropy_real, entropy_estimate)\",\n \"def ensemble_entropy(self):\\n return transition_matrices.entropy_measure(self.ensemble_transition_matrix)\",\n \"def entropy(self):\\n return self._normal.entropy()\",\n \"def entropy(message):\\n n = len(message)\\n message = letter_freq(message)\\n h = 0\\n for n_i in message.values():\\n p_i = n_i/n\\n h += -p_i*(log2(p_i))\\n return h\",\n \"def H(self, data):\\n entropy = 0\\n\\n if not data:\\n return entropy\\n\\n for x in range(256):\\n p_x = float(data.count(chr(x))) / len(data)\\n if p_x > 0:\\n entropy -= p_x * math.log(p_x, 2)\\n\\n return entropy\",\n \"def entropy(l):\\n\\n probabilities = np.bincount(l) / len(l)\\n with np.errstate(divide='ignore'): # ignore log(0) errors, we'll handle\\n log_probabilities = np.log2(probabilities)\\n log_probabilities[~np.isfinite(log_probabilities)] = 0\\n return -np.sum(probabilities * log_probabilities)\",\n \"def entropy(self):\\n ent = 0.0\\n for f in self.byte_freq:\\n if f > 0:\\n freq = float(f) / self.byte_total\\n ent = ent + freq * math.log(freq, 2)\\n return -ent\",\n \"def entropy(message):\\n message = letter_freq(message)\\n n = sum(message.values())\\n h = 0\\n for n_i in message.values():\\n p_i = n_i / n\\n h += -p_i * log2(p_i)\\n return h\",\n \"def get_entropy(self, rows):\\n label_count = defaultdict(int)\\n total_count = 0\\n for row in rows:\\n label = row[self.target_attribute]\\n label_count[label] += 1\\n total_count += 1\\n return sum([-(float(label_count[label]) /\\n total_count) * np.log2(float(label_count[label]) / total_count)\\n for label in label_count.keys()])\",\n \"def entropy(y):\\r\\n\\r\\n # INSERT YOUR CODE HERE\\r\\n value, count = np.unique(y,return_counts = True)\\r\\n Hy = 0.0\\r\\n prob = count.astype(float)/len(y)\\r\\n for p in prob:\\r\\n Hy += -(p)*(np.log2(p))\\r\\n return Hy\\r\\n raise Exception('Function not yet implemented!')\",\n \"def cal_localEntropy(self):\\n if len(self.locality) == 0:\\n proportion = np.asarray(self.pop / self.pop_sum)\\n\\n else:\\n polygon_sum = np.sum(self.locality, axis=1).reshape(self.n_location, 1)\\n proportion = np.asarray(self.locality / polygon_sum)\\n\\n entropy = proportion * np.log(1 / proportion)\\n\\n entropy[np.isnan(entropy)] = 0\\n entropy[np.isinf(entropy)] = 0\\n entropy = np.sum(entropy, axis=1)\\n entropy = entropy.reshape((self.n_location, 1))\\n\\n return entropy\",\n \"def shannon_entropy(c):\\n\\n c_normalized = c / float(np.sum(c))\\n c_normalized_nonzero = c_normalized[np.nonzero(c_normalized)] # gives 1D array\\n entropy = -sum(c_normalized_nonzero * np.log2(c_normalized_nonzero)) # unit in bits\\n return entropy\",\n \"def entropy(y):\\n return -1 * sum(\\n [\\n pipe(np.sum(y == value) / len(y), lambda ratio: ratio * np.log(ratio))\\n for value in set(y)\\n ]\\n )\",\n \"def entropy( freq ):\\n N = 0.0\\n entropy = 0.0\\n for x, v in freq.items( ):\\n N += v\\n entropy -= v * math.log( v, 2 )\\n return (N * math.log( N, 2 ) + entropy) / N\",\n \"def GetEntropy(flanks):\\n countA = 0\\n countT = 0\\n countG = 0\\n countC = 0\\n for i in flanks:\\n if i == \\\"A\\\":\\n countA += 1\\n elif i == \\\"T\\\":\\n countT += 1\\n elif i == \\\"C\\\":\\n countC += 1\\n elif i == \\\"G\\\":\\n countG += 1\\n else: pass\\n total = countA+countT+countG+countC\\n fractions = [item*1.0/total for item in [countA,countT,countG,countC]]\\n entropy = sum([-1.0*item*math.log(item,2) for item in fractions if item != 0])\\n return entropy\",\n \"def entropy(img):\\n # by calculating\\n histogram = img.histogram()\\n histogram_size = sum(histogram)\\n histogram = [float(h) / histogram_size for h in histogram]\\n\\n return -sum([p * math.log(p, 2) for p in histogram if p != 0])\",\n \"def div(self):\\n freqList = [i / sum(self.has.values()) for i in self.has.values()]\\n entropies = [i * math.log(i, 2) for i in freqList]\\n entropy = -sum(entropies)\\n return entropy\",\n \"def entropy(s):\\n p, lns = Counter(s), float(len(s))\\n return -sum( count/lns * math.log(count/lns, 2) for count in p.values())\",\n \"def cal_globalEntropy(self):\\n group_score = []\\n pop_total = np.sum(self.pop_sum)\\n prop = np.asarray(np.sum(self.pop, axis=0))[0]\\n\\n # loop at sum of each population groups\\n for group in prop:\\n group_idx = group / pop_total * np.log(1 / (group / pop_total))\\n group_score.append(group_idx)\\n entropy = np.sum(group_score)\\n\\n return entropy\",\n \"def entropy(self):\\n return self._entropy_func\",\n \"def entropy(self, y):\\n n = y.size\\n if n <= 1:\\n return 0\\n\\n labels, counts = unique(y, return_counts=True)\\n\\n if counts.size <= 1:\\n return 0\\n\\n probs = counts / n\\n entropy = -sum([p * log(p, 2) for p in probs])\\n return entropy\",\n \"def entropy(self,classData):\\n\\n\\t\\t###### your implementation below ######\\n\\t\\ttotalEntropy = 0\\n\\t\\ttempSet = {}\\n\\t\\tfor i in range(len(classData)):\\n\\t\\t\\ttempSet[classData[i]] = (classData.count(classData[i]))\\n\\t\\ttotal = sum(tempSet.values())\\n\\t\\tfor x in tempSet:\\n\\t\\t\\ttotalEntropy += -(tempSet[x]/total * math.log(tempSet[x]/total, 2))\\n\\t\\treturn totalEntropy\",\n \"def entropy(data):\\n strings, lens = Counter(data), np.float(len(data))\\n return -sum(count / lens * np.log2(count / lens) for count in strings.values())\",\n \"def entropy(y):\\n p = _proba(y)\\n return (-p * np.log2(p)).sum()\",\n \"def entropy(Y):\\n\\n temp = np.unique(Y, return_counts=True)\\n uniq_Y = list(temp[0])\\n Y_count = list(temp[1])\\n \\n total = sum(Y_count)\\n\\n ent = 0\\n for elem in uniq_Y:\\n prob = Y_count[uniq_Y.index(elem)] / total\\n # print(\\\"prob:\\\", prob)\\n ent -= (prob * (math.log2(prob)))\\n # print(\\\"ent:\\\",ent)\\n\\n return ent\",\n \"def entropy(counts):\\n assert (counts >= 0).all()\\n probs = counts / counts.sum()\\n probs = probs[probs > 0] # Avoid log(0)\\n return - np.sum(probs * np.log2(probs))\",\n \"def _entropy(self):\\n return self.rv.entropy(*self._pymc_dists_to_value(self.args), **self.kwds)\",\n \"def calc_entropy(frequency_wn, temperature):\\r\\n\\tentropy = 0\\r\\n\\tfrequency = [entry * SPEED_OF_LIGHT for entry in frequency_wn]\\r\\n\\tfor entry in frequency:\\r\\n\\t\\tfactor = ((PLANCK_CONSTANT*entry)/(BOLTZMANN_CONSTANT*temperature))\\r\\n\\t\\ttemp = factor*(1/(math.exp(factor)-1)) - math.log(1-math.exp(-factor))\\r\\n\\t\\ttemp = temp*GAS_CONSTANT/4.184\\r\\n\\t\\tentropy = entropy + temp \\r\\n\\treturn entropy\",\n \"def entropy_analysis(data_df):\\n seq_list = data_df['sequence'].values.tolist()\\n base_set = set([])\\n for seq in seq_list:\\n base_set.update(set(seq))\\n H_list = []\\n null_freq_list = []\\n STEP = ceil(len(seq_list[0]) / 10)\\n for base_idx in range(len(seq_list[0])):\\n if base_idx % STEP == 0:\\n logging.info('Entropy analysis in progress: {}% completed.'.format(10 * base_idx // STEP))\\n H, null_freq = base_entropy_masked(seq_list, base_set, base_idx)\\n H_list.append(H,)\\n null_freq_list.append(null_freq)\\n logging.info('Entropy analysis in progress: DONE.')\\n return H_list, null_freq_list\",\n \"def entropy(data, idxList):\\n df = data.loc[idxList]\\n counts = df.value_counts().to_numpy()\\n counts = counts.reshape(1, -1).astype(np.float32)\\n counts /= np.sum(counts)\\n log_sum = counts @ np.log2(counts.T)\\n return -log_sum[0, 0]\",\n \"def entropy(*args):\\n\\n\\n values = []\\n leaf = -1\\n\\n for i, val in enumerate(args):\\n if(val != 0):\\n values.append(val * math.log(val, len(args)))\\n if(val == 1):\\n leaf = i\\n \\n return -sum(values), leaf\",\n \"def _entropy(data):\\n hist = np.array(PIL.Image.fromarray(data).histogram())\\n hist = hist / hist.sum()\\n hist = hist[hist != 0]\\n return -np.sum(hist * np.log2(hist))\",\n \"def entropy(data):\\n\\n freqs = {}\\n suma = len(data)\\n\\n for i in range(0, len(data)):\\n freqs[data[i]] = 1.0 + freqs.get(data[i], 0)\\n\\n res = 0.0\\n for i in freqs:\\n res += (freqs[i] / suma) * log((freqs[i] / suma), 2)\\n return -res\",\n \"def entropy(dist):\\n #dist = array([max(d,1e-100) for d in dist])\\n dist = dist + 1e-20\\n return dot(dist,(log(1.0/dist) * (1.0/log(2.0))).T)\",\n \"def cal_localIndexH(self):\\n local_entropy = self.cal_localEntropy()\\n global_entropy = self.cal_globalEntropy()\\n\\n et = global_entropy * np.sum(self.pop_sum)\\n eei = np.asarray(global_entropy - local_entropy)\\n h_local = np.asarray(self.pop_sum) * eei / et\\n\\n return h_local\",\n \"def entropy(d, total, word_count):\\n\\t# Entropie je - Sum_morf p(morf) * log_2 p(morf)\\n\\t# p(morf) = c(morf) / c(all)\\n\\te = 0\\n\\tfor count in d.values():\\n\\t\\tp = count/total\\n\\t\\ttype_e = - p * log2(p)\\n\\t\\te += type_e * count\\n\\treturn e / word_count\",\n \"def entropy_balance(self):\\n return\",\n \"def entropy_balance(self):\\n return\",\n \"def entropy(y):\\n EPS = 0.0005\\n\\n # YOUR CODE HERE\\n if len(y) == 0:\\n return 0.\\n \\n pk = np.mean(y, axis=0)\\n \\n return - np.sum(pk * np.log(pk + EPS))\",\n \"def entropy(self):\\r\\n return 1/2 * (self.dim * (_LOG_2PI + 1) + self._log_det_cov)\",\n \"def entropy ( target_array ):\\n return -1 * sum (\\n [\\n pipe ( np.sum ( target_array == value ) / len ( target_array ), lambda ratio: ratio * np.log ( ratio ) )\\n for value in set ( target_array )\\n ]\\n ) # End entropy()\",\n \"def entropy(y):\\n total = y.size\\n value_counts = np.bincount(y).astype(\\\"float\\\")\\n proportions = value_counts / y.size\\n\\n return sum(-i * np.log(i) for i in proportions if i)\",\n \"def c_entropy(state,target=None,log_base=2):\\n if target!=None:\\n state = state.ptrace(target)\\n return entropy(com_measure(state),base=log_base)\",\n \"def entropy(self, priors=None):\\n def entropy_f(x):\\n x[x != 0] *= np.log(x[x != 0])\\n return -x.sum(axis=0)\\n return self.utility(entropy_f, priors)\",\n \"def entropy(self, policy_params):\\n return self.head.entropy(policy_params)\",\n \"def _entropy(self, y):\\n # Get size\\n n = y.shape[0]\\n summation = 0\\n\\n # Summatory\\n for c_i in np.unique(y):\\n prob = sum(y == c_i) / float(n)\\n summation += prob * np.log2(prob)\\n\\n return -summation\",\n \"def entropy(class_probabilities):\\n return sum(-p * math.log(p, 2)\\n for p in class_probabilities\\n if p) #ignore 0's\",\n \"def entropy(x):\\n x_max, x_min = x.max(), x.min()\\n assert (x_min >= 0) and (x_max <= 1)\\n if x_min == x_max == 0:\\n return np.float32(0.)\\n # Take only non-zero values as log(0) = 0 :\\n nnz_x = x[np.nonzero(x)]\\n entr = -np.sum(nnz_x * np.log2(nnz_x))\\n\\n return entr\",\n \"def conditional_entropy_hyper(self) -> float:\\n pass\",\n \"def entropy(n_bits):\\n return n_bits and random.getrandbits(n_bits)\",\n \"def entropyDistributed(distribution):\\n return -sum(map(lambda p : p * log(p, 2), distribution))\",\n \"def entropy(self, complete=True, dl=False, partition_dl=True, edges_dl=True,\\n degree_dl=True, dense=False, multigraph=True, norm=False,\\n dl_ent=False, **kwargs):\\n\\n S = 0\\n if self.layers:\\n if dl and partition_dl:\\n S += self.total_state.entropy(dl=True, partition_dl=True,\\n degree_dl=False, edges_dl=False,\\n norm=False) - \\\\\\n self.total_state.entropy(dl=False, norm=False)\\n for state in self.states[1:]:\\n S += state.entropy(complete=complete, dl=dl, partition_dl=False,\\n edges_dl=False, degree_dl=degree_dl,\\n dense=dense, multigraph=multigraph,\\n norm=False, dl_ent=dl_ent, **kwargs)\\n else:\\n for l, state in enumerate(self.states):\\n if l == 0:\\n S += state.entropy(complete=complete, dl=dl,\\n partition_dl=partition_dl,\\n edges_dl=False, degree_dl=degree_dl,\\n dense=dense, multigraph=multigraph,\\n norm=False, dl_ent=dl_ent, **kwargs)\\n S -= libcommunity.covariate_entropy(state.bg._Graph__graph,\\n _prop(\\\"e\\\", state.bg,\\n state.mrs))\\n if multigraph:\\n S -= libcommunity.entropy_parallel(state.g._Graph__graph,\\n _prop(\\\"e\\\", state.g,\\n state.eweight))\\n else:\\n S += libcommunity.covariate_entropy(state.bg._Graph__graph,\\n _prop(\\\"e\\\", state.bg,\\n state.mrs))\\n if multigraph:\\n S += libcommunity.entropy_parallel(state.g._Graph__graph,\\n _prop(\\\"e\\\", state.g,\\n state.eweight))\\n\\n if dl and edges_dl:\\n bstate = self.get_block_state(b=zeros(self.B), layers=True)[0]\\n S += bstate.entropy(dense=True, multigraph=True, dl=False,\\n norm=False)\\n\\n if dl:\\n S += self.__get_layer_entropy()\\n\\n if norm:\\n S /= self.E\\n return S\",\n \"def calculate_entropy():\\n\\tstat = {} # dictionary - chars and number of repetitions\\n\\tallchar = 0.0 # total number of characters\\n\\tentropy = 0.0 # initial entropy\\n\\n\\tfor line in sys.stdin.readlines():\\n\\t\\tline = re.sub(r'\\\\s', '', line)\\n\\t\\tfor znak in line:\\n\\t\\t\\tif znak in stat:\\n\\t\\t\\t\\tstat[znak] += 1\\n\\t\\t\\telse:\\n\\t\\t\\t\\tstat[znak] = 1\\n\\t\\t\\tallchar += 1\\n\\n\\tfor znak in stat:\\n\\t\\tstat[znak] = stat[znak]/allchar\\n\\t\\tentropy += stat[znak] * log(stat[znak], 2)\\n\\n\\tentropy *= -1\\n\\treturn entropy\",\n \"def calculate_entropy(dataset) :\\n\\n num_entries = len(dataset)\\n label_counts = {}\\n for vector in dataset :\\n # the label is at the last index of the data set\\n current_label = vector[-1]\\n if current_label not in label_counts :\\n label_counts[current_label] = 0\\n label_counts[current_label] += 1\\n # Calculate the entropy\\n entropy = 0.0\\n for label in label_counts :\\n # Calculate probability of each label within the dataset\\n prob_of_label = label_counts[label]/num_entries\\n # Since the entropy is the negative of the sum of all probability,\\n # simply subtract it\\n entropy -= prob_of_label * log(prob_of_label, 2)\\n return entropy\",\n \"def entropy(class_probabilities):\\n return sum(-p * math.log(p,2)\\n for p in class_probabilities\\n if p)\",\n \"def __compute_entropy(row:pd.Series) -> float:\\n probability = row.value_counts(normalize=True).values\\n entropy = EntropicSolver.__compute_entropy_probability(probability)\\n return entropy\",\n \"def entropy(probabilities):\\n return -(sum([p * log(p, 2) if p > 0 else 0 for p in probabilities]))\",\n \"def entropy(Y):\\n unique, count = np.unique(Y, return_counts=True, axis=0)\\n prob = count/len(Y)\\n en = np.sum((-1)*prob*np.log2(prob))\\n return en\",\n \"def compute_entropy_all(self) -> pd.Series:\\n if self.game_matrix is None:\\n return self.word_matrix.apply(self.__compute_entropy, axis=1)\\n return self.game_matrix.apply(self.__compute_entropy, axis=1)\",\n \"def entropy(target_col):\\n elements,counts = np.unique(target_col,return_counts = True)\\n entropy = np.sum([(-counts[i]/np.sum(counts))*np.log2(counts[i]/np.sum(counts)) for i in range(len(elements))])\\n return entropy\",\n \"def get_entropy(df: pd.DataFrame) -> int:\\r\\n\\r\\n # get unique values and their counts for the last column of df\\r\\n values, val_freq = np.unique(df[df.columns[-1]], return_counts=True)\\r\\n val_freq = val_freq / val_freq.sum()\\r\\n\\r\\n entropy = 0\\r\\n for freq in val_freq:\\r\\n entropy -= freq * np.log2(freq)\\r\\n return entropy\",\n \"def entropy(self) -> float:\\n probabilities = np.array([len(players) for players in self.answers.values()])\\n probabilities = probabilities / sum(probabilities)\\n return sc.stats.entropy(probabilities)\",\n \"def calEntropy(vList):\\n from collections import Counter\\n counter = Counter(vList)\\n entropy, N = 0, len(vList)\\n for v in counter:\\n p = counter[v] / N\\n entropy += - p * np.log(p)\\n return entropy\",\n \"def entropy(P):\\n P_nan = P.copy()\\n P_nan[P_nan == 0] = np.nan\\n return np.nansum(np.multiply(P_nan, np.log2(1 / P_nan)))\",\n \"def entropy_function(c, n):\\n return -(c*1.0/n)*math.log(c*1.0/n,2)\",\n \"def calc_entropy(data_set): #calculates total entropy of the dataset\\r\\n republicans = 0\\r\\n democrats = 0\\r\\n total = 0\\r\\n for data_point in data_set:\\r\\n party = data_point.dat_party\\r\\n if party == \\\"R\\\":\\r\\n republicans+=1\\r\\n elif party == \\\"D\\\":\\r\\n democrats+=1\\r\\n total+=1\\r\\n\\r\\n if total == 0: return 0\\r\\n prob_dem = democrats/total\\r\\n prob_rep = republicans/total\\r\\n if prob_dem == 0: return -(prob_rep * math.log(prob_rep, 2))\\r\\n if prob_rep == 0: return -(prob_dem * math.log(prob_dem, 2))\\r\\n\\r\\n entropy = (-prob_dem * math.log(prob_dem, 2)) -(prob_rep * math.log(prob_rep, 2))\\r\\n return entropy\",\n \"def h(self, state):\\n loc = dict((val, (i, j)) for i, row in enumerate(self.goal_state)\\n for j, val in enumerate(row))\\n\\n def calculate_cost(i, j):\\n val = state[i][j]\\n goal_i, goal_j = loc[val]\\n return abs(goal_i - i) + abs(goal_j - j)\\n\\n return sum(\\n calculate_cost(i, j) for i in xrange(self.size)\\n for j in xrange(self.size) if state[i][j])\",\n \"def get_entropy_df(self, df=None):\\n if df is None:\\n df = self.df\\n target = self.target\\n\\n entropy = 0\\n values = df[target].unique()\\n\\n for value in values:\\n # Fraction of values of 'value' in target feature\\n fraction = df[target].value_counts()[value]/len(df[target])\\n entropy += -fraction*np.log2(fraction)\\n\\n return entropy\",\n \"def stateOccupationProbabilityGeneration(self):\\n self.L = zeros((self.noOfEmmittingStates, self.T))\\n\\n for j in range(self.noOfEmmittingStates):\\n for t in range(self.T):\\n self.L[j,t] = (self.alpha[j+1, t+1] * self.beta[j+1, t+1]) / self.observationLikelihood\",\n \"def computeEntropy(self, img):\\n hist, bins = np.histogram(img.ravel(), bins=256, density=True)\\n return scipy.stats.entropy(hist)\",\n \"def conditional_entropy(self) -> float:\\n pass\",\n \"def entropy(a):\\n a = a.upper()\\n\\n freq = collections.defaultdict(int) # int() is the default constructor for non existent item, and returns 0\\n for c in a:\\n freq[c] = freq[c] + 1\\n\\n e = 0.0\\n for f in freq.values():\\n if f:\\n p = f / len(a)\\n e += p * math.log(p)\\n\\n return -e\",\n \"def point_entropy(self, unlabeled_sequence):\\n unlabeled_sequence = self._transform(unlabeled_sequence)\\n\\n T = len(unlabeled_sequence)\\n N = len(self._states)\\n\\n alpha = self._forward_probability(unlabeled_sequence)\\n beta = self._backward_probability(unlabeled_sequence)\\n normalisation = logsumexp2(alpha[T - 1])\\n\\n entropies = np.zeros(T, np.float64)\\n probs = np.zeros(N, np.float64)\\n for t in range(T):\\n for s in range(N):\\n probs[s] = alpha[t, s] + beta[t, s] - normalisation\\n\\n for s in range(N):\\n entropies[t] -= 2 ** (probs[s]) * probs[s]\\n\\n return entropies\",\n \"def entropy(rows, resCol = None):\\n from math import log\\n if not resCol: #create the dictionary of counts for each class using pure python\\n total = len(rows)\\n counts = __uniqueCounts(rows)\\n else: #Create the dictionary of counts for each class using pandas.\\n assert 'index' in dir(rows)\\n total = len(rows.index)\\n counts = __uniqueCountsPandas(rows, resCol)\\n log2 = lambda x:log(x)/log(2) #Create a function to take log-base 2 of a number\\n ent = 0 #Initialize the entropy at zero\\n #Implement the formula for entropy, using log-base2\\n fracs = [float(x)/total for x in counts.values()]\\n for x in fracs:\\n ent += -x*log2(x)\\n return ent\",\n \"def entropy(data):\\n n, m = np.shape(data)\\n data = np.tanh(data)\\n data = data / np.sum(data, axis=0)\\n a = data * 1.0\\n a[np.where(data == 0)] = 0.000001\\n\\n e = (-1.0 / np.log(n)) * np.sum(data * np.log(a), axis=0)\\n w = (1 - e) / np.sum(1 - e)\\n return w\",\n \"def compute_entropy(bincounts, include_zeros):\\n num_total = tf.cast(tf.reduce_sum(bincounts), tf.float64)\\n if not include_zeros:\\n bincounts = bincounts[1:]\\n mask = tf.greater(bincounts, 0)\\n nonzero_bincounts = tf.cast(\\n tf.boolean_mask(bincounts, mask), tf.float64)\\n num_nonzero = tf.cast(tf.reduce_sum(nonzero_bincounts), tf.float64)\\n log_nonzero_bincounts = tf.math.log(nonzero_bincounts)\\n log_prob = log_nonzero_bincounts - tf.reduce_logsumexp(\\n log_nonzero_bincounts)\\n entropy = tf.math.reduce_sum(\\n log_prob * tf.exp(log_prob)) / -tf.math.log(tf.cast(2, tf.float64))\\n return entropy * num_nonzero / num_total\",\n \"def _entropy(self, feature, node):\\n entropy = 0\\n categories = np.unique(feature)\\n num_point = len(feature)\\n for category in categories:\\n # for each category in that feature\\n num_category = len(feature[feature == category])\\n for c in self.num_class:\\n # count the number of each class\\n num_category_class = len(feature[np.logical_and(feature == category, node.y == c)])\\n if num_category_class == 0:\\n continue\\n # compute entropy/information gain or classification error\\n entropy += num_category / num_point * (\\n -num_category_class / num_category * log2(num_category_class / num_category))\\n return entropy\",\n \"def image_entropy(img):\\r\\n hist = img.histogram()\\r\\n hist_size = sum(hist)\\r\\n hist = [float(h) / hist_size for h in hist]\\r\\n\\r\\n return -sum([p * math.log(p, 2) for p in hist if p != 0])\",\n \"def exploreEntropy(self, position):\\n pass\",\n \"def get_entropy(*labels):\\n entropies = [] #list of entropy values from each subset\\n total = 0 #total number of datapoints\\n for subset in labels:\\n n = len(subset)\\n total += n\\n counts = np.unique(subset, return_counts=True)[1] #frequency of unique values\\n entropy = np.sum([-(i/n) * np.log2(i/n) for i in counts]) #subset entropy calcuation\\n entropies.append((entropy, n))\\n return np.sum([(n/total) * ent for n, ent in iter(entropies)])\",\n \"def shannon_entropy(counts):\\n freq = np.array(counts) * 1.0 / np.sum(counts)\\n return -np.sum([f * np.log2(f) for f in freq if f != 0])\",\n \"def calculate_entropy(y):\\n\\tlog2 = lambda x: math.log(x) / math.log(2)\\n\\tunique_labels = np.unique(y)\\n\\tentropy = 0\\n\\tfor label in unique_labels:\\n\\t\\tcount = len(y[y == label])\\n\\t\\tp = count / len(y)\\n\\t\\tentropy += -p * log2(p)\\n\\treturn entropy\",\n \"def calc_entropy_one(states, T):\\n\\n # S = - kB \\\\sum_i p_i \\\\log p_i = - k_B <\\\\log p_i>\\n P = calc_probabilities_one(states, T)\\n return -kB * np.sum(P * np.log(P))\",\n \"def get_entropy(distribution, samples):\\n entropy = -tf.reduce_sum(distribution.log_prob(samples), axis=1)\\n return entropy\",\n \"def __entropy(self, labels):\\n class_probs = np.unique(labels, return_counts=True)[1] / labels.size\\n class_prob_logs = np.log2(class_probs)\\n entropy = -np.sum(class_probs * class_prob_logs)\\n return entropy\",\n \"def calc_entropy(data_set):\\n size = len(data_set)\\n label_counts = {}\\n for feat_vector in data_set:\\n label = feat_vector[-1]\\n label_counts.setdefault(label, 0)\\n label_counts[label] += 1\\n\\n entropy = 0.0\\n for key, count in label_counts.iteritems():\\n prob = float(count) / size\\n entropy -= prob * log(prob, 2)\\n\\n return entropy\",\n \"def transfer_entropy(X, Y):\\n coords = Counter(zip(Y[1:], X[:-1], Y[:-1]))\\n\\n p_dist = np.zeros((config.NUM_STATES, config.NUM_STATES, config.NUM_STATES))\\n for y_f, x_p, y_p in coords.keys():\\n p_dist[y_p, y_f, x_p] = coords[(y_f, x_p, y_p)] / (len(X) - 1)\\n\\n p_yp = p_dist.sum(axis=2).sum(axis=1)\\n p_joint_cond_yp = p_dist / p_yp[:, None, None]\\n p_yf_cond_yp = p_dist.sum(axis=2) / p_yp[:, None]\\n p_xp_cond_yp = p_dist.sum(axis=1) / p_yp[:, None]\\n\\n denominator = np.multiply(p_yf_cond_yp, p_xp_cond_yp)\\n denominator[denominator == 0] = np.nan\\n\\n division = np.divide(p_joint_cond_yp, denominator[:, :, None])\\n division[division == 0] = np.nan\\n\\n log = np.log2(division)\\n\\n return np.nansum(np.multiply(p_dist, log))\",\n \"def entropy(p):\\n ent = tf.where(p > np.finfo(np.float32).eps, -p * tf.log(p), tf.zeros_like(p))\\n ent = tf.reduce_sum(ent, axis=1)\\n return ent\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.67780423","0.6692582","0.66096693","0.6583241","0.646438","0.6463834","0.64497703","0.643623","0.6350943","0.6336463","0.6297908","0.62889063","0.62844735","0.6246048","0.62386346","0.62371117","0.6227964","0.62140536","0.6190457","0.618255","0.6164896","0.61534065","0.61434424","0.613901","0.6099791","0.6081295","0.60710585","0.60515374","0.60494435","0.6042749","0.6040821","0.6030353","0.59956914","0.59877366","0.5987647","0.5985964","0.59812754","0.59741443","0.59741116","0.595347","0.59406793","0.5937973","0.593123","0.5920657","0.59086716","0.59063387","0.5902275","0.59006196","0.58955586","0.589256","0.589256","0.5872183","0.58556783","0.58508694","0.5847746","0.5846385","0.58425456","0.5839454","0.5836809","0.58360565","0.58153963","0.58062303","0.5798828","0.5797042","0.579681","0.5789489","0.5786957","0.577163","0.5771089","0.5768587","0.5767156","0.57647663","0.5764647","0.5751291","0.57501537","0.5738673","0.57359695","0.5720341","0.5718052","0.571384","0.571144","0.57075197","0.57065886","0.5703031","0.5694639","0.5687653","0.5673408","0.5646502","0.56394374","0.5632122","0.56238115","0.56124455","0.5603496","0.55921954","0.5590687","0.5587474","0.5587136","0.5579451","0.5575571","0.5551954","0.5550965"],"string":"[\n \"0.67780423\",\n \"0.6692582\",\n \"0.66096693\",\n \"0.6583241\",\n \"0.646438\",\n \"0.6463834\",\n \"0.64497703\",\n \"0.643623\",\n \"0.6350943\",\n \"0.6336463\",\n \"0.6297908\",\n \"0.62889063\",\n \"0.62844735\",\n \"0.6246048\",\n \"0.62386346\",\n \"0.62371117\",\n \"0.6227964\",\n \"0.62140536\",\n \"0.6190457\",\n \"0.618255\",\n \"0.6164896\",\n \"0.61534065\",\n \"0.61434424\",\n \"0.613901\",\n \"0.6099791\",\n \"0.6081295\",\n \"0.60710585\",\n \"0.60515374\",\n \"0.60494435\",\n \"0.6042749\",\n \"0.6040821\",\n \"0.6030353\",\n \"0.59956914\",\n \"0.59877366\",\n \"0.5987647\",\n \"0.5985964\",\n \"0.59812754\",\n \"0.59741443\",\n \"0.59741116\",\n \"0.595347\",\n \"0.59406793\",\n \"0.5937973\",\n \"0.593123\",\n \"0.5920657\",\n \"0.59086716\",\n \"0.59063387\",\n \"0.5902275\",\n \"0.59006196\",\n \"0.58955586\",\n \"0.589256\",\n \"0.589256\",\n \"0.5872183\",\n \"0.58556783\",\n \"0.58508694\",\n \"0.5847746\",\n \"0.5846385\",\n \"0.58425456\",\n \"0.5839454\",\n \"0.5836809\",\n \"0.58360565\",\n \"0.58153963\",\n \"0.58062303\",\n \"0.5798828\",\n \"0.5797042\",\n \"0.579681\",\n \"0.5789489\",\n \"0.5786957\",\n \"0.577163\",\n \"0.5771089\",\n \"0.5768587\",\n \"0.5767156\",\n \"0.57647663\",\n \"0.5764647\",\n \"0.5751291\",\n \"0.57501537\",\n \"0.5738673\",\n \"0.57359695\",\n \"0.5720341\",\n \"0.5718052\",\n \"0.571384\",\n \"0.571144\",\n \"0.57075197\",\n \"0.57065886\",\n \"0.5703031\",\n \"0.5694639\",\n \"0.5687653\",\n \"0.5673408\",\n \"0.5646502\",\n \"0.56394374\",\n \"0.5632122\",\n \"0.56238115\",\n \"0.56124455\",\n \"0.5603496\",\n \"0.55921954\",\n \"0.5590687\",\n \"0.5587474\",\n \"0.5587136\",\n \"0.5579451\",\n \"0.5575571\",\n \"0.5551954\",\n \"0.5550965\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":22494,"cells":{"query":{"kind":"string","value":"Test the connection of the bot."},"document":{"kind":"string","value":"async def ping(self, ctx):\n msg_time = ctx.message.created_at\n cur_time = datetime.utcnow()\n delay = (cur_time - msg_time) / timedelta(milliseconds=1)\n await ctx.send(f\"Pong! ({str(delay)} ms)\")"},"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":["async def test_connection(self):\n await self.webhook_connection.connect()\n assert self.webhook_connection.is_connected is True","def test_connection(self):\n r = main.List.connection()\n self.assertTrue(r.ping(), \"Connection failed.\")","def test_connect(rgd):\n assert rgd.connected is True","def test_check_connection(self):\n self.assertIsNotNone(app.check_connection())","def test_connection_is_established(self):\n assert self.connection_client_1.is_connected is True\n assert self.connection_client_2.is_connected is True","def test_connection_is_established(self):\n assert self.connection_node_1.is_connected is True\n assert self.connection_node_2.is_connected is True\n assert self.connection_client_1.is_connected is True\n assert self.connection_client_2.is_connected is True","def test_verify_connection_to_a_device():","def test_connection(self):\n req = requests.get(\"http://{}:{}\".format(self.config.options.get(\"Server\", \"ListenAddress\"),\n self.config.options.get(\"Server\", \"Port\")))\n\n self.assertEqual(req.status_code, 200)","def test_connection_is_established(self):\n for conn in self.connections:\n assert conn.is_connected is True","def test_control(self, ping_fixture):\n engine = ping_fixture\n\n with expect_raises_message(\n exc.DBAPIError, \"unhandled disconnect situation\"\n ):\n engine.connect()","def test_connection_to_server(self):\n @asyncio.coroutine\n def wait_and_go():\n \"\"\"Wait a little for the server to go up, then connect.\"\"\"\n yield from asyncio.sleep(0.01, loop=self.loop)\n # reader, writer = ...\n _ = yield from asyncio.open_connection(\n *TEST_ADDRESS, loop=self.loop)\n\n self.loop.run_until_complete(wait_and_go())","def test_agentpi_connection(self):\n response = socket_utils.sendJson(conn, { \"authenticated\": True })\n\n self.assertTrue(response.text == \"authenticated\")","def test_connection(self) -> None:\n test_connection_fn = get_test_connection_fn(self.service_connection)\n test_connection_fn(self.engine)","def test_connection(self):\n try:\n response = self.call(\"auth.test\")\n response.validate()\n except SlackApiError as e:\n return False, str(e)\n except Exception as e:\n return False, f\"Unknown error occurred while testing connection: {e}\"\n\n if isinstance(response.data, bytes):\n # If response data binary then return simple message\n return True, f\"Connection successfully tested (url: {response.api_url}).\"\n\n try:\n return True, json.dumps(response.data)\n except TypeError:\n return True, str(response)","def test_connect(server):\n assert server","def test_connection(self):\n self._bind_to_service()","async def test_listen(self):\n connector = ConnectorWebexTeams({}, opsdroid=OpsDroid())\n self.assertEqual(await connector.listen(), None)","async def test_plain_smtp_connect(preset_client):\n await preset_client.connect()\n assert preset_client.is_connected\n\n await preset_client.quit()\n assert not preset_client.is_connected","def test_run_without_message(connection, events, loop):\n loop.run_until_complete(connection.run())\n assert events.triggered(\"CLIENT_CONNECT\")\n assert events.triggered(\"CLIENT_DISCONNECT\")","def test_connection():\r\n try:\r\n connect()\r\n except:\r\n pass\r\n print ('Unable to connect.')\r\n else:\r\n main()","def test_connect(connection, events, writer, schedule, flush):\n schedule(connection.connect())\n flush()\n assert connection.connected\n assert not writer.closed\n assert events.triggered(\"CLIENT_CONNECT\")","def TestConnection(self):\n return self._analyzer.TestConnection()","def test_connection():\n result = run(\"uname -a\")\n if result.failed:\n _pretty_output(\"Could not connect to remote server. Please check your configuration\")\n abort(\"Cannot continue. Aborting...\")","def connect(self):\n auth = self.botconfig.find('auth')\n logging.info(\"Connecting ...\" )\n if not auth.get('server', None):\n # we don't know the server, but the lib can probably figure it out\n super(SleekBot, self).connect()\n else:\n super(SleekBot, self).connect((auth.attrib['server'], auth.get('port', 5222)))","def testConn(*args, **kwargs):\n\ttempConnParams = sptFunctions.getConnParams()\n\tmainObj.stormConn.changeBase(**tempConnParams) # Change for the test\n\ttest = snakeStorm.method('Utilities/Info/ping', mainObj.stormConn)\n\tif 'snakeStormError' in test.request():\n\t\tmainObj.widgets['messageDialog'].set_markup('<span size=\"xx-large\" weight=\"ultrabold\">There was an error.</span>')\n\t\tmainObj.widgets['messageDialog'].format_secondary_text('There may be an error with either the API or snakeStorm - but it\\'s most likely you have bad settings.')\n\telse:\n\t\tmainObj.widgets['messageDialog'].set_markup('<span size=\"xx-large\" weight=\"ultrabold\">Success!</span>')\n\t\tmainObj.widgets['messageDialog'].format_secondary_text('It appears things are working!')\n\tmainObj.widgets['messageDialog'].set_transient_for(args[0])\n\tmainObj.widgets['messageDialog'].set_title('Connection Test Result')\n\tmainObj.widgets['messageDialog'].run()\n\tmainObj.stormConn.changeBase(**mainObj.connParams) # Change back after the test\n\tdel test","async def test_initialization(self):\n assert self.webhook_connection.address == self.identity.address","def test_connection(self):\n\n self.speed_test()\n if self.runs >= self.min_runs:\n self.lg.debug('Minimum number of speed tests performed.')\n self.check_performance()\n if self.bad_performance:\n self.lg.debug('Performance is below tolerance level.')\n self.notify_ISP()\n self.results_up.pop(0)\n self.results_down.pop(0)\n self.results_timestamp.pop(0)\n self.runs += 1","def test_send_before_connected(connection, writer):\n assert not connection.connected\n connection.send(\"test\")\n assert not writer.used","def check_connection(self):\n pass","def test_ApiConnectionWillAuthenticate_ValidCredentials_Successfully(self):\n connection = ApiConnection(self.userId, self.testToken)\n self.assertTrue(connection.connected())","def test_is_connected__not_alive(self):\n connection = MagicMock()\n connection.is_alive.return_value = True\n connection.is_established.return_value = True\n self.switch.connection = connection\n self.switch.is_active = MagicMock()\n self.switch.is_active.return_value = False\n\n self.assertFalse(self.switch.is_connected())","async def test_disconnect(self):\n await self.webhook_connection.connect()\n assert self.webhook_connection.is_connected is True\n\n await self.webhook_connection.disconnect()\n assert self.webhook_connection.is_connected is False","def connect():\n logging.info('Client connected')","def test_004_connect(self):\n HEADING()\n self.db.connect()\n\n result = True\n assert result","def _test_con(self) -> bool:\n self.reset_buffers()\n try:\n serial_number = self.get_exact(b\"<GETSERIAL>>\", size=7)\n except Exception as e:\n # Unsure of exception types.\n logger.warning(f\"{e}\", exc_info=True)\n return False\n # timeout error if wrong\n if len(serial_number) == 7:\n # Not 100% sure... no prescribed method of confirming\n # we're connected to a GMC device in specs\n logger.debug(f\"Test connection serial: {serial_number}\")\n return True\n else:\n logger.warning(f\"Unexpected response: {serial_number}\")\n return False","def test_get_connection_established(self):\n module = MagicMock()\n connection = slxos.get_connection(module)\n self.assertEqual(connection, module.slxos_connection)","async def test_connection_scope(path):\n communicator = WebsocketCommunicator(ConnectionScopeValidator(), path)\n connected, _ = await communicator.connect()\n assert connected\n await communicator.disconnect()","def _testConnectionProps(self, connection):\n self.assertEquals(36, len(connection.id))\n self.assertEquals(self.channel.name, connection.channel_name)\n self.assertTrue(connection.connected)\n self.assertFalse(connection.authenticated)\n self.assertEquals(None, connection.flex_user)\n self.assertTrue(connection.last_active > 0)\n self.assertTrue(time.time() * 1000 > connection.last_active)","def test_correct_connection(self, mock_execute, mock_parse):\n mock_parse.return_value = ARGS_INPUT\n result = CONNECTION1\n mock_execute.return_value = result\n actual_result = connection_to_client()\n self.assertIs(actual_result.exit_code, 0)\n self.assertIs(actual_result.error, \"\")\n self.assertIs(actual_result.exit_code_execution, 0)\n self.assertIs(actual_result.output, IPERF_PARSER_EXPECTED_RESULT)","def connect():\r\n # print ('Testing connection..')\r\n get_qlik_sense.get_about()","def can_connect(test_url):\n try:\n requests.get(test_url)\n except (OSError):#connection error\n logger.warning('couldn\\'t reach server on: {test_url}')\n return False\n return True","def test_is_connected__not_connection(self):\n self.switch.connection = None\n self.switch.is_active = MagicMock()\n self.switch.is_active.return_value = True\n\n self.assertFalse(self.switch.is_connected())","def test_correct_connectiom(self, mock_execute, mock_parse):\n mock_parse.return_value = ARGS_INPUT\n result = CONNECTION1\n mock_execute.return_value = result\n actual_result = connection_to_server()\n self.assertIs(actual_result.error, \"\")\n self.assertIs(actual_result.exit_code, 0)\n self.assertIs(actual_result.exit_code_execution, 0)\n self.assertIs(actual_result.output, IPERF_PARSER_EXPECTED_RESULT)","def init_conn(self):\n \n SERVER_ADDRESS = '192.168.0.21'\n PORT = 8018\n SERVER_PASSWORD = \"biratkingofcomedy\" \n connected = False\n \n # check if test module is being run\n if self.testing == 'n': \n while not connected:\n self.socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n \n try:\n self.socket.connect((SERVER_ADDRESS, PORT))\n \n # server verification\n self.socket.sendall(self.make_packet(\"DATA\", SERVER_PASSWORD))\n \n response = self.socket.recv(4096)\n \n if response:\n response_hdr, response_msg, response_sdr = self.parse_packet(response)\n \n if response_hdr == \"ERROR\" and response_msg == \"IDENTIFY FAILED\":\n raise Exception(\"PASSWORD FAIL\")\n \n elif response_hdr == \"DATA\" and response_msg == \"CONNECTED\":\n connected = True\n \n else:\n raise Exception(\"CONNECTION FAIL\") \n \n except Exception as e:\n if e == \"PASSWORD FAIL\":\n print(\"DEBUG: server connection failed (invalid credentials)\")\n print(\"DEBUG: quitting\")\n break\n \n else:\n print(e)\n print(\"DEBUG: server connection failed (could not connect), trying again in 10s\")\n time.sleep(10)\n \n else:\n print(\"DEBUG: socket setup skipped\")","def test_im_chat_sessions(self):\n pass","async def test_connect_and_disconnect(database_url):\n database = Database(database_url)\n\n assert not database.is_connected\n await database.connect()\n assert database.is_connected\n await database.disconnect()\n assert not database.is_connected","def test_interface(self):\n reactor = self.buildReactor()\n connector = self.connect(reactor, ClientFactory())\n self.assertTrue(verifyObject(IConnector, connector))","def connect(self):\n self.sock = socket.socket()\n self.sock.connect(self.addr)\n self.send(\"PASS {}\".format(self.password))\n self.send(\"NICK {}\".format(self.nick))\n self.send(\"JOIN {}\".format(self.channel))\n self.send(\"CAP REQ :twitch.tv/tags\")\n self.is_connected = True\n # TODO: error handling","def test_say(self):\n self.client.say(\"thechannel\", \"the message\")\n self.assertEqual(self.client.lines, [\"PRIVMSG #thechannel :the message\"])","def test_init(self):\n connector = ConnectorTelegram(\n {\"name\": \"telegram\", \"token\": \"test\"}, opsdroid=OpsDroid()\n )\n self.assertEqual(None, connector.default_target)\n self.assertEqual(\"telegram\", connector.name)","def test_connect():\n\n\tprint(\"Testing connecting to the server\")\n\ttry:\n\t\twith socket.socket() as s:\n\t\t\ts.connect((\"localhost\", 8000))\n\t\tprint(\"Connection attempt succeeded.\")\n\t\treturn None\n\texcept socket.error:\n\t\treturn \"Server didn't answer on localhost port 8000. Is it running?\"","def verifyWalabotIsConnected():\n while True:\n try:\n wlbt.ConnectAny()\n except wlbt.WalabotError as err:\n input(\"- Connect Walabot and press 'Enter'.\")\n else:\n print('- Connection to Walabot established.')\n return","def isConnected():","async def test_libp2pclientconnection_connect_disconnect(self):\n assert self.connection.is_connected is False\n try:\n await self.connection_node.connect()\n await self.connection.connect()\n assert self.connection.is_connected is True\n\n await self.connection.disconnect()\n assert self.connection.is_connected is False\n except Exception:\n raise\n finally:\n await self.connection_node.disconnect()","def test_get_connection(self):\n\n http_client = bce_http_client.BceHttpClient(None)\n\n # test unknown protocol\n test_protocol = Expando({'name': 'unknown', 'default_port': 65535})\n host = \"1.2.3.4\"\n port = 8080\n connection_timeout = 1000\n self.assertRaises(ValueError,\n http_client._get_connection,\n test_protocol, host, port, connection_timeout)\n\n # test http protocol\n test_protocol = protocol.HTTP\n host = \"1.2.3.4\"\n port = 8080\n connection_timeout = 1000\n conn = http_client._get_connection(test_protocol, host, port, connection_timeout)\n self.assertEqual(conn.host, \"1.2.3.4\")\n self.assertEqual(conn.port, 8080)\n self.assertEqual(conn.timeout, 1)\n\n # test https protocol\n test_protocol = protocol.HTTPS\n host = \"1.2.3.4\"\n port = 8080\n connection_timeout = 1000\n conn = http_client._get_connection(test_protocol, host, port, connection_timeout)","async def conn(self) -> None:\n self.bot.db = await aiosqlite.connect('database.db')","def test_passing_connection(self):\n Pet.init_db(Redis(host=REDIS_HOST, port=REDIS_PORT))\n self.assertIsNotNone(Pet.redis)","def testConnectionMade(self):\n d = self._getClientConnection()\n d.addCallback(lambda _: self.client.disconnect())\n return d","def test_neo4j_connected(self):\n\t\tclient = Client()\n\t\tresponse = client.post(self.uri_add_selectable+self.bob_the_socialaware_twin_profile.token+'/',\n\t\t content_type='application/json', data=page_one_objects(self.bob_the_socialaware_twin_auth, 0))\n\t\tstatus = json.loads(response.content.decode('utf-8'))['inter']\n\t\tself.assertNotEqual(status,'e_neo4j_Disconnected')","def test_connect(self, gateway):\n assert not gateway._devs","def test_database_connection(self):\n\t\t\n\t\tself.assertTrue(database.connect())","def check_connection(self):\n return False","def test_connected(self):\n networktables_mock = unittest.mock.Mock()\n networktables_mock.isConnected.side_effect = [True, False]\n\n network_instance = network.Network(networktables_mock, None, None)\n self.assertTrue(network_instance.connected())\n self.assertFalse(network_instance.connected())","def test_api_connection(server, username, password, api_key):\n global api_url\n api_url = \"http://\" + server + \"/i-doit/src/jsonrpc.php\"\n\n global headers\n headers = {}\n headers[\"Content-Type\"] = \"application/json\"\n headers[\"X-RPC-Auth-Username\"] = username\n headers[\"X-RPC-Auth-Password\"] = password\n\n global apikey\n apikey = api_key\n\n print(blue + \"\\n>>> \" + reset + \"Checking API connection...\")\n\n login_body = json.loads(\"{\\\"version\\\": \\\"2.0\\\",\\\"method\\\": \\\"idoit.login\\\",\\\"params\\\": {\\\"apikey\\\": \\\"\" +\n apikey + \"\\\",\\\"language\\\": \\\"en\\\"},\\\"id\\\": 1}\")\n\n try:\n s = requests.Session()\n login_request = s.post(api_url, json=login_body, headers=headers)\n login = login_request.json()\n if \"error\" in login:\n print(red + \"\\n>>> \" + reset +\n \"Unable to connect to the API. Please verify the connection information.\")\n return False\n else:\n print(green + \"\\n>>> \" + reset + \"Successfully connected.\")\n return True\n except requests.exceptions.RequestException:\n print(red + \"\\n>>> \" + reset +\n \"Unable to connect to the API. Please verify the connection information.\")\n return False","def testGetConn(self):\n self.u = Usuario.Usuario(\"pruebaConexion\", \"User0\", 0)\n self.assertEqual(\"pruebaConexion\", self.u.getConn())\n self.u.setConn(\"cambioConexion\")\n self.assertEqual(\"cambioConexion\", self.u.getConn())","def connect(self, host):\n return False","async def test_connect_on_trigger(self) -> None:\n self.assertFalse(self.client.triggers, 'predefined triggers found')\n self.assertIsNone(self.client.websocket, 'preexisting websocket found')\n # Add trigger and wait for ready\n trigger = auraxium.Trigger(auraxium.event.BattleRankUp)\n self.client.add_trigger(trigger)\n await self.client.wait_ready()\n # Check for websocket activity\n self.assertIsNotNone(self.client.websocket, 'missing websocket')\n self.assertListEqual(\n self.client.triggers, [trigger], 'trigger not found')","def setUp(self):\n self.transport = StringTransport()\n self.protocol = IRCClient()\n self.protocol.performLogin = False\n self.protocol.makeConnection(self.transport)\n\n # Sanity check - we don't want anything to have happened at this\n # point, since we're not in a test yet.\n self.assertEqualBufferValue(self.transport.value(), \"\")\n\n self.addCleanup(self.transport.loseConnection)\n self.addCleanup(self.protocol.connectionLost, None)","async def _test_credentials(self, username, password):\n try:\n session = async_create_clientsession(self.hass)\n client = SwitchBotCloudApiClient(session)\n await client.authenticate(username, password)\n return True\n except Exception: # pylint: disable=broad-except\n pass\n return False","def step_impl(context):\n log.info(\"====> Checking if the CME server is alive via SSH\")\n assert context.cme_session.is_server_alive(), \" ****> The server is not available via SSH\"","def check_connection():\n r = requests.get('https://www.google.com')\n if r.status_code == 200:\n print (colored(\"Connected.\", 'green'))\n else:\n print (colored(\"Not Connected.\", 'red'))","def test_connect_disconnect(self):\n ms_srv = common.MatchMakingServer(2222)\n ms_srv.start()\n time.sleep(2)\n\n clients = common.start_multiple_clients(ms_srv.port, 1)\n\n # Disconnect the client by killing it.\n # The sleeps here ensure the MS server and client are given enough time\n # to interact for log entries to be written.\n time.sleep(5)\n clients[0].kill()\n time.sleep(5)\n\n starting_game_found = False\n player_count_found = False\n with open(ms_srv.local_log_path) as log_file:\n for line in log_file:\n if \"Starting Game\" in line:\n starting_game_found = True\n continue\n elif \"1 players\" in line:\n player_count_found = True\n continue\n\n self.assertTrue(player_count_found,\n \"MS server should have been connected to\")\n self.assertFalse(starting_game_found, \"Game should not have started\")","async def connect(self, **kwargs) -> bool:\n return await self._backend.connect(**kwargs)","def rpc_test_connection(client, rpc_server, rpc_user=BTC_RPC_USER, rpc_password=BTC_RPC_PASSWD, rpc_port=BTC_RPC_PORT):\n try:\n get_info = rpc_getinfo(client, rpc_server, rpc_user=rpc_user, rpc_password=rpc_password, rpc_port=rpc_port)\n print(get_info)\n return True\n except JSONRPCException as err:\n return False","def test_irc_JOIN(self):\n self.client.irc_JOIN(self.user, [self.channel])\n self.client.irc_JOIN(\"Svadilfari!~svadi@yok.utu.fi\", [\"#python\"])\n self.assertEqual(\n self.client.methods,\n [(\"joined\", (self.channel,)), (\"userJoined\", (\"Svadilfari\", \"#python\"))],\n )","async def test_send(self):\n await self.webhook_connection.connect()\n assert self.webhook_connection.is_connected is True\n\n http_message = HttpMessage(\n dialogue_reference=(\"\", \"\"),\n target=0,\n message_id=1,\n performative=HttpMessage.Performative.REQUEST,\n method=\"get\",\n url=\"/\",\n headers=\"\",\n body=\"\",\n version=\"\",\n )\n envelope = Envelope(\n to=\"addr\",\n sender=\"my_id\",\n message=http_message,\n )\n with patch.object(self.webhook_connection.logger, \"warning\") as mock_logger:\n await self.webhook_connection.send(envelope)\n await asyncio.sleep(0.01)\n mock_logger.assert_any_call(\n RegexComparator(\n \"Dropping envelope=.* as sending via the webhook is not possible!\"\n )\n )","def test_connect_success():\n\n t = Thread(target=setup_socket)\n t.start()\n\n data_sender = DataSender('127.0.0.1', 12345)\n server_response = data_sender.notify('test')\n\n assert server_response == 'ok'\n\n data_sender.close()\n t.join()","async def connect(self):\n pass","def test_postMessage(self): #GIVEN the appropriate environment variables are configured\n testBot = bot.Bot(os.environ['bot_id'], os.environ['token'], os.environ['group_ID'])\n status = testBot.postMessage('Zygium') #WHEN the bot posts a message\n self.assertTrue(status == 202) # a status code of 202 should be returned","def is_connected(self, test=False):\n return self._server.is_connected()","def test_connection(server_address, server_username=None, server_password=None, verbose=True):\n\n try:\n if server_username is None and server_password is None:\n r = requests.get(url=server_address)\n else:\n r = requests.get(url=server_address, auth=(server_username, server_password))\n if r.ok:\n if verbose:\n print(\"Network connectivity: VERIFIED. Server \" + server_address + \" is reachable!\")\n return True\n else:\n print(\"Something wrong during connection!\")\n return False\n\n except Exception as e:\n print(e)\n return False","def is_connected():\r\n global connection\r\n if connection is None:\r\n return False\r\n else:\r\n return True","def test_is_connected():\n with Replacer() as r:\n queue_manager = uuid4().hex\n channel = uuid4().hex\n host = uuid4().hex\n port = \"1431\"\n conn_info = \"%s(%s)\" % (host, port)\n\n for expected in(True, False):\n\n def _connectTCPClient(*ignored_args, **ignored_kwargs):\n pass\n\n def _getattr(self, name):\n if expected:\n class _DummyMethod(object):\n pass\n # The mere fact of not raising an exception will suffice\n # for QueueManager._is_connected to understand it as an\n # all's OK condition.\n return _DummyMethod\n else:\n raise Exception()\n\n r.replace('pymqi.QueueManager.connectTCPClient', _connectTCPClient)\n r.replace('pymqi.PCFExecute.__getattr__', _getattr)\n\n qmgr = pymqi.QueueManager(None)\n qmgr.connectTCPClient(queue_manager, pymqi.cd(), channel, conn_info)\n\n eq_(qmgr.is_connected, expected)","def test_connection(self):\n with vertica_python.connect(**conn_info) as connection:\n print(\"Connected to {} on host{} \".format(conn_info['database'],conn_info['host']))\n cur = connection.cursor()\n cur.execute(self.TEST_QUERY)\n for row in cur.iterate():\n print(\"The row is {}\".format(row))","def test_connection(self):\n try:\n if 0 <= self.get_wavelength() <= 10e-6:\n return True\n except Exception:\n print(\"Self test failed.\")\n return False","def startedConnecting(self, connector):\n logger.log_info(\"Connecting to Discord...\")","def test_call(self):\n\n with Client('username', 'password') as client:\n self.setSessionResponse(content='{\"ok\": true}')\n data = client.call(**self.build_parameters)\n self.assertEqual(data, '{\"ok\": true}')","def _test_connection(self, connection_string):\n try:\n engine = create_engine(connection_string)\n connection = engine.connect()\n connection.close()\n return True\n except Exception as e:\n if options.debug:\n logging.exception(\"Database connection failed: %s\" % e)\n return False","def test_for_client():","def verify_core_connection():\n if not base_url or not api_credentials:\n retrieve_connection_info()\n return","def verify_core_connection():\n if not base_url or not api_credentials:\n retrieve_connection_info()\n return","def reconnecting(self) -> bool:","def setUp(self):\n\t\tself.conn = Client([\"127.0.0.1:11211\"], debug = 1)","async def connect(self, retry=2):\n # pylint: disable=too-many-return-statements\n url = self._endpoint + PLUGWISE_PING_ENDPOINT\n try:\n with async_timeout.timeout(self._timeout):\n resp = await self.websession.get(url,auth=self._auth)\n except (asyncio.TimeoutError, aiohttp.ClientError):\n if retry < 1:\n _LOGGER.error(\"Error connecting to Plugwise\", exc_info=True)\n return False\n return await self.connect(retry - 1)\n\n result = await resp.text()\n if not 'error' in result:\n _LOGGER.error('Connected but expected text not returned, we got %s',result)\n return False\n\n return True","def Connected(self) -> bool:","def Connected(self) -> bool:","def test_server_connection():\n response = client.get(\"/\")\n assert response.ok\n assert response.json() == {\"ID\": \"8dbaaa72-ff7a-4f95-887c-e3109e577edd\"}","def __CheckConnectStatus(self):\r\n if not self.tn:\r\n print \"Connection is down!\"\r\n return False\r\n else:\r\n print \"Connection is alive!\"\r\n return True","def test_connection(remote=False):\n import socket\n remote_server = 'www.google.com' if not remote else remote # TODO: maybe improve for China\n try:\n # does the host name resolve?\n host = socket.gethostbyname(remote_server)\n # can we establish a connection to the host name?\n con = socket.create_connection((host, 80), 2)\n return True\n except:\n print(\"Can't connect to a server...\")\n pass\n return False","def bot():\n log(log.DEBUG, 'Starting bot')\n bot = BotSad()\n bot.listen()","async def test_api_ping(hassio_handler, aioclient_mock: AiohttpClientMocker) -> None:\n aioclient_mock.get(\"http://127.0.0.1/supervisor/ping\", json={\"result\": \"ok\"})\n\n assert await hassio_handler.is_connected()\n assert aioclient_mock.call_count == 1"],"string":"[\n \"async def test_connection(self):\\n await self.webhook_connection.connect()\\n assert self.webhook_connection.is_connected is True\",\n \"def test_connection(self):\\n r = main.List.connection()\\n self.assertTrue(r.ping(), \\\"Connection failed.\\\")\",\n \"def test_connect(rgd):\\n assert rgd.connected is True\",\n \"def test_check_connection(self):\\n self.assertIsNotNone(app.check_connection())\",\n \"def test_connection_is_established(self):\\n assert self.connection_client_1.is_connected is True\\n assert self.connection_client_2.is_connected is True\",\n \"def test_connection_is_established(self):\\n assert self.connection_node_1.is_connected is True\\n assert self.connection_node_2.is_connected is True\\n assert self.connection_client_1.is_connected is True\\n assert self.connection_client_2.is_connected is True\",\n \"def test_verify_connection_to_a_device():\",\n \"def test_connection(self):\\n req = requests.get(\\\"http://{}:{}\\\".format(self.config.options.get(\\\"Server\\\", \\\"ListenAddress\\\"),\\n self.config.options.get(\\\"Server\\\", \\\"Port\\\")))\\n\\n self.assertEqual(req.status_code, 200)\",\n \"def test_connection_is_established(self):\\n for conn in self.connections:\\n assert conn.is_connected is True\",\n \"def test_control(self, ping_fixture):\\n engine = ping_fixture\\n\\n with expect_raises_message(\\n exc.DBAPIError, \\\"unhandled disconnect situation\\\"\\n ):\\n engine.connect()\",\n \"def test_connection_to_server(self):\\n @asyncio.coroutine\\n def wait_and_go():\\n \\\"\\\"\\\"Wait a little for the server to go up, then connect.\\\"\\\"\\\"\\n yield from asyncio.sleep(0.01, loop=self.loop)\\n # reader, writer = ...\\n _ = yield from asyncio.open_connection(\\n *TEST_ADDRESS, loop=self.loop)\\n\\n self.loop.run_until_complete(wait_and_go())\",\n \"def test_agentpi_connection(self):\\n response = socket_utils.sendJson(conn, { \\\"authenticated\\\": True })\\n\\n self.assertTrue(response.text == \\\"authenticated\\\")\",\n \"def test_connection(self) -> None:\\n test_connection_fn = get_test_connection_fn(self.service_connection)\\n test_connection_fn(self.engine)\",\n \"def test_connection(self):\\n try:\\n response = self.call(\\\"auth.test\\\")\\n response.validate()\\n except SlackApiError as e:\\n return False, str(e)\\n except Exception as e:\\n return False, f\\\"Unknown error occurred while testing connection: {e}\\\"\\n\\n if isinstance(response.data, bytes):\\n # If response data binary then return simple message\\n return True, f\\\"Connection successfully tested (url: {response.api_url}).\\\"\\n\\n try:\\n return True, json.dumps(response.data)\\n except TypeError:\\n return True, str(response)\",\n \"def test_connect(server):\\n assert server\",\n \"def test_connection(self):\\n self._bind_to_service()\",\n \"async def test_listen(self):\\n connector = ConnectorWebexTeams({}, opsdroid=OpsDroid())\\n self.assertEqual(await connector.listen(), None)\",\n \"async def test_plain_smtp_connect(preset_client):\\n await preset_client.connect()\\n assert preset_client.is_connected\\n\\n await preset_client.quit()\\n assert not preset_client.is_connected\",\n \"def test_run_without_message(connection, events, loop):\\n loop.run_until_complete(connection.run())\\n assert events.triggered(\\\"CLIENT_CONNECT\\\")\\n assert events.triggered(\\\"CLIENT_DISCONNECT\\\")\",\n \"def test_connection():\\r\\n try:\\r\\n connect()\\r\\n except:\\r\\n pass\\r\\n print ('Unable to connect.')\\r\\n else:\\r\\n main()\",\n \"def test_connect(connection, events, writer, schedule, flush):\\n schedule(connection.connect())\\n flush()\\n assert connection.connected\\n assert not writer.closed\\n assert events.triggered(\\\"CLIENT_CONNECT\\\")\",\n \"def TestConnection(self):\\n return self._analyzer.TestConnection()\",\n \"def test_connection():\\n result = run(\\\"uname -a\\\")\\n if result.failed:\\n _pretty_output(\\\"Could not connect to remote server. Please check your configuration\\\")\\n abort(\\\"Cannot continue. Aborting...\\\")\",\n \"def connect(self):\\n auth = self.botconfig.find('auth')\\n logging.info(\\\"Connecting ...\\\" )\\n if not auth.get('server', None):\\n # we don't know the server, but the lib can probably figure it out\\n super(SleekBot, self).connect()\\n else:\\n super(SleekBot, self).connect((auth.attrib['server'], auth.get('port', 5222)))\",\n \"def testConn(*args, **kwargs):\\n\\ttempConnParams = sptFunctions.getConnParams()\\n\\tmainObj.stormConn.changeBase(**tempConnParams) # Change for the test\\n\\ttest = snakeStorm.method('Utilities/Info/ping', mainObj.stormConn)\\n\\tif 'snakeStormError' in test.request():\\n\\t\\tmainObj.widgets['messageDialog'].set_markup('<span size=\\\"xx-large\\\" weight=\\\"ultrabold\\\">There was an error.</span>')\\n\\t\\tmainObj.widgets['messageDialog'].format_secondary_text('There may be an error with either the API or snakeStorm - but it\\\\'s most likely you have bad settings.')\\n\\telse:\\n\\t\\tmainObj.widgets['messageDialog'].set_markup('<span size=\\\"xx-large\\\" weight=\\\"ultrabold\\\">Success!</span>')\\n\\t\\tmainObj.widgets['messageDialog'].format_secondary_text('It appears things are working!')\\n\\tmainObj.widgets['messageDialog'].set_transient_for(args[0])\\n\\tmainObj.widgets['messageDialog'].set_title('Connection Test Result')\\n\\tmainObj.widgets['messageDialog'].run()\\n\\tmainObj.stormConn.changeBase(**mainObj.connParams) # Change back after the test\\n\\tdel test\",\n \"async def test_initialization(self):\\n assert self.webhook_connection.address == self.identity.address\",\n \"def test_connection(self):\\n\\n self.speed_test()\\n if self.runs >= self.min_runs:\\n self.lg.debug('Minimum number of speed tests performed.')\\n self.check_performance()\\n if self.bad_performance:\\n self.lg.debug('Performance is below tolerance level.')\\n self.notify_ISP()\\n self.results_up.pop(0)\\n self.results_down.pop(0)\\n self.results_timestamp.pop(0)\\n self.runs += 1\",\n \"def test_send_before_connected(connection, writer):\\n assert not connection.connected\\n connection.send(\\\"test\\\")\\n assert not writer.used\",\n \"def check_connection(self):\\n pass\",\n \"def test_ApiConnectionWillAuthenticate_ValidCredentials_Successfully(self):\\n connection = ApiConnection(self.userId, self.testToken)\\n self.assertTrue(connection.connected())\",\n \"def test_is_connected__not_alive(self):\\n connection = MagicMock()\\n connection.is_alive.return_value = True\\n connection.is_established.return_value = True\\n self.switch.connection = connection\\n self.switch.is_active = MagicMock()\\n self.switch.is_active.return_value = False\\n\\n self.assertFalse(self.switch.is_connected())\",\n \"async def test_disconnect(self):\\n await self.webhook_connection.connect()\\n assert self.webhook_connection.is_connected is True\\n\\n await self.webhook_connection.disconnect()\\n assert self.webhook_connection.is_connected is False\",\n \"def connect():\\n logging.info('Client connected')\",\n \"def test_004_connect(self):\\n HEADING()\\n self.db.connect()\\n\\n result = True\\n assert result\",\n \"def _test_con(self) -> bool:\\n self.reset_buffers()\\n try:\\n serial_number = self.get_exact(b\\\"<GETSERIAL>>\\\", size=7)\\n except Exception as e:\\n # Unsure of exception types.\\n logger.warning(f\\\"{e}\\\", exc_info=True)\\n return False\\n # timeout error if wrong\\n if len(serial_number) == 7:\\n # Not 100% sure... no prescribed method of confirming\\n # we're connected to a GMC device in specs\\n logger.debug(f\\\"Test connection serial: {serial_number}\\\")\\n return True\\n else:\\n logger.warning(f\\\"Unexpected response: {serial_number}\\\")\\n return False\",\n \"def test_get_connection_established(self):\\n module = MagicMock()\\n connection = slxos.get_connection(module)\\n self.assertEqual(connection, module.slxos_connection)\",\n \"async def test_connection_scope(path):\\n communicator = WebsocketCommunicator(ConnectionScopeValidator(), path)\\n connected, _ = await communicator.connect()\\n assert connected\\n await communicator.disconnect()\",\n \"def _testConnectionProps(self, connection):\\n self.assertEquals(36, len(connection.id))\\n self.assertEquals(self.channel.name, connection.channel_name)\\n self.assertTrue(connection.connected)\\n self.assertFalse(connection.authenticated)\\n self.assertEquals(None, connection.flex_user)\\n self.assertTrue(connection.last_active > 0)\\n self.assertTrue(time.time() * 1000 > connection.last_active)\",\n \"def test_correct_connection(self, mock_execute, mock_parse):\\n mock_parse.return_value = ARGS_INPUT\\n result = CONNECTION1\\n mock_execute.return_value = result\\n actual_result = connection_to_client()\\n self.assertIs(actual_result.exit_code, 0)\\n self.assertIs(actual_result.error, \\\"\\\")\\n self.assertIs(actual_result.exit_code_execution, 0)\\n self.assertIs(actual_result.output, IPERF_PARSER_EXPECTED_RESULT)\",\n \"def connect():\\r\\n # print ('Testing connection..')\\r\\n get_qlik_sense.get_about()\",\n \"def can_connect(test_url):\\n try:\\n requests.get(test_url)\\n except (OSError):#connection error\\n logger.warning('couldn\\\\'t reach server on: {test_url}')\\n return False\\n return True\",\n \"def test_is_connected__not_connection(self):\\n self.switch.connection = None\\n self.switch.is_active = MagicMock()\\n self.switch.is_active.return_value = True\\n\\n self.assertFalse(self.switch.is_connected())\",\n \"def test_correct_connectiom(self, mock_execute, mock_parse):\\n mock_parse.return_value = ARGS_INPUT\\n result = CONNECTION1\\n mock_execute.return_value = result\\n actual_result = connection_to_server()\\n self.assertIs(actual_result.error, \\\"\\\")\\n self.assertIs(actual_result.exit_code, 0)\\n self.assertIs(actual_result.exit_code_execution, 0)\\n self.assertIs(actual_result.output, IPERF_PARSER_EXPECTED_RESULT)\",\n \"def init_conn(self):\\n \\n SERVER_ADDRESS = '192.168.0.21'\\n PORT = 8018\\n SERVER_PASSWORD = \\\"biratkingofcomedy\\\" \\n connected = False\\n \\n # check if test module is being run\\n if self.testing == 'n': \\n while not connected:\\n self.socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\\n \\n try:\\n self.socket.connect((SERVER_ADDRESS, PORT))\\n \\n # server verification\\n self.socket.sendall(self.make_packet(\\\"DATA\\\", SERVER_PASSWORD))\\n \\n response = self.socket.recv(4096)\\n \\n if response:\\n response_hdr, response_msg, response_sdr = self.parse_packet(response)\\n \\n if response_hdr == \\\"ERROR\\\" and response_msg == \\\"IDENTIFY FAILED\\\":\\n raise Exception(\\\"PASSWORD FAIL\\\")\\n \\n elif response_hdr == \\\"DATA\\\" and response_msg == \\\"CONNECTED\\\":\\n connected = True\\n \\n else:\\n raise Exception(\\\"CONNECTION FAIL\\\") \\n \\n except Exception as e:\\n if e == \\\"PASSWORD FAIL\\\":\\n print(\\\"DEBUG: server connection failed (invalid credentials)\\\")\\n print(\\\"DEBUG: quitting\\\")\\n break\\n \\n else:\\n print(e)\\n print(\\\"DEBUG: server connection failed (could not connect), trying again in 10s\\\")\\n time.sleep(10)\\n \\n else:\\n print(\\\"DEBUG: socket setup skipped\\\")\",\n \"def test_im_chat_sessions(self):\\n pass\",\n \"async def test_connect_and_disconnect(database_url):\\n database = Database(database_url)\\n\\n assert not database.is_connected\\n await database.connect()\\n assert database.is_connected\\n await database.disconnect()\\n assert not database.is_connected\",\n \"def test_interface(self):\\n reactor = self.buildReactor()\\n connector = self.connect(reactor, ClientFactory())\\n self.assertTrue(verifyObject(IConnector, connector))\",\n \"def connect(self):\\n self.sock = socket.socket()\\n self.sock.connect(self.addr)\\n self.send(\\\"PASS {}\\\".format(self.password))\\n self.send(\\\"NICK {}\\\".format(self.nick))\\n self.send(\\\"JOIN {}\\\".format(self.channel))\\n self.send(\\\"CAP REQ :twitch.tv/tags\\\")\\n self.is_connected = True\\n # TODO: error handling\",\n \"def test_say(self):\\n self.client.say(\\\"thechannel\\\", \\\"the message\\\")\\n self.assertEqual(self.client.lines, [\\\"PRIVMSG #thechannel :the message\\\"])\",\n \"def test_init(self):\\n connector = ConnectorTelegram(\\n {\\\"name\\\": \\\"telegram\\\", \\\"token\\\": \\\"test\\\"}, opsdroid=OpsDroid()\\n )\\n self.assertEqual(None, connector.default_target)\\n self.assertEqual(\\\"telegram\\\", connector.name)\",\n \"def test_connect():\\n\\n\\tprint(\\\"Testing connecting to the server\\\")\\n\\ttry:\\n\\t\\twith socket.socket() as s:\\n\\t\\t\\ts.connect((\\\"localhost\\\", 8000))\\n\\t\\tprint(\\\"Connection attempt succeeded.\\\")\\n\\t\\treturn None\\n\\texcept socket.error:\\n\\t\\treturn \\\"Server didn't answer on localhost port 8000. Is it running?\\\"\",\n \"def verifyWalabotIsConnected():\\n while True:\\n try:\\n wlbt.ConnectAny()\\n except wlbt.WalabotError as err:\\n input(\\\"- Connect Walabot and press 'Enter'.\\\")\\n else:\\n print('- Connection to Walabot established.')\\n return\",\n \"def isConnected():\",\n \"async def test_libp2pclientconnection_connect_disconnect(self):\\n assert self.connection.is_connected is False\\n try:\\n await self.connection_node.connect()\\n await self.connection.connect()\\n assert self.connection.is_connected is True\\n\\n await self.connection.disconnect()\\n assert self.connection.is_connected is False\\n except Exception:\\n raise\\n finally:\\n await self.connection_node.disconnect()\",\n \"def test_get_connection(self):\\n\\n http_client = bce_http_client.BceHttpClient(None)\\n\\n # test unknown protocol\\n test_protocol = Expando({'name': 'unknown', 'default_port': 65535})\\n host = \\\"1.2.3.4\\\"\\n port = 8080\\n connection_timeout = 1000\\n self.assertRaises(ValueError,\\n http_client._get_connection,\\n test_protocol, host, port, connection_timeout)\\n\\n # test http protocol\\n test_protocol = protocol.HTTP\\n host = \\\"1.2.3.4\\\"\\n port = 8080\\n connection_timeout = 1000\\n conn = http_client._get_connection(test_protocol, host, port, connection_timeout)\\n self.assertEqual(conn.host, \\\"1.2.3.4\\\")\\n self.assertEqual(conn.port, 8080)\\n self.assertEqual(conn.timeout, 1)\\n\\n # test https protocol\\n test_protocol = protocol.HTTPS\\n host = \\\"1.2.3.4\\\"\\n port = 8080\\n connection_timeout = 1000\\n conn = http_client._get_connection(test_protocol, host, port, connection_timeout)\",\n \"async def conn(self) -> None:\\n self.bot.db = await aiosqlite.connect('database.db')\",\n \"def test_passing_connection(self):\\n Pet.init_db(Redis(host=REDIS_HOST, port=REDIS_PORT))\\n self.assertIsNotNone(Pet.redis)\",\n \"def testConnectionMade(self):\\n d = self._getClientConnection()\\n d.addCallback(lambda _: self.client.disconnect())\\n return d\",\n \"def test_neo4j_connected(self):\\n\\t\\tclient = Client()\\n\\t\\tresponse = client.post(self.uri_add_selectable+self.bob_the_socialaware_twin_profile.token+'/',\\n\\t\\t content_type='application/json', data=page_one_objects(self.bob_the_socialaware_twin_auth, 0))\\n\\t\\tstatus = json.loads(response.content.decode('utf-8'))['inter']\\n\\t\\tself.assertNotEqual(status,'e_neo4j_Disconnected')\",\n \"def test_connect(self, gateway):\\n assert not gateway._devs\",\n \"def test_database_connection(self):\\n\\t\\t\\n\\t\\tself.assertTrue(database.connect())\",\n \"def check_connection(self):\\n return False\",\n \"def test_connected(self):\\n networktables_mock = unittest.mock.Mock()\\n networktables_mock.isConnected.side_effect = [True, False]\\n\\n network_instance = network.Network(networktables_mock, None, None)\\n self.assertTrue(network_instance.connected())\\n self.assertFalse(network_instance.connected())\",\n \"def test_api_connection(server, username, password, api_key):\\n global api_url\\n api_url = \\\"http://\\\" + server + \\\"/i-doit/src/jsonrpc.php\\\"\\n\\n global headers\\n headers = {}\\n headers[\\\"Content-Type\\\"] = \\\"application/json\\\"\\n headers[\\\"X-RPC-Auth-Username\\\"] = username\\n headers[\\\"X-RPC-Auth-Password\\\"] = password\\n\\n global apikey\\n apikey = api_key\\n\\n print(blue + \\\"\\\\n>>> \\\" + reset + \\\"Checking API connection...\\\")\\n\\n login_body = json.loads(\\\"{\\\\\\\"version\\\\\\\": \\\\\\\"2.0\\\\\\\",\\\\\\\"method\\\\\\\": \\\\\\\"idoit.login\\\\\\\",\\\\\\\"params\\\\\\\": {\\\\\\\"apikey\\\\\\\": \\\\\\\"\\\" +\\n apikey + \\\"\\\\\\\",\\\\\\\"language\\\\\\\": \\\\\\\"en\\\\\\\"},\\\\\\\"id\\\\\\\": 1}\\\")\\n\\n try:\\n s = requests.Session()\\n login_request = s.post(api_url, json=login_body, headers=headers)\\n login = login_request.json()\\n if \\\"error\\\" in login:\\n print(red + \\\"\\\\n>>> \\\" + reset +\\n \\\"Unable to connect to the API. Please verify the connection information.\\\")\\n return False\\n else:\\n print(green + \\\"\\\\n>>> \\\" + reset + \\\"Successfully connected.\\\")\\n return True\\n except requests.exceptions.RequestException:\\n print(red + \\\"\\\\n>>> \\\" + reset +\\n \\\"Unable to connect to the API. Please verify the connection information.\\\")\\n return False\",\n \"def testGetConn(self):\\n self.u = Usuario.Usuario(\\\"pruebaConexion\\\", \\\"User0\\\", 0)\\n self.assertEqual(\\\"pruebaConexion\\\", self.u.getConn())\\n self.u.setConn(\\\"cambioConexion\\\")\\n self.assertEqual(\\\"cambioConexion\\\", self.u.getConn())\",\n \"def connect(self, host):\\n return False\",\n \"async def test_connect_on_trigger(self) -> None:\\n self.assertFalse(self.client.triggers, 'predefined triggers found')\\n self.assertIsNone(self.client.websocket, 'preexisting websocket found')\\n # Add trigger and wait for ready\\n trigger = auraxium.Trigger(auraxium.event.BattleRankUp)\\n self.client.add_trigger(trigger)\\n await self.client.wait_ready()\\n # Check for websocket activity\\n self.assertIsNotNone(self.client.websocket, 'missing websocket')\\n self.assertListEqual(\\n self.client.triggers, [trigger], 'trigger not found')\",\n \"def setUp(self):\\n self.transport = StringTransport()\\n self.protocol = IRCClient()\\n self.protocol.performLogin = False\\n self.protocol.makeConnection(self.transport)\\n\\n # Sanity check - we don't want anything to have happened at this\\n # point, since we're not in a test yet.\\n self.assertEqualBufferValue(self.transport.value(), \\\"\\\")\\n\\n self.addCleanup(self.transport.loseConnection)\\n self.addCleanup(self.protocol.connectionLost, None)\",\n \"async def _test_credentials(self, username, password):\\n try:\\n session = async_create_clientsession(self.hass)\\n client = SwitchBotCloudApiClient(session)\\n await client.authenticate(username, password)\\n return True\\n except Exception: # pylint: disable=broad-except\\n pass\\n return False\",\n \"def step_impl(context):\\n log.info(\\\"====> Checking if the CME server is alive via SSH\\\")\\n assert context.cme_session.is_server_alive(), \\\" ****> The server is not available via SSH\\\"\",\n \"def check_connection():\\n r = requests.get('https://www.google.com')\\n if r.status_code == 200:\\n print (colored(\\\"Connected.\\\", 'green'))\\n else:\\n print (colored(\\\"Not Connected.\\\", 'red'))\",\n \"def test_connect_disconnect(self):\\n ms_srv = common.MatchMakingServer(2222)\\n ms_srv.start()\\n time.sleep(2)\\n\\n clients = common.start_multiple_clients(ms_srv.port, 1)\\n\\n # Disconnect the client by killing it.\\n # The sleeps here ensure the MS server and client are given enough time\\n # to interact for log entries to be written.\\n time.sleep(5)\\n clients[0].kill()\\n time.sleep(5)\\n\\n starting_game_found = False\\n player_count_found = False\\n with open(ms_srv.local_log_path) as log_file:\\n for line in log_file:\\n if \\\"Starting Game\\\" in line:\\n starting_game_found = True\\n continue\\n elif \\\"1 players\\\" in line:\\n player_count_found = True\\n continue\\n\\n self.assertTrue(player_count_found,\\n \\\"MS server should have been connected to\\\")\\n self.assertFalse(starting_game_found, \\\"Game should not have started\\\")\",\n \"async def connect(self, **kwargs) -> bool:\\n return await self._backend.connect(**kwargs)\",\n \"def rpc_test_connection(client, rpc_server, rpc_user=BTC_RPC_USER, rpc_password=BTC_RPC_PASSWD, rpc_port=BTC_RPC_PORT):\\n try:\\n get_info = rpc_getinfo(client, rpc_server, rpc_user=rpc_user, rpc_password=rpc_password, rpc_port=rpc_port)\\n print(get_info)\\n return True\\n except JSONRPCException as err:\\n return False\",\n \"def test_irc_JOIN(self):\\n self.client.irc_JOIN(self.user, [self.channel])\\n self.client.irc_JOIN(\\\"Svadilfari!~svadi@yok.utu.fi\\\", [\\\"#python\\\"])\\n self.assertEqual(\\n self.client.methods,\\n [(\\\"joined\\\", (self.channel,)), (\\\"userJoined\\\", (\\\"Svadilfari\\\", \\\"#python\\\"))],\\n )\",\n \"async def test_send(self):\\n await self.webhook_connection.connect()\\n assert self.webhook_connection.is_connected is True\\n\\n http_message = HttpMessage(\\n dialogue_reference=(\\\"\\\", \\\"\\\"),\\n target=0,\\n message_id=1,\\n performative=HttpMessage.Performative.REQUEST,\\n method=\\\"get\\\",\\n url=\\\"/\\\",\\n headers=\\\"\\\",\\n body=\\\"\\\",\\n version=\\\"\\\",\\n )\\n envelope = Envelope(\\n to=\\\"addr\\\",\\n sender=\\\"my_id\\\",\\n message=http_message,\\n )\\n with patch.object(self.webhook_connection.logger, \\\"warning\\\") as mock_logger:\\n await self.webhook_connection.send(envelope)\\n await asyncio.sleep(0.01)\\n mock_logger.assert_any_call(\\n RegexComparator(\\n \\\"Dropping envelope=.* as sending via the webhook is not possible!\\\"\\n )\\n )\",\n \"def test_connect_success():\\n\\n t = Thread(target=setup_socket)\\n t.start()\\n\\n data_sender = DataSender('127.0.0.1', 12345)\\n server_response = data_sender.notify('test')\\n\\n assert server_response == 'ok'\\n\\n data_sender.close()\\n t.join()\",\n \"async def connect(self):\\n pass\",\n \"def test_postMessage(self): #GIVEN the appropriate environment variables are configured\\n testBot = bot.Bot(os.environ['bot_id'], os.environ['token'], os.environ['group_ID'])\\n status = testBot.postMessage('Zygium') #WHEN the bot posts a message\\n self.assertTrue(status == 202) # a status code of 202 should be returned\",\n \"def is_connected(self, test=False):\\n return self._server.is_connected()\",\n \"def test_connection(server_address, server_username=None, server_password=None, verbose=True):\\n\\n try:\\n if server_username is None and server_password is None:\\n r = requests.get(url=server_address)\\n else:\\n r = requests.get(url=server_address, auth=(server_username, server_password))\\n if r.ok:\\n if verbose:\\n print(\\\"Network connectivity: VERIFIED. Server \\\" + server_address + \\\" is reachable!\\\")\\n return True\\n else:\\n print(\\\"Something wrong during connection!\\\")\\n return False\\n\\n except Exception as e:\\n print(e)\\n return False\",\n \"def is_connected():\\r\\n global connection\\r\\n if connection is None:\\r\\n return False\\r\\n else:\\r\\n return True\",\n \"def test_is_connected():\\n with Replacer() as r:\\n queue_manager = uuid4().hex\\n channel = uuid4().hex\\n host = uuid4().hex\\n port = \\\"1431\\\"\\n conn_info = \\\"%s(%s)\\\" % (host, port)\\n\\n for expected in(True, False):\\n\\n def _connectTCPClient(*ignored_args, **ignored_kwargs):\\n pass\\n\\n def _getattr(self, name):\\n if expected:\\n class _DummyMethod(object):\\n pass\\n # The mere fact of not raising an exception will suffice\\n # for QueueManager._is_connected to understand it as an\\n # all's OK condition.\\n return _DummyMethod\\n else:\\n raise Exception()\\n\\n r.replace('pymqi.QueueManager.connectTCPClient', _connectTCPClient)\\n r.replace('pymqi.PCFExecute.__getattr__', _getattr)\\n\\n qmgr = pymqi.QueueManager(None)\\n qmgr.connectTCPClient(queue_manager, pymqi.cd(), channel, conn_info)\\n\\n eq_(qmgr.is_connected, expected)\",\n \"def test_connection(self):\\n with vertica_python.connect(**conn_info) as connection:\\n print(\\\"Connected to {} on host{} \\\".format(conn_info['database'],conn_info['host']))\\n cur = connection.cursor()\\n cur.execute(self.TEST_QUERY)\\n for row in cur.iterate():\\n print(\\\"The row is {}\\\".format(row))\",\n \"def test_connection(self):\\n try:\\n if 0 <= self.get_wavelength() <= 10e-6:\\n return True\\n except Exception:\\n print(\\\"Self test failed.\\\")\\n return False\",\n \"def startedConnecting(self, connector):\\n logger.log_info(\\\"Connecting to Discord...\\\")\",\n \"def test_call(self):\\n\\n with Client('username', 'password') as client:\\n self.setSessionResponse(content='{\\\"ok\\\": true}')\\n data = client.call(**self.build_parameters)\\n self.assertEqual(data, '{\\\"ok\\\": true}')\",\n \"def _test_connection(self, connection_string):\\n try:\\n engine = create_engine(connection_string)\\n connection = engine.connect()\\n connection.close()\\n return True\\n except Exception as e:\\n if options.debug:\\n logging.exception(\\\"Database connection failed: %s\\\" % e)\\n return False\",\n \"def test_for_client():\",\n \"def verify_core_connection():\\n if not base_url or not api_credentials:\\n retrieve_connection_info()\\n return\",\n \"def verify_core_connection():\\n if not base_url or not api_credentials:\\n retrieve_connection_info()\\n return\",\n \"def reconnecting(self) -> bool:\",\n \"def setUp(self):\\n\\t\\tself.conn = Client([\\\"127.0.0.1:11211\\\"], debug = 1)\",\n \"async def connect(self, retry=2):\\n # pylint: disable=too-many-return-statements\\n url = self._endpoint + PLUGWISE_PING_ENDPOINT\\n try:\\n with async_timeout.timeout(self._timeout):\\n resp = await self.websession.get(url,auth=self._auth)\\n except (asyncio.TimeoutError, aiohttp.ClientError):\\n if retry < 1:\\n _LOGGER.error(\\\"Error connecting to Plugwise\\\", exc_info=True)\\n return False\\n return await self.connect(retry - 1)\\n\\n result = await resp.text()\\n if not 'error' in result:\\n _LOGGER.error('Connected but expected text not returned, we got %s',result)\\n return False\\n\\n return True\",\n \"def Connected(self) -> bool:\",\n \"def Connected(self) -> bool:\",\n \"def test_server_connection():\\n response = client.get(\\\"/\\\")\\n assert response.ok\\n assert response.json() == {\\\"ID\\\": \\\"8dbaaa72-ff7a-4f95-887c-e3109e577edd\\\"}\",\n \"def __CheckConnectStatus(self):\\r\\n if not self.tn:\\r\\n print \\\"Connection is down!\\\"\\r\\n return False\\r\\n else:\\r\\n print \\\"Connection is alive!\\\"\\r\\n return True\",\n \"def test_connection(remote=False):\\n import socket\\n remote_server = 'www.google.com' if not remote else remote # TODO: maybe improve for China\\n try:\\n # does the host name resolve?\\n host = socket.gethostbyname(remote_server)\\n # can we establish a connection to the host name?\\n con = socket.create_connection((host, 80), 2)\\n return True\\n except:\\n print(\\\"Can't connect to a server...\\\")\\n pass\\n return False\",\n \"def bot():\\n log(log.DEBUG, 'Starting bot')\\n bot = BotSad()\\n bot.listen()\",\n \"async def test_api_ping(hassio_handler, aioclient_mock: AiohttpClientMocker) -> None:\\n aioclient_mock.get(\\\"http://127.0.0.1/supervisor/ping\\\", json={\\\"result\\\": \\\"ok\\\"})\\n\\n assert await hassio_handler.is_connected()\\n assert aioclient_mock.call_count == 1\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7984916","0.73088425","0.70733136","0.6972396","0.6922865","0.68271625","0.6753107","0.67112476","0.667889","0.6632673","0.66180444","0.66106814","0.65947783","0.65855265","0.6513005","0.64952236","0.6448094","0.6343192","0.63170284","0.63074887","0.63046765","0.63009566","0.63005745","0.62996215","0.62958837","0.6243791","0.6230994","0.62120926","0.6206492","0.6182653","0.6171127","0.61529994","0.6133834","0.612942","0.61147624","0.6085203","0.6075095","0.60688984","0.60526496","0.6033569","0.60294634","0.6017661","0.6011923","0.6011789","0.5997301","0.5989346","0.5985346","0.598227","0.59812903","0.59475297","0.5945485","0.5937613","0.5930049","0.5928397","0.59236246","0.5906969","0.5901598","0.590052","0.5890884","0.58739036","0.5872789","0.5871739","0.58633316","0.5860915","0.5837006","0.5835685","0.5833128","0.58315504","0.5821542","0.58142173","0.5808095","0.5807307","0.5796544","0.5792676","0.5788109","0.5774092","0.576447","0.5763651","0.5756089","0.57406276","0.57390785","0.5739018","0.5737229","0.5731714","0.57315874","0.5715341","0.5714226","0.5712278","0.5709586","0.5706055","0.5706055","0.5705476","0.5704602","0.5700031","0.5694824","0.5694824","0.5694678","0.56910336","0.56907725","0.5689397","0.5684574"],"string":"[\n \"0.7984916\",\n \"0.73088425\",\n \"0.70733136\",\n \"0.6972396\",\n \"0.6922865\",\n \"0.68271625\",\n \"0.6753107\",\n \"0.67112476\",\n \"0.667889\",\n \"0.6632673\",\n \"0.66180444\",\n \"0.66106814\",\n \"0.65947783\",\n \"0.65855265\",\n \"0.6513005\",\n \"0.64952236\",\n \"0.6448094\",\n \"0.6343192\",\n \"0.63170284\",\n \"0.63074887\",\n \"0.63046765\",\n \"0.63009566\",\n \"0.63005745\",\n \"0.62996215\",\n \"0.62958837\",\n \"0.6243791\",\n \"0.6230994\",\n \"0.62120926\",\n \"0.6206492\",\n \"0.6182653\",\n \"0.6171127\",\n \"0.61529994\",\n \"0.6133834\",\n \"0.612942\",\n \"0.61147624\",\n \"0.6085203\",\n \"0.6075095\",\n \"0.60688984\",\n \"0.60526496\",\n \"0.6033569\",\n \"0.60294634\",\n \"0.6017661\",\n \"0.6011923\",\n \"0.6011789\",\n \"0.5997301\",\n \"0.5989346\",\n \"0.5985346\",\n \"0.598227\",\n \"0.59812903\",\n \"0.59475297\",\n \"0.5945485\",\n \"0.5937613\",\n \"0.5930049\",\n \"0.5928397\",\n \"0.59236246\",\n \"0.5906969\",\n \"0.5901598\",\n \"0.590052\",\n \"0.5890884\",\n \"0.58739036\",\n \"0.5872789\",\n \"0.5871739\",\n \"0.58633316\",\n \"0.5860915\",\n \"0.5837006\",\n \"0.5835685\",\n \"0.5833128\",\n \"0.58315504\",\n \"0.5821542\",\n \"0.58142173\",\n \"0.5808095\",\n \"0.5807307\",\n \"0.5796544\",\n \"0.5792676\",\n \"0.5788109\",\n \"0.5774092\",\n \"0.576447\",\n \"0.5763651\",\n \"0.5756089\",\n \"0.57406276\",\n \"0.57390785\",\n \"0.5739018\",\n \"0.5737229\",\n \"0.5731714\",\n \"0.57315874\",\n \"0.5715341\",\n \"0.5714226\",\n \"0.5712278\",\n \"0.5709586\",\n \"0.5706055\",\n \"0.5706055\",\n \"0.5705476\",\n \"0.5704602\",\n \"0.5700031\",\n \"0.5694824\",\n \"0.5694824\",\n \"0.5694678\",\n \"0.56910336\",\n \"0.56907725\",\n \"0.5689397\",\n \"0.5684574\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":22495,"cells":{"query":{"kind":"string","value":"Link a spotify account to the bot."},"document":{"kind":"string","value":"async def link(self, ctx):\n if not is_linked(ctx.author.id):\n token = str(uuid.uuid4())\n valid_until = int((datetime.utcnow() + timedelta(days=1)).timestamp())\n add_token(ctx.author.display_name, ctx.author.id, token, valid_until, str(ctx.author.avatar_url))\n web_base_url = get_setting('web_base_url')\n await ctx.author.send(f\"Please visit {web_base_url}/link/{token} to link your Spotify account. \"\n f\"This link will expire after 24 hours.\")\n if ctx.guild is not None:\n await ctx.message.add_reaction('📬')\n else:\n await ctx.reply(\"You have already linked a spotify account!\")"},"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":["async def link(ctx, bot: typing.Union[discord.Member, discord.User]):\n if not bot.bot:\n return await r(ctx, \"Not a bot.\")\n await r(ctx, f'<https://www.motiondevelopment.top/bots/{bot.id}>')","async def info(self, ctx):\n if ctx.guild is not None:\n await ctx.reply(\"This command can only be used in DMs, because of privacy reasons.\")\n raise commands.CommandError(\"Invoker not in DMs.\")\n\n if not is_linked(ctx.author.id):\n await ctx.reply(f\"You don't have a Spotify account linked. Please link one using \"\n f\"`{self.bot_config['prefix']}link`.\")\n raise commands.CommandError(\"User has no spotify account linked.\")\n\n sp = init_spotify(ctx.author.id)\n result = sp.me()\n msg_embed = Embed()\n msg_embed.title = \"Linked Spotify account\"\n msg_embed.url = result['external_urls'].get('spotify', None)\n if len(result['images']) > 0:\n msg_embed.set_image(url=result['images'][0]['url'])\n msg_embed.add_field(name=\"Display name\", value=result['display_name'])\n msg_embed.add_field(name=\"Subscription type\", value=result.get('product', 'free'))\n if result.get('product', None) != \"premium\":\n msg_embed.add_field(name=\"Warning!\",\n value=\"Only accounts with Spotify Premium can use this bot!\",\n inline=False)\n await ctx.reply(embed=msg_embed)","async def join(self, ctx):\n if ctx.guild is None:\n await ctx.reply(\"This command can only be used in a server, not in DMs.\")\n raise commands.CommandError(\"Invoker not in a guild.\")\n\n if not is_linked(ctx.author.id):\n await ctx.reply(f\"You don't have a Spotify account linked. Please link one using \"\n f\"`{self.bot_config['prefix']}link`.\")\n raise commands.CommandError(\"User has no spotify account linked.\")\n\n if ctx.author.voice is None or ctx.author.voice.channel is None:\n await ctx.reply(\"You need to be in a voice channel to use this command.\")\n raise commands.CommandError(\"Invoker not connected to a voice channel.\")\n\n if ctx.voice_client is not None and ctx.author.voice.channel != ctx.voice_client.channel:\n await ctx.reply(\"You need to be in the same voice channel as the bot to use this command.\")\n raise commands.CommandError(\"Invoker not in same voice channel as bot.\")\n\n # Connect to voice channel that the invoker is in (if we're not already connected somewhere else)\n try:\n controller_instance = await ctx.author.voice.channel.connect(reconnect=False)\n except asyncio.TimeoutError:\n await ctx.reply(\"Timeout error while connecting to the voice channel. Please try again later.\")\n return\n except discord.ClientException:\n await ctx.reply(\"I'm already connected to a voice channel, please disconnect me first!\")\n return\n except OpusNotLoaded:\n await ctx.reply(\"Opus library was not loaded. Please try again later.\")\n return\n\n if controller_instance is not None:\n # Create a listening socket for the future incoming audio connection\n try:\n controller = SpotifyController.create(controller_instance.channel.id,\n controller_instance.channel.bitrate,\n ctx.author.id)\n except ValueError as e:\n await ctx.reply(e)\n return\n\n controller.get_or_create_playlist()\n\n await ctx.author.send(f\"Please enter the following code in your client application and click \"\n f\"'connect' to start playing music!\\nCode: `{controller.link_code}`\")\n await ctx.reply(f\"Ready and waiting for a connection! I've DM'ed you a code to fill in in your client app.\"\n f\"\\nIn the mean time, you can start adding songs with `{self.bot_config['prefix']}add`, \"\n f\"and view the queue with `{self.bot_config['prefix']}queue`\")","async def invite(self, ctx):\r\n myInvite = discord.utils.oauth_url(self.bot.user.id, permissions=discord.Permissions(permissions=8))\r\n await ctx.channel.send('Invite me to *your* server with this link: \\n\\n<{}>'.format(myInvite))","async def invite(self):\n link = \"https://discordapp.com/oauth2/authorize?client_id=282765243862614016&scope=bot&permissions=19456\"\n await self.bot.say(\"Invite me to your server with this link!\\n\" + link)","async def inviteme(self):\r\n\r\n #Your code will go here\r\n await self.bot.say(\"Here is a link to Invite Me: http://bit.ly/TacoBot\")","async def invite(self, ctx):\n link = \"https://discordapp.com/oauth2/authorize?client_id=282765243862614016&scope=bot&permissions=19456\"\n await ctx.send(\"Invite me to your server with this link!\\n\" + link)","async def spotify(self, ctx):\n if ctx.invoked_subcommand is None:\n await ctx.send('Invalid command passed. Use the `?help spotify` command to learn more.')","def share_link(cls, user, link):","def share_link(cls, user, link):","async def invite(self, ctx):\n await ctx.send(f\"**{ctx.author.name}**, use this URL to invite me\\n<{discord.utils.oauth_url(self.bot.user.id)}>\")","async def link_to(self, *args):\n pass","async def invite(ctx):\n permissions = 2134207679\n url = discord.utils.oauth_url(client_id=bot.user.id, permissions=discord.Permissions(permissions=permissions),\n scopes=(\"bot\", \"applications.commands\"))\n view = discord.ui.View()\n view.add_item(discord.ui.Button(label=\"Invite\", url=url))\n await ctx.respond(\"I'm glad you want to add me to your server, here's a link!\", view=view)","def wrap_spotify_link(item, text=''):\n\n # generate default text if no text has been given\n if not text:\n name = item['name']\n if item['type'] == 'playlist':\n user = SP.user(item['owner']['id'])['display_name']\n text = f'{name} by {user}'\n elif item['type'] == 'artist':\n text = name\n else:\n artist = item['artists'][0]['name']\n text = f'{name} by {artist}'\n\n link = item['external_urls']['spotify']\n return f'<a href=\"{link}\">{text}</a>'","async def user_aclient(user_token):\n sender = tk.RetryingSender(sender=tk.AsyncSender())\n yield tk.Spotify(user_token, sender=sender)\n await sender.close()","async def spotify(self, ctx, *, query):\n\n # Setup the headers with the token that should be here\n headers = {\"Authorization\": \"Bearer {}\".format(self._token)}\n opts = {\"q\": query, \"type\": \"track\"}\n url = \"https://api.spotify.com/v1/search\"\n response = await utils.request(url, headers=headers, payload=opts)\n try:\n await ctx.send(\n response.get(\"tracks\")\n .get(\"items\")[0]\n .get(\"external_urls\")\n .get(\"spotify\")\n )\n except (KeyError, AttributeError, IndexError):\n await ctx.send(\"Couldn't find a song for:\\n{}\".format(query))","async def links(self, ctx):\n await ctx.send(\"https://discordapp.com/channels/566451504332931073/681617252814159904/755489156146397311\")","def use_account(self, token, url=QE_URL, **kwargs):\n credentials = Credentials(token, url, **kwargs)\n\n self._append_account(credentials)","async def invite(self, ctx):\n embed = discord.Embed(title=\"Invite\", description=f\"**{ctx.author.name}**, use this URL to invite me\\n[link](https://discord.com/oauth2/authorize?client_id=749629426777456691&permissions=8&scope=bot)\", color=0xeff0f1)\n await ctx.send(embed=embed)","def auth(self):\n token = spotipy.util.prompt_for_user_token(self.username,\n self.scope,\n client_id = self.client_id,\n client_secret = self.client_secret,\n redirect_uri= self.redirect_uri)\n if token:\n self.spotify = spotipy.Spotify(auth=token)\n else:\n print(colored.stylize(\"\"\"\\n[*] \"\"\", colored.fg(\"light_red\")) + 'Cant get token for: %s\\n' % (self.username))\n exit()","async def github(self, ctx):\n await ctx.send('https://github.com/nick411077/nickcan_bot')","async def server():\n await bot.say(\"https://discord.gg/Eau7uhf\")","def shareNote(self, authenticationToken, guid):\r\n pass","async def addme(self, ctx):\n invite_url = discord.utils.oauth_url(self.bot.user.id, permissions=discord.Permissions(8))\n embed = self.bot.embeds.embed_builder(title='Add this bot to your own Discord server',\n description=invite_url,\n has_footer=False)\n await ctx.send(embed=embed)","async def _invite(self, ctx: Context):\n\n perm_int = discord.Permissions(268494928)\n\n data = await self.bot.application_info()\n invite_url = discord.utils.oauth_url(data.id, permissions=perm_int)\n\n value = (\n f\"Invite TvM Assistant to your bot by [clicking here]({invite_url}).\"\n \"\\n\\nInviting the bot will give it some management permissions. You can\"\n \" review them when you use the link.\"\n )\n\n embed = discord.Embed(color=await ctx.embed_colour(), description=value)\n embed.set_author(name=f\"Invite TvM Assistant\", icon_url=ctx.me.avatar_url)\n\n try:\n await ctx.send(embed=embed)\n except discord.Forbidden:\n return await ctx.send(\n f\"{invite_url}\\n\\nInviting the bot will give it some management permissions.\"\n \" You can review them when you use the link.\"\n )","def link(self):\n return f\"https://{DOMAIN}/invite/{self.code}\"","async def invite(self, ctx, plain_url: bool = False):\n\n if not plain_url:\n try:\n await ctx.embed(\n 'Click to invite me to your server!',\n title_url=self.bot.invite_url,\n colour='blue',\n icon=\"https://i.imgur.com/DtPWJPG.png\"\n )\n except discord.errors.Forbidden:\n pass\n else:\n return\n\n await ctx.send(f\"Invite URL: <{self.bot.invite_url}>\")","async def _discord(self, ctx: Context):\n\n await ctx.send(\n f\"You can join the Brawlcord community server by using this link: {COMMUNITY_SERVER}\"\n )","def link(self, pin):\n headers = {\n 'Content-Type': 'application/x-www-form-urlencoded',\n 'X-Plex-Product': 'Plex SSO'\n }\n data = {'code': pin}\n self.query(self.LINK, self._session.put, headers=headers, data=data)","async def _invite(self, ctx: Context):\n\n # read_messages=True,\n # send_messages=True,\n # manage_messages=True,\n # embed_links=True,\n # attach_files=True,\n # external_emojis=True,\n # add_reactions=True\n perms = discord.Permissions(322624)\n\n try:\n data = await self.bot.application_info()\n invite_url = discord.utils.oauth_url(data.id, permissions=perms)\n value = (\n \"Add Brawlcord to your server by **[clicking here]\"\n f\"({invite_url})**.\\n\\n**Note:** By using the link\"\n \" above, Brawlcord will be able to\"\n \" read messages,\"\n \" send messages,\"\n \" manage messages,\"\n \" embed links,\"\n \" attach files,\"\n \" add reactions,\"\n \" and use external emojis\"\n \" wherever allowed.\\n\\n*You can remove the permissions manually,\"\n \" but that may break the bot.*\"\n )\n except Exception as exc:\n invite_url = None\n value = (\n f\"Error \\\"{exc}\\\" while generating invite link.\"\n \" Notify bot owner using the `-report` command.\"\n )\n\n embed = discord.Embed(color=EMBED_COLOR, description=value)\n embed.set_author(\n name=f\"Invite {ctx.me.name}\", icon_url=ctx.me.avatar_url)\n # embed.add_field(name=\"__**Invite Link:**__\", value=value)\n\n try:\n await ctx.send(embed=embed)\n except discord.Forbidden:\n return await ctx.send(\n \"I do not have the permission to embed a link.\"\n \" Please give/ask someone to give me that permission.\"\n )","async def invite(self, ctx):\n embed = discord.Embed(title='Invite links for NOVA',\n description='[<:news:730866149109137520> Required Permissions](https://discord.com/api/'\n 'oauth2/authorize?client_id=709922850953494598&permissions=1573252215&scope='\n 'bot)\\n'\n '[<:news:730866149109137520> No Permissions]'\n '(https://discord.com/api/oauth2/authorize?client_id=709922850953494598&permi'\n 'ssions=0&scope=bot)\\n[<:news:730866149109137520> All Permissions (admin)]'\n '(https://discord.com/api/oauth2/authorize?client_id=709922850953494598&perm'\n 'issions=8&scope=bot)', color=0x5643fd)\n embed.set_footer(text='Developed by YeetVegetabales', icon_url='https://cdn.discordapp.com/avatars'\n '/569374429218603019'\n '/a_6dac6946906e498650f6c2466aa82200.gif?size'\n '=256&f=.gif')\n embed.set_thumbnail(url='https://images-ext-2.discordapp.net/external/54Mim4lahztGCP4hgmpy4lOdEUc4'\n '-dOeNA_x6hVHMlc/%3Fsize%3D4096/https/cdn.discordapp.com/avatars/709922850953494598'\n '/f78ed19924e8c95abc30f406d47670d7.png')\n await ctx.send(embed=embed)","def Connect(self,scope):\n\n \"\"\"\n Calling util.prompt_for_user_token will open Spotify’s application authorization\n page in your browser (and require you to log in if you are not already logged in\n to spotify.com), unless a locally cached access token exist from a previous authorization/authentication.\n \"\"\"\n try:\n token = util.prompt_for_user_token(\n self.username,\n scope,\n self.client_id,\n self.secret_id,\n self.redirect_uri)\n except ImportError:\n self._isConnected = False\n print(\" onnecting to Spotify failed\") \n\n\n if token:\n sp = spotipy.Spotify(auth=token)\n self._isConnected = True\n return sp\n else:\n print(\"Can't get token for\", self.username)\n self._isConnected = False","def user_client(user_token):\n sender = tk.RetryingSender(sender=tk.SyncSender())\n yield tk.Spotify(user_token, sender=sender)\n sender.close()","def update_access_token(self):\n self.token = util.prompt_for_user_token(self._username, scope,\n client_id=const.CLIENT_ID,\n client_secret=const.CLIENT_SECRET,\n redirect_uri=const.REDIRECT_URL)\n self._client = spotipy.Spotify(auth=self.token)","async def sendPing(self, title, progress, link, image):\n embed = discord.Embed(title=title, description=progress, color=discord.Color.green())\n embed.add_field(name=\"Link\", value=f\"[{link}]({link})\")\n embed.set_thumbnail(url=image)\n await self.bot.get_cog('UserCog').channel.send(self.bot.get_cog('UserCog').discordUser.mention, embed=embed)","async def botinvite_command(self, ctx):\n invite = f\"https://discord.com/api/oauth2/authorize?client_id={self.client.user.id}&permissions=1374809815&scope=bot\"\n await ctx.send(invite)","def follow_someone(screen_name):\n twitter.create_friendship(screen_name=screen_name)","def link_account(self, accountid, provider, providerid):\n auth = 'appkey='+ self._lr_object._get_api_key()+ '&appsecret='+ self._lr_object._get_api_secret()\n payload = {'accountid': accountid, 'provider': provider, 'providerid': providerid}\n url = SECURE_API_URL + \"raas/v1/account/link\" + \"?\" + auth\n return self._lr_object._post_json(url, payload)","async def invite(self, ctx):\n await ctx.send(f'🐱You can invite me to your server using the following url:\\n{self.invite_url}'\n '\\n\\nYou will need the **Manage Server** permission to add me to a server. '\n f'Run `{self.heleus.command_prefix[0]}help` to see what you can customise!')","async def set_bot():\n\n self = await LOCAL.APP.get_me()\n LOCAL.bot_id = self.id\n LOCAL.bot_name = self.first_name\n LOCAL.bot_username = self.username","def linkTrackToChannel(*args, **kwargs):\n pass","async def info(ctx):\n embed = discord.Embed(title=\"Zane Bot\", description=\"All hail the hypnotoad!\", color=0x0091C5)\n\n # give info about you here\n embed.add_field(name=\"Author\", value=\"Zanexius\")\n\n # Shows the number of servers the bot is member of.\n embed.add_field(name=\"Server count\", value=f\"{len(bot.guilds)}\")\n\n # give users a link to invite thsi bot to their server\n embed.add_field(name=\"Invite\", value=\"[Invite link](<insert your OAuth invitation link here>)\")\n\n await ctx.send(embed=embed)","async def app_aclient(app_token):\n sender = tk.RetryingSender(sender=tk.AsyncSender())\n yield tk.Spotify(app_token, sender=sender)\n await sender.close()","async def link(self, ctx: Context) -> None:\r\n try:\r\n params: List[str] = get_cmd_params(ctx)\r\n\r\n if len(params) < 1 or not params[0].isdigit() or int(params[0]) > len(self.yt_result.ids):\r\n await ctx.send(\"Please enter a valid video number from 0 to 5\")\r\n return\r\n\r\n self.yt_link = await self.message.edit(content=self.yt_result.get_link(int(params[0])))\r\n await ctx.message.delete()\r\n except Exception as e:\r\n await self.channels.log_error(e, \"ytl\")","async def invite(self, ctx):\n perms = discord.Permissions.text()\n perms.update(read_messages=True, manage_messages=True,\n mention_everyone=False, send_tts_messages=False)\n await ctx.send(f'Invite me here:\\n<{discord.utils.oauth_url(self.bot.user.id, perms)}>')","def create_link(self):\n self.filename = App.get_running_app().root.ids.camera_screen.capture()\n self.url = FileSharer(self.filename).share()\n self.ids.label.text = self.url","async def inspire(self, ctx):\n async with aiohttp.ClientSession() as session:\n async with session.get('http://inspirobot.me/api?generate=true') as response:\n if(response.status == 200):\n imgurl = await response.text()\n embed = discord.Embed(colour=discord.Colour.dark_blue())\n embed.set_image(url=imgurl)\n embed.set_footer(text='http://inspirobot.me/')\n await ctx.bot.send_message(ctx.message.channel, embed=embed)","async def discord(self, ctx):\n embed = discord.Embed(title='Join the discord today!', color=0x5643fd, description=\"This server is where \"\n \"all of \"\n \"NOVA's updates and \"\n \"important \"\n \"announcements will pass \"\n \"through. The creator of \"\n \"this \"\n \"bot, YeetVegetabales#5313, \"\n \"will also be there testing \"\n \"and letting the communtiy \"\n \"in \"\n \"on things first hand!\")\n embed.set_thumbnail(url='https://images-ext-2.discordapp.net/external/AQCEqCF4Yl_PWAfuA-GReZoDify6'\n '--y4hXOJVkqaDHo/%3Fsize%3D1024/https/cdn.discordapp.com/avatars/709922850953494598'\n '/f78ed19924e8c95abc30f406d47670d7.png')\n embed.add_field(name='Server Invite', value='<:news:730866149109137520> '\n '[Join here](https://discord.gg/Uqh9NXY)')\n await ctx.send(embed=embed)","def contact_linkup(self, request, pk):\n obj_api = api()\n title_contact = \"Tu contacto Linkup\"\n token = request.session['token']\n resp = obj_api.get(slug='sellers/' + pk + \"/\", token=token)\n return render(request, 'frontend/actors/client/my_account.html', {'data_user': resp, \n 'title_contact': title_contact})","def click(cls, user, link):\n pass","def __init__(self, username):\n self.spotify = spotipy.Spotify(simple_auth_token(username))","async def vote(self, ctx):\n embed = discord.Embed(title = \"Here are some bot lists that you can vote for me on, voters may soon™ recieve perks\", color = discord.Color.blurple())\n embed.add_field(name = \"Bots For Discord\", value = \"[Click Here](https://botsfordiscord.com/bot/592811241756688405/vote)\")\n embed.add_field(name = \"Discord Boats\", value = \"[Click Here](https://discord.boats/bot/592811241756688405/vote)\")\n embed.add_field(name = \"Divine Discord Bots\", value = \"[Click Here](https://divinediscordbots.com/bot/592811241756688405/vote)\") \n embed.add_field(name = \"Botlist.space\", value = \"[Click Here](https://botlist.space/bot/592811241756688405/upvote)\") \n embed.set_thumbnail(url = self.bot.user.avatar_url)\n await ctx.send(embed = embed)","def status(verbose):\n user_data = Spotify.request('me', method='GET')\n click.echo('Logged in as {}'.format(user_data['display_name']))\n if verbose:\n click.echo('Credentials stored in {}'.format(CREDS_PATH))\n return","async def inspire(self, ctx):\n async with aiohttp.ClientSession() as session:\n async with session.get('http://inspirobot.me/api?generate=true') as response:\n if(response.status == 200):\n imgurl = await response.text()\n embed = discord.Embed(colour=discord.Colour.dark_blue())\n embed.set_image(url=imgurl)\n embed.set_footer(text='http://inspirobot.me/')\n await ctx.send(embed=embed)","async def add(self, ctx, query):\n if ctx.guild is None:\n await ctx.reply(\"This command can only be used in a server, not in DMs.\")\n raise commands.CommandError(\"Invoker not in a guild.\")\n\n if ctx.voice_client is None or ctx.voice_client.channel is None:\n await ctx.reply(f\"I am not in a voice channel, invite me first with `{self.bot_config['prefix']}join`.\")\n raise commands.CommandError(\"Bot not connected to a voice channel.\")\n\n if ctx.author.voice is None or ctx.author.voice.channel is None:\n await ctx.reply(\"You need to be in a voice channel to use this command.\")\n raise commands.CommandError(\"Invoker not connected to a voice channel.\")\n\n if ctx.voice_client is not None and ctx.author.voice.channel != ctx.voice_client.channel:\n await ctx.reply(\"You need to be in the same voice channel as the bot to use this command.\")\n raise commands.CommandError(\"Invoker not in same voice channel as bot.\")\n\n if ctx.voice_client is not None and ctx.voice_client.channel is not None:\n controller = SpotifyController.get_instance(ctx.voice_client.channel.id)\n if controller is None:\n await ctx.reply(f\"I'm not playing anything at the moment.\")\n raise commands.CommandError(\"Bot not connected to active spotify session.\")\n else:\n await ctx.reply(f\"I am not in a voice channel, invite me first with `{self.bot_config['prefix']}join`.\")\n raise commands.CommandError(\"Bot not connected to a voice channel.\")\n\n print(f\"Adding {query} to playlist\")\n controller = SpotifyController.get_instance(ctx.voice_client.channel.id)\n sp = controller.get_playlist_api()\n\n uri = None\n item_info = None\n item_type = None\n\n # If link, queue by link\n if query.startswith(\"http://\") or query.startswith(\"https://\"):\n m = SPOTIFY_LINK_REGEX.match(query)\n if m:\n uri = f\"spotify:{m.group('type')}:{m.group('id')}\"\n item_type = m.group('type')\n if item_type == \"track\":\n try:\n item_info = sp.track(m.group('id'))\n except SpotifyException:\n await ctx.send(f\"Cannot add! Invalid track!\")\n return\n elif item_type == \"album\":\n try:\n item_info = sp.album(m.group('id'))\n except SpotifyException:\n await ctx.send(f\"Cannot add! Invalid album!\")\n return\n elif item_type == \"playlist\":\n try:\n item_info = sp.playlist(m.group('id'))\n except SpotifyException:\n await ctx.send(f\"Cannot add! Invalid or private playlist!\")\n return\n else:\n await ctx.send(f\"Type {item_type} not supported!\")\n return\n\n print(f\"Converted link to ID '{uri}'\")\n else:\n await ctx.send(f\"Only spotify links are supported!\")\n return\n\n # If spotify uri, queue by link\n if uri is None:\n m = SPOTIFY_URI_REGEX.match(query)\n if m:\n uri = f\"spotify:{m.group('type')}:{m.group('id')}\"\n item_type = m.group('type')\n if item_type == \"track\":\n try:\n item_info = sp.track(m.group('id'))\n except SpotifyException:\n await ctx.send(f\"Cannot add! Invalid track!\")\n return\n elif item_type == \"album\":\n try:\n item_info = sp.album(m.group('id'))\n except SpotifyException:\n await ctx.send(f\"Cannot add! Invalid album!\")\n return\n elif item_type == \"playlist\":\n try:\n item_info = sp.playlist(m.group('id'))\n except SpotifyException:\n await ctx.send(f\"Cannot add! Invalid or private playlist!\")\n return\n else:\n await ctx.send(f\"Type {item_type} not supported!\")\n return\n print(f\"Converted URI to ID '{uri}'\")\n\n # Else, try to search\n if uri is None:\n await ctx.send(f'Searching not supported yet.')\n return\n\n # Add URI\n if uri is not None:\n if item_type == \"track\":\n sp.playlist_add_items(controller.playlist[\"id\"], items=[uri])\n elif item_type == \"album\":\n album_tracks = controller.get_album_tracks(item_info['id'])\n i, max_tracks = 0, 50\n while i < len(album_tracks):\n block = [t['uri'] for t in album_tracks[i:i+max_tracks]]\n sp.playlist_add_items(controller.playlist[\"id\"], items=block)\n i += max_tracks\n elif item_type == \"playlist\":\n playlist_tracks = controller.get_playlist_tracks(item_info['id'])\n i, max_tracks = 0, 50\n while i < len(playlist_tracks):\n block = [t['uri'] for t in playlist_tracks[i:i+max_tracks]]\n sp.playlist_add_items(controller.playlist[\"id\"], items=block)\n i += max_tracks\n else:\n await ctx.send(f\"Cannot add! Type {item_type} not supported!\")\n return\n\n try:\n controller.update_playlist()\n except IndexError as e:\n print(e, file=sys.stderr)\n\n msg_embed = Embed()\n if item_type == \"track\":\n full_title = SpotifyController.format_full_title(item_info)\n try:\n thumbnail = item_info['album']['images'][0]['url']\n except IndexError:\n thumbnail = None\n msg_embed.description = f\"Added [{full_title}]({item_info['external_urls']['spotify']}) to queue!\"\n msg_embed.set_thumbnail(url=thumbnail)\n elif item_type == \"album\":\n full_title = SpotifyController.format_full_title(item_info)\n try:\n thumbnail = item_info['images'][0]['url']\n except IndexError:\n thumbnail = None\n num_tracks = item_info['tracks']['total']\n msg_embed.description = f\"Added album [{full_title}]({item_info['external_urls']['spotify']}) \" \\\n f\"({num_tracks} tracks) to queue!\"\n msg_embed.set_thumbnail(url=thumbnail)\n elif item_type == \"playlist\":\n title = item_info['name']\n try:\n thumbnail = item_info['images'][0]['url']\n except IndexError:\n thumbnail = None\n num_tracks = item_info['tracks']['total']\n msg_embed.description = f\"Added playlist [{title}]({item_info['external_urls']['spotify']}) \" \\\n f\"({num_tracks} tracks) to queue!\"\n msg_embed.set_thumbnail(url=thumbnail)\n else:\n # Shouldn't happen, but lets add a message anyway...\n msg_embed.description = f\"Unknown {item_type} item added to queue!\"\n await ctx.reply(embed=msg_embed)","def click(cls, user, link):\r\n pass","async def botlink(self, ctx):\n async with self.bot.pool.acquire() as conn:\n async with conn.cursor() as cur:\n await cur.execute('SELECT * FROM BLChannels WHERE \"id\"=%s;', (ctx.message.channel.id,))\n isregistered = await cur.fetchone()\n if isregistered: \n return\n else: \n embed = discord.Embed(title=\"Mafu, the multi use bot\", colour=discord.Colour(0x6c56b0),url=\"https://discordapp.com/oauth2/authorize?client_id=432292171371118592&scope=bot\") \n await ctx.send(embed=embed)","async def now_playing(self, ctx):\n if ctx.guild is None:\n await ctx.reply(\"This command can only be used in a server, not in DMs.\")\n raise commands.CommandError(\"Invoker not in a guild.\")\n\n if ctx.voice_client is None or ctx.voice_client.channel is None:\n await ctx.reply(f\"I am not in a voice channel, invite me first with `{self.bot_config['prefix']}join`.\")\n raise commands.CommandError(\"Bot not connected to a voice channel.\")\n\n if ctx.author.voice is None or ctx.author.voice.channel is None:\n await ctx.reply(\"You need to be in a voice channel to use this command.\")\n raise commands.CommandError(\"Invoker not connected to a voice channel.\")\n\n if ctx.voice_client is not None and ctx.author.voice.channel != ctx.voice_client.channel:\n await ctx.reply(\"You need to be in the same voice channel as the bot to use this command.\")\n raise commands.CommandError(\"Invoker not in same voice channel as bot.\")\n\n if ctx.voice_client is not None and ctx.voice_client.channel is not None:\n controller = SpotifyController.get_instance(ctx.voice_client.channel.id)\n if controller is None:\n await ctx.reply(f\"I'm not playing anything at the moment.\")\n raise commands.CommandError(\"Bot not connected to active spotify session.\")\n else:\n await ctx.reply(f\"I am not in a voice channel, invite me first with `{self.bot_config['prefix']}join`.\")\n raise commands.CommandError(\"Bot not connected to a voice channel.\")\n\n sp = controller.get_api()\n info = sp.current_playback()\n if not controller.is_playing_on_bot():\n await ctx.send(\"Not playing anything at the moment...\")\n return\n\n # Add URI\n if info is not None:\n track_info = info['item']\n try:\n thumbnail = track_info['album']['images'][0]\n except IndexError:\n thumbnail = None\n msg_embed = Embed()\n artist_url = track_info['artists'][0]['external_urls']['spotify']\n msg_embed.set_author(name=SpotifyController.format_artist(track_info), url=artist_url)\n msg_embed.title = SpotifyController.format_title(track_info)\n msg_embed.description = f\"{SpotifyController.format_album_name(track_info)}\\n\"\n msg_embed.url = track_info['external_urls']['spotify']\n msg_embed.set_thumbnail(url=thumbnail['url'])\n msg_embed.set_footer(text=f\"{SpotifyController.format_progress(info)}\")\n await ctx.reply(embed=msg_embed)\n else:\n await ctx.send(\"Not playing anything at the moment...\")","def login():\n url = AUTH_URL + '&state=' + str(uuid1())\n try:\n import webbrowser\n webbrowser.open(url)\n except:\n pass\n \n print('Go to the following link in your browser:\\n\\n\\t{}\\n'.format(url))\n\n auth_code = input('Enter verification code: ')\n print('\\nObtaining access token...')\n Spotify.refresh(auth_code)\n print('Credentials saved to {}'.format(CREDS_PATH))\n return","def link(request, link_id):\n bkmrk_id = Bookmark.decode_id(link_id)\n bookmark = get_object_or_404(Bookmark, pk=bkmrk_id)\n if request.user.is_authenticated():\n Click.objects.create(human=request.user, bookmark=bookmark)\n else:\n Click.objects.create(bookmark=bookmark)\n return redirect(bookmark.url)","def get_token_link(self):\n return self.env['ir.config_parameter'].search([('key', '=', 'web.base.url')]).value + \"/web/signup?inv_id={}\".format(self.name)","async def meow(self, ctx):\n url = 'http://placekitten.com/'\n async with aiohttp.ClientSession() as session:\n async with session.get(url) as response:\n source = await response.read()\n art = str(source[559:1000], 'utf8')\n embed = discord.Embed(title='{}:'.format(ctx.message.author.name),\n description='```{}```'.format(art),\n colour=0xf20006)\n a = await self.bot.say(embed=embed)\n await self.bot.add_reaction(a, self.emojiUnicode['succes'])","async def support(self, ctx):\n await ctx.send('Join the support server here: https://discord.gg/bAq8Ec5JPQ')","def login_link(self):\n\t\tresponse = self.client.post(self._endpoint + \"/loginLink\")\n\t\treturn response.json[\"link\"]","async def follow(follow):\n await follow.edit(\n f\"`FOLLOW {DEFAULTUSER} ON` \\n\\n\"\n f\"[InstaGram](https://www.instagram.com/mayur_karaniya) \\n\\n\"\n f\"[FaceBook](https://www.facebook.com/mkaraniya) \\n\\n\"\n f\"[YouTube](https://www.youtube.com/channel/UCeKQxQK7XZ3jGi3541uWATg?sub_confirmation=1) \"\n )","async def twitter_follow(self, ctx, handle):\n discord_channel = ctx.message.channel\n\n # Check for required permissions\n if not discord_channel.permissions_for(discord_channel.server.me).embed_links:\n raise TwitterError('\\N{WARNING SIGN} The `Embed Links` permission in this channel is required to display tweets properly. \\N{WARNING SIGN}')\n\n sane_handle = handle.lower().lstrip('@')\n conf = dutils.get(self.conf.follows, screen_name=sane_handle)\n if conf is None:\n # New Twitter channel, retrieve the user info\n partial = functools.partial(self.api.get_user, screen_name=sane_handle)\n try:\n user = await self.bot.loop.run_in_executor(None, partial)\n except tweepy.TweepError as e:\n if e.api_code == 50:\n raise TwitterError('User \"{}\" not found.'.format(handle)) from e\n else:\n log.error(str(e))\n raise TwitterError('Unknown error, this has been logged.') from e\n\n # The Twitter API does not support following protected users\n # https://dev.twitter.com/streaming/overview/request-parameters#follow\n if user.protected:\n raise TwitterError('This channel is protected and cannot be followed.')\n\n # Register the new channel\n conf = FollowConfig(user.id_str, user.screen_name)\n self.conf.follows.append(conf)\n\n try:\n # Restart the stream\n await self.stream.start()\n except tweepy.TweepError as e:\n self.conf.follows.remove(conf)\n log.error(str(e))\n raise TwitterError('Unknown error, this has been logged.') from e\n elif dutils.get(conf.discord_channels, id=discord_channel.id):\n raise TwitterError('Already following {} on this channel.'.format(handle))\n\n # Add new Discord channel\n conf.discord_channels.append(ChannelConfig(discord_channel.id))\n self.conf.save()\n await self.bot.say('\\N{OK HAND SIGN}')","async def source(self, context):\n await context.channel.send(\"https://github.com/balfroim/TengriBOT\")","async def steamlink(self, ctx, steam_id, user: discord.Member=None):\n\n if not user:\n user = ctx.message.author\n\n game_list = get_library()\n\n # Either use given 64-bit Steam ID, or convert given name to a 64-bit Steam ID\n try:\n int(steam_id)\n except ValueError:\n key = get_steam_key()\n\n if key:\n url = \"https://api.steampowered.com/ISteamUser/ResolveVanityURL/v0001/?key={key}&vanityurl={id}&format=json\".format(\n key=key, id=steam_id)\n r = requests.get(url)\n response = json.loads(r.text).get('response')\n\n if response.get('success') == 1:\n game_list[user.id][\"steam_name\"] = steam_id\n game_list[user.id][\"steam_id\"] = response.get('steamid')\n else:\n await self.bot.say(\"{}, there was a problem linking your Steam name. Please try again with your 64-bit Steam ID instead.\".format(user.mention))\n return\n else:\n await self.bot.say(\"Sorry, you need a Steam API key to make requests to Steam. Use `{}game steamkey` for more information.\".format(ctx.prefix))\n return\n else:\n game_list[user.id][\"steam_id\"] = steam_id\n finally:\n dataIO.save_json(\"data/game/games.json\", game_list)\n\n await self.bot.say(\"{}'s account has been linked with Steam.\".format(user.mention))\n\n # Update the user's Steam games with their permission\n await self.bot.say(question(\"Do you want to update your library with your Steam games? (yes/no)\"))\n response = await self.bot.wait_for_message(author=user, timeout=15, check=check_response)\n\n if response:\n response = response.content.strip().lower()\n\n if response in \"yes\":\n set_steam_games(game_list[user.id][\"steam_id\"], user.id)\n await self.bot.say(\"{}, your Steam games have been updated!\".format(user.mention))\n elif response in \"no\":\n await self.bot.say(\"Fair enough. If you would like to update your games later, please run `{}game update`.\".format(ctx.prefix))\n else:\n await self.bot.say(\"Too late, but you can still use `{}game update` to update your games.\".format(ctx.prefix))","def create_trackurl(self, context):\n t_url, created = TrackableURL.objects.get_or_create(url=self.url)\n t_url.save()\n\n # key = generate_url_key()\n redirect, created = RedirectUrl.objects.get_or_create(user=context['user'], target_url=t_url)\n if created:\n redirect.save()\n\n text = self.url\n if hasattr(self, 'display_text') and self.display_text is not None:\n text = self.display_text\n else:\n text = self.url\n if self.mode == 'link':\n return \"<a href='{0}' target='_blank'>{1}</a>\".format(reverse('api_redirect', kwargs={'key': redirect.redirect_key}), text)\n else:\n return reverse('api_redirect', kwargs={'key': redirect.redirect_key})","async def invite(self, ctx):\n invite = f\"https://discordapp.com/api/oauth2/authorize?client_id={self.bot.user.id}&permissions=67584&scope=bot\"\n await ctx.send(embed=discord.Embed(\n color=discord.colour.Colour.teal(),\n description=f\":mailbox_with_mail: [Invite]({invite}) me to your server!\"))","def app_client(app_token):\n sender = tk.RetryingSender(sender=tk.SyncSender())\n yield tk.Spotify(app_token, sender=sender)\n sender.close()","async def token(self, ctx):\n logger.info(\"token command issued by {0}\".format(ctx.message.author.name))\n await ctx.message.delete()\n spotify_token = spotipy.util.prompt_for_user_token_auto(self.spotify_username, self.spotify_scope, self.spotify_id, self.spotify_secret)\n self.spotify_client = spotipy.Spotify(auth=spotify_token)\n await ctx.send(\"Spotify refresh token updated\")","async def support(self, ctx):\n link = \"https://discord.gg/J78uAgZ\"\n await ctx.send(\"Join my support server if you need help with commands!\\n \" + link)","async def avatarurl(self, ctx: \"IceTeaContext\", target: discord.Member = None):\n target = target or ctx.author\n embed = discord.Embed(description=f\"{target} Profile Picture\")\n embed.set_image(url=str(target.avatar_url))\n await ctx.send(embed=embed)","def account_setup(remote, token, resp):\n with db.session.begin_nested():\n # Retrieve ORCID from response.\n orcid = resp.get(\"orcid\")\n full_name = resp.get(\"name\")\n\n # Set ORCID in extra_data.\n token.remote_account.extra_data = {\n \"orcid\": orcid,\n \"full_name\": full_name,\n \"allow_push\": current_app.config.get(\"ORCID_ALLOW_PUSH_DEFAULT\", False),\n }\n\n user = token.remote_account.user\n\n # Create user <-> external id link.\n oauth_link_external_id(user, {\"id\": orcid, \"method\": \"orcid\"})","def example_alias2():\n\n # Define app_id and secret\n my_app_id = 'my_app_id'\n my_secret = 'my_secret'\n # Create a Pushbots instance\n pushbots = Pushbots(app_id=my_app_id, secret=my_secret)\n # Define data\n data = {'platform': Pushbots.PLATFORM_ANDROID, 'alias': 'my_new_alias',\n 'current_alias': 'my_current_alias'}\n code, message = pushbots.alias(data=data)\n print('Returned code: {0}'.format(code))\n print('Returned message: {0}'.format(message))","def authenticate(redirect_uri, client_cred_manager, username, scope,client_id,client_secret):\r\n\r\n sp = spotipy.Spotify(client_credentials_manager = client_cred_manager)\r\n token = util.prompt_for_user_token(username, scope, client_id, client_secret, redirect_uri)\r\n if token:\r\n sp = spotipy.Spotify(auth=token)\r\n else:\r\n print(\"Can't get token for\", username)\r\n return sp","async def info(self, context):\n await context.send('creador: debellisnahuel@gmail.com\\ncolabs:\\n emi: https://twitter.com/emilianosce/ o https://www.instagram.com/emilianosce/ \\n garza: https://twitter.com/Matias_Garcia00 o https://www.twitch.tv/garzangb')","async def async_setup(hass, config):\n conf = config[DOMAIN]\n\n username = conf[CONF_USERNAME]\n password = conf[CONF_PASSWORD]\n accounts = conf.get(CONF_ACCOUNTS)\n\n @callback\n def websocket_handle_playlists(hass, connection, msg):\n \"\"\"Handle get playlist\"\"\"\n import spotipy\n access_token, expires = get_spotify_token(username=username, password=password)\n client = spotipy.Spotify(auth=access_token)\n resp = client._get('views/made-for-x?content_limit=10&locale=en&platform=web&types=album%2Cplaylist%2Cartist%2Cshow%2Cstation', limit=10,\n offset=0)\n connection.send_message(\n websocket_api.result_message(msg[\"id\"], resp)\n )\n\n def get_spotify_token(username, password):\n import spotify_token as st\n data = st.start_session(username, password)\n access_token = data[0]\n # token_expires = data[1]\n expires = data[1] - int(time.time())\n return access_token, expires\n\n def play(client, spotify_device_id, uri, random_song, repeat):\n # import spotipy\n # import http.client as http_client\n # spotipy.trace = True\n # spotipy.trace_out = True\n # http_client.HTTPConnection.debuglevel = 1\n\n _LOGGER.debug('Version: %s, playing URI: %s on device-id: %s', _VERSION, uri, spotify_device_id)\n if uri.find('track') > 0:\n _LOGGER.debug('Playing track using uris= for uri: %s', uri)\n client.start_playback(device_id=spotify_device_id, uris=[uri])\n else:\n if uri == 'random':\n _LOGGER.debug('Cool, you found the easter egg with playing a random playlist')\n playlists = client.user_playlists('me', 50)\n no_playlists = len(playlists['items'])\n uri = playlists['items'][random.randint(0, no_playlists - 1)]['uri']\n kwargs = {'device_id': spotify_device_id, 'context_uri': uri}\n if random_song:\n results = client.user_playlist_tracks(\"me\", uri)\n position = random.randint(0, results['total'] - 1)\n _LOGGER.debug('Start playback at random position: %s', position)\n kwargs['offset'] = {'position': position}\n\n _LOGGER.debug('Playing context uri using context_uri for uri: \"%s\" (random_song: %s)', uri, random_song)\n client.start_playback(**kwargs)\n if repeat:\n _LOGGER.debug('Turning repeat on')\n time.sleep(5)\n client.repeat(state=repeat, device_id=spotify_device_id)\n\n def get_account_credentials(call):\n \"\"\" Get credentials for account \"\"\"\n account = call.data.get(CONF_SPOTIFY_ACCOUNT)\n user = username\n pwd = password\n if account is not None:\n _LOGGER.debug('setting up with different account than default %s', account)\n user = accounts.get(account).get(CONF_USERNAME)\n pwd = accounts.get(account).get(CONF_PASSWORD)\n return user, pwd\n\n def shouldTransferPlayback(call, client):\n \"\"\" Check if something is playing \"\"\"\n uri = call.data.get(CONF_SPOTIFY_URI)\n if uri is None or uri.strip() == '' or call.data.get(CONF_TRANSFER_PLAYBACK):\n current_playback = client.current_playback()\n if current_playback is not None:\n _LOGGER.debug('current_playback from spotipy: %s', current_playback)\n return True\n return False\n\n async def start_casting(call):\n \"\"\"service called.\"\"\"\n import spotipy\n\n uri = call.data.get(CONF_SPOTIFY_URI)\n random_song = call.data.get(CONF_RANDOM, False)\n repeat = call.data.get(CONF_REPEAT)\n\n # Account\n user, pwd = get_account_credentials(call)\n\n # login as real browser to get powerful token\n access_token, expires = get_spotify_token(username=user, password=pwd)\n\n # get the spotify web api client\n client = spotipy.Spotify(auth=access_token)\n\n # launch the app on chromecast\n spotify_cast_device = SpotifyCastDevice(hass, call.data.get(CONF_DEVICE_NAME), call.data.get(CONF_ENTITY_ID))\n spotify_cast_device.startSpotifyController(access_token, expires)\n spotify_device_id = spotify_cast_device.getSpotifyDeviceId(client)\n\n transfer_playback = shouldTransferPlayback(call, client)\n if transfer_playback == True:\n _LOGGER.debug('Transfering playback')\n client.transfer_playback(\n device_id=spotify_device_id, force_play=True)\n else:\n play(client, spotify_device_id, uri, random_song, repeat)\n\n # Register websocket and service\n hass.components.websocket_api.async_register_command(\n WS_TYPE_SPOTCAST_PLAYLISTS, websocket_handle_playlists, SCHEMA_PLAYLISTS\n )\n\n hass.services.async_register(DOMAIN, 'start', start_casting,\n schema=SERVICE_START_COMMAND_SCHEMA)\n\n return True","def set_card_link(self):\n self.response.card.type = 'LinkAccount'","def link(self):\n return (\n reverse_lazy(\"passbook_core:auth-sign-up\") + f\"?invitation={self.uuid.hex}\"\n )","def add_bot(self, bot):\n self.add_user(bot)","async def invite(ctx):\r\n await ctx.send(\"https://discordapp.com/oauth2/authorize?client_id=457903893079392256&scope=bot&permissions=2146958591\")\r\n ctx.counter(n)","def data_client(user_token):\n sender = tk.RetryingSender(sender=tk.SyncSender())\n yield tk.Spotify(user_token, sender=sender)\n sender.close()","async def support(self):\n link = \"https://discord.gg/J78uAgZ\"\n await self.bot.say(\"Join my support server if you need help with commands!\\n \" + link)","def echo(update, context):\n update.message.reply_text('Did you mean? https://www.youtube.com/watch?v=dQw4w9WgXcQ ?')","def example_alias1():\n\n # Define app_id and secret\n my_app_id = 'my_app_id'\n my_secret = 'my_secret'\n # Create a Pushbots instance\n pushbots = Pushbots(app_id=my_app_id, secret=my_secret)\n # Define new and current alias and platform\n new_alias = 'my_new_alias'\n old_alias = 'my_current_alias'\n platform = Pushbots.PLATFORM_ANDROID\n code, message = pushbots.alias(platform=platform, alias=new_alias,\n current_alias=old_alias)\n print('Returned code: {0}'.format(code))\n print('Returned message: {0}'.format(message))","async def userinfo(self, ctx: \"IceTeaContext\", target: discord.Member = None):\n target = target or ctx.author\n target_data = ctx.author_data if target == ctx.author else await ctx.get_user_data(target)\n if target_data:\n nicknames = await target_data.get_nicknames()\n else:\n nicknames = []\n shared_servers = len([member for member in ctx.bot.get_all_members() if member == target])\n embed = discord.Embed(title=f\"{target.nick or target.name} Profile\")\n embed.set_author(name=f\"{target.name} ({target.id})\", icon_url=target.avatar_url)\n embed.set_thumbnail(url=target.avatar_url)\n embed.add_field(name=\"Shared Servers\", value=f\"{shared_servers} Shared\")\n embed.add_field(name=\"Created\",\n value=f\"\"\"{timeago.format(target.created_at)} ({target.created_at.strftime(\"%b %d, %Y\")})\"\"\")\n embed.add_field(name=\"Joined\",\n value=f\"\"\"{timeago.format(target.joined_at)} ({target.joined_at.strftime(\"%b %d, %Y\")})\"\"\")\n embed.set_footer(text=\"Last Spoke In server\")\n if target_data:\n embed.timestamp = target_data.last_spoke\n else:\n embed.timestamp = ctx.message.created_at\n if len(nicknames) > 0:\n embed.add_field(name=\"Nicknames\", value=\" , \".join(str(nick) for nick in nicknames[:5]), inline=False)\n embed.add_field(name=\"Roles\", value=\" , \".join([role.name for role in target.roles[:5] if len(role.name) > 0]),\n inline=False)\n if target.activity:\n if isinstance(target.activity, discord.Spotify):\n embed.add_field(name=\"Currently Listening to\",\n value=f\"**{target.activity.title}** by {target.activity.artist} \")\n else:\n embed.add_field(name=\"Currently Playing Since\",\n value=f\"{target.activity.name}\\n{target.activity.details}\\n{target.activity.state}\")\n await ctx.send(embed=embed)","def authenticate_spotify_api(SPOTIPY_CLIENT_ID, SPOTIPY_CLIENT_SECRET):\r\n auth_manager = SpotifyClientCredentials(client_id = SPOTIPY_CLIENT_ID, \r\n client_secret=SPOTIPY_CLIENT_SECRET)\r\n \r\n return spotipy.Spotify(auth_manager=auth_manager)","def connect(self):\n auth = self.botconfig.find('auth')\n logging.info(\"Connecting ...\" )\n if not auth.get('server', None):\n # we don't know the server, but the lib can probably figure it out\n super(SleekBot, self).connect()\n else:\n super(SleekBot, self).connect((auth.attrib['server'], auth.get('port', 5222)))","def onTuxLinkClicked(self, linkId=None):\n self.LinkTux.emit()","def linkTo(sharedProxyOrItem):\n if isinstance(sharedProxyOrItem, sharing.SharedProxy):\n userStore = sharing.itemFromProxy(sharedProxyOrItem).store\n else:\n userStore = sharedProxyOrItem.store\n appStore = isAppStore(userStore)\n if appStore:\n # This code-path should be fixed by #2703; PublicWeb is deprecated.\n from xmantissa.publicweb import PublicWeb\n substore = userStore.parent.getItemByID(userStore.idInParent)\n pw = userStore.parent.findUnique(PublicWeb, PublicWeb.application == substore)\n path = [pw.prefixURL.encode('ascii')]\n else:\n for lm in userbase.getLoginMethods(userStore):\n if lm.internal:\n path = ['users', lm.localpart.encode('ascii')]\n break\n else:\n raise RuntimeError(\n \"Shared item is in a user store with no\"\n \" internal username -- can't generate a link.\")\n if (sharedProxyOrItem.shareID == getDefaultShareID(userStore)):\n shareID = sharedProxyOrItem.shareID\n path.append('')\n else:\n shareID = None\n path.append(sharedProxyOrItem.shareID)\n return _ShareURL(shareID, scheme='', netloc='', pathsegs=path)","def link(self, obj):\n return format_html(\n '<a href=\"{url}\">{url}</a>',\n url='https://sms.cam.ac.uk/media/{}'.format(obj.id)\n )","async def invite(self, context: Context) -> None:\n embed = discord.Embed(\n description=f\"Invite me by clicking [here](https://discordapp.com/oauth2/authorize?&client_id={self.bot.config['application_id']}&scope=bot+applications.commands&permissions={self.bot.config['permissions']}).\",\n color=0xD75BF4,\n )\n try:\n # To know what permissions to give to your bot, please see here: https://discordapi.com/permissions.html and remember to not give Administrator permissions.\n await context.author.send(embed=embed)\n await context.send(\"I sent you a private message!\")\n except discord.Forbidden:\n await context.send(embed=embed)","async def join(self, ctx):\n player = self.bot.lavalink.players.get(ctx.guild.id)\n if player.is_connected:\n return await ctx.send(\"I'm already in a voice channel :no_entry:\")\n else:\n if not ctx.author.voice or not ctx.author.voice.channel:\n return await ctx.send(\"You are not in a voice channel :no_entry:\")\n player.store('sessionowner', ctx.author.id)\n player.store('channel', ctx.channel.id)\n await player.connect(ctx.author.voice.channel.id)\n await ctx.send(\"Summoned to `{}` <:done:403285928233402378>\".format(ctx.author.voice.channel.name))","def _create_user_object(self) -> None:\n\n token = util.prompt_for_user_token(self._USERNAME, self.scope, self._CLIENT_ID, self._CLIENT_SECRET, self.redirect_uri)\n self.spotipyObject = spotipy.Spotify(auth=token)","def _next(verbose=0, quiet=False):\n Spotify.request('me/player/next', method='POST')\n if not quiet:\n from cli.commands.status import status\n status.callback(verbose=verbose)\n\n return","async def server(self, context: Context) -> None:\n embed = discord.Embed(\n description=f\"Join the support server for the bot by clicking [here](https://discord.gg/mTBrXyWxAF).\",\n color=0xD75BF4,\n )\n try:\n await context.author.send(embed=embed)\n await context.send(\"I sent you a private message!\")\n except discord.Forbidden:\n await context.send(embed=embed)","def spotify(request):\n\n logger.debug(\"Spotify Album Called\")\n response_data = {}\n\n spotify_pre_auth = spotify_auth(request)\n# if type(spotify_pre_auth) is JsonResponse:\n# return spotify_pre_auth\n\n response = spotify_pre_auth.get('result','')\n album_url = spotify_pre_auth.get('album_url','')\n response_code=spotify_pre_auth.get('status_code','')\n\n if response_code == 200:\n auth_data = json.loads(response)\n access_token = auth_data['access_token']\n\n headers = {\"Authorization\": \"Bearer %s\" % access_token}\n album_url = \"https://api.spotify.com/v1/albums/\" + album_url.split(\"/\")[-1]\n logger.debug(\"Spotify get album: %s\" % album_url)\n res = requests.get(album_url, headers=headers)\n if res.status_code == 200:\n album = json.loads(res.text)\n logger.debug(\"Found on Spotify the album: %(name)s\" % album)\n return json.loads(res.text)\n else:\n response = res.text\n response_code = res.status_code\n response_body = {\"result\": \"failure\", \"message\": \"Spotify album failed. Check the url or the connections\", \"status_code\": response_code}\n return response_body","def view_newsletter_tracking_link(request, slug, uidb36, token, link_id):\n newsletter = get_object_or_404(Newsletter, slug=slug)\n contact = untokenize(uidb36, token)\n link = get_object_or_404(Link, pk=link_id)\n ContactMailingStatus.objects.create(newsletter=newsletter,\n contact=contact,\n status=ContactMailingStatus.LINK_OPENED,\n link=link)\n return HttpResponseRedirect(link.url)"],"string":"[\n \"async def link(ctx, bot: typing.Union[discord.Member, discord.User]):\\n if not bot.bot:\\n return await r(ctx, \\\"Not a bot.\\\")\\n await r(ctx, f'<https://www.motiondevelopment.top/bots/{bot.id}>')\",\n \"async def info(self, ctx):\\n if ctx.guild is not None:\\n await ctx.reply(\\\"This command can only be used in DMs, because of privacy reasons.\\\")\\n raise commands.CommandError(\\\"Invoker not in DMs.\\\")\\n\\n if not is_linked(ctx.author.id):\\n await ctx.reply(f\\\"You don't have a Spotify account linked. Please link one using \\\"\\n f\\\"`{self.bot_config['prefix']}link`.\\\")\\n raise commands.CommandError(\\\"User has no spotify account linked.\\\")\\n\\n sp = init_spotify(ctx.author.id)\\n result = sp.me()\\n msg_embed = Embed()\\n msg_embed.title = \\\"Linked Spotify account\\\"\\n msg_embed.url = result['external_urls'].get('spotify', None)\\n if len(result['images']) > 0:\\n msg_embed.set_image(url=result['images'][0]['url'])\\n msg_embed.add_field(name=\\\"Display name\\\", value=result['display_name'])\\n msg_embed.add_field(name=\\\"Subscription type\\\", value=result.get('product', 'free'))\\n if result.get('product', None) != \\\"premium\\\":\\n msg_embed.add_field(name=\\\"Warning!\\\",\\n value=\\\"Only accounts with Spotify Premium can use this bot!\\\",\\n inline=False)\\n await ctx.reply(embed=msg_embed)\",\n \"async def join(self, ctx):\\n if ctx.guild is None:\\n await ctx.reply(\\\"This command can only be used in a server, not in DMs.\\\")\\n raise commands.CommandError(\\\"Invoker not in a guild.\\\")\\n\\n if not is_linked(ctx.author.id):\\n await ctx.reply(f\\\"You don't have a Spotify account linked. Please link one using \\\"\\n f\\\"`{self.bot_config['prefix']}link`.\\\")\\n raise commands.CommandError(\\\"User has no spotify account linked.\\\")\\n\\n if ctx.author.voice is None or ctx.author.voice.channel is None:\\n await ctx.reply(\\\"You need to be in a voice channel to use this command.\\\")\\n raise commands.CommandError(\\\"Invoker not connected to a voice channel.\\\")\\n\\n if ctx.voice_client is not None and ctx.author.voice.channel != ctx.voice_client.channel:\\n await ctx.reply(\\\"You need to be in the same voice channel as the bot to use this command.\\\")\\n raise commands.CommandError(\\\"Invoker not in same voice channel as bot.\\\")\\n\\n # Connect to voice channel that the invoker is in (if we're not already connected somewhere else)\\n try:\\n controller_instance = await ctx.author.voice.channel.connect(reconnect=False)\\n except asyncio.TimeoutError:\\n await ctx.reply(\\\"Timeout error while connecting to the voice channel. Please try again later.\\\")\\n return\\n except discord.ClientException:\\n await ctx.reply(\\\"I'm already connected to a voice channel, please disconnect me first!\\\")\\n return\\n except OpusNotLoaded:\\n await ctx.reply(\\\"Opus library was not loaded. Please try again later.\\\")\\n return\\n\\n if controller_instance is not None:\\n # Create a listening socket for the future incoming audio connection\\n try:\\n controller = SpotifyController.create(controller_instance.channel.id,\\n controller_instance.channel.bitrate,\\n ctx.author.id)\\n except ValueError as e:\\n await ctx.reply(e)\\n return\\n\\n controller.get_or_create_playlist()\\n\\n await ctx.author.send(f\\\"Please enter the following code in your client application and click \\\"\\n f\\\"'connect' to start playing music!\\\\nCode: `{controller.link_code}`\\\")\\n await ctx.reply(f\\\"Ready and waiting for a connection! I've DM'ed you a code to fill in in your client app.\\\"\\n f\\\"\\\\nIn the mean time, you can start adding songs with `{self.bot_config['prefix']}add`, \\\"\\n f\\\"and view the queue with `{self.bot_config['prefix']}queue`\\\")\",\n \"async def invite(self, ctx):\\r\\n myInvite = discord.utils.oauth_url(self.bot.user.id, permissions=discord.Permissions(permissions=8))\\r\\n await ctx.channel.send('Invite me to *your* server with this link: \\\\n\\\\n<{}>'.format(myInvite))\",\n \"async def invite(self):\\n link = \\\"https://discordapp.com/oauth2/authorize?client_id=282765243862614016&scope=bot&permissions=19456\\\"\\n await self.bot.say(\\\"Invite me to your server with this link!\\\\n\\\" + link)\",\n \"async def inviteme(self):\\r\\n\\r\\n #Your code will go here\\r\\n await self.bot.say(\\\"Here is a link to Invite Me: http://bit.ly/TacoBot\\\")\",\n \"async def invite(self, ctx):\\n link = \\\"https://discordapp.com/oauth2/authorize?client_id=282765243862614016&scope=bot&permissions=19456\\\"\\n await ctx.send(\\\"Invite me to your server with this link!\\\\n\\\" + link)\",\n \"async def spotify(self, ctx):\\n if ctx.invoked_subcommand is None:\\n await ctx.send('Invalid command passed. Use the `?help spotify` command to learn more.')\",\n \"def share_link(cls, user, link):\",\n \"def share_link(cls, user, link):\",\n \"async def invite(self, ctx):\\n await ctx.send(f\\\"**{ctx.author.name}**, use this URL to invite me\\\\n<{discord.utils.oauth_url(self.bot.user.id)}>\\\")\",\n \"async def link_to(self, *args):\\n pass\",\n \"async def invite(ctx):\\n permissions = 2134207679\\n url = discord.utils.oauth_url(client_id=bot.user.id, permissions=discord.Permissions(permissions=permissions),\\n scopes=(\\\"bot\\\", \\\"applications.commands\\\"))\\n view = discord.ui.View()\\n view.add_item(discord.ui.Button(label=\\\"Invite\\\", url=url))\\n await ctx.respond(\\\"I'm glad you want to add me to your server, here's a link!\\\", view=view)\",\n \"def wrap_spotify_link(item, text=''):\\n\\n # generate default text if no text has been given\\n if not text:\\n name = item['name']\\n if item['type'] == 'playlist':\\n user = SP.user(item['owner']['id'])['display_name']\\n text = f'{name} by {user}'\\n elif item['type'] == 'artist':\\n text = name\\n else:\\n artist = item['artists'][0]['name']\\n text = f'{name} by {artist}'\\n\\n link = item['external_urls']['spotify']\\n return f'<a href=\\\"{link}\\\">{text}</a>'\",\n \"async def user_aclient(user_token):\\n sender = tk.RetryingSender(sender=tk.AsyncSender())\\n yield tk.Spotify(user_token, sender=sender)\\n await sender.close()\",\n \"async def spotify(self, ctx, *, query):\\n\\n # Setup the headers with the token that should be here\\n headers = {\\\"Authorization\\\": \\\"Bearer {}\\\".format(self._token)}\\n opts = {\\\"q\\\": query, \\\"type\\\": \\\"track\\\"}\\n url = \\\"https://api.spotify.com/v1/search\\\"\\n response = await utils.request(url, headers=headers, payload=opts)\\n try:\\n await ctx.send(\\n response.get(\\\"tracks\\\")\\n .get(\\\"items\\\")[0]\\n .get(\\\"external_urls\\\")\\n .get(\\\"spotify\\\")\\n )\\n except (KeyError, AttributeError, IndexError):\\n await ctx.send(\\\"Couldn't find a song for:\\\\n{}\\\".format(query))\",\n \"async def links(self, ctx):\\n await ctx.send(\\\"https://discordapp.com/channels/566451504332931073/681617252814159904/755489156146397311\\\")\",\n \"def use_account(self, token, url=QE_URL, **kwargs):\\n credentials = Credentials(token, url, **kwargs)\\n\\n self._append_account(credentials)\",\n \"async def invite(self, ctx):\\n embed = discord.Embed(title=\\\"Invite\\\", description=f\\\"**{ctx.author.name}**, use this URL to invite me\\\\n[link](https://discord.com/oauth2/authorize?client_id=749629426777456691&permissions=8&scope=bot)\\\", color=0xeff0f1)\\n await ctx.send(embed=embed)\",\n \"def auth(self):\\n token = spotipy.util.prompt_for_user_token(self.username,\\n self.scope,\\n client_id = self.client_id,\\n client_secret = self.client_secret,\\n redirect_uri= self.redirect_uri)\\n if token:\\n self.spotify = spotipy.Spotify(auth=token)\\n else:\\n print(colored.stylize(\\\"\\\"\\\"\\\\n[*] \\\"\\\"\\\", colored.fg(\\\"light_red\\\")) + 'Cant get token for: %s\\\\n' % (self.username))\\n exit()\",\n \"async def github(self, ctx):\\n await ctx.send('https://github.com/nick411077/nickcan_bot')\",\n \"async def server():\\n await bot.say(\\\"https://discord.gg/Eau7uhf\\\")\",\n \"def shareNote(self, authenticationToken, guid):\\r\\n pass\",\n \"async def addme(self, ctx):\\n invite_url = discord.utils.oauth_url(self.bot.user.id, permissions=discord.Permissions(8))\\n embed = self.bot.embeds.embed_builder(title='Add this bot to your own Discord server',\\n description=invite_url,\\n has_footer=False)\\n await ctx.send(embed=embed)\",\n \"async def _invite(self, ctx: Context):\\n\\n perm_int = discord.Permissions(268494928)\\n\\n data = await self.bot.application_info()\\n invite_url = discord.utils.oauth_url(data.id, permissions=perm_int)\\n\\n value = (\\n f\\\"Invite TvM Assistant to your bot by [clicking here]({invite_url}).\\\"\\n \\\"\\\\n\\\\nInviting the bot will give it some management permissions. You can\\\"\\n \\\" review them when you use the link.\\\"\\n )\\n\\n embed = discord.Embed(color=await ctx.embed_colour(), description=value)\\n embed.set_author(name=f\\\"Invite TvM Assistant\\\", icon_url=ctx.me.avatar_url)\\n\\n try:\\n await ctx.send(embed=embed)\\n except discord.Forbidden:\\n return await ctx.send(\\n f\\\"{invite_url}\\\\n\\\\nInviting the bot will give it some management permissions.\\\"\\n \\\" You can review them when you use the link.\\\"\\n )\",\n \"def link(self):\\n return f\\\"https://{DOMAIN}/invite/{self.code}\\\"\",\n \"async def invite(self, ctx, plain_url: bool = False):\\n\\n if not plain_url:\\n try:\\n await ctx.embed(\\n 'Click to invite me to your server!',\\n title_url=self.bot.invite_url,\\n colour='blue',\\n icon=\\\"https://i.imgur.com/DtPWJPG.png\\\"\\n )\\n except discord.errors.Forbidden:\\n pass\\n else:\\n return\\n\\n await ctx.send(f\\\"Invite URL: <{self.bot.invite_url}>\\\")\",\n \"async def _discord(self, ctx: Context):\\n\\n await ctx.send(\\n f\\\"You can join the Brawlcord community server by using this link: {COMMUNITY_SERVER}\\\"\\n )\",\n \"def link(self, pin):\\n headers = {\\n 'Content-Type': 'application/x-www-form-urlencoded',\\n 'X-Plex-Product': 'Plex SSO'\\n }\\n data = {'code': pin}\\n self.query(self.LINK, self._session.put, headers=headers, data=data)\",\n \"async def _invite(self, ctx: Context):\\n\\n # read_messages=True,\\n # send_messages=True,\\n # manage_messages=True,\\n # embed_links=True,\\n # attach_files=True,\\n # external_emojis=True,\\n # add_reactions=True\\n perms = discord.Permissions(322624)\\n\\n try:\\n data = await self.bot.application_info()\\n invite_url = discord.utils.oauth_url(data.id, permissions=perms)\\n value = (\\n \\\"Add Brawlcord to your server by **[clicking here]\\\"\\n f\\\"({invite_url})**.\\\\n\\\\n**Note:** By using the link\\\"\\n \\\" above, Brawlcord will be able to\\\"\\n \\\" read messages,\\\"\\n \\\" send messages,\\\"\\n \\\" manage messages,\\\"\\n \\\" embed links,\\\"\\n \\\" attach files,\\\"\\n \\\" add reactions,\\\"\\n \\\" and use external emojis\\\"\\n \\\" wherever allowed.\\\\n\\\\n*You can remove the permissions manually,\\\"\\n \\\" but that may break the bot.*\\\"\\n )\\n except Exception as exc:\\n invite_url = None\\n value = (\\n f\\\"Error \\\\\\\"{exc}\\\\\\\" while generating invite link.\\\"\\n \\\" Notify bot owner using the `-report` command.\\\"\\n )\\n\\n embed = discord.Embed(color=EMBED_COLOR, description=value)\\n embed.set_author(\\n name=f\\\"Invite {ctx.me.name}\\\", icon_url=ctx.me.avatar_url)\\n # embed.add_field(name=\\\"__**Invite Link:**__\\\", value=value)\\n\\n try:\\n await ctx.send(embed=embed)\\n except discord.Forbidden:\\n return await ctx.send(\\n \\\"I do not have the permission to embed a link.\\\"\\n \\\" Please give/ask someone to give me that permission.\\\"\\n )\",\n \"async def invite(self, ctx):\\n embed = discord.Embed(title='Invite links for NOVA',\\n description='[<:news:730866149109137520> Required Permissions](https://discord.com/api/'\\n 'oauth2/authorize?client_id=709922850953494598&permissions=1573252215&scope='\\n 'bot)\\\\n'\\n '[<:news:730866149109137520> No Permissions]'\\n '(https://discord.com/api/oauth2/authorize?client_id=709922850953494598&permi'\\n 'ssions=0&scope=bot)\\\\n[<:news:730866149109137520> All Permissions (admin)]'\\n '(https://discord.com/api/oauth2/authorize?client_id=709922850953494598&perm'\\n 'issions=8&scope=bot)', color=0x5643fd)\\n embed.set_footer(text='Developed by YeetVegetabales', icon_url='https://cdn.discordapp.com/avatars'\\n '/569374429218603019'\\n '/a_6dac6946906e498650f6c2466aa82200.gif?size'\\n '=256&f=.gif')\\n embed.set_thumbnail(url='https://images-ext-2.discordapp.net/external/54Mim4lahztGCP4hgmpy4lOdEUc4'\\n '-dOeNA_x6hVHMlc/%3Fsize%3D4096/https/cdn.discordapp.com/avatars/709922850953494598'\\n '/f78ed19924e8c95abc30f406d47670d7.png')\\n await ctx.send(embed=embed)\",\n \"def Connect(self,scope):\\n\\n \\\"\\\"\\\"\\n Calling util.prompt_for_user_token will open Spotify’s application authorization\\n page in your browser (and require you to log in if you are not already logged in\\n to spotify.com), unless a locally cached access token exist from a previous authorization/authentication.\\n \\\"\\\"\\\"\\n try:\\n token = util.prompt_for_user_token(\\n self.username,\\n scope,\\n self.client_id,\\n self.secret_id,\\n self.redirect_uri)\\n except ImportError:\\n self._isConnected = False\\n print(\\\" onnecting to Spotify failed\\\") \\n\\n\\n if token:\\n sp = spotipy.Spotify(auth=token)\\n self._isConnected = True\\n return sp\\n else:\\n print(\\\"Can't get token for\\\", self.username)\\n self._isConnected = False\",\n \"def user_client(user_token):\\n sender = tk.RetryingSender(sender=tk.SyncSender())\\n yield tk.Spotify(user_token, sender=sender)\\n sender.close()\",\n \"def update_access_token(self):\\n self.token = util.prompt_for_user_token(self._username, scope,\\n client_id=const.CLIENT_ID,\\n client_secret=const.CLIENT_SECRET,\\n redirect_uri=const.REDIRECT_URL)\\n self._client = spotipy.Spotify(auth=self.token)\",\n \"async def sendPing(self, title, progress, link, image):\\n embed = discord.Embed(title=title, description=progress, color=discord.Color.green())\\n embed.add_field(name=\\\"Link\\\", value=f\\\"[{link}]({link})\\\")\\n embed.set_thumbnail(url=image)\\n await self.bot.get_cog('UserCog').channel.send(self.bot.get_cog('UserCog').discordUser.mention, embed=embed)\",\n \"async def botinvite_command(self, ctx):\\n invite = f\\\"https://discord.com/api/oauth2/authorize?client_id={self.client.user.id}&permissions=1374809815&scope=bot\\\"\\n await ctx.send(invite)\",\n \"def follow_someone(screen_name):\\n twitter.create_friendship(screen_name=screen_name)\",\n \"def link_account(self, accountid, provider, providerid):\\n auth = 'appkey='+ self._lr_object._get_api_key()+ '&appsecret='+ self._lr_object._get_api_secret()\\n payload = {'accountid': accountid, 'provider': provider, 'providerid': providerid}\\n url = SECURE_API_URL + \\\"raas/v1/account/link\\\" + \\\"?\\\" + auth\\n return self._lr_object._post_json(url, payload)\",\n \"async def invite(self, ctx):\\n await ctx.send(f'🐱You can invite me to your server using the following url:\\\\n{self.invite_url}'\\n '\\\\n\\\\nYou will need the **Manage Server** permission to add me to a server. '\\n f'Run `{self.heleus.command_prefix[0]}help` to see what you can customise!')\",\n \"async def set_bot():\\n\\n self = await LOCAL.APP.get_me()\\n LOCAL.bot_id = self.id\\n LOCAL.bot_name = self.first_name\\n LOCAL.bot_username = self.username\",\n \"def linkTrackToChannel(*args, **kwargs):\\n pass\",\n \"async def info(ctx):\\n embed = discord.Embed(title=\\\"Zane Bot\\\", description=\\\"All hail the hypnotoad!\\\", color=0x0091C5)\\n\\n # give info about you here\\n embed.add_field(name=\\\"Author\\\", value=\\\"Zanexius\\\")\\n\\n # Shows the number of servers the bot is member of.\\n embed.add_field(name=\\\"Server count\\\", value=f\\\"{len(bot.guilds)}\\\")\\n\\n # give users a link to invite thsi bot to their server\\n embed.add_field(name=\\\"Invite\\\", value=\\\"[Invite link](<insert your OAuth invitation link here>)\\\")\\n\\n await ctx.send(embed=embed)\",\n \"async def app_aclient(app_token):\\n sender = tk.RetryingSender(sender=tk.AsyncSender())\\n yield tk.Spotify(app_token, sender=sender)\\n await sender.close()\",\n \"async def link(self, ctx: Context) -> None:\\r\\n try:\\r\\n params: List[str] = get_cmd_params(ctx)\\r\\n\\r\\n if len(params) < 1 or not params[0].isdigit() or int(params[0]) > len(self.yt_result.ids):\\r\\n await ctx.send(\\\"Please enter a valid video number from 0 to 5\\\")\\r\\n return\\r\\n\\r\\n self.yt_link = await self.message.edit(content=self.yt_result.get_link(int(params[0])))\\r\\n await ctx.message.delete()\\r\\n except Exception as e:\\r\\n await self.channels.log_error(e, \\\"ytl\\\")\",\n \"async def invite(self, ctx):\\n perms = discord.Permissions.text()\\n perms.update(read_messages=True, manage_messages=True,\\n mention_everyone=False, send_tts_messages=False)\\n await ctx.send(f'Invite me here:\\\\n<{discord.utils.oauth_url(self.bot.user.id, perms)}>')\",\n \"def create_link(self):\\n self.filename = App.get_running_app().root.ids.camera_screen.capture()\\n self.url = FileSharer(self.filename).share()\\n self.ids.label.text = self.url\",\n \"async def inspire(self, ctx):\\n async with aiohttp.ClientSession() as session:\\n async with session.get('http://inspirobot.me/api?generate=true') as response:\\n if(response.status == 200):\\n imgurl = await response.text()\\n embed = discord.Embed(colour=discord.Colour.dark_blue())\\n embed.set_image(url=imgurl)\\n embed.set_footer(text='http://inspirobot.me/')\\n await ctx.bot.send_message(ctx.message.channel, embed=embed)\",\n \"async def discord(self, ctx):\\n embed = discord.Embed(title='Join the discord today!', color=0x5643fd, description=\\\"This server is where \\\"\\n \\\"all of \\\"\\n \\\"NOVA's updates and \\\"\\n \\\"important \\\"\\n \\\"announcements will pass \\\"\\n \\\"through. The creator of \\\"\\n \\\"this \\\"\\n \\\"bot, YeetVegetabales#5313, \\\"\\n \\\"will also be there testing \\\"\\n \\\"and letting the communtiy \\\"\\n \\\"in \\\"\\n \\\"on things first hand!\\\")\\n embed.set_thumbnail(url='https://images-ext-2.discordapp.net/external/AQCEqCF4Yl_PWAfuA-GReZoDify6'\\n '--y4hXOJVkqaDHo/%3Fsize%3D1024/https/cdn.discordapp.com/avatars/709922850953494598'\\n '/f78ed19924e8c95abc30f406d47670d7.png')\\n embed.add_field(name='Server Invite', value='<:news:730866149109137520> '\\n '[Join here](https://discord.gg/Uqh9NXY)')\\n await ctx.send(embed=embed)\",\n \"def contact_linkup(self, request, pk):\\n obj_api = api()\\n title_contact = \\\"Tu contacto Linkup\\\"\\n token = request.session['token']\\n resp = obj_api.get(slug='sellers/' + pk + \\\"/\\\", token=token)\\n return render(request, 'frontend/actors/client/my_account.html', {'data_user': resp, \\n 'title_contact': title_contact})\",\n \"def click(cls, user, link):\\n pass\",\n \"def __init__(self, username):\\n self.spotify = spotipy.Spotify(simple_auth_token(username))\",\n \"async def vote(self, ctx):\\n embed = discord.Embed(title = \\\"Here are some bot lists that you can vote for me on, voters may soon™ recieve perks\\\", color = discord.Color.blurple())\\n embed.add_field(name = \\\"Bots For Discord\\\", value = \\\"[Click Here](https://botsfordiscord.com/bot/592811241756688405/vote)\\\")\\n embed.add_field(name = \\\"Discord Boats\\\", value = \\\"[Click Here](https://discord.boats/bot/592811241756688405/vote)\\\")\\n embed.add_field(name = \\\"Divine Discord Bots\\\", value = \\\"[Click Here](https://divinediscordbots.com/bot/592811241756688405/vote)\\\") \\n embed.add_field(name = \\\"Botlist.space\\\", value = \\\"[Click Here](https://botlist.space/bot/592811241756688405/upvote)\\\") \\n embed.set_thumbnail(url = self.bot.user.avatar_url)\\n await ctx.send(embed = embed)\",\n \"def status(verbose):\\n user_data = Spotify.request('me', method='GET')\\n click.echo('Logged in as {}'.format(user_data['display_name']))\\n if verbose:\\n click.echo('Credentials stored in {}'.format(CREDS_PATH))\\n return\",\n \"async def inspire(self, ctx):\\n async with aiohttp.ClientSession() as session:\\n async with session.get('http://inspirobot.me/api?generate=true') as response:\\n if(response.status == 200):\\n imgurl = await response.text()\\n embed = discord.Embed(colour=discord.Colour.dark_blue())\\n embed.set_image(url=imgurl)\\n embed.set_footer(text='http://inspirobot.me/')\\n await ctx.send(embed=embed)\",\n \"async def add(self, ctx, query):\\n if ctx.guild is None:\\n await ctx.reply(\\\"This command can only be used in a server, not in DMs.\\\")\\n raise commands.CommandError(\\\"Invoker not in a guild.\\\")\\n\\n if ctx.voice_client is None or ctx.voice_client.channel is None:\\n await ctx.reply(f\\\"I am not in a voice channel, invite me first with `{self.bot_config['prefix']}join`.\\\")\\n raise commands.CommandError(\\\"Bot not connected to a voice channel.\\\")\\n\\n if ctx.author.voice is None or ctx.author.voice.channel is None:\\n await ctx.reply(\\\"You need to be in a voice channel to use this command.\\\")\\n raise commands.CommandError(\\\"Invoker not connected to a voice channel.\\\")\\n\\n if ctx.voice_client is not None and ctx.author.voice.channel != ctx.voice_client.channel:\\n await ctx.reply(\\\"You need to be in the same voice channel as the bot to use this command.\\\")\\n raise commands.CommandError(\\\"Invoker not in same voice channel as bot.\\\")\\n\\n if ctx.voice_client is not None and ctx.voice_client.channel is not None:\\n controller = SpotifyController.get_instance(ctx.voice_client.channel.id)\\n if controller is None:\\n await ctx.reply(f\\\"I'm not playing anything at the moment.\\\")\\n raise commands.CommandError(\\\"Bot not connected to active spotify session.\\\")\\n else:\\n await ctx.reply(f\\\"I am not in a voice channel, invite me first with `{self.bot_config['prefix']}join`.\\\")\\n raise commands.CommandError(\\\"Bot not connected to a voice channel.\\\")\\n\\n print(f\\\"Adding {query} to playlist\\\")\\n controller = SpotifyController.get_instance(ctx.voice_client.channel.id)\\n sp = controller.get_playlist_api()\\n\\n uri = None\\n item_info = None\\n item_type = None\\n\\n # If link, queue by link\\n if query.startswith(\\\"http://\\\") or query.startswith(\\\"https://\\\"):\\n m = SPOTIFY_LINK_REGEX.match(query)\\n if m:\\n uri = f\\\"spotify:{m.group('type')}:{m.group('id')}\\\"\\n item_type = m.group('type')\\n if item_type == \\\"track\\\":\\n try:\\n item_info = sp.track(m.group('id'))\\n except SpotifyException:\\n await ctx.send(f\\\"Cannot add! Invalid track!\\\")\\n return\\n elif item_type == \\\"album\\\":\\n try:\\n item_info = sp.album(m.group('id'))\\n except SpotifyException:\\n await ctx.send(f\\\"Cannot add! Invalid album!\\\")\\n return\\n elif item_type == \\\"playlist\\\":\\n try:\\n item_info = sp.playlist(m.group('id'))\\n except SpotifyException:\\n await ctx.send(f\\\"Cannot add! Invalid or private playlist!\\\")\\n return\\n else:\\n await ctx.send(f\\\"Type {item_type} not supported!\\\")\\n return\\n\\n print(f\\\"Converted link to ID '{uri}'\\\")\\n else:\\n await ctx.send(f\\\"Only spotify links are supported!\\\")\\n return\\n\\n # If spotify uri, queue by link\\n if uri is None:\\n m = SPOTIFY_URI_REGEX.match(query)\\n if m:\\n uri = f\\\"spotify:{m.group('type')}:{m.group('id')}\\\"\\n item_type = m.group('type')\\n if item_type == \\\"track\\\":\\n try:\\n item_info = sp.track(m.group('id'))\\n except SpotifyException:\\n await ctx.send(f\\\"Cannot add! Invalid track!\\\")\\n return\\n elif item_type == \\\"album\\\":\\n try:\\n item_info = sp.album(m.group('id'))\\n except SpotifyException:\\n await ctx.send(f\\\"Cannot add! Invalid album!\\\")\\n return\\n elif item_type == \\\"playlist\\\":\\n try:\\n item_info = sp.playlist(m.group('id'))\\n except SpotifyException:\\n await ctx.send(f\\\"Cannot add! Invalid or private playlist!\\\")\\n return\\n else:\\n await ctx.send(f\\\"Type {item_type} not supported!\\\")\\n return\\n print(f\\\"Converted URI to ID '{uri}'\\\")\\n\\n # Else, try to search\\n if uri is None:\\n await ctx.send(f'Searching not supported yet.')\\n return\\n\\n # Add URI\\n if uri is not None:\\n if item_type == \\\"track\\\":\\n sp.playlist_add_items(controller.playlist[\\\"id\\\"], items=[uri])\\n elif item_type == \\\"album\\\":\\n album_tracks = controller.get_album_tracks(item_info['id'])\\n i, max_tracks = 0, 50\\n while i < len(album_tracks):\\n block = [t['uri'] for t in album_tracks[i:i+max_tracks]]\\n sp.playlist_add_items(controller.playlist[\\\"id\\\"], items=block)\\n i += max_tracks\\n elif item_type == \\\"playlist\\\":\\n playlist_tracks = controller.get_playlist_tracks(item_info['id'])\\n i, max_tracks = 0, 50\\n while i < len(playlist_tracks):\\n block = [t['uri'] for t in playlist_tracks[i:i+max_tracks]]\\n sp.playlist_add_items(controller.playlist[\\\"id\\\"], items=block)\\n i += max_tracks\\n else:\\n await ctx.send(f\\\"Cannot add! Type {item_type} not supported!\\\")\\n return\\n\\n try:\\n controller.update_playlist()\\n except IndexError as e:\\n print(e, file=sys.stderr)\\n\\n msg_embed = Embed()\\n if item_type == \\\"track\\\":\\n full_title = SpotifyController.format_full_title(item_info)\\n try:\\n thumbnail = item_info['album']['images'][0]['url']\\n except IndexError:\\n thumbnail = None\\n msg_embed.description = f\\\"Added [{full_title}]({item_info['external_urls']['spotify']}) to queue!\\\"\\n msg_embed.set_thumbnail(url=thumbnail)\\n elif item_type == \\\"album\\\":\\n full_title = SpotifyController.format_full_title(item_info)\\n try:\\n thumbnail = item_info['images'][0]['url']\\n except IndexError:\\n thumbnail = None\\n num_tracks = item_info['tracks']['total']\\n msg_embed.description = f\\\"Added album [{full_title}]({item_info['external_urls']['spotify']}) \\\" \\\\\\n f\\\"({num_tracks} tracks) to queue!\\\"\\n msg_embed.set_thumbnail(url=thumbnail)\\n elif item_type == \\\"playlist\\\":\\n title = item_info['name']\\n try:\\n thumbnail = item_info['images'][0]['url']\\n except IndexError:\\n thumbnail = None\\n num_tracks = item_info['tracks']['total']\\n msg_embed.description = f\\\"Added playlist [{title}]({item_info['external_urls']['spotify']}) \\\" \\\\\\n f\\\"({num_tracks} tracks) to queue!\\\"\\n msg_embed.set_thumbnail(url=thumbnail)\\n else:\\n # Shouldn't happen, but lets add a message anyway...\\n msg_embed.description = f\\\"Unknown {item_type} item added to queue!\\\"\\n await ctx.reply(embed=msg_embed)\",\n \"def click(cls, user, link):\\r\\n pass\",\n \"async def botlink(self, ctx):\\n async with self.bot.pool.acquire() as conn:\\n async with conn.cursor() as cur:\\n await cur.execute('SELECT * FROM BLChannels WHERE \\\"id\\\"=%s;', (ctx.message.channel.id,))\\n isregistered = await cur.fetchone()\\n if isregistered: \\n return\\n else: \\n embed = discord.Embed(title=\\\"Mafu, the multi use bot\\\", colour=discord.Colour(0x6c56b0),url=\\\"https://discordapp.com/oauth2/authorize?client_id=432292171371118592&scope=bot\\\") \\n await ctx.send(embed=embed)\",\n \"async def now_playing(self, ctx):\\n if ctx.guild is None:\\n await ctx.reply(\\\"This command can only be used in a server, not in DMs.\\\")\\n raise commands.CommandError(\\\"Invoker not in a guild.\\\")\\n\\n if ctx.voice_client is None or ctx.voice_client.channel is None:\\n await ctx.reply(f\\\"I am not in a voice channel, invite me first with `{self.bot_config['prefix']}join`.\\\")\\n raise commands.CommandError(\\\"Bot not connected to a voice channel.\\\")\\n\\n if ctx.author.voice is None or ctx.author.voice.channel is None:\\n await ctx.reply(\\\"You need to be in a voice channel to use this command.\\\")\\n raise commands.CommandError(\\\"Invoker not connected to a voice channel.\\\")\\n\\n if ctx.voice_client is not None and ctx.author.voice.channel != ctx.voice_client.channel:\\n await ctx.reply(\\\"You need to be in the same voice channel as the bot to use this command.\\\")\\n raise commands.CommandError(\\\"Invoker not in same voice channel as bot.\\\")\\n\\n if ctx.voice_client is not None and ctx.voice_client.channel is not None:\\n controller = SpotifyController.get_instance(ctx.voice_client.channel.id)\\n if controller is None:\\n await ctx.reply(f\\\"I'm not playing anything at the moment.\\\")\\n raise commands.CommandError(\\\"Bot not connected to active spotify session.\\\")\\n else:\\n await ctx.reply(f\\\"I am not in a voice channel, invite me first with `{self.bot_config['prefix']}join`.\\\")\\n raise commands.CommandError(\\\"Bot not connected to a voice channel.\\\")\\n\\n sp = controller.get_api()\\n info = sp.current_playback()\\n if not controller.is_playing_on_bot():\\n await ctx.send(\\\"Not playing anything at the moment...\\\")\\n return\\n\\n # Add URI\\n if info is not None:\\n track_info = info['item']\\n try:\\n thumbnail = track_info['album']['images'][0]\\n except IndexError:\\n thumbnail = None\\n msg_embed = Embed()\\n artist_url = track_info['artists'][0]['external_urls']['spotify']\\n msg_embed.set_author(name=SpotifyController.format_artist(track_info), url=artist_url)\\n msg_embed.title = SpotifyController.format_title(track_info)\\n msg_embed.description = f\\\"{SpotifyController.format_album_name(track_info)}\\\\n\\\"\\n msg_embed.url = track_info['external_urls']['spotify']\\n msg_embed.set_thumbnail(url=thumbnail['url'])\\n msg_embed.set_footer(text=f\\\"{SpotifyController.format_progress(info)}\\\")\\n await ctx.reply(embed=msg_embed)\\n else:\\n await ctx.send(\\\"Not playing anything at the moment...\\\")\",\n \"def login():\\n url = AUTH_URL + '&state=' + str(uuid1())\\n try:\\n import webbrowser\\n webbrowser.open(url)\\n except:\\n pass\\n \\n print('Go to the following link in your browser:\\\\n\\\\n\\\\t{}\\\\n'.format(url))\\n\\n auth_code = input('Enter verification code: ')\\n print('\\\\nObtaining access token...')\\n Spotify.refresh(auth_code)\\n print('Credentials saved to {}'.format(CREDS_PATH))\\n return\",\n \"def link(request, link_id):\\n bkmrk_id = Bookmark.decode_id(link_id)\\n bookmark = get_object_or_404(Bookmark, pk=bkmrk_id)\\n if request.user.is_authenticated():\\n Click.objects.create(human=request.user, bookmark=bookmark)\\n else:\\n Click.objects.create(bookmark=bookmark)\\n return redirect(bookmark.url)\",\n \"def get_token_link(self):\\n return self.env['ir.config_parameter'].search([('key', '=', 'web.base.url')]).value + \\\"/web/signup?inv_id={}\\\".format(self.name)\",\n \"async def meow(self, ctx):\\n url = 'http://placekitten.com/'\\n async with aiohttp.ClientSession() as session:\\n async with session.get(url) as response:\\n source = await response.read()\\n art = str(source[559:1000], 'utf8')\\n embed = discord.Embed(title='{}:'.format(ctx.message.author.name),\\n description='```{}```'.format(art),\\n colour=0xf20006)\\n a = await self.bot.say(embed=embed)\\n await self.bot.add_reaction(a, self.emojiUnicode['succes'])\",\n \"async def support(self, ctx):\\n await ctx.send('Join the support server here: https://discord.gg/bAq8Ec5JPQ')\",\n \"def login_link(self):\\n\\t\\tresponse = self.client.post(self._endpoint + \\\"/loginLink\\\")\\n\\t\\treturn response.json[\\\"link\\\"]\",\n \"async def follow(follow):\\n await follow.edit(\\n f\\\"`FOLLOW {DEFAULTUSER} ON` \\\\n\\\\n\\\"\\n f\\\"[InstaGram](https://www.instagram.com/mayur_karaniya) \\\\n\\\\n\\\"\\n f\\\"[FaceBook](https://www.facebook.com/mkaraniya) \\\\n\\\\n\\\"\\n f\\\"[YouTube](https://www.youtube.com/channel/UCeKQxQK7XZ3jGi3541uWATg?sub_confirmation=1) \\\"\\n )\",\n \"async def twitter_follow(self, ctx, handle):\\n discord_channel = ctx.message.channel\\n\\n # Check for required permissions\\n if not discord_channel.permissions_for(discord_channel.server.me).embed_links:\\n raise TwitterError('\\\\N{WARNING SIGN} The `Embed Links` permission in this channel is required to display tweets properly. \\\\N{WARNING SIGN}')\\n\\n sane_handle = handle.lower().lstrip('@')\\n conf = dutils.get(self.conf.follows, screen_name=sane_handle)\\n if conf is None:\\n # New Twitter channel, retrieve the user info\\n partial = functools.partial(self.api.get_user, screen_name=sane_handle)\\n try:\\n user = await self.bot.loop.run_in_executor(None, partial)\\n except tweepy.TweepError as e:\\n if e.api_code == 50:\\n raise TwitterError('User \\\"{}\\\" not found.'.format(handle)) from e\\n else:\\n log.error(str(e))\\n raise TwitterError('Unknown error, this has been logged.') from e\\n\\n # The Twitter API does not support following protected users\\n # https://dev.twitter.com/streaming/overview/request-parameters#follow\\n if user.protected:\\n raise TwitterError('This channel is protected and cannot be followed.')\\n\\n # Register the new channel\\n conf = FollowConfig(user.id_str, user.screen_name)\\n self.conf.follows.append(conf)\\n\\n try:\\n # Restart the stream\\n await self.stream.start()\\n except tweepy.TweepError as e:\\n self.conf.follows.remove(conf)\\n log.error(str(e))\\n raise TwitterError('Unknown error, this has been logged.') from e\\n elif dutils.get(conf.discord_channels, id=discord_channel.id):\\n raise TwitterError('Already following {} on this channel.'.format(handle))\\n\\n # Add new Discord channel\\n conf.discord_channels.append(ChannelConfig(discord_channel.id))\\n self.conf.save()\\n await self.bot.say('\\\\N{OK HAND SIGN}')\",\n \"async def source(self, context):\\n await context.channel.send(\\\"https://github.com/balfroim/TengriBOT\\\")\",\n \"async def steamlink(self, ctx, steam_id, user: discord.Member=None):\\n\\n if not user:\\n user = ctx.message.author\\n\\n game_list = get_library()\\n\\n # Either use given 64-bit Steam ID, or convert given name to a 64-bit Steam ID\\n try:\\n int(steam_id)\\n except ValueError:\\n key = get_steam_key()\\n\\n if key:\\n url = \\\"https://api.steampowered.com/ISteamUser/ResolveVanityURL/v0001/?key={key}&vanityurl={id}&format=json\\\".format(\\n key=key, id=steam_id)\\n r = requests.get(url)\\n response = json.loads(r.text).get('response')\\n\\n if response.get('success') == 1:\\n game_list[user.id][\\\"steam_name\\\"] = steam_id\\n game_list[user.id][\\\"steam_id\\\"] = response.get('steamid')\\n else:\\n await self.bot.say(\\\"{}, there was a problem linking your Steam name. Please try again with your 64-bit Steam ID instead.\\\".format(user.mention))\\n return\\n else:\\n await self.bot.say(\\\"Sorry, you need a Steam API key to make requests to Steam. Use `{}game steamkey` for more information.\\\".format(ctx.prefix))\\n return\\n else:\\n game_list[user.id][\\\"steam_id\\\"] = steam_id\\n finally:\\n dataIO.save_json(\\\"data/game/games.json\\\", game_list)\\n\\n await self.bot.say(\\\"{}'s account has been linked with Steam.\\\".format(user.mention))\\n\\n # Update the user's Steam games with their permission\\n await self.bot.say(question(\\\"Do you want to update your library with your Steam games? (yes/no)\\\"))\\n response = await self.bot.wait_for_message(author=user, timeout=15, check=check_response)\\n\\n if response:\\n response = response.content.strip().lower()\\n\\n if response in \\\"yes\\\":\\n set_steam_games(game_list[user.id][\\\"steam_id\\\"], user.id)\\n await self.bot.say(\\\"{}, your Steam games have been updated!\\\".format(user.mention))\\n elif response in \\\"no\\\":\\n await self.bot.say(\\\"Fair enough. If you would like to update your games later, please run `{}game update`.\\\".format(ctx.prefix))\\n else:\\n await self.bot.say(\\\"Too late, but you can still use `{}game update` to update your games.\\\".format(ctx.prefix))\",\n \"def create_trackurl(self, context):\\n t_url, created = TrackableURL.objects.get_or_create(url=self.url)\\n t_url.save()\\n\\n # key = generate_url_key()\\n redirect, created = RedirectUrl.objects.get_or_create(user=context['user'], target_url=t_url)\\n if created:\\n redirect.save()\\n\\n text = self.url\\n if hasattr(self, 'display_text') and self.display_text is not None:\\n text = self.display_text\\n else:\\n text = self.url\\n if self.mode == 'link':\\n return \\\"<a href='{0}' target='_blank'>{1}</a>\\\".format(reverse('api_redirect', kwargs={'key': redirect.redirect_key}), text)\\n else:\\n return reverse('api_redirect', kwargs={'key': redirect.redirect_key})\",\n \"async def invite(self, ctx):\\n invite = f\\\"https://discordapp.com/api/oauth2/authorize?client_id={self.bot.user.id}&permissions=67584&scope=bot\\\"\\n await ctx.send(embed=discord.Embed(\\n color=discord.colour.Colour.teal(),\\n description=f\\\":mailbox_with_mail: [Invite]({invite}) me to your server!\\\"))\",\n \"def app_client(app_token):\\n sender = tk.RetryingSender(sender=tk.SyncSender())\\n yield tk.Spotify(app_token, sender=sender)\\n sender.close()\",\n \"async def token(self, ctx):\\n logger.info(\\\"token command issued by {0}\\\".format(ctx.message.author.name))\\n await ctx.message.delete()\\n spotify_token = spotipy.util.prompt_for_user_token_auto(self.spotify_username, self.spotify_scope, self.spotify_id, self.spotify_secret)\\n self.spotify_client = spotipy.Spotify(auth=spotify_token)\\n await ctx.send(\\\"Spotify refresh token updated\\\")\",\n \"async def support(self, ctx):\\n link = \\\"https://discord.gg/J78uAgZ\\\"\\n await ctx.send(\\\"Join my support server if you need help with commands!\\\\n \\\" + link)\",\n \"async def avatarurl(self, ctx: \\\"IceTeaContext\\\", target: discord.Member = None):\\n target = target or ctx.author\\n embed = discord.Embed(description=f\\\"{target} Profile Picture\\\")\\n embed.set_image(url=str(target.avatar_url))\\n await ctx.send(embed=embed)\",\n \"def account_setup(remote, token, resp):\\n with db.session.begin_nested():\\n # Retrieve ORCID from response.\\n orcid = resp.get(\\\"orcid\\\")\\n full_name = resp.get(\\\"name\\\")\\n\\n # Set ORCID in extra_data.\\n token.remote_account.extra_data = {\\n \\\"orcid\\\": orcid,\\n \\\"full_name\\\": full_name,\\n \\\"allow_push\\\": current_app.config.get(\\\"ORCID_ALLOW_PUSH_DEFAULT\\\", False),\\n }\\n\\n user = token.remote_account.user\\n\\n # Create user <-> external id link.\\n oauth_link_external_id(user, {\\\"id\\\": orcid, \\\"method\\\": \\\"orcid\\\"})\",\n \"def example_alias2():\\n\\n # Define app_id and secret\\n my_app_id = 'my_app_id'\\n my_secret = 'my_secret'\\n # Create a Pushbots instance\\n pushbots = Pushbots(app_id=my_app_id, secret=my_secret)\\n # Define data\\n data = {'platform': Pushbots.PLATFORM_ANDROID, 'alias': 'my_new_alias',\\n 'current_alias': 'my_current_alias'}\\n code, message = pushbots.alias(data=data)\\n print('Returned code: {0}'.format(code))\\n print('Returned message: {0}'.format(message))\",\n \"def authenticate(redirect_uri, client_cred_manager, username, scope,client_id,client_secret):\\r\\n\\r\\n sp = spotipy.Spotify(client_credentials_manager = client_cred_manager)\\r\\n token = util.prompt_for_user_token(username, scope, client_id, client_secret, redirect_uri)\\r\\n if token:\\r\\n sp = spotipy.Spotify(auth=token)\\r\\n else:\\r\\n print(\\\"Can't get token for\\\", username)\\r\\n return sp\",\n \"async def info(self, context):\\n await context.send('creador: debellisnahuel@gmail.com\\\\ncolabs:\\\\n emi: https://twitter.com/emilianosce/ o https://www.instagram.com/emilianosce/ \\\\n garza: https://twitter.com/Matias_Garcia00 o https://www.twitch.tv/garzangb')\",\n \"async def async_setup(hass, config):\\n conf = config[DOMAIN]\\n\\n username = conf[CONF_USERNAME]\\n password = conf[CONF_PASSWORD]\\n accounts = conf.get(CONF_ACCOUNTS)\\n\\n @callback\\n def websocket_handle_playlists(hass, connection, msg):\\n \\\"\\\"\\\"Handle get playlist\\\"\\\"\\\"\\n import spotipy\\n access_token, expires = get_spotify_token(username=username, password=password)\\n client = spotipy.Spotify(auth=access_token)\\n resp = client._get('views/made-for-x?content_limit=10&locale=en&platform=web&types=album%2Cplaylist%2Cartist%2Cshow%2Cstation', limit=10,\\n offset=0)\\n connection.send_message(\\n websocket_api.result_message(msg[\\\"id\\\"], resp)\\n )\\n\\n def get_spotify_token(username, password):\\n import spotify_token as st\\n data = st.start_session(username, password)\\n access_token = data[0]\\n # token_expires = data[1]\\n expires = data[1] - int(time.time())\\n return access_token, expires\\n\\n def play(client, spotify_device_id, uri, random_song, repeat):\\n # import spotipy\\n # import http.client as http_client\\n # spotipy.trace = True\\n # spotipy.trace_out = True\\n # http_client.HTTPConnection.debuglevel = 1\\n\\n _LOGGER.debug('Version: %s, playing URI: %s on device-id: %s', _VERSION, uri, spotify_device_id)\\n if uri.find('track') > 0:\\n _LOGGER.debug('Playing track using uris= for uri: %s', uri)\\n client.start_playback(device_id=spotify_device_id, uris=[uri])\\n else:\\n if uri == 'random':\\n _LOGGER.debug('Cool, you found the easter egg with playing a random playlist')\\n playlists = client.user_playlists('me', 50)\\n no_playlists = len(playlists['items'])\\n uri = playlists['items'][random.randint(0, no_playlists - 1)]['uri']\\n kwargs = {'device_id': spotify_device_id, 'context_uri': uri}\\n if random_song:\\n results = client.user_playlist_tracks(\\\"me\\\", uri)\\n position = random.randint(0, results['total'] - 1)\\n _LOGGER.debug('Start playback at random position: %s', position)\\n kwargs['offset'] = {'position': position}\\n\\n _LOGGER.debug('Playing context uri using context_uri for uri: \\\"%s\\\" (random_song: %s)', uri, random_song)\\n client.start_playback(**kwargs)\\n if repeat:\\n _LOGGER.debug('Turning repeat on')\\n time.sleep(5)\\n client.repeat(state=repeat, device_id=spotify_device_id)\\n\\n def get_account_credentials(call):\\n \\\"\\\"\\\" Get credentials for account \\\"\\\"\\\"\\n account = call.data.get(CONF_SPOTIFY_ACCOUNT)\\n user = username\\n pwd = password\\n if account is not None:\\n _LOGGER.debug('setting up with different account than default %s', account)\\n user = accounts.get(account).get(CONF_USERNAME)\\n pwd = accounts.get(account).get(CONF_PASSWORD)\\n return user, pwd\\n\\n def shouldTransferPlayback(call, client):\\n \\\"\\\"\\\" Check if something is playing \\\"\\\"\\\"\\n uri = call.data.get(CONF_SPOTIFY_URI)\\n if uri is None or uri.strip() == '' or call.data.get(CONF_TRANSFER_PLAYBACK):\\n current_playback = client.current_playback()\\n if current_playback is not None:\\n _LOGGER.debug('current_playback from spotipy: %s', current_playback)\\n return True\\n return False\\n\\n async def start_casting(call):\\n \\\"\\\"\\\"service called.\\\"\\\"\\\"\\n import spotipy\\n\\n uri = call.data.get(CONF_SPOTIFY_URI)\\n random_song = call.data.get(CONF_RANDOM, False)\\n repeat = call.data.get(CONF_REPEAT)\\n\\n # Account\\n user, pwd = get_account_credentials(call)\\n\\n # login as real browser to get powerful token\\n access_token, expires = get_spotify_token(username=user, password=pwd)\\n\\n # get the spotify web api client\\n client = spotipy.Spotify(auth=access_token)\\n\\n # launch the app on chromecast\\n spotify_cast_device = SpotifyCastDevice(hass, call.data.get(CONF_DEVICE_NAME), call.data.get(CONF_ENTITY_ID))\\n spotify_cast_device.startSpotifyController(access_token, expires)\\n spotify_device_id = spotify_cast_device.getSpotifyDeviceId(client)\\n\\n transfer_playback = shouldTransferPlayback(call, client)\\n if transfer_playback == True:\\n _LOGGER.debug('Transfering playback')\\n client.transfer_playback(\\n device_id=spotify_device_id, force_play=True)\\n else:\\n play(client, spotify_device_id, uri, random_song, repeat)\\n\\n # Register websocket and service\\n hass.components.websocket_api.async_register_command(\\n WS_TYPE_SPOTCAST_PLAYLISTS, websocket_handle_playlists, SCHEMA_PLAYLISTS\\n )\\n\\n hass.services.async_register(DOMAIN, 'start', start_casting,\\n schema=SERVICE_START_COMMAND_SCHEMA)\\n\\n return True\",\n \"def set_card_link(self):\\n self.response.card.type = 'LinkAccount'\",\n \"def link(self):\\n return (\\n reverse_lazy(\\\"passbook_core:auth-sign-up\\\") + f\\\"?invitation={self.uuid.hex}\\\"\\n )\",\n \"def add_bot(self, bot):\\n self.add_user(bot)\",\n \"async def invite(ctx):\\r\\n await ctx.send(\\\"https://discordapp.com/oauth2/authorize?client_id=457903893079392256&scope=bot&permissions=2146958591\\\")\\r\\n ctx.counter(n)\",\n \"def data_client(user_token):\\n sender = tk.RetryingSender(sender=tk.SyncSender())\\n yield tk.Spotify(user_token, sender=sender)\\n sender.close()\",\n \"async def support(self):\\n link = \\\"https://discord.gg/J78uAgZ\\\"\\n await self.bot.say(\\\"Join my support server if you need help with commands!\\\\n \\\" + link)\",\n \"def echo(update, context):\\n update.message.reply_text('Did you mean? https://www.youtube.com/watch?v=dQw4w9WgXcQ ?')\",\n \"def example_alias1():\\n\\n # Define app_id and secret\\n my_app_id = 'my_app_id'\\n my_secret = 'my_secret'\\n # Create a Pushbots instance\\n pushbots = Pushbots(app_id=my_app_id, secret=my_secret)\\n # Define new and current alias and platform\\n new_alias = 'my_new_alias'\\n old_alias = 'my_current_alias'\\n platform = Pushbots.PLATFORM_ANDROID\\n code, message = pushbots.alias(platform=platform, alias=new_alias,\\n current_alias=old_alias)\\n print('Returned code: {0}'.format(code))\\n print('Returned message: {0}'.format(message))\",\n \"async def userinfo(self, ctx: \\\"IceTeaContext\\\", target: discord.Member = None):\\n target = target or ctx.author\\n target_data = ctx.author_data if target == ctx.author else await ctx.get_user_data(target)\\n if target_data:\\n nicknames = await target_data.get_nicknames()\\n else:\\n nicknames = []\\n shared_servers = len([member for member in ctx.bot.get_all_members() if member == target])\\n embed = discord.Embed(title=f\\\"{target.nick or target.name} Profile\\\")\\n embed.set_author(name=f\\\"{target.name} ({target.id})\\\", icon_url=target.avatar_url)\\n embed.set_thumbnail(url=target.avatar_url)\\n embed.add_field(name=\\\"Shared Servers\\\", value=f\\\"{shared_servers} Shared\\\")\\n embed.add_field(name=\\\"Created\\\",\\n value=f\\\"\\\"\\\"{timeago.format(target.created_at)} ({target.created_at.strftime(\\\"%b %d, %Y\\\")})\\\"\\\"\\\")\\n embed.add_field(name=\\\"Joined\\\",\\n value=f\\\"\\\"\\\"{timeago.format(target.joined_at)} ({target.joined_at.strftime(\\\"%b %d, %Y\\\")})\\\"\\\"\\\")\\n embed.set_footer(text=\\\"Last Spoke In server\\\")\\n if target_data:\\n embed.timestamp = target_data.last_spoke\\n else:\\n embed.timestamp = ctx.message.created_at\\n if len(nicknames) > 0:\\n embed.add_field(name=\\\"Nicknames\\\", value=\\\" , \\\".join(str(nick) for nick in nicknames[:5]), inline=False)\\n embed.add_field(name=\\\"Roles\\\", value=\\\" , \\\".join([role.name for role in target.roles[:5] if len(role.name) > 0]),\\n inline=False)\\n if target.activity:\\n if isinstance(target.activity, discord.Spotify):\\n embed.add_field(name=\\\"Currently Listening to\\\",\\n value=f\\\"**{target.activity.title}** by {target.activity.artist} \\\")\\n else:\\n embed.add_field(name=\\\"Currently Playing Since\\\",\\n value=f\\\"{target.activity.name}\\\\n{target.activity.details}\\\\n{target.activity.state}\\\")\\n await ctx.send(embed=embed)\",\n \"def authenticate_spotify_api(SPOTIPY_CLIENT_ID, SPOTIPY_CLIENT_SECRET):\\r\\n auth_manager = SpotifyClientCredentials(client_id = SPOTIPY_CLIENT_ID, \\r\\n client_secret=SPOTIPY_CLIENT_SECRET)\\r\\n \\r\\n return spotipy.Spotify(auth_manager=auth_manager)\",\n \"def connect(self):\\n auth = self.botconfig.find('auth')\\n logging.info(\\\"Connecting ...\\\" )\\n if not auth.get('server', None):\\n # we don't know the server, but the lib can probably figure it out\\n super(SleekBot, self).connect()\\n else:\\n super(SleekBot, self).connect((auth.attrib['server'], auth.get('port', 5222)))\",\n \"def onTuxLinkClicked(self, linkId=None):\\n self.LinkTux.emit()\",\n \"def linkTo(sharedProxyOrItem):\\n if isinstance(sharedProxyOrItem, sharing.SharedProxy):\\n userStore = sharing.itemFromProxy(sharedProxyOrItem).store\\n else:\\n userStore = sharedProxyOrItem.store\\n appStore = isAppStore(userStore)\\n if appStore:\\n # This code-path should be fixed by #2703; PublicWeb is deprecated.\\n from xmantissa.publicweb import PublicWeb\\n substore = userStore.parent.getItemByID(userStore.idInParent)\\n pw = userStore.parent.findUnique(PublicWeb, PublicWeb.application == substore)\\n path = [pw.prefixURL.encode('ascii')]\\n else:\\n for lm in userbase.getLoginMethods(userStore):\\n if lm.internal:\\n path = ['users', lm.localpart.encode('ascii')]\\n break\\n else:\\n raise RuntimeError(\\n \\\"Shared item is in a user store with no\\\"\\n \\\" internal username -- can't generate a link.\\\")\\n if (sharedProxyOrItem.shareID == getDefaultShareID(userStore)):\\n shareID = sharedProxyOrItem.shareID\\n path.append('')\\n else:\\n shareID = None\\n path.append(sharedProxyOrItem.shareID)\\n return _ShareURL(shareID, scheme='', netloc='', pathsegs=path)\",\n \"def link(self, obj):\\n return format_html(\\n '<a href=\\\"{url}\\\">{url}</a>',\\n url='https://sms.cam.ac.uk/media/{}'.format(obj.id)\\n )\",\n \"async def invite(self, context: Context) -> None:\\n embed = discord.Embed(\\n description=f\\\"Invite me by clicking [here](https://discordapp.com/oauth2/authorize?&client_id={self.bot.config['application_id']}&scope=bot+applications.commands&permissions={self.bot.config['permissions']}).\\\",\\n color=0xD75BF4,\\n )\\n try:\\n # To know what permissions to give to your bot, please see here: https://discordapi.com/permissions.html and remember to not give Administrator permissions.\\n await context.author.send(embed=embed)\\n await context.send(\\\"I sent you a private message!\\\")\\n except discord.Forbidden:\\n await context.send(embed=embed)\",\n \"async def join(self, ctx):\\n player = self.bot.lavalink.players.get(ctx.guild.id)\\n if player.is_connected:\\n return await ctx.send(\\\"I'm already in a voice channel :no_entry:\\\")\\n else:\\n if not ctx.author.voice or not ctx.author.voice.channel:\\n return await ctx.send(\\\"You are not in a voice channel :no_entry:\\\")\\n player.store('sessionowner', ctx.author.id)\\n player.store('channel', ctx.channel.id)\\n await player.connect(ctx.author.voice.channel.id)\\n await ctx.send(\\\"Summoned to `{}` <:done:403285928233402378>\\\".format(ctx.author.voice.channel.name))\",\n \"def _create_user_object(self) -> None:\\n\\n token = util.prompt_for_user_token(self._USERNAME, self.scope, self._CLIENT_ID, self._CLIENT_SECRET, self.redirect_uri)\\n self.spotipyObject = spotipy.Spotify(auth=token)\",\n \"def _next(verbose=0, quiet=False):\\n Spotify.request('me/player/next', method='POST')\\n if not quiet:\\n from cli.commands.status import status\\n status.callback(verbose=verbose)\\n\\n return\",\n \"async def server(self, context: Context) -> None:\\n embed = discord.Embed(\\n description=f\\\"Join the support server for the bot by clicking [here](https://discord.gg/mTBrXyWxAF).\\\",\\n color=0xD75BF4,\\n )\\n try:\\n await context.author.send(embed=embed)\\n await context.send(\\\"I sent you a private message!\\\")\\n except discord.Forbidden:\\n await context.send(embed=embed)\",\n \"def spotify(request):\\n\\n logger.debug(\\\"Spotify Album Called\\\")\\n response_data = {}\\n\\n spotify_pre_auth = spotify_auth(request)\\n# if type(spotify_pre_auth) is JsonResponse:\\n# return spotify_pre_auth\\n\\n response = spotify_pre_auth.get('result','')\\n album_url = spotify_pre_auth.get('album_url','')\\n response_code=spotify_pre_auth.get('status_code','')\\n\\n if response_code == 200:\\n auth_data = json.loads(response)\\n access_token = auth_data['access_token']\\n\\n headers = {\\\"Authorization\\\": \\\"Bearer %s\\\" % access_token}\\n album_url = \\\"https://api.spotify.com/v1/albums/\\\" + album_url.split(\\\"/\\\")[-1]\\n logger.debug(\\\"Spotify get album: %s\\\" % album_url)\\n res = requests.get(album_url, headers=headers)\\n if res.status_code == 200:\\n album = json.loads(res.text)\\n logger.debug(\\\"Found on Spotify the album: %(name)s\\\" % album)\\n return json.loads(res.text)\\n else:\\n response = res.text\\n response_code = res.status_code\\n response_body = {\\\"result\\\": \\\"failure\\\", \\\"message\\\": \\\"Spotify album failed. Check the url or the connections\\\", \\\"status_code\\\": response_code}\\n return response_body\",\n \"def view_newsletter_tracking_link(request, slug, uidb36, token, link_id):\\n newsletter = get_object_or_404(Newsletter, slug=slug)\\n contact = untokenize(uidb36, token)\\n link = get_object_or_404(Link, pk=link_id)\\n ContactMailingStatus.objects.create(newsletter=newsletter,\\n contact=contact,\\n status=ContactMailingStatus.LINK_OPENED,\\n link=link)\\n return HttpResponseRedirect(link.url)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6603521","0.63787323","0.6339056","0.6307534","0.62112045","0.6183406","0.60342115","0.6027001","0.60145694","0.60145694","0.5984008","0.59183764","0.5639046","0.5622354","0.5614502","0.5600881","0.5547755","0.5491989","0.5484494","0.54839873","0.5476929","0.5442474","0.5434192","0.54155725","0.54124016","0.53911066","0.5378274","0.5357755","0.53518236","0.5349578","0.53478295","0.53295135","0.5302093","0.5266099","0.5243785","0.5243077","0.5221872","0.52152944","0.52114296","0.52081084","0.5178433","0.5174221","0.51652974","0.51651114","0.5164095","0.51576215","0.514796","0.51355886","0.5123083","0.51216906","0.51052195","0.5098135","0.5096596","0.5095267","0.509311","0.5072366","0.5068375","0.50599676","0.5057778","0.50235456","0.5021712","0.50210214","0.50205934","0.50095356","0.5003756","0.5001134","0.50001806","0.499677","0.49766657","0.49686265","0.49259704","0.49172282","0.49120206","0.48992053","0.48885807","0.48829967","0.48723677","0.4862","0.48585474","0.4857994","0.48475403","0.48440674","0.48359546","0.48356837","0.48353046","0.4832557","0.48287633","0.4820411","0.48136413","0.4806871","0.4806666","0.48060825","0.4801997","0.48017666","0.4800396","0.47950438","0.47871324","0.47825456","0.4782016","0.47787935"],"string":"[\n \"0.6603521\",\n \"0.63787323\",\n \"0.6339056\",\n \"0.6307534\",\n \"0.62112045\",\n \"0.6183406\",\n \"0.60342115\",\n \"0.6027001\",\n \"0.60145694\",\n \"0.60145694\",\n \"0.5984008\",\n \"0.59183764\",\n \"0.5639046\",\n \"0.5622354\",\n \"0.5614502\",\n \"0.5600881\",\n \"0.5547755\",\n \"0.5491989\",\n \"0.5484494\",\n \"0.54839873\",\n \"0.5476929\",\n \"0.5442474\",\n \"0.5434192\",\n \"0.54155725\",\n \"0.54124016\",\n \"0.53911066\",\n \"0.5378274\",\n \"0.5357755\",\n \"0.53518236\",\n \"0.5349578\",\n \"0.53478295\",\n \"0.53295135\",\n \"0.5302093\",\n \"0.5266099\",\n \"0.5243785\",\n \"0.5243077\",\n \"0.5221872\",\n \"0.52152944\",\n \"0.52114296\",\n \"0.52081084\",\n \"0.5178433\",\n \"0.5174221\",\n \"0.51652974\",\n \"0.51651114\",\n \"0.5164095\",\n \"0.51576215\",\n \"0.514796\",\n \"0.51355886\",\n \"0.5123083\",\n \"0.51216906\",\n \"0.51052195\",\n \"0.5098135\",\n \"0.5096596\",\n \"0.5095267\",\n \"0.509311\",\n \"0.5072366\",\n \"0.5068375\",\n \"0.50599676\",\n \"0.5057778\",\n \"0.50235456\",\n \"0.5021712\",\n \"0.50210214\",\n \"0.50205934\",\n \"0.50095356\",\n \"0.5003756\",\n \"0.5001134\",\n \"0.50001806\",\n \"0.499677\",\n \"0.49766657\",\n \"0.49686265\",\n \"0.49259704\",\n \"0.49172282\",\n \"0.49120206\",\n \"0.48992053\",\n \"0.48885807\",\n \"0.48829967\",\n \"0.48723677\",\n \"0.4862\",\n \"0.48585474\",\n \"0.4857994\",\n \"0.48475403\",\n \"0.48440674\",\n \"0.48359546\",\n \"0.48356837\",\n \"0.48353046\",\n \"0.4832557\",\n \"0.48287633\",\n \"0.4820411\",\n \"0.48136413\",\n \"0.4806871\",\n \"0.4806666\",\n \"0.48060825\",\n \"0.4801997\",\n \"0.48017666\",\n \"0.4800396\",\n \"0.47950438\",\n \"0.47871324\",\n \"0.47825456\",\n \"0.4782016\",\n \"0.47787935\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7880789"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":22496,"cells":{"query":{"kind":"string","value":"Unlink a spotify account from the bot."},"document":{"kind":"string","value":"async def unlink(self, ctx):\n # Remove all link tokens and spotify details for this user\n remove_tokens(ctx.author.id)\n remove_spotify_details(ctx.author.id)\n await ctx.reply(\"All your linked accounts were removed, if you had any!\")"},"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 unlink(self, link_id):","def unfollow_profile(self):\n self.find_clickable_element(self.ISFOLLOWED_BTN).click()","async def twitter_unfollow(self, ctx, handle):\n sane_handle = handle.lower().lstrip('@')\n conf = dutils.get(self.conf.follows, screen_name=sane_handle)\n chan_conf = dutils.get(conf.discord_channels, id=ctx.message.channel.id) if conf is not None else None\n\n if chan_conf is None:\n raise TwitterError('Not following {} on this channel.'.format(handle))\n\n # Remove the Discord channel from the Twitter channel conf\n conf.discord_channels.remove(chan_conf)\n if not conf.discord_channels:\n # If there are no more Discord channel to feed, unfollow the Twitter channel\n self.conf.follows.remove(conf)\n del conf\n\n # Update the tweepy stream\n if len(self.conf.follows) > 0:\n await self.stream.start()\n else:\n self.stream.stop()\n\n self.conf.save()\n\n await self.bot.say('\\N{OK HAND SIGN}')","def disconnect():\n\n\tglob.tokens.deleteToken(glob.tokens.getTokenFromUserID(999))","def unfollow(alias):\n s = db.Series.alias_lookup(alias)\n s.following = False\n db.session.commit()\n output.series('Removing follow for {}'.format(s.name))","def closeaccount(request):\n get_user_model().objects.get(username=request.user.get_username()).delete()\n return Response({}, status=status.HTTP_200_OK)","def unregister(url):\n return Client.get_client().unregister(url)","async def unplonk(ctx, user: typing.Union[discord.Member, discord.User]):\n await bot.plonk.delete(user.id)\n await r(ctx, f'Unplonked **{user}**')","def _disconnect(remote, *args, **kwargs):\n if not current_user.is_authenticated:\n return current_app.login_manager.unauthorized()\n\n account = RemoteAccount.get(\n user_id=current_user.get_id(), client_id=remote.consumer_key\n )\n if account:\n external_id = account.extra_data.get(\"external_id\")\n\n if external_id:\n oauth_unlink_external_id(dict(id=external_id, method=\"cern_openid\"))\n\n with db.session.begin_nested():\n account.delete()\n\n disconnect_identity(g.identity)","def deauth(nick):\n global auth_list\n if nick in auth_list:\n a = auth_list.index(nick)\n del(auth_list[a])","def leave(self):\n self.remove(\n self.subreddit._reddit.config.username or self.subreddit._reddit.user.me()\n )","async def unlink(self, ctx: MyContext):\n query = \"SELECT * FROM wormhole_channel WHERE channelID = ?\"\n wh_channel = self.bot.db_query(\n query, (ctx.channel.id,), astuple=True, fetchone=True\n )\n # comes as: (name, channelID, guildID, type, webhookID, webhookTOKEN)\n if len(wh_channel) == 0:\n await ctx.send(await self.bot._(ctx.guild.id, \"wormhole.error.not-linked\"))\n return\n query = \"DELETE FROM wormhole_channel WHERE channelID = ? AND name = ?\"\n async with ClientSession() as session:\n webhook = discord.Webhook.partial(\n wh_channel[4], wh_channel[5], session=session\n )\n await webhook.delete()\n self.bot.db_query(query, (wh_channel[0], ctx.channel.id))\n await ctx.send(\n await self.bot._(ctx.guild.id, \"wormhole.success.channel-unlinked\")\n )","def unlink(self):\n if self.resource is None:\n self.resource = self.client.get_resource(self.href)\n self.client.post_linked_resource(\n self.resource, RelationType.UNLINK_FROM_TEMPLATE,\n EntityType.ROLE.value, None)","def unfollow_me(self):\n return self.follow_me('unfollow_topic')","def unfollow(request, usertostopfollow):\n stop_follow = Member.objects.get(user__username=usertostopfollow)\n user = Member.objects.get(user=request.user)\n user.following.remove(stop_follow)\n user.save()\n return redirect(request.META['HTTP_REFERER'])","def unfollow(self, name):\r\n url = '{0}/{1}/{2}'.format(self.get_url(), 'following', name)\r\n\r\n return http.Request('DELETE', url), parsers.parse_empty","def delete_account(self, account):\n \n pass","def unfollow(self, other):\n\t\tif self.follows(other):\n\t\t\tself.followed.remove(other)","def unShare(sharedItem):\n sharedItem.store.query(Share, Share.sharedItem == sharedItem).deleteFromStore()","def remove_link():","def disassociate_member_account(memberAccountId=None):\n pass","def detele_account():\n session_id = request.args.get('session-id', None)\n user_id = request.args.get('user-id', None)\n delete_user(user_id)\n delete_session(session_id)\n return render_template('home.html', cars_list=get_cars_preview(), news_list=get_news_list(), authjs=False,\n preview_length=get_cars_preview().__len__(), user_deleted=user_id)","def unfollow_friend(username):\n\n if not g.user:\n print \"401\"\n abort(401)\n whom_id = get_user_id(username)\n print whom_id\n if whom_id is None:\n abort(404)\n unfollow_query(whom_id)\n flash('You are no longer following \"%s\"' % username)\n name = {'name of unfollowing user': username}\n ############### REDIS cache invalidate #####################\n R_SERVER.delete(user_timeline_key)\n return jsonify(Username=name, Status_code=status.HTTP_200_OK)","def unfollow_user(username):\n if not g.user:\n abort(401)\n whom_id = get_user_id(username)\n if whom_id is None:\n abort(404)\n db = get_db()\n db.execute('delete from follower where who_id=? and whom_id=?',\n [session['user_id'], whom_id])\n db.commit()\n flash('You are no longer following \"%s\"' % username)\n return redirect(url_for('user_timeline', username=username))","def unlink_account(self, accountid, provider, providerid):\n auth = 'appkey='+ self._lr_object._get_api_key()+ '&appsecret='+ self._lr_object._get_api_secret()\n payload = {'accountid': accountid, 'provider': provider, 'providerid': providerid}\n url = SECURE_API_URL + \"raas/v1/account/unlink\" + \"?\" + auth\n return self._lr_object._post_json(url, payload)","def revoke_token(token):\n token.delete_instance()","async def disconnect(self, ctx):\n player = self.bot.lavalink.players.get(ctx.guild.id)\n if not player.is_connected:\n return await ctx.send(\"I'm not currently connected to a voice channel :no_entry:\")\n if not ctx.author.voice or (player.is_connected and player.connected_channel.id != ctx.author.voice.channel.id):\n return await ctx.send(\"You have to be in my voice channel to disconnect :no_entry:\")\n if player.fetch(\"sessionowner\") == ctx.author.id:\n player.queue.clear()\n await player.disconnect()\n player.delete(\"votes\")\n await ctx.send(\"Disconnected <:done:403285928233402378>\")\n else:\n await ctx.send(\"Only the session owner can disconnect the bot :no_entry:\")","def disconnect(self, login_session):\n\n # Only disconnect a connected user.\n credentials = login_session.get('credentials')\n\n if 'gplus_id' in login_session:\n del login_session['gplus_id']\n if 'credentials' in login_session:\n del login_session['credentials']\n\n if credentials is None:\n response = make_response(\n json.dumps('Current user not connected.'), 401)\n response.headers['Content-Type'] = 'application/json'\n return response\n\n access_token = credentials.access_token\n url = 'https://accounts.google.com/o/oauth2/revoke?token=%s' % access_token\n h = httplib2.Http()\n result = h.request(url, 'GET')[0]\n if result['status'] != '200':\n # For whatever reason, the given token was invalid.\n response = make_response(\n json.dumps('Failed to revoke token for given user.', 400))\n response.headers['Content-Type'] = 'application/json'\n return response\n else:\n return \"You have been logged out.\"","async def disconnect(ctx):\n if ctx.voice_client is not None:\n await ctx.voice_client.disconnect()","def unregister(self):\n if self.hub.is_connected:\n self._is_registered = False\n self.hub.unregister(self._private_key)\n self._hub_id = None\n self._public_id = None\n self._private_key = None\n else:\n raise SAMPClientError(\n \"Unable to unregister from the SAMP Hub. Hub proxy not connected.\"\n )","def delete_account(self):\n Credential.account_list.remove(self)","async def off(self, ctx, *, nickname=\"\"):\n nickname = nickname.strip()\n if nickname == \"\":\n nickname = None\n try:\n await self.bot.change_nickname(ctx.message.server.me, nickname)\n await self.bot.delete_message(ctx.message)\n await self.bot.say(\"RIP music lol\")\n except discord.Forbidden:\n await self.bot.say(\"I cannot do that, I miss the `Change Nickname` or `Manage Messages` permission\")","def disconnect_identity(identity):\n session.pop(\"cern_resource\", None)\n key = current_app.config.get(\n \"OAUTHCLIENT_CERN_OPENID_SESSION_KEY\",\n OAUTHCLIENT_CERN_OPENID_SESSION_KEY,\n )\n provides = session.pop(key, set())\n identity.provides -= provides","def del_user(self, username):\n pass","def stopSharingNote(self, authenticationToken, guid):\r\n pass","def user_unfollow():\n data = request.get_json(force=True)\n follower = User.query.get(data['follower'])\n following = User.query.get(data['following'])\n follower.followcheck.remove(following)\n db.session.commit()\n return {'unfollowed': True}","def unfollow(self, user):\n f = self.followed.filter_by(followed_id=user.id).first()\n if f:\n db.session.delete(f)","async def unban(self, ctx, name: str):\n try:\n bans = await self.bot.get_bans(ctx.message.server)\n user = discord.utils.get(bans, name=name)\n if user is not None:\n await self.bot.unban(ctx.message.server, user)\n except discord.Forbidden:\n await self.bot.say('I do not have the proper permissions')\n except discord.HTTPException:\n await self.bot.say('Unbanning failed')\n else:\n await self.bot.say('\\N{OK HAND SIGN}')","def fb_deauth(self, request):\n signed_request = request.data.get('signed_request')\n if signed_request:\n parsed_signed_request = facebook_controller.parse_signed_request(signed_request)\n facebook_user_id = parsed_signed_request.get('user_id')\n if facebook_user_id:\n facebook_controller.delete_linked_facebook_account(facebook_user_id)\n return Response('OK')","def delete(account):\n account.stripe_account.delete()\n account.delete()","def unlink_venue_account(venue_account, after_action, owner, user):\n if after_action == 'move_events':\n _transfer_venue_events_to_owner(venue_account, owner, user)\n elif after_action == 'remove_events':\n _delete_venue_events(venue_account, user)\n\n venue_account.delete()","def sign_off(self):\n self.log(\"Bot player signing off.\")\n return self.complete_questionnaire()","def fbdisconnect():\n\n facebook_id = login_session['facebook_id']\n url = 'https://graph.facebook.com/%s/permissions' % facebook_id\n h = httplib2.Http()\n result = h.request(url, 'DELETE')[1]\n del login_session['facebook_id']\n return \"you have been logged out\"","def delete(self):\n url = \"https://api.imgur.com/3/account/{0}\".format(self.name)\n return self._imgur._send_request(url, needs_auth=True, method='DELETE')","def deltoken(confirm, name):\n stm = shared_morphene_instance()\n if mph.rpc is not None:\n mph.rpc.rpcconnect()\n if not unlock_wallet(stm):\n return\n mph.wallet.removeTokenFromPublicName(name)\n set_shared_morphene_instance(stm)","def spiderbotDelete(spiderbotid):\n sclogic.spiderbotDelete(spiderbotid)","def unfollowUser(following):\n \n cur, user_id, con = initialise(3, True)\n cur.execute(\"DELETE FROM followers WHERE user = (SELECT username FROM users WHERE id = ?) AND following = ?\", (user_id, following))\n finish(con)","def unlink(address):","def untagAll(self, authenticationToken, guid):\r\n pass","def unfollow(self, followerId, followeeId):\r\n self.follows[followerId].discard(followeeId)","def revoke(self, token):\n client = self.connect(VAULT_TOKEN)\n client.revoke_token(token)","def unlink_pivot(remote, pivot_id):\n cmd = mmapi.StoredCommands()\n cmd_key = cmd.AppendSceneCommand_UnlinkPivot(pivot_id)\n remote.runCommand(cmd)","async def logout(self):\n try:\n user = self.request.session.get(\"user\")\n chat = self.request.session.get(\"chat\")\n active_sockets = self.request.app.active_sockets\n active_sockets.get_chat(chat).del_user(user)\n\n self.request.session.pop(\"user\")\n self.request.user = None\n self.request.chat = None\n\n return {\n \"Type\": \"account\",\n \"Command\": \"logout\",\n \"Status\": \"success\"\n }\n except KeyError:\n return {\"Type\": \"account\", \"Command\": \"logout\", \"Status\": \"error\"}","def logout(self):\n self.account = None\n return \"Welcome to the bank!\"","def unblock(self):\n data = {'container': self._reddit.user.me().fullname,\n 'name': str(self), 'type': 'enemy'}\n url = API_PATH['unfriend'].format(subreddit='all')\n # PRAW5 REMOVE (return statement)\n return self._reddit.post(url, data=data)","def forget(self, uid):","async def _logout(self, ctx):\n self.bot.log.info('Bot logging off.')\n await self.bot.logout()","async def logout(ctx):\n await ctx.send(f\"Hey {ctx.author.mention}, I am now logging out :wave:\")\n await bot.logout()","def remove_account(self, account_name):\n del self._accounts[account_name]","def logout(token):\n with client.connect_to_server_with_auth(token) as auth_conn:\n client_stub = strongdoc_pb2_grpc.StrongDocServiceStub(auth_conn)\n\n request = accounts_pb2.LogoutReq()\n\n response = client_stub.Logout(request, timeout=constants.GRPC_TIMEOUT)\n\n return response.status","def get(self):\n\n try:\n health.UnlinkProfile()\n except gdata.service.RevokingOAuthTokenFailed:\n pass\n\n self.redirect('/')","async def remove(self, context):\n try: \n url_tournament = Tournament.last_url_tournament\n await context.send(url_tournament)\n except Exception as error:\n print(error)","def remove_user(self):\n self.currentuser = None\n self.carlocked = False","def del_user(self, server, username, quiet=False):\n self._op_user(\"del\", server, {\"username\": username}, quiet)","def logout(client):\n\n return client.post('/v1/auth/revoke')","async def disconnect(self, ctx):\r\n if ctx.voice_client:\r\n await ctx.voice_client.disconnect()\r\n print(\"[INFO] Bot disconnected from channel {0}\".format(self.channel_name))\r\n await ctx.message.delete()","def logout(connection, verbose=False):\n response = requests.post(url=connection.base_url + '/auth/logout',\n headers={'X-MSTR-AuthToken': connection.auth_token},\n cookies=connection.cookies,\n verify=connection.ssl_verify)\n if verbose:\n print(response.url)\n return response","def remove(url: str):\n authenticated = credentials.authenticate(url)\n REMOVER_REGISTRY.get_handler(authenticated.scheme).remove(authenticated)","def disassociate_song(self, song):\n self.songs.remove(song)","def untag():\n form = TagSubscriptionForm(hidden_mode=True)\n if not form.validate_on_submit():\n abort(403)\n\n subscription = current_user.subscriptions.filter_by(\n channel_id=form.channel_id.data\n ).first_or_404()\n tag = current_user.tags.filter_by(name=form.tag_name.data).first_or_404()\n\n results = subscription.untag(tag.id)\n response = {\"success\": results}\n return jsonify(response)","def unbookmark(user_id, item_id):\n Bookmark.objects.filter(user=user_id, item=item_id).delete()","def unfollow(self, user):\n if self.is_following(user):\n self.followed.remove(user)\n return self","def delete_account(request):\n ubanks = request.user.userbank.all()\n for ubank in ubanks:\n ubank.delete()\n user = request.user\n log_out(request)\n user.delete()\n return HttpResponse(\"Account succesfully deleted\")","async def tourney_ping_off(ctx):\n\n user = find_user_in_db(ctx.author.id)\n if user is None:\n await ctx.send(\n f\"{ctx.author.mention} you are not even being notified.\\n If you want to be notified use `{prefix}pingme`.\")\n return\n\n delete_user_from_db(ctx.author.id)\n\n await ctx.send(f\"{ctx.author.mention} you won't be notified ever again unless you type `{prefix}pingme` again.\")\n return","def logout():\n rino.login.logout()","def unfollow(self, followerId: int, followeeId: int) -> None:\n self.users[followerId].discard(followeeId)","def remove(self, session: \"pwncat.manager.Session\"):","def __delitem__(self, userid):\r\n self.removePlayer(userid)","def gdisconnect():\n\taccess_token = session.get('access_token')\n\tuname = session.get('username')\n\n\tif not access_token:\n\t\tresponse = make_response(\n\t\t\tjson.dumps('Current user not connected.'), 401)\n\t\tresponse.headers['Content-Type'] = 'application/json'\n\t\treturn response\n\n\turl = 'https://accounts.google.com/o/oauth2/revoke?token=%s' % access_token\n\th = httplib2.Http()\n\tresult = h.request(url, 'GET')[0]\n\n\tif result['status'] != '200':\n\t\t# For whatever reason, the given token was invalid.\n\t\tresponse = make_response(\n\t\t\tjson.dumps('Failed to revoke token for given user.'), 400)\n\t\tresponse.headers['Content-Type'] = 'application/json'\n\t\t[session.pop(k, None) for k, _ in session.items()]\n\t\treturn response\n\t#Clearing out session data\n\t[session.pop(k, None) for k, _ in session.items()]\n\treturn redirect(request.referrer)","def do_logout():\n del session[CURRENT_USER_KEY]","def logout_user(session):\n del session['user']","def backwards(self, orm):\n \n user_type = orm['auth.User']\n user = user_type.objects.get(username=self.suUsername)\n \n profile_type = orm['mooi.Profile']\n userProfile = profile_type.objects.get(user=user)\n \n userProfile.delete()\n user.delete()","async def unfollow(self, TargetId: int):\n data = {\n 'targetUserId': TargetId\n }\n e = await self.request.request(url=f'https://friends.roblox.com/v1/users/{TargetId}/unfollow', method='post',\n data=data)\n return e","def unfollow_user(username):\n user_ID = before_request()\n user_ID = None\n if user_ID != None:\n user_ID = str(g.user['_id'])\n if not g.user:\n abort(401)\n whom_id = get_user_id(username)\n if whom_id is None:\n abort(404)\n mongo.db.users.update({'_id': g.user['_id']}, {\n '$pull': {'follows': whom_id}})\n flash('You are no longer following \"%s\"' % username)\n if redis_obj.get(user_ID):\n return redirect(url_for('user_timeline', username=username, userId=pickle.loads(redis_obj.get(user_ID))))\n else:\n redis_obj.delete(session['user_id'])\n print \"Invalidating cache after Unfollow\"\n return redirect(url_for('user_timeline', username=username))","def unlike(id):\n spinner = Halo(text=\"Unliking photo...\", spinner=\"dots\").start()\n try:\n api.unlike(id)\n spinner.succeed(\"Unliked\")\n except Exception:\n spinner.fail(\"Failed to unlike photo.\")","def logOut(self):\n self.client.logout()","def unjoin():\n event_id = request.args.get('eventId')\n print(\"-=-=-=-=-\",event_id)\n register = db.session.query(Register).filter(Register.event_id == event_id).first()\n db.session.delete(register)\n db.session.commit()\n return redirect('/')","def unfollow(self,id):\n # DELETE /followings/$id\n debugMain('unfollow')\n resource = '/followings/%s'%id\n requestUrl = self.apiRootUrls[0] + resource\n debugRequest('unfollowing: %s'%requestUrl)\n r = self.session.delete(requestUrl)\n \n debugDetail('request headers:')\n debugJson(r.request.headers)\n debugDetail()\n debugDetail(' -- -- -- --')\n debugDetail()\n debugDetail('response headers:')\n debugJson(r.headers)\n debugDetail()\n \n if r.status_code is not 200:\n debugError('failed to unfollow.')\n debugDetail()\n return False\n return True","def delete_remote_access_session(arn=None):\n pass","def delete_my_account():\n # Remove user ownerships\n for p in current_user.projects:\n p.user_id = None\n p.save()\n # Delete user posts\n [ a.delete() for a in current_user.activities ]\n # Delete user account\n current_user.delete()\n logout_user()\n flash('We are sorry to see you go. Your profile has been deleted.', 'info')\n return redirect(url_for('public.home'))","def unban_member(self, *args, **kwargs):\n return self.bot.unban_chat_member(self.id, *args, **kwargs)","def unfriend(self, user_id, target_id):\n if self.database is None:\n raise Exception(\"No database.\")\n if user_id is None or len(user_id) == 0:\n raise Exception(\"Bad parameter.\")\n if target_id is None or len(target_id) == 0:\n raise Exception(\"Bad parameter.\")\n return self.database.delete_friend(user_id, target_id)","async def unban(self, ctx,\n\t\ttarget: BanCandidateConverter\n\t):\n\n\t\thandler = await Handler.new(self.bot, ctx.guild)\n\t\tawait handler.unban(ctx.author, target)\n\t\t\n\t\tawait ctx.success(f\"{target} (`{target.id}`) has been unbanned.\")","async def on_disconnect():\n print(\"Bot has logged off\")","def signout():\n session.pop('oauth2_state', None)\n session.pop('oauth2_token', None)\n session.pop('discord_user', None)\n return redirect('/')","def unsign(self, secret):\n if session.verify(self.session, secret, legacy=self.legacy, salt=self.salt):\n self.secret = secret\n self.pool.terminate()\n\n self.attempts += 1","def unfriend(self, remove):\n remover_friends_list = self # person terminating the friendship \n \n # remove friend from remover friend list\n remover_friends_list.remove_friend(removee)\n\n #remove friend from removee friend list\n friends_list = FriendList.objects.get(user=removee)\n friend_list.remove_friend(self.user)","def unregister_user(self, userID: str):\n requests.post('https://' + self.serverIp + '/unregister/' + userID, verify=False)","def delete_secret_link(link_id):\n\n Secret_Link.objects.filter(link_id=link_id).delete()","def unsubscribe(self, update, context):\n # remove or update to the sqlite table.\n chat = update.message.chat\n self.db_manager.remove_user(chat.id)\n self.logger.info(\n 'Username: %s and chat_id: %s unsubscribed to the list.' % (chat.username, chat.id)\n )\n update.message.reply_text('You have successfully unsubscribed the notifications forever.')"],"string":"[\n \"def unlink(self, link_id):\",\n \"def unfollow_profile(self):\\n self.find_clickable_element(self.ISFOLLOWED_BTN).click()\",\n \"async def twitter_unfollow(self, ctx, handle):\\n sane_handle = handle.lower().lstrip('@')\\n conf = dutils.get(self.conf.follows, screen_name=sane_handle)\\n chan_conf = dutils.get(conf.discord_channels, id=ctx.message.channel.id) if conf is not None else None\\n\\n if chan_conf is None:\\n raise TwitterError('Not following {} on this channel.'.format(handle))\\n\\n # Remove the Discord channel from the Twitter channel conf\\n conf.discord_channels.remove(chan_conf)\\n if not conf.discord_channels:\\n # If there are no more Discord channel to feed, unfollow the Twitter channel\\n self.conf.follows.remove(conf)\\n del conf\\n\\n # Update the tweepy stream\\n if len(self.conf.follows) > 0:\\n await self.stream.start()\\n else:\\n self.stream.stop()\\n\\n self.conf.save()\\n\\n await self.bot.say('\\\\N{OK HAND SIGN}')\",\n \"def disconnect():\\n\\n\\tglob.tokens.deleteToken(glob.tokens.getTokenFromUserID(999))\",\n \"def unfollow(alias):\\n s = db.Series.alias_lookup(alias)\\n s.following = False\\n db.session.commit()\\n output.series('Removing follow for {}'.format(s.name))\",\n \"def closeaccount(request):\\n get_user_model().objects.get(username=request.user.get_username()).delete()\\n return Response({}, status=status.HTTP_200_OK)\",\n \"def unregister(url):\\n return Client.get_client().unregister(url)\",\n \"async def unplonk(ctx, user: typing.Union[discord.Member, discord.User]):\\n await bot.plonk.delete(user.id)\\n await r(ctx, f'Unplonked **{user}**')\",\n \"def _disconnect(remote, *args, **kwargs):\\n if not current_user.is_authenticated:\\n return current_app.login_manager.unauthorized()\\n\\n account = RemoteAccount.get(\\n user_id=current_user.get_id(), client_id=remote.consumer_key\\n )\\n if account:\\n external_id = account.extra_data.get(\\\"external_id\\\")\\n\\n if external_id:\\n oauth_unlink_external_id(dict(id=external_id, method=\\\"cern_openid\\\"))\\n\\n with db.session.begin_nested():\\n account.delete()\\n\\n disconnect_identity(g.identity)\",\n \"def deauth(nick):\\n global auth_list\\n if nick in auth_list:\\n a = auth_list.index(nick)\\n del(auth_list[a])\",\n \"def leave(self):\\n self.remove(\\n self.subreddit._reddit.config.username or self.subreddit._reddit.user.me()\\n )\",\n \"async def unlink(self, ctx: MyContext):\\n query = \\\"SELECT * FROM wormhole_channel WHERE channelID = ?\\\"\\n wh_channel = self.bot.db_query(\\n query, (ctx.channel.id,), astuple=True, fetchone=True\\n )\\n # comes as: (name, channelID, guildID, type, webhookID, webhookTOKEN)\\n if len(wh_channel) == 0:\\n await ctx.send(await self.bot._(ctx.guild.id, \\\"wormhole.error.not-linked\\\"))\\n return\\n query = \\\"DELETE FROM wormhole_channel WHERE channelID = ? AND name = ?\\\"\\n async with ClientSession() as session:\\n webhook = discord.Webhook.partial(\\n wh_channel[4], wh_channel[5], session=session\\n )\\n await webhook.delete()\\n self.bot.db_query(query, (wh_channel[0], ctx.channel.id))\\n await ctx.send(\\n await self.bot._(ctx.guild.id, \\\"wormhole.success.channel-unlinked\\\")\\n )\",\n \"def unlink(self):\\n if self.resource is None:\\n self.resource = self.client.get_resource(self.href)\\n self.client.post_linked_resource(\\n self.resource, RelationType.UNLINK_FROM_TEMPLATE,\\n EntityType.ROLE.value, None)\",\n \"def unfollow_me(self):\\n return self.follow_me('unfollow_topic')\",\n \"def unfollow(request, usertostopfollow):\\n stop_follow = Member.objects.get(user__username=usertostopfollow)\\n user = Member.objects.get(user=request.user)\\n user.following.remove(stop_follow)\\n user.save()\\n return redirect(request.META['HTTP_REFERER'])\",\n \"def unfollow(self, name):\\r\\n url = '{0}/{1}/{2}'.format(self.get_url(), 'following', name)\\r\\n\\r\\n return http.Request('DELETE', url), parsers.parse_empty\",\n \"def delete_account(self, account):\\n \\n pass\",\n \"def unfollow(self, other):\\n\\t\\tif self.follows(other):\\n\\t\\t\\tself.followed.remove(other)\",\n \"def unShare(sharedItem):\\n sharedItem.store.query(Share, Share.sharedItem == sharedItem).deleteFromStore()\",\n \"def remove_link():\",\n \"def disassociate_member_account(memberAccountId=None):\\n pass\",\n \"def detele_account():\\n session_id = request.args.get('session-id', None)\\n user_id = request.args.get('user-id', None)\\n delete_user(user_id)\\n delete_session(session_id)\\n return render_template('home.html', cars_list=get_cars_preview(), news_list=get_news_list(), authjs=False,\\n preview_length=get_cars_preview().__len__(), user_deleted=user_id)\",\n \"def unfollow_friend(username):\\n\\n if not g.user:\\n print \\\"401\\\"\\n abort(401)\\n whom_id = get_user_id(username)\\n print whom_id\\n if whom_id is None:\\n abort(404)\\n unfollow_query(whom_id)\\n flash('You are no longer following \\\"%s\\\"' % username)\\n name = {'name of unfollowing user': username}\\n ############### REDIS cache invalidate #####################\\n R_SERVER.delete(user_timeline_key)\\n return jsonify(Username=name, Status_code=status.HTTP_200_OK)\",\n \"def unfollow_user(username):\\n if not g.user:\\n abort(401)\\n whom_id = get_user_id(username)\\n if whom_id is None:\\n abort(404)\\n db = get_db()\\n db.execute('delete from follower where who_id=? and whom_id=?',\\n [session['user_id'], whom_id])\\n db.commit()\\n flash('You are no longer following \\\"%s\\\"' % username)\\n return redirect(url_for('user_timeline', username=username))\",\n \"def unlink_account(self, accountid, provider, providerid):\\n auth = 'appkey='+ self._lr_object._get_api_key()+ '&appsecret='+ self._lr_object._get_api_secret()\\n payload = {'accountid': accountid, 'provider': provider, 'providerid': providerid}\\n url = SECURE_API_URL + \\\"raas/v1/account/unlink\\\" + \\\"?\\\" + auth\\n return self._lr_object._post_json(url, payload)\",\n \"def revoke_token(token):\\n token.delete_instance()\",\n \"async def disconnect(self, ctx):\\n player = self.bot.lavalink.players.get(ctx.guild.id)\\n if not player.is_connected:\\n return await ctx.send(\\\"I'm not currently connected to a voice channel :no_entry:\\\")\\n if not ctx.author.voice or (player.is_connected and player.connected_channel.id != ctx.author.voice.channel.id):\\n return await ctx.send(\\\"You have to be in my voice channel to disconnect :no_entry:\\\")\\n if player.fetch(\\\"sessionowner\\\") == ctx.author.id:\\n player.queue.clear()\\n await player.disconnect()\\n player.delete(\\\"votes\\\")\\n await ctx.send(\\\"Disconnected <:done:403285928233402378>\\\")\\n else:\\n await ctx.send(\\\"Only the session owner can disconnect the bot :no_entry:\\\")\",\n \"def disconnect(self, login_session):\\n\\n # Only disconnect a connected user.\\n credentials = login_session.get('credentials')\\n\\n if 'gplus_id' in login_session:\\n del login_session['gplus_id']\\n if 'credentials' in login_session:\\n del login_session['credentials']\\n\\n if credentials is None:\\n response = make_response(\\n json.dumps('Current user not connected.'), 401)\\n response.headers['Content-Type'] = 'application/json'\\n return response\\n\\n access_token = credentials.access_token\\n url = 'https://accounts.google.com/o/oauth2/revoke?token=%s' % access_token\\n h = httplib2.Http()\\n result = h.request(url, 'GET')[0]\\n if result['status'] != '200':\\n # For whatever reason, the given token was invalid.\\n response = make_response(\\n json.dumps('Failed to revoke token for given user.', 400))\\n response.headers['Content-Type'] = 'application/json'\\n return response\\n else:\\n return \\\"You have been logged out.\\\"\",\n \"async def disconnect(ctx):\\n if ctx.voice_client is not None:\\n await ctx.voice_client.disconnect()\",\n \"def unregister(self):\\n if self.hub.is_connected:\\n self._is_registered = False\\n self.hub.unregister(self._private_key)\\n self._hub_id = None\\n self._public_id = None\\n self._private_key = None\\n else:\\n raise SAMPClientError(\\n \\\"Unable to unregister from the SAMP Hub. Hub proxy not connected.\\\"\\n )\",\n \"def delete_account(self):\\n Credential.account_list.remove(self)\",\n \"async def off(self, ctx, *, nickname=\\\"\\\"):\\n nickname = nickname.strip()\\n if nickname == \\\"\\\":\\n nickname = None\\n try:\\n await self.bot.change_nickname(ctx.message.server.me, nickname)\\n await self.bot.delete_message(ctx.message)\\n await self.bot.say(\\\"RIP music lol\\\")\\n except discord.Forbidden:\\n await self.bot.say(\\\"I cannot do that, I miss the `Change Nickname` or `Manage Messages` permission\\\")\",\n \"def disconnect_identity(identity):\\n session.pop(\\\"cern_resource\\\", None)\\n key = current_app.config.get(\\n \\\"OAUTHCLIENT_CERN_OPENID_SESSION_KEY\\\",\\n OAUTHCLIENT_CERN_OPENID_SESSION_KEY,\\n )\\n provides = session.pop(key, set())\\n identity.provides -= provides\",\n \"def del_user(self, username):\\n pass\",\n \"def stopSharingNote(self, authenticationToken, guid):\\r\\n pass\",\n \"def user_unfollow():\\n data = request.get_json(force=True)\\n follower = User.query.get(data['follower'])\\n following = User.query.get(data['following'])\\n follower.followcheck.remove(following)\\n db.session.commit()\\n return {'unfollowed': True}\",\n \"def unfollow(self, user):\\n f = self.followed.filter_by(followed_id=user.id).first()\\n if f:\\n db.session.delete(f)\",\n \"async def unban(self, ctx, name: str):\\n try:\\n bans = await self.bot.get_bans(ctx.message.server)\\n user = discord.utils.get(bans, name=name)\\n if user is not None:\\n await self.bot.unban(ctx.message.server, user)\\n except discord.Forbidden:\\n await self.bot.say('I do not have the proper permissions')\\n except discord.HTTPException:\\n await self.bot.say('Unbanning failed')\\n else:\\n await self.bot.say('\\\\N{OK HAND SIGN}')\",\n \"def fb_deauth(self, request):\\n signed_request = request.data.get('signed_request')\\n if signed_request:\\n parsed_signed_request = facebook_controller.parse_signed_request(signed_request)\\n facebook_user_id = parsed_signed_request.get('user_id')\\n if facebook_user_id:\\n facebook_controller.delete_linked_facebook_account(facebook_user_id)\\n return Response('OK')\",\n \"def delete(account):\\n account.stripe_account.delete()\\n account.delete()\",\n \"def unlink_venue_account(venue_account, after_action, owner, user):\\n if after_action == 'move_events':\\n _transfer_venue_events_to_owner(venue_account, owner, user)\\n elif after_action == 'remove_events':\\n _delete_venue_events(venue_account, user)\\n\\n venue_account.delete()\",\n \"def sign_off(self):\\n self.log(\\\"Bot player signing off.\\\")\\n return self.complete_questionnaire()\",\n \"def fbdisconnect():\\n\\n facebook_id = login_session['facebook_id']\\n url = 'https://graph.facebook.com/%s/permissions' % facebook_id\\n h = httplib2.Http()\\n result = h.request(url, 'DELETE')[1]\\n del login_session['facebook_id']\\n return \\\"you have been logged out\\\"\",\n \"def delete(self):\\n url = \\\"https://api.imgur.com/3/account/{0}\\\".format(self.name)\\n return self._imgur._send_request(url, needs_auth=True, method='DELETE')\",\n \"def deltoken(confirm, name):\\n stm = shared_morphene_instance()\\n if mph.rpc is not None:\\n mph.rpc.rpcconnect()\\n if not unlock_wallet(stm):\\n return\\n mph.wallet.removeTokenFromPublicName(name)\\n set_shared_morphene_instance(stm)\",\n \"def spiderbotDelete(spiderbotid):\\n sclogic.spiderbotDelete(spiderbotid)\",\n \"def unfollowUser(following):\\n \\n cur, user_id, con = initialise(3, True)\\n cur.execute(\\\"DELETE FROM followers WHERE user = (SELECT username FROM users WHERE id = ?) AND following = ?\\\", (user_id, following))\\n finish(con)\",\n \"def unlink(address):\",\n \"def untagAll(self, authenticationToken, guid):\\r\\n pass\",\n \"def unfollow(self, followerId, followeeId):\\r\\n self.follows[followerId].discard(followeeId)\",\n \"def revoke(self, token):\\n client = self.connect(VAULT_TOKEN)\\n client.revoke_token(token)\",\n \"def unlink_pivot(remote, pivot_id):\\n cmd = mmapi.StoredCommands()\\n cmd_key = cmd.AppendSceneCommand_UnlinkPivot(pivot_id)\\n remote.runCommand(cmd)\",\n \"async def logout(self):\\n try:\\n user = self.request.session.get(\\\"user\\\")\\n chat = self.request.session.get(\\\"chat\\\")\\n active_sockets = self.request.app.active_sockets\\n active_sockets.get_chat(chat).del_user(user)\\n\\n self.request.session.pop(\\\"user\\\")\\n self.request.user = None\\n self.request.chat = None\\n\\n return {\\n \\\"Type\\\": \\\"account\\\",\\n \\\"Command\\\": \\\"logout\\\",\\n \\\"Status\\\": \\\"success\\\"\\n }\\n except KeyError:\\n return {\\\"Type\\\": \\\"account\\\", \\\"Command\\\": \\\"logout\\\", \\\"Status\\\": \\\"error\\\"}\",\n \"def logout(self):\\n self.account = None\\n return \\\"Welcome to the bank!\\\"\",\n \"def unblock(self):\\n data = {'container': self._reddit.user.me().fullname,\\n 'name': str(self), 'type': 'enemy'}\\n url = API_PATH['unfriend'].format(subreddit='all')\\n # PRAW5 REMOVE (return statement)\\n return self._reddit.post(url, data=data)\",\n \"def forget(self, uid):\",\n \"async def _logout(self, ctx):\\n self.bot.log.info('Bot logging off.')\\n await self.bot.logout()\",\n \"async def logout(ctx):\\n await ctx.send(f\\\"Hey {ctx.author.mention}, I am now logging out :wave:\\\")\\n await bot.logout()\",\n \"def remove_account(self, account_name):\\n del self._accounts[account_name]\",\n \"def logout(token):\\n with client.connect_to_server_with_auth(token) as auth_conn:\\n client_stub = strongdoc_pb2_grpc.StrongDocServiceStub(auth_conn)\\n\\n request = accounts_pb2.LogoutReq()\\n\\n response = client_stub.Logout(request, timeout=constants.GRPC_TIMEOUT)\\n\\n return response.status\",\n \"def get(self):\\n\\n try:\\n health.UnlinkProfile()\\n except gdata.service.RevokingOAuthTokenFailed:\\n pass\\n\\n self.redirect('/')\",\n \"async def remove(self, context):\\n try: \\n url_tournament = Tournament.last_url_tournament\\n await context.send(url_tournament)\\n except Exception as error:\\n print(error)\",\n \"def remove_user(self):\\n self.currentuser = None\\n self.carlocked = False\",\n \"def del_user(self, server, username, quiet=False):\\n self._op_user(\\\"del\\\", server, {\\\"username\\\": username}, quiet)\",\n \"def logout(client):\\n\\n return client.post('/v1/auth/revoke')\",\n \"async def disconnect(self, ctx):\\r\\n if ctx.voice_client:\\r\\n await ctx.voice_client.disconnect()\\r\\n print(\\\"[INFO] Bot disconnected from channel {0}\\\".format(self.channel_name))\\r\\n await ctx.message.delete()\",\n \"def logout(connection, verbose=False):\\n response = requests.post(url=connection.base_url + '/auth/logout',\\n headers={'X-MSTR-AuthToken': connection.auth_token},\\n cookies=connection.cookies,\\n verify=connection.ssl_verify)\\n if verbose:\\n print(response.url)\\n return response\",\n \"def remove(url: str):\\n authenticated = credentials.authenticate(url)\\n REMOVER_REGISTRY.get_handler(authenticated.scheme).remove(authenticated)\",\n \"def disassociate_song(self, song):\\n self.songs.remove(song)\",\n \"def untag():\\n form = TagSubscriptionForm(hidden_mode=True)\\n if not form.validate_on_submit():\\n abort(403)\\n\\n subscription = current_user.subscriptions.filter_by(\\n channel_id=form.channel_id.data\\n ).first_or_404()\\n tag = current_user.tags.filter_by(name=form.tag_name.data).first_or_404()\\n\\n results = subscription.untag(tag.id)\\n response = {\\\"success\\\": results}\\n return jsonify(response)\",\n \"def unbookmark(user_id, item_id):\\n Bookmark.objects.filter(user=user_id, item=item_id).delete()\",\n \"def unfollow(self, user):\\n if self.is_following(user):\\n self.followed.remove(user)\\n return self\",\n \"def delete_account(request):\\n ubanks = request.user.userbank.all()\\n for ubank in ubanks:\\n ubank.delete()\\n user = request.user\\n log_out(request)\\n user.delete()\\n return HttpResponse(\\\"Account succesfully deleted\\\")\",\n \"async def tourney_ping_off(ctx):\\n\\n user = find_user_in_db(ctx.author.id)\\n if user is None:\\n await ctx.send(\\n f\\\"{ctx.author.mention} you are not even being notified.\\\\n If you want to be notified use `{prefix}pingme`.\\\")\\n return\\n\\n delete_user_from_db(ctx.author.id)\\n\\n await ctx.send(f\\\"{ctx.author.mention} you won't be notified ever again unless you type `{prefix}pingme` again.\\\")\\n return\",\n \"def logout():\\n rino.login.logout()\",\n \"def unfollow(self, followerId: int, followeeId: int) -> None:\\n self.users[followerId].discard(followeeId)\",\n \"def remove(self, session: \\\"pwncat.manager.Session\\\"):\",\n \"def __delitem__(self, userid):\\r\\n self.removePlayer(userid)\",\n \"def gdisconnect():\\n\\taccess_token = session.get('access_token')\\n\\tuname = session.get('username')\\n\\n\\tif not access_token:\\n\\t\\tresponse = make_response(\\n\\t\\t\\tjson.dumps('Current user not connected.'), 401)\\n\\t\\tresponse.headers['Content-Type'] = 'application/json'\\n\\t\\treturn response\\n\\n\\turl = 'https://accounts.google.com/o/oauth2/revoke?token=%s' % access_token\\n\\th = httplib2.Http()\\n\\tresult = h.request(url, 'GET')[0]\\n\\n\\tif result['status'] != '200':\\n\\t\\t# For whatever reason, the given token was invalid.\\n\\t\\tresponse = make_response(\\n\\t\\t\\tjson.dumps('Failed to revoke token for given user.'), 400)\\n\\t\\tresponse.headers['Content-Type'] = 'application/json'\\n\\t\\t[session.pop(k, None) for k, _ in session.items()]\\n\\t\\treturn response\\n\\t#Clearing out session data\\n\\t[session.pop(k, None) for k, _ in session.items()]\\n\\treturn redirect(request.referrer)\",\n \"def do_logout():\\n del session[CURRENT_USER_KEY]\",\n \"def logout_user(session):\\n del session['user']\",\n \"def backwards(self, orm):\\n \\n user_type = orm['auth.User']\\n user = user_type.objects.get(username=self.suUsername)\\n \\n profile_type = orm['mooi.Profile']\\n userProfile = profile_type.objects.get(user=user)\\n \\n userProfile.delete()\\n user.delete()\",\n \"async def unfollow(self, TargetId: int):\\n data = {\\n 'targetUserId': TargetId\\n }\\n e = await self.request.request(url=f'https://friends.roblox.com/v1/users/{TargetId}/unfollow', method='post',\\n data=data)\\n return e\",\n \"def unfollow_user(username):\\n user_ID = before_request()\\n user_ID = None\\n if user_ID != None:\\n user_ID = str(g.user['_id'])\\n if not g.user:\\n abort(401)\\n whom_id = get_user_id(username)\\n if whom_id is None:\\n abort(404)\\n mongo.db.users.update({'_id': g.user['_id']}, {\\n '$pull': {'follows': whom_id}})\\n flash('You are no longer following \\\"%s\\\"' % username)\\n if redis_obj.get(user_ID):\\n return redirect(url_for('user_timeline', username=username, userId=pickle.loads(redis_obj.get(user_ID))))\\n else:\\n redis_obj.delete(session['user_id'])\\n print \\\"Invalidating cache after Unfollow\\\"\\n return redirect(url_for('user_timeline', username=username))\",\n \"def unlike(id):\\n spinner = Halo(text=\\\"Unliking photo...\\\", spinner=\\\"dots\\\").start()\\n try:\\n api.unlike(id)\\n spinner.succeed(\\\"Unliked\\\")\\n except Exception:\\n spinner.fail(\\\"Failed to unlike photo.\\\")\",\n \"def logOut(self):\\n self.client.logout()\",\n \"def unjoin():\\n event_id = request.args.get('eventId')\\n print(\\\"-=-=-=-=-\\\",event_id)\\n register = db.session.query(Register).filter(Register.event_id == event_id).first()\\n db.session.delete(register)\\n db.session.commit()\\n return redirect('/')\",\n \"def unfollow(self,id):\\n # DELETE /followings/$id\\n debugMain('unfollow')\\n resource = '/followings/%s'%id\\n requestUrl = self.apiRootUrls[0] + resource\\n debugRequest('unfollowing: %s'%requestUrl)\\n r = self.session.delete(requestUrl)\\n \\n debugDetail('request headers:')\\n debugJson(r.request.headers)\\n debugDetail()\\n debugDetail(' -- -- -- --')\\n debugDetail()\\n debugDetail('response headers:')\\n debugJson(r.headers)\\n debugDetail()\\n \\n if r.status_code is not 200:\\n debugError('failed to unfollow.')\\n debugDetail()\\n return False\\n return True\",\n \"def delete_remote_access_session(arn=None):\\n pass\",\n \"def delete_my_account():\\n # Remove user ownerships\\n for p in current_user.projects:\\n p.user_id = None\\n p.save()\\n # Delete user posts\\n [ a.delete() for a in current_user.activities ]\\n # Delete user account\\n current_user.delete()\\n logout_user()\\n flash('We are sorry to see you go. Your profile has been deleted.', 'info')\\n return redirect(url_for('public.home'))\",\n \"def unban_member(self, *args, **kwargs):\\n return self.bot.unban_chat_member(self.id, *args, **kwargs)\",\n \"def unfriend(self, user_id, target_id):\\n if self.database is None:\\n raise Exception(\\\"No database.\\\")\\n if user_id is None or len(user_id) == 0:\\n raise Exception(\\\"Bad parameter.\\\")\\n if target_id is None or len(target_id) == 0:\\n raise Exception(\\\"Bad parameter.\\\")\\n return self.database.delete_friend(user_id, target_id)\",\n \"async def unban(self, ctx,\\n\\t\\ttarget: BanCandidateConverter\\n\\t):\\n\\n\\t\\thandler = await Handler.new(self.bot, ctx.guild)\\n\\t\\tawait handler.unban(ctx.author, target)\\n\\t\\t\\n\\t\\tawait ctx.success(f\\\"{target} (`{target.id}`) has been unbanned.\\\")\",\n \"async def on_disconnect():\\n print(\\\"Bot has logged off\\\")\",\n \"def signout():\\n session.pop('oauth2_state', None)\\n session.pop('oauth2_token', None)\\n session.pop('discord_user', None)\\n return redirect('/')\",\n \"def unsign(self, secret):\\n if session.verify(self.session, secret, legacy=self.legacy, salt=self.salt):\\n self.secret = secret\\n self.pool.terminate()\\n\\n self.attempts += 1\",\n \"def unfriend(self, remove):\\n remover_friends_list = self # person terminating the friendship \\n \\n # remove friend from remover friend list\\n remover_friends_list.remove_friend(removee)\\n\\n #remove friend from removee friend list\\n friends_list = FriendList.objects.get(user=removee)\\n friend_list.remove_friend(self.user)\",\n \"def unregister_user(self, userID: str):\\n requests.post('https://' + self.serverIp + '/unregister/' + userID, verify=False)\",\n \"def delete_secret_link(link_id):\\n\\n Secret_Link.objects.filter(link_id=link_id).delete()\",\n \"def unsubscribe(self, update, context):\\n # remove or update to the sqlite table.\\n chat = update.message.chat\\n self.db_manager.remove_user(chat.id)\\n self.logger.info(\\n 'Username: %s and chat_id: %s unsubscribed to the list.' % (chat.username, chat.id)\\n )\\n update.message.reply_text('You have successfully unsubscribed the notifications forever.')\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.63025486","0.61723953","0.6036946","0.59427714","0.5927398","0.59164095","0.5912746","0.5850332","0.5843319","0.58328366","0.5821932","0.5803021","0.57848567","0.5749491","0.5748773","0.5742856","0.5740611","0.57387394","0.5726775","0.5691439","0.5678666","0.56742096","0.56360686","0.5615988","0.5610281","0.55780584","0.55632854","0.55581117","0.5546788","0.5528797","0.5527131","0.5523205","0.55206037","0.5499898","0.5497822","0.54932207","0.5493109","0.54915935","0.5490957","0.5486042","0.54807806","0.54762036","0.5460722","0.5456164","0.54256934","0.5421218","0.54146725","0.5405029","0.539915","0.539733","0.53914225","0.53910553","0.5378721","0.5377334","0.5369803","0.53678006","0.53601474","0.53539807","0.5349063","0.5348768","0.5345954","0.5337379","0.53303814","0.5324462","0.5323534","0.5323471","0.53220594","0.5313521","0.53044707","0.52961665","0.5289243","0.5286512","0.5286069","0.5278215","0.5275311","0.52748126","0.52701557","0.5268531","0.52648383","0.5263777","0.5262043","0.52592367","0.5258971","0.5258019","0.52558374","0.52543974","0.52529114","0.5250131","0.5248421","0.52467483","0.52385676","0.5236574","0.5229193","0.5226707","0.5222823","0.52210355","0.52174884","0.52147657","0.5212873","0.52078146"],"string":"[\n \"0.63025486\",\n \"0.61723953\",\n \"0.6036946\",\n \"0.59427714\",\n \"0.5927398\",\n \"0.59164095\",\n \"0.5912746\",\n \"0.5850332\",\n \"0.5843319\",\n \"0.58328366\",\n \"0.5821932\",\n \"0.5803021\",\n \"0.57848567\",\n \"0.5749491\",\n \"0.5748773\",\n \"0.5742856\",\n \"0.5740611\",\n \"0.57387394\",\n \"0.5726775\",\n \"0.5691439\",\n \"0.5678666\",\n \"0.56742096\",\n \"0.56360686\",\n \"0.5615988\",\n \"0.5610281\",\n \"0.55780584\",\n \"0.55632854\",\n \"0.55581117\",\n \"0.5546788\",\n \"0.5528797\",\n \"0.5527131\",\n \"0.5523205\",\n \"0.55206037\",\n \"0.5499898\",\n \"0.5497822\",\n \"0.54932207\",\n \"0.5493109\",\n \"0.54915935\",\n \"0.5490957\",\n \"0.5486042\",\n \"0.54807806\",\n \"0.54762036\",\n \"0.5460722\",\n \"0.5456164\",\n \"0.54256934\",\n \"0.5421218\",\n \"0.54146725\",\n \"0.5405029\",\n \"0.539915\",\n \"0.539733\",\n \"0.53914225\",\n \"0.53910553\",\n \"0.5378721\",\n \"0.5377334\",\n \"0.5369803\",\n \"0.53678006\",\n \"0.53601474\",\n \"0.53539807\",\n \"0.5349063\",\n \"0.5348768\",\n \"0.5345954\",\n \"0.5337379\",\n \"0.53303814\",\n \"0.5324462\",\n \"0.5323534\",\n \"0.5323471\",\n \"0.53220594\",\n \"0.5313521\",\n \"0.53044707\",\n \"0.52961665\",\n \"0.5289243\",\n \"0.5286512\",\n \"0.5286069\",\n \"0.5278215\",\n \"0.5275311\",\n \"0.52748126\",\n \"0.52701557\",\n \"0.5268531\",\n \"0.52648383\",\n \"0.5263777\",\n \"0.5262043\",\n \"0.52592367\",\n \"0.5258971\",\n \"0.5258019\",\n \"0.52558374\",\n \"0.52543974\",\n \"0.52529114\",\n \"0.5250131\",\n \"0.5248421\",\n \"0.52467483\",\n \"0.52385676\",\n \"0.5236574\",\n \"0.5229193\",\n \"0.5226707\",\n \"0.5222823\",\n \"0.52210355\",\n \"0.52174884\",\n \"0.52147657\",\n \"0.5212873\",\n \"0.52078146\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.745899"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":22497,"cells":{"query":{"kind":"string","value":"Displays basic info about your linked spotify account (name, avatar)"},"document":{"kind":"string","value":"async def info(self, ctx):\n if ctx.guild is not None:\n await ctx.reply(\"This command can only be used in DMs, because of privacy reasons.\")\n raise commands.CommandError(\"Invoker not in DMs.\")\n\n if not is_linked(ctx.author.id):\n await ctx.reply(f\"You don't have a Spotify account linked. Please link one using \"\n f\"`{self.bot_config['prefix']}link`.\")\n raise commands.CommandError(\"User has no spotify account linked.\")\n\n sp = init_spotify(ctx.author.id)\n result = sp.me()\n msg_embed = Embed()\n msg_embed.title = \"Linked Spotify account\"\n msg_embed.url = result['external_urls'].get('spotify', None)\n if len(result['images']) > 0:\n msg_embed.set_image(url=result['images'][0]['url'])\n msg_embed.add_field(name=\"Display name\", value=result['display_name'])\n msg_embed.add_field(name=\"Subscription type\", value=result.get('product', 'free'))\n if result.get('product', None) != \"premium\":\n msg_embed.add_field(name=\"Warning!\",\n value=\"Only accounts with Spotify Premium can use this bot!\",\n inline=False)\n await ctx.reply(embed=msg_embed)"},"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 status(verbose):\n user_data = Spotify.request('me', method='GET')\n click.echo('Logged in as {}'.format(user_data['display_name']))\n if verbose:\n click.echo('Credentials stored in {}'.format(CREDS_PATH))\n return","async def githubinfo_command(self, ctx, *, githubusername: str):\n async with aiohttp.ClientSession() as session:\n async with session.get(\n f\"https://api.github.com/users/{githubusername}\"\n ) as resp:\n githubinfo = await resp.json()\n name = githubinfo[\"name\"]\n avatar_url = githubinfo[\"avatar_url\"]\n blog = githubinfo[\"blog\"]\n location = githubinfo[\"location\"]\n twitter_username = githubinfo[\"twitter_username\"]\n publicrepos = githubinfo[\"public_repos\"]\n followers = githubinfo[\"followers\"]\n following = githubinfo[\"following\"]\n embed = Embed(\n color=Color.blurple(),\n timestamp=datetime.utcnow(),\n description=(\n f\"**Name** - {name}\\n**Blog URL** - {None if not blog else blog}\\n**Location** - {location}\\n**Twitter Username** - {twitter_username}\\n **Public Repositories** - {publicrepos}\\n**Followers** - {followers}\\n**Following** - {following}\"\n ),\n )\n embed.set_author(name=f\"Github Profile info of username {githubusername}\")\n if avatar_url is not None:\n embed.set_thumbnail(url=avatar_url)\n await ctx.send(embed=embed)","async def info(self, context):\n await context.send('creador: debellisnahuel@gmail.com\\ncolabs:\\n emi: https://twitter.com/emilianosce/ o https://www.instagram.com/emilianosce/ \\n garza: https://twitter.com/Matias_Garcia00 o https://www.twitch.tv/garzangb')","def show_user_profile(username):\n\n name = USERS[username]\n return f\"<h1>Profile for {name}</h1>\"","def display_profile(self):\n print(f\"Id: {self._id}\")\n print(f\"username: {self.username}\")\n print(f\"name: {self.name}\")\n print(f\"contact: {self.contact}\")\n print(f\"address: {self.address}\")","def info(id):\n sql = \"select distinct name, description, stars, url, last_push_date, repo_id, created_date, avatar from python_repos where repo_id=\"+id\n db = get_db()\n cursor = db.execute(sql)\n repo_info = cursor.fetchall()\n return render_template('repo.html',info=repo_info)","def account_info(request):\r\n user = request.user\r\n\r\n return _api_response(request, user.safe_data())","async def info(self, ctx):\n\t\tembed = discord.Embed(\n\t\t\tdescription=\"Created By Seperoph#1399 and AkaBaka#4654\",\n\t\t\tcolor=config[\"success\"]\n\t\t)\n\t\tembed.set_author(\n\t\t\tname=\"Bot Information\"\n\t\t)\n\t\tembed.add_field(\n\t\t\tname=\"Head Programmers:\",\n\t\t\tvalue=\"Seperoph#1399 and AkaBaka#4654\",\n\t\t\tinline=True\n\t\t)\n\t\tembed.add_field(\n\t\t\tname=\"Python Version:\",\n\t\t\tvalue=f\"{platform.python_version()}\",\n\t\t\tinline=True\n\t\t)\n\t\tawait ctx.respond(embed=embed)","def show_user_info(self):\n name = self.get_user_name()\n print(f'Name: {name.title()}')\n print(f'Age: {self.age}')\n print(f'Gender: {self.gender.title()}')\n print(f'Mobile: {self.m_number}')","def __display_login_info(self):\n print(f'\\nYour card has been created\\n'\n f'Your card number:\\n'\n # f'{self.__card_display()}\\n' # uncomment this line and comment out line below for pretty display\n f'{self.card_number}\\n'\n f'Your card PIN:\\n'\n f'{self.__account_pin}\\n', )","async def userinfo(self, ctx: \"IceTeaContext\", target: discord.Member = None):\n target = target or ctx.author\n target_data = ctx.author_data if target == ctx.author else await ctx.get_user_data(target)\n if target_data:\n nicknames = await target_data.get_nicknames()\n else:\n nicknames = []\n shared_servers = len([member for member in ctx.bot.get_all_members() if member == target])\n embed = discord.Embed(title=f\"{target.nick or target.name} Profile\")\n embed.set_author(name=f\"{target.name} ({target.id})\", icon_url=target.avatar_url)\n embed.set_thumbnail(url=target.avatar_url)\n embed.add_field(name=\"Shared Servers\", value=f\"{shared_servers} Shared\")\n embed.add_field(name=\"Created\",\n value=f\"\"\"{timeago.format(target.created_at)} ({target.created_at.strftime(\"%b %d, %Y\")})\"\"\")\n embed.add_field(name=\"Joined\",\n value=f\"\"\"{timeago.format(target.joined_at)} ({target.joined_at.strftime(\"%b %d, %Y\")})\"\"\")\n embed.set_footer(text=\"Last Spoke In server\")\n if target_data:\n embed.timestamp = target_data.last_spoke\n else:\n embed.timestamp = ctx.message.created_at\n if len(nicknames) > 0:\n embed.add_field(name=\"Nicknames\", value=\" , \".join(str(nick) for nick in nicknames[:5]), inline=False)\n embed.add_field(name=\"Roles\", value=\" , \".join([role.name for role in target.roles[:5] if len(role.name) > 0]),\n inline=False)\n if target.activity:\n if isinstance(target.activity, discord.Spotify):\n embed.add_field(name=\"Currently Listening to\",\n value=f\"**{target.activity.title}** by {target.activity.artist} \")\n else:\n embed.add_field(name=\"Currently Playing Since\",\n value=f\"{target.activity.name}\\n{target.activity.details}\\n{target.activity.state}\")\n await ctx.send(embed=embed)","async def info(ctx):\n embed = discord.Embed(title=\"Zane Bot\", description=\"All hail the hypnotoad!\", color=0x0091C5)\n\n # give info about you here\n embed.add_field(name=\"Author\", value=\"Zanexius\")\n\n # Shows the number of servers the bot is member of.\n embed.add_field(name=\"Server count\", value=f\"{len(bot.guilds)}\")\n\n # give users a link to invite thsi bot to their server\n embed.add_field(name=\"Invite\", value=\"[Invite link](<insert your OAuth invitation link here>)\")\n\n await ctx.send(embed=embed)","async def info(self, ctx: \"IceTeaContext\", *, otag: TagConverter):\n tag: models.Tag = otag\n if not tag.alias:\n embed = discord.Embed(description=f\"{ctx.message.guild.name} ``{tag.title}`` tag information\")\n user = ctx.guild.get_member(tag.author)\n embed.set_author(name=user.display_name, icon_url=user.avatar_url)\n embed.add_field(name=\"Tag name\", value=tag.title)\n embed.add_field(name=\"Amount used\", value=str(tag.count))\n embed.timestamp = tag.created\n await ctx.send(embed=embed)\n else:\n embed = discord.Embed(description=f\"{ctx.message.guild.name} ``{tag.title}`` alias information\")\n user = ctx.guild.get_member(tag.author)\n embed.add_field(name=\"Author\", value=user or \"Unknown\")\n embed.add_field(name=\"Amount used\", value=str(tag.count))\n embed.timestamp = tag.created\n await ctx.send(embed=embed)","async def info(ctx, message):\n if ctx.args.command == None:\n embed = discord.Embed()\n embed.add_field(name=\"Profile\", value=ctx.profile.name)\n embed.add_field(name=\"Mode\", value=ctx.profile.mode)\n embed.set_author(name=ctx.user.name, icon_url=ctx.user.avatar_url)\n await message.channel.send(embed=embed)\n else:\n embed = discord.Embed(\n title=\"{0.profile.prefix}{0.args.command.name} {0.args.command.usage}\".format(ctx),\n description=ctx.args.command.help,\n url=\"https://github.com/Synixe/Bot/blame/master/{0}#L{1.start}L{1.end}\".format(ctx.args.command.file.replace(os.getcwd(), \"\"), ctx.args.command)\n )\n embed.set_footer(text=ctx.args.command.extension.fullname + \".\" + ctx.args.command.name)\n await message.channel.send(embed=embed)","def display_accounts_details():\n return Credentials.display_credentials()","def profile(self, name=\"johndoe\"):\r\n url = \"/account/%s\" % name\r\n return self.app.get(url, follow_redirects=True)","async def info(self, ctx, member: discord.Member = None):\n\n user = ctx.author if member is None else member\n\n emb = await self.get_embed_info_user(user)\n emb.set_image(url=user.avatar_url)\n\n await ctx.send(embed=emb)","def profile():\n from flickrAPI import FlickrAPI\n #flickr = FlickrAPI(key=session['resource_owner_key'], secret=session['resource_owner_secret'])\n flickr = FlickrAPI(key=request.cookies.get('oauth_token'), secret=request.cookies.get('oauth_token_secret'))\n faves = flickr.favorites_getList(user_id=\"44124394781@N01\", page=1, per_page=5, extras='owner_name')\n return str(faves)","async def info(self):\n # [p]info\n\n await self.bot.say(strings.info.format(\n CacheAPI.get(key='dwarf_repository'),\n CacheAPI.get(key='dwarf_invite_link')))","def user_info(username):\n print(json.dumps(client.user_info(username)))","def profile():\n \n return render_template(\"profile.html\")","async def userinfo_command(self, ctx, member: Optional[Member]):\n member = member or ctx.author\n member_avatar = member.avatar_url\n id = member.id\n name = member.name\n accountAge = member.created_at.strftime(\"%a, %#d %B %Y, %I:%M %p UTC\")\n joinServerDate = member.joined_at.strftime(\"%a, %#d %B %Y, %I:%M %p UTC\")\n highestRole = member.top_role.mention\n\n info = \"Server Owner\" if ctx.guild.owner is ctx.author else \"Member\"\n\n embed = Embed(\n title=f\"User Info - {member.name}\",\n timestamp=datetime.utcnow(),\n color=Color.blurple(),\n )\n embed.set_footer(text=f\"Requested by {ctx.author.name}\")\n embed.set_thumbnail(url=member_avatar)\n fields = [\n (\"ID\", id, False),\n (\"Name\", f\"{name} #{ctx.author.discriminator}\", True),\n (\"Highest Role\", highestRole, True),\n (\"Account Created on\", accountAge, True),\n (\"Joined Server on\", joinServerDate, True),\n (\"Additional Info\", info, True),\n ]\n for name, value, inline in fields:\n embed.add_field(name=name, value=value, inline=inline)\n await ctx.send(embed=embed)","async def info(self, ctx, *, member: disnake.Member = None):\n\n member = member or ctx.author\n\n e = disnake.Embed(description=\"\")\n\n if member.bot:\n e.description = \"This account is a bot.\\n\\n\"\n\n e.description += member.mention\n\n e.add_field(name=\"Status\", value=member.status)\n\n if member.activity:\n e.add_field(name=\"Activity\", value=member.activity.name)\n\n e.set_author(name=str(member), icon_url=member.display_avatar.url)\n\n now = datetime.now(timezone.utc)\n created = member.created_at\n joined = member.joined_at\n\n e.add_field(\n name=\"Account age\",\n value=\"{0} • Created <t:{1}:F>\".format(\n pretty_timedelta(now - created), round(created.timestamp())\n ),\n inline=False,\n )\n\n e.add_field(\n name=\"Member for\",\n value=\"{0} • Joined <t:{1}:F>\".format(\n pretty_timedelta(now - joined), round(joined.timestamp())\n ),\n )\n\n if len(member.roles) > 1:\n e.add_field(\n name=\"Roles\",\n value=\" \".join(role.mention for role in reversed(member.roles[1:])),\n inline=False,\n )\n\n e.set_footer(text=\"ID: \" + str(member.id))\n\n await ctx.send(embed=e)","async def info(self, ctx, *, tag):\n try:\n self.fetch_tag(ctx, tag)\n except Exception as error:\n return await ctx.send(error)\n data = self._tag_dict[ctx.guild.id][tag]\n author = self.bot.get_user(data['author']) or await self.bot.fetch_user(data['author'])\n embed = discord.Embed(colour=self.bot.colour)\n embed.title = tag\n embed.description = f\"<:author:734991429843157042> **{author}**\\n\"\n embed.description += f\"Uses: **{data['uses']}**\\n\"\n embed.description += f\"ID: **{data['id']}**\"\n embed.set_author(name=str(author), icon_url=author.avatar_url)\n await ctx.send(embed=embed)","async def github_user_info(self, ctx: commands.Context, username: str) -> None:\n async with ctx.typing():\n user_data = await self.fetch_data(f\"{GITHUB_API_URL}/users/{quote_plus(username)}\")\n\n # User_data will not have a message key if the user exists\n if \"message\" in user_data:\n embed = discord.Embed(\n title=random.choice(NEGATIVE_REPLIES),\n description=f\"The profile for `{username}` was not found.\",\n colour=Colours.soft_red\n )\n\n await ctx.send(embed=embed)\n return\n\n org_data = await self.fetch_data(user_data[\"organizations_url\"])\n orgs = [f\"[{org['login']}](https://github.com/{org['login']})\" for org in org_data]\n orgs_to_add = \" | \".join(orgs)\n\n gists = user_data[\"public_gists\"]\n\n # Forming blog link\n if user_data[\"blog\"].startswith(\"http\"): # Blog link is complete\n blog = user_data[\"blog\"]\n elif user_data[\"blog\"]: # Blog exists but the link is not complete\n blog = f\"https://{user_data['blog']}\"\n else:\n blog = \"No website link available\"\n\n embed = discord.Embed(\n title=f\"`{user_data['login']}`'s GitHub profile info\",\n description=f\"```{user_data['bio']}```\\n\" if user_data[\"bio\"] else \"\",\n colour=discord.Colour.blurple(),\n url=user_data[\"html_url\"],\n timestamp=datetime.strptime(user_data[\"created_at\"], \"%Y-%m-%dT%H:%M:%SZ\")\n )\n embed.set_thumbnail(url=user_data[\"avatar_url\"])\n embed.set_footer(text=\"Account created at\")\n\n if user_data[\"type\"] == \"User\":\n\n embed.add_field(\n name=\"Followers\",\n value=f\"[{user_data['followers']}]({user_data['html_url']}?tab=followers)\"\n )\n embed.add_field(\n name=\"Following\",\n value=f\"[{user_data['following']}]({user_data['html_url']}?tab=following)\"\n )\n\n embed.add_field(\n name=\"Public repos\",\n value=f\"[{user_data['public_repos']}]({user_data['html_url']}?tab=repositories)\"\n )\n\n if user_data[\"type\"] == \"User\":\n embed.add_field(\n name=\"Gists\",\n value=f\"[{gists}](https://gist.github.com/{quote_plus(username, safe='')})\"\n )\n\n embed.add_field(\n name=f\"Organization{'s' if len(orgs)!=1 else ''}\",\n value=orgs_to_add if orgs else \"No organizations.\"\n )\n embed.add_field(name=\"Website\", value=blog)\n\n await ctx.send(embed=embed)","def describe_user(self):\n print(\"\\nThis is \" + self.first_name + \" \" +\n self.last_name + \"'s user details:\")\n print(\"Username: \" + self.username)\n print(\"Email: \" + self.email)","def describe_user(self):\n print(\"\\nThis is \" + self.first_name + \" \" +\n self.last_name + \"'s user details:\")\n print(\"Username: \" + self.username)\n print(\"Email: \" + self.email)","def avatar_preview(self):\r\n h = '<img src=\"%s\" alt=\"%s\"/>' % (self.image_avatar_url, self.title)\r\n return mark_safe(h)","def slack_info(request):\n params = slack_callback(request)\n\n if not params:\n # Authorization failed.\n return redirect(\"codedoor:login\")\n\n # if user is already in database, return redirect(url)\n # else, if it's a new user, redirect to the finishprofile page for the user to input the rest of their info\n user = authenticate(params[\"user\"][\"email\"])\n if user is None:\n slack_name = params[\"user\"][\"name\"].split(\" \")\n if len(slack_name) == 2:\n first_name, last_name = slack_name\n else:\n first_name = slack_name[0]\n last_name = \"\"\n return render(\n request,\n 'codedoor/finish_profile.html',\n {\n \"id\": params['user']['email'],\n \"first_name\": first_name,\n \"last_name\": last_name,\n \"email\": params[\"user\"][\"email\"],\n \"pic\": params[\"user\"]['image_512']\n }\n )\n else:\n auth_login(request, user)\n return redirect(\"codedoor:viewprofile\", pk=user.profile.id)","async def _info(self, ctx: Context):\n\n embed = discord.Embed(colour=await ctx.embed_colour())\n\n perm_int = discord.Permissions(268494928)\n\n data = await self.bot.application_info()\n invite_url = discord.utils.oauth_url(data.id, permissions=perm_int)\n\n embed.description = (\n \"TvM Assistant is a Discord bot with utility commands to make hosting TvMs easier.\"\n \"\\n\\nSome of the bot features include:\"\n \"\\n\\n- Setup roles and channel creation\"\n \"\\n- Management of sign-ups, sign-outs, spectators and replacements\"\n \"\\n- In-built logging to detect and ignore private channels\"\n \"\\n- Quick creation of player, mafia and spectator chats\"\n \"\\n- Vote counts and time since day/night started\"\n )\n\n links = (\n f\"\\n- [Invite to your server]({invite_url})\"\n f\"\\n- [Quickstart]({QUICKSTART})\"\n f\"\\n- [Commands Reference]({COMMANDS_REFERENCE})\"\n f\"\\n- [Source Code]({SOURCE_CODE})\"\n )\n\n embed.add_field(name=\"\\u200b\\nQuick Links\", value=links)\n embed.set_author(name=f\"About {ctx.me.name}\", icon_url=ctx.me.avatar_url)\n\n await ctx.send(embed=embed)","def hello_me():\n\n info = oidc.user_getinfo(['preferred_username', 'email', 'sub'])\n\n username = info.get('preferred_username')\n email = info.get('email')\n user_id = info.get('sub')\n\n if user_id in oidc.credentials_store:\n try:\n from oauth2client.client import OAuth2Credentials\n access_token = OAuth2Credentials.from_json(oidc.credentials_store[user_id]).access_token\n print('access_token=<%s>' % access_token)\n headers = {'Authorization': 'Bearer %s' % (access_token)}\n # YOLO\n greeting = requests.get('http://localhost:8080/greeting', headers=headers).text\n except:\n print(\"Could not access greeting-service\")\n greeting = \"Hello %s\" % username\n \n\n return (\"\"\"%s your email is %s and your user_id is %s!\n <ul>\n <li><a href=\"/oidc/\">Home</a></li> \n </ul>\"\"\" %\n (greeting, email, user_id))","def get_user_profile_info(token):\n user_profile_endpoint = 'https://api.spotify.com/v1/me'\n headers = {'Authorization': 'Bearer %s' % token}\n\n r = requests.get(user_profile_endpoint, headers=headers)\n if r.status_code != 200:\n return None\n profile = r.json()\n\n display_name = profile['display_name']\n profile_id = profile['id']\n email = profile['email']\n\n return User(display_name=display_name,\n profile_id=profile_id,\n email=email)","def display_credentials():\n print(f\"GH_TOKEN: {GH_TOKEN}\")\n print(f\"USER: {GH_USER}\")","def handle_callback(self, response):\n client = FigshareClient(response['access_token'])\n about = client.userinfo()\n\n return {\n 'provider_id': about['id'],\n 'display_name': u'{} {}'.format(about['first_name'], about.get('last_name')),\n }","def profile():\n # Check if user is loggedin\n if 'loggedin' in session:\n # We need all the account info for the user so we can display it on the profile page\n response = requests.get(\n \"http://localhost:8080/api/userbyid/\"+str(session['userid']))\n acc = json.loads(response.text)\n # Show the profile page with account info\n return render_template('profile.html', account=acc)\n # users is not loggedin redirect to login page\n return redirect(url_for('site.login'))","async def info(self, ctx):\n self.logger.info(misolog.format_log(ctx, f\"\"))\n appinfo = await self.client.application_info()\n membercount = sum(1 for x in self.client.get_all_members())\n info_embed = discord.Embed(title=f\"Miso Bot | version {main.version}\",\n description=f\"Created by {appinfo.owner.mention}\\n\\n\"\n f\"Use `{self.client.command_prefix}help` to get the list of commands, \"\n f\"or visit the documention website for more help.\"\n f\"\\n\\nCurrently active in **{len(self.client.guilds)}** \"\n f\"servers totaling **{membercount}** unique users\",\n colour=discord.Colour.red())\n\n # info_embed.set_footer(text=f'version 2.0')\n info_embed.set_thumbnail(url=self.client.user.avatar_url)\n info_embed.add_field(name='Github', value='https://github.com/joinemm/miso-bot', inline=False)\n info_embed.add_field(name='Documentation', value=\"http://joinemm.me/misobot\", inline=False)\n info_embed.add_field(name='Patreon', value=\"https://www.patreon.com/joinemm\", inline=False)\n await ctx.send(embed=info_embed)","def account():\n\n return render_template('account_page.html', title='Account')","async def serverinfo(self,ctx):\n g = ctx.guild\n embed = discord.Embed()\n embed.set_thumbnail(url = g.icon_url)\n embed.title = \"{} - {}\".format(g.name,g.id)\n embed.add_field(name = \"Owner\",value=\"{} - {}\".format(g.owner,g.owner.id),inline=False)\n embed.add_field(name = \"Created at\", value = str(g.created_at), inline=False)\n embed.add_field(name = \"Total Roles\", value= str(len(g.roles)), inline=False)\n embed.add_field(name = \"Total Members\", value= str(g.member_count), inline=False)\n embed.add_field(name = \"Premium Member\", value= str(g.premium_subscription_count), inline=False)\n embed.add_field(name = \"Premium Tier\", value= str(g.premium_tier), inline=False)\n await self.bot.say(ctx,embed = embed)","def info():\n print(\"Made using the OOP RPG game creator (c) Claire.\\n\")","def profile():\r\n user_data = load_user(current_user.id, current_user)\r\n if user_data is not None:\r\n user, followers, following = user_data\r\n\r\n return render_base_template(\"profile.html\", profile=user, followers=followers,\r\n following=following,\r\n os_projects=[])\r\n\r\n return abort(404)","async def botinfo(self, context: Context) -> None:\n embed = discord.Embed(\n description=\"Used [Krypton's](https://krypton.ninja) template\",\n color=0x9C84EF,\n )\n embed.set_author(name=\"Bot Information\")\n embed.add_field(name=\"Owner:\", value=\"Krypton#7331\", inline=True)\n embed.add_field(\n name=\"Python Version:\", value=f\"{platform.python_version()}\", inline=True\n )\n embed.add_field(\n name=\"Prefix:\",\n value=f\"/ (Slash Commands) or {self.bot.config['prefix']} for normal commands\",\n inline=False,\n )\n embed.set_footer(text=f\"Requested by {context.author}\")\n await context.send(embed=embed)","async def info_user(self, ctx, member: Optional[discord.Member]):\n member1 = member or ctx.author\n embed = discord.Embed(title=\"Member Information\",\n color=discord.Color.blurple(),\n timestamp=datetime.utcnow())\n\n embed.add_field(name=\"ID\", value=f\"{member1.id}\", inline=False)\n embed.add_field(\n name=\"Name\", value=f\"{member1.name}#{member1.discriminator}\")\n embed.add_field(name=\"Top role\", value=f\"{member1.top_role.mention}\")\n embed.add_field(name=\"status\",\n value=f\"{str(member1.activity.type).split('.') if member1.activity else 'N/A'} {member1.activity.name if member1.activity else ''}\")\n embed.add_field(\n name=\"created at\", value=f\"{member1.created_at.strftime('%d/%m/%y %H:%M:%S')}\")\n embed.add_field(\n name=\"Joined at\", value=f\"{member1.joined_at.strftime('%d/%m/%y %H:%M:%S')}\")\n embed.add_field(name=\"Boosted?\", value=f\"{member1.premium_since}\")\n\n await ctx.reply(embed=embed)","def describe_user(self):\n print(self.first_name + \"\\n\" + self.last_name + \"\\n\" + self.Screen_name)","def profile(username):\n username = mongo.db.users.find_one(\n {\"username\": session[\"user\"]})[\"username\"]\n return render_template(\"profile.html\", username=username)","def profile(request):\n user = Info.objects.all()\n return render(request, 'kvent/profile.html',{user:'user'})","def info(ctx: CLIContext) -> None:\n fields = [\n keypair_fields['user_id'],\n keypair_fields['full_name'],\n keypair_fields['access_key'],\n keypair_fields['secret_key'],\n keypair_fields['is_active'],\n keypair_fields['is_admin'],\n keypair_fields['created_at'],\n keypair_fields['last_used'],\n keypair_fields['resource_policy'],\n keypair_fields['rate_limit'],\n keypair_fields['concurrency_used'],\n ]\n with Session() as session:\n try:\n kp = session.KeyPair(session.config.access_key)\n item = kp.info(fields=fields)\n ctx.output.print_item(item, fields)\n except Exception as e:\n ctx.output.print_error(e)\n sys.exit(1)","def show_user():\n\n return render_template('user/show_by_user.html', title='Show Profile', user = current_user)","def describe_user(self):\n print(\"We have stored next information about user \" +\n self.first_name.title() + \" \" + self.last_name.title() +\n \":\")\n print(\"- Username: \" + self.username)\n print(\"- Age: \" + str(self.age))\n print(\"- Location: \" + self.location.title())","def info(self, login=None, full_name=None):\r\n params = base.get_params(None, locals())\r\n return self._get('info', params)","def profile():\n recipes = mongo.db.recipes.find({\"created_by\": session[\"user\"]})\n return render_template(\"accounts/profile.html\", recipes=recipes)","def describe_user(self):\r\n print('\\nFirst Name: ' + self.first_name.title(), end='\\n',)\r\n print('Last Name: ' + self.last_name.title(), end='\\n')\r\n print('Address: ' + self.address.title(), end='\\n',)\r\n print('State: ' + self.state.title(), end='\\n',)\r\n print('Country: ' + self.country.title())","def describe_user(self):\n\t\tprint(f\"\\n{self.first_name.title()} {self.last_name.title()} \"\n\t\t\tf\"is from {self.location.title()}.\")","def profile(username):\n username = mongo.db.users.find_one(\n {\"username\": session[\"user\"]})[\"username\"]\n\n tips = list(mongo.db.tips.find())\n return render_template(\"profile.html\", tips=tips)\n\n if session[\"user\"]:\n return render_template(\"profile.html\", username=username)\n\n return redirect(url_for(\"login\"))","async def info(self,ctx,*,person:discord.Member = None):\n\n if not person:\n guild = len(self.bot.guilds)\n member = len(set(self.bot.get_all_members()))\n app = await self.bot.application_info()\n msg = \"Name:{}\".format(self.bot.user)\n if ctx.message.guild.me.nick:\n msg += \"\\nNickname:{}\".format(ctx.message.guild.me.nick)\n msg += \"\\nCreator: {}\".format(app.owner)\n msg += \"\\nServer:{}\\nMembers:{}\".format(guild,member)\n link = \"If you want to invite this bot to your server, you can check it out here <http://nurevam.site>!\"\n return await self.bot.say(ctx,content = \"```xl\\n{}\\n```\\n{}\\n\".format(msg,link))\n else:\n e = discord.Embed()\n e.title = \"{} - {}\".format(person,person.id)\n e.set_thumbnail(url = person.avatar_url)\n e.add_field(name = \"Created at\", value=\"{} - ({})\".format(person.created_at,self.get_time_delta(person.created_at)),inline=False)\n e.add_field(name = \"Joined at\", value=\"{} - ({})\".format(person.joined_at,self.get_time_delta(person.joined_at)),inline=False)\n e.add_field(name = \"Total Roles\", value=str(len(person.roles)),inline=False)\n\n if person.colour.value:\n e.colour = person.color\n await self.bot.say(ctx,embed = e)","def info(self, membership, callback=None):","def show_site_info(site_name):\n\n site = Site.query.filter_by(route_name=site_name).first()\n site_url = site.url\n sites = Site.query.all()\n return render_template('site-info.html', site_url=site_url, sites=sites)","def describe_my_user_profile():\n pass","def describe_user(self):\r\n print(self.first_name + \" \" + self.last_name + \": \")\r\n print(\" - First name: \" + self.first_name)\r\n print(\" - Last name: \" + self.last_name)\r\n print(\" - Username: \" + self.username)\r\n print(\" - Email address: \" + self.email_address)","def print_user_profile(self):\n print ('User Name: {name}').format(name=self.name)\n print ('User Email: {email}').format(email=self.email)\n print ('User Phone Number: {birthday}').format(birthday=self.phone_number)\n print ('User Country: {country}').format(country=self.country)\n print ('User Address: {address}').format(address=self.address)","async def profile(self, ctx, member: discord.Member = None):\n if member is None:\n member = ctx.author\n name = member.display_name\n id = member.id\n avatar = member.avatar_url\n created = member.created_at\n embed = discord.Embed(\n title=name,\n description=name + \"'s profile!\",\n color=discord.Colour.purple()\n )\n embed.set_thumbnail(url=avatar)\n embed.add_field(name=\"ID:\", value=str(id))\n embed.add_field(name=\"Created At:\", value=str(created))\n if ctx.guild is not None:\n joined_at = member.joined_at\n embed.add_field(name=\"Joined Server At:\", value=str(joined_at))\n if member.bot:\n embed.add_field(name=\"Bot?\", value=\"Yeah\")\n if member.premium_since is not None:\n premium = str(member.premium_since)\n embed.add_field(name=\"Premium Member Since: \", value=premium)\n else:\n embed.add_field(name=\"Premium?\", value=\"Nope\")\n top_role = str(member.top_role)\n embed.add_field(name=\"Top Role:\", value=top_role)\n await ctx.send(\"\", embed=embed)","def profile(request):\n return render(request, 'profile.html', context)","def info():\n print 'Loading info page'\n\n team_list = datastore.get_all_teams(engine)\n\n return render_template('info.html', rows=team_list)","def extra_data(self, user, uid, response, details):\n try:\n return self.get_steam_profile(response)\n except:\n return \"\"","def show_user_info(user_id):\n\n user = User.query.get_or_404(user_id)\n return render_template(\"user_details.html\", user=user)","def account():\n return render_template('user/account.html')","def show_info(title, message):\n\n pass","def boston_info():\n # rendering text\n return 'Welcome to the Boston Info Slack Bot'","async def serverinfo(self, context: Context) -> None:\n roles = [role.name for role in context.guild.roles]\n if len(roles) > 50:\n roles = roles[:50]\n roles.append(f\">>>> Displaying[50/{len(roles)}] Roles\")\n roles = \", \".join(roles)\n\n embed = discord.Embed(\n title=\"**Server Name:**\", description=f\"{context.guild}\", color=0x9C84EF\n )\n if context.guild.icon is not None:\n embed.set_thumbnail(url=context.guild.icon.url)\n embed.add_field(name=\"Server ID\", value=context.guild.id)\n embed.add_field(name=\"Member Count\", value=context.guild.member_count)\n embed.add_field(\n name=\"Text/Voice Channels\", value=f\"{len(context.guild.channels)}\"\n )\n embed.add_field(name=f\"Roles ({len(context.guild.roles)})\", value=roles)\n embed.set_footer(text=f\"Created at: {context.guild.created_at}\")\n await context.send(embed=embed)","def describe_user(self):\n print(\"\\n Name: \" + self.f_name.title() + ' ' + self.l_name.title())\n print(\"Age: \" + str(self.age)) \n print(\"Birthplace: \" + self.birthplace.title())","async def about(self, ctx):\n\n author_repo = \"https://github.com/scragly\"\n bot_repo = author_repo + \"/Firetail\"\n server_url = \"https://discord.gg/ZWmzTP3\"\n owner = \"Discord: Scragly#5146\\nEVE: Kyo Kuronami\"\n member_count = sum(g.member_count for g in self.bot.guilds)\n server_count = len(self.bot.guilds)\n\n description = (\n \"Made for EVE Online Communities\\n\"\n f\"[**Docs & Source**]({bot_repo})\\n\"\n f\"[**Support Server**]({server_url})\\n\"\n f\"[**Invite Me**]({self.bot.invite_url})\\n\"\n \"\\n\"\n f\"See available commands with:\\n`{ctx.prefix}help`\\n\"\n \"\\n\"\n f\"**Maintained and Developed by**\\n\"\n f\"{owner}\\n\"\n \"\\n\"\n f\"**Servers:** {server_count}\\n\"\n f\"**Members:** {member_count}\\n\"\n f\"**Uptime:** {self.bot.uptime_str}\"\n )\n\n try:\n await ctx.info(\"About Firetail\", description, thumbnail=self.bot.user.avatar_url_as(format='png'))\n\n except discord.HTTPException:\n await ctx.send(\"I need the `Embed links` permission for this command.\")","def show_profile():\n print('LOGIN SESSION:', login_session)\n if 'userid' in login_session:\n category = session.query(Category).first()\n item = session.query(Item).first()\n return render_template('profile.html', login_session=login_session, root=app.instance_path, category=category,\n item=item)\n flash('Unfortunately you need to be logged in to see your profile', 'error')\n return redirect(url_for('show_homepage'))","async def about(self, ctx):\n embed = discord.Embed(title = f\"About {self.bot.user.name}\", color = discord.Color.blurple())\n embed.set_thumbnail(url = self.bot.user.avatar_url)\n embed.add_field(name = \"Developers\", value = \"Kowlin#4417 & A Trash Coder#0981\", inline = False)\n embed.add_field(name = \"Library\", value = \"discord.py rewrite\", inline = False)\n embed.add_field(name = \"Source Code\", value = \"[Click here](https://github.com/kowlintechnologies/DHB)\", inline = False)\n embed.add_field(name = \"Links\", value = \"[Docs](https://dhb-documentation.readthedocs.io/en/latest/index.html) | [Support](https://discord.gg/KEkwrwd) | [Invite](https://discordapp.com/api/oauth2/authorize?client_id=592811241756688405&permissions=2080762998&scope=bot)\")\n await ctx.send(embed = embed)","def __str__(self):\n return self.user.username + \"'s Profile\"","def print_player_info(self):\n\t\tclear_screen()\n\n\t\tprint(\"# PLAYER INFO #\\n\")\n\t\tprint(\"Name{:.>17} \".format(self.info['Name']))\n\t\tprint(\"Race{:.>17} \".format(self.info['Race']))\n\t\tprint(\"Level{:.>16} \".format(self.stats['Level']))\n\t\tprint(\"Hit Points{:.>11} \".format(self.stats['HP']))\n\t\tprint(\"Gold Pieces{:.>10} \".format(self.stats['GOLD']))\n\t\n\t\tpress_enter()","async def spotify(self, ctx):\n if ctx.invoked_subcommand is None:\n await ctx.send('Invalid command passed. Use the `?help spotify` command to learn more.')","async def avatarurl(self, ctx: \"IceTeaContext\", target: discord.Member = None):\n target = target or ctx.author\n embed = discord.Embed(description=f\"{target} Profile Picture\")\n embed.set_image(url=str(target.avatar_url))\n await ctx.send(embed=embed)","def logo():\n print (\"\"\"\\\n _ _\n| |_ ___ ___ ___ _ _ _| |\n| | . | | -_| | | . |\n|_|_|___|_|_|___|_ |___|\n |___|\n \"\"\")\n print ('Author: Peter Sooky <448291@mail.muni.cz>')\n print ('Honeyd-python {0}'.format(honeyd.__version__))","async def avatar(self, ctx, *, member: disnake.Member = None):\n if member is None:\n member = ctx.author\n await ctx.send(member.display_avatar.url)","def account_info(self):\n url, params, headers = self.request(\"/account/info\", method='GET')\n\n return self.rest_client.GET(url, headers)","def profile(request):\n profile = request.user.profile\n return render(request, 'accounts/profile.html', {'profile': profile})","def account():\n user = Users.query.filter_by(id=current_user.id).first_or_404(description=f'There is no user {current_user.id}')\n\n return render_template('auth/account.html', user=user)","def user_details():\n url = 'https://api.github.com/orgs/facebook/repos'\n json_obj = urllib2.urlopen(url)\n userdata = json.load(json_obj)\n if 'error' in userdata:\n print 'errors are scanned in data'\n for data in userdata:\n if 'name' in data:\n if data['name'] == 'codemod':\n print 'language used'\n print data['language']\n print 'number of watchers'\n print data['watchers']\n print 'git url'\n print data['git_url']\n print 'open issues'\n print data['open_issues']\n print 'permissions for user'\n print 'push'\n print data['permissions']['push']\n print 'pull'\n print data['permissions']['pull']","def spotify(request):\n\n logger.debug(\"Spotify Album Called\")\n response_data = {}\n\n spotify_pre_auth = spotify_auth(request)\n# if type(spotify_pre_auth) is JsonResponse:\n# return spotify_pre_auth\n\n response = spotify_pre_auth.get('result','')\n album_url = spotify_pre_auth.get('album_url','')\n response_code=spotify_pre_auth.get('status_code','')\n\n if response_code == 200:\n auth_data = json.loads(response)\n access_token = auth_data['access_token']\n\n headers = {\"Authorization\": \"Bearer %s\" % access_token}\n album_url = \"https://api.spotify.com/v1/albums/\" + album_url.split(\"/\")[-1]\n logger.debug(\"Spotify get album: %s\" % album_url)\n res = requests.get(album_url, headers=headers)\n if res.status_code == 200:\n album = json.loads(res.text)\n logger.debug(\"Found on Spotify the album: %(name)s\" % album)\n return json.loads(res.text)\n else:\n response = res.text\n response_code = res.status_code\n response_body = {\"result\": \"failure\", \"message\": \"Spotify album failed. Check the url or the connections\", \"status_code\": response_code}\n return response_body","def display_student(s_info):\n print('')\n print('Your information:')\n print(f'{s_info.student_id} - {s_info.first_name} {s_info.last_name}')","async def avatar(message, user: ParamType.MIXER_USER):\n return \"link to @{} avatar: {}\".format(user.username, user.avatar_url)","def display_user_info(user):\n\n ornament = '=' * 30\n print(ornament)\n print('LOGGED USER')\n print(ornament)\n print('Name: {}\\nSurname: {}\\nPermissions: {}'.format(user.name, user.surname, user.__class__.__name__))\n print(ornament + '\\n')","def profile(request, user_name=None):\n \n # get the viewed user\n if user_name is None:\n user = request.user.get_profile()\n else:\n user = get_object_or_404(User, username=user_name)\n user = user.get_profile()\n \n # set display name\n if len(user.user.first_name) <= 0:\n user.display_name = user.user.username\n else:\n user.display_name = user.user.first_name + \" \" + user.user.last_name\n \n # set avatar path\n if len(user.avatar.name) <= 0:\n user.avatar_url = settings.MEDIA_URL + \"avatar/noavatar.png\"\n else:\n user.avatar_url = user.avatar.url\n \n # get tracked list, ownedlist and playlist\n trackedlist = user.trackedrecordlist_set.all()\n ownedlist = user.userentry_set.all()\n playlist = user.playlist_set.all()\n context = {\n 'profile_user': user,\n 'trackedlist': trackedlist,\n 'ownedlist': ownedlist,\n 'playlist': playlist\n }\n return render_to_response(\n 'usermgr/profile.html',\n context,\n context_instance = RequestContext(request))","def get_account_info(self):\n resp = requests.get(\n self.URL + 'info/',\n headers={'Authorization': 'Token ' + self.api_key}\n )\n\n return self.__handle_response(resp)","def show_user(title, fname, lname):\n person = \"{} {} {}\".format(title, fname, lname)\n logger.info(\"{}\".format(person))\n return person","async def avatar(self, ctx):\n e = discord.Embed(title=\"Here is a avatar image for you {}.\".format(ctx.author.name), color=discord.Color.magenta())\n e.set_image(url=nekos.img('avatar'))\n await ctx.send(embed=e)","def info(ctx, schain_name):\n skale = ctx.obj['skale']\n info = get_schain_info(skale, schain_name)\n print(json.dumps(info, indent=2))","async def info(self, ctx):\n\n uptime = func.time_(self.bot.launch_time)\n users = sum(1 for _ in self.bot.get_all_members())\n channels = sum(1 for _ in self.bot.get_all_channels())\n\n author = self.bot.get_user(299879858572492802)\n\n invite = 'https://discordapp.com/oauth2/authorize?client_id=347205176903335937&scope=bot&permissions=470150359'\n about = ('Infamous is a actively developed bot that gets updated daily.'\n f' It is written with passion by {author} using the Rewrite branch of the discord.py library.')\n\n links = (f'**[[Invite Bot]]({invite})** \\n'\n '**[[Fame Discord]](https://discord.gg/NY2MSA3)** \\n'\n '**[[Discord.py]](https://github.com/Rapptz/discord.py/tree/rewrite)** \\n'\n '**[[Support]](https://discord.gg/JyJTh4H)**')\n\n # From Modelmat\n cpu_usage = self.process.cpu_percent() / psutil.cpu_count()\n ram_usage = self.process.memory_full_info().uss / 1024 ** 2\n\n embed = discord.Embed(color=self.bot.embed_color)\n embed.set_author(name=self.bot.user.name, icon_url=self.bot.user.avatar_url)\n embed.description = 'A multi-purpose bot with image manipulation, wiki pages and it\\'s own rpg; originally a ' \\\n 'community bot for ★ Fame ★'\n embed.set_thumbnail(\n url=self.bot.user.avatar_url)\n\n embed.add_field(name='About', value=about, inline=False)\n\n embed.add_field(name='Statistics 📈',\n value=(f'**{len(self.bot.guilds)} guilds.**\\n'\n f'**{channels} channels.**\\n'\n f'**{users} users.** \\n'\n f'**{self.bot.lines} lines**'), inline=True)\n\n embed.add_field(name='Uptime ⏰', value=(f'**{uptime[0]} days.** \\n'\n f'**{uptime[1]} hours.** \\n'\n f'**{uptime[2]} minutes.** \\n'\n f'**{uptime[3]} seconds.**'), inline=True)\n\n embed.add_field(name='Developer 🕵', value=author)\n embed.add_field(name='Resources 💻', value='`CPU:` {:.2f}% \\n`MEM:` {:.2f}'.format(cpu_usage, ram_usage))\n embed.add_field(name='Links 🔗', value=links, inline=True)\n\n await ctx.send(embed=embed)","def show_me():\n # Scumbag thumbnail code\n try:\n from PIL import Image\n except ImportError:\n pass\n else:\n filename = os.path.join(app.static_folder, 'img', 'badumtss.png')\n image = Image.open(filename)\n\n return render_template('show_me.html')","def show_me_user(user_id):\n\n user = crud.get_user_by_id(user_id)\n\n return render_template('user_details.html', user=user)","def my_account(request):\n context = {}\n\n return render(request, 'classroom_main/my_account.html', context)","async def info(self, ctx,*users:discord.User):\n try:\n if not users:\n user = ctx.message.author\n server = ctx.message.server\n seen = str(len(set([member.server.name for member in self.bot.get_all_members() if member.name == user.name])))\n x = 'Your Info:\\nUsername: \"{0.name}\"\\nID #: \"{1.id}\"\\nDiscriminator #: \"{2.discriminator}\"\\nAvatar: \"{3.avatar_url}\"\\nStatus: \"{4}\"\\nGame: \"{5}\"\\nVoice Channel: \"{6}\"\\nSeen On: \"{7}\" servers\\nJoined Server On: \"{8}\"\\nRoles: \"{9}\"'.format(user, user, user, user, str(user.status), str(user.game), str(user.voice_channel), seen, str(user.joined_at), ', '.join(map(str, user.roles)).replace(\"@\", \"@\\u200b\"))\n await self.bot.say(cool.format(x))\n else:\n for user in users:\n server = ctx.message.server\n seen = str(len(set([member.server.name for member in self.bot.get_all_members() if member.name == user.name])))\n x = 'User Info:\\nUsername: \"{0}\"\\nID #: \"{1}\"\\nDiscriminator #: \"{2}\"\\nAvatar: \"{3}\"\\nStatus: \"{4}\"\\nGame: \"{5}\"\\nVoice Channel: \"{6}\"\\nSeen On: \"{7} servers\"\\nJoined Server On: \"{8}\"\\nRoles: \"{9}\"'.format(user.name, user.id, user.discriminator, user.avatar_url, str(user.status), str(user.game), str(user.voice_channel), seen, str(user.joined_at), ', '.join(map(str, user.roles)).replace(\"@\", \"@\\u200b\"))\n await self.bot.say(cool.format(x))\n except Exception as e:\n await self.bot.say(code.format(type(e).__name__ + ': ' + str(e)))","async def profilepicture(self, ctx):\n if not ctx.message.mentions:\n await ctx.send(embed=discord.Embed().set_image(\n url=ctx.author.avatar_url))\n else:\n for member in ctx.message.mentions:\n await ctx.send(embed=discord.Embed().set_image(\n url=member.avatar_url))","def displayMeta(ctx, data, vid):\n\t\n\ts = \"\"\n\ts += \"Title: %s \" % data.getElementsByTagName(\"title\")[0].firstChild.data\n\ts += \" • By: %s\" % data.getElementsByTagName(\"author\")[0].getElementsByTagName(\"name\")[0].firstChild.data\n\n\tshowRest = True\n\n\tr = data.getElementsByTagName(\"yt:state\")\n\tif len(r):\n\t\tr = r[0]\n\t\tif r.getAttribute(\"name\") == \"restricted\":\n\t\t\tshowRest = r.getAttribute(\"reasonCode\") == \"limitedSyndication\"\n\t\t\tif showRest:\n\t\t\t\ts += \" • Syndication Limited.\"\n\t\t\telse:\n\t\t\t\ts += \" • Video is unavailable: %s\" % r.firstChild.data\n\n\tif showRest:\n\t\ts += \" • Length: %s\" % prettyTime(data.getElementsByTagName(\"yt:duration\")[0].getAttribute(\"seconds\"))\n\t\ts += \" • View Count: %s\" % prettyNumber(data.getElementsByTagName(\"yt:statistics\")[0].getAttribute(\"viewCount\"))\n\n\t\tr = data.getElementsByTagName(\"gd:rating\")\n\t\tif len(r):\n\t\t\tr = r[0]\n\t\t\ts += \" • Average Rating: %1.2f/5 over %s people\" % (\n\t\t\t\tfloat(r.getAttribute(\"average\")),\n\t\t\t\tprettyNumber(r.getAttribute(\"numRaters\"))\n\t\t\t\t)\n\t\telse:\n\t\t\ts += \" • No ratings\"\n\t\n\ts += \" • https://youtu.be/%s\" % vid\n\tctx.reply(s, \"YouTube\")","def __str__(self):\n return f\"Profile for user {self.user}\"","def get_student():\n\n # github = \"jhacks\"\n github = request.args.get('github','jhacks')\n first, last, github = hackbright.get_student_by_github(github)\n return render_template(\"student_info.html\" , first=first, gorilla=last, giraffe=github)\n # return \"%s is the GitHub account for %s %s\" % (github, first, last)"],"string":"[\n \"def status(verbose):\\n user_data = Spotify.request('me', method='GET')\\n click.echo('Logged in as {}'.format(user_data['display_name']))\\n if verbose:\\n click.echo('Credentials stored in {}'.format(CREDS_PATH))\\n return\",\n \"async def githubinfo_command(self, ctx, *, githubusername: str):\\n async with aiohttp.ClientSession() as session:\\n async with session.get(\\n f\\\"https://api.github.com/users/{githubusername}\\\"\\n ) as resp:\\n githubinfo = await resp.json()\\n name = githubinfo[\\\"name\\\"]\\n avatar_url = githubinfo[\\\"avatar_url\\\"]\\n blog = githubinfo[\\\"blog\\\"]\\n location = githubinfo[\\\"location\\\"]\\n twitter_username = githubinfo[\\\"twitter_username\\\"]\\n publicrepos = githubinfo[\\\"public_repos\\\"]\\n followers = githubinfo[\\\"followers\\\"]\\n following = githubinfo[\\\"following\\\"]\\n embed = Embed(\\n color=Color.blurple(),\\n timestamp=datetime.utcnow(),\\n description=(\\n f\\\"**Name** - {name}\\\\n**Blog URL** - {None if not blog else blog}\\\\n**Location** - {location}\\\\n**Twitter Username** - {twitter_username}\\\\n **Public Repositories** - {publicrepos}\\\\n**Followers** - {followers}\\\\n**Following** - {following}\\\"\\n ),\\n )\\n embed.set_author(name=f\\\"Github Profile info of username {githubusername}\\\")\\n if avatar_url is not None:\\n embed.set_thumbnail(url=avatar_url)\\n await ctx.send(embed=embed)\",\n \"async def info(self, context):\\n await context.send('creador: debellisnahuel@gmail.com\\\\ncolabs:\\\\n emi: https://twitter.com/emilianosce/ o https://www.instagram.com/emilianosce/ \\\\n garza: https://twitter.com/Matias_Garcia00 o https://www.twitch.tv/garzangb')\",\n \"def show_user_profile(username):\\n\\n name = USERS[username]\\n return f\\\"<h1>Profile for {name}</h1>\\\"\",\n \"def display_profile(self):\\n print(f\\\"Id: {self._id}\\\")\\n print(f\\\"username: {self.username}\\\")\\n print(f\\\"name: {self.name}\\\")\\n print(f\\\"contact: {self.contact}\\\")\\n print(f\\\"address: {self.address}\\\")\",\n \"def info(id):\\n sql = \\\"select distinct name, description, stars, url, last_push_date, repo_id, created_date, avatar from python_repos where repo_id=\\\"+id\\n db = get_db()\\n cursor = db.execute(sql)\\n repo_info = cursor.fetchall()\\n return render_template('repo.html',info=repo_info)\",\n \"def account_info(request):\\r\\n user = request.user\\r\\n\\r\\n return _api_response(request, user.safe_data())\",\n \"async def info(self, ctx):\\n\\t\\tembed = discord.Embed(\\n\\t\\t\\tdescription=\\\"Created By Seperoph#1399 and AkaBaka#4654\\\",\\n\\t\\t\\tcolor=config[\\\"success\\\"]\\n\\t\\t)\\n\\t\\tembed.set_author(\\n\\t\\t\\tname=\\\"Bot Information\\\"\\n\\t\\t)\\n\\t\\tembed.add_field(\\n\\t\\t\\tname=\\\"Head Programmers:\\\",\\n\\t\\t\\tvalue=\\\"Seperoph#1399 and AkaBaka#4654\\\",\\n\\t\\t\\tinline=True\\n\\t\\t)\\n\\t\\tembed.add_field(\\n\\t\\t\\tname=\\\"Python Version:\\\",\\n\\t\\t\\tvalue=f\\\"{platform.python_version()}\\\",\\n\\t\\t\\tinline=True\\n\\t\\t)\\n\\t\\tawait ctx.respond(embed=embed)\",\n \"def show_user_info(self):\\n name = self.get_user_name()\\n print(f'Name: {name.title()}')\\n print(f'Age: {self.age}')\\n print(f'Gender: {self.gender.title()}')\\n print(f'Mobile: {self.m_number}')\",\n \"def __display_login_info(self):\\n print(f'\\\\nYour card has been created\\\\n'\\n f'Your card number:\\\\n'\\n # f'{self.__card_display()}\\\\n' # uncomment this line and comment out line below for pretty display\\n f'{self.card_number}\\\\n'\\n f'Your card PIN:\\\\n'\\n f'{self.__account_pin}\\\\n', )\",\n \"async def userinfo(self, ctx: \\\"IceTeaContext\\\", target: discord.Member = None):\\n target = target or ctx.author\\n target_data = ctx.author_data if target == ctx.author else await ctx.get_user_data(target)\\n if target_data:\\n nicknames = await target_data.get_nicknames()\\n else:\\n nicknames = []\\n shared_servers = len([member for member in ctx.bot.get_all_members() if member == target])\\n embed = discord.Embed(title=f\\\"{target.nick or target.name} Profile\\\")\\n embed.set_author(name=f\\\"{target.name} ({target.id})\\\", icon_url=target.avatar_url)\\n embed.set_thumbnail(url=target.avatar_url)\\n embed.add_field(name=\\\"Shared Servers\\\", value=f\\\"{shared_servers} Shared\\\")\\n embed.add_field(name=\\\"Created\\\",\\n value=f\\\"\\\"\\\"{timeago.format(target.created_at)} ({target.created_at.strftime(\\\"%b %d, %Y\\\")})\\\"\\\"\\\")\\n embed.add_field(name=\\\"Joined\\\",\\n value=f\\\"\\\"\\\"{timeago.format(target.joined_at)} ({target.joined_at.strftime(\\\"%b %d, %Y\\\")})\\\"\\\"\\\")\\n embed.set_footer(text=\\\"Last Spoke In server\\\")\\n if target_data:\\n embed.timestamp = target_data.last_spoke\\n else:\\n embed.timestamp = ctx.message.created_at\\n if len(nicknames) > 0:\\n embed.add_field(name=\\\"Nicknames\\\", value=\\\" , \\\".join(str(nick) for nick in nicknames[:5]), inline=False)\\n embed.add_field(name=\\\"Roles\\\", value=\\\" , \\\".join([role.name for role in target.roles[:5] if len(role.name) > 0]),\\n inline=False)\\n if target.activity:\\n if isinstance(target.activity, discord.Spotify):\\n embed.add_field(name=\\\"Currently Listening to\\\",\\n value=f\\\"**{target.activity.title}** by {target.activity.artist} \\\")\\n else:\\n embed.add_field(name=\\\"Currently Playing Since\\\",\\n value=f\\\"{target.activity.name}\\\\n{target.activity.details}\\\\n{target.activity.state}\\\")\\n await ctx.send(embed=embed)\",\n \"async def info(ctx):\\n embed = discord.Embed(title=\\\"Zane Bot\\\", description=\\\"All hail the hypnotoad!\\\", color=0x0091C5)\\n\\n # give info about you here\\n embed.add_field(name=\\\"Author\\\", value=\\\"Zanexius\\\")\\n\\n # Shows the number of servers the bot is member of.\\n embed.add_field(name=\\\"Server count\\\", value=f\\\"{len(bot.guilds)}\\\")\\n\\n # give users a link to invite thsi bot to their server\\n embed.add_field(name=\\\"Invite\\\", value=\\\"[Invite link](<insert your OAuth invitation link here>)\\\")\\n\\n await ctx.send(embed=embed)\",\n \"async def info(self, ctx: \\\"IceTeaContext\\\", *, otag: TagConverter):\\n tag: models.Tag = otag\\n if not tag.alias:\\n embed = discord.Embed(description=f\\\"{ctx.message.guild.name} ``{tag.title}`` tag information\\\")\\n user = ctx.guild.get_member(tag.author)\\n embed.set_author(name=user.display_name, icon_url=user.avatar_url)\\n embed.add_field(name=\\\"Tag name\\\", value=tag.title)\\n embed.add_field(name=\\\"Amount used\\\", value=str(tag.count))\\n embed.timestamp = tag.created\\n await ctx.send(embed=embed)\\n else:\\n embed = discord.Embed(description=f\\\"{ctx.message.guild.name} ``{tag.title}`` alias information\\\")\\n user = ctx.guild.get_member(tag.author)\\n embed.add_field(name=\\\"Author\\\", value=user or \\\"Unknown\\\")\\n embed.add_field(name=\\\"Amount used\\\", value=str(tag.count))\\n embed.timestamp = tag.created\\n await ctx.send(embed=embed)\",\n \"async def info(ctx, message):\\n if ctx.args.command == None:\\n embed = discord.Embed()\\n embed.add_field(name=\\\"Profile\\\", value=ctx.profile.name)\\n embed.add_field(name=\\\"Mode\\\", value=ctx.profile.mode)\\n embed.set_author(name=ctx.user.name, icon_url=ctx.user.avatar_url)\\n await message.channel.send(embed=embed)\\n else:\\n embed = discord.Embed(\\n title=\\\"{0.profile.prefix}{0.args.command.name} {0.args.command.usage}\\\".format(ctx),\\n description=ctx.args.command.help,\\n url=\\\"https://github.com/Synixe/Bot/blame/master/{0}#L{1.start}L{1.end}\\\".format(ctx.args.command.file.replace(os.getcwd(), \\\"\\\"), ctx.args.command)\\n )\\n embed.set_footer(text=ctx.args.command.extension.fullname + \\\".\\\" + ctx.args.command.name)\\n await message.channel.send(embed=embed)\",\n \"def display_accounts_details():\\n return Credentials.display_credentials()\",\n \"def profile(self, name=\\\"johndoe\\\"):\\r\\n url = \\\"/account/%s\\\" % name\\r\\n return self.app.get(url, follow_redirects=True)\",\n \"async def info(self, ctx, member: discord.Member = None):\\n\\n user = ctx.author if member is None else member\\n\\n emb = await self.get_embed_info_user(user)\\n emb.set_image(url=user.avatar_url)\\n\\n await ctx.send(embed=emb)\",\n \"def profile():\\n from flickrAPI import FlickrAPI\\n #flickr = FlickrAPI(key=session['resource_owner_key'], secret=session['resource_owner_secret'])\\n flickr = FlickrAPI(key=request.cookies.get('oauth_token'), secret=request.cookies.get('oauth_token_secret'))\\n faves = flickr.favorites_getList(user_id=\\\"44124394781@N01\\\", page=1, per_page=5, extras='owner_name')\\n return str(faves)\",\n \"async def info(self):\\n # [p]info\\n\\n await self.bot.say(strings.info.format(\\n CacheAPI.get(key='dwarf_repository'),\\n CacheAPI.get(key='dwarf_invite_link')))\",\n \"def user_info(username):\\n print(json.dumps(client.user_info(username)))\",\n \"def profile():\\n \\n return render_template(\\\"profile.html\\\")\",\n \"async def userinfo_command(self, ctx, member: Optional[Member]):\\n member = member or ctx.author\\n member_avatar = member.avatar_url\\n id = member.id\\n name = member.name\\n accountAge = member.created_at.strftime(\\\"%a, %#d %B %Y, %I:%M %p UTC\\\")\\n joinServerDate = member.joined_at.strftime(\\\"%a, %#d %B %Y, %I:%M %p UTC\\\")\\n highestRole = member.top_role.mention\\n\\n info = \\\"Server Owner\\\" if ctx.guild.owner is ctx.author else \\\"Member\\\"\\n\\n embed = Embed(\\n title=f\\\"User Info - {member.name}\\\",\\n timestamp=datetime.utcnow(),\\n color=Color.blurple(),\\n )\\n embed.set_footer(text=f\\\"Requested by {ctx.author.name}\\\")\\n embed.set_thumbnail(url=member_avatar)\\n fields = [\\n (\\\"ID\\\", id, False),\\n (\\\"Name\\\", f\\\"{name} #{ctx.author.discriminator}\\\", True),\\n (\\\"Highest Role\\\", highestRole, True),\\n (\\\"Account Created on\\\", accountAge, True),\\n (\\\"Joined Server on\\\", joinServerDate, True),\\n (\\\"Additional Info\\\", info, True),\\n ]\\n for name, value, inline in fields:\\n embed.add_field(name=name, value=value, inline=inline)\\n await ctx.send(embed=embed)\",\n \"async def info(self, ctx, *, member: disnake.Member = None):\\n\\n member = member or ctx.author\\n\\n e = disnake.Embed(description=\\\"\\\")\\n\\n if member.bot:\\n e.description = \\\"This account is a bot.\\\\n\\\\n\\\"\\n\\n e.description += member.mention\\n\\n e.add_field(name=\\\"Status\\\", value=member.status)\\n\\n if member.activity:\\n e.add_field(name=\\\"Activity\\\", value=member.activity.name)\\n\\n e.set_author(name=str(member), icon_url=member.display_avatar.url)\\n\\n now = datetime.now(timezone.utc)\\n created = member.created_at\\n joined = member.joined_at\\n\\n e.add_field(\\n name=\\\"Account age\\\",\\n value=\\\"{0} • Created <t:{1}:F>\\\".format(\\n pretty_timedelta(now - created), round(created.timestamp())\\n ),\\n inline=False,\\n )\\n\\n e.add_field(\\n name=\\\"Member for\\\",\\n value=\\\"{0} • Joined <t:{1}:F>\\\".format(\\n pretty_timedelta(now - joined), round(joined.timestamp())\\n ),\\n )\\n\\n if len(member.roles) > 1:\\n e.add_field(\\n name=\\\"Roles\\\",\\n value=\\\" \\\".join(role.mention for role in reversed(member.roles[1:])),\\n inline=False,\\n )\\n\\n e.set_footer(text=\\\"ID: \\\" + str(member.id))\\n\\n await ctx.send(embed=e)\",\n \"async def info(self, ctx, *, tag):\\n try:\\n self.fetch_tag(ctx, tag)\\n except Exception as error:\\n return await ctx.send(error)\\n data = self._tag_dict[ctx.guild.id][tag]\\n author = self.bot.get_user(data['author']) or await self.bot.fetch_user(data['author'])\\n embed = discord.Embed(colour=self.bot.colour)\\n embed.title = tag\\n embed.description = f\\\"<:author:734991429843157042> **{author}**\\\\n\\\"\\n embed.description += f\\\"Uses: **{data['uses']}**\\\\n\\\"\\n embed.description += f\\\"ID: **{data['id']}**\\\"\\n embed.set_author(name=str(author), icon_url=author.avatar_url)\\n await ctx.send(embed=embed)\",\n \"async def github_user_info(self, ctx: commands.Context, username: str) -> None:\\n async with ctx.typing():\\n user_data = await self.fetch_data(f\\\"{GITHUB_API_URL}/users/{quote_plus(username)}\\\")\\n\\n # User_data will not have a message key if the user exists\\n if \\\"message\\\" in user_data:\\n embed = discord.Embed(\\n title=random.choice(NEGATIVE_REPLIES),\\n description=f\\\"The profile for `{username}` was not found.\\\",\\n colour=Colours.soft_red\\n )\\n\\n await ctx.send(embed=embed)\\n return\\n\\n org_data = await self.fetch_data(user_data[\\\"organizations_url\\\"])\\n orgs = [f\\\"[{org['login']}](https://github.com/{org['login']})\\\" for org in org_data]\\n orgs_to_add = \\\" | \\\".join(orgs)\\n\\n gists = user_data[\\\"public_gists\\\"]\\n\\n # Forming blog link\\n if user_data[\\\"blog\\\"].startswith(\\\"http\\\"): # Blog link is complete\\n blog = user_data[\\\"blog\\\"]\\n elif user_data[\\\"blog\\\"]: # Blog exists but the link is not complete\\n blog = f\\\"https://{user_data['blog']}\\\"\\n else:\\n blog = \\\"No website link available\\\"\\n\\n embed = discord.Embed(\\n title=f\\\"`{user_data['login']}`'s GitHub profile info\\\",\\n description=f\\\"```{user_data['bio']}```\\\\n\\\" if user_data[\\\"bio\\\"] else \\\"\\\",\\n colour=discord.Colour.blurple(),\\n url=user_data[\\\"html_url\\\"],\\n timestamp=datetime.strptime(user_data[\\\"created_at\\\"], \\\"%Y-%m-%dT%H:%M:%SZ\\\")\\n )\\n embed.set_thumbnail(url=user_data[\\\"avatar_url\\\"])\\n embed.set_footer(text=\\\"Account created at\\\")\\n\\n if user_data[\\\"type\\\"] == \\\"User\\\":\\n\\n embed.add_field(\\n name=\\\"Followers\\\",\\n value=f\\\"[{user_data['followers']}]({user_data['html_url']}?tab=followers)\\\"\\n )\\n embed.add_field(\\n name=\\\"Following\\\",\\n value=f\\\"[{user_data['following']}]({user_data['html_url']}?tab=following)\\\"\\n )\\n\\n embed.add_field(\\n name=\\\"Public repos\\\",\\n value=f\\\"[{user_data['public_repos']}]({user_data['html_url']}?tab=repositories)\\\"\\n )\\n\\n if user_data[\\\"type\\\"] == \\\"User\\\":\\n embed.add_field(\\n name=\\\"Gists\\\",\\n value=f\\\"[{gists}](https://gist.github.com/{quote_plus(username, safe='')})\\\"\\n )\\n\\n embed.add_field(\\n name=f\\\"Organization{'s' if len(orgs)!=1 else ''}\\\",\\n value=orgs_to_add if orgs else \\\"No organizations.\\\"\\n )\\n embed.add_field(name=\\\"Website\\\", value=blog)\\n\\n await ctx.send(embed=embed)\",\n \"def describe_user(self):\\n print(\\\"\\\\nThis is \\\" + self.first_name + \\\" \\\" +\\n self.last_name + \\\"'s user details:\\\")\\n print(\\\"Username: \\\" + self.username)\\n print(\\\"Email: \\\" + self.email)\",\n \"def describe_user(self):\\n print(\\\"\\\\nThis is \\\" + self.first_name + \\\" \\\" +\\n self.last_name + \\\"'s user details:\\\")\\n print(\\\"Username: \\\" + self.username)\\n print(\\\"Email: \\\" + self.email)\",\n \"def avatar_preview(self):\\r\\n h = '<img src=\\\"%s\\\" alt=\\\"%s\\\"/>' % (self.image_avatar_url, self.title)\\r\\n return mark_safe(h)\",\n \"def slack_info(request):\\n params = slack_callback(request)\\n\\n if not params:\\n # Authorization failed.\\n return redirect(\\\"codedoor:login\\\")\\n\\n # if user is already in database, return redirect(url)\\n # else, if it's a new user, redirect to the finishprofile page for the user to input the rest of their info\\n user = authenticate(params[\\\"user\\\"][\\\"email\\\"])\\n if user is None:\\n slack_name = params[\\\"user\\\"][\\\"name\\\"].split(\\\" \\\")\\n if len(slack_name) == 2:\\n first_name, last_name = slack_name\\n else:\\n first_name = slack_name[0]\\n last_name = \\\"\\\"\\n return render(\\n request,\\n 'codedoor/finish_profile.html',\\n {\\n \\\"id\\\": params['user']['email'],\\n \\\"first_name\\\": first_name,\\n \\\"last_name\\\": last_name,\\n \\\"email\\\": params[\\\"user\\\"][\\\"email\\\"],\\n \\\"pic\\\": params[\\\"user\\\"]['image_512']\\n }\\n )\\n else:\\n auth_login(request, user)\\n return redirect(\\\"codedoor:viewprofile\\\", pk=user.profile.id)\",\n \"async def _info(self, ctx: Context):\\n\\n embed = discord.Embed(colour=await ctx.embed_colour())\\n\\n perm_int = discord.Permissions(268494928)\\n\\n data = await self.bot.application_info()\\n invite_url = discord.utils.oauth_url(data.id, permissions=perm_int)\\n\\n embed.description = (\\n \\\"TvM Assistant is a Discord bot with utility commands to make hosting TvMs easier.\\\"\\n \\\"\\\\n\\\\nSome of the bot features include:\\\"\\n \\\"\\\\n\\\\n- Setup roles and channel creation\\\"\\n \\\"\\\\n- Management of sign-ups, sign-outs, spectators and replacements\\\"\\n \\\"\\\\n- In-built logging to detect and ignore private channels\\\"\\n \\\"\\\\n- Quick creation of player, mafia and spectator chats\\\"\\n \\\"\\\\n- Vote counts and time since day/night started\\\"\\n )\\n\\n links = (\\n f\\\"\\\\n- [Invite to your server]({invite_url})\\\"\\n f\\\"\\\\n- [Quickstart]({QUICKSTART})\\\"\\n f\\\"\\\\n- [Commands Reference]({COMMANDS_REFERENCE})\\\"\\n f\\\"\\\\n- [Source Code]({SOURCE_CODE})\\\"\\n )\\n\\n embed.add_field(name=\\\"\\\\u200b\\\\nQuick Links\\\", value=links)\\n embed.set_author(name=f\\\"About {ctx.me.name}\\\", icon_url=ctx.me.avatar_url)\\n\\n await ctx.send(embed=embed)\",\n \"def hello_me():\\n\\n info = oidc.user_getinfo(['preferred_username', 'email', 'sub'])\\n\\n username = info.get('preferred_username')\\n email = info.get('email')\\n user_id = info.get('sub')\\n\\n if user_id in oidc.credentials_store:\\n try:\\n from oauth2client.client import OAuth2Credentials\\n access_token = OAuth2Credentials.from_json(oidc.credentials_store[user_id]).access_token\\n print('access_token=<%s>' % access_token)\\n headers = {'Authorization': 'Bearer %s' % (access_token)}\\n # YOLO\\n greeting = requests.get('http://localhost:8080/greeting', headers=headers).text\\n except:\\n print(\\\"Could not access greeting-service\\\")\\n greeting = \\\"Hello %s\\\" % username\\n \\n\\n return (\\\"\\\"\\\"%s your email is %s and your user_id is %s!\\n <ul>\\n <li><a href=\\\"/oidc/\\\">Home</a></li> \\n </ul>\\\"\\\"\\\" %\\n (greeting, email, user_id))\",\n \"def get_user_profile_info(token):\\n user_profile_endpoint = 'https://api.spotify.com/v1/me'\\n headers = {'Authorization': 'Bearer %s' % token}\\n\\n r = requests.get(user_profile_endpoint, headers=headers)\\n if r.status_code != 200:\\n return None\\n profile = r.json()\\n\\n display_name = profile['display_name']\\n profile_id = profile['id']\\n email = profile['email']\\n\\n return User(display_name=display_name,\\n profile_id=profile_id,\\n email=email)\",\n \"def display_credentials():\\n print(f\\\"GH_TOKEN: {GH_TOKEN}\\\")\\n print(f\\\"USER: {GH_USER}\\\")\",\n \"def handle_callback(self, response):\\n client = FigshareClient(response['access_token'])\\n about = client.userinfo()\\n\\n return {\\n 'provider_id': about['id'],\\n 'display_name': u'{} {}'.format(about['first_name'], about.get('last_name')),\\n }\",\n \"def profile():\\n # Check if user is loggedin\\n if 'loggedin' in session:\\n # We need all the account info for the user so we can display it on the profile page\\n response = requests.get(\\n \\\"http://localhost:8080/api/userbyid/\\\"+str(session['userid']))\\n acc = json.loads(response.text)\\n # Show the profile page with account info\\n return render_template('profile.html', account=acc)\\n # users is not loggedin redirect to login page\\n return redirect(url_for('site.login'))\",\n \"async def info(self, ctx):\\n self.logger.info(misolog.format_log(ctx, f\\\"\\\"))\\n appinfo = await self.client.application_info()\\n membercount = sum(1 for x in self.client.get_all_members())\\n info_embed = discord.Embed(title=f\\\"Miso Bot | version {main.version}\\\",\\n description=f\\\"Created by {appinfo.owner.mention}\\\\n\\\\n\\\"\\n f\\\"Use `{self.client.command_prefix}help` to get the list of commands, \\\"\\n f\\\"or visit the documention website for more help.\\\"\\n f\\\"\\\\n\\\\nCurrently active in **{len(self.client.guilds)}** \\\"\\n f\\\"servers totaling **{membercount}** unique users\\\",\\n colour=discord.Colour.red())\\n\\n # info_embed.set_footer(text=f'version 2.0')\\n info_embed.set_thumbnail(url=self.client.user.avatar_url)\\n info_embed.add_field(name='Github', value='https://github.com/joinemm/miso-bot', inline=False)\\n info_embed.add_field(name='Documentation', value=\\\"http://joinemm.me/misobot\\\", inline=False)\\n info_embed.add_field(name='Patreon', value=\\\"https://www.patreon.com/joinemm\\\", inline=False)\\n await ctx.send(embed=info_embed)\",\n \"def account():\\n\\n return render_template('account_page.html', title='Account')\",\n \"async def serverinfo(self,ctx):\\n g = ctx.guild\\n embed = discord.Embed()\\n embed.set_thumbnail(url = g.icon_url)\\n embed.title = \\\"{} - {}\\\".format(g.name,g.id)\\n embed.add_field(name = \\\"Owner\\\",value=\\\"{} - {}\\\".format(g.owner,g.owner.id),inline=False)\\n embed.add_field(name = \\\"Created at\\\", value = str(g.created_at), inline=False)\\n embed.add_field(name = \\\"Total Roles\\\", value= str(len(g.roles)), inline=False)\\n embed.add_field(name = \\\"Total Members\\\", value= str(g.member_count), inline=False)\\n embed.add_field(name = \\\"Premium Member\\\", value= str(g.premium_subscription_count), inline=False)\\n embed.add_field(name = \\\"Premium Tier\\\", value= str(g.premium_tier), inline=False)\\n await self.bot.say(ctx,embed = embed)\",\n \"def info():\\n print(\\\"Made using the OOP RPG game creator (c) Claire.\\\\n\\\")\",\n \"def profile():\\r\\n user_data = load_user(current_user.id, current_user)\\r\\n if user_data is not None:\\r\\n user, followers, following = user_data\\r\\n\\r\\n return render_base_template(\\\"profile.html\\\", profile=user, followers=followers,\\r\\n following=following,\\r\\n os_projects=[])\\r\\n\\r\\n return abort(404)\",\n \"async def botinfo(self, context: Context) -> None:\\n embed = discord.Embed(\\n description=\\\"Used [Krypton's](https://krypton.ninja) template\\\",\\n color=0x9C84EF,\\n )\\n embed.set_author(name=\\\"Bot Information\\\")\\n embed.add_field(name=\\\"Owner:\\\", value=\\\"Krypton#7331\\\", inline=True)\\n embed.add_field(\\n name=\\\"Python Version:\\\", value=f\\\"{platform.python_version()}\\\", inline=True\\n )\\n embed.add_field(\\n name=\\\"Prefix:\\\",\\n value=f\\\"/ (Slash Commands) or {self.bot.config['prefix']} for normal commands\\\",\\n inline=False,\\n )\\n embed.set_footer(text=f\\\"Requested by {context.author}\\\")\\n await context.send(embed=embed)\",\n \"async def info_user(self, ctx, member: Optional[discord.Member]):\\n member1 = member or ctx.author\\n embed = discord.Embed(title=\\\"Member Information\\\",\\n color=discord.Color.blurple(),\\n timestamp=datetime.utcnow())\\n\\n embed.add_field(name=\\\"ID\\\", value=f\\\"{member1.id}\\\", inline=False)\\n embed.add_field(\\n name=\\\"Name\\\", value=f\\\"{member1.name}#{member1.discriminator}\\\")\\n embed.add_field(name=\\\"Top role\\\", value=f\\\"{member1.top_role.mention}\\\")\\n embed.add_field(name=\\\"status\\\",\\n value=f\\\"{str(member1.activity.type).split('.') if member1.activity else 'N/A'} {member1.activity.name if member1.activity else ''}\\\")\\n embed.add_field(\\n name=\\\"created at\\\", value=f\\\"{member1.created_at.strftime('%d/%m/%y %H:%M:%S')}\\\")\\n embed.add_field(\\n name=\\\"Joined at\\\", value=f\\\"{member1.joined_at.strftime('%d/%m/%y %H:%M:%S')}\\\")\\n embed.add_field(name=\\\"Boosted?\\\", value=f\\\"{member1.premium_since}\\\")\\n\\n await ctx.reply(embed=embed)\",\n \"def describe_user(self):\\n print(self.first_name + \\\"\\\\n\\\" + self.last_name + \\\"\\\\n\\\" + self.Screen_name)\",\n \"def profile(username):\\n username = mongo.db.users.find_one(\\n {\\\"username\\\": session[\\\"user\\\"]})[\\\"username\\\"]\\n return render_template(\\\"profile.html\\\", username=username)\",\n \"def profile(request):\\n user = Info.objects.all()\\n return render(request, 'kvent/profile.html',{user:'user'})\",\n \"def info(ctx: CLIContext) -> None:\\n fields = [\\n keypair_fields['user_id'],\\n keypair_fields['full_name'],\\n keypair_fields['access_key'],\\n keypair_fields['secret_key'],\\n keypair_fields['is_active'],\\n keypair_fields['is_admin'],\\n keypair_fields['created_at'],\\n keypair_fields['last_used'],\\n keypair_fields['resource_policy'],\\n keypair_fields['rate_limit'],\\n keypair_fields['concurrency_used'],\\n ]\\n with Session() as session:\\n try:\\n kp = session.KeyPair(session.config.access_key)\\n item = kp.info(fields=fields)\\n ctx.output.print_item(item, fields)\\n except Exception as e:\\n ctx.output.print_error(e)\\n sys.exit(1)\",\n \"def show_user():\\n\\n return render_template('user/show_by_user.html', title='Show Profile', user = current_user)\",\n \"def describe_user(self):\\n print(\\\"We have stored next information about user \\\" +\\n self.first_name.title() + \\\" \\\" + self.last_name.title() +\\n \\\":\\\")\\n print(\\\"- Username: \\\" + self.username)\\n print(\\\"- Age: \\\" + str(self.age))\\n print(\\\"- Location: \\\" + self.location.title())\",\n \"def info(self, login=None, full_name=None):\\r\\n params = base.get_params(None, locals())\\r\\n return self._get('info', params)\",\n \"def profile():\\n recipes = mongo.db.recipes.find({\\\"created_by\\\": session[\\\"user\\\"]})\\n return render_template(\\\"accounts/profile.html\\\", recipes=recipes)\",\n \"def describe_user(self):\\r\\n print('\\\\nFirst Name: ' + self.first_name.title(), end='\\\\n',)\\r\\n print('Last Name: ' + self.last_name.title(), end='\\\\n')\\r\\n print('Address: ' + self.address.title(), end='\\\\n',)\\r\\n print('State: ' + self.state.title(), end='\\\\n',)\\r\\n print('Country: ' + self.country.title())\",\n \"def describe_user(self):\\n\\t\\tprint(f\\\"\\\\n{self.first_name.title()} {self.last_name.title()} \\\"\\n\\t\\t\\tf\\\"is from {self.location.title()}.\\\")\",\n \"def profile(username):\\n username = mongo.db.users.find_one(\\n {\\\"username\\\": session[\\\"user\\\"]})[\\\"username\\\"]\\n\\n tips = list(mongo.db.tips.find())\\n return render_template(\\\"profile.html\\\", tips=tips)\\n\\n if session[\\\"user\\\"]:\\n return render_template(\\\"profile.html\\\", username=username)\\n\\n return redirect(url_for(\\\"login\\\"))\",\n \"async def info(self,ctx,*,person:discord.Member = None):\\n\\n if not person:\\n guild = len(self.bot.guilds)\\n member = len(set(self.bot.get_all_members()))\\n app = await self.bot.application_info()\\n msg = \\\"Name:{}\\\".format(self.bot.user)\\n if ctx.message.guild.me.nick:\\n msg += \\\"\\\\nNickname:{}\\\".format(ctx.message.guild.me.nick)\\n msg += \\\"\\\\nCreator: {}\\\".format(app.owner)\\n msg += \\\"\\\\nServer:{}\\\\nMembers:{}\\\".format(guild,member)\\n link = \\\"If you want to invite this bot to your server, you can check it out here <http://nurevam.site>!\\\"\\n return await self.bot.say(ctx,content = \\\"```xl\\\\n{}\\\\n```\\\\n{}\\\\n\\\".format(msg,link))\\n else:\\n e = discord.Embed()\\n e.title = \\\"{} - {}\\\".format(person,person.id)\\n e.set_thumbnail(url = person.avatar_url)\\n e.add_field(name = \\\"Created at\\\", value=\\\"{} - ({})\\\".format(person.created_at,self.get_time_delta(person.created_at)),inline=False)\\n e.add_field(name = \\\"Joined at\\\", value=\\\"{} - ({})\\\".format(person.joined_at,self.get_time_delta(person.joined_at)),inline=False)\\n e.add_field(name = \\\"Total Roles\\\", value=str(len(person.roles)),inline=False)\\n\\n if person.colour.value:\\n e.colour = person.color\\n await self.bot.say(ctx,embed = e)\",\n \"def info(self, membership, callback=None):\",\n \"def show_site_info(site_name):\\n\\n site = Site.query.filter_by(route_name=site_name).first()\\n site_url = site.url\\n sites = Site.query.all()\\n return render_template('site-info.html', site_url=site_url, sites=sites)\",\n \"def describe_my_user_profile():\\n pass\",\n \"def describe_user(self):\\r\\n print(self.first_name + \\\" \\\" + self.last_name + \\\": \\\")\\r\\n print(\\\" - First name: \\\" + self.first_name)\\r\\n print(\\\" - Last name: \\\" + self.last_name)\\r\\n print(\\\" - Username: \\\" + self.username)\\r\\n print(\\\" - Email address: \\\" + self.email_address)\",\n \"def print_user_profile(self):\\n print ('User Name: {name}').format(name=self.name)\\n print ('User Email: {email}').format(email=self.email)\\n print ('User Phone Number: {birthday}').format(birthday=self.phone_number)\\n print ('User Country: {country}').format(country=self.country)\\n print ('User Address: {address}').format(address=self.address)\",\n \"async def profile(self, ctx, member: discord.Member = None):\\n if member is None:\\n member = ctx.author\\n name = member.display_name\\n id = member.id\\n avatar = member.avatar_url\\n created = member.created_at\\n embed = discord.Embed(\\n title=name,\\n description=name + \\\"'s profile!\\\",\\n color=discord.Colour.purple()\\n )\\n embed.set_thumbnail(url=avatar)\\n embed.add_field(name=\\\"ID:\\\", value=str(id))\\n embed.add_field(name=\\\"Created At:\\\", value=str(created))\\n if ctx.guild is not None:\\n joined_at = member.joined_at\\n embed.add_field(name=\\\"Joined Server At:\\\", value=str(joined_at))\\n if member.bot:\\n embed.add_field(name=\\\"Bot?\\\", value=\\\"Yeah\\\")\\n if member.premium_since is not None:\\n premium = str(member.premium_since)\\n embed.add_field(name=\\\"Premium Member Since: \\\", value=premium)\\n else:\\n embed.add_field(name=\\\"Premium?\\\", value=\\\"Nope\\\")\\n top_role = str(member.top_role)\\n embed.add_field(name=\\\"Top Role:\\\", value=top_role)\\n await ctx.send(\\\"\\\", embed=embed)\",\n \"def profile(request):\\n return render(request, 'profile.html', context)\",\n \"def info():\\n print 'Loading info page'\\n\\n team_list = datastore.get_all_teams(engine)\\n\\n return render_template('info.html', rows=team_list)\",\n \"def extra_data(self, user, uid, response, details):\\n try:\\n return self.get_steam_profile(response)\\n except:\\n return \\\"\\\"\",\n \"def show_user_info(user_id):\\n\\n user = User.query.get_or_404(user_id)\\n return render_template(\\\"user_details.html\\\", user=user)\",\n \"def account():\\n return render_template('user/account.html')\",\n \"def show_info(title, message):\\n\\n pass\",\n \"def boston_info():\\n # rendering text\\n return 'Welcome to the Boston Info Slack Bot'\",\n \"async def serverinfo(self, context: Context) -> None:\\n roles = [role.name for role in context.guild.roles]\\n if len(roles) > 50:\\n roles = roles[:50]\\n roles.append(f\\\">>>> Displaying[50/{len(roles)}] Roles\\\")\\n roles = \\\", \\\".join(roles)\\n\\n embed = discord.Embed(\\n title=\\\"**Server Name:**\\\", description=f\\\"{context.guild}\\\", color=0x9C84EF\\n )\\n if context.guild.icon is not None:\\n embed.set_thumbnail(url=context.guild.icon.url)\\n embed.add_field(name=\\\"Server ID\\\", value=context.guild.id)\\n embed.add_field(name=\\\"Member Count\\\", value=context.guild.member_count)\\n embed.add_field(\\n name=\\\"Text/Voice Channels\\\", value=f\\\"{len(context.guild.channels)}\\\"\\n )\\n embed.add_field(name=f\\\"Roles ({len(context.guild.roles)})\\\", value=roles)\\n embed.set_footer(text=f\\\"Created at: {context.guild.created_at}\\\")\\n await context.send(embed=embed)\",\n \"def describe_user(self):\\n print(\\\"\\\\n Name: \\\" + self.f_name.title() + ' ' + self.l_name.title())\\n print(\\\"Age: \\\" + str(self.age)) \\n print(\\\"Birthplace: \\\" + self.birthplace.title())\",\n \"async def about(self, ctx):\\n\\n author_repo = \\\"https://github.com/scragly\\\"\\n bot_repo = author_repo + \\\"/Firetail\\\"\\n server_url = \\\"https://discord.gg/ZWmzTP3\\\"\\n owner = \\\"Discord: Scragly#5146\\\\nEVE: Kyo Kuronami\\\"\\n member_count = sum(g.member_count for g in self.bot.guilds)\\n server_count = len(self.bot.guilds)\\n\\n description = (\\n \\\"Made for EVE Online Communities\\\\n\\\"\\n f\\\"[**Docs & Source**]({bot_repo})\\\\n\\\"\\n f\\\"[**Support Server**]({server_url})\\\\n\\\"\\n f\\\"[**Invite Me**]({self.bot.invite_url})\\\\n\\\"\\n \\\"\\\\n\\\"\\n f\\\"See available commands with:\\\\n`{ctx.prefix}help`\\\\n\\\"\\n \\\"\\\\n\\\"\\n f\\\"**Maintained and Developed by**\\\\n\\\"\\n f\\\"{owner}\\\\n\\\"\\n \\\"\\\\n\\\"\\n f\\\"**Servers:** {server_count}\\\\n\\\"\\n f\\\"**Members:** {member_count}\\\\n\\\"\\n f\\\"**Uptime:** {self.bot.uptime_str}\\\"\\n )\\n\\n try:\\n await ctx.info(\\\"About Firetail\\\", description, thumbnail=self.bot.user.avatar_url_as(format='png'))\\n\\n except discord.HTTPException:\\n await ctx.send(\\\"I need the `Embed links` permission for this command.\\\")\",\n \"def show_profile():\\n print('LOGIN SESSION:', login_session)\\n if 'userid' in login_session:\\n category = session.query(Category).first()\\n item = session.query(Item).first()\\n return render_template('profile.html', login_session=login_session, root=app.instance_path, category=category,\\n item=item)\\n flash('Unfortunately you need to be logged in to see your profile', 'error')\\n return redirect(url_for('show_homepage'))\",\n \"async def about(self, ctx):\\n embed = discord.Embed(title = f\\\"About {self.bot.user.name}\\\", color = discord.Color.blurple())\\n embed.set_thumbnail(url = self.bot.user.avatar_url)\\n embed.add_field(name = \\\"Developers\\\", value = \\\"Kowlin#4417 & A Trash Coder#0981\\\", inline = False)\\n embed.add_field(name = \\\"Library\\\", value = \\\"discord.py rewrite\\\", inline = False)\\n embed.add_field(name = \\\"Source Code\\\", value = \\\"[Click here](https://github.com/kowlintechnologies/DHB)\\\", inline = False)\\n embed.add_field(name = \\\"Links\\\", value = \\\"[Docs](https://dhb-documentation.readthedocs.io/en/latest/index.html) | [Support](https://discord.gg/KEkwrwd) | [Invite](https://discordapp.com/api/oauth2/authorize?client_id=592811241756688405&permissions=2080762998&scope=bot)\\\")\\n await ctx.send(embed = embed)\",\n \"def __str__(self):\\n return self.user.username + \\\"'s Profile\\\"\",\n \"def print_player_info(self):\\n\\t\\tclear_screen()\\n\\n\\t\\tprint(\\\"# PLAYER INFO #\\\\n\\\")\\n\\t\\tprint(\\\"Name{:.>17} \\\".format(self.info['Name']))\\n\\t\\tprint(\\\"Race{:.>17} \\\".format(self.info['Race']))\\n\\t\\tprint(\\\"Level{:.>16} \\\".format(self.stats['Level']))\\n\\t\\tprint(\\\"Hit Points{:.>11} \\\".format(self.stats['HP']))\\n\\t\\tprint(\\\"Gold Pieces{:.>10} \\\".format(self.stats['GOLD']))\\n\\t\\n\\t\\tpress_enter()\",\n \"async def spotify(self, ctx):\\n if ctx.invoked_subcommand is None:\\n await ctx.send('Invalid command passed. Use the `?help spotify` command to learn more.')\",\n \"async def avatarurl(self, ctx: \\\"IceTeaContext\\\", target: discord.Member = None):\\n target = target or ctx.author\\n embed = discord.Embed(description=f\\\"{target} Profile Picture\\\")\\n embed.set_image(url=str(target.avatar_url))\\n await ctx.send(embed=embed)\",\n \"def logo():\\n print (\\\"\\\"\\\"\\\\\\n _ _\\n| |_ ___ ___ ___ _ _ _| |\\n| | . | | -_| | | . |\\n|_|_|___|_|_|___|_ |___|\\n |___|\\n \\\"\\\"\\\")\\n print ('Author: Peter Sooky <448291@mail.muni.cz>')\\n print ('Honeyd-python {0}'.format(honeyd.__version__))\",\n \"async def avatar(self, ctx, *, member: disnake.Member = None):\\n if member is None:\\n member = ctx.author\\n await ctx.send(member.display_avatar.url)\",\n \"def account_info(self):\\n url, params, headers = self.request(\\\"/account/info\\\", method='GET')\\n\\n return self.rest_client.GET(url, headers)\",\n \"def profile(request):\\n profile = request.user.profile\\n return render(request, 'accounts/profile.html', {'profile': profile})\",\n \"def account():\\n user = Users.query.filter_by(id=current_user.id).first_or_404(description=f'There is no user {current_user.id}')\\n\\n return render_template('auth/account.html', user=user)\",\n \"def user_details():\\n url = 'https://api.github.com/orgs/facebook/repos'\\n json_obj = urllib2.urlopen(url)\\n userdata = json.load(json_obj)\\n if 'error' in userdata:\\n print 'errors are scanned in data'\\n for data in userdata:\\n if 'name' in data:\\n if data['name'] == 'codemod':\\n print 'language used'\\n print data['language']\\n print 'number of watchers'\\n print data['watchers']\\n print 'git url'\\n print data['git_url']\\n print 'open issues'\\n print data['open_issues']\\n print 'permissions for user'\\n print 'push'\\n print data['permissions']['push']\\n print 'pull'\\n print data['permissions']['pull']\",\n \"def spotify(request):\\n\\n logger.debug(\\\"Spotify Album Called\\\")\\n response_data = {}\\n\\n spotify_pre_auth = spotify_auth(request)\\n# if type(spotify_pre_auth) is JsonResponse:\\n# return spotify_pre_auth\\n\\n response = spotify_pre_auth.get('result','')\\n album_url = spotify_pre_auth.get('album_url','')\\n response_code=spotify_pre_auth.get('status_code','')\\n\\n if response_code == 200:\\n auth_data = json.loads(response)\\n access_token = auth_data['access_token']\\n\\n headers = {\\\"Authorization\\\": \\\"Bearer %s\\\" % access_token}\\n album_url = \\\"https://api.spotify.com/v1/albums/\\\" + album_url.split(\\\"/\\\")[-1]\\n logger.debug(\\\"Spotify get album: %s\\\" % album_url)\\n res = requests.get(album_url, headers=headers)\\n if res.status_code == 200:\\n album = json.loads(res.text)\\n logger.debug(\\\"Found on Spotify the album: %(name)s\\\" % album)\\n return json.loads(res.text)\\n else:\\n response = res.text\\n response_code = res.status_code\\n response_body = {\\\"result\\\": \\\"failure\\\", \\\"message\\\": \\\"Spotify album failed. Check the url or the connections\\\", \\\"status_code\\\": response_code}\\n return response_body\",\n \"def display_student(s_info):\\n print('')\\n print('Your information:')\\n print(f'{s_info.student_id} - {s_info.first_name} {s_info.last_name}')\",\n \"async def avatar(message, user: ParamType.MIXER_USER):\\n return \\\"link to @{} avatar: {}\\\".format(user.username, user.avatar_url)\",\n \"def display_user_info(user):\\n\\n ornament = '=' * 30\\n print(ornament)\\n print('LOGGED USER')\\n print(ornament)\\n print('Name: {}\\\\nSurname: {}\\\\nPermissions: {}'.format(user.name, user.surname, user.__class__.__name__))\\n print(ornament + '\\\\n')\",\n \"def profile(request, user_name=None):\\n \\n # get the viewed user\\n if user_name is None:\\n user = request.user.get_profile()\\n else:\\n user = get_object_or_404(User, username=user_name)\\n user = user.get_profile()\\n \\n # set display name\\n if len(user.user.first_name) <= 0:\\n user.display_name = user.user.username\\n else:\\n user.display_name = user.user.first_name + \\\" \\\" + user.user.last_name\\n \\n # set avatar path\\n if len(user.avatar.name) <= 0:\\n user.avatar_url = settings.MEDIA_URL + \\\"avatar/noavatar.png\\\"\\n else:\\n user.avatar_url = user.avatar.url\\n \\n # get tracked list, ownedlist and playlist\\n trackedlist = user.trackedrecordlist_set.all()\\n ownedlist = user.userentry_set.all()\\n playlist = user.playlist_set.all()\\n context = {\\n 'profile_user': user,\\n 'trackedlist': trackedlist,\\n 'ownedlist': ownedlist,\\n 'playlist': playlist\\n }\\n return render_to_response(\\n 'usermgr/profile.html',\\n context,\\n context_instance = RequestContext(request))\",\n \"def get_account_info(self):\\n resp = requests.get(\\n self.URL + 'info/',\\n headers={'Authorization': 'Token ' + self.api_key}\\n )\\n\\n return self.__handle_response(resp)\",\n \"def show_user(title, fname, lname):\\n person = \\\"{} {} {}\\\".format(title, fname, lname)\\n logger.info(\\\"{}\\\".format(person))\\n return person\",\n \"async def avatar(self, ctx):\\n e = discord.Embed(title=\\\"Here is a avatar image for you {}.\\\".format(ctx.author.name), color=discord.Color.magenta())\\n e.set_image(url=nekos.img('avatar'))\\n await ctx.send(embed=e)\",\n \"def info(ctx, schain_name):\\n skale = ctx.obj['skale']\\n info = get_schain_info(skale, schain_name)\\n print(json.dumps(info, indent=2))\",\n \"async def info(self, ctx):\\n\\n uptime = func.time_(self.bot.launch_time)\\n users = sum(1 for _ in self.bot.get_all_members())\\n channels = sum(1 for _ in self.bot.get_all_channels())\\n\\n author = self.bot.get_user(299879858572492802)\\n\\n invite = 'https://discordapp.com/oauth2/authorize?client_id=347205176903335937&scope=bot&permissions=470150359'\\n about = ('Infamous is a actively developed bot that gets updated daily.'\\n f' It is written with passion by {author} using the Rewrite branch of the discord.py library.')\\n\\n links = (f'**[[Invite Bot]]({invite})** \\\\n'\\n '**[[Fame Discord]](https://discord.gg/NY2MSA3)** \\\\n'\\n '**[[Discord.py]](https://github.com/Rapptz/discord.py/tree/rewrite)** \\\\n'\\n '**[[Support]](https://discord.gg/JyJTh4H)**')\\n\\n # From Modelmat\\n cpu_usage = self.process.cpu_percent() / psutil.cpu_count()\\n ram_usage = self.process.memory_full_info().uss / 1024 ** 2\\n\\n embed = discord.Embed(color=self.bot.embed_color)\\n embed.set_author(name=self.bot.user.name, icon_url=self.bot.user.avatar_url)\\n embed.description = 'A multi-purpose bot with image manipulation, wiki pages and it\\\\'s own rpg; originally a ' \\\\\\n 'community bot for ★ Fame ★'\\n embed.set_thumbnail(\\n url=self.bot.user.avatar_url)\\n\\n embed.add_field(name='About', value=about, inline=False)\\n\\n embed.add_field(name='Statistics 📈',\\n value=(f'**{len(self.bot.guilds)} guilds.**\\\\n'\\n f'**{channels} channels.**\\\\n'\\n f'**{users} users.** \\\\n'\\n f'**{self.bot.lines} lines**'), inline=True)\\n\\n embed.add_field(name='Uptime ⏰', value=(f'**{uptime[0]} days.** \\\\n'\\n f'**{uptime[1]} hours.** \\\\n'\\n f'**{uptime[2]} minutes.** \\\\n'\\n f'**{uptime[3]} seconds.**'), inline=True)\\n\\n embed.add_field(name='Developer 🕵', value=author)\\n embed.add_field(name='Resources 💻', value='`CPU:` {:.2f}% \\\\n`MEM:` {:.2f}'.format(cpu_usage, ram_usage))\\n embed.add_field(name='Links 🔗', value=links, inline=True)\\n\\n await ctx.send(embed=embed)\",\n \"def show_me():\\n # Scumbag thumbnail code\\n try:\\n from PIL import Image\\n except ImportError:\\n pass\\n else:\\n filename = os.path.join(app.static_folder, 'img', 'badumtss.png')\\n image = Image.open(filename)\\n\\n return render_template('show_me.html')\",\n \"def show_me_user(user_id):\\n\\n user = crud.get_user_by_id(user_id)\\n\\n return render_template('user_details.html', user=user)\",\n \"def my_account(request):\\n context = {}\\n\\n return render(request, 'classroom_main/my_account.html', context)\",\n \"async def info(self, ctx,*users:discord.User):\\n try:\\n if not users:\\n user = ctx.message.author\\n server = ctx.message.server\\n seen = str(len(set([member.server.name for member in self.bot.get_all_members() if member.name == user.name])))\\n x = 'Your Info:\\\\nUsername: \\\"{0.name}\\\"\\\\nID #: \\\"{1.id}\\\"\\\\nDiscriminator #: \\\"{2.discriminator}\\\"\\\\nAvatar: \\\"{3.avatar_url}\\\"\\\\nStatus: \\\"{4}\\\"\\\\nGame: \\\"{5}\\\"\\\\nVoice Channel: \\\"{6}\\\"\\\\nSeen On: \\\"{7}\\\" servers\\\\nJoined Server On: \\\"{8}\\\"\\\\nRoles: \\\"{9}\\\"'.format(user, user, user, user, str(user.status), str(user.game), str(user.voice_channel), seen, str(user.joined_at), ', '.join(map(str, user.roles)).replace(\\\"@\\\", \\\"@\\\\u200b\\\"))\\n await self.bot.say(cool.format(x))\\n else:\\n for user in users:\\n server = ctx.message.server\\n seen = str(len(set([member.server.name for member in self.bot.get_all_members() if member.name == user.name])))\\n x = 'User Info:\\\\nUsername: \\\"{0}\\\"\\\\nID #: \\\"{1}\\\"\\\\nDiscriminator #: \\\"{2}\\\"\\\\nAvatar: \\\"{3}\\\"\\\\nStatus: \\\"{4}\\\"\\\\nGame: \\\"{5}\\\"\\\\nVoice Channel: \\\"{6}\\\"\\\\nSeen On: \\\"{7} servers\\\"\\\\nJoined Server On: \\\"{8}\\\"\\\\nRoles: \\\"{9}\\\"'.format(user.name, user.id, user.discriminator, user.avatar_url, str(user.status), str(user.game), str(user.voice_channel), seen, str(user.joined_at), ', '.join(map(str, user.roles)).replace(\\\"@\\\", \\\"@\\\\u200b\\\"))\\n await self.bot.say(cool.format(x))\\n except Exception as e:\\n await self.bot.say(code.format(type(e).__name__ + ': ' + str(e)))\",\n \"async def profilepicture(self, ctx):\\n if not ctx.message.mentions:\\n await ctx.send(embed=discord.Embed().set_image(\\n url=ctx.author.avatar_url))\\n else:\\n for member in ctx.message.mentions:\\n await ctx.send(embed=discord.Embed().set_image(\\n url=member.avatar_url))\",\n \"def displayMeta(ctx, data, vid):\\n\\t\\n\\ts = \\\"\\\"\\n\\ts += \\\"Title: %s \\\" % data.getElementsByTagName(\\\"title\\\")[0].firstChild.data\\n\\ts += \\\" • By: %s\\\" % data.getElementsByTagName(\\\"author\\\")[0].getElementsByTagName(\\\"name\\\")[0].firstChild.data\\n\\n\\tshowRest = True\\n\\n\\tr = data.getElementsByTagName(\\\"yt:state\\\")\\n\\tif len(r):\\n\\t\\tr = r[0]\\n\\t\\tif r.getAttribute(\\\"name\\\") == \\\"restricted\\\":\\n\\t\\t\\tshowRest = r.getAttribute(\\\"reasonCode\\\") == \\\"limitedSyndication\\\"\\n\\t\\t\\tif showRest:\\n\\t\\t\\t\\ts += \\\" • Syndication Limited.\\\"\\n\\t\\t\\telse:\\n\\t\\t\\t\\ts += \\\" • Video is unavailable: %s\\\" % r.firstChild.data\\n\\n\\tif showRest:\\n\\t\\ts += \\\" • Length: %s\\\" % prettyTime(data.getElementsByTagName(\\\"yt:duration\\\")[0].getAttribute(\\\"seconds\\\"))\\n\\t\\ts += \\\" • View Count: %s\\\" % prettyNumber(data.getElementsByTagName(\\\"yt:statistics\\\")[0].getAttribute(\\\"viewCount\\\"))\\n\\n\\t\\tr = data.getElementsByTagName(\\\"gd:rating\\\")\\n\\t\\tif len(r):\\n\\t\\t\\tr = r[0]\\n\\t\\t\\ts += \\\" • Average Rating: %1.2f/5 over %s people\\\" % (\\n\\t\\t\\t\\tfloat(r.getAttribute(\\\"average\\\")),\\n\\t\\t\\t\\tprettyNumber(r.getAttribute(\\\"numRaters\\\"))\\n\\t\\t\\t\\t)\\n\\t\\telse:\\n\\t\\t\\ts += \\\" • No ratings\\\"\\n\\t\\n\\ts += \\\" • https://youtu.be/%s\\\" % vid\\n\\tctx.reply(s, \\\"YouTube\\\")\",\n \"def __str__(self):\\n return f\\\"Profile for user {self.user}\\\"\",\n \"def get_student():\\n\\n # github = \\\"jhacks\\\"\\n github = request.args.get('github','jhacks')\\n first, last, github = hackbright.get_student_by_github(github)\\n return render_template(\\\"student_info.html\\\" , first=first, gorilla=last, giraffe=github)\\n # return \\\"%s is the GitHub account for %s %s\\\" % (github, first, last)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.65351516","0.6477003","0.64630944","0.62933874","0.62896085","0.6278736","0.6263717","0.6239856","0.62017554","0.61626744","0.61613786","0.6148831","0.6129267","0.6054022","0.60383016","0.6036688","0.6031312","0.6021112","0.5999502","0.599438","0.5987369","0.5961587","0.5911696","0.59076726","0.5840414","0.5837207","0.5837207","0.58251953","0.5823577","0.58053696","0.5798808","0.57862717","0.5777484","0.5767723","0.57664454","0.57592356","0.57553786","0.5725495","0.57239","0.5712331","0.5712285","0.5711306","0.5682394","0.5678397","0.5672659","0.5668509","0.56624925","0.5657754","0.5657315","0.56554407","0.5649513","0.56393164","0.5589809","0.5589225","0.5570194","0.556521","0.5561419","0.55550855","0.55536276","0.55393237","0.55333143","0.55313855","0.5518156","0.5514925","0.5507613","0.5506429","0.54997027","0.54985136","0.54864943","0.5485125","0.5471443","0.54708266","0.5458197","0.54519963","0.5451095","0.5450204","0.5448186","0.54454273","0.5445171","0.5444557","0.54409117","0.54256856","0.5425041","0.5424547","0.5414255","0.5412266","0.54062724","0.5402789","0.53976953","0.5374191","0.5370485","0.53702813","0.53697103","0.5362925","0.53575295","0.53525835","0.5344418","0.5341334","0.5339467","0.5338304"],"string":"[\n \"0.65351516\",\n \"0.6477003\",\n \"0.64630944\",\n \"0.62933874\",\n \"0.62896085\",\n \"0.6278736\",\n \"0.6263717\",\n \"0.6239856\",\n \"0.62017554\",\n \"0.61626744\",\n \"0.61613786\",\n \"0.6148831\",\n \"0.6129267\",\n \"0.6054022\",\n \"0.60383016\",\n \"0.6036688\",\n \"0.6031312\",\n \"0.6021112\",\n \"0.5999502\",\n \"0.599438\",\n \"0.5987369\",\n \"0.5961587\",\n \"0.5911696\",\n \"0.59076726\",\n \"0.5840414\",\n \"0.5837207\",\n \"0.5837207\",\n \"0.58251953\",\n \"0.5823577\",\n \"0.58053696\",\n \"0.5798808\",\n \"0.57862717\",\n \"0.5777484\",\n \"0.5767723\",\n \"0.57664454\",\n \"0.57592356\",\n \"0.57553786\",\n \"0.5725495\",\n \"0.57239\",\n \"0.5712331\",\n \"0.5712285\",\n \"0.5711306\",\n \"0.5682394\",\n \"0.5678397\",\n \"0.5672659\",\n \"0.5668509\",\n \"0.56624925\",\n \"0.5657754\",\n \"0.5657315\",\n \"0.56554407\",\n \"0.5649513\",\n \"0.56393164\",\n \"0.5589809\",\n \"0.5589225\",\n \"0.5570194\",\n \"0.556521\",\n \"0.5561419\",\n \"0.55550855\",\n \"0.55536276\",\n \"0.55393237\",\n \"0.55333143\",\n \"0.55313855\",\n \"0.5518156\",\n \"0.5514925\",\n \"0.5507613\",\n \"0.5506429\",\n \"0.54997027\",\n \"0.54985136\",\n \"0.54864943\",\n \"0.5485125\",\n \"0.5471443\",\n \"0.54708266\",\n \"0.5458197\",\n \"0.54519963\",\n \"0.5451095\",\n \"0.5450204\",\n \"0.5448186\",\n \"0.54454273\",\n \"0.5445171\",\n \"0.5444557\",\n \"0.54409117\",\n \"0.54256856\",\n \"0.5425041\",\n \"0.5424547\",\n \"0.5414255\",\n \"0.5412266\",\n \"0.54062724\",\n \"0.5402789\",\n \"0.53976953\",\n \"0.5374191\",\n \"0.5370485\",\n \"0.53702813\",\n \"0.53697103\",\n \"0.5362925\",\n \"0.53575295\",\n \"0.53525835\",\n \"0.5344418\",\n \"0.5341334\",\n \"0.5339467\",\n \"0.5338304\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7391768"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":22498,"cells":{"query":{"kind":"string","value":"Makes the bot join your voice channel"},"document":{"kind":"string","value":"async def join(self, ctx):\n if ctx.guild is None:\n await ctx.reply(\"This command can only be used in a server, not in DMs.\")\n raise commands.CommandError(\"Invoker not in a guild.\")\n\n if not is_linked(ctx.author.id):\n await ctx.reply(f\"You don't have a Spotify account linked. Please link one using \"\n f\"`{self.bot_config['prefix']}link`.\")\n raise commands.CommandError(\"User has no spotify account linked.\")\n\n if ctx.author.voice is None or ctx.author.voice.channel is None:\n await ctx.reply(\"You need to be in a voice channel to use this command.\")\n raise commands.CommandError(\"Invoker not connected to a voice channel.\")\n\n if ctx.voice_client is not None and ctx.author.voice.channel != ctx.voice_client.channel:\n await ctx.reply(\"You need to be in the same voice channel as the bot to use this command.\")\n raise commands.CommandError(\"Invoker not in same voice channel as bot.\")\n\n # Connect to voice channel that the invoker is in (if we're not already connected somewhere else)\n try:\n controller_instance = await ctx.author.voice.channel.connect(reconnect=False)\n except asyncio.TimeoutError:\n await ctx.reply(\"Timeout error while connecting to the voice channel. Please try again later.\")\n return\n except discord.ClientException:\n await ctx.reply(\"I'm already connected to a voice channel, please disconnect me first!\")\n return\n except OpusNotLoaded:\n await ctx.reply(\"Opus library was not loaded. Please try again later.\")\n return\n\n if controller_instance is not None:\n # Create a listening socket for the future incoming audio connection\n try:\n controller = SpotifyController.create(controller_instance.channel.id,\n controller_instance.channel.bitrate,\n ctx.author.id)\n except ValueError as e:\n await ctx.reply(e)\n return\n\n controller.get_or_create_playlist()\n\n await ctx.author.send(f\"Please enter the following code in your client application and click \"\n f\"'connect' to start playing music!\\nCode: `{controller.link_code}`\")\n await ctx.reply(f\"Ready and waiting for a connection! I've DM'ed you a code to fill in in your client app.\"\n f\"\\nIn the mean time, you can start adding songs with `{self.bot_config['prefix']}add`, \"\n f\"and view the queue with `{self.bot_config['prefix']}queue`\")"},"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":["async def join(self, ctx):\n voice = ctx.author.voice\n\n if voice and voice.channel:\n channel = voice.channel\n \n try:\n await channel.connect()\n except discord.errors.ClientException:\n await ctx.guild.get_member(self.bot.user.id).move_to(channel) \n \n await ctx.send(f\"Joined `{channel.name}`.\")\n else:\n await ctx.message.add_reaction('\\U0001F615');\n await ctx.send(\"You're not in a voice channel.\")","async def joinForce(self, ctx):\n print(\"joining\")\n channel = ctx.author.voice.channel\n await channel.connect()","async def join(ctx):\r\n channel = ctx.message.author.voice.channel\r\n if ctx.voice_client is not None:\r\n return await ctx.voice_client.move_to(channel)\r\n\r\n await channel.connect()","async def _join(self, ctx: commands.Context):\n\n destination = ctx.author.voice.channel\n if ctx.voice_state.voice:\n await ctx.voice_state.voice.move_to(destination)\n return\n ctx.voice_state.voice = await destination.connect()\n await ctx.message.add_reaction('✅')","async def join(self, ctx, *, channel: discord.VoiceChannel):\n \n if ctx.voice_client is not None:\n return await ctx.voice_client.move_to(channel)\n else:\n return await ctx.voice_client.move_to(bot.get_channel(247531960488951815))\n \n await channel.connect()","async def join(self, ctx, *, channel: discord.VoiceChannel):\n\n if ctx.voice_client is not None:\n return await ctx.voice_client.move_to(channel)\n\n await channel.connect()","async def _join(self, ctx: commands.Context):\n\n destination = ctx.author.voice.channel\n if ctx.voice_state.voice:\n await ctx.voice_state.voice.move_to(destination)\n return\n\n ctx.voice_state.voice = await destination.connect()","async def _join(self, ctx: commands.Context):\n destination = ctx.author.voice.channel # Destination is the author's voice channel\n if ctx.voice_state.voice:\n await ctx.voice_state.voice.move_to(destination)\n return\n\n ctx.voice_state.voice = await destination.connect()","async def join(self, ctx):\n player = self.bot.lavalink.players.get(ctx.guild.id)\n if player.is_connected:\n return await ctx.send(\"I'm already in a voice channel :no_entry:\")\n else:\n if not ctx.author.voice or not ctx.author.voice.channel:\n return await ctx.send(\"You are not in a voice channel :no_entry:\")\n player.store('sessionowner', ctx.author.id)\n player.store('channel', ctx.channel.id)\n await player.connect(ctx.author.voice.channel.id)\n await ctx.send(\"Summoned to `{}` <:done:403285928233402378>\".format(ctx.author.voice.channel.name))","async def join(self, ctx, *, channel_name: str):\n channels = [c for c in self.get_all_channels() if c.type.name == \"voice\" and c.name == channel_name]\n\n if len(channels) != 1:\n await ctx.send(f\"Unknown channel: {channel_name}\")\n return\n\n channel = channels[0]\n\n if ctx.voice_client is not None:\n return await ctx.voice_client.move_to(channel)\n await channel.connect()","async def join(self, msg, *, channel: discord.VoiceChannel = None):\n if msg.voice_client is not None:\n return await msg.send(f\"Bot is already in a voice channel\\nDid you mean to use {msg.prefix}moveTo\")\n\n if msg.voice_client is None:\n if channel is None:\n return await msg.author.voice.channel.connect(), await msg.message.add_reaction(emoji='✅')\n\n return await channel.connect(), await msg.message.add_reaction(emoji='✅')\n\n else:\n if msg.voice_client.is_playing() is False and not self.player[msg.guild.id]['queue']:\n return await msg.author.voice.channel.connect(), await msg.message.add_reaction(emoji='✅')","async def join(self, ctx, *, channel: discord.VoiceChannel=None):\n\n if not channel:\n try:\n channel = ctx.author.voice.channel\n await channel.connect()\n except AttributeError:\n raise commands.CommandError('No channel to join. Please either specify a valid channel or join one.')\n\n vc = ctx.voice_client\n if vc:\n if vc.channel.id == channel.id:\n return\n try:\n await vc.move_to(channel)\n except asyncio.TimeoutError:\n raise commands.CommandError(f'Moving to channel: <{channel}> timed out.')\n else:\n try:\n await channel.connect()\n except asyncio.TimeoutError:\n raise commands.CommandError(f'Connecting to channel: <{channel}> timed out.')","async def join(self, *, channel: discord.Channel):\n try:\n await self.create_voice_client(channel)\n except discord.InvalidArgument:\n await self.bot.say('That\\'s not a voice channel!')\n except discord.ClientException:\n await self.bot.say('Already in a voice channel.')\n else:\n await self.bot.say('Ready to play audio in **' + channel.name + '**!')","async def join(self, ctx: commands.Context) -> Optional[discord.VoiceChannel]:\n\n if ctx.voice_client and ctx.voice_client.is_connected():\n await self.call_event(\n \"on_music_error\",\n ctx,\n AlreadyConnected(\"Client is already connected to a voice channel\"),\n )\n return\n\n if not ctx.author.voice:\n await self.call_event(\n \"on_music_error\",\n ctx,\n UserNotConnected(\"User is not connected to a voice channel\"),\n )\n return\n\n channel = ctx.author.voice.channel\n await channel.connect(\n cls=LavalinkPlayer\n if self.type == ManagerType.LAVALINK\n else discord.VoiceClient\n )\n return channel","def on_join(self, event):\n self.pre_check(event)\n state = event.guild.get_member(event.author).get_voice_state()\n if not state:\n return api_loop(\n event.channel.send_message,\n \"You must be in a voice channel to use that command.\",\n )\n if event.guild.id not in self.guilds:\n try:\n client = state.channel.connect(mute=False)\n except VoiceException as e:\n return api_loop(\n event.channel.send_message,\n \"Failed to connect to voice: `{}`\".format(e),\n )\n else:\n self.guilds[event.guild.id] = psuedo_queue(\n self,\n player=Player(client),\n guild_id=event.guild.id,\n )\n return","async def connect(self, ctx, *, channel_name: str = None):\r\n if channel_name:\r\n voice_channel = discord.utils.get(ctx.guild.channels, name=channel_name)\r\n else:\r\n voice_channel = ctx.author.voice.channel\r\n\r\n if voice_channel:\r\n self.channel_name = voice_channel.name\r\n await voice_channel.connect()\r\n print(\"[INFO] Bot connected to channel {0}\".format(self.channel_name))\r\n await ctx.message.delete()","async def _join(self, bot, server_id, voice_channel):\n if not voice_channel:\n return\n srv = self.get_server_dict(server_id)\n if srv['voice']:\n if voice_channel != srv['voice'].channel:\n await srv['voice'].move_to(voice_channel)\n else:\n srv['voice'] = await bot.join_voice_channel(voice_channel)","def start(update,context):\r\n update.message.reply_text('welcome to voice bot')","async def _summon(self,\n ctx: commands.Context,\n *,\n channel: discord.VoiceChannel = None):\n\n if not channel and not ctx.author.voice:\n raise VoiceError(\n 'You are neither connected to a voice channel nor specified a channel to join.'\n )\n\n destination = channel or ctx.author.voice.channel\n if ctx.voice_state.voice:\n await ctx.voice_state.voice.move_to(destination)\n return\n\n ctx.voice_state.voice = await destination.connect()\n await ctx.message.add_reaction('✅')","async def join(message):\n voice_client = await get_voice_client(message.author, move_to=True, any_channel=True)\n return \"I'm in {}\".format(voice_client.channel.mention)","async def _summon(self, ctx: commands.Context, *, channel: discord.VoiceChannel = None):\n\n if not channel and not ctx.author.voice:\n raise VoiceError('You are neither connected to a voice channel nor specified a channel to join.')\n\n destination = channel or ctx.author.voice.channel\n if ctx.voice_state.voice:\n await ctx.voice_state.voice.move_to(destination)\n return\n\n ctx.voice_state.voice = await destination.connect()","async def _summon(self, ctx: commands.Context, *, channel: discord.VoiceChannel = None):\n if not channel and not ctx.author.voice:\n raise commands.CommandError('You are neither connected to a voice channel nor specified a channel to join.')\n # raise VoiceError()\n\n destination = channel or ctx.author.voice.channel\n if ctx.voice_state.voice:\n await ctx.voice_state.voice.move_to(destination)\n return\n\n ctx.voice_state.voice = await destination.connect()","async def ensure_voice(self, ctx):\n player = self.bot.lavalink.player_manager.create(\n ctx.guild.id, endpoint=str(ctx.guild.region))\n # Create returns a player if one exists, otherwise creates.\n # This line is important because it ensures that a player always exists for a guild.\n\n # Most people might consider this a waste of resources for guilds that aren't playing, but this is\n # the easiest and simplest way of ensuring players are created.\n\n # These are commands that require the bot to join a voicechannel (i.e. initiating playback).\n # Commands such as volume/skip etc don't require the bot to be in a voicechannel so don't need listing here.\n should_connect = ctx.command.name in ('play',)\n\n if not ctx.author.voice or not ctx.author.voice.channel:\n # Our cog_command_error handler catches this and sends it to the voicechannel.\n # Exceptions allow us to \"short-circuit\" command invocation via checks so the\n # execution state of the command goes no further.\n raise commands.CommandInvokeError(\n \"You aren't connected to voice channel.\")\n\n if not player.is_connected:\n if not should_connect:\n raise commands.CommandInvokeError('Player is not connected.')\n\n permissions = ctx.author.voice.channel.permissions_for(ctx.me)\n\n if not permissions.connect or not permissions.speak: # Check user limit too?\n raise commands.CommandInvokeError(\n 'I need to have both `connect` and `speak` permissions.')\n\n player.store('channel', ctx.channel.id)\n await ctx.guild.change_voice_state(channel=ctx.author.voice.channel)\n else:\n if int(player.channel_id) != ctx.author.voice.channel.id:\n raise commands.CommandInvokeError(\n 'You need to be connected to the same voice channel.')","async def start(self, ctx):\n if ctx.guild is None:\n await ctx.reply(\"This command can only be used in a server, not in DMs.\")\n raise commands.CommandError(\"Invoker not in a guild.\")\n\n if ctx.voice_client is None or ctx.voice_client.channel is None:\n await ctx.reply(f\"I am not in a voice channel, invite me first with `{self.bot_config['prefix']}join`.\")\n raise commands.CommandError(\"Bot not connected to a voice channel.\")\n\n if ctx.author.voice is None or ctx.author.voice.channel is None:\n await ctx.reply(\"You need to be in a voice channel to use this command.\")\n raise commands.CommandError(\"Invoker not connected to a voice channel.\")\n\n if ctx.voice_client is not None and ctx.author.voice.channel != ctx.voice_client.channel:\n await ctx.reply(\"You need to be in the same voice channel as the bot to use this command.\")\n raise commands.CommandError(\"Invoker not in same voice channel as bot.\")\n\n if ctx.voice_client is not None and ctx.voice_client.channel is not None:\n controller = SpotifyController.get_instance(ctx.voice_client.channel.id)\n if controller is None:\n await ctx.reply(f\"I'm not playing anything at the moment.\")\n raise commands.CommandError(\"Bot not connected to active spotify session.\")\n else:\n await ctx.reply(f\"I am not in a voice channel, invite me first with `{self.bot_config['prefix']}join`.\")\n raise commands.CommandError(\"Bot not connected to a voice channel.\")\n\n queue, is_playing, current_index, current_progress_ms = controller.get_queue()\n\n if current_index is not None:\n await ctx.reply(f\"I'm already playing the room playlist!\")\n raise commands.CommandError(\"Bot not connected to a voice channel.\")\n\n controller.start_playback()\n await ctx.message.add_reaction(\"👍\")","def voice(self,nick):\n self.logger.debug(\"voicing %s\" % nick)\n self.connection.mode(self.config[\"IRC/channel\"],\"+v \"+nick)","async def join(ctx):\n if ctx.message.channel.name.lower() not in tod_channels:\n return\n\n room = ctx.message.channel.name.lower()\n if room not in tod_games:\n await amor_manager.say(\"Truth Or Dare not in progress in {}\".format(room))\n return\n player = ctx.message.author.name\n if player.lower() in list(tod_games[room]['participants'].keys()):\n await amor_manager.say(\"{}... you're already playing Truth or Dare here!\".format(room))\n else:\n tod_games[room]['participants'][player.lower()] = {'spins': 0}\n await amor_manager.say(\"{} has joined Truth or Dare!\".format(player))","async def voice(self, ctx, voice: str):\n global voice_type\n\n voice_dict = {\n 'IN_F': 'en-IN-Wavenet-A',\n 'IN_M': 'en-IN-Wavenet-C',\n 'US_F': 'en-US-Wavenet-G',\n 'US_M': 'en-US-Wavenet-B',\n 'GB_F': 'en-GB-Wavenet-A',\n 'GB_M': 'en-GB-Wavenet-B',\n 'AU_F': 'en-AU-Wavenet-C',\n 'AU_M': 'en-AU-Wavenet-B'\n }\n if voice in voice_dict:\n voice_type = voice_dict[voice]\n else:\n voice_type = voice\n await ctx.send(\"New voice set to: \" + voice_type)\n audioClip = TTSSource(voice_type, \"New voice set.\")\n while not audioClip.done:\n await asyncio.sleep(1)\n source = discord.PCMVolumeTransformer(discord.FFmpegPCMAudio(audioClip.filename, **ffmpeg_options))\n ctx.voice_client.play(source, after=lambda e: print('Player error: %s' % e) if e else None)","async def joinChat(self,ctx):\n await ctx.send('Joining the chat kiddo')","async def tod_join(self, ctx, *args):\n if ctx.author not in self.players:\n self.players.append(ctx.author)\n message = f\"{ctx.author.mention} has been added to the game!\"\n await ctx.send(message)\n else:\n message = f\"{ctx.author.mention} has already joined!\"\n await ctx.send(message)\n\n # Updates the role if channel exists\n for channel in ctx.guild.channels:\n if channel.name.startswith(\"truth-or-dare\"):\n role = discord.utils.get(ctx.guild.roles, name=\"Player\")\n await ctx.author.add_roles(role)\n return\n\n # Creates the channel if it doesn't exist\n role = discord.utils.get(ctx.guild.roles, name=\"Player\")\n bots = discord.utils.get(ctx.guild.roles, name=\"Bots\")\n overwrites = {\n ctx.guild.default_role: discord.PermissionOverwrite(read_messages=False, send_messages=False),\n bots: discord.PermissionOverwrite(read_messages=True, send_messages=True),\n role: discord.PermissionOverwrite(read_messages=True, send_messages=True, connect=True, speak=True)\n }\n await ctx.guild.create_text_channel('truth-or-dare', overwrites=overwrites)\n await ctx.guild.create_voice_channel('secret-voice', overwrites=overwrites)\n\n # Adds the role\n role = discord.utils.get(ctx.guild.roles, name=\"Player\")\n await ctx.author.add_roles(role)","def connect_to_voice(self, event):\n\n vs = event.guild.get_member(event.author).get_voice_state()\n \n if not vs:\n raise CommandFail('You\\'re not in a voice channel.')\n if event.guild.id in self.players:\n if self.players[event.guild.id].channel_id == vs.channel_id:\n raise CommandFail('I\\'m already in that channel.')\n else:\n self.players[event.guild.id].set_channel(vs.channel)\n return\n\n player = MusicPlayer(event, vs)\n\n self.players[event.guild.id] = player","async def summon(self, ctx):\n summoned_channel = ctx.message.author.voice_channel\n if summoned_channel is None:\n await self.bot.say('You are not in a voice channel.')\n return False\n\n state = self.get_voice_state(ctx.message.server)\n if state.voice is None:\n state.voice = await self.bot.join_voice_channel(summoned_channel)\n else:\n await state.voice.move_to(summoned_channel)\n\n return True","async def summon(self, channel: discord.Channel):\n if self.voice.is_connected():\n await self.voice.move_to(channel)\n else:\n await self.voice.connect(channel)","async def on(self, ctx, *, nickname=\"\"):\n nickname = nickname.strip()\n mention_here = True\n mention_everyone = True\n if nickname == \"\":\n nickname = \"Dank Bot |Music on voice!\"\n try:\n await self.bot.change_nickname(ctx.message.server.me, nickname)\n await self.bot.say(\"Hey, music is playing on voice channel come! @here\")\n await self.bot.delete_message(ctx.message)\n except discord.Forbidden:\n await self.bot.say(\"I cannot do that, I miss the `Change Nickname` or `Manage Messages` permission\")","async def _discord(self, ctx: Context):\n\n await ctx.send(\n f\"You can join the Brawlcord community server by using this link: {COMMUNITY_SERVER}\"\n )","async def katıl(con,channel=None):\r\n check = str(con.message.channel)\r\n\r\n if check == 'Direct Message with {}'.format(con.message.author.name):#COMMAND IS IN DM\r\n await bot.send_message(con.message.channel, \"**You must be in a `server voice channel` to use this command**\")\r\n\r\n if check != 'Direct Message with {}'.format(con.message.author.name):#COMMAND NOT IN DM\r\n voice_status = bot.is_voice_connected(con.message.server)\r\n\r\n if voice_status == False:#VOICE NOT CONNECTED\r\n await bot.join_voice_channel(con.message.author.voice.voice_channel)\r\n\r\n if voice_status == True:#VOICE ALREADY CONNECTED\r\n await bot.send_message(con.message.channel, \"**Bot zaten bir kanala bağlı !**\")","async def connect(\n self, ctx: commands.Context, *, channel: t.Optional[discord.VoiceChannel]\n ) -> None:\n # We prioritise user-specified channels if they are available, else we use the author's current channel.\n destination = (\n channel\n if channel is not None\n else getattr(ctx.author.voice, \"channel\", None)\n )\n\n if destination is not None:\n player = self.get_player(ctx.guild)\n await player.connect(destination.id)\n else:\n fail = Embeds.status(success=False, desc=None)\n items = ctx.message.content.split()[1:]\n\n # Check that the user is invoking the connect command with an invalid channel.\n if len(items) > 0 and ctx.command.qualified_name == \"connect\":\n fail.description = f\"`{' '.join(items)}` is not a valid voice channel.\"\n else:\n fail.description = \"You aren't connected to a voice channel!\"\n\n await ctx.send(embed=fail)","async def join(self, ctx, key: str):\n await ctx.message.delete()\n async with ctx.typing():\n data = await self.config.guild(ctx.guild).all()\n if data[\"private\"]:\n try:\n if ctx.author.voice.channel.id == data[\"pstart\"]:\n if key in data[\"pchannels\"]:\n await ctx.author.move_to(ctx.guild.get_channel(data[\"pchannels\"][key]))\n else:\n await self.sendNotInStartChannelMessage(ctx, data[\"pstart\"])\n except AttributeError:\n await self.sendNotInStartChannelMessage(ctx, data[\"pstart\"])\n else:\n await ctx.send(_(\"Private rooms are not enabled on this server.\"))","async def summon(self, ctx):\n summoned_channel = ctx.message.author.voice_channel\n if not summoned_channel:\n await self.bot.say('You aren\\'t in a voice channel.')\n return False\n\n state = self.get_voice_state(ctx.message.server)\n if state.voice is None:\n state.voice = await self.bot.join_voice_channel(summoned_channel)\n try:\n await state.voice.enable_voice_events()\n except AttributeError:\n pass\n else:\n await state.voice.move_to(summoned_channel)\n await self.bot.say('Ready to play audio in **' + summoned_channel.name + '**!')\n\n return True","async def summon(self, ctx):\r\n if ctx.message.channel.id != 701868237771505695:\r\n return await ctx.send(\"**Error:** Music Bot commands are only available in <#701868237771505695>\")\r\n if self.music_off:\r\n return await ctx.send(\"**Error:** Music Bot features are currently off\")\r\n\r\n self.played_time = time.time()\r\n summoned_channel = ctx.message.author.voice\r\n if summoned_channel is None:\r\n return await ctx.send(\"**Error:** You are not in a voice channel.\")\r\n\r\n if ctx.voice_client is not None:\r\n if self.songs != [\"None\"]:\r\n return await ctx.send(\"**Error:** There are songs playing in the other voice channel\")\r\n self.voice = ctx.voice_client\r\n self.voice_channel = ctx.message.channel\r\n return await ctx.voice_client.move_to(summoned_channel.channel)\r\n\r\n await summoned_channel.channel.connect()\r\n self.voice = ctx.voice_client\r\n self.voice_channel = ctx.message.channel\r\n self.processing_songs = 0\r\n await ctx.send(\"Summoned\")\r\n # await ctx.send(\"**Notice:** youtube_dl is currently experiencing slowdowns when handling YouTube requests (it will still work though). Until this is fixed, consider using other audio sources, such as Bandcamp or Soundcloud\")\r","async def startchannel(self, ctx, vc: discord.VoiceChannel):\n await self.config.guild(ctx.guild).pstart.set(vc.id)\n await self.config.guild(ctx.guild).pcat.set(vc.category_id)\n await ctx.send(\n _(\n \"Private starting channel set. Users can join this channel to use all features of private rooms.\\nI recommend not allowing members to speak in this channel.\"\n )\n )","async def on_ready():\n # channel = client.get_channel(695669957891194952)\n # await channel.send(\"Who wants to play The Game of 99?\")\n print(\"Who wants to play The Game of 99?\")","async def voicekick(self, ctx, member: discord.Member, reason: str=None):\n if not (member.voice is not None and member.voice.channel is not None):\n return await ctx.send('That user is not in a voice channel.')\n overwrite = discord.PermissionOverwrite(connect=False)\n overwrite = {ctx.guild.default_role: overwrite}\n _reason = 'Voice kick initiated by {}'.format(ctx.author)\n if reason is not None:\n _reason += ', for reason \"{}\"'.format(reason)\n try:\n channel = await ctx.guild.create_voice_channel('Voice Kick', overwrites=overwrite, reason=_reason)\n await member.move_to(channel, reason=_reason)\n await channel.delete(reason=_reason)\n await ctx.send('Done.')\n except discord.Forbidden:\n await ctx.send('Sorry, I don\\'t have permission to voice kick that person.')","async def create_voice_client(self, channel):\n voice = await self.bot.join_voice_channel(channel)\n try:\n await voice.enable_voice_events()\n except AttributeError:\n pass\n state = self.get_voice_state(channel.server)\n state.voice = voice","async def enable(self, ctx):\n await self.config.guild(ctx.guild).auto.set(True)\n await ctx.send(_(\"Automatic voicechannel creation enabled.\"))","async def inviteme(self):\r\n\r\n #Your code will go here\r\n await self.bot.say(\"Here is a link to Invite Me: http://bit.ly/TacoBot\")","async def çıkış(con):\r\n check=str(con.message.channel)\r\n if check == 'Direct Message with {}'.format(con.message.author.name):#COMMAND USED IN DM\r\n await bot.send_message(con.message.channel,\"**You must be in a `server voice channel` to use this command**\")\r\n\r\n if check != 'Direct Message with {}'.format(con.message.author.name):#COMMAND NOT IN DM\r\n \r\n # IF VOICE IS NOT CONNECTED\r\n if bot.is_voice_connected(con.message.server) == False:\r\n await bot.send_message(con.message.channel,\"**Bot kanala bağlanmamış !**\")\r\n\r\n # VOICE ALREADY CONNECTED\r\n if bot.is_voice_connected(con.message.server) == True:\r\n bot.loop.create_task(queue_songs(con,True))","def join_channel(self, channel):\r\n self._send('JOIN #%s\\r\\n' % channel)","def joingroup_command(update,context):\n update.message.reply_text('Want to chat with other CTF players or ask questions to admins? Use the following channel:\\r\\nhttps://t.me/joinchat/CYsj-xwzlFqIbQPPeo04bw')","def voice(phenny, input):\n if not input.admin:\n return phenny.say(\"Not an admin!\")\n if not input.sender.startswith('#'):\n return phenny.say(\"Command must be given in channel!\")\n nick = input.group(2)\n verify = auth_check(phenny, input.nick, nick)\n if verify:\n channel = input.sender\n if not nick:\n nick = input.nick\n phenny.write(['MODE', channel, \"+v\", nick])\n else:\n phenny.say(\"Nick not verified\")","async def connect_voice_channel(client: \"Client\", *args, **kwargs) -> None:\n total_args_len = len(args) + len(kwargs)\n\n # use this rather peculiar method to allow **kwargs and *args interchangeably\n if total_args_len == 1:\n await _connect_voice_channel_channel(client, *args, **kwargs)\n else:\n await _connect_voice_channel_guild_channel(client, *args, **kwargs)","async def hello(self, ctx):\n await ctx.send(random.choice(self.greetings))","def signedOn(self):\n self.join(self.channel)","async def joinchannel(self, ctx: commands.Context, *channels: str):\n for channel in channels:\n channel_query = self._channel_query(channel)\n\n if channel_query == None:\n await ctx.send(f\"Unable to join {channel}.\")\n continue\n\n channel = self.bot.get_channel(channel_query.id)\n guild = self.bot.get_guild(SERVER_ID)\n member = guild.get_member(ctx.author.id)\n\n if channel == None:\n await ctx.send(f\"Unable to join {channel}.\")\n continue\n\n # Don't let a user join the channel again if they are already in it.\n if channel.permissions_for(member).is_superset(JOINED_PERMISSIONS):\n await ctx.send(f\"You're already a member of {channel}.\")\n continue\n\n await channel.set_permissions(member, read_messages=True, reason=\"UQCSbot added.\")\n join_message = await channel.send(f\"{member.display_name} joined {channel.mention}\")\n await join_message.add_reaction(\"👋\")\n await ctx.send(f\"You've joined {channel.mention}.\")","def help_command(update,context):\r\n update.message.reply_text('I am a Voice bot')","async def leave(self, ctx):\n if ctx.guild.voice_client:\n await ctx.guild.voice_client.disconnect()\n await ctx.send(\"Left voice channel.\")\n else:\n await ctx.message.add_reaction('\\U0001F615');\n await ctx.send(\"Not in a voice channel.\")","async def turn(ctx):\n if ctx.message.channel.name.lower() not in tod_channels:\n return\n\n room = ctx.message.channel.name.lower()\n if room not in tod_games:\n await amor_manager.say(\"Truth Or Dare not in progress in {}\".format(room))\n else:\n await amor_manager.say(\"Current Player is {}\".format(tod_games[room]['current']))","def signedOn(self):\n self.join(self.factory.channel)","async def now_playing(self, ctx):\n if ctx.guild is None:\n await ctx.reply(\"This command can only be used in a server, not in DMs.\")\n raise commands.CommandError(\"Invoker not in a guild.\")\n\n if ctx.voice_client is None or ctx.voice_client.channel is None:\n await ctx.reply(f\"I am not in a voice channel, invite me first with `{self.bot_config['prefix']}join`.\")\n raise commands.CommandError(\"Bot not connected to a voice channel.\")\n\n if ctx.author.voice is None or ctx.author.voice.channel is None:\n await ctx.reply(\"You need to be in a voice channel to use this command.\")\n raise commands.CommandError(\"Invoker not connected to a voice channel.\")\n\n if ctx.voice_client is not None and ctx.author.voice.channel != ctx.voice_client.channel:\n await ctx.reply(\"You need to be in the same voice channel as the bot to use this command.\")\n raise commands.CommandError(\"Invoker not in same voice channel as bot.\")\n\n if ctx.voice_client is not None and ctx.voice_client.channel is not None:\n controller = SpotifyController.get_instance(ctx.voice_client.channel.id)\n if controller is None:\n await ctx.reply(f\"I'm not playing anything at the moment.\")\n raise commands.CommandError(\"Bot not connected to active spotify session.\")\n else:\n await ctx.reply(f\"I am not in a voice channel, invite me first with `{self.bot_config['prefix']}join`.\")\n raise commands.CommandError(\"Bot not connected to a voice channel.\")\n\n sp = controller.get_api()\n info = sp.current_playback()\n if not controller.is_playing_on_bot():\n await ctx.send(\"Not playing anything at the moment...\")\n return\n\n # Add URI\n if info is not None:\n track_info = info['item']\n try:\n thumbnail = track_info['album']['images'][0]\n except IndexError:\n thumbnail = None\n msg_embed = Embed()\n artist_url = track_info['artists'][0]['external_urls']['spotify']\n msg_embed.set_author(name=SpotifyController.format_artist(track_info), url=artist_url)\n msg_embed.title = SpotifyController.format_title(track_info)\n msg_embed.description = f\"{SpotifyController.format_album_name(track_info)}\\n\"\n msg_embed.url = track_info['external_urls']['spotify']\n msg_embed.set_thumbnail(url=thumbnail['url'])\n msg_embed.set_footer(text=f\"{SpotifyController.format_progress(info)}\")\n await ctx.reply(embed=msg_embed)\n else:\n await ctx.send(\"Not playing anything at the moment...\")","def joinedChannel(self, channel, users):\n pass","async def add(self, ctx, *, channel: discord.VoiceChannel):\n auto_channels = await self.config.guild(ctx.guild).auto_channels()\n if auto_channels is None:\n auto_channels = []\n auto_channels.append(channel.id)\n await self.config.guild(ctx.guild).auto_channels.set(auto_channels)\n await ctx.send(\n _(\"Startchannel used for automatic voicechannels added: {channel}\").format(\n channel=channel.name\n )\n )","def join(self, channel):\n raise NotImplementedError","async def p(con,*,url):\r\n check = str(con.message.channel)\r\n if check == 'Direct Message with {}'.format(con.message.author.name):\r\n await bot.send_message(con.message.channel, \"**You must be in a `server voice channel ` to use this command**\")\r\n\r\n if check != 'Direct Message with {}'.format(con.message.author.name):\r\n if bot.is_voice_connected(con.message.server) == False:\r\n await bot.join_voice_channel(con.message.author.voice.voice_channel)\r\n\r\n if bot.is_voice_connected(con.message.server) == True:\r\n if player_status[con.message.server.id]==True:\r\n song_names[con.message.server.id].append(url)\r\n await bot.send_message(con.message.channel, \"**Şarkı sıraya alındı :white_check_mark:**\")\r\n\r\n\r\n \r\n if player_status[con.message.server.id]==False:\r\n player_status[con.message.server.id]=True\r\n song_names[con.message.server.id].append(url)\r\n song=await bot.voice_client_in(con.message.server).create_ytdl_player(song_names[con.message.server.id][0], ytdl_options=opts, after=lambda: bot.loop.create_task(after_song(con,False)))\r\n servers_songs[con.message.server.id]=song\r\n servers_songs[con.message.server.id].start()\r\n msg = await bot.send_message(con.message.channel, \"**Şuanda oynatılan > {}**\".format(servers_songs[con.message.server.id].title))\r\n now_playing[con.message.server.id]=msg\r\n song_names[con.message.server.id].pop(0)","async def x5lol(ctx):\n #Shortcut to Author of the Message\n atr = ctx.author\n #Shortcut to Author Current Voice Channel\n currentvc = atr.voice.channel.name\n #Shortcut to Voice Channel Members list\n usrs = atr.voice.channel.members\n #Specify what role will use \n role = ctx.guild.get_role(\"\"\"Insert role id\"\"\")\n \n pot = []\n #await ctx.send(atr.name +' '+currentvc)\n for i in usrs:\n if role in i.roles:\n #await ctx.send('O ' + str(i) + ' É ' + str(role))\n nick = (str(i.nick))\n if nick != 'None':\n pot.append(str(i.nick))\n else:\n pot.append(str(i))\n \n \n #NOTE:The math part of team ballance isn't done yet\n # For now, Bot shuffle the names, then send to Text Channel on Discord \n random.shuffle(pot)\n await ctx.send(pot)","async def on_voice_state_update(member, member_before_voice_state, member_after_voice_state):\n server = member.guild\n channel = find_channel(server)\n \n voice_channel_before = member_before_voice_state.channel\n voice_channel_after = member_after_voice_state.channel\n \n if voice_channel_before == voice_channel_after:\n # No change\n return\n\n if voice_channel_before is None:\n # The member was not on a voice channel before the change\n # Discord api sets voice_channel to None if there was no channel to begin with.\n msg = \"**\\'%s#%s\\'** joined voice channel **\\'_%s_\\'** at %s\" % (member.name, member.discriminator, voice_channel_after.name, datetime.datetime.now().strftime(\"%m/%d/%Y, %H:%M:%S\"))\n else:\n # The member was on a voice channel before the change\n if voice_channel_after is None:\n # The member is no longer on a voice channel after the change\n msg = \"**\\'%s#%s\\'** left voice channel **\\'_%s_\\'** at %s\" % (member.name, member.discriminator, voice_channel_before.name, datetime.datetime.now().strftime(\"%m/%d/%Y, %H:%M:%S\"))\n else:\n # The member is still on a voice channel after the change\n msg = \"**\\'%s#%s\\'** switched from voice channel **\\'_%s_\\'** to **\\'_%s_\\'** at %s\" % (member.name, member.discriminator,\n voice_channel_before.name, voice_channel_after.name, datetime.datetime.now().strftime(\"%m/%d/%Y, %H:%M:%S\"))\n \n # Try to log the voice event to the channel\n try:\n await channel.send(msg)\n\n except:\n # No message could be sent to the channel; force refresh the channel cache and try again\n channel = find_channel(server, refresh=True)\n if channel is None:\n # The channel could not be found\n print(\"Error: channel #%s does not exist on server %s.\" % (channel_name, server))\n else:\n # Try sending a message again\n try:\n await channel.send(msg)\n except discord.DiscordException as exception:\n # Print the exception\n print(\"Error: no message could be sent to channel #%s on server %s. Exception: %s\" % (channel_name, server, exception))","def handle_autovoiceon(bot, ievent):\n try: ievent.chan.data.autovoice = 1\n except TypeError: ievent.reply('no %s in channel database' % ievent.channel) ; return\n ievent.reply('autovoice enabled on %s' % ievent.channel)","async def react_join(a: Message):\n if a.action.member_id == club_id:\n await a.answer(r_register_help)\n stats.jincr()","async def tick(self):\n room = self.bot.Room.load('19961884194@chatroom')\n await room.ready()\n await room.say(f'i love you -> {datetime.now()}')","def joined(self, channel):\n log.msg(\"GbRobot joined \" + self.factory.channel)","async def test_silence_voice_sibling_channel(self):\n with unittest.mock.patch.object(silence, \"VOICE_CHANNELS\") as mock_voice_channels:\n mock_voice_channels.get.return_value = self.text_channels[1].id\n\n message = \"This should show up as {channel}.\"\n await self.cog.send_message(message, self.text_channels[1], self.voice_channel, alert_target=True)\n\n updated_message = message.format(channel=self.voice_channel.mention)\n self.text_channels[1].send.assert_awaited_once_with(updated_message)\n\n mock_voice_channels.get.assert_called_once_with(self.voice_channel.id)\n self.bot.get_channel.assert_called_once_with(self.text_channels[1].id)","async def welcome(self, ctx, member: discord.Member = None):\n\n await ctx.message.delete()\n if not member:\n return await ctx.send('Inform a member!')\n\n bots_and_commands_channel = discord.utils.get(ctx.guild.channels, id=bots_and_commands_channel_id)\n await bots_and_commands_channel.send(\n f'''__**Welcome to the Language Sloth**__! {member.mention}\nThis is a community of people who are practicing and studying languages from all around the world! While you're here, you will also make tons of new friends! There is a lot to do here in the server but there are some things you should do to start off.\n\n1. Make sure you go check out the <#688967996512665655> and the informations page. These rules are very important and are taken seriously here on the server.\n2. After you have finished reading those, you can assign yourself some roles at <#679333977705676830> <#683987207065042944> <#688037387561205824> and <#562019509477703697>! These roles will give you access to different voice and text channels! To choose your role click on the flag that best represents your native language.\n\nIf you have any questions feel free to ask! And if you experience any type of problem make sure you let a staff member know right away''')","def joined(self, channel):\n log.msg(\"[I have joined %s]\" % channel)\n self.msg(channel, \"user1: bonbon\")","async def support(self, ctx):\n await ctx.send('Join the support server here: https://discord.gg/bAq8Ec5JPQ')","def start(update: Update, context: CallbackContext) -> None:\n if update.effective_chat:\n context.bot.send_message(chat_id=update.effective_chat.id,\n text=\"I'm a bot, please talk to me!\")","async def rebind(self, ctx, channel: discord.TextChannel=None):\n player = self.bot.lavalink.players.get(ctx.guild.id)\n if not player.is_connected:\n return await ctx.send(\"I'm not connected to a voice channel :no_entry:\")\n if not channel:\n channel = ctx.channel\n player.store('channel', channel.id)\n await ctx.send(\"Messages will now be sent in {} <:done:403285928233402378>\".format(channel.mention))","def vc_only():\n\n async def check(ctx):\n if ctx.guild and ctx.author.voice:\n if not ctx.guild.me.voice or ctx.author.voice.channel == ctx.guild.me.voice.channel:\n return True\n await ctx.reply(\"I'm already in another voice channel!\")\n return False\n await ctx.reply('You must join a server voice channel first!')\n return False\n\n return commands.check(check)","def join(var, wrapper, message):\n # keep this and the event in fjoin() in sync\n evt = Event(\"join\", {\n \"join_player\": join_player,\n \"join_deadchat\": join_deadchat,\n \"vote_gamemode\": vote_gamemode\n })\n if not evt.dispatch(var, wrapper, message, forced=False):\n return\n if var.PHASE in (\"none\", \"join\"):\n if wrapper.private:\n return\n if var.ACCOUNTS_ONLY:\n if wrapper.source.account is None:\n wrapper.pm(messages[\"not_logged_in\"])\n return\n if evt.data[\"join_player\"](var, wrapper) and message:\n evt.data[\"vote_gamemode\"](var, wrapper, message.lower().split()[0], doreply=False)\n\n else: # join deadchat\n if wrapper.private and wrapper.source is not wrapper.target:\n evt.data[\"join_deadchat\"](var, wrapper.source)","async def support(self, ctx):\n link = \"https://discord.gg/J78uAgZ\"\n await ctx.send(\"Join my support server if you need help with commands!\\n \" + link)","async def pause(self, ctx):\n voice = discord.utils.get(self.bot.voice_clients, guild=ctx.guild)\n\n if voice:\n if voice.is_playing():\n voice.pause()\n await ctx.send(\"Playing paused.\")\n else:\n await ctx.message.add_reaction('\\U0001F615');\n await ctx.send(\"Not playing anything right now.\")\n else:\n await ctx.message.add_reaction('\\U0001F615')\n await ctx.send(\"Not in a voice channel.\")","async def _99join(ctx):\n NNB.add_player(ctx.message.author)\n await ctx.send(\"Welcome to the game, {}\".format(ctx.message.author.name))\n print(\"Added {} to the game.\".format(ctx.message.author.name))","async def _bot(ctx):\n await ctx.send('Yes, the bot is cool.')","def join_channel(self) -> None:\n response = self.client.conversations_list()\n for channel in response.data[\"channels\"]:\n if channel[\"name\"] == BaseConfig.SLACK_CHANNEL:\n self.channel_id = channel[\"id\"]\n if not self.channel_id:\n raise Exception(f\"Channel: {BaseConfig.SLACK_CHANNEL} was not found\")\n self.client.conversations_join(channel=self.channel_id)","async def enable_channel(self, ctx, *channels: discord.TextChannel):\n channels = channels or (ctx.channel, )\n await ctx.guild_profile.permissions.enable_channels(channels)\n await ctx.send_line(f\"{ctx.emotes.web_emotion.galka} Bot commands and messages has been enabled in specified channels.\")","async def _join_money(ctx):\n await join_money(bot, ctx.message.author)","def leaveroom(self, the_id):\r\n the_id = Client.toroomid(the_id)\r\n self.tx_cmd(FCTYPE.JOINCHAN, 0, the_id, FCCHAN.PART)","async def managechannels(self, ctx:commands.Context):","async def request_to_speak(self) -> None:\n if self.voice is None or self.voice.channel is None:\n raise ClientException('Cannot request to speak while not connected to a voice channel.')\n\n payload = {\n 'channel_id': self.voice.channel.id,\n 'request_to_speak_timestamp': datetime.datetime.utcnow().isoformat(),\n }\n\n if self._state.self_id != self.id:\n payload['suppress'] = False\n await self._state.http.edit_voice_state(self.guild.id, self.id, payload)\n else:\n await self._state.http.edit_my_voice_state(self.guild.id, payload)","def newJoin(user, channel):\n\n message = \"\"\"\nWelcome to the official Slack for ZenCash!\n\n\nThe official links are:\nhttps://github.com/ZenCashOfficial/\nhttps://zencashofficial.io/\n\nNOTICE:\nDue to the recent plague that is SlackBot spamming with `/remind`, please do not click any links sent to you in a DM from slackbot.\n\nAdditionally, please copy the message to #spam so the Admins can ban the user.\n\nPlease remember to be civil, and have a great day!\n\"\"\"\n\n\n # General\n if channel == 'C4QGQ8SEM':\n return message\n\n # Bottesting\n if channel == \"C5JCER3NG\":\n return message","def start(bot, update):\n update.message.reply_text('Hi!')","async def invite(self, ctx):\n perms = discord.Permissions.text()\n perms.update(read_messages=True, manage_messages=True,\n mention_everyone=False, send_tts_messages=False)\n await ctx.send(f'Invite me here:\\n<{discord.utils.oauth_url(self.bot.user.id, perms)}>')","async def on_voice_state_update(self, member, before, after):\n # This makes sure that the bot leaves\n # the voice channel after a settable timeout\n # for performance improvements.\n timeout = 600 # seconds\n if not member.id == self.bot.user.id:\n return\n elif before.channel is None:\n player = self.bot.lavalink.player_manager.get(member.guild.id)\n time = 0\n while True:\n await asyncio.sleep(1)\n time += 1\n\n # Check if after channel none & clear queue\n if after.channel is None:\n player.queue.clear()\n # Stop the current track so Lavalink consumes less resources.\n await player.stop()\n break\n\n # Timeout after `timeout` seconds for performance improvements.\n if player.is_playing:\n time = 0\n if time == timeout:\n ch = await self.bot.fetch_channel(\n self.preferred_channels[str(player.guild_id)])\n await member.guild.change_voice_state(channel=None)\n await ch.send(embed=self.reply_embed(\"Left voice channel after no activity for `10 minutes`.\"))\n del self.preferred_channels[str(player.guild_id)]\n if not player.is_connected:\n break","def play_command(update,context):\n update.message.reply_text('Rkrt: Welcome on board. Let\\'s see if you are worth the challenge. To find the invite code and land on planet hackazon you will need to solve this first. Ready for a ride?!')\n time.sleep(5)\n update.message.reply_text('Mx: During intergalactical travel, time does not matter. Any enemies could be listening in at any time. This is why the crew is sometimes forced to used coded languages to exchange messages between vessels. To decrypt messages every crew member can use the key on their hardware tokens.')\n time.sleep(10)\n update.message.reply_text('Jms: Mx we are getting a distress signal from vessel Vigenere. Do you copy?')\n time.sleep(3)\n update.message.reply_text('Mx: [gasps...]')\n time.sleep(1)\n update.message.reply_text('Mx: This one is for you rookie... See you on the other side.')\n update.message.reply_text('Kyjkda kghc tir Yeevobyj: BgXfsGofrCyrDouwfh\\r\\nUsfcfqg zb dywzv lcfy ij cqff hsnal jjoa:\\r\\nCKJ{en55td2my6jse8361a427p3xf319tf12}')","async def move(self, ctx, *, channel_name: str):\r\n if ctx.voice_client:\r\n if discord.utils.get(ctx.guild.channels, name=channel_name):\r\n await ctx.voice_client.move_to(discord.utils.get(ctx.guild.channels, name=channel_name))\r\n print(\"[INFO] Bot moved to channel {0}\".format(channel_name))\r\n self.channel_name = channel_name\r\n else:\r\n await ctx.send(\"Channel {0} not found\".format(channel_name))\r\n else:\r\n await self.connect(ctx, channel_name=channel_name)\r\n await ctx.message.delete()","async def test_force_voice_sync_no_channel(self):\n await self.cog.cog_load()\n\n channel = MockVoiceChannel(guild=MockGuild(afk_channel=None))\n new_channel = MockVoiceChannel(delete=AsyncMock())\n channel.guild.create_voice_channel.return_value = new_channel\n\n await self.cog._force_voice_sync(channel)\n\n # Check channel creation\n overwrites = {\n channel.guild.default_role: PermissionOverwrite(speak=False, connect=False, view_channel=False)\n }\n channel.guild.create_voice_channel.assert_awaited_once_with(\"mute-temp\", overwrites=overwrites)\n\n # Check bot deleted channel\n new_channel.delete.assert_awaited_once()","async def channel(self, ctx):\n pass","async def test_silence_voice(self):\n message = \"This should show up just here.\"\n await self.cog.send_message(message, self.text_channels[0], self.voice_channel, alert_target=False)\n self.text_channels[0].send.assert_awaited_once_with(message)\n self.text_channels[1].send.assert_not_called()","def sendVoiceMessage(self,data):\n\n self.controlProtocol.sendVoiceMessage(data)","async def links(self, ctx):\n await ctx.send(\"https://discordapp.com/channels/566451504332931073/681617252814159904/755489156146397311\")","def join(bot, event, *args):\n yield from _joinEvent(bot, event, list(args))","async def CoMLegendBuilder(self, ctx):\n me = CoachService.discord_user_to_coach(ctx.author)\n data = getattr(special_play, inspect.currentframe().f_code.co_name)(ctx.channel.name, me)\n await self.send_embed(data, ctx)","def join(self, channel):\n self.channels[channel.name.lower()] = channel\n channel.protocol = self.protocol\n self.protocol.join(channel.name)"],"string":"[\n \"async def join(self, ctx):\\n voice = ctx.author.voice\\n\\n if voice and voice.channel:\\n channel = voice.channel\\n \\n try:\\n await channel.connect()\\n except discord.errors.ClientException:\\n await ctx.guild.get_member(self.bot.user.id).move_to(channel) \\n \\n await ctx.send(f\\\"Joined `{channel.name}`.\\\")\\n else:\\n await ctx.message.add_reaction('\\\\U0001F615');\\n await ctx.send(\\\"You're not in a voice channel.\\\")\",\n \"async def joinForce(self, ctx):\\n print(\\\"joining\\\")\\n channel = ctx.author.voice.channel\\n await channel.connect()\",\n \"async def join(ctx):\\r\\n channel = ctx.message.author.voice.channel\\r\\n if ctx.voice_client is not None:\\r\\n return await ctx.voice_client.move_to(channel)\\r\\n\\r\\n await channel.connect()\",\n \"async def _join(self, ctx: commands.Context):\\n\\n destination = ctx.author.voice.channel\\n if ctx.voice_state.voice:\\n await ctx.voice_state.voice.move_to(destination)\\n return\\n ctx.voice_state.voice = await destination.connect()\\n await ctx.message.add_reaction('✅')\",\n \"async def join(self, ctx, *, channel: discord.VoiceChannel):\\n \\n if ctx.voice_client is not None:\\n return await ctx.voice_client.move_to(channel)\\n else:\\n return await ctx.voice_client.move_to(bot.get_channel(247531960488951815))\\n \\n await channel.connect()\",\n \"async def join(self, ctx, *, channel: discord.VoiceChannel):\\n\\n if ctx.voice_client is not None:\\n return await ctx.voice_client.move_to(channel)\\n\\n await channel.connect()\",\n \"async def _join(self, ctx: commands.Context):\\n\\n destination = ctx.author.voice.channel\\n if ctx.voice_state.voice:\\n await ctx.voice_state.voice.move_to(destination)\\n return\\n\\n ctx.voice_state.voice = await destination.connect()\",\n \"async def _join(self, ctx: commands.Context):\\n destination = ctx.author.voice.channel # Destination is the author's voice channel\\n if ctx.voice_state.voice:\\n await ctx.voice_state.voice.move_to(destination)\\n return\\n\\n ctx.voice_state.voice = await destination.connect()\",\n \"async def join(self, ctx):\\n player = self.bot.lavalink.players.get(ctx.guild.id)\\n if player.is_connected:\\n return await ctx.send(\\\"I'm already in a voice channel :no_entry:\\\")\\n else:\\n if not ctx.author.voice or not ctx.author.voice.channel:\\n return await ctx.send(\\\"You are not in a voice channel :no_entry:\\\")\\n player.store('sessionowner', ctx.author.id)\\n player.store('channel', ctx.channel.id)\\n await player.connect(ctx.author.voice.channel.id)\\n await ctx.send(\\\"Summoned to `{}` <:done:403285928233402378>\\\".format(ctx.author.voice.channel.name))\",\n \"async def join(self, ctx, *, channel_name: str):\\n channels = [c for c in self.get_all_channels() if c.type.name == \\\"voice\\\" and c.name == channel_name]\\n\\n if len(channels) != 1:\\n await ctx.send(f\\\"Unknown channel: {channel_name}\\\")\\n return\\n\\n channel = channels[0]\\n\\n if ctx.voice_client is not None:\\n return await ctx.voice_client.move_to(channel)\\n await channel.connect()\",\n \"async def join(self, msg, *, channel: discord.VoiceChannel = None):\\n if msg.voice_client is not None:\\n return await msg.send(f\\\"Bot is already in a voice channel\\\\nDid you mean to use {msg.prefix}moveTo\\\")\\n\\n if msg.voice_client is None:\\n if channel is None:\\n return await msg.author.voice.channel.connect(), await msg.message.add_reaction(emoji='✅')\\n\\n return await channel.connect(), await msg.message.add_reaction(emoji='✅')\\n\\n else:\\n if msg.voice_client.is_playing() is False and not self.player[msg.guild.id]['queue']:\\n return await msg.author.voice.channel.connect(), await msg.message.add_reaction(emoji='✅')\",\n \"async def join(self, ctx, *, channel: discord.VoiceChannel=None):\\n\\n if not channel:\\n try:\\n channel = ctx.author.voice.channel\\n await channel.connect()\\n except AttributeError:\\n raise commands.CommandError('No channel to join. Please either specify a valid channel or join one.')\\n\\n vc = ctx.voice_client\\n if vc:\\n if vc.channel.id == channel.id:\\n return\\n try:\\n await vc.move_to(channel)\\n except asyncio.TimeoutError:\\n raise commands.CommandError(f'Moving to channel: <{channel}> timed out.')\\n else:\\n try:\\n await channel.connect()\\n except asyncio.TimeoutError:\\n raise commands.CommandError(f'Connecting to channel: <{channel}> timed out.')\",\n \"async def join(self, *, channel: discord.Channel):\\n try:\\n await self.create_voice_client(channel)\\n except discord.InvalidArgument:\\n await self.bot.say('That\\\\'s not a voice channel!')\\n except discord.ClientException:\\n await self.bot.say('Already in a voice channel.')\\n else:\\n await self.bot.say('Ready to play audio in **' + channel.name + '**!')\",\n \"async def join(self, ctx: commands.Context) -> Optional[discord.VoiceChannel]:\\n\\n if ctx.voice_client and ctx.voice_client.is_connected():\\n await self.call_event(\\n \\\"on_music_error\\\",\\n ctx,\\n AlreadyConnected(\\\"Client is already connected to a voice channel\\\"),\\n )\\n return\\n\\n if not ctx.author.voice:\\n await self.call_event(\\n \\\"on_music_error\\\",\\n ctx,\\n UserNotConnected(\\\"User is not connected to a voice channel\\\"),\\n )\\n return\\n\\n channel = ctx.author.voice.channel\\n await channel.connect(\\n cls=LavalinkPlayer\\n if self.type == ManagerType.LAVALINK\\n else discord.VoiceClient\\n )\\n return channel\",\n \"def on_join(self, event):\\n self.pre_check(event)\\n state = event.guild.get_member(event.author).get_voice_state()\\n if not state:\\n return api_loop(\\n event.channel.send_message,\\n \\\"You must be in a voice channel to use that command.\\\",\\n )\\n if event.guild.id not in self.guilds:\\n try:\\n client = state.channel.connect(mute=False)\\n except VoiceException as e:\\n return api_loop(\\n event.channel.send_message,\\n \\\"Failed to connect to voice: `{}`\\\".format(e),\\n )\\n else:\\n self.guilds[event.guild.id] = psuedo_queue(\\n self,\\n player=Player(client),\\n guild_id=event.guild.id,\\n )\\n return\",\n \"async def connect(self, ctx, *, channel_name: str = None):\\r\\n if channel_name:\\r\\n voice_channel = discord.utils.get(ctx.guild.channels, name=channel_name)\\r\\n else:\\r\\n voice_channel = ctx.author.voice.channel\\r\\n\\r\\n if voice_channel:\\r\\n self.channel_name = voice_channel.name\\r\\n await voice_channel.connect()\\r\\n print(\\\"[INFO] Bot connected to channel {0}\\\".format(self.channel_name))\\r\\n await ctx.message.delete()\",\n \"async def _join(self, bot, server_id, voice_channel):\\n if not voice_channel:\\n return\\n srv = self.get_server_dict(server_id)\\n if srv['voice']:\\n if voice_channel != srv['voice'].channel:\\n await srv['voice'].move_to(voice_channel)\\n else:\\n srv['voice'] = await bot.join_voice_channel(voice_channel)\",\n \"def start(update,context):\\r\\n update.message.reply_text('welcome to voice bot')\",\n \"async def _summon(self,\\n ctx: commands.Context,\\n *,\\n channel: discord.VoiceChannel = None):\\n\\n if not channel and not ctx.author.voice:\\n raise VoiceError(\\n 'You are neither connected to a voice channel nor specified a channel to join.'\\n )\\n\\n destination = channel or ctx.author.voice.channel\\n if ctx.voice_state.voice:\\n await ctx.voice_state.voice.move_to(destination)\\n return\\n\\n ctx.voice_state.voice = await destination.connect()\\n await ctx.message.add_reaction('✅')\",\n \"async def join(message):\\n voice_client = await get_voice_client(message.author, move_to=True, any_channel=True)\\n return \\\"I'm in {}\\\".format(voice_client.channel.mention)\",\n \"async def _summon(self, ctx: commands.Context, *, channel: discord.VoiceChannel = None):\\n\\n if not channel and not ctx.author.voice:\\n raise VoiceError('You are neither connected to a voice channel nor specified a channel to join.')\\n\\n destination = channel or ctx.author.voice.channel\\n if ctx.voice_state.voice:\\n await ctx.voice_state.voice.move_to(destination)\\n return\\n\\n ctx.voice_state.voice = await destination.connect()\",\n \"async def _summon(self, ctx: commands.Context, *, channel: discord.VoiceChannel = None):\\n if not channel and not ctx.author.voice:\\n raise commands.CommandError('You are neither connected to a voice channel nor specified a channel to join.')\\n # raise VoiceError()\\n\\n destination = channel or ctx.author.voice.channel\\n if ctx.voice_state.voice:\\n await ctx.voice_state.voice.move_to(destination)\\n return\\n\\n ctx.voice_state.voice = await destination.connect()\",\n \"async def ensure_voice(self, ctx):\\n player = self.bot.lavalink.player_manager.create(\\n ctx.guild.id, endpoint=str(ctx.guild.region))\\n # Create returns a player if one exists, otherwise creates.\\n # This line is important because it ensures that a player always exists for a guild.\\n\\n # Most people might consider this a waste of resources for guilds that aren't playing, but this is\\n # the easiest and simplest way of ensuring players are created.\\n\\n # These are commands that require the bot to join a voicechannel (i.e. initiating playback).\\n # Commands such as volume/skip etc don't require the bot to be in a voicechannel so don't need listing here.\\n should_connect = ctx.command.name in ('play',)\\n\\n if not ctx.author.voice or not ctx.author.voice.channel:\\n # Our cog_command_error handler catches this and sends it to the voicechannel.\\n # Exceptions allow us to \\\"short-circuit\\\" command invocation via checks so the\\n # execution state of the command goes no further.\\n raise commands.CommandInvokeError(\\n \\\"You aren't connected to voice channel.\\\")\\n\\n if not player.is_connected:\\n if not should_connect:\\n raise commands.CommandInvokeError('Player is not connected.')\\n\\n permissions = ctx.author.voice.channel.permissions_for(ctx.me)\\n\\n if not permissions.connect or not permissions.speak: # Check user limit too?\\n raise commands.CommandInvokeError(\\n 'I need to have both `connect` and `speak` permissions.')\\n\\n player.store('channel', ctx.channel.id)\\n await ctx.guild.change_voice_state(channel=ctx.author.voice.channel)\\n else:\\n if int(player.channel_id) != ctx.author.voice.channel.id:\\n raise commands.CommandInvokeError(\\n 'You need to be connected to the same voice channel.')\",\n \"async def start(self, ctx):\\n if ctx.guild is None:\\n await ctx.reply(\\\"This command can only be used in a server, not in DMs.\\\")\\n raise commands.CommandError(\\\"Invoker not in a guild.\\\")\\n\\n if ctx.voice_client is None or ctx.voice_client.channel is None:\\n await ctx.reply(f\\\"I am not in a voice channel, invite me first with `{self.bot_config['prefix']}join`.\\\")\\n raise commands.CommandError(\\\"Bot not connected to a voice channel.\\\")\\n\\n if ctx.author.voice is None or ctx.author.voice.channel is None:\\n await ctx.reply(\\\"You need to be in a voice channel to use this command.\\\")\\n raise commands.CommandError(\\\"Invoker not connected to a voice channel.\\\")\\n\\n if ctx.voice_client is not None and ctx.author.voice.channel != ctx.voice_client.channel:\\n await ctx.reply(\\\"You need to be in the same voice channel as the bot to use this command.\\\")\\n raise commands.CommandError(\\\"Invoker not in same voice channel as bot.\\\")\\n\\n if ctx.voice_client is not None and ctx.voice_client.channel is not None:\\n controller = SpotifyController.get_instance(ctx.voice_client.channel.id)\\n if controller is None:\\n await ctx.reply(f\\\"I'm not playing anything at the moment.\\\")\\n raise commands.CommandError(\\\"Bot not connected to active spotify session.\\\")\\n else:\\n await ctx.reply(f\\\"I am not in a voice channel, invite me first with `{self.bot_config['prefix']}join`.\\\")\\n raise commands.CommandError(\\\"Bot not connected to a voice channel.\\\")\\n\\n queue, is_playing, current_index, current_progress_ms = controller.get_queue()\\n\\n if current_index is not None:\\n await ctx.reply(f\\\"I'm already playing the room playlist!\\\")\\n raise commands.CommandError(\\\"Bot not connected to a voice channel.\\\")\\n\\n controller.start_playback()\\n await ctx.message.add_reaction(\\\"👍\\\")\",\n \"def voice(self,nick):\\n self.logger.debug(\\\"voicing %s\\\" % nick)\\n self.connection.mode(self.config[\\\"IRC/channel\\\"],\\\"+v \\\"+nick)\",\n \"async def join(ctx):\\n if ctx.message.channel.name.lower() not in tod_channels:\\n return\\n\\n room = ctx.message.channel.name.lower()\\n if room not in tod_games:\\n await amor_manager.say(\\\"Truth Or Dare not in progress in {}\\\".format(room))\\n return\\n player = ctx.message.author.name\\n if player.lower() in list(tod_games[room]['participants'].keys()):\\n await amor_manager.say(\\\"{}... you're already playing Truth or Dare here!\\\".format(room))\\n else:\\n tod_games[room]['participants'][player.lower()] = {'spins': 0}\\n await amor_manager.say(\\\"{} has joined Truth or Dare!\\\".format(player))\",\n \"async def voice(self, ctx, voice: str):\\n global voice_type\\n\\n voice_dict = {\\n 'IN_F': 'en-IN-Wavenet-A',\\n 'IN_M': 'en-IN-Wavenet-C',\\n 'US_F': 'en-US-Wavenet-G',\\n 'US_M': 'en-US-Wavenet-B',\\n 'GB_F': 'en-GB-Wavenet-A',\\n 'GB_M': 'en-GB-Wavenet-B',\\n 'AU_F': 'en-AU-Wavenet-C',\\n 'AU_M': 'en-AU-Wavenet-B'\\n }\\n if voice in voice_dict:\\n voice_type = voice_dict[voice]\\n else:\\n voice_type = voice\\n await ctx.send(\\\"New voice set to: \\\" + voice_type)\\n audioClip = TTSSource(voice_type, \\\"New voice set.\\\")\\n while not audioClip.done:\\n await asyncio.sleep(1)\\n source = discord.PCMVolumeTransformer(discord.FFmpegPCMAudio(audioClip.filename, **ffmpeg_options))\\n ctx.voice_client.play(source, after=lambda e: print('Player error: %s' % e) if e else None)\",\n \"async def joinChat(self,ctx):\\n await ctx.send('Joining the chat kiddo')\",\n \"async def tod_join(self, ctx, *args):\\n if ctx.author not in self.players:\\n self.players.append(ctx.author)\\n message = f\\\"{ctx.author.mention} has been added to the game!\\\"\\n await ctx.send(message)\\n else:\\n message = f\\\"{ctx.author.mention} has already joined!\\\"\\n await ctx.send(message)\\n\\n # Updates the role if channel exists\\n for channel in ctx.guild.channels:\\n if channel.name.startswith(\\\"truth-or-dare\\\"):\\n role = discord.utils.get(ctx.guild.roles, name=\\\"Player\\\")\\n await ctx.author.add_roles(role)\\n return\\n\\n # Creates the channel if it doesn't exist\\n role = discord.utils.get(ctx.guild.roles, name=\\\"Player\\\")\\n bots = discord.utils.get(ctx.guild.roles, name=\\\"Bots\\\")\\n overwrites = {\\n ctx.guild.default_role: discord.PermissionOverwrite(read_messages=False, send_messages=False),\\n bots: discord.PermissionOverwrite(read_messages=True, send_messages=True),\\n role: discord.PermissionOverwrite(read_messages=True, send_messages=True, connect=True, speak=True)\\n }\\n await ctx.guild.create_text_channel('truth-or-dare', overwrites=overwrites)\\n await ctx.guild.create_voice_channel('secret-voice', overwrites=overwrites)\\n\\n # Adds the role\\n role = discord.utils.get(ctx.guild.roles, name=\\\"Player\\\")\\n await ctx.author.add_roles(role)\",\n \"def connect_to_voice(self, event):\\n\\n vs = event.guild.get_member(event.author).get_voice_state()\\n \\n if not vs:\\n raise CommandFail('You\\\\'re not in a voice channel.')\\n if event.guild.id in self.players:\\n if self.players[event.guild.id].channel_id == vs.channel_id:\\n raise CommandFail('I\\\\'m already in that channel.')\\n else:\\n self.players[event.guild.id].set_channel(vs.channel)\\n return\\n\\n player = MusicPlayer(event, vs)\\n\\n self.players[event.guild.id] = player\",\n \"async def summon(self, ctx):\\n summoned_channel = ctx.message.author.voice_channel\\n if summoned_channel is None:\\n await self.bot.say('You are not in a voice channel.')\\n return False\\n\\n state = self.get_voice_state(ctx.message.server)\\n if state.voice is None:\\n state.voice = await self.bot.join_voice_channel(summoned_channel)\\n else:\\n await state.voice.move_to(summoned_channel)\\n\\n return True\",\n \"async def summon(self, channel: discord.Channel):\\n if self.voice.is_connected():\\n await self.voice.move_to(channel)\\n else:\\n await self.voice.connect(channel)\",\n \"async def on(self, ctx, *, nickname=\\\"\\\"):\\n nickname = nickname.strip()\\n mention_here = True\\n mention_everyone = True\\n if nickname == \\\"\\\":\\n nickname = \\\"Dank Bot |Music on voice!\\\"\\n try:\\n await self.bot.change_nickname(ctx.message.server.me, nickname)\\n await self.bot.say(\\\"Hey, music is playing on voice channel come! @here\\\")\\n await self.bot.delete_message(ctx.message)\\n except discord.Forbidden:\\n await self.bot.say(\\\"I cannot do that, I miss the `Change Nickname` or `Manage Messages` permission\\\")\",\n \"async def _discord(self, ctx: Context):\\n\\n await ctx.send(\\n f\\\"You can join the Brawlcord community server by using this link: {COMMUNITY_SERVER}\\\"\\n )\",\n \"async def katıl(con,channel=None):\\r\\n check = str(con.message.channel)\\r\\n\\r\\n if check == 'Direct Message with {}'.format(con.message.author.name):#COMMAND IS IN DM\\r\\n await bot.send_message(con.message.channel, \\\"**You must be in a `server voice channel` to use this command**\\\")\\r\\n\\r\\n if check != 'Direct Message with {}'.format(con.message.author.name):#COMMAND NOT IN DM\\r\\n voice_status = bot.is_voice_connected(con.message.server)\\r\\n\\r\\n if voice_status == False:#VOICE NOT CONNECTED\\r\\n await bot.join_voice_channel(con.message.author.voice.voice_channel)\\r\\n\\r\\n if voice_status == True:#VOICE ALREADY CONNECTED\\r\\n await bot.send_message(con.message.channel, \\\"**Bot zaten bir kanala bağlı !**\\\")\",\n \"async def connect(\\n self, ctx: commands.Context, *, channel: t.Optional[discord.VoiceChannel]\\n ) -> None:\\n # We prioritise user-specified channels if they are available, else we use the author's current channel.\\n destination = (\\n channel\\n if channel is not None\\n else getattr(ctx.author.voice, \\\"channel\\\", None)\\n )\\n\\n if destination is not None:\\n player = self.get_player(ctx.guild)\\n await player.connect(destination.id)\\n else:\\n fail = Embeds.status(success=False, desc=None)\\n items = ctx.message.content.split()[1:]\\n\\n # Check that the user is invoking the connect command with an invalid channel.\\n if len(items) > 0 and ctx.command.qualified_name == \\\"connect\\\":\\n fail.description = f\\\"`{' '.join(items)}` is not a valid voice channel.\\\"\\n else:\\n fail.description = \\\"You aren't connected to a voice channel!\\\"\\n\\n await ctx.send(embed=fail)\",\n \"async def join(self, ctx, key: str):\\n await ctx.message.delete()\\n async with ctx.typing():\\n data = await self.config.guild(ctx.guild).all()\\n if data[\\\"private\\\"]:\\n try:\\n if ctx.author.voice.channel.id == data[\\\"pstart\\\"]:\\n if key in data[\\\"pchannels\\\"]:\\n await ctx.author.move_to(ctx.guild.get_channel(data[\\\"pchannels\\\"][key]))\\n else:\\n await self.sendNotInStartChannelMessage(ctx, data[\\\"pstart\\\"])\\n except AttributeError:\\n await self.sendNotInStartChannelMessage(ctx, data[\\\"pstart\\\"])\\n else:\\n await ctx.send(_(\\\"Private rooms are not enabled on this server.\\\"))\",\n \"async def summon(self, ctx):\\n summoned_channel = ctx.message.author.voice_channel\\n if not summoned_channel:\\n await self.bot.say('You aren\\\\'t in a voice channel.')\\n return False\\n\\n state = self.get_voice_state(ctx.message.server)\\n if state.voice is None:\\n state.voice = await self.bot.join_voice_channel(summoned_channel)\\n try:\\n await state.voice.enable_voice_events()\\n except AttributeError:\\n pass\\n else:\\n await state.voice.move_to(summoned_channel)\\n await self.bot.say('Ready to play audio in **' + summoned_channel.name + '**!')\\n\\n return True\",\n \"async def summon(self, ctx):\\r\\n if ctx.message.channel.id != 701868237771505695:\\r\\n return await ctx.send(\\\"**Error:** Music Bot commands are only available in <#701868237771505695>\\\")\\r\\n if self.music_off:\\r\\n return await ctx.send(\\\"**Error:** Music Bot features are currently off\\\")\\r\\n\\r\\n self.played_time = time.time()\\r\\n summoned_channel = ctx.message.author.voice\\r\\n if summoned_channel is None:\\r\\n return await ctx.send(\\\"**Error:** You are not in a voice channel.\\\")\\r\\n\\r\\n if ctx.voice_client is not None:\\r\\n if self.songs != [\\\"None\\\"]:\\r\\n return await ctx.send(\\\"**Error:** There are songs playing in the other voice channel\\\")\\r\\n self.voice = ctx.voice_client\\r\\n self.voice_channel = ctx.message.channel\\r\\n return await ctx.voice_client.move_to(summoned_channel.channel)\\r\\n\\r\\n await summoned_channel.channel.connect()\\r\\n self.voice = ctx.voice_client\\r\\n self.voice_channel = ctx.message.channel\\r\\n self.processing_songs = 0\\r\\n await ctx.send(\\\"Summoned\\\")\\r\\n # await ctx.send(\\\"**Notice:** youtube_dl is currently experiencing slowdowns when handling YouTube requests (it will still work though). Until this is fixed, consider using other audio sources, such as Bandcamp or Soundcloud\\\")\\r\",\n \"async def startchannel(self, ctx, vc: discord.VoiceChannel):\\n await self.config.guild(ctx.guild).pstart.set(vc.id)\\n await self.config.guild(ctx.guild).pcat.set(vc.category_id)\\n await ctx.send(\\n _(\\n \\\"Private starting channel set. Users can join this channel to use all features of private rooms.\\\\nI recommend not allowing members to speak in this channel.\\\"\\n )\\n )\",\n \"async def on_ready():\\n # channel = client.get_channel(695669957891194952)\\n # await channel.send(\\\"Who wants to play The Game of 99?\\\")\\n print(\\\"Who wants to play The Game of 99?\\\")\",\n \"async def voicekick(self, ctx, member: discord.Member, reason: str=None):\\n if not (member.voice is not None and member.voice.channel is not None):\\n return await ctx.send('That user is not in a voice channel.')\\n overwrite = discord.PermissionOverwrite(connect=False)\\n overwrite = {ctx.guild.default_role: overwrite}\\n _reason = 'Voice kick initiated by {}'.format(ctx.author)\\n if reason is not None:\\n _reason += ', for reason \\\"{}\\\"'.format(reason)\\n try:\\n channel = await ctx.guild.create_voice_channel('Voice Kick', overwrites=overwrite, reason=_reason)\\n await member.move_to(channel, reason=_reason)\\n await channel.delete(reason=_reason)\\n await ctx.send('Done.')\\n except discord.Forbidden:\\n await ctx.send('Sorry, I don\\\\'t have permission to voice kick that person.')\",\n \"async def create_voice_client(self, channel):\\n voice = await self.bot.join_voice_channel(channel)\\n try:\\n await voice.enable_voice_events()\\n except AttributeError:\\n pass\\n state = self.get_voice_state(channel.server)\\n state.voice = voice\",\n \"async def enable(self, ctx):\\n await self.config.guild(ctx.guild).auto.set(True)\\n await ctx.send(_(\\\"Automatic voicechannel creation enabled.\\\"))\",\n \"async def inviteme(self):\\r\\n\\r\\n #Your code will go here\\r\\n await self.bot.say(\\\"Here is a link to Invite Me: http://bit.ly/TacoBot\\\")\",\n \"async def çıkış(con):\\r\\n check=str(con.message.channel)\\r\\n if check == 'Direct Message with {}'.format(con.message.author.name):#COMMAND USED IN DM\\r\\n await bot.send_message(con.message.channel,\\\"**You must be in a `server voice channel` to use this command**\\\")\\r\\n\\r\\n if check != 'Direct Message with {}'.format(con.message.author.name):#COMMAND NOT IN DM\\r\\n \\r\\n # IF VOICE IS NOT CONNECTED\\r\\n if bot.is_voice_connected(con.message.server) == False:\\r\\n await bot.send_message(con.message.channel,\\\"**Bot kanala bağlanmamış !**\\\")\\r\\n\\r\\n # VOICE ALREADY CONNECTED\\r\\n if bot.is_voice_connected(con.message.server) == True:\\r\\n bot.loop.create_task(queue_songs(con,True))\",\n \"def join_channel(self, channel):\\r\\n self._send('JOIN #%s\\\\r\\\\n' % channel)\",\n \"def joingroup_command(update,context):\\n update.message.reply_text('Want to chat with other CTF players or ask questions to admins? Use the following channel:\\\\r\\\\nhttps://t.me/joinchat/CYsj-xwzlFqIbQPPeo04bw')\",\n \"def voice(phenny, input):\\n if not input.admin:\\n return phenny.say(\\\"Not an admin!\\\")\\n if not input.sender.startswith('#'):\\n return phenny.say(\\\"Command must be given in channel!\\\")\\n nick = input.group(2)\\n verify = auth_check(phenny, input.nick, nick)\\n if verify:\\n channel = input.sender\\n if not nick:\\n nick = input.nick\\n phenny.write(['MODE', channel, \\\"+v\\\", nick])\\n else:\\n phenny.say(\\\"Nick not verified\\\")\",\n \"async def connect_voice_channel(client: \\\"Client\\\", *args, **kwargs) -> None:\\n total_args_len = len(args) + len(kwargs)\\n\\n # use this rather peculiar method to allow **kwargs and *args interchangeably\\n if total_args_len == 1:\\n await _connect_voice_channel_channel(client, *args, **kwargs)\\n else:\\n await _connect_voice_channel_guild_channel(client, *args, **kwargs)\",\n \"async def hello(self, ctx):\\n await ctx.send(random.choice(self.greetings))\",\n \"def signedOn(self):\\n self.join(self.channel)\",\n \"async def joinchannel(self, ctx: commands.Context, *channels: str):\\n for channel in channels:\\n channel_query = self._channel_query(channel)\\n\\n if channel_query == None:\\n await ctx.send(f\\\"Unable to join {channel}.\\\")\\n continue\\n\\n channel = self.bot.get_channel(channel_query.id)\\n guild = self.bot.get_guild(SERVER_ID)\\n member = guild.get_member(ctx.author.id)\\n\\n if channel == None:\\n await ctx.send(f\\\"Unable to join {channel}.\\\")\\n continue\\n\\n # Don't let a user join the channel again if they are already in it.\\n if channel.permissions_for(member).is_superset(JOINED_PERMISSIONS):\\n await ctx.send(f\\\"You're already a member of {channel}.\\\")\\n continue\\n\\n await channel.set_permissions(member, read_messages=True, reason=\\\"UQCSbot added.\\\")\\n join_message = await channel.send(f\\\"{member.display_name} joined {channel.mention}\\\")\\n await join_message.add_reaction(\\\"👋\\\")\\n await ctx.send(f\\\"You've joined {channel.mention}.\\\")\",\n \"def help_command(update,context):\\r\\n update.message.reply_text('I am a Voice bot')\",\n \"async def leave(self, ctx):\\n if ctx.guild.voice_client:\\n await ctx.guild.voice_client.disconnect()\\n await ctx.send(\\\"Left voice channel.\\\")\\n else:\\n await ctx.message.add_reaction('\\\\U0001F615');\\n await ctx.send(\\\"Not in a voice channel.\\\")\",\n \"async def turn(ctx):\\n if ctx.message.channel.name.lower() not in tod_channels:\\n return\\n\\n room = ctx.message.channel.name.lower()\\n if room not in tod_games:\\n await amor_manager.say(\\\"Truth Or Dare not in progress in {}\\\".format(room))\\n else:\\n await amor_manager.say(\\\"Current Player is {}\\\".format(tod_games[room]['current']))\",\n \"def signedOn(self):\\n self.join(self.factory.channel)\",\n \"async def now_playing(self, ctx):\\n if ctx.guild is None:\\n await ctx.reply(\\\"This command can only be used in a server, not in DMs.\\\")\\n raise commands.CommandError(\\\"Invoker not in a guild.\\\")\\n\\n if ctx.voice_client is None or ctx.voice_client.channel is None:\\n await ctx.reply(f\\\"I am not in a voice channel, invite me first with `{self.bot_config['prefix']}join`.\\\")\\n raise commands.CommandError(\\\"Bot not connected to a voice channel.\\\")\\n\\n if ctx.author.voice is None or ctx.author.voice.channel is None:\\n await ctx.reply(\\\"You need to be in a voice channel to use this command.\\\")\\n raise commands.CommandError(\\\"Invoker not connected to a voice channel.\\\")\\n\\n if ctx.voice_client is not None and ctx.author.voice.channel != ctx.voice_client.channel:\\n await ctx.reply(\\\"You need to be in the same voice channel as the bot to use this command.\\\")\\n raise commands.CommandError(\\\"Invoker not in same voice channel as bot.\\\")\\n\\n if ctx.voice_client is not None and ctx.voice_client.channel is not None:\\n controller = SpotifyController.get_instance(ctx.voice_client.channel.id)\\n if controller is None:\\n await ctx.reply(f\\\"I'm not playing anything at the moment.\\\")\\n raise commands.CommandError(\\\"Bot not connected to active spotify session.\\\")\\n else:\\n await ctx.reply(f\\\"I am not in a voice channel, invite me first with `{self.bot_config['prefix']}join`.\\\")\\n raise commands.CommandError(\\\"Bot not connected to a voice channel.\\\")\\n\\n sp = controller.get_api()\\n info = sp.current_playback()\\n if not controller.is_playing_on_bot():\\n await ctx.send(\\\"Not playing anything at the moment...\\\")\\n return\\n\\n # Add URI\\n if info is not None:\\n track_info = info['item']\\n try:\\n thumbnail = track_info['album']['images'][0]\\n except IndexError:\\n thumbnail = None\\n msg_embed = Embed()\\n artist_url = track_info['artists'][0]['external_urls']['spotify']\\n msg_embed.set_author(name=SpotifyController.format_artist(track_info), url=artist_url)\\n msg_embed.title = SpotifyController.format_title(track_info)\\n msg_embed.description = f\\\"{SpotifyController.format_album_name(track_info)}\\\\n\\\"\\n msg_embed.url = track_info['external_urls']['spotify']\\n msg_embed.set_thumbnail(url=thumbnail['url'])\\n msg_embed.set_footer(text=f\\\"{SpotifyController.format_progress(info)}\\\")\\n await ctx.reply(embed=msg_embed)\\n else:\\n await ctx.send(\\\"Not playing anything at the moment...\\\")\",\n \"def joinedChannel(self, channel, users):\\n pass\",\n \"async def add(self, ctx, *, channel: discord.VoiceChannel):\\n auto_channels = await self.config.guild(ctx.guild).auto_channels()\\n if auto_channels is None:\\n auto_channels = []\\n auto_channels.append(channel.id)\\n await self.config.guild(ctx.guild).auto_channels.set(auto_channels)\\n await ctx.send(\\n _(\\\"Startchannel used for automatic voicechannels added: {channel}\\\").format(\\n channel=channel.name\\n )\\n )\",\n \"def join(self, channel):\\n raise NotImplementedError\",\n \"async def p(con,*,url):\\r\\n check = str(con.message.channel)\\r\\n if check == 'Direct Message with {}'.format(con.message.author.name):\\r\\n await bot.send_message(con.message.channel, \\\"**You must be in a `server voice channel ` to use this command**\\\")\\r\\n\\r\\n if check != 'Direct Message with {}'.format(con.message.author.name):\\r\\n if bot.is_voice_connected(con.message.server) == False:\\r\\n await bot.join_voice_channel(con.message.author.voice.voice_channel)\\r\\n\\r\\n if bot.is_voice_connected(con.message.server) == True:\\r\\n if player_status[con.message.server.id]==True:\\r\\n song_names[con.message.server.id].append(url)\\r\\n await bot.send_message(con.message.channel, \\\"**Şarkı sıraya alındı :white_check_mark:**\\\")\\r\\n\\r\\n\\r\\n \\r\\n if player_status[con.message.server.id]==False:\\r\\n player_status[con.message.server.id]=True\\r\\n song_names[con.message.server.id].append(url)\\r\\n song=await bot.voice_client_in(con.message.server).create_ytdl_player(song_names[con.message.server.id][0], ytdl_options=opts, after=lambda: bot.loop.create_task(after_song(con,False)))\\r\\n servers_songs[con.message.server.id]=song\\r\\n servers_songs[con.message.server.id].start()\\r\\n msg = await bot.send_message(con.message.channel, \\\"**Şuanda oynatılan > {}**\\\".format(servers_songs[con.message.server.id].title))\\r\\n now_playing[con.message.server.id]=msg\\r\\n song_names[con.message.server.id].pop(0)\",\n \"async def x5lol(ctx):\\n #Shortcut to Author of the Message\\n atr = ctx.author\\n #Shortcut to Author Current Voice Channel\\n currentvc = atr.voice.channel.name\\n #Shortcut to Voice Channel Members list\\n usrs = atr.voice.channel.members\\n #Specify what role will use \\n role = ctx.guild.get_role(\\\"\\\"\\\"Insert role id\\\"\\\"\\\")\\n \\n pot = []\\n #await ctx.send(atr.name +' '+currentvc)\\n for i in usrs:\\n if role in i.roles:\\n #await ctx.send('O ' + str(i) + ' É ' + str(role))\\n nick = (str(i.nick))\\n if nick != 'None':\\n pot.append(str(i.nick))\\n else:\\n pot.append(str(i))\\n \\n \\n #NOTE:The math part of team ballance isn't done yet\\n # For now, Bot shuffle the names, then send to Text Channel on Discord \\n random.shuffle(pot)\\n await ctx.send(pot)\",\n \"async def on_voice_state_update(member, member_before_voice_state, member_after_voice_state):\\n server = member.guild\\n channel = find_channel(server)\\n \\n voice_channel_before = member_before_voice_state.channel\\n voice_channel_after = member_after_voice_state.channel\\n \\n if voice_channel_before == voice_channel_after:\\n # No change\\n return\\n\\n if voice_channel_before is None:\\n # The member was not on a voice channel before the change\\n # Discord api sets voice_channel to None if there was no channel to begin with.\\n msg = \\\"**\\\\'%s#%s\\\\'** joined voice channel **\\\\'_%s_\\\\'** at %s\\\" % (member.name, member.discriminator, voice_channel_after.name, datetime.datetime.now().strftime(\\\"%m/%d/%Y, %H:%M:%S\\\"))\\n else:\\n # The member was on a voice channel before the change\\n if voice_channel_after is None:\\n # The member is no longer on a voice channel after the change\\n msg = \\\"**\\\\'%s#%s\\\\'** left voice channel **\\\\'_%s_\\\\'** at %s\\\" % (member.name, member.discriminator, voice_channel_before.name, datetime.datetime.now().strftime(\\\"%m/%d/%Y, %H:%M:%S\\\"))\\n else:\\n # The member is still on a voice channel after the change\\n msg = \\\"**\\\\'%s#%s\\\\'** switched from voice channel **\\\\'_%s_\\\\'** to **\\\\'_%s_\\\\'** at %s\\\" % (member.name, member.discriminator,\\n voice_channel_before.name, voice_channel_after.name, datetime.datetime.now().strftime(\\\"%m/%d/%Y, %H:%M:%S\\\"))\\n \\n # Try to log the voice event to the channel\\n try:\\n await channel.send(msg)\\n\\n except:\\n # No message could be sent to the channel; force refresh the channel cache and try again\\n channel = find_channel(server, refresh=True)\\n if channel is None:\\n # The channel could not be found\\n print(\\\"Error: channel #%s does not exist on server %s.\\\" % (channel_name, server))\\n else:\\n # Try sending a message again\\n try:\\n await channel.send(msg)\\n except discord.DiscordException as exception:\\n # Print the exception\\n print(\\\"Error: no message could be sent to channel #%s on server %s. Exception: %s\\\" % (channel_name, server, exception))\",\n \"def handle_autovoiceon(bot, ievent):\\n try: ievent.chan.data.autovoice = 1\\n except TypeError: ievent.reply('no %s in channel database' % ievent.channel) ; return\\n ievent.reply('autovoice enabled on %s' % ievent.channel)\",\n \"async def react_join(a: Message):\\n if a.action.member_id == club_id:\\n await a.answer(r_register_help)\\n stats.jincr()\",\n \"async def tick(self):\\n room = self.bot.Room.load('19961884194@chatroom')\\n await room.ready()\\n await room.say(f'i love you -> {datetime.now()}')\",\n \"def joined(self, channel):\\n log.msg(\\\"GbRobot joined \\\" + self.factory.channel)\",\n \"async def test_silence_voice_sibling_channel(self):\\n with unittest.mock.patch.object(silence, \\\"VOICE_CHANNELS\\\") as mock_voice_channels:\\n mock_voice_channels.get.return_value = self.text_channels[1].id\\n\\n message = \\\"This should show up as {channel}.\\\"\\n await self.cog.send_message(message, self.text_channels[1], self.voice_channel, alert_target=True)\\n\\n updated_message = message.format(channel=self.voice_channel.mention)\\n self.text_channels[1].send.assert_awaited_once_with(updated_message)\\n\\n mock_voice_channels.get.assert_called_once_with(self.voice_channel.id)\\n self.bot.get_channel.assert_called_once_with(self.text_channels[1].id)\",\n \"async def welcome(self, ctx, member: discord.Member = None):\\n\\n await ctx.message.delete()\\n if not member:\\n return await ctx.send('Inform a member!')\\n\\n bots_and_commands_channel = discord.utils.get(ctx.guild.channels, id=bots_and_commands_channel_id)\\n await bots_and_commands_channel.send(\\n f'''__**Welcome to the Language Sloth**__! {member.mention}\\nThis is a community of people who are practicing and studying languages from all around the world! While you're here, you will also make tons of new friends! There is a lot to do here in the server but there are some things you should do to start off.\\n\\n1. Make sure you go check out the <#688967996512665655> and the informations page. These rules are very important and are taken seriously here on the server.\\n2. After you have finished reading those, you can assign yourself some roles at <#679333977705676830> <#683987207065042944> <#688037387561205824> and <#562019509477703697>! These roles will give you access to different voice and text channels! To choose your role click on the flag that best represents your native language.\\n\\nIf you have any questions feel free to ask! And if you experience any type of problem make sure you let a staff member know right away''')\",\n \"def joined(self, channel):\\n log.msg(\\\"[I have joined %s]\\\" % channel)\\n self.msg(channel, \\\"user1: bonbon\\\")\",\n \"async def support(self, ctx):\\n await ctx.send('Join the support server here: https://discord.gg/bAq8Ec5JPQ')\",\n \"def start(update: Update, context: CallbackContext) -> None:\\n if update.effective_chat:\\n context.bot.send_message(chat_id=update.effective_chat.id,\\n text=\\\"I'm a bot, please talk to me!\\\")\",\n \"async def rebind(self, ctx, channel: discord.TextChannel=None):\\n player = self.bot.lavalink.players.get(ctx.guild.id)\\n if not player.is_connected:\\n return await ctx.send(\\\"I'm not connected to a voice channel :no_entry:\\\")\\n if not channel:\\n channel = ctx.channel\\n player.store('channel', channel.id)\\n await ctx.send(\\\"Messages will now be sent in {} <:done:403285928233402378>\\\".format(channel.mention))\",\n \"def vc_only():\\n\\n async def check(ctx):\\n if ctx.guild and ctx.author.voice:\\n if not ctx.guild.me.voice or ctx.author.voice.channel == ctx.guild.me.voice.channel:\\n return True\\n await ctx.reply(\\\"I'm already in another voice channel!\\\")\\n return False\\n await ctx.reply('You must join a server voice channel first!')\\n return False\\n\\n return commands.check(check)\",\n \"def join(var, wrapper, message):\\n # keep this and the event in fjoin() in sync\\n evt = Event(\\\"join\\\", {\\n \\\"join_player\\\": join_player,\\n \\\"join_deadchat\\\": join_deadchat,\\n \\\"vote_gamemode\\\": vote_gamemode\\n })\\n if not evt.dispatch(var, wrapper, message, forced=False):\\n return\\n if var.PHASE in (\\\"none\\\", \\\"join\\\"):\\n if wrapper.private:\\n return\\n if var.ACCOUNTS_ONLY:\\n if wrapper.source.account is None:\\n wrapper.pm(messages[\\\"not_logged_in\\\"])\\n return\\n if evt.data[\\\"join_player\\\"](var, wrapper) and message:\\n evt.data[\\\"vote_gamemode\\\"](var, wrapper, message.lower().split()[0], doreply=False)\\n\\n else: # join deadchat\\n if wrapper.private and wrapper.source is not wrapper.target:\\n evt.data[\\\"join_deadchat\\\"](var, wrapper.source)\",\n \"async def support(self, ctx):\\n link = \\\"https://discord.gg/J78uAgZ\\\"\\n await ctx.send(\\\"Join my support server if you need help with commands!\\\\n \\\" + link)\",\n \"async def pause(self, ctx):\\n voice = discord.utils.get(self.bot.voice_clients, guild=ctx.guild)\\n\\n if voice:\\n if voice.is_playing():\\n voice.pause()\\n await ctx.send(\\\"Playing paused.\\\")\\n else:\\n await ctx.message.add_reaction('\\\\U0001F615');\\n await ctx.send(\\\"Not playing anything right now.\\\")\\n else:\\n await ctx.message.add_reaction('\\\\U0001F615')\\n await ctx.send(\\\"Not in a voice channel.\\\")\",\n \"async def _99join(ctx):\\n NNB.add_player(ctx.message.author)\\n await ctx.send(\\\"Welcome to the game, {}\\\".format(ctx.message.author.name))\\n print(\\\"Added {} to the game.\\\".format(ctx.message.author.name))\",\n \"async def _bot(ctx):\\n await ctx.send('Yes, the bot is cool.')\",\n \"def join_channel(self) -> None:\\n response = self.client.conversations_list()\\n for channel in response.data[\\\"channels\\\"]:\\n if channel[\\\"name\\\"] == BaseConfig.SLACK_CHANNEL:\\n self.channel_id = channel[\\\"id\\\"]\\n if not self.channel_id:\\n raise Exception(f\\\"Channel: {BaseConfig.SLACK_CHANNEL} was not found\\\")\\n self.client.conversations_join(channel=self.channel_id)\",\n \"async def enable_channel(self, ctx, *channels: discord.TextChannel):\\n channels = channels or (ctx.channel, )\\n await ctx.guild_profile.permissions.enable_channels(channels)\\n await ctx.send_line(f\\\"{ctx.emotes.web_emotion.galka} Bot commands and messages has been enabled in specified channels.\\\")\",\n \"async def _join_money(ctx):\\n await join_money(bot, ctx.message.author)\",\n \"def leaveroom(self, the_id):\\r\\n the_id = Client.toroomid(the_id)\\r\\n self.tx_cmd(FCTYPE.JOINCHAN, 0, the_id, FCCHAN.PART)\",\n \"async def managechannels(self, ctx:commands.Context):\",\n \"async def request_to_speak(self) -> None:\\n if self.voice is None or self.voice.channel is None:\\n raise ClientException('Cannot request to speak while not connected to a voice channel.')\\n\\n payload = {\\n 'channel_id': self.voice.channel.id,\\n 'request_to_speak_timestamp': datetime.datetime.utcnow().isoformat(),\\n }\\n\\n if self._state.self_id != self.id:\\n payload['suppress'] = False\\n await self._state.http.edit_voice_state(self.guild.id, self.id, payload)\\n else:\\n await self._state.http.edit_my_voice_state(self.guild.id, payload)\",\n \"def newJoin(user, channel):\\n\\n message = \\\"\\\"\\\"\\nWelcome to the official Slack for ZenCash!\\n\\n\\nThe official links are:\\nhttps://github.com/ZenCashOfficial/\\nhttps://zencashofficial.io/\\n\\nNOTICE:\\nDue to the recent plague that is SlackBot spamming with `/remind`, please do not click any links sent to you in a DM from slackbot.\\n\\nAdditionally, please copy the message to #spam so the Admins can ban the user.\\n\\nPlease remember to be civil, and have a great day!\\n\\\"\\\"\\\"\\n\\n\\n # General\\n if channel == 'C4QGQ8SEM':\\n return message\\n\\n # Bottesting\\n if channel == \\\"C5JCER3NG\\\":\\n return message\",\n \"def start(bot, update):\\n update.message.reply_text('Hi!')\",\n \"async def invite(self, ctx):\\n perms = discord.Permissions.text()\\n perms.update(read_messages=True, manage_messages=True,\\n mention_everyone=False, send_tts_messages=False)\\n await ctx.send(f'Invite me here:\\\\n<{discord.utils.oauth_url(self.bot.user.id, perms)}>')\",\n \"async def on_voice_state_update(self, member, before, after):\\n # This makes sure that the bot leaves\\n # the voice channel after a settable timeout\\n # for performance improvements.\\n timeout = 600 # seconds\\n if not member.id == self.bot.user.id:\\n return\\n elif before.channel is None:\\n player = self.bot.lavalink.player_manager.get(member.guild.id)\\n time = 0\\n while True:\\n await asyncio.sleep(1)\\n time += 1\\n\\n # Check if after channel none & clear queue\\n if after.channel is None:\\n player.queue.clear()\\n # Stop the current track so Lavalink consumes less resources.\\n await player.stop()\\n break\\n\\n # Timeout after `timeout` seconds for performance improvements.\\n if player.is_playing:\\n time = 0\\n if time == timeout:\\n ch = await self.bot.fetch_channel(\\n self.preferred_channels[str(player.guild_id)])\\n await member.guild.change_voice_state(channel=None)\\n await ch.send(embed=self.reply_embed(\\\"Left voice channel after no activity for `10 minutes`.\\\"))\\n del self.preferred_channels[str(player.guild_id)]\\n if not player.is_connected:\\n break\",\n \"def play_command(update,context):\\n update.message.reply_text('Rkrt: Welcome on board. Let\\\\'s see if you are worth the challenge. To find the invite code and land on planet hackazon you will need to solve this first. Ready for a ride?!')\\n time.sleep(5)\\n update.message.reply_text('Mx: During intergalactical travel, time does not matter. Any enemies could be listening in at any time. This is why the crew is sometimes forced to used coded languages to exchange messages between vessels. To decrypt messages every crew member can use the key on their hardware tokens.')\\n time.sleep(10)\\n update.message.reply_text('Jms: Mx we are getting a distress signal from vessel Vigenere. Do you copy?')\\n time.sleep(3)\\n update.message.reply_text('Mx: [gasps...]')\\n time.sleep(1)\\n update.message.reply_text('Mx: This one is for you rookie... See you on the other side.')\\n update.message.reply_text('Kyjkda kghc tir Yeevobyj: BgXfsGofrCyrDouwfh\\\\r\\\\nUsfcfqg zb dywzv lcfy ij cqff hsnal jjoa:\\\\r\\\\nCKJ{en55td2my6jse8361a427p3xf319tf12}')\",\n \"async def move(self, ctx, *, channel_name: str):\\r\\n if ctx.voice_client:\\r\\n if discord.utils.get(ctx.guild.channels, name=channel_name):\\r\\n await ctx.voice_client.move_to(discord.utils.get(ctx.guild.channels, name=channel_name))\\r\\n print(\\\"[INFO] Bot moved to channel {0}\\\".format(channel_name))\\r\\n self.channel_name = channel_name\\r\\n else:\\r\\n await ctx.send(\\\"Channel {0} not found\\\".format(channel_name))\\r\\n else:\\r\\n await self.connect(ctx, channel_name=channel_name)\\r\\n await ctx.message.delete()\",\n \"async def test_force_voice_sync_no_channel(self):\\n await self.cog.cog_load()\\n\\n channel = MockVoiceChannel(guild=MockGuild(afk_channel=None))\\n new_channel = MockVoiceChannel(delete=AsyncMock())\\n channel.guild.create_voice_channel.return_value = new_channel\\n\\n await self.cog._force_voice_sync(channel)\\n\\n # Check channel creation\\n overwrites = {\\n channel.guild.default_role: PermissionOverwrite(speak=False, connect=False, view_channel=False)\\n }\\n channel.guild.create_voice_channel.assert_awaited_once_with(\\\"mute-temp\\\", overwrites=overwrites)\\n\\n # Check bot deleted channel\\n new_channel.delete.assert_awaited_once()\",\n \"async def channel(self, ctx):\\n pass\",\n \"async def test_silence_voice(self):\\n message = \\\"This should show up just here.\\\"\\n await self.cog.send_message(message, self.text_channels[0], self.voice_channel, alert_target=False)\\n self.text_channels[0].send.assert_awaited_once_with(message)\\n self.text_channels[1].send.assert_not_called()\",\n \"def sendVoiceMessage(self,data):\\n\\n self.controlProtocol.sendVoiceMessage(data)\",\n \"async def links(self, ctx):\\n await ctx.send(\\\"https://discordapp.com/channels/566451504332931073/681617252814159904/755489156146397311\\\")\",\n \"def join(bot, event, *args):\\n yield from _joinEvent(bot, event, list(args))\",\n \"async def CoMLegendBuilder(self, ctx):\\n me = CoachService.discord_user_to_coach(ctx.author)\\n data = getattr(special_play, inspect.currentframe().f_code.co_name)(ctx.channel.name, me)\\n await self.send_embed(data, ctx)\",\n \"def join(self, channel):\\n self.channels[channel.name.lower()] = channel\\n channel.protocol = self.protocol\\n self.protocol.join(channel.name)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8252359","0.8217584","0.8208582","0.7949153","0.79472905","0.78682876","0.786053","0.77314293","0.7688857","0.76547545","0.75633955","0.75611305","0.7534342","0.753236","0.7313502","0.71708095","0.7167358","0.7105433","0.70923823","0.6946052","0.6941861","0.68807536","0.68545514","0.6847914","0.6792473","0.67827594","0.67591435","0.6739941","0.6677625","0.66401595","0.6623254","0.660976","0.6603921","0.65363395","0.65117466","0.6483533","0.6412413","0.6411179","0.6409071","0.63684285","0.633795","0.62947845","0.6288552","0.6251798","0.6235826","0.62353915","0.62268406","0.6221526","0.62100214","0.6202718","0.6197466","0.61353976","0.61318564","0.6115696","0.61150134","0.61135834","0.6103898","0.61009955","0.6073101","0.6062551","0.6061634","0.60582334","0.6055785","0.60524654","0.6047923","0.604489","0.60246044","0.6011098","0.60054547","0.60022426","0.5968997","0.59679097","0.59676325","0.59620494","0.59594905","0.59536296","0.5935952","0.59167427","0.5912264","0.59090024","0.59021056","0.5871402","0.58696955","0.58339196","0.5830637","0.58255607","0.5811075","0.58038694","0.5799541","0.5796719","0.57858944","0.5785329","0.57768375","0.57690996","0.57659405","0.5762884","0.5758879","0.5758516","0.57577735","0.5742073"],"string":"[\n \"0.8252359\",\n \"0.8217584\",\n \"0.8208582\",\n \"0.7949153\",\n \"0.79472905\",\n \"0.78682876\",\n \"0.786053\",\n \"0.77314293\",\n \"0.7688857\",\n \"0.76547545\",\n \"0.75633955\",\n \"0.75611305\",\n \"0.7534342\",\n \"0.753236\",\n \"0.7313502\",\n \"0.71708095\",\n \"0.7167358\",\n \"0.7105433\",\n \"0.70923823\",\n \"0.6946052\",\n \"0.6941861\",\n \"0.68807536\",\n \"0.68545514\",\n \"0.6847914\",\n \"0.6792473\",\n \"0.67827594\",\n \"0.67591435\",\n \"0.6739941\",\n \"0.6677625\",\n \"0.66401595\",\n \"0.6623254\",\n \"0.660976\",\n \"0.6603921\",\n \"0.65363395\",\n \"0.65117466\",\n \"0.6483533\",\n \"0.6412413\",\n \"0.6411179\",\n \"0.6409071\",\n \"0.63684285\",\n \"0.633795\",\n \"0.62947845\",\n \"0.6288552\",\n \"0.6251798\",\n \"0.6235826\",\n \"0.62353915\",\n \"0.62268406\",\n \"0.6221526\",\n \"0.62100214\",\n \"0.6202718\",\n \"0.6197466\",\n \"0.61353976\",\n \"0.61318564\",\n \"0.6115696\",\n \"0.61150134\",\n \"0.61135834\",\n \"0.6103898\",\n \"0.61009955\",\n \"0.6073101\",\n \"0.6062551\",\n \"0.6061634\",\n \"0.60582334\",\n \"0.6055785\",\n \"0.60524654\",\n \"0.6047923\",\n \"0.604489\",\n \"0.60246044\",\n \"0.6011098\",\n \"0.60054547\",\n \"0.60022426\",\n \"0.5968997\",\n \"0.59679097\",\n \"0.59676325\",\n \"0.59620494\",\n \"0.59594905\",\n \"0.59536296\",\n \"0.5935952\",\n \"0.59167427\",\n \"0.5912264\",\n \"0.59090024\",\n \"0.59021056\",\n \"0.5871402\",\n \"0.58696955\",\n \"0.58339196\",\n \"0.5830637\",\n \"0.58255607\",\n \"0.5811075\",\n \"0.58038694\",\n \"0.5799541\",\n \"0.5796719\",\n \"0.57858944\",\n \"0.5785329\",\n \"0.57768375\",\n \"0.57690996\",\n \"0.57659405\",\n \"0.5762884\",\n \"0.5758879\",\n \"0.5758516\",\n \"0.57577735\",\n \"0.5742073\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.69570655"},"document_rank":{"kind":"string","value":"19"}}},{"rowIdx":22499,"cells":{"query":{"kind":"string","value":"Makes the bot leave your voice channel"},"document":{"kind":"string","value":"async def leave(self, ctx):\n if ctx.guild is None:\n await ctx.reply(\"This command can only be used in a server, not in DMs.\")\n raise commands.CommandError(\"Invoker not in a guild.\")\n\n if ctx.author.voice is None or ctx.author.voice.channel is None:\n await ctx.reply(\"You need to be in a voice channel to use this command.\")\n raise commands.CommandError(\"Invoker not connected to a voice channel.\")\n\n if ctx.voice_client is not None and ctx.author.voice.channel != ctx.voice_client.channel:\n await ctx.reply(\"You need to be in the same voice channel as the bot to use this command.\")\n raise commands.CommandError(\"Invoker not in same voice channel as bot.\")\n\n if ctx.voice_client is not None:\n SpotifyController.stop_for_channel(ctx.voice_client.channel.id)\n await ctx.voice_client.disconnect()\n return\n await ctx.send('I am not connected to a voice channel...')"},"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":["async def leave(self, ctx):\n if ctx.guild.voice_client:\n await ctx.guild.voice_client.disconnect()\n await ctx.send(\"Left voice channel.\")\n else:\n await ctx.message.add_reaction('\\U0001F615');\n await ctx.send(\"Not in a voice channel.\")","async def _leave(self, ctx: commands.Context):\n\n if not ctx.voice_state.voice:\n return await ctx.send('Not connected to any voice channel.')\n\n await ctx.voice_state.stop()\n del self.voice_states[ctx.guild.id]\n await ctx.message.add_reaction('👋')","async def _leave(self, ctx: commands.Context):\n\n if not ctx.voice_state.voice:\n return await ctx.send('Not connected to any voice channel.')\n\n await ctx.voice_state.stop()\n del self.voice_states[ctx.guild.id]","async def leave(self, msg):\n if msg.author.voice is not None and msg.voice_client is not None:\n if msg.voice_client.is_playing() is True or self.player[msg.guild.id]['queue']:\n self.player[msg.guild.id]['queue'].clear()\n msg.voice_client.stop()\n return await msg.voice_client.disconnect(), await msg.message.add_reaction(emoji='✅')\n\n return await msg.voice_client.disconnect(), await msg.message.add_reaction(emoji='✅')\n\n if msg.author.voice is None:\n return await msg.send(\"You must be in the same voice channel as bot to disconnect it via command\")","async def leave(self, ctx):\n\n if ctx.voice_client is not None:\n return await ctx.voice_client.disconnect()","async def leave(self, ctx: commands.Context) -> Optional[discord.VoiceChannel]:\n\n if ctx.guild.id in self.queue:\n self.queue[ctx.guild.id].cleanup()\n del self.queue[ctx.guild.id]\n\n await maybe_coroutine(ctx.voice_client.stop)\n\n channel = ctx.voice_client.channel\n await ctx.voice_client.disconnect(force=True)\n return channel","async def leave(self, ctx):\n player = self.bot.lavalink.player_manager.get(ctx.guild.id)\n\n if not player.is_connected:\n # We can't disconnect, if we're not connected.\n return await ctx.send(embed=self.error_embed(f'Not playing. [{ctx.message.author.mention}]'))\n\n if not ctx.author.voice or (player.is_connected and ctx.author.voice.channel.id != int(player.channel_id)):\n # Abuse prevention. Users not in voice channels, or not in the same voice channel as the bot\n # may not disconnect the bot.\n return await ctx.send(embed=self.error_embed(f'Not connected to the same voice channel. [{ctx.message.author.mention}]'))\n\n # Clear the queue to ensure old tracks don't start playing\n # when someone else queues something.\n player.queue.clear()\n # Stop the current track so Lavalink consumes less resources.\n await player.stop()\n # Disconnect from the voice channel.\n await ctx.send(embed=self.reply_embed(f\"Left channel `{ctx.message.author.voice.channel}` [{ctx.message.author.mention}]\"))\n await ctx.guild.change_voice_state(channel=None)","async def leaveForce(self, ctx):\n await ctx.voice_client.disconnect()","async def _leave(self, ctx: commands.Context):\n\n await ctx.voice_state.stop()\n del self.voice_states[ctx.guild.id]","async def stop(self, ctx):\n voice = discord.utils.get(self.bot.voice_clients, guild=ctx.guild)\n\n if voice:\n if voice.is_playing():\n voice.stop()\n await ctx.send(\"Playing stopped.\") \n else:\n await ctx.message.add_reaction('\\U0001F615')\n await ctx.send(\"Not playing anything right now.\")\n else:\n await ctx.message.add_reaction('\\U0001F615')\n await ctx.send(\"Not in a voice channel.\")","async def stop(ctx):\r\n\r\n await ctx.voice_client.disconnect()","async def disconnect(ctx):\n if ctx.voice_client is not None:\n await ctx.voice_client.disconnect()","async def disconnect(self, ctx):\r\n if ctx.voice_client:\r\n await ctx.voice_client.disconnect()\r\n print(\"[INFO] Bot disconnected from channel {0}\".format(self.channel_name))\r\n await ctx.message.delete()","def voice_decrease():\n request_command(tv_command=TVCommand.voice_decrease)","async def disconnect(self, ctx):\n player = self.bot.lavalink.players.get(ctx.guild.id)\n if not player.is_connected:\n return await ctx.send(\"I'm not currently connected to a voice channel :no_entry:\")\n if not ctx.author.voice or (player.is_connected and player.connected_channel.id != ctx.author.voice.channel.id):\n return await ctx.send(\"You have to be in my voice channel to disconnect :no_entry:\")\n if player.fetch(\"sessionowner\") == ctx.author.id:\n player.queue.clear()\n await player.disconnect()\n player.delete(\"votes\")\n await ctx.send(\"Disconnected <:done:403285928233402378>\")\n else:\n await ctx.send(\"Only the session owner can disconnect the bot :no_entry:\")","async def stop(self, ctx):\n if ctx.voice_client != None:\n await ctx.voice_client.disconnect()","async def stop(self, ctx):\n\n await ctx.voice_client.disconnect()","async def stop(self, ctx):\n\n await ctx.voice_client.disconnect()","async def _leave(self, server_id):\n srv = self.get_server_dict(server_id)\n if srv['voice'] and srv['voice'].channel:\n await srv['voice'].disconnect()\n srv['voice'] = None","async def disconnect(self, ctx):\r\n if ctx.message.channel.id != 701868237771505695:\r\n return await ctx.send(\"**Error:** Music Bot commands are only available in <#701868237771505695>\")\r\n if self.music_off:\r\n return await ctx.send(\"**Error:** Music Bot features are currently off\")\r\n if ctx.voice_client is None or ctx.voice_client is not self.voice:\r\n return await ctx.send(\"**Error:** You must be connected to the voice channel.\")\r\n try:\r\n await ctx.voice_client.stop()\r\n except:\r\n pass\r\n await ctx.voice_client.disconnect()\r\n self.songs = [\"None\"]\r\n self.processing_songs = 0\r\n self.del_all_files()\r\n self.voice = None\r\n await ctx.send(\"Disconnected\")","def leave(bot, event, conversation_id=None, *args):\n\n leave_quietly = False\n convs = []\n\n if not conversation_id:\n convs.append(event.conv.id_)\n elif conversation_id==\"quietly\":\n convs.append(event.conv.id_)\n leave_quietly = True\n else:\n convs.append(conversation_id)\n\n for c_id in convs:\n if not leave_quietly:\n bot.send_message_parsed(c_id, _('I\\'ll be back!'))\n yield from bot._conv_list.leave_conversation(c_id)\n try:\n \"\"\"support convmem plugin\"\"\"\n bot.call_shared(\"convmem.removeconv\", bot, c_id)\n except KeyError:\n print(\"bot left {}, convmem plugin not available\".format(c_id))","async def endGame(self, ctx):\n print(\"Ending game ...\")\n await self.movePlayer(ctx=ctx, voiceChannel=self.lastVoiceChannel, reason=\"Fin de partie.\")\n await self.deleteCategory(ctx=ctx, reason=\"Fin de partie.\")\n await self.deleteRole(ctx=ctx, reason=\"Fin de partie.\")\n print(\"Game ended\")\n await self.delete()","async def leave(ctx):\n if ctx.message.channel.name.lower() not in tod_channels:\n return\n\n room = ctx.message.channel.name.lower()\n if room not in tod_games:\n await amor_manager.say(\"Truth Or Dare not in progress in {}\".format(room))\n else:\n player = ctx.message.author.name\n if player.lower() not in list(tod_games[room]['participants'].keys()):\n await amor_manager.say(\"{}, you cannot leave the game if you have not joined\".format(player))\n elif player == tod_games[room]['host']:\n await amor_manager.say(\"{}, you cannot leave the game you're the host\".format(player))\n else:\n del tod_games[room]['participants'][player.lower()]\n await amor_manager.say(\"{} has left Truth or Dare.\".format(player))","def handle_autovoiceoff(bot, ievent):\n try:\n ievent.chan.data.autovoice = 0\n ievent.reply('autovoice disabled on %s' % ievent.channel)\n except TypeError: ievent.reply('no %s channel in database' % ievent.channel)","async def end(ctx):\n if ctx.message.channel.name.lower() not in tod_channels:\n return\n\n room = ctx.message.channel.name.lower()\n if room not in tod_games:\n await amor_manager.say(\"Truth Or Dare not in progress in {}\".format(room))\n else:\n host = tod_games[room]['host']\n await amor_manager.say(\"Game Over in {}! Thank you to {} for hosting this game!\".format(room, host))\n del tod_games[room]","def devoice(self,nick):\n self.logger.debug(\"devoicing %s\" % nick)\n self.connection.mode(self.config[\"IRC/channel\"],\"-v \"+nick)","def leave(bot, event, conversation_id=None, *args):\n\n arglist = list(args)\n\n if conversation_id == \"quietly\":\n arglist.append(\"quietly\")\n conversation_id = False\n\n if not conversation_id:\n conversation_id = event.conv_id\n\n yield from command.run(bot, event, *[\"convleave\", \"id:\" + conversation_id, \" \".join(arglist)])","def stop_speaking(self):\n self.ts.send_message(\"Okay, I'll shut up for a bit. !start_speaking when you want me to speak again.\")\n self.allowed_to_chat = False","async def disconnect_voice_channel(client: \"Client\", guild: Union[\"Guild\", int]) -> None:\n guild_id = guild if isinstance(guild, int) else guild.id\n await update_voice_state(client, guild_id, None)","async def stop(self, ctx):\n server = ctx.message.server\n state = self.get_voice_state(server)\n\n if state.is_playing():\n player = state.player\n player.stop()\n\n try:\n state.speech_player.cancel()\n state.audio_player.cancel()\n del self.voice_states[server.id]\n await state.voice.disconnect()\n await self.bot.say('Stopped.')\n except:\n await self.bot.say('Couldn\\'t stop.')\n pass","async def stop(self, msg):\n if msg.voice_client is None:\n return await msg.send(\"Bot is not connect to a voice channel\")\n\n if msg.author.voice is None:\n return await msg.send(\"You must be in the same voice channel as the bot\")\n\n if msg.author.voice is not None and msg.voice_client is not None:\n if msg.voice_client.is_playing() is True or self.player[msg.guild.id]['queue']:\n self.player[msg.guild.id]['queue'].clear()\n self.player[msg.guild.id]['repeat'] = False\n msg.voice_client.stop()\n return await msg.message.add_reaction(emoji='✅')\n\n return await msg.send(f\"**{msg.author.display_name}, there is no audio currently playing or songs in queue**\")","def _stop_bot(_event):\n pass","async def leave(ctx, *, check=\"\"):\r\n # if botv.isAdmin(ctx.message.author) and check == \"now, bot\":\r\n # if necessary, save checks can go here; check presently commented out because botv can\r\n # fail to initialize in testing\r\n await bot.say(\"Allan, please add dialogue!\")\r\n quit()","async def _stop(self, ctx: commands.Context):\n\n ctx.voice_state.songs.clear()\n\n if ctx.voice_state.is_playing:\n ctx.voice_state.voice.stop()\n await ctx.message.add_reaction('⏹')","async def _stop(self, ctx: commands.Context):\n\n ctx.voice_state.songs.clear()\n\n if not ctx.voice_state.is_playing:\n ctx.voice_state.voice.stop()\n await ctx.message.add_reaction('⏹')","async def pause(self, ctx):\n voice = discord.utils.get(self.bot.voice_clients, guild=ctx.guild)\n\n if voice:\n if voice.is_playing():\n voice.pause()\n await ctx.send(\"Playing paused.\")\n else:\n await ctx.message.add_reaction('\\U0001F615');\n await ctx.send(\"Not playing anything right now.\")\n else:\n await ctx.message.add_reaction('\\U0001F615')\n await ctx.send(\"Not in a voice channel.\")","async def leavechannel(self, ctx: commands.Context, channel=\"\"):\n\n # If a channel is not specified, attempt to leave the current channel.\n if (channel == \"\"):\n channel = ctx.channel.name\n dm_notify = True\n\n channel_query = self._channel_query(channel)\n\n if channel_query == None:\n await ctx.send(\"Unable to leave that channel.\")\n return\n\n channel = self.bot.get_channel(channel_query.id)\n guild = self.bot.get_guild(SERVER_ID)\n member = guild.get_member(ctx.author.id)\n\n # You can't leave a channel that doesn't exist or you're not in.\n if channel == None or channel.permissions_for(member).is_strict_subset(JOINED_PERMISSIONS):\n await ctx.send(\"Unable to leave that channel.\")\n return\n\n await channel.set_permissions(member, read_messages=False, reason=\"UQCSbot removed.\")\n if dm_notify:\n await ctx.author.send(f\"You've left {channel.mention}\")\n else:\n await ctx.send(f\"You've left {channel.mention}\")","async def _msgvote_off(self, ctx):\n\n channel = ctx.message.channel\n if channel.id not in self.settings[\"channels_enabled\"]:\n await self.bot.say(\"Msgvote mode is already off in this channel.\")\n else:\n self.settings[\"channels_enabled\"].remove(channel.id)\n dataIO.save_json(self.settings_path, self.settings)\n await self.bot.say(\"Msgvote mode is now off in this channel.\")","def shutdown(self):\n ev3.Sound.speak(\"Goodbye\")","async def move(self, ctx, *, channel_name: str):\r\n if ctx.voice_client:\r\n if discord.utils.get(ctx.guild.channels, name=channel_name):\r\n await ctx.voice_client.move_to(discord.utils.get(ctx.guild.channels, name=channel_name))\r\n print(\"[INFO] Bot moved to channel {0}\".format(channel_name))\r\n self.channel_name = channel_name\r\n else:\r\n await ctx.send(\"Channel {0} not found\".format(channel_name))\r\n else:\r\n await self.connect(ctx, channel_name=channel_name)\r\n await ctx.message.delete()","async def quit(self, ctx):\n await self.bot.logout()","async def poweroff(ctx):\n await ctx.send(\"Bye\")\n await bot.logout()","def leave(self, *args, **kwargs):\n return self.bot.leave_chat(self.id, *args, **kwargs)","async def tc_close(self, ctx):\n async with self.config.guild(ctx.guild).private_textchannels() as textchannels:\n if ctx.channel.id in textchannels:\n await ctx.channel.delete(reason=_(\"Private text channel deleted.\"))\n del textchannels[ctx.channel.id]\n else:\n await ctx.send(_(\"Use this command in a private text channel.\"))","def shutdown(self):\n self.left_motor.stop()\n ev3.Leds.set_color(ev3.Leds.LEFT, ev3.Leds.GREEN)\n self.right_motor.stop()\n ev3.Leds.set_color(ev3.Leds.RIGHT, ev3.Leds.GREEN)\n self.running = False\n print('Good Bye')\n ev3.Sound.speak(\"Good Bye\")","async def join(ctx):\r\n channel = ctx.message.author.voice.channel\r\n if ctx.voice_client is not None:\r\n return await ctx.voice_client.move_to(channel)\r\n\r\n await channel.connect()","def leave(self):\n print('%r: leaving', self)\n self.telepathy_text_chan.Close()","async def remove(self, ctx, *, channel: discord.VoiceChannel):\n auto_channels = await self.config.guild(ctx.guild).auto_channels()\n auto_channels.remove(channel.id)\n await self.config.guild(ctx.guild).auto_channels.set(auto_channels)\n await ctx.send(\n _(\"Startchannel used for automatic voicechannels removed: {channel}\").format(\n channel=channel.name\n )\n )","async def shutdownBot(ctx):\n if str(ctx.message.author) == \"Etheren#6893\":\n await ctx.send('Shutting the bot down.')\n await bot.close()\n else: \n await ctx.send('Only Etheren#6893 can shut the bot down.')","async def on_voice_state_update(self, member, before, after):\n # This makes sure that the bot leaves\n # the voice channel after a settable timeout\n # for performance improvements.\n timeout = 600 # seconds\n if not member.id == self.bot.user.id:\n return\n elif before.channel is None:\n player = self.bot.lavalink.player_manager.get(member.guild.id)\n time = 0\n while True:\n await asyncio.sleep(1)\n time += 1\n\n # Check if after channel none & clear queue\n if after.channel is None:\n player.queue.clear()\n # Stop the current track so Lavalink consumes less resources.\n await player.stop()\n break\n\n # Timeout after `timeout` seconds for performance improvements.\n if player.is_playing:\n time = 0\n if time == timeout:\n ch = await self.bot.fetch_channel(\n self.preferred_channels[str(player.guild_id)])\n await member.guild.change_voice_state(channel=None)\n await ch.send(embed=self.reply_embed(\"Left voice channel after no activity for `10 minutes`.\"))\n del self.preferred_channels[str(player.guild_id)]\n if not player.is_connected:\n break","async def voice_check(self, msg):\n if msg.voice_client is not None:\n await asyncio.sleep(120)\n if msg.voice_client is not None and msg.voice_client.is_playing() is False and msg.voice_client.is_paused() is False:\n await msg.voice_client.disconnect()","async def _pause(self, ctx: commands.Context):\n\n if not ctx.voice_state.is_playing and ctx.voice_state.voice.is_playing():\n ctx.voice_state.voice.pause()\n await ctx.message.add_reaction('⏯')","async def _pause(self, ctx: commands.Context):\n\n if ctx.voice_state.is_playing and ctx.voice_state.voice.is_playing():\n ctx.voice_state.voice.pause()\n await ctx.message.add_reaction('⏯')","def turn_bot_off_on_ctrl_c(signal, frame):\n global keep_bot_on\n keep_bot_on = False","async def _join(self, ctx: commands.Context):\n\n destination = ctx.author.voice.channel\n if ctx.voice_state.voice:\n await ctx.voice_state.voice.move_to(destination)\n return\n ctx.voice_state.voice = await destination.connect()\n await ctx.message.add_reaction('✅')","async def music_voice(self, user, before, after):\n if after.channel is None and user.id == self.bot.user.id:\n try:\n self.player[user.guild.id]['queue'].clear()\n except KeyError:\n # NOTE: server ID not in bot's local self.player dict\n # Server ID lost or was not in data before disconnecting\n print(f\"Failed to get guild id {user.guild.id}\")","async def connect(self, ctx, *, channel_name: str = None):\r\n if channel_name:\r\n voice_channel = discord.utils.get(ctx.guild.channels, name=channel_name)\r\n else:\r\n voice_channel = ctx.author.voice.channel\r\n\r\n if voice_channel:\r\n self.channel_name = voice_channel.name\r\n await voice_channel.connect()\r\n print(\"[INFO] Bot connected to channel {0}\".format(self.channel_name))\r\n await ctx.message.delete()","async def goodbye_channel(self, ctx, *, channel: discord.TextChannel):\n await queries.update_setting(ctx, \"goodbye_settings\", \"channel_id\", channel.id)\n await util.send_success(ctx, f\"Goodbye messages channel is now {channel.mention}\")","async def joinForce(self, ctx):\n print(\"joining\")\n channel = ctx.author.voice.channel\n await channel.connect()","async def join(self, ctx):\n voice = ctx.author.voice\n\n if voice and voice.channel:\n channel = voice.channel\n \n try:\n await channel.connect()\n except discord.errors.ClientException:\n await ctx.guild.get_member(self.bot.user.id).move_to(channel) \n \n await ctx.send(f\"Joined `{channel.name}`.\")\n else:\n await ctx.message.add_reaction('\\U0001F615');\n await ctx.send(\"You're not in a voice channel.\")","def leave(self):\n self.pleaseQuit=1","def cb_stop(self, update, context):\n\n print(f\"Unsubscribing chat_id '{update.message.chat_id}'\")\n try:\n self.clientChatIds.remove(update.message.chat_id)\n answer = \"You sucessfully unsubscribed.\"\n self.saveToFile(self.configFile)\n except KeyError:\n answer = \"You are not subscribed.\"\n\n update.message.reply_text(answer)","async def kill(ctx):\n\n def check_author(message):\n return message.author.id == ctx.author.id\n\n await ctx.send(\"You are about to shut down the bot, are you sure you want to do this? (Y/N)\")\n\n response = await gompei.wait_for('message', check=check_author)\n\n if response.content.lower() == \"y\" or response.content.lower() == \"yes\":\n Config.set_close_time()\n if Config.dm_channel is not None:\n end_embed = discord.Embed(title=\"Bot shutting down\", color=0xbe4041)\n end_embed.set_author(name=gompei.user.name + \"#\" + gompei.user.discriminator, icon_url=gompei.user.avatar_url)\n end_embed.set_footer(text=\"ID: \" + str(gompei.user.id))\n end_embed.timestamp = datetime.utcnow()\n\n await Config.dm_channel.send(embed=end_embed)\n await gompei.close()\n else:\n await ctx.send(\"Not shutting down the bot\")","def devoice(phenny, input):\n if not input.admin or not input.sender.startswith('#'):\n return\n nick = input.group(2)\n verify = auth_check(phenny, input.nick, nick)\n if verify:\n channel = input.sender\n if not nick:\n nick = input.nick\n phenny.write(['MODE', channel, \"-v\", nick])","async def _stop(self, ctx: commands.Context):\n ctx.voice_state.songs.clear()\n\n if ctx.voice_state.is_playing:\n ctx.voice_state.voice.stop()\n return await ctx.send(embed=embed_msg(description=\"🛑 Stopped the music\"))\n\n else:\n return await ctx.send('Cannot stop. Not playing any song right now.')","async def rebind(self, ctx, channel: discord.TextChannel=None):\n player = self.bot.lavalink.players.get(ctx.guild.id)\n if not player.is_connected:\n return await ctx.send(\"I'm not connected to a voice channel :no_entry:\")\n if not channel:\n channel = ctx.channel\n player.store('channel', channel.id)\n await ctx.send(\"Messages will now be sent in {} <:done:403285928233402378>\".format(channel.mention))","def stop(update, context) -> None:\n update.message.reply_text('Okay, bye.')\n\n logger.info(\"User [%s] canceled conversation using command [/stop].\",\n update.message.from_user.first_name)\n return END","async def on_disconnect():\n print(\"Bot has logged off\")","async def reset(self, msg):\n if msg.voice_client is None:\n return await msg.send(f\"**{msg.author.display_name}, there is no audio currently playing from the bot.**\")\n\n if msg.author.voice is None or msg.author.voice.channel != msg.voice_client.channel:\n return await msg.send(f\"**{msg.author.display_name}, you must be in the same voice channel as the bot.**\")\n\n if self.player[msg.guild.id]['queue'] and msg.voice_client.is_playing() is False:\n return await msg.send(\"**No audio currently playing or songs in queue**\".title(), delete_after=25)\n\n self.player[msg.guild.id]['reset'] = True\n msg.voice_client.stop()","def stop(self):\n self.running = False\n self.hop_channel(\"auto\")","def stop_telescope_move():\n\ttarget = send_command('stoptel')","async def disable(self, ctx):\n await self.config.guild(ctx.guild).auto.set(True)\n await ctx.send(_(\"Automatic voicechannel creation disabled.\"))","async def _summon(self,\n ctx: commands.Context,\n *,\n channel: discord.VoiceChannel = None):\n\n if not channel and not ctx.author.voice:\n raise VoiceError(\n 'You are neither connected to a voice channel nor specified a channel to join.'\n )\n\n destination = channel or ctx.author.voice.channel\n if ctx.voice_state.voice:\n await ctx.voice_state.voice.move_to(destination)\n return\n\n ctx.voice_state.voice = await destination.connect()\n await ctx.message.add_reaction('✅')","async def _join(self, ctx: commands.Context):\n\n destination = ctx.author.voice.channel\n if ctx.voice_state.voice:\n await ctx.voice_state.voice.move_to(destination)\n return\n\n ctx.voice_state.voice = await destination.connect()","async def test_silence_voice(self):\n message = \"This should show up just here.\"\n await self.cog.send_message(message, self.text_channels[0], self.voice_channel, alert_target=False)\n self.text_channels[0].send.assert_awaited_once_with(message)\n self.text_channels[1].send.assert_not_called()","async def join(self, ctx, *, channel: discord.VoiceChannel):\n \n if ctx.voice_client is not None:\n return await ctx.voice_client.move_to(channel)\n else:\n return await ctx.voice_client.move_to(bot.get_channel(247531960488951815))\n \n await channel.connect()","def do_session_stop(bot, msg, **kwargs):\n channel = kwargs.get('event').get('channel')\n bot.post_msg(\n text='`Closing election session...',\n channel_name_or_id=bot.current_channel\n )\n bot.stats.get(channel)['election_status'] = False\n bot.db.session.query(bot.db.Office).filter_by(channel=channel).first().election_status= False\n bot.db.session.commit()\n bot.update_election_status(channel, False)\n bot.post_msg(\n text='`Election session closed.`',\n channel_name_or_id=bot.current_channel\n )\n bot.post_msg(\n text='`Reporting to team members...`',\n channel_name_or_id=bot.current_channel\n )\n\n members_who_voted = [vote.voter.userid for vote in bot.db.session.query(bot.db.Vote).all()]\n print 'Voters: %r' %members_who_voted\n if 'DEBUG' in dir(bot.config) or 'TESTING' in dir(bot.config):\n for test_userid in bot.config.TEST_USERS.values():\n #test_userid = bot.config.TEST_USERS.values()[0]\n if test_userid in members_who_voted:\n voted_msg = 'Thank you for your participation in the process. Looking forward to more contributions from you in the community.'\n else:\n voted_msg = 'You did not participate in this one, but the community would be glad to have your contribution the next time. :simple_smile:'\n\n bot.direct_message(\n text=textwrap.dedent(\n '''\n Hello {user},\n\n Elections for *{office}* have just been concluded. These are the results:\n \n {stats}\n\n Any reviews would be communicated by the team administrators.\n\n {voted_msg}\n\n You may now head over to the <#C0NA61U1X|general> channel to talk about this with the rest of the community.\n '''.format(\n user=bot.format_user_mention(test_userid),\n office=bot.stats.get(channel).get('office'),\n stats=bot.get_stats(channel).strip().split('\\n',1)[1].strip(),\n voted_msg=voted_msg\n )\n ),\n userid=test_userid\n )\n else:\n for member in bot.team_members:\n if member.get('id') in members_who_voted:\n voted_msg = 'Thank you for your participation in the process. Looking forward to more contributions from you in the community.'\n else:\n voted_msg = 'You did not participate in this one, but the community would be glad to have your contribution the next time. :simple_smile:'\n\n bot.direct_message(\n text=textwrap.dedent(\n '''\n Hello {user},\n \n Elections for *{office}* have just been concluded. These are the results:\n \n {stats}\n\n Any reviews would be communicated by the team administrators.\n\n {voted_msg}\n\n You may now head over to the <#C0NA61U1X|general> channel to talk about this with the rest of the community.\n '''.format(\n user=member.get('profile').get('first_name') or bot.format_user_mention(member.get('id')),\n office=bot.stats.get(channel).get('office'),\n stats=bot.get_stats(channel).strip().split('\\n',1)[1].strip(),\n voted_msg=voted_msg\n )\n ),\n userid=member.get('id')\n )\n bot.post_msg(\n text='`Done.`',\n channel_name_or_id=bot.current_channel\n )\n return True","def leave_on_empty(\n client: discord.Client,\n interval: float = 30,\n) -> LoopType:\n\n @tasks.loop(seconds=interval)\n async def _leaver() -> None:\n for voice_client in client.voice_clients:\n if (\n isinstance(voice_client, discord.VoiceClient)\n and len(voice_client.channel.members) <= 1\n ):\n logger.info(\n f'leaving voice channel {voice_client.channel.name} in '\n f'{voice_client.channel.guild.name} due to inactivity',\n )\n await voice_client.disconnect()\n\n return _leaver","async def resume(self, ctx):\n voice = discord.utils.get(self.bot.voice_clients, guild=ctx.guild)\n\n if voice:\n if voice.is_paused():\n voice.resume()\n await ctx.send(\"Playing resumed.\")\n else:\n await ctx.message.add_reaction('\\U0001F615');\n await ctx.send(\"Nothing paused right now.\")\n else:\n await ctx.message.add_reaction('\\U0001F615')\n await ctx.send(\"Not in a voice channel.\")","async def join(self, ctx, *, channel: discord.VoiceChannel):\n\n if ctx.voice_client is not None:\n return await ctx.voice_client.move_to(channel)\n\n await channel.connect()","def __del__(self):\n self.bot.close()","async def clear(self, ctx):\n if ctx.voice_client is None or ctx.voice_client.channel is None:\n await ctx.reply(f\"I am not in a voice channel, invite me first with `{self.bot_config['prefix']}join`.\")\n raise commands.CommandError(\"Bot not connected to a voice channel.\")\n\n if ctx.author.voice is None or ctx.author.voice.channel is None:\n await ctx.reply(\"You need to be in a voice channel to use this command.\")\n raise commands.CommandError(\"Invoker not connected to a voice channel.\")\n\n if ctx.voice_client is not None and ctx.author.voice.channel != ctx.voice_client.channel:\n await ctx.reply(\"You need to be in the same voice channel as the bot to use this command.\")\n raise commands.CommandError(\"Invoker not in same voice channel as bot.\")\n\n controller = SpotifyController.get_instance(ctx.voice_client.channel.id)\n controller.stop_playlist_playback()\n controller.clear_playlist()\n await ctx.send(f\"Queue cleared!\")","async def stop_alert(self, ctx):\n cyphon = discord.utils.get(ctx.message.server.members, id=\"186835826699665409\")\n\n if self.check_channel(ctx):\n if self.check_permission(ctx) or ctx.message.author == cyphon:\n channel = ctx.message.channel\n\n to_delete = []\n\n for s in self.twitch_streams:\n if channel.id in s[\"CHANNEL\"]:\n to_delete.append(s)\n\n for s in to_delete:\n self.twitch_streams.remove(s)\n\n dataIO.save_json(\"data/streams/twitch.json\", self.twitch_streams)\n\n await self.bot.say(\"There will be no more stream alerts in the stream \"\n \"channel.\")\n else:\n await self.bot.send_message(ctx.message.author, \"You don't have permission to execute that command.\")","def turn_off(self, **kwargs):\n self.robot.pause_cleaning()\n time.sleep(1)\n self.robot.send_to_base()","def stop_conversation(self):\n self.redis.publish('conversation_stopped_by_operator', json.dumps(self.operator_token))\n self.conversation_started = False","async def off(self, ctx, *, nickname=\"\"):\n nickname = nickname.strip()\n if nickname == \"\":\n nickname = None\n try:\n await self.bot.change_nickname(ctx.message.server.me, nickname)\n await self.bot.delete_message(ctx.message)\n await self.bot.say(\"RIP music lol\")\n except discord.Forbidden:\n await self.bot.say(\"I cannot do that, I miss the `Change Nickname` or `Manage Messages` permission\")","async def on(self, ctx, *, nickname=\"\"):\n nickname = nickname.strip()\n mention_here = True\n mention_everyone = True\n if nickname == \"\":\n nickname = \"Dank Bot |Music on voice!\"\n try:\n await self.bot.change_nickname(ctx.message.server.me, nickname)\n await self.bot.say(\"Hey, music is playing on voice channel come! @here\")\n await self.bot.delete_message(ctx.message)\n except discord.Forbidden:\n await self.bot.say(\"I cannot do that, I miss the `Change Nickname` or `Manage Messages` permission\")","async def shutdown(self, ctx: commands.Context):\r\n try:\r\n await self.bot.logout()\r\n await self.bot.close()\r\n except Exception as ex:\r\n await self.bot.logger.warning(\"An error occurred trying to logout\", exc_info=ex)\r\n else:\r\n await ctx.message.add_reaction(\"👌\")","def stopped(self, event, source):\n\n self.log('Disconnecting IRC bot')\n self.fire(disconnect())","def on_close(self):\n self.subscrib.unsubscribe(self.channel)\n self.thread.stop()","def endGame(self):\n pass","async def shutdown(self, ctx):\n await self.bot.session.close()\n await self.bot.logout()","async def _summon(self, ctx: commands.Context, *, channel: discord.VoiceChannel = None):\n\n if not channel and not ctx.author.voice:\n raise VoiceError('You are neither connected to a voice channel nor specified a channel to join.')\n\n destination = channel or ctx.author.voice.channel\n if ctx.voice_state.voice:\n await ctx.voice_state.voice.move_to(destination)\n return\n\n ctx.voice_state.voice = await destination.connect()","def stopGame(event):\n if event.action == sense_hat.ACTION_RELEASED:\n global playAgain, alive\n playAgain = False\n alive = False","async def logout(ctx):\n await ctx.send(f\"Hey {ctx.author.mention}, I am now logging out :wave:\")\n await bot.logout()","def handle_audio_end(_):\n if config.get(\"listener\").get(\"mute_during_output\"):\n loop.unmute() # restore","def stop(self): \r\n print(\"Thank you for playing the game, hope you had fun!!!\")\r\n self.reset()","def end(update, context) -> int:\n update.callback_query.edit_message_text(\n 'Bye! I hope we can talk again some day.')\n\n logger.info(\"User [%s] exited the conversation, [Exit], from [Main Menu / Results State].\",\n update.callback_query.message.chat.first_name)\n return ConversationHandler.END","def leave(msg: telebot.types.Message):\n if utils.in_menu(msg.from_user):\n bot.reply_to(\n msg,\n 'This command outside of game is useless.'\n )\n return\n\n game, user, opponent = utils.get_game_user_opponent(msg.from_user)\n if not game or not user:\n # todo log something\n return\n\n user.state = states.USER_IN_MENU\n user.losses += 1\n utils.update_user(user)\n bot.send_message(\n user.user_id,\n 'You surrendered.'\n )\n\n if opponent:\n opponent.state = states.USER_IN_MENU\n opponent.wins += 1\n utils.update_user(opponent)\n bot.send_message(\n opponent.user_id,\n 'Your opponent surrendered'\n )\n\n field = json.loads(game.field)\n sig = 1 if user == game.user1 else 2\n\n # changes users emojis to poop\n for i in range(len(field)):\n for j in range(len(field[i])):\n if field[i][j] == sig:\n field[i][j] = 4\n\n if opponent:\n utils.send_updated_field(bot, field, game, opponent)\n Game.delete_by_id(game.id)","async def _join(self, ctx: commands.Context):\n destination = ctx.author.voice.channel # Destination is the author's voice channel\n if ctx.voice_state.voice:\n await ctx.voice_state.voice.move_to(destination)\n return\n\n ctx.voice_state.voice = await destination.connect()"],"string":"[\n \"async def leave(self, ctx):\\n if ctx.guild.voice_client:\\n await ctx.guild.voice_client.disconnect()\\n await ctx.send(\\\"Left voice channel.\\\")\\n else:\\n await ctx.message.add_reaction('\\\\U0001F615');\\n await ctx.send(\\\"Not in a voice channel.\\\")\",\n \"async def _leave(self, ctx: commands.Context):\\n\\n if not ctx.voice_state.voice:\\n return await ctx.send('Not connected to any voice channel.')\\n\\n await ctx.voice_state.stop()\\n del self.voice_states[ctx.guild.id]\\n await ctx.message.add_reaction('👋')\",\n \"async def _leave(self, ctx: commands.Context):\\n\\n if not ctx.voice_state.voice:\\n return await ctx.send('Not connected to any voice channel.')\\n\\n await ctx.voice_state.stop()\\n del self.voice_states[ctx.guild.id]\",\n \"async def leave(self, msg):\\n if msg.author.voice is not None and msg.voice_client is not None:\\n if msg.voice_client.is_playing() is True or self.player[msg.guild.id]['queue']:\\n self.player[msg.guild.id]['queue'].clear()\\n msg.voice_client.stop()\\n return await msg.voice_client.disconnect(), await msg.message.add_reaction(emoji='✅')\\n\\n return await msg.voice_client.disconnect(), await msg.message.add_reaction(emoji='✅')\\n\\n if msg.author.voice is None:\\n return await msg.send(\\\"You must be in the same voice channel as bot to disconnect it via command\\\")\",\n \"async def leave(self, ctx):\\n\\n if ctx.voice_client is not None:\\n return await ctx.voice_client.disconnect()\",\n \"async def leave(self, ctx: commands.Context) -> Optional[discord.VoiceChannel]:\\n\\n if ctx.guild.id in self.queue:\\n self.queue[ctx.guild.id].cleanup()\\n del self.queue[ctx.guild.id]\\n\\n await maybe_coroutine(ctx.voice_client.stop)\\n\\n channel = ctx.voice_client.channel\\n await ctx.voice_client.disconnect(force=True)\\n return channel\",\n \"async def leave(self, ctx):\\n player = self.bot.lavalink.player_manager.get(ctx.guild.id)\\n\\n if not player.is_connected:\\n # We can't disconnect, if we're not connected.\\n return await ctx.send(embed=self.error_embed(f'Not playing. [{ctx.message.author.mention}]'))\\n\\n if not ctx.author.voice or (player.is_connected and ctx.author.voice.channel.id != int(player.channel_id)):\\n # Abuse prevention. Users not in voice channels, or not in the same voice channel as the bot\\n # may not disconnect the bot.\\n return await ctx.send(embed=self.error_embed(f'Not connected to the same voice channel. [{ctx.message.author.mention}]'))\\n\\n # Clear the queue to ensure old tracks don't start playing\\n # when someone else queues something.\\n player.queue.clear()\\n # Stop the current track so Lavalink consumes less resources.\\n await player.stop()\\n # Disconnect from the voice channel.\\n await ctx.send(embed=self.reply_embed(f\\\"Left channel `{ctx.message.author.voice.channel}` [{ctx.message.author.mention}]\\\"))\\n await ctx.guild.change_voice_state(channel=None)\",\n \"async def leaveForce(self, ctx):\\n await ctx.voice_client.disconnect()\",\n \"async def _leave(self, ctx: commands.Context):\\n\\n await ctx.voice_state.stop()\\n del self.voice_states[ctx.guild.id]\",\n \"async def stop(self, ctx):\\n voice = discord.utils.get(self.bot.voice_clients, guild=ctx.guild)\\n\\n if voice:\\n if voice.is_playing():\\n voice.stop()\\n await ctx.send(\\\"Playing stopped.\\\") \\n else:\\n await ctx.message.add_reaction('\\\\U0001F615')\\n await ctx.send(\\\"Not playing anything right now.\\\")\\n else:\\n await ctx.message.add_reaction('\\\\U0001F615')\\n await ctx.send(\\\"Not in a voice channel.\\\")\",\n \"async def stop(ctx):\\r\\n\\r\\n await ctx.voice_client.disconnect()\",\n \"async def disconnect(ctx):\\n if ctx.voice_client is not None:\\n await ctx.voice_client.disconnect()\",\n \"async def disconnect(self, ctx):\\r\\n if ctx.voice_client:\\r\\n await ctx.voice_client.disconnect()\\r\\n print(\\\"[INFO] Bot disconnected from channel {0}\\\".format(self.channel_name))\\r\\n await ctx.message.delete()\",\n \"def voice_decrease():\\n request_command(tv_command=TVCommand.voice_decrease)\",\n \"async def disconnect(self, ctx):\\n player = self.bot.lavalink.players.get(ctx.guild.id)\\n if not player.is_connected:\\n return await ctx.send(\\\"I'm not currently connected to a voice channel :no_entry:\\\")\\n if not ctx.author.voice or (player.is_connected and player.connected_channel.id != ctx.author.voice.channel.id):\\n return await ctx.send(\\\"You have to be in my voice channel to disconnect :no_entry:\\\")\\n if player.fetch(\\\"sessionowner\\\") == ctx.author.id:\\n player.queue.clear()\\n await player.disconnect()\\n player.delete(\\\"votes\\\")\\n await ctx.send(\\\"Disconnected <:done:403285928233402378>\\\")\\n else:\\n await ctx.send(\\\"Only the session owner can disconnect the bot :no_entry:\\\")\",\n \"async def stop(self, ctx):\\n if ctx.voice_client != None:\\n await ctx.voice_client.disconnect()\",\n \"async def stop(self, ctx):\\n\\n await ctx.voice_client.disconnect()\",\n \"async def stop(self, ctx):\\n\\n await ctx.voice_client.disconnect()\",\n \"async def _leave(self, server_id):\\n srv = self.get_server_dict(server_id)\\n if srv['voice'] and srv['voice'].channel:\\n await srv['voice'].disconnect()\\n srv['voice'] = None\",\n \"async def disconnect(self, ctx):\\r\\n if ctx.message.channel.id != 701868237771505695:\\r\\n return await ctx.send(\\\"**Error:** Music Bot commands are only available in <#701868237771505695>\\\")\\r\\n if self.music_off:\\r\\n return await ctx.send(\\\"**Error:** Music Bot features are currently off\\\")\\r\\n if ctx.voice_client is None or ctx.voice_client is not self.voice:\\r\\n return await ctx.send(\\\"**Error:** You must be connected to the voice channel.\\\")\\r\\n try:\\r\\n await ctx.voice_client.stop()\\r\\n except:\\r\\n pass\\r\\n await ctx.voice_client.disconnect()\\r\\n self.songs = [\\\"None\\\"]\\r\\n self.processing_songs = 0\\r\\n self.del_all_files()\\r\\n self.voice = None\\r\\n await ctx.send(\\\"Disconnected\\\")\",\n \"def leave(bot, event, conversation_id=None, *args):\\n\\n leave_quietly = False\\n convs = []\\n\\n if not conversation_id:\\n convs.append(event.conv.id_)\\n elif conversation_id==\\\"quietly\\\":\\n convs.append(event.conv.id_)\\n leave_quietly = True\\n else:\\n convs.append(conversation_id)\\n\\n for c_id in convs:\\n if not leave_quietly:\\n bot.send_message_parsed(c_id, _('I\\\\'ll be back!'))\\n yield from bot._conv_list.leave_conversation(c_id)\\n try:\\n \\\"\\\"\\\"support convmem plugin\\\"\\\"\\\"\\n bot.call_shared(\\\"convmem.removeconv\\\", bot, c_id)\\n except KeyError:\\n print(\\\"bot left {}, convmem plugin not available\\\".format(c_id))\",\n \"async def endGame(self, ctx):\\n print(\\\"Ending game ...\\\")\\n await self.movePlayer(ctx=ctx, voiceChannel=self.lastVoiceChannel, reason=\\\"Fin de partie.\\\")\\n await self.deleteCategory(ctx=ctx, reason=\\\"Fin de partie.\\\")\\n await self.deleteRole(ctx=ctx, reason=\\\"Fin de partie.\\\")\\n print(\\\"Game ended\\\")\\n await self.delete()\",\n \"async def leave(ctx):\\n if ctx.message.channel.name.lower() not in tod_channels:\\n return\\n\\n room = ctx.message.channel.name.lower()\\n if room not in tod_games:\\n await amor_manager.say(\\\"Truth Or Dare not in progress in {}\\\".format(room))\\n else:\\n player = ctx.message.author.name\\n if player.lower() not in list(tod_games[room]['participants'].keys()):\\n await amor_manager.say(\\\"{}, you cannot leave the game if you have not joined\\\".format(player))\\n elif player == tod_games[room]['host']:\\n await amor_manager.say(\\\"{}, you cannot leave the game you're the host\\\".format(player))\\n else:\\n del tod_games[room]['participants'][player.lower()]\\n await amor_manager.say(\\\"{} has left Truth or Dare.\\\".format(player))\",\n \"def handle_autovoiceoff(bot, ievent):\\n try:\\n ievent.chan.data.autovoice = 0\\n ievent.reply('autovoice disabled on %s' % ievent.channel)\\n except TypeError: ievent.reply('no %s channel in database' % ievent.channel)\",\n \"async def end(ctx):\\n if ctx.message.channel.name.lower() not in tod_channels:\\n return\\n\\n room = ctx.message.channel.name.lower()\\n if room not in tod_games:\\n await amor_manager.say(\\\"Truth Or Dare not in progress in {}\\\".format(room))\\n else:\\n host = tod_games[room]['host']\\n await amor_manager.say(\\\"Game Over in {}! Thank you to {} for hosting this game!\\\".format(room, host))\\n del tod_games[room]\",\n \"def devoice(self,nick):\\n self.logger.debug(\\\"devoicing %s\\\" % nick)\\n self.connection.mode(self.config[\\\"IRC/channel\\\"],\\\"-v \\\"+nick)\",\n \"def leave(bot, event, conversation_id=None, *args):\\n\\n arglist = list(args)\\n\\n if conversation_id == \\\"quietly\\\":\\n arglist.append(\\\"quietly\\\")\\n conversation_id = False\\n\\n if not conversation_id:\\n conversation_id = event.conv_id\\n\\n yield from command.run(bot, event, *[\\\"convleave\\\", \\\"id:\\\" + conversation_id, \\\" \\\".join(arglist)])\",\n \"def stop_speaking(self):\\n self.ts.send_message(\\\"Okay, I'll shut up for a bit. !start_speaking when you want me to speak again.\\\")\\n self.allowed_to_chat = False\",\n \"async def disconnect_voice_channel(client: \\\"Client\\\", guild: Union[\\\"Guild\\\", int]) -> None:\\n guild_id = guild if isinstance(guild, int) else guild.id\\n await update_voice_state(client, guild_id, None)\",\n \"async def stop(self, ctx):\\n server = ctx.message.server\\n state = self.get_voice_state(server)\\n\\n if state.is_playing():\\n player = state.player\\n player.stop()\\n\\n try:\\n state.speech_player.cancel()\\n state.audio_player.cancel()\\n del self.voice_states[server.id]\\n await state.voice.disconnect()\\n await self.bot.say('Stopped.')\\n except:\\n await self.bot.say('Couldn\\\\'t stop.')\\n pass\",\n \"async def stop(self, msg):\\n if msg.voice_client is None:\\n return await msg.send(\\\"Bot is not connect to a voice channel\\\")\\n\\n if msg.author.voice is None:\\n return await msg.send(\\\"You must be in the same voice channel as the bot\\\")\\n\\n if msg.author.voice is not None and msg.voice_client is not None:\\n if msg.voice_client.is_playing() is True or self.player[msg.guild.id]['queue']:\\n self.player[msg.guild.id]['queue'].clear()\\n self.player[msg.guild.id]['repeat'] = False\\n msg.voice_client.stop()\\n return await msg.message.add_reaction(emoji='✅')\\n\\n return await msg.send(f\\\"**{msg.author.display_name}, there is no audio currently playing or songs in queue**\\\")\",\n \"def _stop_bot(_event):\\n pass\",\n \"async def leave(ctx, *, check=\\\"\\\"):\\r\\n # if botv.isAdmin(ctx.message.author) and check == \\\"now, bot\\\":\\r\\n # if necessary, save checks can go here; check presently commented out because botv can\\r\\n # fail to initialize in testing\\r\\n await bot.say(\\\"Allan, please add dialogue!\\\")\\r\\n quit()\",\n \"async def _stop(self, ctx: commands.Context):\\n\\n ctx.voice_state.songs.clear()\\n\\n if ctx.voice_state.is_playing:\\n ctx.voice_state.voice.stop()\\n await ctx.message.add_reaction('⏹')\",\n \"async def _stop(self, ctx: commands.Context):\\n\\n ctx.voice_state.songs.clear()\\n\\n if not ctx.voice_state.is_playing:\\n ctx.voice_state.voice.stop()\\n await ctx.message.add_reaction('⏹')\",\n \"async def pause(self, ctx):\\n voice = discord.utils.get(self.bot.voice_clients, guild=ctx.guild)\\n\\n if voice:\\n if voice.is_playing():\\n voice.pause()\\n await ctx.send(\\\"Playing paused.\\\")\\n else:\\n await ctx.message.add_reaction('\\\\U0001F615');\\n await ctx.send(\\\"Not playing anything right now.\\\")\\n else:\\n await ctx.message.add_reaction('\\\\U0001F615')\\n await ctx.send(\\\"Not in a voice channel.\\\")\",\n \"async def leavechannel(self, ctx: commands.Context, channel=\\\"\\\"):\\n\\n # If a channel is not specified, attempt to leave the current channel.\\n if (channel == \\\"\\\"):\\n channel = ctx.channel.name\\n dm_notify = True\\n\\n channel_query = self._channel_query(channel)\\n\\n if channel_query == None:\\n await ctx.send(\\\"Unable to leave that channel.\\\")\\n return\\n\\n channel = self.bot.get_channel(channel_query.id)\\n guild = self.bot.get_guild(SERVER_ID)\\n member = guild.get_member(ctx.author.id)\\n\\n # You can't leave a channel that doesn't exist or you're not in.\\n if channel == None or channel.permissions_for(member).is_strict_subset(JOINED_PERMISSIONS):\\n await ctx.send(\\\"Unable to leave that channel.\\\")\\n return\\n\\n await channel.set_permissions(member, read_messages=False, reason=\\\"UQCSbot removed.\\\")\\n if dm_notify:\\n await ctx.author.send(f\\\"You've left {channel.mention}\\\")\\n else:\\n await ctx.send(f\\\"You've left {channel.mention}\\\")\",\n \"async def _msgvote_off(self, ctx):\\n\\n channel = ctx.message.channel\\n if channel.id not in self.settings[\\\"channels_enabled\\\"]:\\n await self.bot.say(\\\"Msgvote mode is already off in this channel.\\\")\\n else:\\n self.settings[\\\"channels_enabled\\\"].remove(channel.id)\\n dataIO.save_json(self.settings_path, self.settings)\\n await self.bot.say(\\\"Msgvote mode is now off in this channel.\\\")\",\n \"def shutdown(self):\\n ev3.Sound.speak(\\\"Goodbye\\\")\",\n \"async def move(self, ctx, *, channel_name: str):\\r\\n if ctx.voice_client:\\r\\n if discord.utils.get(ctx.guild.channels, name=channel_name):\\r\\n await ctx.voice_client.move_to(discord.utils.get(ctx.guild.channels, name=channel_name))\\r\\n print(\\\"[INFO] Bot moved to channel {0}\\\".format(channel_name))\\r\\n self.channel_name = channel_name\\r\\n else:\\r\\n await ctx.send(\\\"Channel {0} not found\\\".format(channel_name))\\r\\n else:\\r\\n await self.connect(ctx, channel_name=channel_name)\\r\\n await ctx.message.delete()\",\n \"async def quit(self, ctx):\\n await self.bot.logout()\",\n \"async def poweroff(ctx):\\n await ctx.send(\\\"Bye\\\")\\n await bot.logout()\",\n \"def leave(self, *args, **kwargs):\\n return self.bot.leave_chat(self.id, *args, **kwargs)\",\n \"async def tc_close(self, ctx):\\n async with self.config.guild(ctx.guild).private_textchannels() as textchannels:\\n if ctx.channel.id in textchannels:\\n await ctx.channel.delete(reason=_(\\\"Private text channel deleted.\\\"))\\n del textchannels[ctx.channel.id]\\n else:\\n await ctx.send(_(\\\"Use this command in a private text channel.\\\"))\",\n \"def shutdown(self):\\n self.left_motor.stop()\\n ev3.Leds.set_color(ev3.Leds.LEFT, ev3.Leds.GREEN)\\n self.right_motor.stop()\\n ev3.Leds.set_color(ev3.Leds.RIGHT, ev3.Leds.GREEN)\\n self.running = False\\n print('Good Bye')\\n ev3.Sound.speak(\\\"Good Bye\\\")\",\n \"async def join(ctx):\\r\\n channel = ctx.message.author.voice.channel\\r\\n if ctx.voice_client is not None:\\r\\n return await ctx.voice_client.move_to(channel)\\r\\n\\r\\n await channel.connect()\",\n \"def leave(self):\\n print('%r: leaving', self)\\n self.telepathy_text_chan.Close()\",\n \"async def remove(self, ctx, *, channel: discord.VoiceChannel):\\n auto_channels = await self.config.guild(ctx.guild).auto_channels()\\n auto_channels.remove(channel.id)\\n await self.config.guild(ctx.guild).auto_channels.set(auto_channels)\\n await ctx.send(\\n _(\\\"Startchannel used for automatic voicechannels removed: {channel}\\\").format(\\n channel=channel.name\\n )\\n )\",\n \"async def shutdownBot(ctx):\\n if str(ctx.message.author) == \\\"Etheren#6893\\\":\\n await ctx.send('Shutting the bot down.')\\n await bot.close()\\n else: \\n await ctx.send('Only Etheren#6893 can shut the bot down.')\",\n \"async def on_voice_state_update(self, member, before, after):\\n # This makes sure that the bot leaves\\n # the voice channel after a settable timeout\\n # for performance improvements.\\n timeout = 600 # seconds\\n if not member.id == self.bot.user.id:\\n return\\n elif before.channel is None:\\n player = self.bot.lavalink.player_manager.get(member.guild.id)\\n time = 0\\n while True:\\n await asyncio.sleep(1)\\n time += 1\\n\\n # Check if after channel none & clear queue\\n if after.channel is None:\\n player.queue.clear()\\n # Stop the current track so Lavalink consumes less resources.\\n await player.stop()\\n break\\n\\n # Timeout after `timeout` seconds for performance improvements.\\n if player.is_playing:\\n time = 0\\n if time == timeout:\\n ch = await self.bot.fetch_channel(\\n self.preferred_channels[str(player.guild_id)])\\n await member.guild.change_voice_state(channel=None)\\n await ch.send(embed=self.reply_embed(\\\"Left voice channel after no activity for `10 minutes`.\\\"))\\n del self.preferred_channels[str(player.guild_id)]\\n if not player.is_connected:\\n break\",\n \"async def voice_check(self, msg):\\n if msg.voice_client is not None:\\n await asyncio.sleep(120)\\n if msg.voice_client is not None and msg.voice_client.is_playing() is False and msg.voice_client.is_paused() is False:\\n await msg.voice_client.disconnect()\",\n \"async def _pause(self, ctx: commands.Context):\\n\\n if not ctx.voice_state.is_playing and ctx.voice_state.voice.is_playing():\\n ctx.voice_state.voice.pause()\\n await ctx.message.add_reaction('⏯')\",\n \"async def _pause(self, ctx: commands.Context):\\n\\n if ctx.voice_state.is_playing and ctx.voice_state.voice.is_playing():\\n ctx.voice_state.voice.pause()\\n await ctx.message.add_reaction('⏯')\",\n \"def turn_bot_off_on_ctrl_c(signal, frame):\\n global keep_bot_on\\n keep_bot_on = False\",\n \"async def _join(self, ctx: commands.Context):\\n\\n destination = ctx.author.voice.channel\\n if ctx.voice_state.voice:\\n await ctx.voice_state.voice.move_to(destination)\\n return\\n ctx.voice_state.voice = await destination.connect()\\n await ctx.message.add_reaction('✅')\",\n \"async def music_voice(self, user, before, after):\\n if after.channel is None and user.id == self.bot.user.id:\\n try:\\n self.player[user.guild.id]['queue'].clear()\\n except KeyError:\\n # NOTE: server ID not in bot's local self.player dict\\n # Server ID lost or was not in data before disconnecting\\n print(f\\\"Failed to get guild id {user.guild.id}\\\")\",\n \"async def connect(self, ctx, *, channel_name: str = None):\\r\\n if channel_name:\\r\\n voice_channel = discord.utils.get(ctx.guild.channels, name=channel_name)\\r\\n else:\\r\\n voice_channel = ctx.author.voice.channel\\r\\n\\r\\n if voice_channel:\\r\\n self.channel_name = voice_channel.name\\r\\n await voice_channel.connect()\\r\\n print(\\\"[INFO] Bot connected to channel {0}\\\".format(self.channel_name))\\r\\n await ctx.message.delete()\",\n \"async def goodbye_channel(self, ctx, *, channel: discord.TextChannel):\\n await queries.update_setting(ctx, \\\"goodbye_settings\\\", \\\"channel_id\\\", channel.id)\\n await util.send_success(ctx, f\\\"Goodbye messages channel is now {channel.mention}\\\")\",\n \"async def joinForce(self, ctx):\\n print(\\\"joining\\\")\\n channel = ctx.author.voice.channel\\n await channel.connect()\",\n \"async def join(self, ctx):\\n voice = ctx.author.voice\\n\\n if voice and voice.channel:\\n channel = voice.channel\\n \\n try:\\n await channel.connect()\\n except discord.errors.ClientException:\\n await ctx.guild.get_member(self.bot.user.id).move_to(channel) \\n \\n await ctx.send(f\\\"Joined `{channel.name}`.\\\")\\n else:\\n await ctx.message.add_reaction('\\\\U0001F615');\\n await ctx.send(\\\"You're not in a voice channel.\\\")\",\n \"def leave(self):\\n self.pleaseQuit=1\",\n \"def cb_stop(self, update, context):\\n\\n print(f\\\"Unsubscribing chat_id '{update.message.chat_id}'\\\")\\n try:\\n self.clientChatIds.remove(update.message.chat_id)\\n answer = \\\"You sucessfully unsubscribed.\\\"\\n self.saveToFile(self.configFile)\\n except KeyError:\\n answer = \\\"You are not subscribed.\\\"\\n\\n update.message.reply_text(answer)\",\n \"async def kill(ctx):\\n\\n def check_author(message):\\n return message.author.id == ctx.author.id\\n\\n await ctx.send(\\\"You are about to shut down the bot, are you sure you want to do this? (Y/N)\\\")\\n\\n response = await gompei.wait_for('message', check=check_author)\\n\\n if response.content.lower() == \\\"y\\\" or response.content.lower() == \\\"yes\\\":\\n Config.set_close_time()\\n if Config.dm_channel is not None:\\n end_embed = discord.Embed(title=\\\"Bot shutting down\\\", color=0xbe4041)\\n end_embed.set_author(name=gompei.user.name + \\\"#\\\" + gompei.user.discriminator, icon_url=gompei.user.avatar_url)\\n end_embed.set_footer(text=\\\"ID: \\\" + str(gompei.user.id))\\n end_embed.timestamp = datetime.utcnow()\\n\\n await Config.dm_channel.send(embed=end_embed)\\n await gompei.close()\\n else:\\n await ctx.send(\\\"Not shutting down the bot\\\")\",\n \"def devoice(phenny, input):\\n if not input.admin or not input.sender.startswith('#'):\\n return\\n nick = input.group(2)\\n verify = auth_check(phenny, input.nick, nick)\\n if verify:\\n channel = input.sender\\n if not nick:\\n nick = input.nick\\n phenny.write(['MODE', channel, \\\"-v\\\", nick])\",\n \"async def _stop(self, ctx: commands.Context):\\n ctx.voice_state.songs.clear()\\n\\n if ctx.voice_state.is_playing:\\n ctx.voice_state.voice.stop()\\n return await ctx.send(embed=embed_msg(description=\\\"🛑 Stopped the music\\\"))\\n\\n else:\\n return await ctx.send('Cannot stop. Not playing any song right now.')\",\n \"async def rebind(self, ctx, channel: discord.TextChannel=None):\\n player = self.bot.lavalink.players.get(ctx.guild.id)\\n if not player.is_connected:\\n return await ctx.send(\\\"I'm not connected to a voice channel :no_entry:\\\")\\n if not channel:\\n channel = ctx.channel\\n player.store('channel', channel.id)\\n await ctx.send(\\\"Messages will now be sent in {} <:done:403285928233402378>\\\".format(channel.mention))\",\n \"def stop(update, context) -> None:\\n update.message.reply_text('Okay, bye.')\\n\\n logger.info(\\\"User [%s] canceled conversation using command [/stop].\\\",\\n update.message.from_user.first_name)\\n return END\",\n \"async def on_disconnect():\\n print(\\\"Bot has logged off\\\")\",\n \"async def reset(self, msg):\\n if msg.voice_client is None:\\n return await msg.send(f\\\"**{msg.author.display_name}, there is no audio currently playing from the bot.**\\\")\\n\\n if msg.author.voice is None or msg.author.voice.channel != msg.voice_client.channel:\\n return await msg.send(f\\\"**{msg.author.display_name}, you must be in the same voice channel as the bot.**\\\")\\n\\n if self.player[msg.guild.id]['queue'] and msg.voice_client.is_playing() is False:\\n return await msg.send(\\\"**No audio currently playing or songs in queue**\\\".title(), delete_after=25)\\n\\n self.player[msg.guild.id]['reset'] = True\\n msg.voice_client.stop()\",\n \"def stop(self):\\n self.running = False\\n self.hop_channel(\\\"auto\\\")\",\n \"def stop_telescope_move():\\n\\ttarget = send_command('stoptel')\",\n \"async def disable(self, ctx):\\n await self.config.guild(ctx.guild).auto.set(True)\\n await ctx.send(_(\\\"Automatic voicechannel creation disabled.\\\"))\",\n \"async def _summon(self,\\n ctx: commands.Context,\\n *,\\n channel: discord.VoiceChannel = None):\\n\\n if not channel and not ctx.author.voice:\\n raise VoiceError(\\n 'You are neither connected to a voice channel nor specified a channel to join.'\\n )\\n\\n destination = channel or ctx.author.voice.channel\\n if ctx.voice_state.voice:\\n await ctx.voice_state.voice.move_to(destination)\\n return\\n\\n ctx.voice_state.voice = await destination.connect()\\n await ctx.message.add_reaction('✅')\",\n \"async def _join(self, ctx: commands.Context):\\n\\n destination = ctx.author.voice.channel\\n if ctx.voice_state.voice:\\n await ctx.voice_state.voice.move_to(destination)\\n return\\n\\n ctx.voice_state.voice = await destination.connect()\",\n \"async def test_silence_voice(self):\\n message = \\\"This should show up just here.\\\"\\n await self.cog.send_message(message, self.text_channels[0], self.voice_channel, alert_target=False)\\n self.text_channels[0].send.assert_awaited_once_with(message)\\n self.text_channels[1].send.assert_not_called()\",\n \"async def join(self, ctx, *, channel: discord.VoiceChannel):\\n \\n if ctx.voice_client is not None:\\n return await ctx.voice_client.move_to(channel)\\n else:\\n return await ctx.voice_client.move_to(bot.get_channel(247531960488951815))\\n \\n await channel.connect()\",\n \"def do_session_stop(bot, msg, **kwargs):\\n channel = kwargs.get('event').get('channel')\\n bot.post_msg(\\n text='`Closing election session...',\\n channel_name_or_id=bot.current_channel\\n )\\n bot.stats.get(channel)['election_status'] = False\\n bot.db.session.query(bot.db.Office).filter_by(channel=channel).first().election_status= False\\n bot.db.session.commit()\\n bot.update_election_status(channel, False)\\n bot.post_msg(\\n text='`Election session closed.`',\\n channel_name_or_id=bot.current_channel\\n )\\n bot.post_msg(\\n text='`Reporting to team members...`',\\n channel_name_or_id=bot.current_channel\\n )\\n\\n members_who_voted = [vote.voter.userid for vote in bot.db.session.query(bot.db.Vote).all()]\\n print 'Voters: %r' %members_who_voted\\n if 'DEBUG' in dir(bot.config) or 'TESTING' in dir(bot.config):\\n for test_userid in bot.config.TEST_USERS.values():\\n #test_userid = bot.config.TEST_USERS.values()[0]\\n if test_userid in members_who_voted:\\n voted_msg = 'Thank you for your participation in the process. Looking forward to more contributions from you in the community.'\\n else:\\n voted_msg = 'You did not participate in this one, but the community would be glad to have your contribution the next time. :simple_smile:'\\n\\n bot.direct_message(\\n text=textwrap.dedent(\\n '''\\n Hello {user},\\n\\n Elections for *{office}* have just been concluded. These are the results:\\n \\n {stats}\\n\\n Any reviews would be communicated by the team administrators.\\n\\n {voted_msg}\\n\\n You may now head over to the <#C0NA61U1X|general> channel to talk about this with the rest of the community.\\n '''.format(\\n user=bot.format_user_mention(test_userid),\\n office=bot.stats.get(channel).get('office'),\\n stats=bot.get_stats(channel).strip().split('\\\\n',1)[1].strip(),\\n voted_msg=voted_msg\\n )\\n ),\\n userid=test_userid\\n )\\n else:\\n for member in bot.team_members:\\n if member.get('id') in members_who_voted:\\n voted_msg = 'Thank you for your participation in the process. Looking forward to more contributions from you in the community.'\\n else:\\n voted_msg = 'You did not participate in this one, but the community would be glad to have your contribution the next time. :simple_smile:'\\n\\n bot.direct_message(\\n text=textwrap.dedent(\\n '''\\n Hello {user},\\n \\n Elections for *{office}* have just been concluded. These are the results:\\n \\n {stats}\\n\\n Any reviews would be communicated by the team administrators.\\n\\n {voted_msg}\\n\\n You may now head over to the <#C0NA61U1X|general> channel to talk about this with the rest of the community.\\n '''.format(\\n user=member.get('profile').get('first_name') or bot.format_user_mention(member.get('id')),\\n office=bot.stats.get(channel).get('office'),\\n stats=bot.get_stats(channel).strip().split('\\\\n',1)[1].strip(),\\n voted_msg=voted_msg\\n )\\n ),\\n userid=member.get('id')\\n )\\n bot.post_msg(\\n text='`Done.`',\\n channel_name_or_id=bot.current_channel\\n )\\n return True\",\n \"def leave_on_empty(\\n client: discord.Client,\\n interval: float = 30,\\n) -> LoopType:\\n\\n @tasks.loop(seconds=interval)\\n async def _leaver() -> None:\\n for voice_client in client.voice_clients:\\n if (\\n isinstance(voice_client, discord.VoiceClient)\\n and len(voice_client.channel.members) <= 1\\n ):\\n logger.info(\\n f'leaving voice channel {voice_client.channel.name} in '\\n f'{voice_client.channel.guild.name} due to inactivity',\\n )\\n await voice_client.disconnect()\\n\\n return _leaver\",\n \"async def resume(self, ctx):\\n voice = discord.utils.get(self.bot.voice_clients, guild=ctx.guild)\\n\\n if voice:\\n if voice.is_paused():\\n voice.resume()\\n await ctx.send(\\\"Playing resumed.\\\")\\n else:\\n await ctx.message.add_reaction('\\\\U0001F615');\\n await ctx.send(\\\"Nothing paused right now.\\\")\\n else:\\n await ctx.message.add_reaction('\\\\U0001F615')\\n await ctx.send(\\\"Not in a voice channel.\\\")\",\n \"async def join(self, ctx, *, channel: discord.VoiceChannel):\\n\\n if ctx.voice_client is not None:\\n return await ctx.voice_client.move_to(channel)\\n\\n await channel.connect()\",\n \"def __del__(self):\\n self.bot.close()\",\n \"async def clear(self, ctx):\\n if ctx.voice_client is None or ctx.voice_client.channel is None:\\n await ctx.reply(f\\\"I am not in a voice channel, invite me first with `{self.bot_config['prefix']}join`.\\\")\\n raise commands.CommandError(\\\"Bot not connected to a voice channel.\\\")\\n\\n if ctx.author.voice is None or ctx.author.voice.channel is None:\\n await ctx.reply(\\\"You need to be in a voice channel to use this command.\\\")\\n raise commands.CommandError(\\\"Invoker not connected to a voice channel.\\\")\\n\\n if ctx.voice_client is not None and ctx.author.voice.channel != ctx.voice_client.channel:\\n await ctx.reply(\\\"You need to be in the same voice channel as the bot to use this command.\\\")\\n raise commands.CommandError(\\\"Invoker not in same voice channel as bot.\\\")\\n\\n controller = SpotifyController.get_instance(ctx.voice_client.channel.id)\\n controller.stop_playlist_playback()\\n controller.clear_playlist()\\n await ctx.send(f\\\"Queue cleared!\\\")\",\n \"async def stop_alert(self, ctx):\\n cyphon = discord.utils.get(ctx.message.server.members, id=\\\"186835826699665409\\\")\\n\\n if self.check_channel(ctx):\\n if self.check_permission(ctx) or ctx.message.author == cyphon:\\n channel = ctx.message.channel\\n\\n to_delete = []\\n\\n for s in self.twitch_streams:\\n if channel.id in s[\\\"CHANNEL\\\"]:\\n to_delete.append(s)\\n\\n for s in to_delete:\\n self.twitch_streams.remove(s)\\n\\n dataIO.save_json(\\\"data/streams/twitch.json\\\", self.twitch_streams)\\n\\n await self.bot.say(\\\"There will be no more stream alerts in the stream \\\"\\n \\\"channel.\\\")\\n else:\\n await self.bot.send_message(ctx.message.author, \\\"You don't have permission to execute that command.\\\")\",\n \"def turn_off(self, **kwargs):\\n self.robot.pause_cleaning()\\n time.sleep(1)\\n self.robot.send_to_base()\",\n \"def stop_conversation(self):\\n self.redis.publish('conversation_stopped_by_operator', json.dumps(self.operator_token))\\n self.conversation_started = False\",\n \"async def off(self, ctx, *, nickname=\\\"\\\"):\\n nickname = nickname.strip()\\n if nickname == \\\"\\\":\\n nickname = None\\n try:\\n await self.bot.change_nickname(ctx.message.server.me, nickname)\\n await self.bot.delete_message(ctx.message)\\n await self.bot.say(\\\"RIP music lol\\\")\\n except discord.Forbidden:\\n await self.bot.say(\\\"I cannot do that, I miss the `Change Nickname` or `Manage Messages` permission\\\")\",\n \"async def on(self, ctx, *, nickname=\\\"\\\"):\\n nickname = nickname.strip()\\n mention_here = True\\n mention_everyone = True\\n if nickname == \\\"\\\":\\n nickname = \\\"Dank Bot |Music on voice!\\\"\\n try:\\n await self.bot.change_nickname(ctx.message.server.me, nickname)\\n await self.bot.say(\\\"Hey, music is playing on voice channel come! @here\\\")\\n await self.bot.delete_message(ctx.message)\\n except discord.Forbidden:\\n await self.bot.say(\\\"I cannot do that, I miss the `Change Nickname` or `Manage Messages` permission\\\")\",\n \"async def shutdown(self, ctx: commands.Context):\\r\\n try:\\r\\n await self.bot.logout()\\r\\n await self.bot.close()\\r\\n except Exception as ex:\\r\\n await self.bot.logger.warning(\\\"An error occurred trying to logout\\\", exc_info=ex)\\r\\n else:\\r\\n await ctx.message.add_reaction(\\\"👌\\\")\",\n \"def stopped(self, event, source):\\n\\n self.log('Disconnecting IRC bot')\\n self.fire(disconnect())\",\n \"def on_close(self):\\n self.subscrib.unsubscribe(self.channel)\\n self.thread.stop()\",\n \"def endGame(self):\\n pass\",\n \"async def shutdown(self, ctx):\\n await self.bot.session.close()\\n await self.bot.logout()\",\n \"async def _summon(self, ctx: commands.Context, *, channel: discord.VoiceChannel = None):\\n\\n if not channel and not ctx.author.voice:\\n raise VoiceError('You are neither connected to a voice channel nor specified a channel to join.')\\n\\n destination = channel or ctx.author.voice.channel\\n if ctx.voice_state.voice:\\n await ctx.voice_state.voice.move_to(destination)\\n return\\n\\n ctx.voice_state.voice = await destination.connect()\",\n \"def stopGame(event):\\n if event.action == sense_hat.ACTION_RELEASED:\\n global playAgain, alive\\n playAgain = False\\n alive = False\",\n \"async def logout(ctx):\\n await ctx.send(f\\\"Hey {ctx.author.mention}, I am now logging out :wave:\\\")\\n await bot.logout()\",\n \"def handle_audio_end(_):\\n if config.get(\\\"listener\\\").get(\\\"mute_during_output\\\"):\\n loop.unmute() # restore\",\n \"def stop(self): \\r\\n print(\\\"Thank you for playing the game, hope you had fun!!!\\\")\\r\\n self.reset()\",\n \"def end(update, context) -> int:\\n update.callback_query.edit_message_text(\\n 'Bye! I hope we can talk again some day.')\\n\\n logger.info(\\\"User [%s] exited the conversation, [Exit], from [Main Menu / Results State].\\\",\\n update.callback_query.message.chat.first_name)\\n return ConversationHandler.END\",\n \"def leave(msg: telebot.types.Message):\\n if utils.in_menu(msg.from_user):\\n bot.reply_to(\\n msg,\\n 'This command outside of game is useless.'\\n )\\n return\\n\\n game, user, opponent = utils.get_game_user_opponent(msg.from_user)\\n if not game or not user:\\n # todo log something\\n return\\n\\n user.state = states.USER_IN_MENU\\n user.losses += 1\\n utils.update_user(user)\\n bot.send_message(\\n user.user_id,\\n 'You surrendered.'\\n )\\n\\n if opponent:\\n opponent.state = states.USER_IN_MENU\\n opponent.wins += 1\\n utils.update_user(opponent)\\n bot.send_message(\\n opponent.user_id,\\n 'Your opponent surrendered'\\n )\\n\\n field = json.loads(game.field)\\n sig = 1 if user == game.user1 else 2\\n\\n # changes users emojis to poop\\n for i in range(len(field)):\\n for j in range(len(field[i])):\\n if field[i][j] == sig:\\n field[i][j] = 4\\n\\n if opponent:\\n utils.send_updated_field(bot, field, game, opponent)\\n Game.delete_by_id(game.id)\",\n \"async def _join(self, ctx: commands.Context):\\n destination = ctx.author.voice.channel # Destination is the author's voice channel\\n if ctx.voice_state.voice:\\n await ctx.voice_state.voice.move_to(destination)\\n return\\n\\n ctx.voice_state.voice = await destination.connect()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8343599","0.77186316","0.7650133","0.76289874","0.758633","0.7526717","0.74780494","0.7415649","0.7250327","0.7245011","0.7157922","0.70776135","0.70071197","0.6973833","0.6932585","0.69310313","0.6870495","0.6870495","0.68690455","0.68123126","0.6805128","0.6641129","0.66123444","0.6560766","0.65141135","0.6481257","0.64475113","0.64318085","0.64069206","0.6400439","0.6394434","0.6362375","0.6338891","0.6332155","0.62985176","0.6277435","0.6245925","0.61949337","0.6116147","0.61156964","0.6106642","0.6102138","0.6092314","0.607465","0.6071301","0.6060327","0.605987","0.6055135","0.59986246","0.59932214","0.5979252","0.59639084","0.5963795","0.59512556","0.5947675","0.59451634","0.5932126","0.5922338","0.5920961","0.5900027","0.58940804","0.58725566","0.58615875","0.5861584","0.5856909","0.5853301","0.58453315","0.58370763","0.58362013","0.58273256","0.5812341","0.58015877","0.5779446","0.57763493","0.5772527","0.57696897","0.5765078","0.576427","0.57585865","0.57536286","0.57391334","0.5737623","0.57270813","0.5718656","0.57177925","0.57104266","0.57092476","0.5700545","0.5650235","0.5649328","0.5646669","0.5644693","0.5634727","0.5630432","0.5618981","0.5614318","0.5613411","0.56085205","0.5599994","0.55992925"],"string":"[\n \"0.8343599\",\n \"0.77186316\",\n \"0.7650133\",\n \"0.76289874\",\n \"0.758633\",\n \"0.7526717\",\n \"0.74780494\",\n \"0.7415649\",\n \"0.7250327\",\n \"0.7245011\",\n \"0.7157922\",\n \"0.70776135\",\n \"0.70071197\",\n \"0.6973833\",\n \"0.6932585\",\n \"0.69310313\",\n \"0.6870495\",\n \"0.6870495\",\n \"0.68690455\",\n \"0.68123126\",\n \"0.6805128\",\n \"0.6641129\",\n \"0.66123444\",\n \"0.6560766\",\n \"0.65141135\",\n \"0.6481257\",\n \"0.64475113\",\n \"0.64318085\",\n \"0.64069206\",\n \"0.6400439\",\n \"0.6394434\",\n \"0.6362375\",\n \"0.6338891\",\n \"0.6332155\",\n \"0.62985176\",\n \"0.6277435\",\n \"0.6245925\",\n \"0.61949337\",\n \"0.6116147\",\n \"0.61156964\",\n \"0.6106642\",\n \"0.6102138\",\n \"0.6092314\",\n \"0.607465\",\n \"0.6071301\",\n \"0.6060327\",\n \"0.605987\",\n \"0.6055135\",\n \"0.59986246\",\n \"0.59932214\",\n \"0.5979252\",\n \"0.59639084\",\n \"0.5963795\",\n \"0.59512556\",\n \"0.5947675\",\n \"0.59451634\",\n \"0.5932126\",\n \"0.5922338\",\n \"0.5920961\",\n \"0.5900027\",\n \"0.58940804\",\n \"0.58725566\",\n \"0.58615875\",\n \"0.5861584\",\n \"0.5856909\",\n \"0.5853301\",\n \"0.58453315\",\n \"0.58370763\",\n \"0.58362013\",\n \"0.58273256\",\n \"0.5812341\",\n \"0.58015877\",\n \"0.5779446\",\n \"0.57763493\",\n \"0.5772527\",\n \"0.57696897\",\n \"0.5765078\",\n \"0.576427\",\n \"0.57585865\",\n \"0.57536286\",\n \"0.57391334\",\n \"0.5737623\",\n \"0.57270813\",\n \"0.5718656\",\n \"0.57177925\",\n \"0.57104266\",\n \"0.57092476\",\n \"0.5700545\",\n \"0.5650235\",\n \"0.5649328\",\n \"0.5646669\",\n \"0.5644693\",\n \"0.5634727\",\n \"0.5630432\",\n \"0.5618981\",\n \"0.5614318\",\n \"0.5613411\",\n \"0.56085205\",\n \"0.5599994\",\n \"0.55992925\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.79554284"},"document_rank":{"kind":"string","value":"1"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":224,"numItemsPerPage":100,"numTotalItems":95772,"offset":22400,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sInJlcG9JZHMiOlsiNjc1Mzc2ODJkMmE2Mjg0NzVhMWJmZmNkIl0sImlhdCI6MTc5MDk1NDgwMywianRpIjoiYzlkMGZjODctZGFiMS00NWMwLTk0MmEtZDFiNWVhMzlkNjU2Iiwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3OTA5NTg0MDMsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.EZbIiwCkPDcGumU1M84_TVjYcF0ULt-32iz6ZLjt6qAhvSss0UpzAtg_QcYoweJp6xfmC83a51-nyVsr_DpTDA","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 |
|---|---|---|---|---|---|---|
Initializes batch normalization axis | def init_bn_axis():
if K.image_data_format() == 'channels_first':
bn_axis = 1
else:
bn_axis = -1
return bn_axis | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _init_norm(self):\n with tf.name_scope('init_norm'):\n flat = tf.reshape(self.v, [-1, self.layer_depth])\n self.g.assign(\n tf.reshape(tf.linalg.norm(flat, axis=0), (self.layer_depth,)))",
"def __init__(self, reduction_indices=None, offset=True, scale=False,\n ... | [
"0.6898164",
"0.689096",
"0.6800002",
"0.67654866",
"0.6455606",
"0.64516187",
"0.6423677",
"0.6414971",
"0.6396319",
"0.6357348",
"0.63528067",
"0.6349475",
"0.63430935",
"0.6318749",
"0.6273592",
"0.62298125",
"0.62289274",
"0.6211508",
"0.619495",
"0.61622876",
"0.6161921"... | 0.61274785 | 23 |
Initializes network input shape | def init_input_shape(image_size, channels=3):
(img_rows, img_cols) = image_size
if K.image_data_format() == 'channels_first':
input_shape = (channels, img_rows, img_cols)
else:
input_shape = (img_rows, img_cols, channels)
return input_shape | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, shape, input_var=None):\n\n self.output = layers.InputLayer(shape, input_var=input_var)",
"def __init__(self, netSize):\n\t\t\n\t\t# TRY THIS FOR RANDOM!\n\t\t#\n\t\t#\n\t\t#\n\t\t\n\t\tself.biases = [self.randomArray(i, 1) for i in netSize[1:]] # Biases do not exist for the ... | [
"0.7029771",
"0.7024046",
"0.6919055",
"0.68907285",
"0.68846923",
"0.68404883",
"0.6822069",
"0.6804236",
"0.6804236",
"0.6804236",
"0.67505586",
"0.66984344",
"0.66806984",
"0.666526",
"0.6639504",
"0.66085154",
"0.6592786",
"0.6583862",
"0.65691096",
"0.65449697",
"0.65282... | 0.0 | -1 |
Initializes network input shape and batch normalization axis | def init_input_shape_and_bn_axis(image_size):
(img_rows, img_cols) = image_size
if K.image_data_format() == 'channels_first':
input_shape = (1, img_rows, img_cols)
bn_axis = 1
else:
input_shape = (img_rows, img_cols, 1)
bn_axis = 3
return (input_shape, bn_axis) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, net, batch):\n self.net = net\n self.train_batch_is(batch)\n self.image_height = len(batch.image_array[0][0])\n self.image_width = len(batch.image_array[0][0][0])\n self.net.reset_forward()",
"def initialize_network(self):\n # intermediate layer size\n... | [
"0.726126",
"0.69356924",
"0.6871046",
"0.6827918",
"0.66717565",
"0.66414213",
"0.65312356",
"0.6525526",
"0.64984953",
"0.645438",
"0.64380443",
"0.63919",
"0.63639694",
"0.6353767",
"0.63438046",
"0.6335529",
"0.633095",
"0.6296713",
"0.62875813",
"0.628072",
"0.6252297",
... | 0.0 | -1 |
Adds "dropout" layer to model | def dropout(keep_prob, net, is_training):
return Dropout(keep_prob)(net) if is_training else net | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_dropout_layer(self):\n return tf.keras.layers.Dropout(rate=self.dropout)",
"def add_drop_out_layer(self, input_layer):\n return tf.nn.dropout(input_layer, self.keep_prob)",
"def dropout(input_var=None):\n\n # Hyperparameters\n hp = Hyperparameters()\n hp('batch_size', 20)\n ... | [
"0.8024405",
"0.7728018",
"0.74729925",
"0.72339785",
"0.71602124",
"0.6933945",
"0.69273853",
"0.6838031",
"0.6837817",
"0.682116",
"0.6715737",
"0.6679599",
"0.65281695",
"0.6525919",
"0.65130067",
"0.65084773",
"0.64376026",
"0.63970506",
"0.62666637",
"0.62647206",
"0.622... | 0.633441 | 18 |
Initializes training cost function | def retrieve_optimizer(flags):
lrate = flags.learning_rate
momentum = flags.momentum
decay = lrate / flags.epochs
if flags.optimizer == 'adam':
optimizer = Adam(lr=lrate, decay=decay)
logger.print_directly(flags, 'ADAM optimizer was configured')
else:
optimizer = SGD(lr=lrat... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _initialize_trainer(self):\n self.cost = mse(0., 0.)\n for task_id in self.task_ids.keys():\n self.cost += self.model.get_layer(task_id + '-loss')\n\n opt = Optimizer(self.cost)\n self.optimizer = opt.get_adagrad(self.learning_rate)",
"def __init__(self, cost_func):\n ... | [
"0.7631753",
"0.7487942",
"0.71219885",
"0.7037173",
"0.694103",
"0.6896091",
"0.6878694",
"0.68467164",
"0.6837462",
"0.6803369",
"0.6757318",
"0.6744617",
"0.6744267",
"0.67090356",
"0.66888",
"0.66572183",
"0.6620065",
"0.661703",
"0.66004735",
"0.6590226",
"0.6585318",
... | 0.0 | -1 |
Compiles network for training | def compile_network_model(model, optimizer, loss_func):
model.compile(optimizer=optimizer,
loss=loss_func, metrics=['accuracy']) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def compile_network(model, optimizer):\n compile_network_model(model, optimizer, categorical_crossentropy)",
"def compile(self):\n logger.info('Define network with dnnet of version : %s'\\\n % dnnet.__version__)\n if self.layers.size == 0:\n msg = 'NeuralNetwork has... | [
"0.7822134",
"0.75386256",
"0.7513529",
"0.7442466",
"0.71998936",
"0.71950305",
"0.7178709",
"0.71079236",
"0.7097527",
"0.70253724",
"0.6996287",
"0.69950825",
"0.6874142",
"0.6854327",
"0.68316686",
"0.679506",
"0.6751136",
"0.67454094",
"0.67450076",
"0.6727265",
"0.67063... | 0.681932 | 15 |
Compiles network for training | def compile_network(model, optimizer):
compile_network_model(model, optimizer, categorical_crossentropy) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def compile(self):\n logger.info('Define network with dnnet of version : %s'\\\n % dnnet.__version__)\n if self.layers.size == 0:\n msg = 'NeuralNetwork has no layer.\\n Add layers before compiling.'\n raise DNNetRuntimeError(msg)\n\n parent = self.laye... | [
"0.7539172",
"0.7513481",
"0.7444233",
"0.72000104",
"0.719475",
"0.71787214",
"0.7108279",
"0.70971787",
"0.70245636",
"0.699772",
"0.699507",
"0.68741465",
"0.6854814",
"0.68306136",
"0.68187803",
"0.67940164",
"0.67512035",
"0.6746178",
"0.67435986",
"0.6729193",
"0.670608... | 0.78212905 | 0 |
Randomly rotate the point clouds to augument the dataset rotation is per shape based along up direction | def rotate_point_cloud(batch_data):
rotated_data = np.zeros(batch_data.shape, dtype=np.float32)
for k in np.arange(batch_data.shape[0]):
rotation_angle = np.random.uniform() * 2 * np.pi
cosval = np.cos(rotation_angle)
sinval = np.sin(rotation_angle)
rotation_matrix = np.array([[c... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def rotate_point_cloud(data):\n rotated_data = np.zeros(data.shape, dtype=np.float32)\n for k in xrange(data.shape[0]):\n rotation_angle = np.random.uniform() * 2 * np.pi\n cosval = np.cos(rotation_angle)\n sinval = np.sin(rotation_angle)\n rotation_matrix ... | [
"0.7384044",
"0.69522613",
"0.6924791",
"0.6924791",
"0.6924791",
"0.68159866",
"0.6813395",
"0.6813395",
"0.6746108",
"0.669056",
"0.6641431",
"0.65933377",
"0.65933377",
"0.65933377",
"0.65219665",
"0.64666146",
"0.641492",
"0.6368018",
"0.62005866",
"0.61693",
"0.6102413",... | 0.6964295 | 1 |
Get maximum depth of given tree by BFS | def max_depth(root):
# basic case
if root is None:
return 0
# breadth-first traversal
queue = collections.deque([root])
depth = 0
while queue:
queue_size = len(queue)
for i in range(queue_size):
curr = queue.popleft()
if curr.left is not None:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _max_depth(self):\n max_depth = 0\n for node, data in self.traverse():\n max_depth = max(max_depth, data['level'])\n return max_depth",
"def max_depth(node):\n if not node:\n return 0\n return max(max_depth(node.left), max_depth(node.right)) + 1",
"def maxDepth(... | [
"0.7669001",
"0.7654562",
"0.7653014",
"0.7614319",
"0.7599487",
"0.74606186",
"0.74326384",
"0.73375744",
"0.7196552",
"0.718703",
"0.7182534",
"0.71651614",
"0.7148998",
"0.71120065",
"0.7107149",
"0.7074876",
"0.706041",
"0.7011375",
"0.6983949",
"0.6968935",
"0.6909913",
... | 0.7672864 | 0 |
Initlize config from YAML file | def __init__(self, configFileName):
config = yaml.load(open(configFileName, "r"))
# Logging
self.log_level = logging.DEBUG
if config['log_level'] == 'INFO':
self.log_level = logging.INFO
if config['log_level'] == 'ERROR':
self.log_level = logging.ERROR
# CPU count
self.multiple... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def config_init(filename='./config.yml'):\n try:\n cfg = yaml.safe_load(open(filename))\n except IOError:\n msg = f'Loading config file ({filename}) failed.'\n raise IOError(msg)\n return cfg",
"def load_config(filename):\n AS[\"config\"] = load_yaml_file(filename)",
"def load_config... | [
"0.78310275",
"0.75019807",
"0.72568125",
"0.7239557",
"0.7188735",
"0.7183867",
"0.71801865",
"0.71490663",
"0.71455586",
"0.71418613",
"0.7119411",
"0.70776075",
"0.70237774",
"0.700751",
"0.69708675",
"0.69608235",
"0.694945",
"0.6896558",
"0.6868082",
"0.6859895",
"0.6859... | 0.0 | -1 |
Util to make path | def mkdir_p(start_path):
try:
os.makedirs(start_path)
except OSError as exc: # Python >2.5
if exc.errno == errno.EEXIST and os.path.isdir(start_path):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def path_creator(rel_path=''):\n if platform.system() != 'Windows':\n if rel_path == '':\n path_list=sys.argv[0].split('/')[:-1]\n return '/'.join(path_list)\n else:\n path_list = sys.argv[0].split('/')[:-1]\n return '/'.join(path_list) + '/' + rel_path\... | [
"0.7679384",
"0.75879025",
"0.7498985",
"0.74565715",
"0.737568",
"0.7094449",
"0.7010396",
"0.6994071",
"0.69334644",
"0.6852602",
"0.6852222",
"0.67747194",
"0.6739483",
"0.6722316",
"0.67053086",
"0.6696218",
"0.66496366",
"0.66158193",
"0.6606479",
"0.66040236",
"0.654456... | 0.0 | -1 |
Util to get total size of path in MB | def dir_size(start_path):
total_size = 0
for dirpath, dirnames, filenames in os.walk(start_path):
for f in filenames:
fp = os.path.join(dirpath, f)
if os.path.exists(fp):
try:
total_size += os.path.getsize(fp)
except:
continue
# convert to MB... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_size(path):\n return str(os.path.getsize(path)/1024) + 'kb'",
"def size(path):",
"def getsize(path):\n return get_instance(path).getsize(path)",
"def getsize(self, path):\n return os.path.getsize(path)",
"def get_size_bytes( path ):\n cmd = [ 'du', '-s', '-B1', '--apparent-size',... | [
"0.842222",
"0.8361867",
"0.8233926",
"0.81820023",
"0.813203",
"0.8001843",
"0.79292035",
"0.7899288",
"0.7849692",
"0.78464174",
"0.77591735",
"0.77309155",
"0.7719227",
"0.7716033",
"0.76963025",
"0.76753175",
"0.7606359",
"0.7567961",
"0.7567961",
"0.7564459",
"0.7554968"... | 0.7675255 | 16 |
Extracts all the data from the crawled pages and appends them to authors list | def parse(self, response, **kwargs):
key_url = response._get_url().rsplit('/')[-2] # get the author nickname after the last slash
name = response.xpath('//*[@id="woe"]/div[2]/div/div[1]/div[2]/h3/text()').extract_first()
job_title = response.xpath('//*[@id="woe"]/div[2]/div/div[1]/div[2]/p/tex... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse(self, response, **kwargs):\n title = response.xpath('//*[@id=\"wrap\"]/h1/text()').extract_first()\n if title:\n url_to_full_version = response._get_url()\n first_160 = ''.join(response.xpath('//*[@id=\"woe\"]/section/div/p/text()').extract())[:160]\n base_d... | [
"0.6515397",
"0.6493973",
"0.64058137",
"0.6364981",
"0.6291647",
"0.6258661",
"0.6218389",
"0.61712474",
"0.61032647",
"0.6082514",
"0.60737187",
"0.602179",
"0.6013204",
"0.5997872",
"0.5973892",
"0.596097",
"0.5955605",
"0.5949766",
"0.5940653",
"0.5926298",
"0.5921988",
... | 0.633091 | 4 |
Crawls each authors pages starting from allauthors main page stored in authors report | def start_requests(self):
authors_pandas = conf.read_from_data('authors.json')
author_link_list = list(
map(lambda obj: (obj['keyUrl'], conf.gd_base_url + obj['article_url'], obj['article_url']),
authors_pandas))
for link in author_link_list:
yield Request... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def scrape_author(self, author_name, min_len=0, max_len=9999):\n search = sc.search_author(author_name)\n author = next(search)\n sc.fill(author, sections=['publications'])\n print(author.keys())\n with open(\n 'loadings\\\\authors_papers\\\\{}.txt'.format(author_name)... | [
"0.66120636",
"0.6457187",
"0.6398303",
"0.6192936",
"0.61002207",
"0.60938454",
"0.6064179",
"0.6056438",
"0.60388",
"0.5980902",
"0.5950264",
"0.59492725",
"0.5898721",
"0.5894304",
"0.58883196",
"0.5852367",
"0.581795",
"0.5807714",
"0.58075255",
"0.5806006",
"0.5805787",
... | 0.64995414 | 1 |
Extracts all the data from the crawled pages and appends them to articles list | def parse(self, response, **kwargs):
title = response.xpath('//*[@id="wrap"]/h1/text()').extract_first()
if title:
url_to_full_version = response._get_url()
first_160 = ''.join(response.xpath('//*[@id="woe"]/section/div/p/text()').extract())[:160]
base_date = response... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_data(self):\n has_next_page = True\n page = 1\n while has_next_page:\n print(f'Getting page {page}')\n response = self.get_articles(\n page=page,\n size=200,\n order_by='extracted_at',\n o... | [
"0.737759",
"0.71375436",
"0.6939972",
"0.68686",
"0.677189",
"0.6745556",
"0.67038983",
"0.66500175",
"0.6645783",
"0.6633248",
"0.6561571",
"0.65434307",
"0.6535052",
"0.65009606",
"0.6485128",
"0.64842933",
"0.6419887",
"0.6404132",
"0.64039326",
"0.63916636",
"0.63700855"... | 0.65011173 | 13 |
initialize a network with normalizing flows. | def __init__(self, n_components=1, init_sigma_params=1e-4,
w_prior_sigma= 1., **kwargs):
self.n_components = n_components
# 1 output for predicting offset.
# if input dependent, then predict mixing proportion, mean and variance
# for each mixing component.
self.w_pri... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def initialize_network(self):\n # intermediate layer size\n ils = int((self.specbinnum + self.numfilters) / 2)\n\n network = lasagne.layers.InputLayer((None, 1, self.specbinnum, self.numtimebins), self.input_var)\n\n network = NormalisationLayer(network, self.specbinnum)\n self.n... | [
"0.726364",
"0.70838445",
"0.7032408",
"0.67254543",
"0.66174716",
"0.6567007",
"0.64898306",
"0.64257777",
"0.6404362",
"0.63733166",
"0.6250138",
"0.61597544",
"0.61556476",
"0.6155297",
"0.6144982",
"0.6139448",
"0.6134628",
"0.6117033",
"0.6114269",
"0.60687244",
"0.60680... | 0.0 | -1 |
construct_network establishes all weight matrices and biases and connects them. The outputs may include parameters of the flow | def construct_network(self, n_units, n_samples=1, noise_dim=0,
keep_p=1., nonlinearity=True, init_params=None, name=""):
print "constructing network, n_units: ",n_units
# TODO use kwargs for more elagant solutions to being called by this
# base class
assert keep_p ==1. and n... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _build_networks(self):\n self.online_convnet = self._create_network(name='Online')\n self.target_convnet = self._create_network(name='Target')\n self._net_outputs = self.online_convnet(self.state_ph, training=True)\n self._q_argmax = tf.argmax(self._net_outputs.q_values, axis=1)[0]\n self._repla... | [
"0.7192482",
"0.7097717",
"0.70156425",
"0.69921356",
"0.69625026",
"0.68106323",
"0.67981684",
"0.6779482",
"0.67386734",
"0.67300445",
"0.6718721",
"0.6694646",
"0.6689944",
"0.6686015",
"0.66773814",
"0.66746324",
"0.6673208",
"0.6619003",
"0.66150516",
"0.6595705",
"0.656... | 0.71916044 | 1 |
construct_flow builds and links together the normalizing flow and establishes the log likelihood of samples. | def construct_mog(self, outputs):
# check for correct number of input dimensions.
assert outputs.shape[-1] == (self.n_components)*3 + 1
out_idx = 0 # keep track of which output we are working with.
self.shift = outputs[0, :, out_idx:out_idx+1]; out_idx += 1
with tf.name_scope("Mi... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def build(self, input_shape):\n with tf.name_scope(self.name):\n # Using the PyTorch default hyperparameters.\n self.batch_norm = tf.keras.layers.BatchNormalization(epsilon=1e-5,\n momentum=0.9)\n self.latent_dim = input_shape[-1]\n i... | [
"0.61279553",
"0.57909757",
"0.5786865",
"0.57724696",
"0.5767959",
"0.57207876",
"0.5715004",
"0.5664029",
"0.5578143",
"0.55653596",
"0.55619967",
"0.55383277",
"0.5536612",
"0.54801136",
"0.5474818",
"0.54728127",
"0.5437271",
"0.5427648",
"0.5400124",
"0.5386498",
"0.5384... | 0.0 | -1 |
Check transaction and journal and stuff. | def beforeEditing(self):
infotext, self.journal, self.parentApp.tmpTransC = (
ledgeradd.check_trans_in_journal(
settings=self.parentApp.S,
transaction=self.parentApp.tmpTransC
)
)
# set form title, color and infotext
self.name = se... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def checkAllTx(self):\n return None",
"def in_transaction(self):\n # We likely just changed data - give it a second to catch up\n time.sleep(0.1) # I think I keep reading journal watermark too soon without this\n \n # Get relevant data\n water_mark = pos.read_journal_wat... | [
"0.670862",
"0.640443",
"0.6317232",
"0.6199903",
"0.60706097",
"0.6067854",
"0.6045636",
"0.59766394",
"0.59426844",
"0.58845556",
"0.5867006",
"0.58334965",
"0.57512766",
"0.57473826",
"0.5746719",
"0.5746719",
"0.5746719",
"0.5746719",
"0.5737928",
"0.5692912",
"0.56395143... | 0.0 | -1 |
Add transaction to the history. | def add_history(self):
# add separator, if there already are history entries
if self.parentApp.History != '':
self.parentApp.History += (
'\n\n--- --- --- --- --- --- --- --- --- --- --- ---\n\n'
)
# add the transaction to it
self.parentApp.Histor... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def addTransaction(self, transaction):\n self.transactions.append(transaction)\n self.transactionIDs.add(transaction.id)",
"def add(self, transaction):\n if isinstance(transaction, Transaction):\n # If the transaction already exists\n if(transaction.hash in self.transac... | [
"0.73509127",
"0.71942204",
"0.7029798",
"0.6894006",
"0.68311554",
"0.6707649",
"0.6418446",
"0.6413464",
"0.6399687",
"0.6394123",
"0.6381334",
"0.63626176",
"0.6356362",
"0.63558763",
"0.63207406",
"0.62853384",
"0.6273537",
"0.6218804",
"0.61947453",
"0.6156782",
"0.61382... | 0.730184 | 1 |
Press cancel go back. | def on_cancel(self, keypress=None):
self.parentApp.switchFormPrevious() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def press_back_button(self):\n self.driver.back()",
"def skip(self):\n self.click_back_button()",
"def back(self):\n self.input_key_event(InputActions.BACK)",
"def back( self ):\n super( ConfirmationScreen, self ).back()\n\n self._current_option = self._current_option - 1\n ... | [
"0.7686845",
"0.76652855",
"0.7510146",
"0.73989546",
"0.7366367",
"0.735423",
"0.7283691",
"0.7280198",
"0.7280198",
"0.7280198",
"0.72527945",
"0.72527945",
"0.7245617",
"0.7226324",
"0.7215707",
"0.7212924",
"0.72069484",
"0.70282036",
"0.7014023",
"0.6985043",
"0.6983128"... | 0.80343944 | 0 |
`parse` should always `yield` a dict that follows the Event Schema | def parse(self, response):
for item in response.css('.listingTable .listingRow'):
start_time = self._parse_start(item)
data = {
'_type': 'event',
'name': self._parse_name(item),
'event_description': '',
'all_day': False,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse_events(events_dict):\n return events_dict['events']",
"def event_parser(\n message, sample_event, response, payload, log_in_file, language\n):\n event = {}\n message = sample_event_key_evaluator(response, payload, message)\n for key, value in sample_event.items():\n event[key] = s... | [
"0.6623319",
"0.6369398",
"0.63349575",
"0.6207194",
"0.61231434",
"0.60706687",
"0.6053306",
"0.6007903",
"0.5988026",
"0.5986127",
"0.5968965",
"0.5869501",
"0.5863868",
"0.58521926",
"0.58474016",
"0.5812221",
"0.5798428",
"0.5786188",
"0.5765936",
"0.5759376",
"0.57146984... | 0.52081865 | 55 |
Parse or generate event name. | def _parse_name(self, item):
return item.css('td[headers=Name]::text').extract_first().strip() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def adjust_event_name(event_name):\n pos=find_first_digit(event_name)\n return event_name[pos:]",
"def event_name(self):\n return dict.get(self, 'event_name', None)",
"def cal_name(self):\n return self.event_name",
"def event_name(self):\n return self._event_name",
"def getEventI... | [
"0.70926106",
"0.6770929",
"0.6751488",
"0.65823394",
"0.63745666",
"0.6276526",
"0.6255812",
"0.62287736",
"0.6173391",
"0.6092006",
"0.6069228",
"0.60093296",
"0.60023534",
"0.5955843",
"0.59454787",
"0.5880808",
"0.5880808",
"0.58638054",
"0.58026296",
"0.5793772",
"0.5783... | 0.0 | -1 |
Parse or generate classification (e.g. public health, education, etc). | def _parse_classification(self, item):
full_name = item.css('td[headers=Name]::text').extract_first()
if "Metra" in full_name and "Board Meeting" in full_name:
return BOARD
elif "Citizens Advisory" in full_name:
return ADVISORY_COMMITTEE
elif "Committee Meeting" ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _parse_classification(self, links):\n for link in links:\n if \"hearing\" in link[\"title\"].lower():\n return FORUM\n return COMMISSION",
"def classification(self) -> 'outputs.CaseClassificationResponse':\n return pulumi.get(self, \"classification\")",
"def _... | [
"0.707388",
"0.6647457",
"0.6626518",
"0.6521891",
"0.6510014",
"0.64428645",
"0.636594",
"0.6330406",
"0.6225585",
"0.61786884",
"0.61213285",
"0.6062574",
"0.6061338",
"0.60570234",
"0.6047572",
"0.60411346",
"0.5997528",
"0.5993576",
"0.5987054",
"0.5979351",
"0.5978588",
... | 0.68485194 | 1 |
Parse start date and time. | def _parse_start(self, item):
raw_date_time = item.css('td[headers~=Date]::text').extract_first()
date_time_str = re.sub(r'\s+', ' ', raw_date_time).strip()
if not date_time_str:
return None
try:
dt = datetime.strptime(date_time_str, '%b %d, %Y - %I:%M %p')
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _parse_start(self, date_str):\n # TODO: Find start time\n return datetime.strptime(date_str.title(), \"%B %d, %Y\")",
"def _parse_start(self, response):\n date_str = response.css(\".date time::attr(datetime)\").extract_first()\n time_str = \"\".join(response.css(\"article.time::te... | [
"0.8012607",
"0.7956383",
"0.79541355",
"0.775254",
"0.7745357",
"0.7708766",
"0.7648467",
"0.7642984",
"0.754136",
"0.74821883",
"0.7399587",
"0.7093819",
"0.697785",
"0.69141096",
"0.6728942",
"0.65998924",
"0.6470897",
"0.6462791",
"0.6380753",
"0.634331",
"0.6262659",
"... | 0.7459805 | 10 |
Parse documents from current and past meetings | def _parse_documents(self, item):
documents = []
agenda_url = item.css('a[href*=Agenda]::attr(href)').extract_first()
if agenda_url:
documents.append({'url': agenda_url, 'note': 'Agenda'})
minutes_url = item.css('a[href*=Minutes]::attr(href)').extract_first()
if minut... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _parse_past_meetings(self, response):\n meetings = []\n for item in response.css('table.table-striped tbody tr'):\n dt_str = item.css('time::text').extract_first()\n meetings.append({\n 'start': {\n 'date': datetime.strptime(dt_str, '%b %d, ... | [
"0.70574003",
"0.6554029",
"0.64571774",
"0.61043",
"0.60476214",
"0.5917609",
"0.59144187",
"0.5843874",
"0.5824725",
"0.5757989",
"0.5725755",
"0.5612276",
"0.5595577",
"0.55906874",
"0.5554109",
"0.5529632",
"0.54651964",
"0.5390686",
"0.53802896",
"0.5373121",
"0.5335325"... | 0.5668923 | 11 |
Determine if the given request is marked for caching and if yes, then look it up in the cache and if found, then return the cached value | def process_resource(self, req, resp, resource, params):
# Step 1: for 'rest-based' and 'rest&time-based' eviction strategies the
# POST/PATCH/PUT/DELETE calls are never cached, they should never be
# loaded from cache as they must always execute,
# so for those we don't need to try to ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cache():\n is_conditional = request.headers.get(\"If-Modified-Since\") or request.headers.get(\n \"If-None-Match\"\n )\n\n if is_conditional is None:\n response = view_get()\n response.headers[\"Last-Modified\"] = http_date()\n response.headers[\"ETag\"] = uuid.uuid4().hex\... | [
"0.71749496",
"0.69655037",
"0.6879033",
"0.6858692",
"0.665096",
"0.66387236",
"0.6592808",
"0.6526735",
"0.65230745",
"0.65191174",
"0.65162516",
"0.6468557",
"0.6466649",
"0.64410686",
"0.64223427",
"0.6418258",
"0.6404368",
"0.63901526",
"0.63901526",
"0.6369117",
"0.6362... | 0.57678896 | 86 |
Cache the response if this request qualifies and has not been cached yet or for restbased and restandtimebased evict the record from the cache if the request method is POST/PATCH/PUT or DELETE | def process_response(self, req, resp, resource, req_succeeded):
# Step 1: for 'rest-based' and 'rest&time-based' eviction strategies the
# POST/PATCH/PUT/DELETE calls are never cached and even more they
# invalidate the record cached by the GET method
if self.cache_config['CACHE_EVICTIO... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def process_resource(self, req, resp, resource, params):\n\n # Step 1: for 'rest-based' and 'rest&time-based' eviction strategies the\n # POST/PATCH/PUT/DELETE calls are never cached, they should never be\n # loaded from cache as they must always execute,\n # so for those we don't need ... | [
"0.7296144",
"0.7083652",
"0.701264",
"0.68986917",
"0.6891372",
"0.6742869",
"0.67302907",
"0.66479725",
"0.6640116",
"0.6626159",
"0.653558",
"0.6509554",
"0.65028024",
"0.6493731",
"0.64882547",
"0.6464503",
"0.61653435",
"0.6115088",
"0.6103556",
"0.6070408",
"0.6057066",... | 0.77516645 | 0 |
Generate the cache key from the request using the path and the method | def generate_cache_key(req, method: str = None) -> str:
path = req.path
if path.endswith('/'):
path = path[:-1]
if not method:
method = req.method
return f'{path}:{method.upper()}' | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _generate_view_response_cache_key( # pylint: disable=unused-argument\n handler: Callable[..., Awaitable[StreamResponse]],\n request: Request,\n *args,\n **kwargs,\n) -> str:\n get_params = request.query\n\n hash_ = sha1(request.path.encode('utf-8'))\n\n for param in sorted(get_params):\n ... | [
"0.7671069",
"0.73947465",
"0.69811064",
"0.69450694",
"0.6926922",
"0.6827275",
"0.66507095",
"0.66111356",
"0.6543088",
"0.65096855",
"0.6467866",
"0.64333147",
"0.6348608",
"0.6315374",
"0.6295006",
"0.6290446",
"0.61842054",
"0.6159068",
"0.6157648",
"0.6108428",
"0.60800... | 0.8796167 | 0 |
Serializes the response, so it can be cached. If CACHE_CONTENT_TYPE_JSON_ONLY = False (default), then we need to keep the response ContentType header, so we need to serialize the response body with the content type with msgpack, which takes away performance. For this reason the user can set CACHE_CONTENT_TYPE_JSON_ONLY... | def serialize(self, req, resp, resource) -> bytes:
if self.cache_config['CACHE_CONTENT_TYPE_JSON_ONLY']:
if FALCONVERSION_MAIN < 3:
return resp.body
else:
return resp.text
else:
if FALCONVERSION_MAIN < 3:
return msgpack.... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def to_http_response(self) -> HttpResponse:\n response = (\n JsonResponse(self.body)\n if (self.headers or {}).get(\"Content-Type\") == \"application/json\"\n else HttpResponse(self.body)\n )\n response.headers = self.headers\n return response",
"def r... | [
"0.64963675",
"0.64194614",
"0.636542",
"0.6278377",
"0.61988986",
"0.6171429",
"0.6129956",
"0.60481405",
"0.6038928",
"0.6000924",
"0.60004413",
"0.59598386",
"0.5909832",
"0.5895042",
"0.5895042",
"0.58946943",
"0.58941776",
"0.5894121",
"0.58709216",
"0.5853939",
"0.58444... | 0.72587997 | 0 |
Deserializes the cached record into the response Body or the ContentType and Body | def deserialize(self, data: bytes) -> Tuple[str, Any]:
if self.cache_config['CACHE_CONTENT_TYPE_JSON_ONLY']:
return data
else:
return msgpack.unpackb(data, raw=False) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def deserialize(self, resp):\r\n return self.serializer.deserialize(resp.content, format=resp['Content-Type'])",
"def deserialize(self, data, caches=None):\n return data",
"def decode(self) -> D:\n if self.has_cached_data():\n return self._data\n\n # Dispatch decoding\n ... | [
"0.66872615",
"0.628563",
"0.6097179",
"0.60517174",
"0.6006489",
"0.60002464",
"0.59839606",
"0.59279156",
"0.5876185",
"0.5767751",
"0.57473266",
"0.5706114",
"0.56609535",
"0.56507987",
"0.562745",
"0.5612654",
"0.5603545",
"0.5589274",
"0.5583386",
"0.5578948",
"0.5569624... | 0.6360597 | 1 |
Takes an incoming socket and either stores the command to the command queue, or performs another action based on the command. | def receive_and_store(self, socket, addr):
# Create the incoming connection
conn = IncomingConnection(addr, socket)
# Receive the data from the connection
data = conn.recv_data()
if not data:
logger.warning("Invalid data received")
return
# Get t... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def perform_command(self, command_str):\n print \"Received command: {}\".format(command_str)\n commands = command_str.split('#')\n if len(commands) == 2:\n data = None\n elif len(commands) > 2:\n data = commands[2:]\n elif command_str == '':\n # G... | [
"0.6953094",
"0.6832541",
"0.6550946",
"0.6328179",
"0.6293573",
"0.6292742",
"0.6161187",
"0.6027672",
"0.5915629",
"0.5909542",
"0.58832407",
"0.5841701",
"0.58089155",
"0.58026075",
"0.5779208",
"0.5778535",
"0.5763915",
"0.5758714",
"0.5742956",
"0.57220924",
"0.5711825",... | 0.7060211 | 0 |
Runs the state machine. Returns in the EXIT state. | def run(self):
# Start the thread to receive commands
self.command_input.start()
# The next time to execute a state
self.next_time = time.time() + self.control_period
# Run forever!
logger.debug("Starting")
while True:
old_state = self.current_state
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main():\n \n\n f = FiniteStatesMachine('stopped', [])\n f.setDefaultTransition(Error, None)\n \n f.addTransitionList('start', 'stopped', starFSMVariables, 'started')\n f.addTransitionList('collect', 'started', collectData, 'collecting')\n f.addTransitionList('collect', ... | [
"0.66520494",
"0.6551841",
"0.6485087",
"0.63386077",
"0.63172615",
"0.6300952",
"0.6278196",
"0.6263265",
"0.6246749",
"0.6161754",
"0.61544645",
"0.6139356",
"0.60383415",
"0.5993939",
"0.5967432",
"0.5961428",
"0.58711255",
"0.5869836",
"0.5837625",
"0.58372927",
"0.581037... | 0.5402872 | 69 |
Show the girth of black pixels along a line. Highlight said line in orig image | def side_traces(x,im):
s0 = x['side-traces'][0]
s1 = x['side-traces'][1]
t1 = Scatter(y=s0)
t2 = Scatter(y=s1)
#put_thing(im,x['abs-line'],(255,0,0),(0,0),3)
groups = []
diff_traces = []
markers = []
y3 = []
TriangleHumps.get_dimensions(x... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def addNonBarrelBlue(self, event):\n # let user draw second ROI\n ROI = RoiPoly(color='r') #let user draw ROI\n plt.show(block=False)\n mask = ROI.get_mask(self.greyimg)\n mask = mask*2\n self.ROI += mask",
"def _hilightcurrent(self, onoff):\n if len(self.canvas[\"ite... | [
"0.61536944",
"0.6099716",
"0.5944608",
"0.59030896",
"0.5892146",
"0.5855114",
"0.58391273",
"0.58272207",
"0.58183736",
"0.5801204",
"0.5785304",
"0.57578826",
"0.57566464",
"0.56925803",
"0.5666854",
"0.56493896",
"0.5643967",
"0.56353635",
"0.56159747",
"0.5602625",
"0.55... | 0.0 | -1 |
get base64 string repr of object or np image | def getbase64(nparr,):
if type(nparr) == type({}):
nparr = nparr['img']
im = Image.fromarray(nparr)
buf = BytesIO()
im.save(buf,format="JPEG")
return base64.b64encode(buf.getvalue()).decode('ascii') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def base64(self):\n image = self.png.getvalue()\n return base64.encodestring(image).decode('utf-8')",
"def data64(self) -> str:\n return Image.encode64(self.data)",
"def base64_string(self) -> global___Expression:",
"def _get_image(x):\n return b64encode(x).decode('ascii')",
"def data... | [
"0.72159815",
"0.6906734",
"0.68322635",
"0.682055",
"0.6737309",
"0.67277545",
"0.6667643",
"0.66208005",
"0.66028064",
"0.6586784",
"0.65735114",
"0.6563396",
"0.65275586",
"0.6448807",
"0.63930434",
"0.63762724",
"0.63067997",
"0.63018715",
"0.62593025",
"0.6230752",
"0.61... | 0.6916091 | 1 |
make a plot of each object and put image next to it. func defines the type of plot and anything that is done to each image,obj pair | def _dump_plotly(objs, images, func):
l = len(objs)
#print(l)
titles = []
for i,x in enumerate(objs):
if 'id' in x:
titles.append('shape id %d' % x.id)
else:
titles.append('item %d' % i)
fig = tools.make_subplots(rows=l, cols=1, subplot_titles = titles,print_g... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def plot_single(potential_func, obstacles, filename, xlim=(-400, 400), ylim=(-400, 400)):\n print \"Generating\", filename\n fig = plt.figure()\n plot = plt.subplot(111)\n show_arrows(plot, potential_func, xlim=xlim, ylim=ylim)\n for obstacle in obstacles:\n show_obstacle(plot, obstacle)\n ... | [
"0.6392929",
"0.6309091",
"0.62998956",
"0.62422526",
"0.62251675",
"0.59421015",
"0.59245807",
"0.590018",
"0.58759063",
"0.5816621",
"0.5809763",
"0.5801232",
"0.57604563",
"0.57517",
"0.5740631",
"0.57335705",
"0.5718802",
"0.5717321",
"0.56906945",
"0.56868666",
"0.568540... | 0.6703569 | 0 |
Integrates contact into protocolaccountcontactmessagedatastructure and returns it | def get_make_contact(root):
root_split = root.split('/')
contact_name = root_split[-1]
#groupchat's folder's end with .chat
group_chat = False
if contact_name.endswith(".chat"):
contact_name = contact_name[:-5]
group_chat = True
account_name = root_split[-2]
protocol_name =... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def import_(cls, contact):\n assert not isinstance(message, PhoneMessage)\n ret = PhoneMessage(peer=message.peer,text=message.text,timestamp=message.timestamp,direction=message.direction,status=message.status)\n yield ret",
"def import_(cls, contact):\n assert not isinstance(message, ... | [
"0.6415014",
"0.6415014",
"0.6144517",
"0.5938548",
"0.5781222",
"0.5781222",
"0.573517",
"0.5672302",
"0.56584066",
"0.5608456",
"0.5603349",
"0.5595631",
"0.5589713",
"0.5581832",
"0.5555969",
"0.5525056",
"0.5495442",
"0.54939127",
"0.54892784",
"0.537718",
"0.5371565",
... | 0.5550638 | 15 |
Adds message to global datastructure | def add_message(name, time, message_text, root):
(contact, account) = get_make_contact(root)
show = None
subject = None
group_chat = False
if root.endswith(".chat"):
group_chat = True
#actions
if message_text and message_text.startswith("***"):
for alias in contact.alias:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_message(self, msg):\n self.messages.append(msg)",
"def handle_message(self, msg):\n self.messages.append({\n 'type': msg.category,\n 'module': msg.module,\n 'obj': msg.obj,\n 'line': msg.line,\n 'column': msg.column,\n 'path'... | [
"0.7409812",
"0.7172363",
"0.70097774",
"0.6925562",
"0.66760397",
"0.6673151",
"0.66208136",
"0.66029525",
"0.660143",
"0.6600997",
"0.65801567",
"0.6517668",
"0.6481048",
"0.6467904",
"0.6448488",
"0.64417195",
"0.6427785",
"0.6414664",
"0.6409843",
"0.639914",
"0.63720566"... | 0.0 | -1 |
Parses pidgin's htmlformated logfiles HTML within messages is converted to normal text, so messages about HTMLcode will get lost | def parse_html(root, filename):
root_filename = os.path.join(root, filename)
match_date = regex_date.findall(filename)
if not match_date:
raise Exception(root_filename, 'r')
year = int(match_date[0][0])
month = int(match_date[0][1])
day = int(match_date[0][2])
file = open(root_fi... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def strip_logfile_html(text):\n out_text = \"\"\n buff = \"\"\n start_tag = \"\"\n end_tag = \"\"\n context = \"none\"\n for i in range(len(text)):\n c = text[i]\n # print \"c = \"+str(c)+\" context = \"+str(context)\n if c == \"<\":\n if context == \"none\":\n ... | [
"0.6302063",
"0.6082321",
"0.60166854",
"0.59433186",
"0.59056187",
"0.57682514",
"0.57220733",
"0.56769717",
"0.5576809",
"0.5557175",
"0.55498254",
"0.5516565",
"0.5464202",
"0.5434003",
"0.5428622",
"0.5424175",
"0.53731394",
"0.5366191",
"0.5362259",
"0.5360732",
"0.53596... | 0.6280499 | 1 |
Parses pidgin's txtformated logfiles | def parse_txt(root, filename):
root_filename = os.path.join(root, filename)
match_date = regex_date.findall(filename)
if not match_date:
raise Exception(root_filename, 'r')
year = int(match_date[0][0])
month = int(match_date[0][1])
day = int(match_date[0][2])
file = open(root_fil... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse_file(self):\n with open(self.file_name, 'r', errors='ignore') as log_file:\n for line in log_file:\n self.process_line(line)",
"def __parse(self):\n lines = self.file.readlines()\n name_idx = 2\n name_idx_found = False\n pathre = re.compile(r... | [
"0.64843875",
"0.64326483",
"0.6343257",
"0.6289032",
"0.62647706",
"0.62052536",
"0.6193887",
"0.61273474",
"0.60964555",
"0.6089907",
"0.6056501",
"0.60470355",
"0.6037005",
"0.60215807",
"0.5989976",
"0.5972458",
"0.5971046",
"0.5943428",
"0.5934065",
"0.5914323",
"0.58844... | 0.60863197 | 10 |
Insert messages (from global datastructure) into db | def database_insert(db):
con = sqlite3.connect(db)
cur = con.cursor()
for protocol in protocols:
if protocol.name == "jabber":
for account in protocol.accounts:
for contact in account.contacts:
print("Inserting", contact.name)
cur.e... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_message_to_db(self, user_name, date_time, message):\n try:\n self.cursor.execute('INSERT INTO {0} (user_name, time_stamp, message) VALUES (?,?,?);'.format(TABLE_NAME),\n (user_name, date_time, message))\n except sqlite3.DatabaseError as err:\n ... | [
"0.6841209",
"0.6574107",
"0.6454141",
"0.64404714",
"0.6375744",
"0.62187374",
"0.6194717",
"0.61241376",
"0.6104334",
"0.6085481",
"0.60429436",
"0.60366213",
"0.60090595",
"0.60085547",
"0.5956678",
"0.59218454",
"0.5910281",
"0.5906215",
"0.58807814",
"0.5849432",
"0.5838... | 0.6568218 | 2 |
Parses files into global datastructure | def parse_dir(root, filenames):
for filename in fnmatch.filter(filenames, '*.html'):
parse_html(root, filename)
for filename in fnmatch.filter(filenames, '*.txt'):
parse_txt(root, filename) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main():\n parse_file(sys.argv[1])",
"def parse(self):\n\t\tself.maincfg_values = self._load_static_file(self.cfg_file)\n\t\t\n\t\tself.cfg_files = self.get_cfg_files()\n\t\t\n\t\tself.resource_values = self.get_resources()\n\t\t\n\t\tself.timestamps = self.get_timestamps()\n\t\t\n\t\t## This loads everyth... | [
"0.68028134",
"0.67118067",
"0.6645361",
"0.6598039",
"0.6507164",
"0.645687",
"0.6445086",
"0.64414036",
"0.6389246",
"0.63524276",
"0.6335487",
"0.6325584",
"0.6236716",
"0.6203235",
"0.6191149",
"0.618047",
"0.61771804",
"0.6165077",
"0.6151517",
"0.61479783",
"0.61439776"... | 0.0 | -1 |
Asks user for own nicks after listing all encountered ones | def names_interaction():
already_printed = []
for protocol in protocols:
for account in protocol.accounts:
for contact in account.contacts:
for message in contact.messages:
if message.name not in already_printed:
already_printed.app... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def take_sticks_ai(self, sticks):\n print(\"\\nThere are {} sticks on the board\".format(sticks))\n sticks_taken = random.choice(self.hats[sticks]['content'])\n self.hats[sticks]['choice'] = sticks_taken\n sticks -= sticks_taken\n return sticks",
"def user_picks():\r\n print... | [
"0.601555",
"0.5969573",
"0.576979",
"0.5542987",
"0.5438632",
"0.5384672",
"0.53576845",
"0.53359205",
"0.5284243",
"0.5240409",
"0.518095",
"0.5168703",
"0.5161023",
"0.5150909",
"0.5149311",
"0.5141872",
"0.5106042",
"0.5085934",
"0.50680995",
"0.5062412",
"0.5058632",
"... | 0.6445882 | 0 |
Updates global datastructure with message type (own, not own) | def message_update_kind(alias_me):
for protocol in protocols:
for account in protocol.accounts:
for contact in account.contacts:
for message in contact.messages:
#if kind not jet known
if message.kind == -1:
if messa... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_message_data(self) -> None:\n if PrimaryFlight.MESSAGETYPE == self.type:\n self.message_data = PrimaryFlight(self.data, self.config)\n elif GPS.MESSAGETYPE == self.type:\n self.message_data = GPS(self.data, self.config)\n elif Attitude.MESSAGETYPE == self.type:\n ... | [
"0.6372461",
"0.6005264",
"0.5914773",
"0.5829452",
"0.56945264",
"0.56803596",
"0.56739694",
"0.5616793",
"0.5577698",
"0.5549897",
"0.5547644",
"0.5545447",
"0.5525357",
"0.5523845",
"0.54576707",
"0.5430875",
"0.53760004",
"0.5334801",
"0.5321741",
"0.5316897",
"0.5307633"... | 0.5454643 | 15 |
Implement the check_unused_args in superclass. | def check_unused_args(self, used_args, args, kwargs):
for k, v in kwargs.items():
if k in used_args:
self._used_kwargs.update({k: v})
else:
self._unused_kwargs.update({k: v}) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _check_args(self, args_):\n\n pass",
"def __init__(self, *unused_args, **unused_kwargs):",
"def __check_args(self):\n self.__check_args_type()\n self.__check_args_val()",
"def ignore(self, *__args): # real signature unknown; restored from __doc__ with multiple overloads\n pass... | [
"0.71085674",
"0.6883384",
"0.67958516",
"0.6730387",
"0.66968954",
"0.6619412",
"0.6390801",
"0.6302647",
"0.6250002",
"0.62224966",
"0.6195048",
"0.6153181",
"0.61491466",
"0.6146345",
"0.6113333",
"0.6072033",
"0.60612833",
"0.60221803",
"0.60128295",
"0.6003403",
"0.59412... | 0.7603612 | 0 |
Clear used and unused dicts before each formatting. | def vformat(self, format_string, args, kwargs):
self._used_kwargs = {}
self._unused_kwargs = {}
return super(MemorizeFormatter, self).vformat(format_string, args, kwargs) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _clear_caches(self):\n self._brushes = {}\n self._formats = {}",
"def clear(self):\n\n for a in self.formats + self.other_clear:\n setattr(self, a, None)\n self.filename = None\n self.timestamp = None\n self.lastfail = None",
"def reset_format(self):\n ... | [
"0.6962335",
"0.66672957",
"0.65036696",
"0.64456564",
"0.6429814",
"0.638011",
"0.63629144",
"0.6338855",
"0.6315553",
"0.62882304",
"0.62856793",
"0.6279111",
"0.6267949",
"0.62281585",
"0.6219763",
"0.6197153",
"0.619033",
"0.6180451",
"0.6177072",
"0.615768",
"0.6153044",... | 0.0 | -1 |
format a string by a map | def format_map(self, format_string, mapping):
return self.vformat(format_string, args=None, kwargs=mapping) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _reprOfStringToValueMap (stringMap : Map) -> String:\n\n entrySeparator = u\"§\"\n entryTemplate = \"%s: %s\"\n keyList = sorted(list(stringMap.keys()))\n result = \"\"\n \n for key in keyList:\n value = stringMap[key] \n result += (iif(result == \"\", \"\", entrySeparator)\n ... | [
"0.7346636",
"0.69570595",
"0.6857461",
"0.67944145",
"0.67872196",
"0.65942025",
"0.64885473",
"0.64343286",
"0.6369347",
"0.6199872",
"0.6082483",
"0.6055873",
"0.6032503",
"0.59989256",
"0.59868157",
"0.59844786",
"0.597163",
"0.59715307",
"0.5968912",
"0.5882509",
"0.5869... | 0.7537712 | 0 |
Get used kwargs after formatting. | def get_used_kwargs(self):
return self._used_kwargs | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_kwargs(self):\n return {}",
"def kwargs(self):\n return self._kwargs",
"def kwargs(self):\n return self._kwargs",
"def format_arguments(self, **kwargs):\n return kwargs",
"def get_kwargs(self):\n return {\n 'user': self.user,\n }",
"def get_kwa... | [
"0.7273864",
"0.7083243",
"0.7083243",
"0.7075471",
"0.67573947",
"0.66603476",
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"0.6377557",
"0.6353454",
"0.6334118",
"0.6269364",
"0.6239159",
"0.62325567",
"0.6228446... | 0.7414289 | 0 |
Get unused kwargs after formatting. | def get_unused_kwargs(self):
return self._unused_kwargs | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_used_kwargs(self):\n return self._used_kwargs",
"def _scrub_kwargs(kwargs: Dict[str, Any]) -> Dict[str, Any]:\n keywords_to_scrub: List[str] = ['extra_arguments', 'kernel_id']\n scrubbed_kwargs = kwargs.copy()\n for kw in keywords_to_scrub:\n scrubbed_kwargs.pop(kw,... | [
"0.6829997",
"0.6761362",
"0.6697653",
"0.6459872",
"0.6360253",
"0.63554084",
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"0.60411596",
"0.599486",
"0.5875321",
"0.5854771",
"0.5847538",
"0.58386594... | 0.7941406 | 0 |
Add element_by alias and extension' methods(if_exists/or_none). | def add_element_extension_method(Klass):
def add_element_method(Klass, using):
locator = using.name.lower()
find_element_name = "element_by_" + locator
find_element_if_exists_name = "element_by_" + locator + "_if_exists"
find_element_or_none_name = "element_by_" + locator + "_or_none... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def contains(self, element):\n pass",
"def add(element):",
"def artAttrTool(*args, exists: Union[AnyStr, bool]=\"\", remove: AnyStr=\"\", q=True, query=True,\n **kwargs)->Union[None, Any]:\n pass",
"def has_element(parent, xpath):\n ele = parent.find('./' + xpath)\n if ele is n... | [
"0.52138686",
"0.5116739",
"0.5036193",
"0.50305545",
"0.49910548",
"0.497446",
"0.48736545",
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"0.48278427",
"0.47965333",
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"0.46641046",
"0.46609923",
"0.46489343",
"0.46479532",
"0.46302179",
"0.45912728",
... | 0.6793267 | 0 |
Fluent interface decorator to return self if method return None. | def fluent(func):
@wraps(func)
def fluent_interface(instance, *args, **kwargs):
ret = func(instance, *args, **kwargs)
if ret is not None:
return ret
return instance
return fluent_interface | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __noop(self, *args, **kwargs):\n return None",
"def method(self):\n return None",
"def return_none() -> None:\n pass",
"def __call__(self, *args, **kwargs):\n return self.__wrapped__(*args, **kwargs)",
"def pass_null(func):\n\n def wrapper(obj, *args, **kwargs):\n if not o... | [
"0.6567935",
"0.6153579",
"0.5921167",
"0.5789551",
"0.57773596",
"0.5770913",
"0.5758091",
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"0.5533451",
"0.5528883",
"0.55121976",
"0.54744726",
"0.5464299",
"0.5359974... | 0.62337273 | 1 |
Convert value to a list of key strokes >>> value_to_key_strokes(123) ['1', '2', '3'] >>> value_to_key_strokes('123') ['1', '2', '3'] >>> value_to_key_strokes([1, 2, 3]) ['1', '2', '3'] >>> value_to_key_strokes(['1', '2', '3']) ['1', '2', '3'] | def value_to_key_strokes(value):
result = []
if isinstance(value, Integral):
value = str(value)
for v in value:
if isinstance(v, Keys):
result.append(v.value)
elif isinstance(v, Integral):
result.append(str(v))
else:
result.append(v)
r... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def to_key_val_list(value):\n if value is None:\n return None\n\n if isinstance(value, (str, bytes, bool, int)):\n raise ValueError('cannot encode objects that are not 2-tuples')\n\n if isinstance(value, collections.Mapping):\n value = v... | [
"0.6031192",
"0.52026004",
"0.5165081",
"0.5160203",
"0.50922084",
"0.5048257",
"0.50478864",
"0.5046137",
"0.50427055",
"0.5000748",
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"0.48172843",
"0.48017225",
"0.47746295",
"0.4763067",
"0.47456744",
"0.4712962",
"0.463... | 0.77909297 | 0 |
Execute code in a namespace. | def exec_(code, globs=None, locs=None):
if globs is None:
frame = sys._getframe(1)
globs = frame.f_globals
if locs is None:
locs = frame.f_locals
del frame
elif locs is None:
locs = globs
exec("""exec code in globs, locs... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def exposed_execute(self, text):\n execute(text, PublicService.exposed_namespace)",
"def sbox_exec(self, source: str):\n return exec(source, self.sbox_globals, self.sbox_locals)",
"def code():",
"def run ( self ) :\n exec self._cmd in self._myglobals,self._mylocals",
"def complie_a... | [
"0.62050563",
"0.60476995",
"0.6043668",
"0.60398364",
"0.59286726",
"0.58017296",
"0.5787538",
"0.57273376",
"0.57222074",
"0.57211214",
"0.5654667",
"0.5628628",
"0.5615811",
"0.55890703",
"0.5584693",
"0.5578245",
"0.5562799",
"0.5552856",
"0.5531261",
"0.55299324",
"0.550... | 0.5353693 | 40 |
Batch normalization on convolutional maps. | def batch_norm(x, phase_train, scope='bn', affine=True):
with tf.variable_scope(scope):
og_shape = x.get_shape().as_list()
if len(og_shape) == 2:
x = tf.reshape(x, [-1, 1, 1, og_shape[1]])
shape = x.get_shape().as_list()
beta = tf.Variable(tf.constant(0.0, shape=[shape[-... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def normalize(batch_img: np.ndarray) -> np.ndarray:\n batch_img = batch_img.astype('float32')\n return batch_img / 127.5 - 1",
"def batch_norm(self, inputs):\n x = inputs\n x = self.bn(x)\n return x",
"def normalize_data(batch_data):\n B, N, C = batch_data.shape\n normal_data =... | [
"0.6931802",
"0.6867194",
"0.67963105",
"0.6757687",
"0.6720907",
"0.6656808",
"0.6617828",
"0.649989",
"0.6443505",
"0.6426401",
"0.6426401",
"0.63924474",
"0.6389737",
"0.637851",
"0.63618445",
"0.63528675",
"0.63390213",
"0.63346064",
"0.6308552",
"0.6296517",
"0.6292161",... | 0.0 | -1 |
Augment image and key points, bounding boxes !! | def img_and_key_point_augmentation(augmentation, img, bbox, key_points):
# img_copy = img.copy()
image_shape = img.shape
h, w = image_shape[0:2]
# Convert the stochastic sequence of augmenters to a deterministic one.
# The deterministic sequence will always apply the exactly same effects to the im... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def im_detect_keypoints_aug(model, im, boxes):\n\n # Collect heatmaps predicted under different transformations\n heatmaps_ts = []\n # Tag predictions computed under downscaling and upscaling transformations\n ds_ts = []\n us_ts = []\n\n def add_heatmaps_t(heatmaps_t, ds_t=False, us_t=False):\n ... | [
"0.6619781",
"0.65760165",
"0.6573601",
"0.64467424",
"0.63438433",
"0.6319195",
"0.6261868",
"0.6209508",
"0.6207679",
"0.62067044",
"0.61714906",
"0.6164397",
"0.6108835",
"0.60427105",
"0.6042169",
"0.60405695",
"0.6033405",
"0.60134506",
"0.6002291",
"0.5998151",
"0.59661... | 0.7612929 | 0 |
Augment image and bounding boxes !! | def img_augmentation(augmentation, img, bbox):
# img_copy = img.copy()
image_shape = img.shape
h, w = image_shape[0:2]
# Convert the stochastic sequence of augmenters to a deterministic one.
# The deterministic sequence will always apply the exactly same effects to the images.
det = augmentati... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def preprocessing(image_data, max_height, max_width):\n img = image_data[\"image\"]\n img = resize_image(img, max_height, max_width)\n gt_boxes = image_data[\"objects\"][\"bbox\"]\n gt_labels = image_data[\"objects\"][\"label\"]\n return img, gt_boxes, gt_labels",
"def to_imgaug(self, image_shape)... | [
"0.68984985",
"0.6853272",
"0.6828715",
"0.6781937",
"0.6781937",
"0.66911954",
"0.66844195",
"0.6655804",
"0.6620257",
"0.6595388",
"0.65874094",
"0.6579807",
"0.65564126",
"0.65507674",
"0.6549536",
"0.65301317",
"0.65256107",
"0.6521426",
"0.6520758",
"0.6511823",
"0.65013... | 0.6885573 | 1 |
Determines which augmenters to apply to masks. | def hook(images, augmenter, parents, default):
return augmenter.__class__.__name__ in MASK_AUGMENTERS | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def hook(images, augmenter, parents, default):\n return augmenter.__class__.__name__ in MASK_AUGMENTERS",
"def hook(images, augmenter, parents, default):\n return augmenter.__class__.__name__ in MASK_AUGMENTERS",
"def hook(images, augmenter, parents, default):\n return (aug... | [
"0.72273487",
"0.7182704",
"0.7110781",
"0.594772",
"0.59287107",
"0.5728177",
"0.56989866",
"0.56986225",
"0.56169343",
"0.5586497",
"0.5522835",
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"0.55029047",
"0.5455861",
"0.54065317",
"0.5406343",
"0.5406252",
"0.53967136",
"0.5390863",
"0.53442645",
"0.5329... | 0.70050114 | 3 |
Augment image and bounding boxes !! | def img_and_mask_augmentation(augmentation, img, mask):
# img_copy = img.copy()
image_shape = img.shape
# Convert the stochastic sequence of augmenters to a deterministic one.
# The deterministic sequence will always apply the exactly same effects to the images.
det = augmentation.to_deterministic... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def preprocessing(image_data, max_height, max_width):\n img = image_data[\"image\"]\n img = resize_image(img, max_height, max_width)\n gt_boxes = image_data[\"objects\"][\"bbox\"]\n gt_labels = image_data[\"objects\"][\"label\"]\n return img, gt_boxes, gt_labels",
"def img_augmentation(augmentatio... | [
"0.68984985",
"0.6885573",
"0.6853272",
"0.6828715",
"0.6781937",
"0.6781937",
"0.66911954",
"0.66844195",
"0.6655804",
"0.6620257",
"0.6595388",
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"0.6579807",
"0.65564126",
"0.65507674",
"0.6549536",
"0.65301317",
"0.65256107",
"0.6521426",
"0.6520758",
"0.65118... | 0.0 | -1 |
Parses the program commandline arguments. Args must be an array containing all arguments. | def parse_commandline_args():
epilog = """
The configuration file must contained a JSON-encoded map. Example: "{"name":"foo"}".
"""
parser = utils.ConnectionArgumentParser(
description="Update config (key/value pairs) on a board", epilog=epilog
)
parser.add_argument(
"-c",
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse_arguments(args):",
"def parse_args(args=None):\n\t\treturn _get_args_parser().parse_args(args)",
"def parse_args(args=None):\n return AP.parse_args(args=args)",
"def parse_args(self, argv=None):\n self.opts, self.args = self.cli_parser.parse_args(argv)\n self._begin_logging()\n ... | [
"0.78749067",
"0.75790143",
"0.73856956",
"0.73781556",
"0.72477776",
"0.7216756",
"0.7198788",
"0.71979994",
"0.7137461",
"0.7108861",
"0.7107137",
"0.710128",
"0.70689136",
"0.7023758",
"0.70063096",
"0.6984291",
"0.69759655",
"0.6971735",
"0.69376504",
"0.6935694",
"0.6925... | 0.0 | -1 |
Init emply player with required settings | def __init__(self):
self.id = None
self.name = None
self.phone = None
self.score = 0 # Running sum of player's score
self.state = None
self.ball_id = None
self.start_x = None # start pos of object thrown in game
self.angle = 0 ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def init_player():\n global active_track_idx\n global track_last_slided_pos\n global track_last_paused_pos\n global track_total_play_time \n\n # INITIALIZE Player\n active_track_idx = -1\n cancel_update_play_time_loop()\n cancel_track_end_event_loop()\n track_status.set(\"---\")\n tra... | [
"0.7290873",
"0.72183263",
"0.69451517",
"0.6945105",
"0.6931438",
"0.67666894",
"0.6618811",
"0.65181553",
"0.64596945",
"0.6405867",
"0.6321414",
"0.6309153",
"0.6307809",
"0.630426",
"0.6285705",
"0.6270282",
"0.6256138",
"0.6255461",
"0.62332505",
"0.622231",
"0.6217058",... | 0.0 | -1 |
update player state based on incomming data | def update(self, data):
# convert to string to json object
# see conversions if you're having problems
# https://docs.python.org/3/library/json.html#encoders-and-decoders
try:
data = json.loads(data)
# updated player state
self.id = data['id']
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setPlayerStates(self, updates):\r\n for upd in updates:\r\n print \"UPD player %s\" % upd['player']\r\n player = self.players[upd['player']]\r\n player.setStatus(upd['status'], upd['jump'], upd['charge'])\r\n\r\n player.health = upd['health']\r\n if... | [
"0.71735924",
"0.6937591",
"0.6606941",
"0.65582985",
"0.65486693",
"0.6540643",
"0.6533953",
"0.6484222",
"0.6477047",
"0.64653736",
"0.6373607",
"0.62698174",
"0.62684196",
"0.62657106",
"0.62476707",
"0.62337685",
"0.6191886",
"0.61768854",
"0.61741716",
"0.6125093",
"0.61... | 0.6615881 | 2 |
Reset the palyer data to ensure no data carries over between rounds | def player_reset(self):
logging.info(
f"Player.player_reset(): Player {self.id} score and time reset\n")
self.id = 0
self.name = ""
self.phone = ""
self.score = 0 # Running sum of player's score
self.state = "innactive"
self.ball_id = ""... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def resetData(self):\n self.currentHoursLeft = self.maxHoursLeft\n self.currentRound = self.currentRound + 1\n # reset empire data\n for empireID, myEmpire in self.empires.iteritems():\n myEmpire.resetData()\n myEmpire.resetRoundData()\n \n # reset sy... | [
"0.7531799",
"0.7322715",
"0.71666825",
"0.7104449",
"0.7095844",
"0.7072128",
"0.7063651",
"0.70372325",
"0.7013312",
"0.6935498",
"0.69190276",
"0.69188154",
"0.6910696",
"0.69025",
"0.6897481",
"0.6871852",
"0.68656075",
"0.68656075",
"0.6861706",
"0.6860858",
"0.68599",
... | 0.0 | -1 |
Collate function that padds examples to the longest sequence in a batch. It is meant for training the question generation model. Used together with SQuAD2.0 dataset preprocesses with preprocess_dataset function. | def dynamic_padding_collate_fn(batch_list):
batch_uncollated = [[] for i in range(3)]
for features in batch_list:
length = features[1].sum().item()
for i, feature in enumerate(features):
batch_uncollated[i].append(feature[:length])
batch_collated = []
for batch in batch_unc... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def collate_fn(self, batch):\r\n batch = list(map(torch.stack, zip(*batch)))\r\n max_seq_len = torch.max(torch.sum(batch[1], 1)).item()\r\n for i in range(len(batch) - 1):\r\n if batch[i].size()[1] > max_seq_len:\r\n batch[i] = batch[i][:, :max_seq_len]\r\n if ... | [
"0.7033074",
"0.6944575",
"0.67516834",
"0.6637126",
"0.65820885",
"0.65329534",
"0.6456716",
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"0.61593163",
"0.6139525",
"0.6039231",
"0.6031318",
"0.59539074",
"0.5948258",
"0.5943981",
"0.59365565... | 0.59552723 | 17 |
Preprocess SQuAD2.0 dataset constructed with load_and_cache_examples function into a form suitable for training the question generation model. | def preprocess_dataset(dataset, tokenizer):
eos = torch.tensor([tokenizer.eos_token_id], dtype=torch.long)
q_start = torch.tensor(tokenizer.encode('question:'), dtype=torch.long)
q_end = torch.tensor(tokenizer.encode(':question'), dtype=torch.long)
tensors = [[] for i in range(3)]
for i in trange(l... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def prepare_data():\n user_name = os.environ.get('USER')\n traintest_corpus = ResumeCorpus('/Users/' + user_name + '/Documents/Data')\n random.shuffle(traintest_corpus.resumes)\n\n for resume in traintest_corpus.resumes:\n try:\n review_text = pre_processing(resume[0])\n re... | [
"0.6381256",
"0.61704665",
"0.6150791",
"0.61070114",
"0.6052635",
"0.60219157",
"0.6011219",
"0.5969777",
"0.5967805",
"0.5960829",
"0.59575015",
"0.5916582",
"0.5907612",
"0.58599246",
"0.58495444",
"0.583701",
"0.58334464",
"0.5833186",
"0.58210236",
"0.5818004",
"0.578608... | 0.6146939 | 3 |
This evaluates the Borehole function using a subprocess running compiled code. Note that the Executor base class submit runs a serial process inplace. This should work on compute nodes so long as there are free contexts. | def subproc_borehole(H, delay):
with open("input", "w") as f:
H["thetas"][0].tofile(f)
H["x"][0].tofile(f)
exctr = Executor.executor
args = "input" + " " + str(delay)
task = exctr.submit(app_name="borehole", app_args=args, stdout="out.txt", stderr="err.txt")
calc_status = exctr.pol... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def run(self):\n try:\n self._execute_func(self._params)\n except Exception, e:\n print str(e)\n self._parallel_executer.release()",
"def __call__(self):\n return self._executor()",
"def _run_executor(ejobs, machine, assignment, timeout, kernel_messages):\n ... | [
"0.6250415",
"0.610581",
"0.6065356",
"0.6052186",
"0.58854985",
"0.5843875",
"0.5820821",
"0.5800925",
"0.5734414",
"0.57106155",
"0.56652045",
"0.56579715",
"0.5598459",
"0.55800426",
"0.5577861",
"0.5566699",
"0.5563498",
"0.55468994",
"0.5540453",
"0.5497623",
"0.54588884... | 0.0 | -1 |
Wraps the borehole function Subprocess to test receiving kill signals from manager | def borehole(H, persis_info, sim_specs, libE_info):
calc_status = UNSET_TAG # Calc_status gets printed in libE_stats.txt
H_o = np.zeros(H["x"].shape[0], dtype=sim_specs["out"])
# Add a delay so subprocessed borehole takes longer
sim_id = libE_info["H_rows"][0]
delay = 0
if sim_id > sim_specs["... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _on_parent_process_kill(self):",
"def remote_kill():",
"def test_stopProcessForcedKill(self):\r\n self.pm.startService()\r\n self.pm.addProcess(\"foo\", [\"foo\"])\r\n self.assertIn(\"foo\", self.pm.protocols)\r\n self.reactor.advance(self.pm.threshold)\r\n proc = self.pm... | [
"0.72107",
"0.69551826",
"0.66956586",
"0.6674258",
"0.64437044",
"0.641544",
"0.6345274",
"0.62977797",
"0.6271215",
"0.624368",
"0.6164998",
"0.6142843",
"0.6131285",
"0.61203116",
"0.61101794",
"0.61093026",
"0.60986966",
"0.60873014",
"0.60727215",
"0.6048453",
"0.602942"... | 0.0 | -1 |
Walk the selected fields (part of the gql query) recursively. | def _optimize_gql_selections(self, selected_fields: List[SelectedField], graphql_type,
store: QueryOptimizerStore = None) -> QueryOptimizerStore:
_logger.info('_optimize_gql_selections %r %r', graphql_type, selected_fields)
if not store:
store = QueryOptimize... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def iter_fields(node):\r\n for field in getattr(node, '_fields', ()) or ():\r\n try:\r\n yield field, getattr(node, field)\r\n except AttributeError:\r\n pass",
"def parse_fields(q, fields):\n for f in fields:\n current_node = q\n last_idx = 0\n quot... | [
"0.60911274",
"0.58325875",
"0.5736242",
"0.5622641",
"0.5581321",
"0.5567092",
"0.55273837",
"0.5496403",
"0.5462031",
"0.545002",
"0.53911525",
"0.53628486",
"0.53516597",
"0.53372884",
"0.5335005",
"0.5310861",
"0.5310636",
"0.5241845",
"0.51789254",
"0.5177498",
"0.513816... | 0.5608912 | 4 |
Add optimization to the store by inspecting the model field type. | def _optimize_field_by_name(self, store: QueryOptimizerStore, model, selection, field_def) -> bool:
name = self._get_name_from_field_dev(field_def)
if not (model_field := self._get_model_field_from_name(model, name)):
return False
_logger.info('_optimize_field_by_name %r %r', name, m... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _optimize_field_by_hints(self, store: QueryOptimizerStore, selected_field, field_def) -> bool:\n if not (optimization_hints := getattr(field_def, 'optimization_hints', None)):\n return False\n args = selected_field.arguments\n self._add_optimization_hints(optimization_hints.sele... | [
"0.53922397",
"0.537939",
"0.49929884",
"0.47674727",
"0.47306097",
"0.46442184",
"0.45238703",
"0.45149407",
"0.45139423",
"0.45052016",
"0.45049003",
"0.44889838",
"0.4485591",
"0.4484575",
"0.4460081",
"0.4451049",
"0.44423994",
"0.44279674",
"0.4426244",
"0.442454",
"0.44... | 0.5479826 | 0 |
Add the optimizations from the resolver_hints decorator to the store. | def _optimize_field_by_hints(self, store: QueryOptimizerStore, selected_field, field_def) -> bool:
if not (optimization_hints := getattr(field_def, 'optimization_hints', None)):
return False
args = selected_field.arguments
self._add_optimization_hints(optimization_hints.select_relate... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _add_hints(self, **hints):\n self._hints.update(hints)",
"def update_optimizer(self, context, optimizer, host):\n pass",
"def setup_optimizers(self, *args, **kwargs):\n\n # self.optimizers.append(...)\n # self.loss.append(...)\n pass",
"def _add_lazyload_options(self, o... | [
"0.6120944",
"0.5490147",
"0.54176563",
"0.52781653",
"0.51241463",
"0.50488275",
"0.49040857",
"0.49034598",
"0.4894648",
"0.4883001",
"0.48744258",
"0.48651642",
"0.48068908",
"0.47903302",
"0.47814724",
"0.473934",
"0.47289",
"0.46801856",
"0.4662181",
"0.46440312",
"0.462... | 0.5351505 | 3 |
Perform an upload of the given file to the current user's account The upload process consists of three steps and a loop. First, we request and receive an "upload ticket" from Vimeo. This ticket represents our place in the queue of videos waiting to upload. After successfully obtaining this ticket, we upload the binary ... | def __call__(self, name, post_check_hook=None):
def do_upload():
video_data, filetype = self.read_file(name)
ticket_id, upload_uri, complete_uri = self.get_upload_ticket()
log.info("Ticket ID: %s" % ticket_id)
_range = 0
hook_break = False
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def upload_file(self, file_upload_parameters, progress=None):\n\n file_upload_parameters._submit_upload_parameters.timeout_in_milliseconds = file_upload_parameters.timeout_in_milliseconds\n operation = self.submit_upload(file_upload_parameters._submit_upload_parameters)\n return self.download_... | [
"0.6913328",
"0.64488506",
"0.6341638",
"0.6326437",
"0.63007796",
"0.62839514",
"0.62739664",
"0.62476707",
"0.6243486",
"0.6209967",
"0.6197469",
"0.6196735",
"0.6184578",
"0.6173266",
"0.6143371",
"0.6109111",
"0.6083066",
"0.60762596",
"0.6070302",
"0.60671276",
"0.604248... | 0.59804696 | 27 |
Open a binary file and return its contents and extension | def read_file(self, filename):
data = None
with open(filename, "rb") as f:
data = f.read()
filetype = filename.split('.')[-1] if '.' in filename.split('/')[-1] else None
return data, filetype | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_binary(fname):\n with open(fname, 'rb+') as f_name:\n data = f_name.read()\n return data",
"def ReadBinaryFile(name):\n\n try:\n fBinary = open(name, 'rb')\n except:\n return None\n try:\n content = fBinary.read()\n except:\n return None\n finally:\... | [
"0.73417205",
"0.6876805",
"0.6709726",
"0.66392875",
"0.6591508",
"0.6574621",
"0.6560591",
"0.6469011",
"0.6448845",
"0.63792706",
"0.63552374",
"0.6351509",
"0.6344689",
"0.6221042",
"0.6202726",
"0.62002987",
"0.61832154",
"0.6179081",
"0.61498123",
"0.6124398",
"0.611265... | 0.62648433 | 13 |
Obtain an upload ticket from the API | def get_upload_ticket(self):
r = HTTPClient().fetch(self.config['apiroot'] + self.ticket_path, method="POST",
body=urlencode({'type': 'streaming'}), headers = self.standard_headers,
validate_cert=not self.config['dev'])
response = json.loads(r.body)
return respons... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_service_ticket():\n\n payload = {'username': APIC_EM_USER, 'password': APIC_EM_PASSW}\n url = 'https://' + APIC_EM + '/ticket'\n header = {'content-type': 'application/json'}\n ticket_response = requests.post(url, data=json.dumps(payload), headers=header, verify=False)\n if not ticket_respon... | [
"0.67064804",
"0.5887511",
"0.58059216",
"0.5711891",
"0.558435",
"0.55571055",
"0.55388236",
"0.5534896",
"0.55088025",
"0.54868495",
"0.54192376",
"0.531124",
"0.5295683",
"0.5222806",
"0.5221019",
"0.51976347",
"0.51389897",
"0.51316166",
"0.51182204",
"0.50823814",
"0.507... | 0.8335117 | 0 |
Upload a piece of a video file to Vimeo Makes a PUT request to the given URL with the given binary data The _range parameter indicates the first byte to send. The first time you attempt an upload, this will be 0. The next time, it will be the number returned from get_last_uploaded_byte, if that number is less than the ... | def upload_segment(self, upload_uri, _range, data, filetype):
content_range = '%d-%d/%d' % (_range, len(data), len(data))
upload_headers = {'Content-Type': 'video/%s' % filetype,
'Content-Length': len(data),
'Content-Range': 'bytes: %s' % content_range}
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def upload_range( # type: ignore\n self, data, # type: bytes\n start_range, # type: int\n end_range, # type: int\n validate_content=False, # type: Optional[bool]\n timeout=None, # type: Optional[int]\n encoding='UTF-8',\n **kwargs\n ... | [
"0.6141114",
"0.5634965",
"0.56206375",
"0.550832",
"0.5482587",
"0.54187346",
"0.5404088",
"0.538972",
"0.5272799",
"0.52320516",
"0.5201052",
"0.511785",
"0.51038563",
"0.5073653",
"0.5050996",
"0.49917027",
"0.49861932",
"0.49765033",
"0.49168584",
"0.49088553",
"0.4880528... | 0.7709178 | 0 |
Get the last byte index of the file successfully uploaded Performs a PUT to the given url, which returns a Range header indicating how much of the video file was successfully uploaded. If less than the total file size, this number is used in subsequent calls to upload_segment | def get_last_uploaded_byte(self, check_uri):
upload_check_headers = {'Content-Range': 'bytes */*'}
request_headers = dict(upload_check_headers.items() + self.standard_headers.items())
try:
HTTPClient().fetch(check_uri, method="PUT", body='', headers=request_headers)
except HT... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_file_size(url: str):\n header = requests.head(url).headers\n if \"Content-Length\" in header and header[\"Content-Length\"] != 0:\n return int(header[\"Content-Length\"])\n elif \"Location\" in header:\n h = requests.head(header[\"Location\"]).headers\n return int(h.get(\"Cont... | [
"0.61535513",
"0.5919975",
"0.5807138",
"0.5694676",
"0.56919193",
"0.5665816",
"0.562974",
"0.55861944",
"0.54725754",
"0.54708993",
"0.54157346",
"0.53107536",
"0.53089446",
"0.5262854",
"0.52135384",
"0.51811814",
"0.5161932",
"0.5133586",
"0.5120558",
"0.5094605",
"0.5090... | 0.6557539 | 0 |
Delete the upload ticket (to be used once get_last_uploaded_byte() == total file size) Makes a DELETE request to the given URI, removing the upload ticket and setting the upload status to "processing" | def delete_upload_ticket(self, complete_uri):
url = self.config['apiroot'] + complete_uri
log.info("Requesting %s" % url)
r = HTTPClient().fetch(url, method="DELETE", headers=self.standard_headers,
validate_cert=not self.config['dev'])
log.info("Upload comp... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def delete(self, _uri):\n print(\"Deleting '%s'\"%(_uri))\n response = self.__httpsRequest('DELETE', _uri, '')",
"def delete(self):\n try:\n flash_message = request.json[\"flash_message\"]\n folder_path = \"{0}/user_uploads/{1}/{2}/\".format(self.__APP_PATH__, ... | [
"0.67701095",
"0.64577615",
"0.6345733",
"0.61198354",
"0.6112959",
"0.6029433",
"0.59412366",
"0.5909342",
"0.5896206",
"0.5832445",
"0.5818534",
"0.57876617",
"0.5784361",
"0.5783744",
"0.5763343",
"0.57614774",
"0.5744645",
"0.57401735",
"0.5735063",
"0.57311624",
"0.57280... | 0.7901534 | 0 |
r"""Helper method for yielding various names + members of modules. | def get_named_members(model, get_members_fn, prefix='', recurse=True):
memo = set()
modules = model.named_modules(prefix=prefix) if recurse else [(prefix, model)]
for i, (module_prefix, module) in enumerate(modules):
members = get_members_fn(module)
for k, v in members:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _named_members(self, get_members_fn, prefix='', recurse=True):\n memo = set()\n modules = self.named_modules(prefix=prefix) if recurse else [(prefix, self)]\n for module_prefix, module in modules:\n members = get_members_fn(module)\n for k, v in members:\n ... | [
"0.72332364",
"0.69934905",
"0.66442704",
"0.6641855",
"0.65818375",
"0.65763384",
"0.6490665",
"0.64709103",
"0.6409749",
"0.6361901",
"0.6215152",
"0.6214206",
"0.6196199",
"0.6162733",
"0.61518407",
"0.61518407",
"0.60533714",
"0.6052606",
"0.59672785",
"0.5960688",
"0.594... | 0.6819973 | 2 |
Init object to store step and trial data. | def init_data(stats_list):
data = {stats_name: {} for stats_name in stats_list}
return data | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self):\n self._trials = [] # private variables _ convention",
"def __init__( self ):\n self._env = None\n self._steps = None\n\n self._initialize( )",
"def init(self, **kwargs):\n self._d = {}\n self._th = None\n self._run = True\n ... | [
"0.716681",
"0.70468086",
"0.6757133",
"0.6672487",
"0.66058046",
"0.6582223",
"0.653331",
"0.65125954",
"0.649741",
"0.64206564",
"0.63828874",
"0.6369628",
"0.6362635",
"0.6338107",
"0.6331931",
"0.6317953",
"0.6301544",
"0.6297562",
"0.62726104",
"0.62723035",
"0.6268866",... | 0.0 | -1 |
Record trial data and model state after a step during navigation. | def get_rec_stats(village, u, s, r, stim, GS, HC, hc_ro, vS, dlS, data,
co_occ_pars, idx=None):
res = {}
if 'anim_data' in data:
# Externally observable variables: action, state, reward, x-y position.
x, y = village.animal_coords()
res['anim_data'] = {'u': u, 's': s, ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def record(self, step):",
"def do_step(self) -> None:",
"def storeState(self):\n\n self.action_history[self.trial] = self.action\n self.ball_history[self.trial] = self.ballcolor",
"def step(self, model):\n pass",
"def step(self, model):\n pass",
"def on_trial_add(self, trial: ... | [
"0.6487955",
"0.6050769",
"0.5991312",
"0.5982613",
"0.5982613",
"0.5968404",
"0.591018",
"0.5843705",
"0.5831286",
"0.5749037",
"0.5696999",
"0.5677254",
"0.56736565",
"0.5658061",
"0.5658061",
"0.5654882",
"0.5654626",
"0.56524915",
"0.5651177",
"0.5645648",
"0.56064904",
... | 0.0 | -1 |
Format recordings of GS HC connectivity. | def format_GS_HC_rec(res, s_hc, GS_HC):
# GS - HC x-y mean position per HC unit.
gs_hc_pos = pd.concat({i: pd.DataFrame(kv, index=['x', 'y'], columns=s_hc)
for i, kv in res['gs_hc_pos'].items()}).unstack()
# GS - HC entropy per HC unit.
gs_hc_h = pd.Series([utils.entropy(gs_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def coregister_formatted():\r\n\r\n print(\"begin coregister_formatted\")\r\n\r\n # check all records for pairs using the recordBegin time\r\n pair_records()\r\n\r\n # establish the beginning and end of the coregistered records\r\n define_pairedRecords()\r\n\r\n # coregister paired data in a sing... | [
"0.5985085",
"0.5772294",
"0.5695159",
"0.5633481",
"0.5580155",
"0.553478",
"0.5474018",
"0.546638",
"0.54650944",
"0.54626495",
"0.54481137",
"0.53700316",
"0.5360647",
"0.5341959",
"0.5339633",
"0.5279129",
"0.527552",
"0.5239246",
"0.5197716",
"0.5166865",
"0.51560336",
... | 0.49148738 | 49 |
Process and format recorded learning results. | def format_learning(res, s_real, s_hc, GS_HC):
# Format results.
res = pd.DataFrame(res).T
res.index.name = 'step'
# Pre-calculate some stats.
s_real_h = pd.concat({i: pd.Series(kv, index=s_real)
for i, kv in res['s_real_h'].items()}).unstack()
s_hc_h = pd.concat({i: ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def process_results(self, episode, eval):\n if episode % 10 == 9:\n ave = np.mean(self.scores[episode - 9:episode])\n print('Episodes: {}, AveScores: {}, Alpha: {}, Steps: {}'.format(\n episode + 1, ave, self.alpha.item(), self.step_count))\n if eval:\n ... | [
"0.66430914",
"0.5997761",
"0.5993275",
"0.5946991",
"0.5923554",
"0.5910248",
"0.5840243",
"0.5838326",
"0.58113766",
"0.5800332",
"0.5793081",
"0.5748188",
"0.5714592",
"0.56949764",
"0.5694171",
"0.5687105",
"0.5685428",
"0.5673243",
"0.5668452",
"0.56547135",
"0.5650398",... | 0.533079 | 58 |
Return summary stats of recorded data. | def summarize_rec_data(data):
# Warning: not all collectible data has a summary stats implemented below!
# See get_rec_stats() above!
stats = {}
if 'hc_ro' in data:
# Entropy across HC units average over samples.
hc_ro_arr = np.array(list(data['hc_ro'].values()))
stats['H HC r... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_summary_stats(self):\r\n n = len(self.results)\r\n\r\n if n == 0:\r\n mean = None\r\n stdev = None\r\n\r\n elif n == 1:\r\n mean = numpy.mean(self.results)\r\n stdev = None\r\n\r\n else:\r\n mean = numpy.mean(self.results)\r... | [
"0.7375736",
"0.71892005",
"0.68082",
"0.675289",
"0.67228687",
"0.6701924",
"0.66873115",
"0.66815513",
"0.6668492",
"0.6614487",
"0.66084427",
"0.65882283",
"0.6587884",
"0.6563548",
"0.65191305",
"0.6514346",
"0.6482753",
"0.64782995",
"0.64565426",
"0.6439179",
"0.6433357... | 0.6649093 | 9 |
Format recorded simulation data. | def format_rec_data(tr_data, village, HC, gs_pars, vfeatures, idx_pars=[]):
print('\nFormatting recorded data...')
ret_list = []
gs_xvec, gs_yvec = [utils.get_copy(gs_pars[v]) for v in ['xvec', 'yvec']]
if 'anim_data' in tr_data:
anim_data = pd.DataFrame(tr_data['anim_data']).T
ret_l... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def format(self, data):",
"def format_data(self, data):",
"def _formatData(self):\r\n assert self._runData is not None\r\n\r\n # Getting Axes data into separate lists\r\n x=[]; y=[]; z=[]\r\n for i in range(len(self._runData)):\r\n ySet = []; xSet = []; zSet = []\r\n ... | [
"0.6369223",
"0.6365222",
"0.585794",
"0.5771995",
"0.5722368",
"0.5701128",
"0.5615324",
"0.56003875",
"0.5577038",
"0.55621386",
"0.5557003",
"0.5556837",
"0.55493116",
"0.5504945",
"0.5502198",
"0.54964334",
"0.5491324",
"0.5473072",
"0.5443231",
"0.54316455",
"0.5413139",... | 0.51327187 | 57 |
Report progress of simulation. | def report_progr(i, n, freq=1000):
if not i % freq:
print('\t{} / {}'.format(str(i).rjust(5), n))
# print('{}%'.format(int(100*istep/nsteps))) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def reportProgress(self):\n \n pass",
"def report_scenario_progress(self):\n pass",
"def report_progress(self):\r\n stats = self.simulation_stats.stats\r\n solutions = len(self.solutions)\r\n round = self.round\r\n scores = stats[round]\r\n best_score = min(s... | [
"0.78183013",
"0.7616774",
"0.7516212",
"0.7346008",
"0.7162934",
"0.7073537",
"0.6910481",
"0.687008",
"0.68546647",
"0.6802137",
"0.670709",
"0.6668817",
"0.6607123",
"0.6585042",
"0.65652347",
"0.6525252",
"0.6525252",
"0.6443862",
"0.6428839",
"0.64268047",
"0.64268047",
... | 0.0 | -1 |
Report progress during learning simulations. | def report_learning_progress(istep, nsteps, VC_HC, GS_HC, norm, mrh, mhh,
rop=None):
rep = '{}%'.format(int(100*istep/nsteps)).rjust(4)
l_vc_hc = analysis.VC_HC_norm(VC_HC, norm).mean()
l_gs_hc = analysis.GS_HC_norm(GS_HC, norm).mean()
prog = '{} | VC-HC: {:.3f}, GS-HC: {:.... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def reportProgress(self):\n \n pass",
"def report_scenario_progress(self):\n pass",
"def show_progress(self, game):\n if self.verbose:\n if self.params.eval_interval is not None and (self.episode % self.params.eval_interval == 0):\n self._print_progress()\n ... | [
"0.71725804",
"0.70678365",
"0.6930796",
"0.6844124",
"0.6836328",
"0.68133104",
"0.67061037",
"0.6654687",
"0.6649508",
"0.64450794",
"0.64312387",
"0.6404172",
"0.6400389",
"0.6358474",
"0.63549787",
"0.6334084",
"0.63017714",
"0.62951624",
"0.62896836",
"0.62857735",
"0.62... | 0.6581055 | 9 |
Return parameterized folder name of simulation. | def sim_dir_name(fdir, nsteps, stim_pars, hc_pars, gs_pars, str_pars):
fn = ('nsteps_{}_GSHCsharp_{}'.format(nsteps, hc_pars['gs_hc_sharp']) +
'_hcpow_{}'.format(hc_pars['ro_pow']) +
'_msig_{}'.format(int(stim_pars['mot_sig'])) +
'_vbeta_{}'.format(stim_pars['vis_beta']) +
'... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def folder(self, step=None):\n if step is None:\n return self._obs_group_folder / self._obs_folder\n else:\n return Path(step) / self._obs_group_folder / self._obs_folder",
"def folder(self, step=None):\n if step is None:\n return self._obs_group_folder / sel... | [
"0.67199093",
"0.67199093",
"0.67199093",
"0.6616217",
"0.656852",
"0.6482704",
"0.6465053",
"0.62389404",
"0.62153554",
"0.615684",
"0.615684",
"0.6155915",
"0.6129946",
"0.61195314",
"0.6086348",
"0.6066995",
"0.60657847",
"0.6050361",
"0.5979955",
"0.58812314",
"0.5849302"... | 0.659045 | 4 |
Return folder name for simulation results. | def get_res_dir_name(random, nx=None, ny=None, p_state=None, p_path=None,
**kws):
if random:
fdir = 'random_env/'
env_dir = 'nx{}_ny{}_pstate{}_ppath{}'.format(nx, ny, p_state, p_path)
fdir += utils.format_to_fname(env_dir) + '/'
else:
fdir = 'village/'
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def folder(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"folder\")",
"def simulation_dir(self):\n try:\n return (self.output_directory / self.sim_id).expand()\n except AttributeError:\n return Path()",
"def name_folder_data(data):\n string = 'results/S0(... | [
"0.7222843",
"0.6902778",
"0.6742704",
"0.6699133",
"0.66006047",
"0.65905297",
"0.65905297",
"0.65905297",
"0.6492835",
"0.6459841",
"0.6366473",
"0.63570267",
"0.630044",
"0.62975377",
"0.6291892",
"0.6234195",
"0.621634",
"0.6193216",
"0.6178031",
"0.617218",
"0.6141358",
... | 0.0 | -1 |
Return name of connectivity configuration. | def conn_config_name(conn_config):
n_conn_config = [cname for cname, on in conn_config.items() if on]
n_conn_config = ' + '.join(n_conn_config)
return n_conn_config | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def name(self):\n return self._config_name",
"def get_config_name(self): # pragma: no cover\n pass",
"def name(self):\n return self._config.get(CONF_NAME)",
"def connection_name(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"connection_name\")",
"def connection_name(s... | [
"0.77077055",
"0.7493195",
"0.745413",
"0.72979784",
"0.7289367",
"0.7035228",
"0.6940678",
"0.685062",
"0.6848523",
"0.6846582",
"0.6830779",
"0.68089354",
"0.6791377",
"0.675848",
"0.67513496",
"0.67148095",
"0.66720396",
"0.6649063",
"0.66395855",
"0.6601104",
"0.6592569",... | 0.7347803 | 3 |
Return world state HC state cooccurance matrix. | def get_co_occ_mat(s_hc_ml, n_s_real, n_s_hc):
co_occs = np.zeros((n_s_hc, n_s_real))
for idx, n in s_hc_ml.items():
co_occs[idx] = n
return co_occs | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def CTMCtoStormpy(h):\n\tstate_labelling = _buildStateLabeling(h)\n\ttransition_matrix = deepcopy(h.matrix)\n\te = array([h.e(s) for s in range(h.nb_states)])\n\ttransition_matrix /= e[:,newaxis]\n\ttransition_matrix = st.build_sparse_matrix(transition_matrix)\n\tcomponents = st.SparseModelComponents(transition_m... | [
"0.635167",
"0.59943235",
"0.58283806",
"0.5816899",
"0.5784517",
"0.57605594",
"0.5751287",
"0.5710274",
"0.57057977",
"0.56717366",
"0.5666582",
"0.56537753",
"0.562778",
"0.56184703",
"0.56027305",
"0.56016433",
"0.5601625",
"0.55812305",
"0.555667",
"0.5529863",
"0.552653... | 0.63109434 | 1 |
Update world state HC state cooccurance matrix. | def update_co_occ_mat(hc_ro, i_s_real, co_occs, co_occ_list, idx, nsteps):
# Go to next element in circular array.
idx = (idx + 1) % nsteps
# Remove estimate from left hand side of time window.
if len(co_occ_list[idx]):
i_s_real_left, hc_ro_left = co_occ_list[idx]
co_occs[:, i_s_real_l... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_world(self):\n pass",
"def update_H(self):\n self.grid.H[self.loc] -= (\n self.grid.courant_number\n * self.grid.inverse_permeability[self.loc]\n * self.phi_H\n )",
"def _update_loc(self) -> None:\n self.state[:, :, Boids.Attr.LOC] += self... | [
"0.59525",
"0.59345645",
"0.58975",
"0.57189",
"0.56345606",
"0.5619074",
"0.5619074",
"0.55726504",
"0.55646217",
"0.5538941",
"0.5527422",
"0.5505704",
"0.5498921",
"0.5483387",
"0.54796284",
"0.54754066",
"0.5472103",
"0.5468159",
"0.54635316",
"0.5440324",
"0.54234046",
... | 0.5303621 | 31 |
Collect data from a single step of connectivity learning. | def record_learning(village, s, GS, HC, hc_ro, dVC_HC, dGS_HC, co_occs, norm):
# Animal's real and estimated position and location + uncertainty.
x, y = village.animal_coords()
gs_x, gs_y = analysis.GS_pos_mean(GS.P, GS.xvec, GS.yvec, GS.circular)
gs_h = utils.entropy(GS.P.flatten())
hc_ml = HC.s_n... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def predict_collect(self, src, collector): # real signature unknown; restored from __doc__\n pass",
"def collect_data(endless):\r\n click.echo(\"start collecting data ...\")\r\n _collect_data(endless)",
"def collect_data(self,sensation,action,reward,next_sensation):\n pass",
"def run(self... | [
"0.58938706",
"0.5764855",
"0.5659415",
"0.555809",
"0.54953295",
"0.54437625",
"0.5437285",
"0.54306674",
"0.542526",
"0.53984416",
"0.5388055",
"0.5355798",
"0.5355798",
"0.5281315",
"0.52788657",
"0.5276297",
"0.5263628",
"0.5263628",
"0.5263628",
"0.5263628",
"0.5263628",... | 0.0 | -1 |
Get number of real and estimated state cooccurances. | def get_co_occ_matrix(res, last_n, s_real, s_hc):
s = np.array(res['s'])[-last_n:]
hc_ml = np.array(res['hc_ml'])[-last_n:]
co_occs = np.zeros((len(s_hc), len(s_real)))
for si, hc_mli in zip(s, hc_ml):
co_occs[s_hc.index(hc_mli), s_real.index(si)] += 1
co_occs /= co_occs.sum()
return c... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def constituent_count(self):\n return self._constituent_count",
"def num_conll(self):\n pass",
"def StateCounts(self):\r\n\t\treturn self._get_attribute('stateCounts')",
"def num_carns(self):\n return self._num_carns",
"def number_of_constituents(bc_class):\n num_trn = 0\n cn = b... | [
"0.72566885",
"0.6694087",
"0.6514297",
"0.65112036",
"0.64747995",
"0.6469168",
"0.64518386",
"0.64387137",
"0.64271784",
"0.64253205",
"0.6421117",
"0.64210594",
"0.6419776",
"0.64074904",
"0.6388321",
"0.63843083",
"0.6365295",
"0.63594586",
"0.63399863",
"0.63103896",
"0.... | 0.0 | -1 |
Return learned HC state order from cooccurance matrix by taking best matching pairs world location HC state pairs. | def get_s_order(co_occs, s_hc=None):
# Greedy approach: just go through items from max to min.
free_rows, free_cols = [list(range(n)) for n in co_occs.shape]
s_ord = -np.ones(co_occs.shape[0], dtype=int)
co_normed = norm_co_occ_matrix(co_occs)
isrtd = np.unravel_index(co_normed.argsort(axis=None)[... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def calc_nearest_state(self): # TODO: Check if we need here state, instead of self.state\n self.stateC = self.toConceptual(self.state)\n CTP, winners = self.find_winner()\n\n state_name = self.find_TPname(filleridx=winners)\n binding = self.find_symBinding(filleridx=winners)\n s... | [
"0.5657669",
"0.5559561",
"0.5372872",
"0.53349",
"0.5301023",
"0.5282986",
"0.52419263",
"0.5127219",
"0.5086206",
"0.50741035",
"0.5065264",
"0.50308555",
"0.5025374",
"0.50204796",
"0.50141317",
"0.5002204",
"0.5002081",
"0.4969301",
"0.49646002",
"0.4962375",
"0.4949405",... | 0.62907976 | 0 |
Return entropy of each village location state across HC activations. | def get_loc_co_occ_entropy(co_occs, v_repl_nan=None):
return utils.get_row_entropy(co_occs, v_repl_nan) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def entropy(self):\n raise NotImplementedError",
"def entropy(self):\n\n \"\"\"Gets the first neighbours, which are the first 2*r+1 cells.\"\"\"\n current_neighbours = []\n amount = [0] * self.k ** (2 * self.r + 1)\n for i in range(2 * self.r + 1):\n current_neighbours.append(self.confi... | [
"0.64681673",
"0.64361626",
"0.6397468",
"0.63540465",
"0.6292579",
"0.6235017",
"0.6153285",
"0.6081148",
"0.60573524",
"0.6038061",
"0.60269433",
"0.60266095",
"0.60248816",
"0.59867597",
"0.5969544",
"0.59659535",
"0.5924177",
"0.5898555",
"0.58884066",
"0.583654",
"0.5814... | 0.0 | -1 |
Return entropy of each HC state across village locations. | def get_hc_co_occ_entropy(co_occs, v_repl_nan=None):
return utils.get_row_entropy(co_occs.T, v_repl_nan) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def entropy(self):\n\n \"\"\"Gets the first neighbours, which are the first 2*r+1 cells.\"\"\"\n current_neighbours = []\n amount = [0] * self.k ** (2 * self.r + 1)\n for i in range(2 * self.r + 1):\n current_neighbours.append(self.config[self.t, i % self.width])\n\n \"\"\"Calculates the rule... | [
"0.67780423",
"0.6692582",
"0.66096693",
"0.6583241",
"0.646438",
"0.6463834",
"0.64497703",
"0.643623",
"0.6350943",
"0.6336463",
"0.6297908",
"0.62889063",
"0.62844735",
"0.6246048",
"0.62386346",
"0.62371117",
"0.6227964",
"0.62140536",
"0.6190457",
"0.618255",
"0.6164896"... | 0.0 | -1 |
Test the connection of the bot. | async def ping(self, ctx):
msg_time = ctx.message.created_at
cur_time = datetime.utcnow()
delay = (cur_time - msg_time) / timedelta(milliseconds=1)
await ctx.send(f"Pong! ({str(delay)} ms)") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def test_connection(self):\n await self.webhook_connection.connect()\n assert self.webhook_connection.is_connected is True",
"def test_connection(self):\n r = main.List.connection()\n self.assertTrue(r.ping(), \"Connection failed.\")",
"def test_connect(rgd):\n assert rgd.c... | [
"0.7984916",
"0.73088425",
"0.70733136",
"0.6972396",
"0.6922865",
"0.68271625",
"0.6753107",
"0.67112476",
"0.667889",
"0.6632673",
"0.66180444",
"0.66106814",
"0.65947783",
"0.65855265",
"0.6513005",
"0.64952236",
"0.6448094",
"0.6343192",
"0.63170284",
"0.63074887",
"0.630... | 0.0 | -1 |
Link a spotify account to the bot. | async def link(self, ctx):
if not is_linked(ctx.author.id):
token = str(uuid.uuid4())
valid_until = int((datetime.utcnow() + timedelta(days=1)).timestamp())
add_token(ctx.author.display_name, ctx.author.id, token, valid_until, str(ctx.author.avatar_url))
web_base_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def link(ctx, bot: typing.Union[discord.Member, discord.User]):\n if not bot.bot:\n return await r(ctx, \"Not a bot.\")\n await r(ctx, f'<https://www.motiondevelopment.top/bots/{bot.id}>')",
"async def info(self, ctx):\n if ctx.guild is not None:\n await ctx.reply(\"This comm... | [
"0.6603521",
"0.63787323",
"0.6339056",
"0.6307534",
"0.62112045",
"0.6183406",
"0.60342115",
"0.6027001",
"0.60145694",
"0.60145694",
"0.5984008",
"0.59183764",
"0.5639046",
"0.5622354",
"0.5614502",
"0.5600881",
"0.5547755",
"0.5491989",
"0.5484494",
"0.54839873",
"0.547692... | 0.7880789 | 0 |
Unlink a spotify account from the bot. | async def unlink(self, ctx):
# Remove all link tokens and spotify details for this user
remove_tokens(ctx.author.id)
remove_spotify_details(ctx.author.id)
await ctx.reply("All your linked accounts were removed, if you had any!") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def unlink(self, link_id):",
"def unfollow_profile(self):\n self.find_clickable_element(self.ISFOLLOWED_BTN).click()",
"async def twitter_unfollow(self, ctx, handle):\n sane_handle = handle.lower().lstrip('@')\n conf = dutils.get(self.conf.follows, screen_name=sane_handle)\n chan_co... | [
"0.63025486",
"0.61723953",
"0.6036946",
"0.59427714",
"0.5927398",
"0.59164095",
"0.5912746",
"0.5850332",
"0.5843319",
"0.58328366",
"0.5821932",
"0.5803021",
"0.57848567",
"0.5749491",
"0.5748773",
"0.5742856",
"0.5740611",
"0.57387394",
"0.5726775",
"0.5691439",
"0.567866... | 0.745899 | 0 |
Displays basic info about your linked spotify account (name, avatar) | async def info(self, ctx):
if ctx.guild is not None:
await ctx.reply("This command can only be used in DMs, because of privacy reasons.")
raise commands.CommandError("Invoker not in DMs.")
if not is_linked(ctx.author.id):
await ctx.reply(f"You don't have a Spotify ac... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def status(verbose):\n user_data = Spotify.request('me', method='GET')\n click.echo('Logged in as {}'.format(user_data['display_name']))\n if verbose:\n click.echo('Credentials stored in {}'.format(CREDS_PATH))\n return",
"async def githubinfo_command(self, ctx, *, githubusername: str):\n ... | [
"0.65351516",
"0.6477003",
"0.64630944",
"0.62933874",
"0.62896085",
"0.6278736",
"0.6263717",
"0.6239856",
"0.62017554",
"0.61626744",
"0.61613786",
"0.6148831",
"0.6129267",
"0.6054022",
"0.60383016",
"0.6036688",
"0.6031312",
"0.6021112",
"0.5999502",
"0.599438",
"0.598736... | 0.7391768 | 0 |
Makes the bot join your voice channel | async def join(self, ctx):
if ctx.guild is None:
await ctx.reply("This command can only be used in a server, not in DMs.")
raise commands.CommandError("Invoker not in a guild.")
if not is_linked(ctx.author.id):
await ctx.reply(f"You don't have a Spotify account linke... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def join(self, ctx):\n voice = ctx.author.voice\n\n if voice and voice.channel:\n channel = voice.channel\n \n try:\n await channel.connect()\n except discord.errors.ClientException:\n await ctx.guild.get_member(self.bot.... | [
"0.8252359",
"0.8217584",
"0.8208582",
"0.7949153",
"0.79472905",
"0.78682876",
"0.786053",
"0.77314293",
"0.7688857",
"0.76547545",
"0.75633955",
"0.75611305",
"0.7534342",
"0.753236",
"0.7313502",
"0.71708095",
"0.7167358",
"0.7105433",
"0.70923823",
"0.6946052",
"0.6941861... | 0.69570655 | 19 |
Makes the bot leave your voice channel | async def leave(self, ctx):
if ctx.guild is None:
await ctx.reply("This command can only be used in a server, not in DMs.")
raise commands.CommandError("Invoker not in a guild.")
if ctx.author.voice is None or ctx.author.voice.channel is None:
await ctx.reply("You ne... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def leave(self, ctx):\n if ctx.guild.voice_client:\n await ctx.guild.voice_client.disconnect()\n await ctx.send(\"Left voice channel.\")\n else:\n await ctx.message.add_reaction('\\U0001F615');\n await ctx.send(\"Not in a voice channel.\")",
"async ... | [
"0.8343599",
"0.77186316",
"0.7650133",
"0.76289874",
"0.758633",
"0.7526717",
"0.74780494",
"0.7415649",
"0.7250327",
"0.7245011",
"0.7157922",
"0.70776135",
"0.70071197",
"0.6973833",
"0.6932585",
"0.69310313",
"0.6870495",
"0.6870495",
"0.68690455",
"0.68123126",
"0.680512... | 0.79554284 | 1 |
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