None:\n if not index:\n index = self._index\n\n # Add doc_store to documents\n for d in documents:\n d.doc_store = self\n # Check ID uniqueness\n check_duplicate_documents(documents)\n # Check type consistency\n check_document_types(documents)\n # Batching\n batches = batch_items(documents)\n\n for batch in batches:\n payload = []\n # Calculate vectors\n vectorise_func(batch, self)\n\n for document in batch:\n # JSON representation of document\n doc_json = document.to_elastic()\n\n # Add correct index\n doc_json[\"_index\"] = index\n\n # Rename id key\n doc_json[\"_id\"] = doc_json[\"id\"]\n del doc_json[\"id\"]\n\n payload.append(doc_json)\n\n # Bulk upload to elasticsearch\n helpers.bulk(self._client, payload)\n\n # Update index\n self._client.indices.refresh(index=self._index)","def upload_pon(pon_df, pon_name, namespace, workspace):\n os.system('mkdir -p PoNs')\n filename = './PoNs/fc_upload_PoN_%s.txt' % (pon_name)\n pon_df.to_csv(filename, '\\t', index=False)\n res = upload_entities_from_tsv(namespace, workspace, 'PoNs/fc_upload_PoN_%s.txt'%pon_name)\n return res","def create_multiple(\n self,\n service_name,\n entries=None,\n additional_data=None\n ):\n return self.page(\n service_name=service_name,\n function=CreateMultiple,\n entries=entries,\n additional_data=additional_data,\n )","def post_create(faker_obj, profile_obj, tag_list, num=3):\n for i in range(num):\n obj = faker_obj\n title = obj.sentence(nb_words=random.randint(5, 10))\n author = User.objects.get(id=profile_obj)\n body = \" \".join(obj.paragraphs(nb=random.randint(8, 20)))\n status = \"published\"\n post = Post.objects.create(title=title, author=author, body=body, status=status)\n post.tags.add(\", \".join(random.sample(tag_list, 1)))\n print(\n \"Created post title:'{}' for user '{}'\".format(post.title, author.username)\n )\n create_comment_list(obj, post)","def finish_documents():\n\n doc_ids = json.loads(request.form['doc_ids'])\n\n for docid in doc_ids:\n\n document = Document.query.filter_by(id=docid).first_or_404()\n\n document.status = \"OK\"\n\n db.session.add(document)\n\n db.session.commit()","def batch(self, requests):\n return AlgoliaUtils_request(self.headers, self.write_hosts, \"POST\", \"/1/indexes/*/batch\", self.timeout, {\"requests\": requests})","def create_indices():\n destroy_indices()\n\n ActionDocument._index.create(ignore=[400, 404])\n ClassificationDocument._index.create(ignore=[400, 404])\n FunctionDocument._index.create(ignore=[400, 404])\n PhaseDocument._index.create(ignore=[400, 404])\n RecordDocument._index.create(ignore=[400, 404])\n\n yield\n\n destroy_indices()","def bulk_insert(cls, path=\"data.json\"):\n from json import load\n from codecs import open\n \n lists = load(open(path, \"r\", \"utf8\"))\n for lst in lists:\n ing = cls(content = lst)\n ing.put()","def processed_bulk(self, pipeline):\n docs = [Document([], text=t) for t in EN_DOCS]\n return pipeline(docs)","def create_indices(self) -> None:\n self.client.indices.create(\n index=\"business\",\n body=BUSINESS_MAPPINGS\n )\n self.client.indices.create(\n index=\"review\",\n body=REVIEW_MAPPINGS\n )\n self.client.indices.create(\n index=\"tip\",\n body=TIP_MAPPINGS\n )","def process_es_bulk(pub_list, es):\n bulk_response = es.bulk(\n body=''.join(pub_list),\n refresh='wait_for',\n request_timeout=3600,\n )\n if bulk_response.get('errors'):\n logger.error('failed on bulk indexing:\\n%s',\n bulk_response)\n raise IndexingErrorException()\n return len(pub_list)","def write(cls, templates, values, *args):\n IndexBacklog = Pool().get('elasticsearch.index_backlog')\n Product = Pool().get('product.product')\n\n rv = super(Template, cls).write(templates, values, *args)\n\n products = []\n for template in templates:\n products.extend([Product(p) for p in template.products])\n IndexBacklog.create_from_records(products)\n return rv","def bulk_entries_create(self, feed_id: int, parsed_entries: List) -> None:\n entries_to_create = []\n for entry in parsed_entries:\n entries_to_create.append(\n Item(\n feed_id=feed_id,\n title=entry[\"title\"],\n link=entry[\"link\"],\n description=entry[\"summary\"],\n published_at=datetime.fromtimestamp(\n mktime(entry[\"published_parsed\"])\n ),\n )\n )\n self.bulk_create(entries_to_create)","def push_bulk(self, obj_list, doc_type=None, refresh=True):\n assert isinstance(obj_list, collections.Sequence)\n assert len(obj_list) > 0\n\n es_obj_list = []\n for obj in obj_list:\n if obj is None:\n logger.warning(\"None object in input list\")\n continue\n\n doc_type, es_repr = self._validate_doc_and_get_type_and_repr(obj, doc_type)\n metadata = {\n '_op_type': 'index',\n \"_index\": self.index_name,\n \"_type\": doc_type,\n }\n es_repr.update(**metadata)\n\n es_obj_list.append(es_repr)\n\n helpers.bulk(client=self.conn.elastic_search_client, actions=es_obj_list,\n stats_only=True, refresh=u'true' if refresh else u'false')","def create(self, dictionaries):\n \n return self.ep.post(self.endpoint, params=dictionaries)","def import_documents(self, index, documents, **kwargs):\n self._logger.info('%s documents to index into %s', len(documents), index)\n response = None\n if 'pipeline' in kwargs:\n pipeline_name = kwargs.get(\"pipeline\")\n response = helpers.bulk(self.es, documents, index=index, doc_type=self.doc_type, pipeline=pipeline_name)\n else:\n response = helpers.bulk(self.es, documents, index=index, doc_type=self.doc_type)\n\n # It returns a tuple with summary information - \n # number of successfully executed actions and either list of errors or number of errors if stats_only is set to True.\n return response","def to_db(self):\n bulk = conn_db().initialize_ordered_bulk_op()\n for fiction in self.fictions:\n bulk.find({'id': fiction.id}).upsert().update({'$set': fiction.__dict__})\n bulk.execute()","def create_multiple_people(sqla, n):\n person_schema = PersonSchema()\n new_people = []\n for i in range(n):\n valid_person = person_schema.load(person_object_factory())\n new_people.append(Person(**valid_person))\n sqla.add_all(new_people)\n sqla.commit()","def pdfProcessing():\n global DATABASE\n conn = db.create_connection(DATABASE)\n DOCUMENT_ORIGIN_CODE = \"DOSSIER_PATIENT\"\n\n pathFolder = \"fichiers source/\"\n extension = \".pdf\"\n pdfFileArrayPath = glob.glob(pathFolder + \"*\" + extension)\n print(\" - Processing pdf\", end=\"\")\n for file in pdfFileArrayPath:\n text = readFile.readPdfFile(file)\n query = getDocumentQuery(text, DOCUMENT_ORIGIN_CODE, file, pathFolder, extension)\n \n db.insert_document(conn, query)\n print(\".\", end = '')\n #commit the changes to db\n conn.commit()\n #close the connection\n conn.close()\n print(\"\\n\")","def build_create_or_update_entities_request(\n *,\n json: Any = None,\n content: Any = None,\n **kwargs: Any\n) -> HttpRequest:\n content_type = kwargs.pop(\"content_type\", None)\n accept = \"application/json\"\n\n # Construct URL\n url = kwargs.pop(\"template_url\", '/atlas/v2/entity/bulk')\n\n # Construct headers\n header_parameters = kwargs.pop(\"headers\", {}) # type: Dict[str, Any]\n if content_type is not None:\n header_parameters['Content-Type'] = _SERIALIZER.header(\"content_type\", content_type, 'str')\n header_parameters['Accept'] = _SERIALIZER.header(\"accept\", accept, 'str')\n\n return HttpRequest(\n method=\"POST\",\n url=url,\n headers=header_parameters,\n json=json,\n content=content,\n **kwargs\n )","def create(cls, vlist):\n IndexBacklog = Pool().get('elasticsearch.index_backlog')\n Product = Pool().get('product.product')\n\n templates = super(Template, cls).create(vlist)\n products = []\n for template in templates:\n products.extend([Product(p) for p in template.products])\n IndexBacklog.create_from_records(products)\n return templates","def set_many(self, mapping, timeout=None):\n values = [self._get_doc(key, value, timeout) for key, value in mapping.iteritems()]\n self.collection.insert_many(values)\n return True","def save_posts(self):\n logger.info(\"Savings posts to database\")\n records = self.df.to_dict(\"records\")\n\n for record in records:\n Company.objects.get_or_create(name=record[\"company\"])\n\n Post.objects.get_or_create(\n title=record[\"title\"],\n company_id=record[\"company\"],\n defaults={\n \"date_posted\": record[\"date_posted\"],\n \"description\": record[\"description\"],\n \"location\": record[\"location\"],\n \"is_sponsored\": False,\n \"date_added_db\": record[\"date_added_db\"],\n \"source_id\": record[\"source\"],\n \"link\": record[\"link\"],\n },\n )","def post_document_bulk_annotation(document_id: int, annotation_list, session=konfuzio_session()):\n url = post_project_api_document_annotations_url(document_id)\n r = session.post(url, json=annotation_list)\n r.raise_for_status()\n return r","def test_get_people_list(self):\n person_1 = Person(\n first_name='Emilia',\n last_name='Clarke',\n aliases='Emi'\n )\n person_2 = Person(\n first_name='Peter',\n last_name='Dinklage',\n )\n person_3 = Person(\n first_name='Thomas',\n last_name='McCarthy',\n aliases='Thom'\n )\n\n Person.objects.bulk_create([person_1, person_2, person_3])\n\n response = self.client.get(self.url)\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n self.assertEqual(response.data.get('count'), Person.objects.count())","def populate(self):\n\n self.create_index()\n self.check_type()\n self.create_mapping()\n\n f = open(self.csv_file, 'rU')\n\n # Read the first line for all the headers\n headers = f.readline().split(',')\n\n # Read the rest of the document\n rows = f.readlines()\n added_counter = 0\n\n actions = []\n for row in rows:\n fields = row.split(',')\n obj = {}\n for header in headers:\n # we call lower-case here because we were originally using\n # analyzed strings in elasticsearch (and they were\n # automatically converted). Code was built based on that so it's\n # easiest to convert for now\n try:\n obj[header.replace('\\n', '')] = float(fields[\n headers.index(header)].replace('\\n', '').lower())\n except ValueError:\n obj[header.replace('\\n', '')] = fields[\n headers.index(header)].replace('\\n', '').lower()\n # check afterwards to replace empty strings with None (which json.dumps hopefully writes to null)\n if obj[header.replace('\\n', '')] == '':\n obj[header.replace('\\n', '')] = None\n try:\n item = {\n '_index': self.es_main_index,\n '_type': self.es_main_type,\n '_source': obj\n }\n\n actions.append(item)\n\n added_counter += 1\n print('%s new records added' % added_counter,\n end='\\r')\n sys.stdout.flush()\n\n if added_counter % self.chunk_size == 0:\n helpers.bulk(self.es, actions)\n actions = []\n\n except ConnectionError:\n print('There was a connection error. Check your Elastic' +\n ' Search setting and make sure Elastic Search is ' +\n 'running.')\n return False\n\n # add the remaining items\n if actions:\n helpers.bulk(self.es, actions)\n\n print('The update is completed. %s new records were added.' %\n added_counter)","def insert(self, index, documents, batch_size=100):\n actions = []\n latest_index_id, begin_timestamp = self.__get_latest_index(index)\n\n for idx, doc in enumerate(documents):\n index_id = latest_index_id\n\n if doc[\"request_time\"] <= begin_timestamp:\n index_id = self.get_query_index(index, doc[\"request_time\"])\n\n action = {\n \"_index\": index + \"_\" + str(index_id),\n \"_type\": \"docs\",\n \"_source\": doc,\n }\n actions.append(action)\n\n if len(actions) == batch_size or idx == len(documents) - 1:\n print(\"Bulk ingesting started...\")\n\n try:\n bulk(self.client, actions, raise_on_error=True, request_timeout=200)\n except:\n print(\"Could not write the data.\")\n raise\n \n actions.clear()\n print(\"Bulk ingesting done\")\n if self.__get_index_size(index, latest_index_id) >= self.THRESHOLD:\n begin_timestamp = self.__update_index_timerange(\n index, latest_index_id\n )\n latest_index_id = self.__create_new_index(\n index, latest_index_id + 1, begin_timestamp\n )","def create_page_objects(self, data):\n for page in data['pages']:\n self.create_page(page)","def store_documents(self, documents: list):\n results = app.Results()\n entries = [\n { \n 'Id': str(uuid1()),\n 'MessageBody': json.dumps(doc)\n }\n for doc in documents\n ]\n ids = [ e['Id'] for e in entries ]\n self.Logger.info(f'Store {ids} in sqs')\n self.Logger.debug(f'Saving {entries} in sqs {self.sqs_queue_url}')\n self.sqs_client.send_message_batch(\n QueueUrl=self.sqs_queue_url,\n Entries=entries\n )\n results.ActionStatus = 0\n results.Results = ids\n return results","def bulk_upsert(self, data, parameters=None, many_to_many_clear: bool = True):\n elements = []\n\n # Skip if no identifier is set\n if not self.unique_identifier or not data:\n return\n\n for element in data:\n if isinstance(parameters, dict):\n element.update(parameters)\n\n element, created = self.upsert(data=element, many_to_many_clear=many_to_many_clear)\n elements.append(element)\n\n return elements","def publish_impl(self) -> None:\n\n LOGGER.warn('ElasticsearchPublisher is being deprecated in favor of using SearchMetadatatoElasticasearchTask\\\n which publishes ES metadata with mappings compatible with amundsensearch >= 4.0.0')\n\n actions = [json.loads(line) for line in self.file_handler.readlines()]\n # ensure new data exists\n if not actions:\n LOGGER.warning(\"received no data to upload to Elasticsearch!\")\n return\n\n # Convert object to json for elasticsearch bulk upload\n # Bulk load JSON format is defined here:\n # https://www.elastic.co/guide/en/elasticsearch/reference/6.2/docs-bulk.html\n bulk_actions = []\n cnt = 0\n\n # create new index with mapping\n self.elasticsearch_client.indices.create(index=self.elasticsearch_new_index, body=self.elasticsearch_mapping)\n\n for action in actions:\n index_row = dict(index=dict(_index=self.elasticsearch_new_index))\n action['resource_type'] = self.elasticsearch_type\n\n bulk_actions.append(index_row)\n bulk_actions.append(action)\n cnt += 1\n if cnt == self.elasticsearch_batch_size:\n self.elasticsearch_client.bulk(bulk_actions)\n LOGGER.info('Publish %i of records to ES', cnt)\n cnt = 0\n bulk_actions = []\n\n # Do the final bulk actions\n if bulk_actions:\n self.elasticsearch_client.bulk(bulk_actions)\n\n # fetch indices that have {elasticsearch_alias} as alias\n elasticsearch_old_indices = self._fetch_old_index()\n\n # update alias to point to the new index\n actions = [{\"add\": {\"index\": self.elasticsearch_new_index, \"alias\": self.elasticsearch_alias}}]\n\n # delete old indices\n delete_actions = [{\"remove_index\": {\"index\": index}} for index in elasticsearch_old_indices]\n actions.extend(delete_actions)\n\n update_action = {\"actions\": actions}\n\n # perform alias update and index delete in single atomic operation\n self.elasticsearch_client.indices.update_aliases(update_action)","def bulkCreateTasks(request, *args, **kwargs):\n import settings\n\n # keep track of our own timelimit (20 seconds)\n timelimit = 20000\n timekeeper = Timekeeper(timelimit)\n\n post_dict = request.POST\n\n bulk_create_key = post_dict.get('bulk_create_key')\n if not bulk_create_key:\n return error_handler.logErrorAndReturnOK(\n 'Not all POST data specified in: %s' % post_dict)\n\n bulk_data = bulk_create_model.GCIBulkCreateData.get(bulk_create_key)\n if not bulk_data:\n return error_handler.logErrorAndReturnOK(\n 'No valid data found for key: %s' % bulk_create_key)\n\n # note that we only query for the quota once\n org_admin = bulk_data.created_by\n task_quota = org_logic.getRemainingTaskQuota(org_admin.scope)\n\n tasks = bulk_data.tasks\n while len(tasks) > 0:\n try:\n # check if we have time\n timekeeper.ping()\n\n if settings.GCI_TASK_QUOTA_LIMIT_ENABLED and task_quota <= 0:\n return error_handler.logErrorAndReturnOK(\n 'Task quota reached for %s' %(org_admin.scope.name))\n\n # remove the first task\n task_as_string = tasks.pop(0)\n\n loaded_task = simplejson.loads(task_as_string)\n task = {}\n for key, value in loaded_task.iteritems():\n # If we don't do this python will complain about kwargs not being\n # strings when we try to save the new task.\n task[key.encode('UTF-8')] = value\n\n logging.info('Uncleaned task: %s' %task)\n # clean the data\n errors = _cleanTask(task, org_admin)\n\n if errors:\n logging.warning(\n 'Invalid task data uploaded, the following errors occurred: %s'\n %errors)\n bulk_data.errors.append(db.Text(\n 'The task in row %i contains the following errors.\\n %s' \\\n %(bulk_data.tasksRemoved(), '\\n'.join(errors))))\n\n # at-most-once semantics for creating tasks\n bulk_data.put()\n\n if errors:\n # do the next task\n continue\n\n # set other properties\n task['link_id'] = 't%i' % (int(time.time()*100))\n task['scope'] = org_admin.scope\n task['scope_path'] = org_admin.scope_path\n task['program'] = org_admin.program\n task['status'] = 'Unpublished'\n task['created_by'] = org_admin\n task['modified_by'] = org_admin\n\n # create the new task\n logging.info('Creating new task with fields: %s' %task)\n task_logic.updateOrCreateFromFields(task)\n task_quota = task_quota - 1\n except DeadlineExceededError:\n # time to bail out\n pass\n\n if len(tasks) == 0:\n # send out a message\n notifications.sendBulkCreationCompleted(bulk_data)\n bulk_data.delete()\n else:\n # there is still work to be done, do a non 500 response and requeue\n task_params = {\n 'bulk_create_key': bulk_data.key().id_or_name()\n }\n new_task = taskqueue.Task(params=task_params,\n url=BULK_CREATE_URL)\n # add to the gci queue\n new_task.add(queue_name='gci-update')\n\n # we're done here\n return http.HttpResponse('OK')","def setup(session: 'Session') -> None:\n\n create_many(session, LEVEL_NAMES, commit=False)","def multipart():\n with commit():\n link_and_create_multipart_volumes()\n reindex_pidtype('docid')\n reindex_pidtype('serid')","def _es_push_indexes(self, content):\n for c in self.es_clients:\n c.create_index(content)","def bulkInsert(self, url, values):\n pass","def create_streams(streams):\n for stream in streams:\n Stream.objects.create(**stream)","def build_es_bulk(line):\n action = '{\"index\": {\"_index\": \"%s\"}}' % EPMC_METADATA_INDEX\n data = line + '\\n'\n return '\\n'.join([action, data])","def spawnBulkCreateTasks(data, org_admin):\n data = StringIO.StringIO(data.encode('UTF-8'))\n tasks = csv.DictReader(data, fieldnames=DATA_HEADERS, restval=\"\")\n\n task_list = []\n for task in tasks:\n # pop any extra columns\n task.pop(None,None)\n task_list.append(db.Text(simplejson.dumps(task)))\n\n bulk_data = bulk_create_model.GCIBulkCreateData(\n tasks=task_list, created_by=org_admin, total_tasks=len(task_list))\n bulk_data.put()\n\n task_params = {\n 'bulk_create_key': bulk_data.key()\n }\n\n logging.info('Enqueued bulk_create with: %s' %task_params)\n new_task = taskqueue.Task(params=task_params,\n url=BULK_CREATE_URL)\n # add to the gci queue\n new_task.add(queue_name='gci-update')","def add_elasticsearch_records(self, data_list):\n actions = [self.create_data_record(data_dict) for data_dict in data_list]\n self.actions_buffer.extend(actions)","def upload(self, documents: List[Document], vectorise_func) -> None:\n\n # Add doc_store to documents\n for d in documents:\n d.doc_store = self\n # Check ID uniqueness\n check_duplicate_documents(documents)\n # Check type consistency\n check_document_types(documents)\n # Batching\n batches = batch_items(documents)\n\n # Update document class conveniently\n if issubclass(type(documents[0]), ChunkedDocument):\n self._doc_class = ChunkedDocument\n\n for batch in batches:\n vectorise_func(batch, self)\n self.documents += batch","def upload_multiple():\n gene_list = request.json['gene_list']\n project_id = request.json['project_id']\n all_message = ''\n html = ''\n added_list = []\n button_list = ''\n\n for gene in sorted(gene_list):\n if gene == \"\" or gene in added_list:\n continue\n dct = create_panel_get_tx(gene, project_id)\n if dct[\"message\"] == \"added\":\n added_list.append(gene)\n else:\n all_message += dct[\"message\"]\n try:\n html += dct[\"html\"]\n except KeyError:\n pass\n try:\n button_list += dct[\"button_list\"]\n except KeyError:\n pass\n\n if len(added_list) > 0:\n added_message = render_template(\"added_list.html\", added_list=enumerate(added_list), length=len(added_list))\n all_message += added_message\n\n return jsonify({'message': all_message, 'html': html, 'button_list': button_list})","def insert_many(self, documents: Iterable[dict]) -> None:\n for i, document in enumerate(documents):\n if isinstance(document, dict):\n self._store_document(document)\n else:\n raise TypeError(\n f\"The document at index {i} was not a dictionary. All documents must be dictionaries.\"\n )\n self._dump()","def upload(self, durations, skill_name, skill_args):\n for dur in durations:\n doc = {\"outcome\": 1, \"error\": \"\", \"name\": skill_name, \"duration\": dur}\n args_dict = dict()\n if skill_args != None:\n for arg in skill_args:\n if len(arg) == 1:\n args_dict[arg[0]] = \".*\"\n else:\n args_dict[arg[0]] = np.random.choice(arg[1:])\n doc[\"args\"] = args_dict\n print(doc)\n if not self.dry_run:\n self.lookup_col.insert_one(doc)","def create(requests, user=None):\n return rest.post_multi(resource=_resource, entities=requests, user=user)","def post_samples(self, text, entities, *args):\n params = {'v': WIT_API_VERSION}\n data = []\n if text and entities:\n data.append({'text': text,'entities': entities})\n if len(args)>1:\n x=0\n while x<len(args):\n data.append({'text': args[x],'entities': args[x+1]})\n x+=2\n return req(self.logger, self.access_token, 'POST', '/samples', params,data=json.dumps(data))","def createParticles(self, type, style, *args):\n if not self.rank:\n logging.info('Creating particles {} with args'.format(type) + (' {}' * len(args)).format(*args))\n\n self.lmp.command('create_atoms {} {}'.format(type, style) + (' {}' * len(args)).format(*args))","def create(cls, vlist):\n IndexBacklog = Pool().get('elasticsearch.index_backlog')\n\n products = super(Product, cls).create(vlist)\n IndexBacklog.create_from_records(products)\n return products","def post(self):\n datastore_hooks.SetPrivilegedRequest()\n urlsafe_keys = self.request.get('keys').split(',')\n keys = [ndb.Key(urlsafe=k) for k in urlsafe_keys]\n results = ndb.get_multi(keys)\n\n tests = []\n entities = []\n\n for e in results:\n if e.key.kind() == 'TestMetadata':\n tests.append(e)\n else:\n entities.append(e)\n\n for t in tests:\n t.UpdateSheriff()\n t.put()\n\n ndb.put_multi(entities)","def index_bulk_from_files(self, files):\r\n\r\n docs = self._mailextractor.extract_jsons(files) # Generator-Iterable\r\n actions = self.convert_docstrs_to_bulk_actions(docs) # Generator-Iterable\r\n\r\n self._cur_print = 0\r\n actions_for_chunk = self.print_chunk_progress(actions) # Generator-Iterable\r\n (cnt_success, errors_index) = es_helpers.bulk(\r\n self._es, actions_for_chunk, chunk_size=constants.ES_BULK_CHUNK_SIZE)\r\n\r\n cnt_total = self._mailextractor.cnt_total\r\n errors_convert = self._mailextractor.errors_convert\r\n cnt_error = len(errors_convert) + len(errors_index)\r\n return Summary(cnt_total=cnt_total, cnt_success=cnt_success, cnt_error=cnt_error,\r\n errors_convert=errors_convert, errors_index=errors_index)","def test_bulk_actors(self):\n file_path = os.path.join(os.path.dirname(__file__),\n self.testdata_folder,\n self.filename_actor)\n data = {\n 'bulk_upload' : open(file_path, 'rb'),\n }\n\n res = self.client.post(self.actor_url, data)\n assert res.status_code == status.HTTP_201_CREATED, (\n responses.get(res.status_code, res.status_code), res.content)","def bulk_indice(self, records: List[dict], index_name: str, pipeline: str) -> None:\n events = []\n for record in records:\n event = {\"_id\": self.calc_hash(record), \"_index\": index_name, \"_source\": record}\n if pipeline != \"\":\n event[\"pipeline\"] = pipeline\n events.append(event)\n bulk(self.es, events, raise_on_error=False)","def Create(self, domainsList) :\n\t\t...","def create(server,idx):\n \n uaModel.__create_type(server,idx)\n uaModel.__create_objects(server,idx)","def _create_articles(articles: List[ArticleDto]):\n if articles:\n existing = ArticleRepo.get_existing_objects(\n id_list=[article.id for article in articles]\n )\n articles = [article for article in articles if article.id not in existing]\n ArticleRepo.create_articles(articles=articles)","def createobj(self, firstname='', middlename='', lasttname='', email='', phno='', gender='', address='',\n peronjson={}, notes='', tenantid=''):\n personobj = {'firstname': firstname, 'middlename': middlename, 'lasttname': lasttname, 'email': email, 'phno':\n phno, 'gender': gender, 'address': address, 'peronjson': peronjson, 'notes': notes, 'tenantid': tenantid}\n self.persontdetailscoll.insert(tenantobj, safe=True)","def insertMasivo(self, objects):\n retorno = 1\n \n try:\n n = len(objects)\n for i in xrange(n):\n self.session.add(objects[i])\n if i % 100 == 0:\n self.session.flush()\n #for object in objects:\n # self.session.add(object)\n self.session.commit()\n except sqlalchemy.exc.DBAPIError, e:\n if self.session is not None:\n self.session.rollback()\n retorno = -1\n print(\"Error!\", e)\n finally:\n if self._DAOAlchemy__cerrarSesion:\n self.session.close()\n return retorno","def create_dataset(request):\n body = json.loads(request.body)\n try:\n org = Organization.objects.get(pk=body['organization_id'])\n except Organization.DoesNotExist:\n return {\"status\": 'error',\n 'message': 'organization_id not provided'}\n record = ImportRecord.objects.create(\n name=body['name'],\n app=\"seed\",\n start_time=datetime.datetime.now(),\n created_at=datetime.datetime.now(),\n last_modified_by=request.user,\n super_organization=org,\n owner=request.user,\n )\n\n return {\n 'status': 'success',\n 'id': record.pk,\n 'name': record.name,\n }","def _batch_write(self):\n if self.to_put:\n db.put(self.to_put)\n self.to_put = []\n if self.to_delete:\n db.delete(self.to_delete)\n self.to_delete = []","def test_bulk(es_bulk, mock_es_client):\n actions = []\n chunk_size = 10\n elasticsearch.bulk(actions=actions, chunk_size=chunk_size)\n\n es_bulk.assert_called_with(\n mock_es_client.return_value,\n actions=actions,\n chunk_size=chunk_size,\n max_chunk_bytes=settings.ES_BULK_MAX_CHUNK_BYTES,\n )","async def insert_many(self, models: t.Sequence[Model]):\n\n for model in models:\n if not isinstance(model, Model):\n raise ValueError('insert_many method expects Model instances.')\n\n data = []\n\n for model in models:\n model_as_dict = model.as_dict\n\n if not model_as_dict.get('_id'):\n model_as_dict.pop('_id')\n\n data.append(model_as_dict)\n\n return await self.collection.insert_many(data)","def BatchCreate(self, request, global_params=None):\n config = self.GetMethodConfig('BatchCreate')\n return self._RunMethod(\n config, request, global_params=global_params)"],"string":"[\n \"def handle(self, *args, **options):\\n self.create_indices()\\n self.bulk()\",\n \"def insert_to_elastic(elastic, paper_authors, papers, authors, index_name):\\n helpers.bulk(elastic, merge_to_elastic(paper_authors, papers, authors, index_name))\",\n \"def bulk_refresh():\\n logger.info(\\\"Refreshing/Updating persuasions in bulk\\\")\\n try:\\n request_data = json.loads(request.data)\\n args = request.args\\n with concurrent.futures.ThreadPoolExecutor(max_workers=settings.MAX_WORKERS) as executor:\\n {executor.submit(PersuasionServices.refresh, data, args): data for data in request_data}\\n\\n return jsonify(\\n dict(status=\\\"success\\\", message=\\\"Your request is in the queue, persuasion will be updated shortly\\\"))\\n except Exception as e:\\n logger.error(\\\"Exception while creating persuasions in bulk - \\\" + repr(e))\\n return jsonify(dict(status=\\\"failure\\\", error=repr(e)))\",\n \"def upload_entities(self, batch):\\n # TODO Include a Do Not Overwrite call\\n results = None\\n atlas_endpoint = self.endpoint_url + \\\"/entity/bulk\\\"\\n\\n payload = AtlasClient._prepare_entity_upload(batch)\\n\\n postBulkEntities = requests.post(\\n atlas_endpoint,\\n json=payload,\\n headers=self.authentication.get_authentication_headers()\\n )\\n\\n results = self._handle_response(postBulkEntities)\\n\\n return results\",\n \"def create_doc_bulk(self, file: str, index: str) -> None:\\n\\n with open(file, \\\"r\\\") as f:\\n bulk(self.es, self.make_documents(f, index))\",\n \"def bulk_create(cls, cb, policy_ids):\\n request = [{\\\"policy_id\\\": s} for s in policy_ids]\\n url = cls.urlobject.format(cb.credentials.org_key) + \\\"/_bulk\\\"\\n resp = cb.post_object(url, body=request)\\n result = resp.json()\\n item_list = result.get(\\\"results\\\", [])\\n return [cls(cb, item[\\\"id\\\"], item) for item in item_list]\",\n \"def bulk_push_to_elastic(elastic_search_url, index, docs):\\n CREATE_TEMPLATE = {\\\"create\\\": {\\\"_index\\\": index, \\\"_type\\\": \\\"_doc\\\", \\\"_id\\\": \\\"\\\"}}\\n\\n bulk_request_body = \\\"\\\"\\n for doc in docs:\\n CREATE_TEMPLATE[\\\"create\\\"][\\\"_id\\\"] = doc[HASH_ID]\\n bulk_request_body += json.dumps(CREATE_TEMPLATE) + NEW_LINE\\n bulk_request_body += json.dumps(doc) + NEW_LINE\\n\\n # Request\\n headers = {\\\"content-type\\\": \\\"application/x-ndjson\\\"}\\n\\n url = elastic_search_url + \\\"/\\\" + \\\"_bulk\\\"\\n\\n response = requests.post(url, data=bulk_request_body, headers=headers)\\n return response\",\n \"def create_instance_bulk(self, context, tenant_id, neutron_ports, vms,\\n port_profiles, sync=False):\",\n \"def bulk(self) -> None:\\n helpers.bulk(self.client, self.gen_business_data(BUSINESS_FP))\\n helpers.bulk(self.client, self.gen_review_data(REVIEW_FP))\\n helpers.bulk(self.client, self.gen_tip_data(TIP_FP))\",\n \"def create_multiple_posts(author, num, ptext = TEXT, visibility = ACL_DEFAULT):\\n posts = []\\n\\n for i in range(num):\\n posts.append(Post.objects.create(content = ptext, author = author, visibility=visibility))\\n\\n return posts\",\n \"def post_bulk(bulk_json):\\n\\n nbtry=0\\n success=False\\n\\n # Bulk insert\\n ####################################################################\\n cnx.request(\\\"POST\\\",config.index+\\\"/\\\"+config.typ+\\\"/_bulk\\\",bulk_json) #\\n ####################################################################\\n\\n # Get and read response from Elastic Search server\\n resp=cnx.getresponse()\\n resp_msg_json= resp.read()\\n #sys.stderr.write( resp_msg_json + \\\"\\\\n\\\")\\n resp_msg=json.loads(resp_msg_json)\\n # Check status: both HTTP and within the Elastic Search answer\\n if resp.status != httplib.OK or resp_msg[\\\"errors\\\"] is True:\\n sys.stderr.write( bulk_json)\\n sys.stderr.write( resp_msg_json +\\\"\\\\n\\\")\\n raise Exception(\\\"ERROR when bulk loading into %s/%s: %d %s\\\\n\\\" % (config.index,config.typ, resp.status, resp.reason))\",\n \"def do_bulk(self, args):\\n pass\",\n \"def schedule_refresh():\\n logger.info(\\\"Refreshing/Updating persuasions in bulk\\\")\\n try:\\n args = request.args\\n if not args.get(\\\"type\\\") or not args.get(\\\"sub_type\\\"):\\n return jsonify(\\n dict(status=\\\"failure\\\", message=\\\"Invalid type/sub_type\\\"))\\n\\n persuasions_request = PersuasionServices.get_persuasions_request_from_es(args)\\n\\n with concurrent.futures.ThreadPoolExecutor(max_workers=settings.MAX_WORKERS) as executor:\\n {executor.submit(PersuasionServices.refresh, data, args): data for data in persuasions_request}\\n\\n return jsonify(\\n dict(status=\\\"success\\\", message=\\\"Your request is in the queue, persuasion will be updated shortly\\\"))\\n except Exception as e:\\n logger.error(\\\"Exception while creating persuasions in bulk - \\\" + repr(e))\\n return jsonify(dict(status=\\\"failure\\\", error=repr(e)))\",\n \"async def insert_many(self, models):\\n\\n pass\",\n \"def bulk_insert(engine, model, entries):\\n with session_scope(engine) as session:\\n session.bulk_insert_mappings(model, entries)\\n session.commit()\",\n \"def bulk_process(self):\\n\\n def actions():\\n try:\\n task = self.queue.get(block=False, timeout=None)\\n\\n if task['action'] == 'index':\\n yield {\\n '_op_type': 'index',\\n '_index': self.ensure_index(task),\\n '_id': task['id'],\\n 'doc': task['properties']\\n }\\n elif task['action'] == 'delete':\\n yield {\\n '_op_type': 'delete',\\n '_index': self.ensure_index(task),\\n '_id': task['id'],\\n 'doc': task['properties']\\n }\\n else:\\n raise NotImplementedError\\n\\n except Empty:\\n pass\\n\\n for success, info in streaming_bulk(self.es_client, actions()):\\n if success:\\n self.queue.task_done()\",\n \"def create_entities(self, entity_type):\\n data = self.read_file(entity_type)\\n base_url = data['url']\\n for entity in data['entities']:\\n url = base_url + entity['url']\\n for data in entity['entities']:\\n r = requests.post(url, json.dumps(data))\\n print(r.text)\",\n \"def bulk_insert(objects, model, session):\\n session.bulk_insert_mappings(model, objects)\",\n \"def bulk_create(cls, cb, approvals):\\n url = cls.urlobject.format(cb.credentials.org_key) + \\\"/_bulk\\\"\\n resp = cb.post_object(url, body=approvals)\\n result = resp.json()\\n item_list = result.get(\\\"results\\\", [])\\n return [cls(cb, item[\\\"id\\\"], item) for item in item_list]\",\n \"def test_otoroshi_controllers_adminapi_tcp_service_api_controller_bulk_create_action(self):\\n pass\",\n \"def bulk_create_samples(request):\\n sample_names = request.data['names']\\n library_uuid = request.data['library']\\n library = SampleGroup.objects.get(pk=library_uuid)\\n try:\\n membership_queryset = request.user.organization_set.filter(pk=library.organization.pk)\\n authorized = membership_queryset.exists()\\n except AttributeError: # occurs if user is not logged in\\n authorized = False\\n if not authorized:\\n raise PermissionDenied(_('Insufficient permissions to get group manifest.'))\\n uuids = []\\n for name in sample_names:\\n sample = library.create_sample(name=name)\\n uuids.append(sample.uuid)\\n return Response({'uuids': uuids}, status=201)\",\n \"def store_index(self, index, doc_type, source_list, init_id):\\n\\n bulk_actions = []\\n doc_id = init_id\\n\\n for source in source_list:\\n data_body = ElasticSearchUtility.__index_data_body(index, doc_type, doc_id, source[\\\"_source\\\"])\\n bulk_actions.append(data_body)\\n doc_id += 1\\n\\n print 'inserting - ', len(bulk_actions)\\n helpers.bulk(self.es, bulk_actions)\",\n \"def insert_mass_data(topics=100, posts=100):\\n user1 = User.query.filter_by(id=1).first()\\n user2 = User.query.filter_by(id=2).first()\\n forum = Forum.query.filter_by(id=1).first()\\n\\n created_posts = 0\\n created_topics = 0\\n\\n if not (user1 or user2 or forum):\\n return False\\n\\n # create 1000 topics\\n for i in range(1, topics + 1):\\n\\n # create a topic\\n topic = Topic()\\n post = Post()\\n\\n topic.title = \\\"Test Title %s\\\" % i\\n post.content = \\\"Test Content\\\"\\n topic.save(post=post, user=user1, forum=forum)\\n created_topics += 1\\n\\n # create 100 posts in each topic\\n for j in range(1, posts + 1):\\n post = Post()\\n post.content = \\\"Test Post\\\"\\n post.save(user=user2, topic=topic)\\n created_posts += 1\\n\\n return created_topics, created_posts\",\n \"def test_create_bulk_academic(self):\\n pass\",\n \"def multiple_document_processing(self) -> List:\\n batch_list = []\\n for doc, idx in self.__documents:\\n entities_idx = {'idx': idx}\\n entities_result = self.create_entity(document=doc)\\n word_cleaned = self.clean_words(doc)\\n entities_idx[self.key_spacy_text] = str(word_cleaned)\\n entities_idx.update(entities_result)\\n batch_list.append(entities_idx)\\n return batch_list\",\n \"def populate_employees():\\n employees = get_employees()\\n\\n db.session.bulk_save_objects(employees)\\n db.session.commit()\",\n \"def createMultipleDocuments(cred, payload):\\n url = cred.base_url + \\\"documents:commit\\\"\\n data = { 'writes': [] }\\n\\n for path, fieldData in payload.iteritems():\\n pathData = createFirestoreDataObject(cred, path, fieldData)\\n del pathData['updateMask']\\n data['writes'].append(pathData)\\n\\n makeRequest(cred, url, 'POST', data)\",\n \"def createDocumentAll(self, documents):\\n docs = []\\n for document in documents:\\n if isinstance(document, couch.Document):\\n document = document.getData()\\n\\n # this is create method, no update allowed\\n if \\\"_rev\\\" in document: del document[\\\"_rev\\\"]\\n if \\\"_deleted\\\" in document: del document[\\\"_deleted\\\"]\\n\\n docs.append(document)\\n\\n return self.client.post(self.name +\\\"/_bulk_docs\\\", None,\\n {\\\"docs\\\": docs}).getBodyData()\",\n \"def bulk_create(cls, raw_list):\\n\\t\\tresource_list = [cls(**item) for item in raw_list]\\n\\t\\tdb.session.add_all(resource_list)\\n\\t\\tdb.session.commit()\\n\\n\\t\\treturn resource_list\",\n \"async def create_many(\\n self,\\n documents: List[Dict[str, Any]],\\n *,\\n unique_key: Optional[str] = None,\\n unique_filter: Optional[Dict[str, Any]] = None,\\n session: Optional[Any] = None,\\n **kwargs: Any,\\n ) -> InsertManyResult:\\n return await self._database.create_many(\\n self.name,\\n documents=documents,\\n unique_key=unique_key,\\n unique_filter=unique_filter,\\n session=session,\\n **kwargs,\\n )\",\n \"async def bulk_insert(self, documents, alias=None):\\n\\n is_valid = True\\n docs_to_insert = []\\n\\n for document_index, document in enumerate(documents):\\n self.update_field_on_save_values(document, document._id is not None)\\n try:\\n is_valid = is_valid and self.validate_document(document)\\n except Exception:\\n err = sys.exc_info()[1]\\n raise ValueError(\\n \\\"Validation for document %d in the documents you are saving failed with: %s\\\"\\n % (document_index, str(err))\\n )\\n\\n if not is_valid:\\n return\\n\\n docs_to_insert.append(document.to_son())\\n\\n if not is_valid:\\n return\\n\\n doc_ids = await self.coll(alias).insert(docs_to_insert)\\n\\n for object_index, object_id in enumerate(doc_ids):\\n documents[object_index]._id = object_id\\n\\n return documents\",\n \"def batch(self, request):\\n return AlgoliaUtils_request(self.client.headers, self.write_hosts, \\\"POST\\\", \\\"/1/indexes/%s/batch\\\" % self.url_index_name, self.client.timeout, request)\",\n \"def bulkSave(self, objList: List[PermissionContext], tokenData: TokenData):\",\n \"def insertmany(cls, *args):\\n return InsertQuery(cls).bulk(True).set(*args)\",\n \"def upload_entities(self, entity_upload_parameters, progress=None):\\n \\n tmp_file = path.join(self.working_directory, '{0}.csv'.format(uuid.uuid1()))\\n with BulkFileWriter(tmp_file) as writer:\\n for entity in entity_upload_parameters.entities:\\n writer.write_entity(entity)\\n\\n if (self.need_to_try_upload_entity_records_sync_first(entity_upload_parameters)):\\n return self.bulkupload_entitie_records(entity_upload_parameters, tmp_file, progress)\\n else:\\n return self.bulkupload_entities(entity_upload_parameters, tmp_file, progress)\",\n \"def bulk_index(data):\\n\\n def bulk_api_string(item):\\n return f\\\"{{\\\\\\\"index\\\\\\\":{{}}\\\\n{json.dumps(item)}\\\"\\n\\n body = '\\\\n'.join([bulk_api_string(item) for item in data]) + '\\\\n'\\n\\n return make_request(\\n requests.post,\\n url=f\\\"{connection.hostname}:{connection.port}/{connection.index}/_bulk\\\",\\n headers={'Content-Type': 'application/json'},\\n auth=auth,\\n data=body\\n )\",\n \"def save_posts(self, posts):\\n return self.collection.insert_many(map(lambda post: post.serialize(), posts))\",\n \"def post(self, request):\\n data = request.data\\n skill_data = data.pop('skills')\\n Department_name = data.pop('department')\\n department = Department.objects.get(name=Department_name)\\n manager_name = data.pop('manager')\\n manager = Manager.objects.get(name=manager_name)\\n Employee = EmployeeDetail.objects.create(department=department, manager=manager, **data)\\n Employee.save()\\n for skill in skill_data:\\n skill_add, create = Skill.objects.get_or_create(name=skill)\\n Employee.skills.add(skill_add)\\n return Response(\\n data=request.data\\n )\",\n \"def upload(self, documents: List[ElasticDocument], vectorise_func, index: str = None) -> None:\\n if not index:\\n index = self._index\\n\\n # Add doc_store to documents\\n for d in documents:\\n d.doc_store = self\\n # Check ID uniqueness\\n check_duplicate_documents(documents)\\n # Check type consistency\\n check_document_types(documents)\\n # Batching\\n batches = batch_items(documents)\\n\\n for batch in batches:\\n payload = []\\n # Calculate vectors\\n vectorise_func(batch, self)\\n\\n for document in batch:\\n # JSON representation of document\\n doc_json = document.to_elastic()\\n\\n # Add correct index\\n doc_json[\\\"_index\\\"] = index\\n\\n # Rename id key\\n doc_json[\\\"_id\\\"] = doc_json[\\\"id\\\"]\\n del doc_json[\\\"id\\\"]\\n\\n payload.append(doc_json)\\n\\n # Bulk upload to elasticsearch\\n helpers.bulk(self._client, payload)\\n\\n # Update index\\n self._client.indices.refresh(index=self._index)\",\n \"def upload_pon(pon_df, pon_name, namespace, workspace):\\n os.system('mkdir -p PoNs')\\n filename = './PoNs/fc_upload_PoN_%s.txt' % (pon_name)\\n pon_df.to_csv(filename, '\\\\t', index=False)\\n res = upload_entities_from_tsv(namespace, workspace, 'PoNs/fc_upload_PoN_%s.txt'%pon_name)\\n return res\",\n \"def create_multiple(\\n self,\\n service_name,\\n entries=None,\\n additional_data=None\\n ):\\n return self.page(\\n service_name=service_name,\\n function=CreateMultiple,\\n entries=entries,\\n additional_data=additional_data,\\n )\",\n \"def post_create(faker_obj, profile_obj, tag_list, num=3):\\n for i in range(num):\\n obj = faker_obj\\n title = obj.sentence(nb_words=random.randint(5, 10))\\n author = User.objects.get(id=profile_obj)\\n body = \\\" \\\".join(obj.paragraphs(nb=random.randint(8, 20)))\\n status = \\\"published\\\"\\n post = Post.objects.create(title=title, author=author, body=body, status=status)\\n post.tags.add(\\\", \\\".join(random.sample(tag_list, 1)))\\n print(\\n \\\"Created post title:'{}' for user '{}'\\\".format(post.title, author.username)\\n )\\n create_comment_list(obj, post)\",\n \"def finish_documents():\\n\\n doc_ids = json.loads(request.form['doc_ids'])\\n\\n for docid in doc_ids:\\n\\n document = Document.query.filter_by(id=docid).first_or_404()\\n\\n document.status = \\\"OK\\\"\\n\\n db.session.add(document)\\n\\n db.session.commit()\",\n \"def batch(self, requests):\\n return AlgoliaUtils_request(self.headers, self.write_hosts, \\\"POST\\\", \\\"/1/indexes/*/batch\\\", self.timeout, {\\\"requests\\\": requests})\",\n \"def create_indices():\\n destroy_indices()\\n\\n ActionDocument._index.create(ignore=[400, 404])\\n ClassificationDocument._index.create(ignore=[400, 404])\\n FunctionDocument._index.create(ignore=[400, 404])\\n PhaseDocument._index.create(ignore=[400, 404])\\n RecordDocument._index.create(ignore=[400, 404])\\n\\n yield\\n\\n destroy_indices()\",\n \"def bulk_insert(cls, path=\\\"data.json\\\"):\\n from json import load\\n from codecs import open\\n \\n lists = load(open(path, \\\"r\\\", \\\"utf8\\\"))\\n for lst in lists:\\n ing = cls(content = lst)\\n ing.put()\",\n \"def processed_bulk(self, pipeline):\\n docs = [Document([], text=t) for t in EN_DOCS]\\n return pipeline(docs)\",\n \"def create_indices(self) -> None:\\n self.client.indices.create(\\n index=\\\"business\\\",\\n body=BUSINESS_MAPPINGS\\n )\\n self.client.indices.create(\\n index=\\\"review\\\",\\n body=REVIEW_MAPPINGS\\n )\\n self.client.indices.create(\\n index=\\\"tip\\\",\\n body=TIP_MAPPINGS\\n )\",\n \"def process_es_bulk(pub_list, es):\\n bulk_response = es.bulk(\\n body=''.join(pub_list),\\n refresh='wait_for',\\n request_timeout=3600,\\n )\\n if bulk_response.get('errors'):\\n logger.error('failed on bulk indexing:\\\\n%s',\\n bulk_response)\\n raise IndexingErrorException()\\n return len(pub_list)\",\n \"def write(cls, templates, values, *args):\\n IndexBacklog = Pool().get('elasticsearch.index_backlog')\\n Product = Pool().get('product.product')\\n\\n rv = super(Template, cls).write(templates, values, *args)\\n\\n products = []\\n for template in templates:\\n products.extend([Product(p) for p in template.products])\\n IndexBacklog.create_from_records(products)\\n return rv\",\n \"def bulk_entries_create(self, feed_id: int, parsed_entries: List) -> None:\\n entries_to_create = []\\n for entry in parsed_entries:\\n entries_to_create.append(\\n Item(\\n feed_id=feed_id,\\n title=entry[\\\"title\\\"],\\n link=entry[\\\"link\\\"],\\n description=entry[\\\"summary\\\"],\\n published_at=datetime.fromtimestamp(\\n mktime(entry[\\\"published_parsed\\\"])\\n ),\\n )\\n )\\n self.bulk_create(entries_to_create)\",\n \"def push_bulk(self, obj_list, doc_type=None, refresh=True):\\n assert isinstance(obj_list, collections.Sequence)\\n assert len(obj_list) > 0\\n\\n es_obj_list = []\\n for obj in obj_list:\\n if obj is None:\\n logger.warning(\\\"None object in input list\\\")\\n continue\\n\\n doc_type, es_repr = self._validate_doc_and_get_type_and_repr(obj, doc_type)\\n metadata = {\\n '_op_type': 'index',\\n \\\"_index\\\": self.index_name,\\n \\\"_type\\\": doc_type,\\n }\\n es_repr.update(**metadata)\\n\\n es_obj_list.append(es_repr)\\n\\n helpers.bulk(client=self.conn.elastic_search_client, actions=es_obj_list,\\n stats_only=True, refresh=u'true' if refresh else u'false')\",\n \"def create(self, dictionaries):\\n \\n return self.ep.post(self.endpoint, params=dictionaries)\",\n \"def import_documents(self, index, documents, **kwargs):\\n self._logger.info('%s documents to index into %s', len(documents), index)\\n response = None\\n if 'pipeline' in kwargs:\\n pipeline_name = kwargs.get(\\\"pipeline\\\")\\n response = helpers.bulk(self.es, documents, index=index, doc_type=self.doc_type, pipeline=pipeline_name)\\n else:\\n response = helpers.bulk(self.es, documents, index=index, doc_type=self.doc_type)\\n\\n # It returns a tuple with summary information - \\n # number of successfully executed actions and either list of errors or number of errors if stats_only is set to True.\\n return response\",\n \"def to_db(self):\\n bulk = conn_db().initialize_ordered_bulk_op()\\n for fiction in self.fictions:\\n bulk.find({'id': fiction.id}).upsert().update({'$set': fiction.__dict__})\\n bulk.execute()\",\n \"def create_multiple_people(sqla, n):\\n person_schema = PersonSchema()\\n new_people = []\\n for i in range(n):\\n valid_person = person_schema.load(person_object_factory())\\n new_people.append(Person(**valid_person))\\n sqla.add_all(new_people)\\n sqla.commit()\",\n \"def pdfProcessing():\\n global DATABASE\\n conn = db.create_connection(DATABASE)\\n DOCUMENT_ORIGIN_CODE = \\\"DOSSIER_PATIENT\\\"\\n\\n pathFolder = \\\"fichiers source/\\\"\\n extension = \\\".pdf\\\"\\n pdfFileArrayPath = glob.glob(pathFolder + \\\"*\\\" + extension)\\n print(\\\" - Processing pdf\\\", end=\\\"\\\")\\n for file in pdfFileArrayPath:\\n text = readFile.readPdfFile(file)\\n query = getDocumentQuery(text, DOCUMENT_ORIGIN_CODE, file, pathFolder, extension)\\n \\n db.insert_document(conn, query)\\n print(\\\".\\\", end = '')\\n #commit the changes to db\\n conn.commit()\\n #close the connection\\n conn.close()\\n print(\\\"\\\\n\\\")\",\n \"def build_create_or_update_entities_request(\\n *,\\n json: Any = None,\\n content: Any = None,\\n **kwargs: Any\\n) -> HttpRequest:\\n content_type = kwargs.pop(\\\"content_type\\\", None)\\n accept = \\\"application/json\\\"\\n\\n # Construct URL\\n url = kwargs.pop(\\\"template_url\\\", '/atlas/v2/entity/bulk')\\n\\n # Construct headers\\n header_parameters = kwargs.pop(\\\"headers\\\", {}) # type: Dict[str, Any]\\n if content_type is not None:\\n header_parameters['Content-Type'] = _SERIALIZER.header(\\\"content_type\\\", content_type, 'str')\\n header_parameters['Accept'] = _SERIALIZER.header(\\\"accept\\\", accept, 'str')\\n\\n return HttpRequest(\\n method=\\\"POST\\\",\\n url=url,\\n headers=header_parameters,\\n json=json,\\n content=content,\\n **kwargs\\n )\",\n \"def create(cls, vlist):\\n IndexBacklog = Pool().get('elasticsearch.index_backlog')\\n Product = Pool().get('product.product')\\n\\n templates = super(Template, cls).create(vlist)\\n products = []\\n for template in templates:\\n products.extend([Product(p) for p in template.products])\\n IndexBacklog.create_from_records(products)\\n return templates\",\n \"def set_many(self, mapping, timeout=None):\\n values = [self._get_doc(key, value, timeout) for key, value in mapping.iteritems()]\\n self.collection.insert_many(values)\\n return True\",\n \"def save_posts(self):\\n logger.info(\\\"Savings posts to database\\\")\\n records = self.df.to_dict(\\\"records\\\")\\n\\n for record in records:\\n Company.objects.get_or_create(name=record[\\\"company\\\"])\\n\\n Post.objects.get_or_create(\\n title=record[\\\"title\\\"],\\n company_id=record[\\\"company\\\"],\\n defaults={\\n \\\"date_posted\\\": record[\\\"date_posted\\\"],\\n \\\"description\\\": record[\\\"description\\\"],\\n \\\"location\\\": record[\\\"location\\\"],\\n \\\"is_sponsored\\\": False,\\n \\\"date_added_db\\\": record[\\\"date_added_db\\\"],\\n \\\"source_id\\\": record[\\\"source\\\"],\\n \\\"link\\\": record[\\\"link\\\"],\\n },\\n )\",\n \"def post_document_bulk_annotation(document_id: int, annotation_list, session=konfuzio_session()):\\n url = post_project_api_document_annotations_url(document_id)\\n r = session.post(url, json=annotation_list)\\n r.raise_for_status()\\n return r\",\n \"def test_get_people_list(self):\\n person_1 = Person(\\n first_name='Emilia',\\n last_name='Clarke',\\n aliases='Emi'\\n )\\n person_2 = Person(\\n first_name='Peter',\\n last_name='Dinklage',\\n )\\n person_3 = Person(\\n first_name='Thomas',\\n last_name='McCarthy',\\n aliases='Thom'\\n )\\n\\n Person.objects.bulk_create([person_1, person_2, person_3])\\n\\n response = self.client.get(self.url)\\n self.assertEqual(response.status_code, status.HTTP_200_OK)\\n self.assertEqual(response.data.get('count'), Person.objects.count())\",\n \"def populate(self):\\n\\n self.create_index()\\n self.check_type()\\n self.create_mapping()\\n\\n f = open(self.csv_file, 'rU')\\n\\n # Read the first line for all the headers\\n headers = f.readline().split(',')\\n\\n # Read the rest of the document\\n rows = f.readlines()\\n added_counter = 0\\n\\n actions = []\\n for row in rows:\\n fields = row.split(',')\\n obj = {}\\n for header in headers:\\n # we call lower-case here because we were originally using\\n # analyzed strings in elasticsearch (and they were\\n # automatically converted). Code was built based on that so it's\\n # easiest to convert for now\\n try:\\n obj[header.replace('\\\\n', '')] = float(fields[\\n headers.index(header)].replace('\\\\n', '').lower())\\n except ValueError:\\n obj[header.replace('\\\\n', '')] = fields[\\n headers.index(header)].replace('\\\\n', '').lower()\\n # check afterwards to replace empty strings with None (which json.dumps hopefully writes to null)\\n if obj[header.replace('\\\\n', '')] == '':\\n obj[header.replace('\\\\n', '')] = None\\n try:\\n item = {\\n '_index': self.es_main_index,\\n '_type': self.es_main_type,\\n '_source': obj\\n }\\n\\n actions.append(item)\\n\\n added_counter += 1\\n print('%s new records added' % added_counter,\\n end='\\\\r')\\n sys.stdout.flush()\\n\\n if added_counter % self.chunk_size == 0:\\n helpers.bulk(self.es, actions)\\n actions = []\\n\\n except ConnectionError:\\n print('There was a connection error. Check your Elastic' +\\n ' Search setting and make sure Elastic Search is ' +\\n 'running.')\\n return False\\n\\n # add the remaining items\\n if actions:\\n helpers.bulk(self.es, actions)\\n\\n print('The update is completed. %s new records were added.' %\\n added_counter)\",\n \"def insert(self, index, documents, batch_size=100):\\n actions = []\\n latest_index_id, begin_timestamp = self.__get_latest_index(index)\\n\\n for idx, doc in enumerate(documents):\\n index_id = latest_index_id\\n\\n if doc[\\\"request_time\\\"] <= begin_timestamp:\\n index_id = self.get_query_index(index, doc[\\\"request_time\\\"])\\n\\n action = {\\n \\\"_index\\\": index + \\\"_\\\" + str(index_id),\\n \\\"_type\\\": \\\"docs\\\",\\n \\\"_source\\\": doc,\\n }\\n actions.append(action)\\n\\n if len(actions) == batch_size or idx == len(documents) - 1:\\n print(\\\"Bulk ingesting started...\\\")\\n\\n try:\\n bulk(self.client, actions, raise_on_error=True, request_timeout=200)\\n except:\\n print(\\\"Could not write the data.\\\")\\n raise\\n \\n actions.clear()\\n print(\\\"Bulk ingesting done\\\")\\n if self.__get_index_size(index, latest_index_id) >= self.THRESHOLD:\\n begin_timestamp = self.__update_index_timerange(\\n index, latest_index_id\\n )\\n latest_index_id = self.__create_new_index(\\n index, latest_index_id + 1, begin_timestamp\\n )\",\n \"def create_page_objects(self, data):\\n for page in data['pages']:\\n self.create_page(page)\",\n \"def store_documents(self, documents: list):\\n results = app.Results()\\n entries = [\\n { \\n 'Id': str(uuid1()),\\n 'MessageBody': json.dumps(doc)\\n }\\n for doc in documents\\n ]\\n ids = [ e['Id'] for e in entries ]\\n self.Logger.info(f'Store {ids} in sqs')\\n self.Logger.debug(f'Saving {entries} in sqs {self.sqs_queue_url}')\\n self.sqs_client.send_message_batch(\\n QueueUrl=self.sqs_queue_url,\\n Entries=entries\\n )\\n results.ActionStatus = 0\\n results.Results = ids\\n return results\",\n \"def bulk_upsert(self, data, parameters=None, many_to_many_clear: bool = True):\\n elements = []\\n\\n # Skip if no identifier is set\\n if not self.unique_identifier or not data:\\n return\\n\\n for element in data:\\n if isinstance(parameters, dict):\\n element.update(parameters)\\n\\n element, created = self.upsert(data=element, many_to_many_clear=many_to_many_clear)\\n elements.append(element)\\n\\n return elements\",\n \"def publish_impl(self) -> None:\\n\\n LOGGER.warn('ElasticsearchPublisher is being deprecated in favor of using SearchMetadatatoElasticasearchTask\\\\\\n which publishes ES metadata with mappings compatible with amundsensearch >= 4.0.0')\\n\\n actions = [json.loads(line) for line in self.file_handler.readlines()]\\n # ensure new data exists\\n if not actions:\\n LOGGER.warning(\\\"received no data to upload to Elasticsearch!\\\")\\n return\\n\\n # Convert object to json for elasticsearch bulk upload\\n # Bulk load JSON format is defined here:\\n # https://www.elastic.co/guide/en/elasticsearch/reference/6.2/docs-bulk.html\\n bulk_actions = []\\n cnt = 0\\n\\n # create new index with mapping\\n self.elasticsearch_client.indices.create(index=self.elasticsearch_new_index, body=self.elasticsearch_mapping)\\n\\n for action in actions:\\n index_row = dict(index=dict(_index=self.elasticsearch_new_index))\\n action['resource_type'] = self.elasticsearch_type\\n\\n bulk_actions.append(index_row)\\n bulk_actions.append(action)\\n cnt += 1\\n if cnt == self.elasticsearch_batch_size:\\n self.elasticsearch_client.bulk(bulk_actions)\\n LOGGER.info('Publish %i of records to ES', cnt)\\n cnt = 0\\n bulk_actions = []\\n\\n # Do the final bulk actions\\n if bulk_actions:\\n self.elasticsearch_client.bulk(bulk_actions)\\n\\n # fetch indices that have {elasticsearch_alias} as alias\\n elasticsearch_old_indices = self._fetch_old_index()\\n\\n # update alias to point to the new index\\n actions = [{\\\"add\\\": {\\\"index\\\": self.elasticsearch_new_index, \\\"alias\\\": self.elasticsearch_alias}}]\\n\\n # delete old indices\\n delete_actions = [{\\\"remove_index\\\": {\\\"index\\\": index}} for index in elasticsearch_old_indices]\\n actions.extend(delete_actions)\\n\\n update_action = {\\\"actions\\\": actions}\\n\\n # perform alias update and index delete in single atomic operation\\n self.elasticsearch_client.indices.update_aliases(update_action)\",\n \"def bulkCreateTasks(request, *args, **kwargs):\\n import settings\\n\\n # keep track of our own timelimit (20 seconds)\\n timelimit = 20000\\n timekeeper = Timekeeper(timelimit)\\n\\n post_dict = request.POST\\n\\n bulk_create_key = post_dict.get('bulk_create_key')\\n if not bulk_create_key:\\n return error_handler.logErrorAndReturnOK(\\n 'Not all POST data specified in: %s' % post_dict)\\n\\n bulk_data = bulk_create_model.GCIBulkCreateData.get(bulk_create_key)\\n if not bulk_data:\\n return error_handler.logErrorAndReturnOK(\\n 'No valid data found for key: %s' % bulk_create_key)\\n\\n # note that we only query for the quota once\\n org_admin = bulk_data.created_by\\n task_quota = org_logic.getRemainingTaskQuota(org_admin.scope)\\n\\n tasks = bulk_data.tasks\\n while len(tasks) > 0:\\n try:\\n # check if we have time\\n timekeeper.ping()\\n\\n if settings.GCI_TASK_QUOTA_LIMIT_ENABLED and task_quota <= 0:\\n return error_handler.logErrorAndReturnOK(\\n 'Task quota reached for %s' %(org_admin.scope.name))\\n\\n # remove the first task\\n task_as_string = tasks.pop(0)\\n\\n loaded_task = simplejson.loads(task_as_string)\\n task = {}\\n for key, value in loaded_task.iteritems():\\n # If we don't do this python will complain about kwargs not being\\n # strings when we try to save the new task.\\n task[key.encode('UTF-8')] = value\\n\\n logging.info('Uncleaned task: %s' %task)\\n # clean the data\\n errors = _cleanTask(task, org_admin)\\n\\n if errors:\\n logging.warning(\\n 'Invalid task data uploaded, the following errors occurred: %s'\\n %errors)\\n bulk_data.errors.append(db.Text(\\n 'The task in row %i contains the following errors.\\\\n %s' \\\\\\n %(bulk_data.tasksRemoved(), '\\\\n'.join(errors))))\\n\\n # at-most-once semantics for creating tasks\\n bulk_data.put()\\n\\n if errors:\\n # do the next task\\n continue\\n\\n # set other properties\\n task['link_id'] = 't%i' % (int(time.time()*100))\\n task['scope'] = org_admin.scope\\n task['scope_path'] = org_admin.scope_path\\n task['program'] = org_admin.program\\n task['status'] = 'Unpublished'\\n task['created_by'] = org_admin\\n task['modified_by'] = org_admin\\n\\n # create the new task\\n logging.info('Creating new task with fields: %s' %task)\\n task_logic.updateOrCreateFromFields(task)\\n task_quota = task_quota - 1\\n except DeadlineExceededError:\\n # time to bail out\\n pass\\n\\n if len(tasks) == 0:\\n # send out a message\\n notifications.sendBulkCreationCompleted(bulk_data)\\n bulk_data.delete()\\n else:\\n # there is still work to be done, do a non 500 response and requeue\\n task_params = {\\n 'bulk_create_key': bulk_data.key().id_or_name()\\n }\\n new_task = taskqueue.Task(params=task_params,\\n url=BULK_CREATE_URL)\\n # add to the gci queue\\n new_task.add(queue_name='gci-update')\\n\\n # we're done here\\n return http.HttpResponse('OK')\",\n \"def setup(session: 'Session') -> None:\\n\\n create_many(session, LEVEL_NAMES, commit=False)\",\n \"def multipart():\\n with commit():\\n link_and_create_multipart_volumes()\\n reindex_pidtype('docid')\\n reindex_pidtype('serid')\",\n \"def _es_push_indexes(self, content):\\n for c in self.es_clients:\\n c.create_index(content)\",\n \"def bulkInsert(self, url, values):\\n pass\",\n \"def create_streams(streams):\\n for stream in streams:\\n Stream.objects.create(**stream)\",\n \"def build_es_bulk(line):\\n action = '{\\\"index\\\": {\\\"_index\\\": \\\"%s\\\"}}' % EPMC_METADATA_INDEX\\n data = line + '\\\\n'\\n return '\\\\n'.join([action, data])\",\n \"def spawnBulkCreateTasks(data, org_admin):\\n data = StringIO.StringIO(data.encode('UTF-8'))\\n tasks = csv.DictReader(data, fieldnames=DATA_HEADERS, restval=\\\"\\\")\\n\\n task_list = []\\n for task in tasks:\\n # pop any extra columns\\n task.pop(None,None)\\n task_list.append(db.Text(simplejson.dumps(task)))\\n\\n bulk_data = bulk_create_model.GCIBulkCreateData(\\n tasks=task_list, created_by=org_admin, total_tasks=len(task_list))\\n bulk_data.put()\\n\\n task_params = {\\n 'bulk_create_key': bulk_data.key()\\n }\\n\\n logging.info('Enqueued bulk_create with: %s' %task_params)\\n new_task = taskqueue.Task(params=task_params,\\n url=BULK_CREATE_URL)\\n # add to the gci queue\\n new_task.add(queue_name='gci-update')\",\n \"def add_elasticsearch_records(self, data_list):\\n actions = [self.create_data_record(data_dict) for data_dict in data_list]\\n self.actions_buffer.extend(actions)\",\n \"def upload(self, documents: List[Document], vectorise_func) -> None:\\n\\n # Add doc_store to documents\\n for d in documents:\\n d.doc_store = self\\n # Check ID uniqueness\\n check_duplicate_documents(documents)\\n # Check type consistency\\n check_document_types(documents)\\n # Batching\\n batches = batch_items(documents)\\n\\n # Update document class conveniently\\n if issubclass(type(documents[0]), ChunkedDocument):\\n self._doc_class = ChunkedDocument\\n\\n for batch in batches:\\n vectorise_func(batch, self)\\n self.documents += batch\",\n \"def upload_multiple():\\n gene_list = request.json['gene_list']\\n project_id = request.json['project_id']\\n all_message = ''\\n html = ''\\n added_list = []\\n button_list = ''\\n\\n for gene in sorted(gene_list):\\n if gene == \\\"\\\" or gene in added_list:\\n continue\\n dct = create_panel_get_tx(gene, project_id)\\n if dct[\\\"message\\\"] == \\\"added\\\":\\n added_list.append(gene)\\n else:\\n all_message += dct[\\\"message\\\"]\\n try:\\n html += dct[\\\"html\\\"]\\n except KeyError:\\n pass\\n try:\\n button_list += dct[\\\"button_list\\\"]\\n except KeyError:\\n pass\\n\\n if len(added_list) > 0:\\n added_message = render_template(\\\"added_list.html\\\", added_list=enumerate(added_list), length=len(added_list))\\n all_message += added_message\\n\\n return jsonify({'message': all_message, 'html': html, 'button_list': button_list})\",\n \"def insert_many(self, documents: Iterable[dict]) -> None:\\n for i, document in enumerate(documents):\\n if isinstance(document, dict):\\n self._store_document(document)\\n else:\\n raise TypeError(\\n f\\\"The document at index {i} was not a dictionary. All documents must be dictionaries.\\\"\\n )\\n self._dump()\",\n \"def upload(self, durations, skill_name, skill_args):\\n for dur in durations:\\n doc = {\\\"outcome\\\": 1, \\\"error\\\": \\\"\\\", \\\"name\\\": skill_name, \\\"duration\\\": dur}\\n args_dict = dict()\\n if skill_args != None:\\n for arg in skill_args:\\n if len(arg) == 1:\\n args_dict[arg[0]] = \\\".*\\\"\\n else:\\n args_dict[arg[0]] = np.random.choice(arg[1:])\\n doc[\\\"args\\\"] = args_dict\\n print(doc)\\n if not self.dry_run:\\n self.lookup_col.insert_one(doc)\",\n \"def create(requests, user=None):\\n return rest.post_multi(resource=_resource, entities=requests, user=user)\",\n \"def post_samples(self, text, entities, *args):\\n params = {'v': WIT_API_VERSION}\\n data = []\\n if text and entities:\\n data.append({'text': text,'entities': entities})\\n if len(args)>1:\\n x=0\\n while x<len(args):\\n data.append({'text': args[x],'entities': args[x+1]})\\n x+=2\\n return req(self.logger, self.access_token, 'POST', '/samples', params,data=json.dumps(data))\",\n \"def createParticles(self, type, style, *args):\\n if not self.rank:\\n logging.info('Creating particles {} with args'.format(type) + (' {}' * len(args)).format(*args))\\n\\n self.lmp.command('create_atoms {} {}'.format(type, style) + (' {}' * len(args)).format(*args))\",\n \"def create(cls, vlist):\\n IndexBacklog = Pool().get('elasticsearch.index_backlog')\\n\\n products = super(Product, cls).create(vlist)\\n IndexBacklog.create_from_records(products)\\n return products\",\n \"def post(self):\\n datastore_hooks.SetPrivilegedRequest()\\n urlsafe_keys = self.request.get('keys').split(',')\\n keys = [ndb.Key(urlsafe=k) for k in urlsafe_keys]\\n results = ndb.get_multi(keys)\\n\\n tests = []\\n entities = []\\n\\n for e in results:\\n if e.key.kind() == 'TestMetadata':\\n tests.append(e)\\n else:\\n entities.append(e)\\n\\n for t in tests:\\n t.UpdateSheriff()\\n t.put()\\n\\n ndb.put_multi(entities)\",\n \"def index_bulk_from_files(self, files):\\r\\n\\r\\n docs = self._mailextractor.extract_jsons(files) # Generator-Iterable\\r\\n actions = self.convert_docstrs_to_bulk_actions(docs) # Generator-Iterable\\r\\n\\r\\n self._cur_print = 0\\r\\n actions_for_chunk = self.print_chunk_progress(actions) # Generator-Iterable\\r\\n (cnt_success, errors_index) = es_helpers.bulk(\\r\\n self._es, actions_for_chunk, chunk_size=constants.ES_BULK_CHUNK_SIZE)\\r\\n\\r\\n cnt_total = self._mailextractor.cnt_total\\r\\n errors_convert = self._mailextractor.errors_convert\\r\\n cnt_error = len(errors_convert) + len(errors_index)\\r\\n return Summary(cnt_total=cnt_total, cnt_success=cnt_success, cnt_error=cnt_error,\\r\\n errors_convert=errors_convert, errors_index=errors_index)\",\n \"def test_bulk_actors(self):\\n file_path = os.path.join(os.path.dirname(__file__),\\n self.testdata_folder,\\n self.filename_actor)\\n data = {\\n 'bulk_upload' : open(file_path, 'rb'),\\n }\\n\\n res = self.client.post(self.actor_url, data)\\n assert res.status_code == status.HTTP_201_CREATED, (\\n responses.get(res.status_code, res.status_code), res.content)\",\n \"def bulk_indice(self, records: List[dict], index_name: str, pipeline: str) -> None:\\n events = []\\n for record in records:\\n event = {\\\"_id\\\": self.calc_hash(record), \\\"_index\\\": index_name, \\\"_source\\\": record}\\n if pipeline != \\\"\\\":\\n event[\\\"pipeline\\\"] = pipeline\\n events.append(event)\\n bulk(self.es, events, raise_on_error=False)\",\n \"def Create(self, domainsList) :\\n\\t\\t...\",\n \"def create(server,idx):\\n \\n uaModel.__create_type(server,idx)\\n uaModel.__create_objects(server,idx)\",\n \"def _create_articles(articles: List[ArticleDto]):\\n if articles:\\n existing = ArticleRepo.get_existing_objects(\\n id_list=[article.id for article in articles]\\n )\\n articles = [article for article in articles if article.id not in existing]\\n ArticleRepo.create_articles(articles=articles)\",\n \"def createobj(self, firstname='', middlename='', lasttname='', email='', phno='', gender='', address='',\\n peronjson={}, notes='', tenantid=''):\\n personobj = {'firstname': firstname, 'middlename': middlename, 'lasttname': lasttname, 'email': email, 'phno':\\n phno, 'gender': gender, 'address': address, 'peronjson': peronjson, 'notes': notes, 'tenantid': tenantid}\\n self.persontdetailscoll.insert(tenantobj, safe=True)\",\n \"def insertMasivo(self, objects):\\n retorno = 1\\n \\n try:\\n n = len(objects)\\n for i in xrange(n):\\n self.session.add(objects[i])\\n if i % 100 == 0:\\n self.session.flush()\\n #for object in objects:\\n # self.session.add(object)\\n self.session.commit()\\n except sqlalchemy.exc.DBAPIError, e:\\n if self.session is not None:\\n self.session.rollback()\\n retorno = -1\\n print(\\\"Error!\\\", e)\\n finally:\\n if self._DAOAlchemy__cerrarSesion:\\n self.session.close()\\n return retorno\",\n \"def create_dataset(request):\\n body = json.loads(request.body)\\n try:\\n org = Organization.objects.get(pk=body['organization_id'])\\n except Organization.DoesNotExist:\\n return {\\\"status\\\": 'error',\\n 'message': 'organization_id not provided'}\\n record = ImportRecord.objects.create(\\n name=body['name'],\\n app=\\\"seed\\\",\\n start_time=datetime.datetime.now(),\\n created_at=datetime.datetime.now(),\\n last_modified_by=request.user,\\n super_organization=org,\\n owner=request.user,\\n )\\n\\n return {\\n 'status': 'success',\\n 'id': record.pk,\\n 'name': record.name,\\n }\",\n \"def _batch_write(self):\\n if self.to_put:\\n db.put(self.to_put)\\n self.to_put = []\\n if self.to_delete:\\n db.delete(self.to_delete)\\n self.to_delete = []\",\n \"def test_bulk(es_bulk, mock_es_client):\\n actions = []\\n chunk_size = 10\\n elasticsearch.bulk(actions=actions, chunk_size=chunk_size)\\n\\n es_bulk.assert_called_with(\\n mock_es_client.return_value,\\n actions=actions,\\n chunk_size=chunk_size,\\n max_chunk_bytes=settings.ES_BULK_MAX_CHUNK_BYTES,\\n )\",\n \"async def insert_many(self, models: t.Sequence[Model]):\\n\\n for model in models:\\n if not isinstance(model, Model):\\n raise ValueError('insert_many method expects Model instances.')\\n\\n data = []\\n\\n for model in models:\\n model_as_dict = model.as_dict\\n\\n if not model_as_dict.get('_id'):\\n model_as_dict.pop('_id')\\n\\n data.append(model_as_dict)\\n\\n return await self.collection.insert_many(data)\",\n \"def BatchCreate(self, request, global_params=None):\\n config = self.GetMethodConfig('BatchCreate')\\n return self._RunMethod(\\n config, request, global_params=global_params)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.60707426","0.6044942","0.6015414","0.5868079","0.58612776","0.58422595","0.57517433","0.5748778","0.5713514","0.56829447","0.56696784","0.5647391","0.5640021","0.56258446","0.5564406","0.5555156","0.5547321","0.5522877","0.5503289","0.5487169","0.5446748","0.5438601","0.54330784","0.54143333","0.5403617","0.53948283","0.5386578","0.53577405","0.53415215","0.53323394","0.53317887","0.53307897","0.53077257","0.53074455","0.5292724","0.5281651","0.52430445","0.52377725","0.5234141","0.5226357","0.522452","0.52168167","0.51752573","0.5157943","0.51574814","0.51542753","0.5106583","0.50939727","0.5087876","0.50590205","0.50474036","0.5044962","0.5029526","0.50212836","0.5020642","0.5017058","0.50074375","0.5002954","0.4995208","0.4988046","0.4986841","0.496571","0.49629536","0.49546656","0.49480656","0.49377048","0.49300596","0.49238423","0.49135062","0.49078763","0.49050105","0.48982006","0.4891108","0.48909172","0.48791447","0.4875368","0.48713124","0.48555833","0.48414445","0.48372746","0.48363447","0.48363215","0.48342556","0.4830828","0.48286915","0.48200876","0.48190466","0.48179471","0.48171675","0.481597","0.4806129","0.48052022","0.4803582","0.47973174","0.47851214","0.47840393","0.47826895","0.47820047","0.47805396","0.4772079"],"string":"[\n \"0.60707426\",\n \"0.6044942\",\n \"0.6015414\",\n \"0.5868079\",\n \"0.58612776\",\n \"0.58422595\",\n \"0.57517433\",\n \"0.5748778\",\n \"0.5713514\",\n \"0.56829447\",\n \"0.56696784\",\n \"0.5647391\",\n \"0.5640021\",\n \"0.56258446\",\n \"0.5564406\",\n \"0.5555156\",\n \"0.5547321\",\n \"0.5522877\",\n \"0.5503289\",\n \"0.5487169\",\n \"0.5446748\",\n \"0.5438601\",\n \"0.54330784\",\n \"0.54143333\",\n \"0.5403617\",\n \"0.53948283\",\n \"0.5386578\",\n \"0.53577405\",\n \"0.53415215\",\n \"0.53323394\",\n \"0.53317887\",\n \"0.53307897\",\n \"0.53077257\",\n \"0.53074455\",\n \"0.5292724\",\n \"0.5281651\",\n \"0.52430445\",\n \"0.52377725\",\n \"0.5234141\",\n \"0.5226357\",\n \"0.522452\",\n \"0.52168167\",\n \"0.51752573\",\n \"0.5157943\",\n \"0.51574814\",\n \"0.51542753\",\n \"0.5106583\",\n \"0.50939727\",\n \"0.5087876\",\n \"0.50590205\",\n \"0.50474036\",\n \"0.5044962\",\n \"0.5029526\",\n \"0.50212836\",\n \"0.5020642\",\n \"0.5017058\",\n \"0.50074375\",\n \"0.5002954\",\n \"0.4995208\",\n \"0.4988046\",\n \"0.4986841\",\n \"0.496571\",\n \"0.49629536\",\n \"0.49546656\",\n \"0.49480656\",\n \"0.49377048\",\n \"0.49300596\",\n \"0.49238423\",\n \"0.49135062\",\n \"0.49078763\",\n \"0.49050105\",\n \"0.48982006\",\n \"0.4891108\",\n \"0.48909172\",\n \"0.48791447\",\n \"0.4875368\",\n \"0.48713124\",\n \"0.48555833\",\n \"0.48414445\",\n \"0.48372746\",\n \"0.48363447\",\n \"0.48363215\",\n \"0.48342556\",\n \"0.4830828\",\n \"0.48286915\",\n \"0.48200876\",\n \"0.48190466\",\n \"0.48179471\",\n \"0.48171675\",\n \"0.481597\",\n \"0.4806129\",\n \"0.48052022\",\n \"0.4803582\",\n \"0.47973174\",\n \"0.47851214\",\n \"0.47840393\",\n \"0.47826895\",\n \"0.47820047\",\n \"0.47805396\",\n \"0.4772079\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.79547685"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":94689,"cells":{"query":{"kind":"string","value":"This will update the persuasion and will return it API /persuasion/refresh?push_to_es=true&push_to_inflow=false {"},"document":{"kind":"string","value":"def refresh_persuasion():\n try:\n request_data = json.loads(request.data)\n meta = request.args\n persuasion = PersuasionServices.refresh(request_data, meta)\n except Exception as e:\n error_msg = \"Getting persuasion details - \" + repr(e)\n logger.error(error_msg)\n return jsonify(dict(status=\"failure\", error=error_msg))\n return jsonify(dict(status=\"success\", data=persuasion if persuasion else dict()))"},"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 schedule_refresh():\n logger.info(\"Refreshing/Updating persuasions in bulk\")\n try:\n args = request.args\n if not args.get(\"type\") or not args.get(\"sub_type\"):\n return jsonify(\n dict(status=\"failure\", message=\"Invalid type/sub_type\"))\n\n persuasions_request = PersuasionServices.get_persuasions_request_from_es(args)\n\n with concurrent.futures.ThreadPoolExecutor(max_workers=settings.MAX_WORKERS) as executor:\n {executor.submit(PersuasionServices.refresh, data, args): data for data in persuasions_request}\n\n return jsonify(\n dict(status=\"success\", message=\"Your request is in the queue, persuasion will be updated shortly\"))\n except Exception as e:\n logger.error(\"Exception while creating persuasions in bulk - \" + repr(e))\n return jsonify(dict(status=\"failure\", error=repr(e)))","def bulk_refresh():\n logger.info(\"Refreshing/Updating persuasions in bulk\")\n try:\n request_data = json.loads(request.data)\n args = request.args\n with concurrent.futures.ThreadPoolExecutor(max_workers=settings.MAX_WORKERS) as executor:\n {executor.submit(PersuasionServices.refresh, data, args): data for data in request_data}\n\n return jsonify(\n dict(status=\"success\", message=\"Your request is in the queue, persuasion will be updated shortly\"))\n except Exception as e:\n logger.error(\"Exception while creating persuasions in bulk - \" + repr(e))\n return jsonify(dict(status=\"failure\", error=repr(e)))","def refresh(self):\n self.dto = self.res.get()\n log.debug(f\"Refreshed {self.url}\")","def fullRefresh(self, p):\n p = self.generalisedRefresh(p)\n return p","def _refresh(self):\n resp = self._cb.get_object(self._build_api_request_uri())\n self._info = resp\n self._last_refresh_time = time.time()\n return True","def _refresh(self):\n resp = self._cb.get_object(self._build_api_request_uri())\n self._info = resp\n self._last_refresh_time = time.time()\n return True","def refresh_details(self) -> None:\n data = request(\n 'get',\n f'/api/v0/projects/{self.id}/',\n ).json()\n self.data.update(data)","def refresh_details(self) -> 'outputs.RefreshDetailsResponse':\n return pulumi.get(self, \"refresh_details\")","def refresh(self):\n\t\tif self.id is None:\n\t\t\tprint(\"({cls}): self.id is None, can't refresh\".format(cls=self.__class__.__name__))\n\t\t\treturn\n\n\t\tif self.needs_project_id and self.project_id is None:\n\t\t\tprint(\"({cls}): self.project_id is None, can't refresh\".format(cls=self.__class__.__name__))\n\t\t\treturn\n\n\t\tif self.needs_project_id:\n\t\t\targs = [self.project_id, self.id]\n\t\telse:\n\t\t\targs = [self.id]\n\n\t\tres = getattr(self._client, \"get_\" + self.method)(*args, raw=True)\n\t\tself._create_fields(res)","def refresh_from_api(self):\n self.populate_from_api(self.get_from_api())","def refresh(request):\n \n t, created = LastUpdate.objects.get_or_create(id=1)\n if created:\n t.save()\n t = t.unix_time()\n else:\n t = t.unix_time()\n latest = int(t) - 2\n return JsonResponse({'latest':int(latest)})","def refresh(self):\n return self._refresh","def refresh(self): \n return self._config.refreshObj(self)","def refresh(self):\n pass","def refresh(self):\n pass","def project_refresh_all():\n project_list = Project.objects()\n analyser.add_repos(current_user.username, [repo.project_name for repo in project_list])\n flash('Refresh all successfully!', 'success')\n return redirect(url_for('main.admin_manage'))","def refresh(self):\n raise NotImplementedError","def refresh(self):\n raise NotImplementedError","def updates(self, request, version):\n task = EtlTask.objects.get_for_model(self.queryset.model)\n if task.last_changes:\n offset = task.last_changes.strftime(\"%Y-%m-%d %H:%M\")\n queryset = self.queryset.filter(last_modify_date__gte=offset)\n else:\n offset = \"none\"\n queryset = self.queryset.all()\n queryset = self.paginate_queryset(queryset)\n serializer = self.get_serializer(queryset, many=True)\n return self.get_paginated_response(serializer.data)\n # return Response(serializer.data,\n # headers={'update-date': offset})","def refresh_parcel_repos(self):\n if self.api_client.api_version < 'v16':\n logger.warning('Detected API version without support '\n 'for refreshParcelRepos (%s). Sleeping instead ...',\n self.api_client.api_version)\n sleep(30)\n else:\n return self.api_client.refresh_parcel_repos()","def refresh_counterfactual_json(request):\t\n\n\n \"\"\"\n the following json are for compound exposures\n \"\"\"\n #json_file = '/home/zhou/Downloads/jsons/compound/json_9.json'\n\tjson_file = '/Users/jiaozi/Downloads/jsons/compound/json_9.json'\n\n exposure_sequence = read_json(json_file)#list of exposures{mean,sd,non_rate}\n\n\tprimeCoordinator = PrimeCoordinator()\n\tprimeCoordinator.get_counterfactual_compound_exposures(exposure_sequence)\n\t\n\t# get the data in the interface\n\tb_output_mortality \t= primeCoordinator.output_baseline_mortality # baseline mortality list for all outcomes\n\tb_output_mortality_num \t= primeCoordinator.output_baseline_mortality_num # baseline mortality sum up for each outcome\n\tb_total_mortality \t= primeCoordinator.output_baseline_mortality_total# baseline mortality sum up for all outcomes\n\tc_output_mortality \t= primeCoordinator.output_counterfactual_mortality# counterfactual mortality for all outcomes\n\tc_output_mortality_num \t= primeCoordinator.output_counterfactual_mortality_num# counterfactual mortality for each outcome\n\tc_total_mortality \t= primeCoordinator.output_counterfactual_mortality_total# counterfactual mortality sum up for all outcomes\n\ttotal_population\t= primeCoordinator.output_total_population\n\tall_mortality_exposure\t= primeCoordinator.output_all_mortality_exposure\n\ttotal_death_averted\t= str(round(primeCoordinator.output_total_death_averted,0))\n\ttotal_death_baseline\t= str(primeCoordinator.output_total_death_baseline)\n\n\t#transmit the parameters\n\ttemplate = loader.get_template('primemodel/index.html')\n\tpara_view = {\n\t\t\t'b_output_mortality_num':\tb_output_mortality_num,\n\t\t\t'b_total_mortality':\t\tb_total_mortality,\n\t\t\t'c_output_mortality_num':\tc_output_mortality_num,\n\t\t\t'c_total_mortality':\t\tc_total_mortality,\n\t\t\t'total_population':\t\ttotal_population,\n\t\t\t'total_death_averted':\t\ttotal_death_averted,\n\t\t\t'total_death_baseline':\t\ttotal_death_baseline,\n\t\t\t'all_mortality_exposure':\tall_mortality_exposure\n\t\t\t}\n\n\t#context to transmit the parameters to show\n\tcontext = Context(para_view)\n\tresponse = template.render(context)\n\treturn HttpResponse(response)","def refresh(self) -> object:\n requestor = Requestor(local_api_key=self._api_key)\n url = self.instance_url()\n response, api_key = requestor.request(method=RequestMethod.GET, url=url, params=self._retrieve_params)\n self.refresh_from(values=response, api_key=api_key)\n return self","def refresh(self) -> None:\n pass","def refresh(self) -> None:\n pass","def refresh(self) -> None:\n pass","def _refresh(self):\n self._value = self._service.get_value(self._entry_point, self._path)","def refresh(self):\n raise NotImplementedError(\"To be implemented\")","def _refresh(self):\n url = self.urlobject_single.format(self._cb.credentials.org_key, self._model_unique_id)\n resp = self._cb.get_object(url)\n self._info = resp\n self._last_refresh_time = time.time()\n return True","def _update_from_rest_data(self) -> None:","def project_refresh(project_name):\n if not db_find_project(project_name):\n abort(404)\n analyser.add_repos(current_user.username, [project_name])\n return redirect(url_for('main.admin_manage'))","def refresh(self):\n\n self._refreshed_on = time.time() * 1000","def _Refresh(self):\n raise NotImplementedError","def refresh(self, new_content):\n pass","def Refresh(self):\n pass","def refresh(self, id):\n exports.execute_export.delay(id)\n return render({\"id\": id})","def refresh(modeladmin, request, queryset):\n if not queryset.exists():\n return # 404\n opts = queryset[0].content_object._meta\n module = '%s.%s' % (opts.app_label, opts.object_name)\n ids = queryset.values_list('object_id', flat=True)\n # Execute get_state isolated on a process to avoid gevent polluting the stack\n # and preventing this silly complain \"gevent is only usable from a single thread\"\n # ids listqueryset is converted in a list in order to be properly serialized\n result = get_state.delay(module, ids=list(ids), lock=False)\n try:\n # Block until finish\n result.get()\n except OperationLocked:\n msg = 'This operation is currently being executed by another process.'\n messages.error(request, msg)\n else:\n msg = 'The state of %d %ss has been updated.' % (queryset.count(), opts.object_name)\n modeladmin.message_user(request, msg)","def refresh():\n DB.drop_all()\n DB.create_all()\n df_meas = open_api.measurements(city='Los Angeles', parameter='pm25', df=True)\n df_meas['date.utc'] = df_meas['date.utc'].astype(str)\n create_DB_records(df_meas)\n DB.session.commit()\n message = 'Data refreshed on: ' + str(datetime.datetime.now())\n over9s = Record.query.filter(Record.value > 9)\n recs = Record.query.filter(Record.id < 20)\n over5s = Record.query.filter(Record.value > 5)\n return render_template('base.html', message=message, over9s=over9s, over5s=over5s, recs=recs)","def refresh(self):\n self._refresh_method()","def refresh():\n\tsocketio.emit('refresh')\n\treturn status()","async def poll_refresh(self) -> None:\n await self._send_message_get_response(OutgoingMessage(OutgoingMessageType.poll_refresh))","def refresh():\n current_user = get_jwt_identity()\n\n user = get_user_by_username(current_user)\n\n if not user:\n return make_response(CONST_LOGIN_MSG, 401, {\n 'WWW-Authenticate': f'Basic realm=\"{CONST_REALM_MSG}\"'})\n\n if user.is_admin:\n claims = {'is_admin': True}\n else:\n claims = {'is_admin': False}\n\n now = datetime.datetime.now(datetime.timezone.utc)\n access_expires = (now + jwt_config.access_expires).timestamp()\n refresh_expires = (now + jwt_config.refresh_expires).timestamp()\n\n response = {\n 'access_token': create_access_token(identity=current_user,\n user_claims=claims),\n 'access_expires': access_expires,\n 'refresh_expires': refresh_expires,\n 'refresh_token': create_refresh_token(identity=current_user),\n 'user': get_user_details(user)\n\n }\n return jsonify(response), 200","async def async_update(self):\n await self._coordinator.async_request_refresh()","async def async_update(self):\n await self._coordinator.async_request_refresh()","async def async_update(self):\n await self._coordinator.async_request_refresh()","def _refresh_query(session, query_id):\n resp = session.post('{}/api/queries/{}/refresh'.format(REDASH_HOST, query_id))\n return resp","def update(self):\n return self._process('update')","def refreshPage(self):\n cmdId = self.executeCommand(Command.REFRESH)\n return cmdId","def refresh_view():\n pass","def refresh(self):\n\n metadata = project_scrape(self.url)\n if metadata:\n if not self.done:\n self.progress.set_fraction(metadata['percent_raised'])\n self.progress.set_text(metadata['pretty_percent'])\n self.progress.set_show_text(True)\n self.pledged.set_text(metadata['pledged'])\n self.backers.set_text(metadata['backers'])\n self.updates.set_label(metadata['updates'])\n\n return True","def refresh(self):\n self.__refresh()","async def async_update(self):\n await self.coordinator.async_request_refresh()","def cmd_update(self):\n self.update_repository()\n results = self.results.getvalue()\n if results:\n print('---')\n print(results, end='')","def refresh(self):\n yield from self._db._update_series(self.provider, self._dbattr(self.provider.IDATTR), dirty=self.providers)","def refresh(self):\n resp = self._imgur._send_request(self._INFO_URL)\n self._populate(resp)\n self._has_fetched = True\n # NOTE: What if the object has been deleted in the meantime? That might\n # give a pretty cryptic error.","def update():","def update():","def refresh():\n current_user = get_jwt_identity()\n ret = {\n 'access_token': create_access_token(identity=current_user)\n }\n return jsonify(ret), 200","def refresh():\n return __apf_cmd(\"-e\")","def next_update(self):\r\n request = http.Request('GET', '/metadata/next_update.json')\r\n return request, parsers.parse_json","def refresh_list(self):\n if self._dominfo_lock.acquire(False):\n try:\n return self._refresh_list()\n finally:\n self._dominfo_lock.release()\n else:\n # wait until the refresh done by the other party is complete\n with self._dominfo_lock:\n pass","async def update(self):\n\t\tstate = await self._client.get_state('command.cgi?cmd=getObject&oid={0}&ot={1}'.format(self._oid,self._ot))\n\t\tif state is not None:\n\t\t\tself._raw_result = state","def refresh(self):\n if self.refresh_token is None:\n raise InvalidInvocation('refresh token not provided')\n self._request_token(grant_type='refresh_token',\n refresh_token=self.refresh_token)","def update(self):\n # TO DO for updating urls if changed\n pass","def refresh():\n print(\"refresh request\")\n old_token = request.get_data()\n new_token = guard.refresh_jwt_token(old_token)\n ret = {'access_token': new_token}\n return ret, 200","async def async_update(self):\n await self.wrapper.async_request_refresh()","async def refresh(ctx):\n await update_tournament_list()\n res = await refresh_algorithm()\n if res == True:\n await ctx.send(\"Successfully refreshed data from sheet.\")\n else:\n await ctx.send(\":warning: Unsuccessfully refreshed data from sheet.\")","def update( ):\r\n pass","def refresh(self):\n self.details = self.workspace.get_job(self.id).details","def refresh(self):\n self.lease = self.blazar.lease.get(self.id)","def _refresh(self):\n self._value = (self._service.get_value(self._entry_point, self._path)=='true')","def refresh(self):\n raise UncodedError","def _refresh(self):\n self._value = int(self._service.get_value(self._entry_point, self._path))","async def refresh(self):\n while True:\n await asyncio.sleep(5/6 * self.lifetime)\n\n request = stun.Message(message_method=stun.Method.REFRESH,\n message_class=stun.Class.REQUEST)\n request.attributes['LIFETIME'] = self.lifetime\n self.__add_authentication(request)\n await self.request(request, self.server)","def refresh(self) -> None:\n self._itempage.get()","def update(self):\n self.arest.update()","def update(self):\n if self._refreshed_at is None or (\n self._refreshed_at + self._refresh_rate <= datetime.datetime.now()):\n\n self.run()","def post(self, request):\n refreshed_data = refresh_token(request, request.DATA['token'])\n if refreshed_data:\n data = {}\n auth_data = string_to_dict(refreshed_data.replace(\"\\\"\", \"\"))\n data['access_token'] = auth_data['access_token']\n data['refresh_token'] = auth_data[' refresh_token']\n response_data = get_response_json(\n uri=request._request.path,\n lang=request.DATA['lang'],\n region=request.DATA['region'],\n created=False,\n responsecode=200,\n start=0,\n count=0,\n total=1,\n data=data)\n return Response(data=response_data, status=status.HTTP_200_OK)\n response_data = get_error_json(\n uri=request._request.path,\n lang=request.query_params.get(\"lang\"),\n region=request.query_params.get(\"region\"),\n description='Bad request',\n detail=\"Bad request\",\n responsecode=400,\n )\n return Response(response_data, status=status.HTTP_400_BAD_REQUEST)","def update_positions(request):\n raw_data = request.raw_post_data\n logging.info(\"isrproxy update_positions: %s\" % raw_data)\n update_positions_data = simplejson.loads(raw_data)\n\n # redirect update to dev server in production environment\n if not settings.DEV:\n dev_positions = filter(lambda p: p.get(\"Operator_ID\") == WB_DEV_OPERATOR_ID, update_positions_data)\n if dev_positions:\n deferred.defer(safe_fetch, url=\"http://dev.latest.waybetter-app.appspot.com/fleet/isrproxy/update/positions/\", payload=simplejson.dumps(dev_positions), method=POST, notify=False)\n update_positions_data = filter(lambda p: p.get(\"Operator_ID\") != WB_DEV_OPERATOR_ID, update_positions_data)\n\n\n ride_positions = []\n for rp_data in update_positions_data:\n station_id = rp_data.get(\"Operator_ID\")\n taxi_id = rp_data.get(\"Vehicle_ID\")\n lat = rp_data.get(\"Lat\")\n lon = rp_data.get(\"Lon\")\n timestamp = rp_data.get(\"Timestamp\")\n\n ride_uuid = ISRProxy.get_taxi_assignment(taxi_id, station_id)\n\n if all([station_id, ride_uuid, taxi_id, lat, lon, timestamp]):\n timestamp = dateutil_parser.parse(timestamp)\n timestamp = normalize_naive_israel_dt(timestamp)\n ride_positions.append(TaxiRidePosition(station_id, taxi_id, ride_uuid, lat, lon, timestamp))\n\n fleet_manager.update_positions(ride_positions)\n return HttpResponse(\"OK\")","def editPublication():\n # Get _id and path from url\n id = request.args.get(\"_id\")\n path = request.args.get(\"path\")\n\n # Execute database query\n active = True\n result = \"\"\n if active == True:\n client = MongoClient(db_config)\n db = client['Publications']\n publications = db['Publications']\n result = publications.find_one({\"_id\": id, \"path\": path})\n\n # Pre-processing\n del result[\"htmlResult\"] # Remove HTML output\n result[\"path\"].replace(\",\", \"/\") # Place slashes in path\n\n # Handle dates\n if len(result[\"date\"]) == 10:\n date = result[\"date\"]\n result[\"date\"] = {\"toggledaterange\": False, \"sdate\": date, \"start\": date, \"end\": date}\n else:\n # Format: {'start': '2014-01-01', 'end': '2015-10-01'}\n print(\"Result Date\")\n date = str(result[\"date\"][0]).replace(\"'\", \"\\\"\")\n dates = json.loads(date)\n start = dates[\"start\"]\n end = dates[\"end\"]\n result[\"date\"] = {\"toggledaterange\": True, \"sdate\": start, \"start\": start, \"end\": end}\n\n # Convert to json stringØ\n jsonStr = json.dumps(result)\n\n return render_template(\"editPublication.html\", results=jsonStr)","def updates(self):\n return self._get_page('updates').json()","def refresh_rate_quote(mortgage_profile):\n api = MortechApi(mortgage_profile)\n api.get_response()","def __refresh(self):\n\n\t\tself.status_code = self.__response.status_code\n\n\t\t# Handle errors. Sometimes there may not be a response body (which is\n\t\t# why ValueError) must be caught.\n\t\ttry:\n\t\t\tif (self.__response.status_code not in [200,204]) or \"error\" in self.__response.json():\n\t\t\t\terror = self.__response.json()\n\t\t\t\traise ResponseError(error[\"error\"][\"message\"], error[\"error\"][\"code\"])\n\t\texcept ValueError:\n\t\t\t# Sometimes DELETE returns nothing. When this is the case, it will\n\t\t\t# have a status code 204\n\t\t\tif self.__response.request.method is not \"DELETE\" and self.__response.status_code is not 204:\n\t\t\t\traise ResponseError(\"Unknown error occured.\", self.__response.status_code)\n\n\t\t# Get information on paging if response is paginated\n\t\tif \"X-Resultset-Total\" in self.__response.headers and self.__response.headers[\"X-Resultset-Total\"] > self.__response.headers[\"X-Resultset-Limit\"]:\n\t\t\tself.is_paginated = True\n\t\t\tself.records = int(self.__response.headers.get(\"X-Resultset-Total\"))\n\t\t\tself.page_size = int(self.__response.headers[\"X-Resultset-Limit\"])\n\t\t\tself.current_page = int(self.__response.headers[\"X-Resultset-Page\"])\n\t\t\tself.max_page = int(math.ceil(float(self.records)/int(self.page_size)))\n\n\t\t# Save the content of the request\n\t\ttry:\n\t\t\tself.json = self.__response.json()\n\t\texcept ValueError:\n\t\t\t# Sometimes DELETE returns nothing. When this is the case, it will have a status code 204\n\t\t\tif self.__response.request.method == \"DELETE\" and self.__response.status_code is 204:\n\t\t\t\tself.json = {\"success\":True}\n\t\t\telse:\n\t\t\t\traise ValueError(\"No JSON object could be decoded\" + self.__response.request.method)","def refresh(self, check_mode=True):\n query = 'ALTER SUBSCRIPTION %s REFRESH PUBLICATION' % self.name\n return self.__exec_sql(query, check_mode=check_mode)","def refresh():\n DB.drop_all()\n DB.create_all()\n results = get_results()\n for i, result in enumerate(results):\n record = Record(id=i, datetime=result[0], value=result[1])\n DB.session.add(record)\n DB.session.commit()\n return redirect(\"/\")","def repo_refresh_for_unfinished():\n project_list = Project.objects()\n crawl_list = []\n for repo in project_list:\n if repo.analyser_progress != \"100%\":\n crawl_list.append(repo.project_name)\n analyser.add_repos(current_user.username, crawl_list)\n flash('Refresh for unfinished successfully!', 'success')\n return redirect(url_for('main.admin_manage'))","def executeForRefresh(self):\n value = random.randint(1, 10000000)\n calcEngine = CalcEngine.factory(self.client_session)\n calcEngine.setNodeProperties(\n \"pyplan_refresh\", [{\"name\": \"definition\", \"value\": \"result = \" + str(value)}])\n return calcEngine.setNodeProperties(\"cub_refresh\", [{\"name\": \"definition\", \"value\": \"result = \" + str(value)}])","def refresh():\n print(\"refresh request\")\n old_token = flask.request.get_data()\n new_token = guard.refresh_jwt_token(old_token)\n ret = {'access_token': new_token}\n return ret, 200","def Refresh(self, *args, **kwargs):\n # type: (*Any, **Any) -> None\n payload = {}\n for i in range(len(args)):\n payload[\"Arg%s\" % (i + 1)] = args[i]\n for item in kwargs.items():\n payload[item[0]] = item[1]\n return self._execute(\"refresh\", payload=payload, response_object=None)","def update(self):\n\n pass","def update(self, params):","def update(self):\n pass","def update(self):\n pass","def update(self):\n pass","def update(self):\n pass","def update(self):\n pass","def update(self):\n pass","def update(self):\n pass","def update(self):\n pass","def update(self):\n pass","def update(self):\n pass"],"string":"[\n \"def schedule_refresh():\\n logger.info(\\\"Refreshing/Updating persuasions in bulk\\\")\\n try:\\n args = request.args\\n if not args.get(\\\"type\\\") or not args.get(\\\"sub_type\\\"):\\n return jsonify(\\n dict(status=\\\"failure\\\", message=\\\"Invalid type/sub_type\\\"))\\n\\n persuasions_request = PersuasionServices.get_persuasions_request_from_es(args)\\n\\n with concurrent.futures.ThreadPoolExecutor(max_workers=settings.MAX_WORKERS) as executor:\\n {executor.submit(PersuasionServices.refresh, data, args): data for data in persuasions_request}\\n\\n return jsonify(\\n dict(status=\\\"success\\\", message=\\\"Your request is in the queue, persuasion will be updated shortly\\\"))\\n except Exception as e:\\n logger.error(\\\"Exception while creating persuasions in bulk - \\\" + repr(e))\\n return jsonify(dict(status=\\\"failure\\\", error=repr(e)))\",\n \"def bulk_refresh():\\n logger.info(\\\"Refreshing/Updating persuasions in bulk\\\")\\n try:\\n request_data = json.loads(request.data)\\n args = request.args\\n with concurrent.futures.ThreadPoolExecutor(max_workers=settings.MAX_WORKERS) as executor:\\n {executor.submit(PersuasionServices.refresh, data, args): data for data in request_data}\\n\\n return jsonify(\\n dict(status=\\\"success\\\", message=\\\"Your request is in the queue, persuasion will be updated shortly\\\"))\\n except Exception as e:\\n logger.error(\\\"Exception while creating persuasions in bulk - \\\" + repr(e))\\n return jsonify(dict(status=\\\"failure\\\", error=repr(e)))\",\n \"def refresh(self):\\n self.dto = self.res.get()\\n log.debug(f\\\"Refreshed {self.url}\\\")\",\n \"def fullRefresh(self, p):\\n p = self.generalisedRefresh(p)\\n return p\",\n \"def _refresh(self):\\n resp = self._cb.get_object(self._build_api_request_uri())\\n self._info = resp\\n self._last_refresh_time = time.time()\\n return True\",\n \"def _refresh(self):\\n resp = self._cb.get_object(self._build_api_request_uri())\\n self._info = resp\\n self._last_refresh_time = time.time()\\n return True\",\n \"def refresh_details(self) -> None:\\n data = request(\\n 'get',\\n f'/api/v0/projects/{self.id}/',\\n ).json()\\n self.data.update(data)\",\n \"def refresh_details(self) -> 'outputs.RefreshDetailsResponse':\\n return pulumi.get(self, \\\"refresh_details\\\")\",\n \"def refresh(self):\\n\\t\\tif self.id is None:\\n\\t\\t\\tprint(\\\"({cls}): self.id is None, can't refresh\\\".format(cls=self.__class__.__name__))\\n\\t\\t\\treturn\\n\\n\\t\\tif self.needs_project_id and self.project_id is None:\\n\\t\\t\\tprint(\\\"({cls}): self.project_id is None, can't refresh\\\".format(cls=self.__class__.__name__))\\n\\t\\t\\treturn\\n\\n\\t\\tif self.needs_project_id:\\n\\t\\t\\targs = [self.project_id, self.id]\\n\\t\\telse:\\n\\t\\t\\targs = [self.id]\\n\\n\\t\\tres = getattr(self._client, \\\"get_\\\" + self.method)(*args, raw=True)\\n\\t\\tself._create_fields(res)\",\n \"def refresh_from_api(self):\\n self.populate_from_api(self.get_from_api())\",\n \"def refresh(request):\\n \\n t, created = LastUpdate.objects.get_or_create(id=1)\\n if created:\\n t.save()\\n t = t.unix_time()\\n else:\\n t = t.unix_time()\\n latest = int(t) - 2\\n return JsonResponse({'latest':int(latest)})\",\n \"def refresh(self):\\n return self._refresh\",\n \"def refresh(self): \\n return self._config.refreshObj(self)\",\n \"def refresh(self):\\n pass\",\n \"def refresh(self):\\n pass\",\n \"def project_refresh_all():\\n project_list = Project.objects()\\n analyser.add_repos(current_user.username, [repo.project_name for repo in project_list])\\n flash('Refresh all successfully!', 'success')\\n return redirect(url_for('main.admin_manage'))\",\n \"def refresh(self):\\n raise NotImplementedError\",\n \"def refresh(self):\\n raise NotImplementedError\",\n \"def updates(self, request, version):\\n task = EtlTask.objects.get_for_model(self.queryset.model)\\n if task.last_changes:\\n offset = task.last_changes.strftime(\\\"%Y-%m-%d %H:%M\\\")\\n queryset = self.queryset.filter(last_modify_date__gte=offset)\\n else:\\n offset = \\\"none\\\"\\n queryset = self.queryset.all()\\n queryset = self.paginate_queryset(queryset)\\n serializer = self.get_serializer(queryset, many=True)\\n return self.get_paginated_response(serializer.data)\\n # return Response(serializer.data,\\n # headers={'update-date': offset})\",\n \"def refresh_parcel_repos(self):\\n if self.api_client.api_version < 'v16':\\n logger.warning('Detected API version without support '\\n 'for refreshParcelRepos (%s). Sleeping instead ...',\\n self.api_client.api_version)\\n sleep(30)\\n else:\\n return self.api_client.refresh_parcel_repos()\",\n \"def refresh_counterfactual_json(request):\\t\\n\\n\\n \\\"\\\"\\\"\\n the following json are for compound exposures\\n \\\"\\\"\\\"\\n #json_file = '/home/zhou/Downloads/jsons/compound/json_9.json'\\n\\tjson_file = '/Users/jiaozi/Downloads/jsons/compound/json_9.json'\\n\\n exposure_sequence = read_json(json_file)#list of exposures{mean,sd,non_rate}\\n\\n\\tprimeCoordinator = PrimeCoordinator()\\n\\tprimeCoordinator.get_counterfactual_compound_exposures(exposure_sequence)\\n\\t\\n\\t# get the data in the interface\\n\\tb_output_mortality \\t= primeCoordinator.output_baseline_mortality # baseline mortality list for all outcomes\\n\\tb_output_mortality_num \\t= primeCoordinator.output_baseline_mortality_num # baseline mortality sum up for each outcome\\n\\tb_total_mortality \\t= primeCoordinator.output_baseline_mortality_total# baseline mortality sum up for all outcomes\\n\\tc_output_mortality \\t= primeCoordinator.output_counterfactual_mortality# counterfactual mortality for all outcomes\\n\\tc_output_mortality_num \\t= primeCoordinator.output_counterfactual_mortality_num# counterfactual mortality for each outcome\\n\\tc_total_mortality \\t= primeCoordinator.output_counterfactual_mortality_total# counterfactual mortality sum up for all outcomes\\n\\ttotal_population\\t= primeCoordinator.output_total_population\\n\\tall_mortality_exposure\\t= primeCoordinator.output_all_mortality_exposure\\n\\ttotal_death_averted\\t= str(round(primeCoordinator.output_total_death_averted,0))\\n\\ttotal_death_baseline\\t= str(primeCoordinator.output_total_death_baseline)\\n\\n\\t#transmit the parameters\\n\\ttemplate = loader.get_template('primemodel/index.html')\\n\\tpara_view = {\\n\\t\\t\\t'b_output_mortality_num':\\tb_output_mortality_num,\\n\\t\\t\\t'b_total_mortality':\\t\\tb_total_mortality,\\n\\t\\t\\t'c_output_mortality_num':\\tc_output_mortality_num,\\n\\t\\t\\t'c_total_mortality':\\t\\tc_total_mortality,\\n\\t\\t\\t'total_population':\\t\\ttotal_population,\\n\\t\\t\\t'total_death_averted':\\t\\ttotal_death_averted,\\n\\t\\t\\t'total_death_baseline':\\t\\ttotal_death_baseline,\\n\\t\\t\\t'all_mortality_exposure':\\tall_mortality_exposure\\n\\t\\t\\t}\\n\\n\\t#context to transmit the parameters to show\\n\\tcontext = Context(para_view)\\n\\tresponse = template.render(context)\\n\\treturn HttpResponse(response)\",\n \"def refresh(self) -> object:\\n requestor = Requestor(local_api_key=self._api_key)\\n url = self.instance_url()\\n response, api_key = requestor.request(method=RequestMethod.GET, url=url, params=self._retrieve_params)\\n self.refresh_from(values=response, api_key=api_key)\\n return self\",\n \"def refresh(self) -> None:\\n pass\",\n \"def refresh(self) -> None:\\n pass\",\n \"def refresh(self) -> None:\\n pass\",\n \"def _refresh(self):\\n self._value = self._service.get_value(self._entry_point, self._path)\",\n \"def refresh(self):\\n raise NotImplementedError(\\\"To be implemented\\\")\",\n \"def _refresh(self):\\n url = self.urlobject_single.format(self._cb.credentials.org_key, self._model_unique_id)\\n resp = self._cb.get_object(url)\\n self._info = resp\\n self._last_refresh_time = time.time()\\n return True\",\n \"def _update_from_rest_data(self) -> None:\",\n \"def project_refresh(project_name):\\n if not db_find_project(project_name):\\n abort(404)\\n analyser.add_repos(current_user.username, [project_name])\\n return redirect(url_for('main.admin_manage'))\",\n \"def refresh(self):\\n\\n self._refreshed_on = time.time() * 1000\",\n \"def _Refresh(self):\\n raise NotImplementedError\",\n \"def refresh(self, new_content):\\n pass\",\n \"def Refresh(self):\\n pass\",\n \"def refresh(self, id):\\n exports.execute_export.delay(id)\\n return render({\\\"id\\\": id})\",\n \"def refresh(modeladmin, request, queryset):\\n if not queryset.exists():\\n return # 404\\n opts = queryset[0].content_object._meta\\n module = '%s.%s' % (opts.app_label, opts.object_name)\\n ids = queryset.values_list('object_id', flat=True)\\n # Execute get_state isolated on a process to avoid gevent polluting the stack\\n # and preventing this silly complain \\\"gevent is only usable from a single thread\\\"\\n # ids listqueryset is converted in a list in order to be properly serialized\\n result = get_state.delay(module, ids=list(ids), lock=False)\\n try:\\n # Block until finish\\n result.get()\\n except OperationLocked:\\n msg = 'This operation is currently being executed by another process.'\\n messages.error(request, msg)\\n else:\\n msg = 'The state of %d %ss has been updated.' % (queryset.count(), opts.object_name)\\n modeladmin.message_user(request, msg)\",\n \"def refresh():\\n DB.drop_all()\\n DB.create_all()\\n df_meas = open_api.measurements(city='Los Angeles', parameter='pm25', df=True)\\n df_meas['date.utc'] = df_meas['date.utc'].astype(str)\\n create_DB_records(df_meas)\\n DB.session.commit()\\n message = 'Data refreshed on: ' + str(datetime.datetime.now())\\n over9s = Record.query.filter(Record.value > 9)\\n recs = Record.query.filter(Record.id < 20)\\n over5s = Record.query.filter(Record.value > 5)\\n return render_template('base.html', message=message, over9s=over9s, over5s=over5s, recs=recs)\",\n \"def refresh(self):\\n self._refresh_method()\",\n \"def refresh():\\n\\tsocketio.emit('refresh')\\n\\treturn status()\",\n \"async def poll_refresh(self) -> None:\\n await self._send_message_get_response(OutgoingMessage(OutgoingMessageType.poll_refresh))\",\n \"def refresh():\\n current_user = get_jwt_identity()\\n\\n user = get_user_by_username(current_user)\\n\\n if not user:\\n return make_response(CONST_LOGIN_MSG, 401, {\\n 'WWW-Authenticate': f'Basic realm=\\\"{CONST_REALM_MSG}\\\"'})\\n\\n if user.is_admin:\\n claims = {'is_admin': True}\\n else:\\n claims = {'is_admin': False}\\n\\n now = datetime.datetime.now(datetime.timezone.utc)\\n access_expires = (now + jwt_config.access_expires).timestamp()\\n refresh_expires = (now + jwt_config.refresh_expires).timestamp()\\n\\n response = {\\n 'access_token': create_access_token(identity=current_user,\\n user_claims=claims),\\n 'access_expires': access_expires,\\n 'refresh_expires': refresh_expires,\\n 'refresh_token': create_refresh_token(identity=current_user),\\n 'user': get_user_details(user)\\n\\n }\\n return jsonify(response), 200\",\n \"async def async_update(self):\\n await self._coordinator.async_request_refresh()\",\n \"async def async_update(self):\\n await self._coordinator.async_request_refresh()\",\n \"async def async_update(self):\\n await self._coordinator.async_request_refresh()\",\n \"def _refresh_query(session, query_id):\\n resp = session.post('{}/api/queries/{}/refresh'.format(REDASH_HOST, query_id))\\n return resp\",\n \"def update(self):\\n return self._process('update')\",\n \"def refreshPage(self):\\n cmdId = self.executeCommand(Command.REFRESH)\\n return cmdId\",\n \"def refresh_view():\\n pass\",\n \"def refresh(self):\\n\\n metadata = project_scrape(self.url)\\n if metadata:\\n if not self.done:\\n self.progress.set_fraction(metadata['percent_raised'])\\n self.progress.set_text(metadata['pretty_percent'])\\n self.progress.set_show_text(True)\\n self.pledged.set_text(metadata['pledged'])\\n self.backers.set_text(metadata['backers'])\\n self.updates.set_label(metadata['updates'])\\n\\n return True\",\n \"def refresh(self):\\n self.__refresh()\",\n \"async def async_update(self):\\n await self.coordinator.async_request_refresh()\",\n \"def cmd_update(self):\\n self.update_repository()\\n results = self.results.getvalue()\\n if results:\\n print('---')\\n print(results, end='')\",\n \"def refresh(self):\\n yield from self._db._update_series(self.provider, self._dbattr(self.provider.IDATTR), dirty=self.providers)\",\n \"def refresh(self):\\n resp = self._imgur._send_request(self._INFO_URL)\\n self._populate(resp)\\n self._has_fetched = True\\n # NOTE: What if the object has been deleted in the meantime? That might\\n # give a pretty cryptic error.\",\n \"def update():\",\n \"def update():\",\n \"def refresh():\\n current_user = get_jwt_identity()\\n ret = {\\n 'access_token': create_access_token(identity=current_user)\\n }\\n return jsonify(ret), 200\",\n \"def refresh():\\n return __apf_cmd(\\\"-e\\\")\",\n \"def next_update(self):\\r\\n request = http.Request('GET', '/metadata/next_update.json')\\r\\n return request, parsers.parse_json\",\n \"def refresh_list(self):\\n if self._dominfo_lock.acquire(False):\\n try:\\n return self._refresh_list()\\n finally:\\n self._dominfo_lock.release()\\n else:\\n # wait until the refresh done by the other party is complete\\n with self._dominfo_lock:\\n pass\",\n \"async def update(self):\\n\\t\\tstate = await self._client.get_state('command.cgi?cmd=getObject&oid={0}&ot={1}'.format(self._oid,self._ot))\\n\\t\\tif state is not None:\\n\\t\\t\\tself._raw_result = state\",\n \"def refresh(self):\\n if self.refresh_token is None:\\n raise InvalidInvocation('refresh token not provided')\\n self._request_token(grant_type='refresh_token',\\n refresh_token=self.refresh_token)\",\n \"def update(self):\\n # TO DO for updating urls if changed\\n pass\",\n \"def refresh():\\n print(\\\"refresh request\\\")\\n old_token = request.get_data()\\n new_token = guard.refresh_jwt_token(old_token)\\n ret = {'access_token': new_token}\\n return ret, 200\",\n \"async def async_update(self):\\n await self.wrapper.async_request_refresh()\",\n \"async def refresh(ctx):\\n await update_tournament_list()\\n res = await refresh_algorithm()\\n if res == True:\\n await ctx.send(\\\"Successfully refreshed data from sheet.\\\")\\n else:\\n await ctx.send(\\\":warning: Unsuccessfully refreshed data from sheet.\\\")\",\n \"def update( ):\\r\\n pass\",\n \"def refresh(self):\\n self.details = self.workspace.get_job(self.id).details\",\n \"def refresh(self):\\n self.lease = self.blazar.lease.get(self.id)\",\n \"def _refresh(self):\\n self._value = (self._service.get_value(self._entry_point, self._path)=='true')\",\n \"def refresh(self):\\n raise UncodedError\",\n \"def _refresh(self):\\n self._value = int(self._service.get_value(self._entry_point, self._path))\",\n \"async def refresh(self):\\n while True:\\n await asyncio.sleep(5/6 * self.lifetime)\\n\\n request = stun.Message(message_method=stun.Method.REFRESH,\\n message_class=stun.Class.REQUEST)\\n request.attributes['LIFETIME'] = self.lifetime\\n self.__add_authentication(request)\\n await self.request(request, self.server)\",\n \"def refresh(self) -> None:\\n self._itempage.get()\",\n \"def update(self):\\n self.arest.update()\",\n \"def update(self):\\n if self._refreshed_at is None or (\\n self._refreshed_at + self._refresh_rate <= datetime.datetime.now()):\\n\\n self.run()\",\n \"def post(self, request):\\n refreshed_data = refresh_token(request, request.DATA['token'])\\n if refreshed_data:\\n data = {}\\n auth_data = string_to_dict(refreshed_data.replace(\\\"\\\\\\\"\\\", \\\"\\\"))\\n data['access_token'] = auth_data['access_token']\\n data['refresh_token'] = auth_data[' refresh_token']\\n response_data = get_response_json(\\n uri=request._request.path,\\n lang=request.DATA['lang'],\\n region=request.DATA['region'],\\n created=False,\\n responsecode=200,\\n start=0,\\n count=0,\\n total=1,\\n data=data)\\n return Response(data=response_data, status=status.HTTP_200_OK)\\n response_data = get_error_json(\\n uri=request._request.path,\\n lang=request.query_params.get(\\\"lang\\\"),\\n region=request.query_params.get(\\\"region\\\"),\\n description='Bad request',\\n detail=\\\"Bad request\\\",\\n responsecode=400,\\n )\\n return Response(response_data, status=status.HTTP_400_BAD_REQUEST)\",\n \"def update_positions(request):\\n raw_data = request.raw_post_data\\n logging.info(\\\"isrproxy update_positions: %s\\\" % raw_data)\\n update_positions_data = simplejson.loads(raw_data)\\n\\n # redirect update to dev server in production environment\\n if not settings.DEV:\\n dev_positions = filter(lambda p: p.get(\\\"Operator_ID\\\") == WB_DEV_OPERATOR_ID, update_positions_data)\\n if dev_positions:\\n deferred.defer(safe_fetch, url=\\\"http://dev.latest.waybetter-app.appspot.com/fleet/isrproxy/update/positions/\\\", payload=simplejson.dumps(dev_positions), method=POST, notify=False)\\n update_positions_data = filter(lambda p: p.get(\\\"Operator_ID\\\") != WB_DEV_OPERATOR_ID, update_positions_data)\\n\\n\\n ride_positions = []\\n for rp_data in update_positions_data:\\n station_id = rp_data.get(\\\"Operator_ID\\\")\\n taxi_id = rp_data.get(\\\"Vehicle_ID\\\")\\n lat = rp_data.get(\\\"Lat\\\")\\n lon = rp_data.get(\\\"Lon\\\")\\n timestamp = rp_data.get(\\\"Timestamp\\\")\\n\\n ride_uuid = ISRProxy.get_taxi_assignment(taxi_id, station_id)\\n\\n if all([station_id, ride_uuid, taxi_id, lat, lon, timestamp]):\\n timestamp = dateutil_parser.parse(timestamp)\\n timestamp = normalize_naive_israel_dt(timestamp)\\n ride_positions.append(TaxiRidePosition(station_id, taxi_id, ride_uuid, lat, lon, timestamp))\\n\\n fleet_manager.update_positions(ride_positions)\\n return HttpResponse(\\\"OK\\\")\",\n \"def editPublication():\\n # Get _id and path from url\\n id = request.args.get(\\\"_id\\\")\\n path = request.args.get(\\\"path\\\")\\n\\n # Execute database query\\n active = True\\n result = \\\"\\\"\\n if active == True:\\n client = MongoClient(db_config)\\n db = client['Publications']\\n publications = db['Publications']\\n result = publications.find_one({\\\"_id\\\": id, \\\"path\\\": path})\\n\\n # Pre-processing\\n del result[\\\"htmlResult\\\"] # Remove HTML output\\n result[\\\"path\\\"].replace(\\\",\\\", \\\"/\\\") # Place slashes in path\\n\\n # Handle dates\\n if len(result[\\\"date\\\"]) == 10:\\n date = result[\\\"date\\\"]\\n result[\\\"date\\\"] = {\\\"toggledaterange\\\": False, \\\"sdate\\\": date, \\\"start\\\": date, \\\"end\\\": date}\\n else:\\n # Format: {'start': '2014-01-01', 'end': '2015-10-01'}\\n print(\\\"Result Date\\\")\\n date = str(result[\\\"date\\\"][0]).replace(\\\"'\\\", \\\"\\\\\\\"\\\")\\n dates = json.loads(date)\\n start = dates[\\\"start\\\"]\\n end = dates[\\\"end\\\"]\\n result[\\\"date\\\"] = {\\\"toggledaterange\\\": True, \\\"sdate\\\": start, \\\"start\\\": start, \\\"end\\\": end}\\n\\n # Convert to json stringØ\\n jsonStr = json.dumps(result)\\n\\n return render_template(\\\"editPublication.html\\\", results=jsonStr)\",\n \"def updates(self):\\n return self._get_page('updates').json()\",\n \"def refresh_rate_quote(mortgage_profile):\\n api = MortechApi(mortgage_profile)\\n api.get_response()\",\n \"def __refresh(self):\\n\\n\\t\\tself.status_code = self.__response.status_code\\n\\n\\t\\t# Handle errors. Sometimes there may not be a response body (which is\\n\\t\\t# why ValueError) must be caught.\\n\\t\\ttry:\\n\\t\\t\\tif (self.__response.status_code not in [200,204]) or \\\"error\\\" in self.__response.json():\\n\\t\\t\\t\\terror = self.__response.json()\\n\\t\\t\\t\\traise ResponseError(error[\\\"error\\\"][\\\"message\\\"], error[\\\"error\\\"][\\\"code\\\"])\\n\\t\\texcept ValueError:\\n\\t\\t\\t# Sometimes DELETE returns nothing. When this is the case, it will\\n\\t\\t\\t# have a status code 204\\n\\t\\t\\tif self.__response.request.method is not \\\"DELETE\\\" and self.__response.status_code is not 204:\\n\\t\\t\\t\\traise ResponseError(\\\"Unknown error occured.\\\", self.__response.status_code)\\n\\n\\t\\t# Get information on paging if response is paginated\\n\\t\\tif \\\"X-Resultset-Total\\\" in self.__response.headers and self.__response.headers[\\\"X-Resultset-Total\\\"] > self.__response.headers[\\\"X-Resultset-Limit\\\"]:\\n\\t\\t\\tself.is_paginated = True\\n\\t\\t\\tself.records = int(self.__response.headers.get(\\\"X-Resultset-Total\\\"))\\n\\t\\t\\tself.page_size = int(self.__response.headers[\\\"X-Resultset-Limit\\\"])\\n\\t\\t\\tself.current_page = int(self.__response.headers[\\\"X-Resultset-Page\\\"])\\n\\t\\t\\tself.max_page = int(math.ceil(float(self.records)/int(self.page_size)))\\n\\n\\t\\t# Save the content of the request\\n\\t\\ttry:\\n\\t\\t\\tself.json = self.__response.json()\\n\\t\\texcept ValueError:\\n\\t\\t\\t# Sometimes DELETE returns nothing. When this is the case, it will have a status code 204\\n\\t\\t\\tif self.__response.request.method == \\\"DELETE\\\" and self.__response.status_code is 204:\\n\\t\\t\\t\\tself.json = {\\\"success\\\":True}\\n\\t\\t\\telse:\\n\\t\\t\\t\\traise ValueError(\\\"No JSON object could be decoded\\\" + self.__response.request.method)\",\n \"def refresh(self, check_mode=True):\\n query = 'ALTER SUBSCRIPTION %s REFRESH PUBLICATION' % self.name\\n return self.__exec_sql(query, check_mode=check_mode)\",\n \"def refresh():\\n DB.drop_all()\\n DB.create_all()\\n results = get_results()\\n for i, result in enumerate(results):\\n record = Record(id=i, datetime=result[0], value=result[1])\\n DB.session.add(record)\\n DB.session.commit()\\n return redirect(\\\"/\\\")\",\n \"def repo_refresh_for_unfinished():\\n project_list = Project.objects()\\n crawl_list = []\\n for repo in project_list:\\n if repo.analyser_progress != \\\"100%\\\":\\n crawl_list.append(repo.project_name)\\n analyser.add_repos(current_user.username, crawl_list)\\n flash('Refresh for unfinished successfully!', 'success')\\n return redirect(url_for('main.admin_manage'))\",\n \"def executeForRefresh(self):\\n value = random.randint(1, 10000000)\\n calcEngine = CalcEngine.factory(self.client_session)\\n calcEngine.setNodeProperties(\\n \\\"pyplan_refresh\\\", [{\\\"name\\\": \\\"definition\\\", \\\"value\\\": \\\"result = \\\" + str(value)}])\\n return calcEngine.setNodeProperties(\\\"cub_refresh\\\", [{\\\"name\\\": \\\"definition\\\", \\\"value\\\": \\\"result = \\\" + str(value)}])\",\n \"def refresh():\\n print(\\\"refresh request\\\")\\n old_token = flask.request.get_data()\\n new_token = guard.refresh_jwt_token(old_token)\\n ret = {'access_token': new_token}\\n return ret, 200\",\n \"def Refresh(self, *args, **kwargs):\\n # type: (*Any, **Any) -> None\\n payload = {}\\n for i in range(len(args)):\\n payload[\\\"Arg%s\\\" % (i + 1)] = args[i]\\n for item in kwargs.items():\\n payload[item[0]] = item[1]\\n return self._execute(\\\"refresh\\\", payload=payload, response_object=None)\",\n \"def update(self):\\n\\n pass\",\n \"def update(self, params):\",\n \"def update(self):\\n pass\",\n \"def update(self):\\n pass\",\n \"def update(self):\\n pass\",\n \"def update(self):\\n pass\",\n \"def update(self):\\n pass\",\n \"def update(self):\\n pass\",\n \"def update(self):\\n pass\",\n \"def update(self):\\n pass\",\n \"def update(self):\\n pass\",\n \"def update(self):\\n pass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.68004","0.65762883","0.6169109","0.6017237","0.58676904","0.58676904","0.5842514","0.58078766","0.571597","0.5683472","0.5669192","0.56609315","0.56607497","0.56477195","0.56477195","0.5646708","0.5581315","0.5581315","0.55265003","0.5518151","0.55140364","0.5512183","0.5511529","0.5511529","0.5511529","0.5502183","0.5484659","0.5458461","0.5452334","0.5433787","0.540576","0.5401757","0.53775686","0.53750736","0.5372829","0.53406304","0.5339116","0.533241","0.5325804","0.5324081","0.531488","0.5304105","0.5304105","0.5304105","0.52984613","0.52977306","0.5275688","0.5269246","0.52646273","0.5250659","0.5245556","0.5244829","0.5219243","0.521019","0.5200389","0.5200389","0.5197647","0.51947063","0.5192644","0.51918447","0.5171495","0.51704365","0.51685524","0.516745","0.51617914","0.51509035","0.51432097","0.51386577","0.51327163","0.5130856","0.5124792","0.51208574","0.5120799","0.51051015","0.5100365","0.50916725","0.508727","0.5079207","0.50754106","0.5060492","0.5059199","0.50579566","0.50553346","0.5051255","0.5048644","0.50448","0.5035971","0.50201863","0.5012335","0.50107104","0.50056523","0.50056523","0.50056523","0.50056523","0.50056523","0.50056523","0.50056523","0.50056523","0.50056523","0.50056523"],"string":"[\n \"0.68004\",\n \"0.65762883\",\n \"0.6169109\",\n \"0.6017237\",\n \"0.58676904\",\n \"0.58676904\",\n \"0.5842514\",\n \"0.58078766\",\n \"0.571597\",\n \"0.5683472\",\n \"0.5669192\",\n \"0.56609315\",\n \"0.56607497\",\n \"0.56477195\",\n \"0.56477195\",\n \"0.5646708\",\n \"0.5581315\",\n \"0.5581315\",\n \"0.55265003\",\n \"0.5518151\",\n \"0.55140364\",\n \"0.5512183\",\n \"0.5511529\",\n \"0.5511529\",\n \"0.5511529\",\n \"0.5502183\",\n \"0.5484659\",\n \"0.5458461\",\n \"0.5452334\",\n \"0.5433787\",\n \"0.540576\",\n \"0.5401757\",\n \"0.53775686\",\n \"0.53750736\",\n \"0.5372829\",\n \"0.53406304\",\n \"0.5339116\",\n \"0.533241\",\n \"0.5325804\",\n \"0.5324081\",\n \"0.531488\",\n \"0.5304105\",\n \"0.5304105\",\n \"0.5304105\",\n \"0.52984613\",\n \"0.52977306\",\n \"0.5275688\",\n \"0.5269246\",\n \"0.52646273\",\n \"0.5250659\",\n \"0.5245556\",\n \"0.5244829\",\n \"0.5219243\",\n \"0.521019\",\n \"0.5200389\",\n \"0.5200389\",\n \"0.5197647\",\n \"0.51947063\",\n \"0.5192644\",\n \"0.51918447\",\n \"0.5171495\",\n \"0.51704365\",\n \"0.51685524\",\n \"0.516745\",\n \"0.51617914\",\n \"0.51509035\",\n \"0.51432097\",\n \"0.51386577\",\n \"0.51327163\",\n \"0.5130856\",\n \"0.5124792\",\n \"0.51208574\",\n \"0.5120799\",\n \"0.51051015\",\n \"0.5100365\",\n \"0.50916725\",\n \"0.508727\",\n \"0.5079207\",\n \"0.50754106\",\n \"0.5060492\",\n \"0.5059199\",\n \"0.50579566\",\n \"0.50553346\",\n \"0.5051255\",\n \"0.5048644\",\n \"0.50448\",\n \"0.5035971\",\n \"0.50201863\",\n \"0.5012335\",\n \"0.50107104\",\n \"0.50056523\",\n \"0.50056523\",\n \"0.50056523\",\n \"0.50056523\",\n \"0.50056523\",\n \"0.50056523\",\n \"0.50056523\",\n \"0.50056523\",\n \"0.50056523\",\n \"0.50056523\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8352617"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":94690,"cells":{"query":{"kind":"string","value":"This will update persuasions in bulk and will push it to ES API /persuasion/bulk/refresh?push_to_es=true&push_to_inflow=false [ {"},"document":{"kind":"string","value":"def bulk_refresh():\n logger.info(\"Refreshing/Updating persuasions in bulk\")\n try:\n request_data = json.loads(request.data)\n args = request.args\n with concurrent.futures.ThreadPoolExecutor(max_workers=settings.MAX_WORKERS) as executor:\n {executor.submit(PersuasionServices.refresh, data, args): data for data in request_data}\n\n return jsonify(\n dict(status=\"success\", message=\"Your request is in the queue, persuasion will be updated shortly\"))\n except Exception as e:\n logger.error(\"Exception while creating persuasions in bulk - \" + repr(e))\n return jsonify(dict(status=\"failure\", error=repr(e)))"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def schedule_refresh():\n logger.info(\"Refreshing/Updating persuasions in bulk\")\n try:\n args = request.args\n if not args.get(\"type\") or not args.get(\"sub_type\"):\n return jsonify(\n dict(status=\"failure\", message=\"Invalid type/sub_type\"))\n\n persuasions_request = PersuasionServices.get_persuasions_request_from_es(args)\n\n with concurrent.futures.ThreadPoolExecutor(max_workers=settings.MAX_WORKERS) as executor:\n {executor.submit(PersuasionServices.refresh, data, args): data for data in persuasions_request}\n\n return jsonify(\n dict(status=\"success\", message=\"Your request is in the queue, persuasion will be updated shortly\"))\n except Exception as e:\n logger.error(\"Exception while creating persuasions in bulk - \" + repr(e))\n return jsonify(dict(status=\"failure\", error=repr(e)))","def refresh_persuasion():\n try:\n request_data = json.loads(request.data)\n meta = request.args\n persuasion = PersuasionServices.refresh(request_data, meta)\n except Exception as e:\n error_msg = \"Getting persuasion details - \" + repr(e)\n logger.error(error_msg)\n return jsonify(dict(status=\"failure\", error=error_msg))\n return jsonify(dict(status=\"success\", data=persuasion if persuasion else dict()))","def bulk_update(self, request):\n serializer = MasterySerializer(\n data=request.data,\n many=True,\n )\n if serializer.is_valid():\n serializer.save()\n return Response(serializer.data)\n else:\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)","def update_embeddings(self, retriever):\n\n docs = self.get_all_documents()\n passages = [d.text for d in docs]\n logger.info(f\"Updating embeddings for {len(passages)} docs ...\")\n embeddings = retriever.embed_passages(passages)\n\n assert len(docs) == len(embeddings)\n\n doc_updates = []\n for doc, emb in zip(docs, embeddings):\n update = {\"_op_type\": \"update\",\n \"_index\": self.index,\n \"_id\": doc.id,\n \"doc\": {self.embedding_field: emb.tolist()},\n }\n doc_updates.append(update)\n\n bulk(self.client, doc_updates, request_timeout=300)","def _batch_update(self, query, mutation):\n logger.info(\"Performing batch update on %s. Mutation: %s\", query, mutation)\n modified = 0\n for doc in self.instances.find(query):\n with lock_instance(doc['_id']):\n pre_update_doc = self.instances.find_one({'_id' : doc['_id']})\n result = self.instances.update_one({'_id': doc['_id']}, mutation)\n assert result.modified_count == 1\n modified += 1\n updated_doc = self.instances.find_one({'_id': doc['_id']})\n instance = FixtureInstance.deserialize_mongodoc(updated_doc)\n try:\n self.axdb_client.update_fixture_instance(instance.axdbdoc())\n except Exception:\n logger.exception(\"Failed to persist updates for %s. Undoing cache update\", instance)\n self.instances.replace_one({'_id' : instance.id}, pre_update_doc)\n raise\n logger.info(\"%s fixture instances modified\", modified)","def step070() -> None:\n logger.logMessage('Begin: elasticsearch bulk update')\n client = es.Elasticsearch(hostlist)\n\n def generate():\n with open(renumFile,'r') as f:\n line = f.readline().rstrip()\n while line != '':\n fields = line.split(';')\n oper = { '_index': fields[3], \n '_op_type': 'update',\n '_id': fields[2].rstrip(),\n '_type': 'doc',\n '_source:': {'doc': {'tsa': fields[0]}}}\n \n yield oper\n line = f.readline().rstrip()\n result = eshelp.bulk(client,generate())\n logger.logMessage('Bulk result: {0}'.format(result))\n logger.logMessage('End : elasticsearch bulk update')","def updateDocumentAll(self, documents):\n docs = []\n for document in documents:\n if isinstance(document, couch.Document):\n document = document.getData()\n\n # these are required params\n if \"_id\" not in document or \"_rev\" not in document:\n raise Exception(\"Both _id & _rev fields are required!\")\n\n docs.append(document)\n\n return self.client.post(self.name +\"/_bulk_docs\", None,\n {\"docs\": docs}).getBodyData()","def project_refresh_all():\n project_list = Project.objects()\n analyser.add_repos(current_user.username, [repo.project_name for repo in project_list])\n flash('Refresh all successfully!', 'success')\n return redirect(url_for('main.admin_manage'))","def updateMultipleDocuments(cred, payload):\n\n url = cred.base_url + \"documents:commit\"\n data = { 'writes': [] }\n\n for path, fieldData in payload.iteritems():\n pathData = createFirestoreDataObject(cred, path, fieldData)\n data['writes'].append(pathData)\n \n makeRequest(cred, url, 'POST', data)","def bulk_process(self):\n\n def actions():\n try:\n task = self.queue.get(block=False, timeout=None)\n\n if task['action'] == 'index':\n yield {\n '_op_type': 'index',\n '_index': self.ensure_index(task),\n '_id': task['id'],\n 'doc': task['properties']\n }\n elif task['action'] == 'delete':\n yield {\n '_op_type': 'delete',\n '_index': self.ensure_index(task),\n '_id': task['id'],\n 'doc': task['properties']\n }\n else:\n raise NotImplementedError\n\n except Empty:\n pass\n\n for success, info in streaming_bulk(self.es_client, actions()):\n if success:\n self.queue.task_done()","def update_batch(self, *args, **kwargs):\n pass","def update_entities(self):\n raise NotImplementedError()","def set_many(self, update_dict):\n for key, value in update_dict.items():\n # We just call `set` directly here, because this is an in-memory representation\n # thus we don't concern ourselves with bulk writes.\n self.set(key, value)","def update_all():\n req_data = request.get_json()\n jobs = JobModel.get_one_job(job_id)\n if not jobs:\n return custom_response({'Error': 'Job Not Found'}, 404)\n\n data, error = job_schema.load(req_data, partial=True)\n if error:\n return custom_response(error, 400)\n\n for job in jobs:\n job.update(data)\n job_message = job_schema.dump(job)\n\n return custom_response(job_message, 200)","def updateall(self, params):\n for gauge in self.gauges:\n self.safexec(gauge.updateall, params)","def _update_bulk(self, iterable):\n self.cursor.executemany(self.UPDATE, iterable)","def bulk_create():\n logger.info(\"Creating persuasions in bulk\")\n try:\n request_data = json.loads(request.data)\n with concurrent.futures.ThreadPoolExecutor(max_workers=settings.MAX_WORKERS) as executor:\n {executor.submit(PersuasionServices.create, data): data for data in request_data}\n\n return jsonify(\n dict(status=\"success\", message=\"Your request is in the queue, persuasion will create shortly\"))\n except Exception as e:\n logger.error(\"Exception while creating persuasions in bulk - \" + repr(e))\n return jsonify(dict(status=\"failure\", error=repr(e)))","def update_targets(self, items):\n items = list(filter(None, chain(*items)))\n items = list(filter(None, items))\n\n if len(items) > 0:\n self.logger.info(\"Updating {} thermo documents\".format(len(items)))\n bulk = self.thermo().initialize_ordered_bulk_op()\n\n for m in items:\n m[self.thermo.lu_field] = datetime.utcnow()\n bulk.find({\"material_id\": m[\"material_id\"]}).upsert().replace_one(m)\n bulk.execute()\n else:\n self.logger.info(\"No items to update\")","def update(self, es, **kwargs):\n pass","def commit(self):\n with self.lock:\n try:\n action_buffer = self.BulkBuffer.get_buffer()\n if action_buffer:\n successes, errors = bulk(self.elastic, action_buffer)\n except Exception as e:\n # Handle errors from bulk indexing request\n raise\n \n retry_until_ok(self.elastic.indices.refresh, index=\"\")","def push_bulk(self, obj_list, doc_type=None, refresh=True):\n assert isinstance(obj_list, collections.Sequence)\n assert len(obj_list) > 0\n\n es_obj_list = []\n for obj in obj_list:\n if obj is None:\n logger.warning(\"None object in input list\")\n continue\n\n doc_type, es_repr = self._validate_doc_and_get_type_and_repr(obj, doc_type)\n metadata = {\n '_op_type': 'index',\n \"_index\": self.index_name,\n \"_type\": doc_type,\n }\n es_repr.update(**metadata)\n\n es_obj_list.append(es_repr)\n\n helpers.bulk(client=self.conn.elastic_search_client, actions=es_obj_list,\n stats_only=True, refresh=u'true' if refresh else u'false')","def refresh(self):\n yield from self._db._update_series(self.provider, self._dbattr(self.provider.IDATTR), dirty=self.providers)","def set_many(self, update_dict):\n for key, value in update_dict.items():\n self.set(key, value)","def updateAll(data):\n if (data.updatePositions):\n data.groups.player.update(data)\n data.groups.projectiles.update(data)\n data.groups.monsters.update(data)\n data.groups.spawners.update(data)","def update_many(\n cls,\n *,\n pks: List[Union[str, int]],\n update: Dict[str, Any],\n sychronize_session: bool = False,\n ) -> None:\n if pks:\n db.session.query(cls).filter(\n getattr(cls, cls.get_primary_key()).in_(pks)\n ).update(update, synchronize_session=sychronize_session)\n db.session.commit()\n cache.delete_many(*(cls.create_cache_key(pk) for pk in pks))","def update_all(self, request):\n\n schema = self.session.info['schema']\n\n for item in self.query().filter_by(schema=schema):\n self.session.delete(item)\n\n for item in ElectionCollection(self.session).query():\n self.update(item, request)\n\n for item in ElectionCompoundCollection(self.session).query():\n self.update(item, request)\n\n for item in VoteCollection(self.session).query():\n self.update(item, request)","def populate_employees():\n employees = get_employees()\n\n db.session.bulk_save_objects(employees)\n db.session.commit()","def entityUpdates(self, *args):\n\t\tfor entity in self.members.values():\n\t\t\tentity.update(*args)","def process_es_bulk(pub_list, es):\n bulk_response = es.bulk(\n body=''.join(pub_list),\n refresh='wait_for',\n request_timeout=3600,\n )\n if bulk_response.get('errors'):\n logger.error('failed on bulk indexing:\\n%s',\n bulk_response)\n raise IndexingErrorException()\n return len(pub_list)","def gbf_pub_update():\r\n LOG.info(\"Start: Update datasets in RLIDGeo warehouse.\")\r\n month_stamps = [\r\n datetime.date.today().strftime(\"%Y_%m\"),\r\n (\r\n datetime.date.today().replace(day=1)\r\n - datetime.timedelta(days=1)\r\n ).strftime(\"%Y_%m\"),\r\n ]\r\n for month_stamp in month_stamps:\r\n snapshot_db_path = SNAPSHOT_DB_PATH.format(month_stamp)\r\n if not os.path.exists(snapshot_db_path):\r\n LOG.warning(\"Snapshot database %s does not exist.\", snapshot_db_path)\r\n continue\r\n\r\n for _dataset in DATASETS:\r\n arcetl.features.update_from_dicts(\r\n dataset_path=_dataset.path(\"pub\"),\r\n update_features=source_rows(snapshot_db_path, _dataset.path(\"source\")),\r\n id_field_names=_dataset.id_field_names,\r\n field_names=_dataset.field_names,\r\n delete_missing_features=False,\r\n use_edit_session=False,\r\n )\r\n LOG.info(\"End: Update.\")","def updateList(self):\n self._recreateJobs()","def update_DB(self, iterable, entry_columns, update):\n conn = self.conn\n bulk = []\n old_bulk = []\n list_of_id_db = list()\n list_of_id_atuais = self.lista_atual()\n list_of_id_afastados = self.lista_afastados()\n\n if update:\n list_of_id_db = conn.execute('SELECT id_parlamentar FROM {}.{}'.format(self.schema, self.table))\n list_of_id_db = [tup[0] for tup in list_of_id_db]\n id_row_historic = list(conn.execute('SELECT MAX(id) FROM {}.{}_historic'.format(self.schema, self.table)))[0][0]\n if not id_row_historic:\n id_row_historic = 0\n\n for senador in tqdm(iterable):\n entry = self.fill_entry_senador(senador,entry_columns)\n id_parlamentar = entry['id_parlamentar']\n\n if id_parlamentar in list_of_id_atuais:\n entry['situacao_parlamentar'] = 'atual'\n elif id_parlamentar in list_of_id_afastados:\n entry['situacao_parlamentar'] = 'afastado'\n\n if id_parlamentar in list_of_id_db:\n compare_columns = 'id_parlamentar, nome_completo, nome_parlamentar_atual, forma_tratamento, sexo_parlamentar, data_nascimento, data_falecimento, sigla_uf_origem, endereco_origem, nome_cidade_origem, codigo_estado_civil, endereco_congresso, fone, fax, website, email, profissao, id_camara, id_senado, cpf, titulo_de_eleitor, descricao_participacao'\n\n old_row = conn.execute(\"SELECT {} FROM {}.{} WHERE id_parlamentar='{}'\".format(compare_columns,self.schema, self.table,id_parlamentar))\n old_row = list(old_row)[0]\n new_row = tuple([entry[column] for column in compare_columns.split(', ')])\n\n if old_row != new_row:\n old_entry = copy.deepcopy(entry_columns)\n\n for key in old_entry.keys():\n old_date = conn.execute(\"SELECT {} FROM {}.{} WHERE id_parlamentar='{}'\".format(key,self.schema, self.table,id_parlamentar))\n old_entry[key] = list(old_date)[0][0]\n old_entry['change_date'] = datetime.datetime.today() #capture of change date\n id_row_historic += 1\n old_entry['id'] = id_row_historic\n\n old_bulk.append(old_entry)\n conn.execute(\"DELETE FROM {}.{} WHERE id_parlamentar='{}'\".format(self.schema, self.table,id_parlamentar))\n\n bulk.append(entry)\n else:\n bulk.append(entry)\n\n if len(bulk) > 0:\n df = pd.DataFrame(bulk)\n df.set_index('id_parlamentar', inplace=True)\n print('Adding {} entries to SQL table {}.{}.'.format(len(df),self.schema, self.table))\n df.to_sql(self.table, con=self.conn, schema=self.schema, if_exists='append')\n\n if len(old_bulk) > 0:\n df2 = pd.DataFrame(old_bulk)\n df2.set_index('id_parlamentar', inplace=True)\n historic_table_name = self.table + '_historic'\n print('Adding {} entries to SQL table {}.{}.'.format(len(df2),self.schema, historic_table_name))\n df2.to_sql(historic_table_name, con=self.conn, schema=self.schema, if_exists='append')","def update_metadata(api_token, base_url, records, updates):\n\n update_url = f\"{base_url}data_files/api_update?auth_token={api_token}\"\n\n counter = 0\n for record in tqdm.tqdm(records):\n # copy in the original ID of the search record into the file_id field of the updates\n updates['file_id'] = record['file_id']\n\n response = requests.post(update_url, data=updates)\n\n # Tally the number of successful updates\n if response.status_code == 200:\n counter += 1\n\n print(f\"{counter} records of {len(records)} successfully updated\")","def update_mds(self, mds):\n submitted,failed = [],[]\n guids = list(mds)\n total = len(guids)\n count = 0\n for guid in guids:\n count+=1\n print(\"\\n\\tPosting '{}' to metadata service\".format(guid))\n mds_api = \"{}/mds/metadata/{}\".format(self._endpoint, guid)\n res = requests.put(mds_api, json=mds[guid], auth=self._auth_provider)\n\n if res.status_code > 199 and res.status_code < 300:\n submitted.append(guid)\n print(\"({}/{}) Submitted '{}' to MDS.\".format(count, total, guid))\n else:\n failed.append(guid)\n print(\"({}/{}) FAILED to submit '{}' to MDS.\".format(count, total, guid))\n print(\"\\n\\t\\t{}\".format(res.text))\n\n return {\"submitted\":submitted, \"failed\":failed}","def update_all_metadata(self, metadata):\n return self.manager.update_all_metadata(self, metadata)","def insert_to_elastic(elastic, paper_authors, papers, authors, index_name):\n helpers.bulk(elastic, merge_to_elastic(paper_authors, papers, authors, index_name))","def update_particles(self):\n for particle in self.particles:\n particle.update_coordinates(self.bounds)","def refresh():\n DB.drop_all()\n DB.create_all()\n samples = pull_pm('Los Angeles', 'pm25')\n for sample in samples:\n measure = Record(datetime = str(sample[0]), value = sample[1])\n DB.session.add(measure)\n DB.session.commit()\n return 'Data refreshed!'","def update_sources(self):\n documents = self.get_docs_sources()\n \n for index,each_doc in enumerate(documents['docs']):\n if each_doc['found']:\n _,update_spec,action_buffer_index = self.doc_to_get[index]\n \n # maybe source already has been taken from elasticsearch\n # and updated. In that case get source from sources\n source = self.get_from_sources(each_doc['_index'], each_doc['_type'], each_doc['_id'])\n if not source:\n source = each_doc['_source']\n updated = self.docman.apply_update(source, update_spec)\n \n #Remove _id field from source\n if '_id' in updated: del updated['_id']\n # Everytime update source to keep it up-to-date\n self.add_to_sources(each_doc,updated)\n \n self.action_buffer[action_buffer_index]['_source'] = self.docman._formatter.format_document(updated)\n else:\n # Document not found in elasticsearch,\n # Seems like something went wrong during replication\n # or you tried to update document which while was inserting\n # didn't contain any field mapped in mongo-connector configuration\n self.doc_to_get = []\n raise errors.OperationFailed(\n \"mGET: Document id: {} has not been found\".format(each_doc['_id']))\n self.doc_to_get = []","async def update_series(self):\n log.info('Updating all series')\n await self.updater.update_series()\n log.info('Done updating all series')","def updateAllRealtime():\n for stockName in db.STOCK_MAP.keys():\n getStock(stockName, \"all\")\n\n db.UPDATING_REALTIME = False","def update_many(\n self,\n *args: Union[dict, Mapping],\n session: Optional[ClientSession] = None\n ) -> UpdateMany:\n return self.update(*args, session=session)","def updateAllEntities():\n entityKeys=list(registeredEntities.keys())\n for currKey in entityKeys:\n try:\n currEntity=registeredEntities[currKey]\n currEntity.update()\n except KeyError:\n #this should only be called if an entity is deleted (like if a rock got hit by a bullet)\n continue","def update(self, params):\n for gauge in self.gauges:\n self.safexec(gauge.update, params)","def refresh():\n DB.drop_all()\n DB.create_all()\n results = get_results()\n for i, result in enumerate(results):\n record = Record(id=i, datetime=result[0], value=result[1])\n DB.session.add(record)\n DB.session.commit()\n return redirect(\"/\")","def refresh_index(self):\n synchronize()\n # TODO: add logger call here\n self._compute_embeddings()","def bulk_update_mappings(\n self, mapper: Mapper[Any], mappings: Iterable[Dict[str, Any]]\n ) -> None:\n self._bulk_save_mappings(\n mapper, mappings, True, False, False, False, False\n )","def batch_process(self, delete_list=[], update_list=[]):\n self.request_url = \"{0}/{1}\".format(self.API_URL, self.USER_BULK_ENDPOINT)\n payload = {\n 'updated': update_list,\n 'deleted': delete_list,\n }\n\n r = self.requests.post(\n self.request_url,\n data=json.dumps(payload),\n headers=self.default_headers,\n timeout=30\n )\n\n return r.status_code, r.json()","def update_documents(self, engine_name, documents):\n endpoint = \"engines/{}/documents\".format(engine_name)\n data = json.dumps(documents)\n\n return self.swiftype_session.request('patch', endpoint, data=data)","def do_bulk(self, args):\n pass","def publish_updates():\n run_subprocess(['osg-batch-update'])","def bulk_modify(self, file, lp, storage_id, remote_path=''):\n client, _ = self._get_storage(remote_path, storage_id=storage_id)\n return client.bulk_modify(lp, file)","def refresh(self):\n if self.is_server_process and self.cache_manager.is_refreshing():\n raise RefreshInProgressError()\n catalogs = MetadataManager(schemaspace=ComponentCatalogs.COMPONENT_CATALOGS_SCHEMASPACE_ID).get_all()\n for catalog in catalogs:\n self._insert_request(self.refresh_queue, catalog, \"modify\")","def store(self):\n articles = []\n for entry in self.feed():\n key = self.datastore_client.key(self.DATASTORE_KIND, entry['id'])\n article = datastore.Entity(key=key)\n article.update(entry)\n articles.append(article)\n self.datastore_client.put_multi(articles)","def execute(self):\n for coll in list(self.__bulks):\n try:\n bulkOp = self.__bulks[coll]\n curr_result = Counter(bulkOp.execute())\n self.update_results(coll, curr_result)\n except BulkWriteError as bwe:\n sys.stderr.write(str(bwe.details))","def refresh():\n DB.drop_all()\n DB.create_all()\n # TODO Get data from OpenAQ, make Record objects with it, and add to db\n DB.session.commit()\n return 'Data refreshed!'","def refresh():\n DB.drop_all()\n DB.create_all()\n # TODO Get data from OpenAQ, make Record objects with it, and add to db\n DB.session.commit()\n return 'Data refreshed!'","def refresh():\n DB.drop_all()\n DB.create_all()\n # TODO Get data from OpenAQ, make Record objects with it, and add to db\n DB.session.commit()\n return 'Data refreshed!'","def _batch_write(self):\n if self.to_put:\n db.put(self.to_put)\n self.to_put = []\n if self.to_delete:\n db.delete(self.to_delete)\n self.to_delete = []","def _update_entities(*entities):\n futures = []\n for entity in entities:\n futures.append(entity.put_async())\n Future.wait_all(futures)\n results = []\n for future in futures:\n results.append(future.get_result())\n return results","def handle_wps_update(self, data):\n\n self.jobs = data","def update_entities(self, entities: List[str]) -> None:\n self._entities = entities or []","def updateAll(self):\n \tself.idToUpdate=''\n \tself.newState=''\n \tself.save()","def refresh():\n DB.drop_all()\n DB.create_all()\n # TODO Get data from OpenAQ, make Record objects with it, and add to db\n aq_data.add_aq_to_db()\n DB.session.commit()\n return 'Data refreshed!'","def update_all_posts():\n for post in CURRENT_POSTS:\n update_tag(post)","def update_project_documents(self, manifest_info):\n\n for proj_name, proj_info in manifest_info.projects.items():\n # See if project document already is in the database and extract\n # for updating if so, otherwise create a new dictionary for\n # population\n key_name = f'project:{proj_name}'\n\n try:\n project_data = self.db.get_document(key_name)\n except cbdatabase_db.NotFoundError:\n project_data = dict(\n type='project', key_=key_name, name=proj_name\n )\n\n remote, repo_url = \\\n manifest_info.get_project_remote_info(proj_name)\n\n if 'remotes' in project_data:\n if remote in project_data['remotes']:\n if repo_url not in project_data['remotes'][remote]:\n project_data['remotes'][remote].append(repo_url)\n else:\n project_data['remotes'][remote] = [repo_url]\n else:\n project_data['remotes'] = {remote: [repo_url]}\n\n self.db.upsert_documents({key_name: project_data})","def repopall(ctx):\n c = ctx.obj['client']\n if not c.login:\n return False\n\n r = requests.request(\"GET\", urljoin(c.BASE_URL, '/apiproxy/JobService.js'), params={'accesskey': c.login, 'method': 'PopulateAllSearches'})\n print(r.status_code, r.text)\n\n if r.status_code == 200:\n return True\n else:\n return False","def bulk_upsert(self, data, parameters=None, many_to_many_clear: bool = True):\n elements = []\n\n # Skip if no identifier is set\n if not self.unique_identifier or not data:\n return\n\n for element in data:\n if isinstance(parameters, dict):\n element.update(parameters)\n\n element, created = self.upsert(data=element, many_to_many_clear=many_to_many_clear)\n elements.append(element)\n\n return elements","def do_refresh_revisions():\n\n rows_by_type = _get_revisions_by_type()\n\n for type_, rows in rows_by_type.iteritems():\n logger.info(\"Updating revisions for: %s\", type_)\n _fix_type_revisions(type_, rows)","def update_archive(self, population, offsprings, generation):\n # Store agents\n did_store = False\n for agent in offsprings:\n agent['stored'] = generation\n if self.archive.store(agent):\n self.emitters_pop[self.emitter_idx].stored += 1\n did_store = True\n\n if did_store:\n # Update emitter distribution (This one needs to be changed if wanting to use improvement emitters)\n solutions = [(genome, -value) for genome, value in zip(offsprings['genome'], offsprings['reward'])]\n self.emitters_pop[self.emitter_idx].tell(solutions)\n else:\n # If the emitter did not store anything, we restart it from another elite\n elite_idx = np.random.choice(self.archive.size)\n self.emitters_pop[self.emitter_idx] = OptimizingEmitter(self.archive['genome'][elite_idx],\n self.archive['id'][elite_idx],\n mutation_rate=0.5,\n bounds=self.bounds,\n parameters=self.params)","def test_otoroshi_controllers_adminapi_tcp_service_api_controller_bulk_update_action(self):\n pass","def update(self):\n self._sync_ranges()\n self._update_params()","def salesforce_collection_update(self, objects):\n for obj in objects:\n assert obj[\n \"id\"\n ], \"Should be a list of objects with Ids returned by Salesforce Collection Insert\"\n if STATUS_KEY in obj:\n del obj[STATUS_KEY]\n\n assert len(objects) <= SF_COLLECTION_INSERTION_LIMIT, (\n \"Cannot update more than %s objects with this keyword\"\n % SF_COLLECTION_INSERTION_LIMIT\n )\n\n records = self.cumulusci.sf.restful(\n \"composite/sobjects\",\n method=\"PATCH\",\n json={\"allOrNone\": True, \"records\": objects},\n )\n\n for record, obj in zip(records, objects):\n obj[STATUS_KEY] = record","def update(self, dt):\n for obj in self.objects:\n obj.update(dt)","def to_db(self):\n bulk = conn_db().initialize_ordered_bulk_op()\n for fiction in self.fictions:\n bulk.find({'id': fiction.id}).upsert().update({'$set': fiction.__dict__})\n bulk.execute()","def batch_process_async(self, delete_list=[], update_list=[]):\n headers = update_dict(self.default_headers, {self.API_VERSION_HEADER: self.API_VERSIONS[\"v2\"]})\n self.request_url = \"{0}/{1}\".format(self.API_URL, self.USER_BULK_ENDPOINT)\n\n payload = {\n 'updated': update_list,\n 'deleted': delete_list,\n }\n\n r = self.requests.post(self.request_url, data=json.dumps(payload), headers=headers, timeout=30)\n\n return r.status_code, r.json()","def do_update(services):\n\n global running_update\n\n for service in services:\n feed = registry[service.name][0]\n try:\n if type(feed) is list:\n entries = []\n for f in feed:\n entries.extend(f(service))\n else:\n entries = feed(service)\n # TODO should be in a transaction\n for entry in entries:\n entry.save()\n service.updated = datetime.utcnow()\n service.save()\n except:\n logging.exception(msg='updater exception for service %s' %service.name, exception=True)\n running_update = False","def update_state(self, slate_documents, responses):","def updateMasivo(self, objects):\n retorno = 1\n try:\n for object in objects:\n self.session.merge(object)\n self.session.commit()\n \n except sqlalchemy.exc.DBAPIError, e:\n if self.session is not None:\n self.session.rollback()\n retorno = -1\n print(\"Error!\", e)\n finally:\n if self._DAOAlchemy__cerrarSesion:\n self.session.close()\n return retorno","def refresh(self, parameters = {}):\n\n self.__enforce_connected()\n self.collection.refresh(self, parameters = parameters)","def update():\n query = db.session.query(db.Series).filter_by(following=True).all()\n output.series('Updating {} series'.format(len(query)))\n for follow in query:\n series = series_by_url(follow.url)\n series.update()\n list_new()","def refresh(self):\r\n self.metadata = self.db.read(self.path).json()","def update_list(self):\n Asset.update_list(self, uri_keys=('ems_sys', 'list'))","def update(self):\n\n\t\tif not self.complete:\n\t\t\tfor vasp_run in self.vasp_run_list:\n\t\t\t\tvasp_run.update()","def flush_batch(self, batch):\n inserts = []\n replacements = []\n\n for action_type, data in batch:\n if action_type == processor.INSERT:\n inserts.append(data)\n elif action_type == processor.REPLACE:\n replacements.append(data)\n\n if inserts:\n write_rows(\n self.clickhouse,\n self.dist_table_name,\n inserts\n )\n\n if self.metrics:\n self.metrics.timing('inserts', len(inserts))\n\n if replacements:\n for key, replacement in replacements:\n self.producer.produce(\n self.replacements_topic,\n key=six.text_type(key).encode('utf-8'),\n value=json.dumps(replacement).encode('utf-8'),\n on_delivery=self.delivery_callback,\n )\n\n self.producer.flush()","def _update(self):\n self.all_params = {}\n self._update_experiment_params()\n self._update_preprocessing_params()\n self._update_model_params()","def update_users_bulk(self, users):\n # type: (list) -> dict\n payload = {'updated': users}\n self.request_url = \"{0}/{1}\".format(self.API_URL, self.USER_BULK_ENDPOINT)\n return self.__create_request(payload=payload, request_type=self.REQUEST_POST, version=\"v1\")","def updates(self, request, version):\n task = EtlTask.objects.get_for_model(self.queryset.model)\n if task.last_changes:\n offset = task.last_changes.strftime(\"%Y-%m-%d %H:%M\")\n queryset = self.queryset.filter(last_modify_date__gte=offset)\n else:\n offset = \"none\"\n queryset = self.queryset.all()\n queryset = self.paginate_queryset(queryset)\n serializer = self.get_serializer(queryset, many=True)\n return self.get_paginated_response(serializer.data)\n # return Response(serializer.data,\n # headers={'update-date': offset})","def bulk_write(collection, iterable, job_id=None, unsafe=False):\n namespace = Namespace(collection.foreign_id)\n stage = get_stage(collection, OP_INDEX, job_id=job_id)\n entities = []\n for item in iterable:\n if not is_mapping(item):\n raise InvalidData(\"Failed to read input data\", errors=item)\n entity = model.get_proxy(item)\n entity = namespace.apply(entity)\n if not unsafe:\n entity = remove_checksums(entity)\n entities.append(entity)\n index_entities(stage, collection, entities)","async def reload_all(ctx):\n await ext_manager.reload_all()\n await ctx.send(\"Successfully reloaded.\")","def refresh():\r\n DB.drop_all()\r\n DB.create_all()\r\n # TODO Get data from OpenAQ, make Record objects with it, and add to db\r\n for i in time_x_values():\r\n DB.session.add(Record(datetime=i[0], value=i[1]))\r\n DB.session.commit()\r\n return 'Data refreshed!'","def update(self, values):\n return self.manager.update(self, values)","def apply_updates(self, updates):\n raise NotImplementedError()","def upload_entities(self, batch):\n # TODO Include a Do Not Overwrite call\n results = None\n atlas_endpoint = self.endpoint_url + \"/entity/bulk\"\n\n payload = AtlasClient._prepare_entity_upload(batch)\n\n postBulkEntities = requests.post(\n atlas_endpoint,\n json=payload,\n headers=self.authentication.get_authentication_headers()\n )\n\n results = self._handle_response(postBulkEntities)\n\n return results","def bulkSave(self, objList: List[PermissionContext], tokenData: TokenData):","def apply(self):\n\n data = self._to_dict()\n\n # read only attributes\n del data['Provider']\n del data['MarkedForDelOn']\n del data['IsDeleted']\n del data['Database']\n\n data = {k: v for k, v in data.items() if v is not None}\n\n # remove empty arrays. Note: pylint will complain about this\n # but zeep will complain even more :)\n if len(data['Attributes']) == 0:\n del data['Attributes']\n\n if len(data['SecondaryDomains']) == 0:\n del data['SecondaryDomains']\n\n updateshopobj = self.sc.get_updateshop_obj(data)\n\n self.sc.update(updateshopobj)\n\n self.refresh()","def flush(self):\n self.save()\n for obs in self.observation_set.all():\n obs.flush();","def merge_to_elastic(paper_authors, papers, authors, index_name):\n columns = list(papers.columns) + ['authors']\n for index, paper in papers.iterrows():\n merger = paper_authors.loc[paper_authors['paper_id'] == index]\n author_ids = merger['author_id'].values\n author_names = [authors.loc[authors['id'] == x, 'name'].values[0] for x in author_ids]\n paper['authors'] = author_names\n yield {\n \"_index\": index_name,\n \"_type\": \"_doc\",\n \"_id\" : f\"{index}\",\n \"_source\": filterKeys(paper, columns),\n }","def update_view(self):\n for row in self.view.obj_list:\n for obj in row:\n obj._update(self.model)","def pools_refresh(self):\n path = \"%s/commands/poolsRefresh\" % self.__base_path\n response = self.__session.post(path)\n self.__check_status_code(response.status_code)\n return response.json()"],"string":"[\n \"def schedule_refresh():\\n logger.info(\\\"Refreshing/Updating persuasions in bulk\\\")\\n try:\\n args = request.args\\n if not args.get(\\\"type\\\") or not args.get(\\\"sub_type\\\"):\\n return jsonify(\\n dict(status=\\\"failure\\\", message=\\\"Invalid type/sub_type\\\"))\\n\\n persuasions_request = PersuasionServices.get_persuasions_request_from_es(args)\\n\\n with concurrent.futures.ThreadPoolExecutor(max_workers=settings.MAX_WORKERS) as executor:\\n {executor.submit(PersuasionServices.refresh, data, args): data for data in persuasions_request}\\n\\n return jsonify(\\n dict(status=\\\"success\\\", message=\\\"Your request is in the queue, persuasion will be updated shortly\\\"))\\n except Exception as e:\\n logger.error(\\\"Exception while creating persuasions in bulk - \\\" + repr(e))\\n return jsonify(dict(status=\\\"failure\\\", error=repr(e)))\",\n \"def refresh_persuasion():\\n try:\\n request_data = json.loads(request.data)\\n meta = request.args\\n persuasion = PersuasionServices.refresh(request_data, meta)\\n except Exception as e:\\n error_msg = \\\"Getting persuasion details - \\\" + repr(e)\\n logger.error(error_msg)\\n return jsonify(dict(status=\\\"failure\\\", error=error_msg))\\n return jsonify(dict(status=\\\"success\\\", data=persuasion if persuasion else dict()))\",\n \"def bulk_update(self, request):\\n serializer = MasterySerializer(\\n data=request.data,\\n many=True,\\n )\\n if serializer.is_valid():\\n serializer.save()\\n return Response(serializer.data)\\n else:\\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\",\n \"def update_embeddings(self, retriever):\\n\\n docs = self.get_all_documents()\\n passages = [d.text for d in docs]\\n logger.info(f\\\"Updating embeddings for {len(passages)} docs ...\\\")\\n embeddings = retriever.embed_passages(passages)\\n\\n assert len(docs) == len(embeddings)\\n\\n doc_updates = []\\n for doc, emb in zip(docs, embeddings):\\n update = {\\\"_op_type\\\": \\\"update\\\",\\n \\\"_index\\\": self.index,\\n \\\"_id\\\": doc.id,\\n \\\"doc\\\": {self.embedding_field: emb.tolist()},\\n }\\n doc_updates.append(update)\\n\\n bulk(self.client, doc_updates, request_timeout=300)\",\n \"def _batch_update(self, query, mutation):\\n logger.info(\\\"Performing batch update on %s. Mutation: %s\\\", query, mutation)\\n modified = 0\\n for doc in self.instances.find(query):\\n with lock_instance(doc['_id']):\\n pre_update_doc = self.instances.find_one({'_id' : doc['_id']})\\n result = self.instances.update_one({'_id': doc['_id']}, mutation)\\n assert result.modified_count == 1\\n modified += 1\\n updated_doc = self.instances.find_one({'_id': doc['_id']})\\n instance = FixtureInstance.deserialize_mongodoc(updated_doc)\\n try:\\n self.axdb_client.update_fixture_instance(instance.axdbdoc())\\n except Exception:\\n logger.exception(\\\"Failed to persist updates for %s. Undoing cache update\\\", instance)\\n self.instances.replace_one({'_id' : instance.id}, pre_update_doc)\\n raise\\n logger.info(\\\"%s fixture instances modified\\\", modified)\",\n \"def step070() -> None:\\n logger.logMessage('Begin: elasticsearch bulk update')\\n client = es.Elasticsearch(hostlist)\\n\\n def generate():\\n with open(renumFile,'r') as f:\\n line = f.readline().rstrip()\\n while line != '':\\n fields = line.split(';')\\n oper = { '_index': fields[3], \\n '_op_type': 'update',\\n '_id': fields[2].rstrip(),\\n '_type': 'doc',\\n '_source:': {'doc': {'tsa': fields[0]}}}\\n \\n yield oper\\n line = f.readline().rstrip()\\n result = eshelp.bulk(client,generate())\\n logger.logMessage('Bulk result: {0}'.format(result))\\n logger.logMessage('End : elasticsearch bulk update')\",\n \"def updateDocumentAll(self, documents):\\n docs = []\\n for document in documents:\\n if isinstance(document, couch.Document):\\n document = document.getData()\\n\\n # these are required params\\n if \\\"_id\\\" not in document or \\\"_rev\\\" not in document:\\n raise Exception(\\\"Both _id & _rev fields are required!\\\")\\n\\n docs.append(document)\\n\\n return self.client.post(self.name +\\\"/_bulk_docs\\\", None,\\n {\\\"docs\\\": docs}).getBodyData()\",\n \"def project_refresh_all():\\n project_list = Project.objects()\\n analyser.add_repos(current_user.username, [repo.project_name for repo in project_list])\\n flash('Refresh all successfully!', 'success')\\n return redirect(url_for('main.admin_manage'))\",\n \"def updateMultipleDocuments(cred, payload):\\n\\n url = cred.base_url + \\\"documents:commit\\\"\\n data = { 'writes': [] }\\n\\n for path, fieldData in payload.iteritems():\\n pathData = createFirestoreDataObject(cred, path, fieldData)\\n data['writes'].append(pathData)\\n \\n makeRequest(cred, url, 'POST', data)\",\n \"def bulk_process(self):\\n\\n def actions():\\n try:\\n task = self.queue.get(block=False, timeout=None)\\n\\n if task['action'] == 'index':\\n yield {\\n '_op_type': 'index',\\n '_index': self.ensure_index(task),\\n '_id': task['id'],\\n 'doc': task['properties']\\n }\\n elif task['action'] == 'delete':\\n yield {\\n '_op_type': 'delete',\\n '_index': self.ensure_index(task),\\n '_id': task['id'],\\n 'doc': task['properties']\\n }\\n else:\\n raise NotImplementedError\\n\\n except Empty:\\n pass\\n\\n for success, info in streaming_bulk(self.es_client, actions()):\\n if success:\\n self.queue.task_done()\",\n \"def update_batch(self, *args, **kwargs):\\n pass\",\n \"def update_entities(self):\\n raise NotImplementedError()\",\n \"def set_many(self, update_dict):\\n for key, value in update_dict.items():\\n # We just call `set` directly here, because this is an in-memory representation\\n # thus we don't concern ourselves with bulk writes.\\n self.set(key, value)\",\n \"def update_all():\\n req_data = request.get_json()\\n jobs = JobModel.get_one_job(job_id)\\n if not jobs:\\n return custom_response({'Error': 'Job Not Found'}, 404)\\n\\n data, error = job_schema.load(req_data, partial=True)\\n if error:\\n return custom_response(error, 400)\\n\\n for job in jobs:\\n job.update(data)\\n job_message = job_schema.dump(job)\\n\\n return custom_response(job_message, 200)\",\n \"def updateall(self, params):\\n for gauge in self.gauges:\\n self.safexec(gauge.updateall, params)\",\n \"def _update_bulk(self, iterable):\\n self.cursor.executemany(self.UPDATE, iterable)\",\n \"def bulk_create():\\n logger.info(\\\"Creating persuasions in bulk\\\")\\n try:\\n request_data = json.loads(request.data)\\n with concurrent.futures.ThreadPoolExecutor(max_workers=settings.MAX_WORKERS) as executor:\\n {executor.submit(PersuasionServices.create, data): data for data in request_data}\\n\\n return jsonify(\\n dict(status=\\\"success\\\", message=\\\"Your request is in the queue, persuasion will create shortly\\\"))\\n except Exception as e:\\n logger.error(\\\"Exception while creating persuasions in bulk - \\\" + repr(e))\\n return jsonify(dict(status=\\\"failure\\\", error=repr(e)))\",\n \"def update_targets(self, items):\\n items = list(filter(None, chain(*items)))\\n items = list(filter(None, items))\\n\\n if len(items) > 0:\\n self.logger.info(\\\"Updating {} thermo documents\\\".format(len(items)))\\n bulk = self.thermo().initialize_ordered_bulk_op()\\n\\n for m in items:\\n m[self.thermo.lu_field] = datetime.utcnow()\\n bulk.find({\\\"material_id\\\": m[\\\"material_id\\\"]}).upsert().replace_one(m)\\n bulk.execute()\\n else:\\n self.logger.info(\\\"No items to update\\\")\",\n \"def update(self, es, **kwargs):\\n pass\",\n \"def commit(self):\\n with self.lock:\\n try:\\n action_buffer = self.BulkBuffer.get_buffer()\\n if action_buffer:\\n successes, errors = bulk(self.elastic, action_buffer)\\n except Exception as e:\\n # Handle errors from bulk indexing request\\n raise\\n \\n retry_until_ok(self.elastic.indices.refresh, index=\\\"\\\")\",\n \"def push_bulk(self, obj_list, doc_type=None, refresh=True):\\n assert isinstance(obj_list, collections.Sequence)\\n assert len(obj_list) > 0\\n\\n es_obj_list = []\\n for obj in obj_list:\\n if obj is None:\\n logger.warning(\\\"None object in input list\\\")\\n continue\\n\\n doc_type, es_repr = self._validate_doc_and_get_type_and_repr(obj, doc_type)\\n metadata = {\\n '_op_type': 'index',\\n \\\"_index\\\": self.index_name,\\n \\\"_type\\\": doc_type,\\n }\\n es_repr.update(**metadata)\\n\\n es_obj_list.append(es_repr)\\n\\n helpers.bulk(client=self.conn.elastic_search_client, actions=es_obj_list,\\n stats_only=True, refresh=u'true' if refresh else u'false')\",\n \"def refresh(self):\\n yield from self._db._update_series(self.provider, self._dbattr(self.provider.IDATTR), dirty=self.providers)\",\n \"def set_many(self, update_dict):\\n for key, value in update_dict.items():\\n self.set(key, value)\",\n \"def updateAll(data):\\n if (data.updatePositions):\\n data.groups.player.update(data)\\n data.groups.projectiles.update(data)\\n data.groups.monsters.update(data)\\n data.groups.spawners.update(data)\",\n \"def update_many(\\n cls,\\n *,\\n pks: List[Union[str, int]],\\n update: Dict[str, Any],\\n sychronize_session: bool = False,\\n ) -> None:\\n if pks:\\n db.session.query(cls).filter(\\n getattr(cls, cls.get_primary_key()).in_(pks)\\n ).update(update, synchronize_session=sychronize_session)\\n db.session.commit()\\n cache.delete_many(*(cls.create_cache_key(pk) for pk in pks))\",\n \"def update_all(self, request):\\n\\n schema = self.session.info['schema']\\n\\n for item in self.query().filter_by(schema=schema):\\n self.session.delete(item)\\n\\n for item in ElectionCollection(self.session).query():\\n self.update(item, request)\\n\\n for item in ElectionCompoundCollection(self.session).query():\\n self.update(item, request)\\n\\n for item in VoteCollection(self.session).query():\\n self.update(item, request)\",\n \"def populate_employees():\\n employees = get_employees()\\n\\n db.session.bulk_save_objects(employees)\\n db.session.commit()\",\n \"def entityUpdates(self, *args):\\n\\t\\tfor entity in self.members.values():\\n\\t\\t\\tentity.update(*args)\",\n \"def process_es_bulk(pub_list, es):\\n bulk_response = es.bulk(\\n body=''.join(pub_list),\\n refresh='wait_for',\\n request_timeout=3600,\\n )\\n if bulk_response.get('errors'):\\n logger.error('failed on bulk indexing:\\\\n%s',\\n bulk_response)\\n raise IndexingErrorException()\\n return len(pub_list)\",\n \"def gbf_pub_update():\\r\\n LOG.info(\\\"Start: Update datasets in RLIDGeo warehouse.\\\")\\r\\n month_stamps = [\\r\\n datetime.date.today().strftime(\\\"%Y_%m\\\"),\\r\\n (\\r\\n datetime.date.today().replace(day=1)\\r\\n - datetime.timedelta(days=1)\\r\\n ).strftime(\\\"%Y_%m\\\"),\\r\\n ]\\r\\n for month_stamp in month_stamps:\\r\\n snapshot_db_path = SNAPSHOT_DB_PATH.format(month_stamp)\\r\\n if not os.path.exists(snapshot_db_path):\\r\\n LOG.warning(\\\"Snapshot database %s does not exist.\\\", snapshot_db_path)\\r\\n continue\\r\\n\\r\\n for _dataset in DATASETS:\\r\\n arcetl.features.update_from_dicts(\\r\\n dataset_path=_dataset.path(\\\"pub\\\"),\\r\\n update_features=source_rows(snapshot_db_path, _dataset.path(\\\"source\\\")),\\r\\n id_field_names=_dataset.id_field_names,\\r\\n field_names=_dataset.field_names,\\r\\n delete_missing_features=False,\\r\\n use_edit_session=False,\\r\\n )\\r\\n LOG.info(\\\"End: Update.\\\")\",\n \"def updateList(self):\\n self._recreateJobs()\",\n \"def update_DB(self, iterable, entry_columns, update):\\n conn = self.conn\\n bulk = []\\n old_bulk = []\\n list_of_id_db = list()\\n list_of_id_atuais = self.lista_atual()\\n list_of_id_afastados = self.lista_afastados()\\n\\n if update:\\n list_of_id_db = conn.execute('SELECT id_parlamentar FROM {}.{}'.format(self.schema, self.table))\\n list_of_id_db = [tup[0] for tup in list_of_id_db]\\n id_row_historic = list(conn.execute('SELECT MAX(id) FROM {}.{}_historic'.format(self.schema, self.table)))[0][0]\\n if not id_row_historic:\\n id_row_historic = 0\\n\\n for senador in tqdm(iterable):\\n entry = self.fill_entry_senador(senador,entry_columns)\\n id_parlamentar = entry['id_parlamentar']\\n\\n if id_parlamentar in list_of_id_atuais:\\n entry['situacao_parlamentar'] = 'atual'\\n elif id_parlamentar in list_of_id_afastados:\\n entry['situacao_parlamentar'] = 'afastado'\\n\\n if id_parlamentar in list_of_id_db:\\n compare_columns = 'id_parlamentar, nome_completo, nome_parlamentar_atual, forma_tratamento, sexo_parlamentar, data_nascimento, data_falecimento, sigla_uf_origem, endereco_origem, nome_cidade_origem, codigo_estado_civil, endereco_congresso, fone, fax, website, email, profissao, id_camara, id_senado, cpf, titulo_de_eleitor, descricao_participacao'\\n\\n old_row = conn.execute(\\\"SELECT {} FROM {}.{} WHERE id_parlamentar='{}'\\\".format(compare_columns,self.schema, self.table,id_parlamentar))\\n old_row = list(old_row)[0]\\n new_row = tuple([entry[column] for column in compare_columns.split(', ')])\\n\\n if old_row != new_row:\\n old_entry = copy.deepcopy(entry_columns)\\n\\n for key in old_entry.keys():\\n old_date = conn.execute(\\\"SELECT {} FROM {}.{} WHERE id_parlamentar='{}'\\\".format(key,self.schema, self.table,id_parlamentar))\\n old_entry[key] = list(old_date)[0][0]\\n old_entry['change_date'] = datetime.datetime.today() #capture of change date\\n id_row_historic += 1\\n old_entry['id'] = id_row_historic\\n\\n old_bulk.append(old_entry)\\n conn.execute(\\\"DELETE FROM {}.{} WHERE id_parlamentar='{}'\\\".format(self.schema, self.table,id_parlamentar))\\n\\n bulk.append(entry)\\n else:\\n bulk.append(entry)\\n\\n if len(bulk) > 0:\\n df = pd.DataFrame(bulk)\\n df.set_index('id_parlamentar', inplace=True)\\n print('Adding {} entries to SQL table {}.{}.'.format(len(df),self.schema, self.table))\\n df.to_sql(self.table, con=self.conn, schema=self.schema, if_exists='append')\\n\\n if len(old_bulk) > 0:\\n df2 = pd.DataFrame(old_bulk)\\n df2.set_index('id_parlamentar', inplace=True)\\n historic_table_name = self.table + '_historic'\\n print('Adding {} entries to SQL table {}.{}.'.format(len(df2),self.schema, historic_table_name))\\n df2.to_sql(historic_table_name, con=self.conn, schema=self.schema, if_exists='append')\",\n \"def update_metadata(api_token, base_url, records, updates):\\n\\n update_url = f\\\"{base_url}data_files/api_update?auth_token={api_token}\\\"\\n\\n counter = 0\\n for record in tqdm.tqdm(records):\\n # copy in the original ID of the search record into the file_id field of the updates\\n updates['file_id'] = record['file_id']\\n\\n response = requests.post(update_url, data=updates)\\n\\n # Tally the number of successful updates\\n if response.status_code == 200:\\n counter += 1\\n\\n print(f\\\"{counter} records of {len(records)} successfully updated\\\")\",\n \"def update_mds(self, mds):\\n submitted,failed = [],[]\\n guids = list(mds)\\n total = len(guids)\\n count = 0\\n for guid in guids:\\n count+=1\\n print(\\\"\\\\n\\\\tPosting '{}' to metadata service\\\".format(guid))\\n mds_api = \\\"{}/mds/metadata/{}\\\".format(self._endpoint, guid)\\n res = requests.put(mds_api, json=mds[guid], auth=self._auth_provider)\\n\\n if res.status_code > 199 and res.status_code < 300:\\n submitted.append(guid)\\n print(\\\"({}/{}) Submitted '{}' to MDS.\\\".format(count, total, guid))\\n else:\\n failed.append(guid)\\n print(\\\"({}/{}) FAILED to submit '{}' to MDS.\\\".format(count, total, guid))\\n print(\\\"\\\\n\\\\t\\\\t{}\\\".format(res.text))\\n\\n return {\\\"submitted\\\":submitted, \\\"failed\\\":failed}\",\n \"def update_all_metadata(self, metadata):\\n return self.manager.update_all_metadata(self, metadata)\",\n \"def insert_to_elastic(elastic, paper_authors, papers, authors, index_name):\\n helpers.bulk(elastic, merge_to_elastic(paper_authors, papers, authors, index_name))\",\n \"def update_particles(self):\\n for particle in self.particles:\\n particle.update_coordinates(self.bounds)\",\n \"def refresh():\\n DB.drop_all()\\n DB.create_all()\\n samples = pull_pm('Los Angeles', 'pm25')\\n for sample in samples:\\n measure = Record(datetime = str(sample[0]), value = sample[1])\\n DB.session.add(measure)\\n DB.session.commit()\\n return 'Data refreshed!'\",\n \"def update_sources(self):\\n documents = self.get_docs_sources()\\n \\n for index,each_doc in enumerate(documents['docs']):\\n if each_doc['found']:\\n _,update_spec,action_buffer_index = self.doc_to_get[index]\\n \\n # maybe source already has been taken from elasticsearch\\n # and updated. In that case get source from sources\\n source = self.get_from_sources(each_doc['_index'], each_doc['_type'], each_doc['_id'])\\n if not source:\\n source = each_doc['_source']\\n updated = self.docman.apply_update(source, update_spec)\\n \\n #Remove _id field from source\\n if '_id' in updated: del updated['_id']\\n # Everytime update source to keep it up-to-date\\n self.add_to_sources(each_doc,updated)\\n \\n self.action_buffer[action_buffer_index]['_source'] = self.docman._formatter.format_document(updated)\\n else:\\n # Document not found in elasticsearch,\\n # Seems like something went wrong during replication\\n # or you tried to update document which while was inserting\\n # didn't contain any field mapped in mongo-connector configuration\\n self.doc_to_get = []\\n raise errors.OperationFailed(\\n \\\"mGET: Document id: {} has not been found\\\".format(each_doc['_id']))\\n self.doc_to_get = []\",\n \"async def update_series(self):\\n log.info('Updating all series')\\n await self.updater.update_series()\\n log.info('Done updating all series')\",\n \"def updateAllRealtime():\\n for stockName in db.STOCK_MAP.keys():\\n getStock(stockName, \\\"all\\\")\\n\\n db.UPDATING_REALTIME = False\",\n \"def update_many(\\n self,\\n *args: Union[dict, Mapping],\\n session: Optional[ClientSession] = None\\n ) -> UpdateMany:\\n return self.update(*args, session=session)\",\n \"def updateAllEntities():\\n entityKeys=list(registeredEntities.keys())\\n for currKey in entityKeys:\\n try:\\n currEntity=registeredEntities[currKey]\\n currEntity.update()\\n except KeyError:\\n #this should only be called if an entity is deleted (like if a rock got hit by a bullet)\\n continue\",\n \"def update(self, params):\\n for gauge in self.gauges:\\n self.safexec(gauge.update, params)\",\n \"def refresh():\\n DB.drop_all()\\n DB.create_all()\\n results = get_results()\\n for i, result in enumerate(results):\\n record = Record(id=i, datetime=result[0], value=result[1])\\n DB.session.add(record)\\n DB.session.commit()\\n return redirect(\\\"/\\\")\",\n \"def refresh_index(self):\\n synchronize()\\n # TODO: add logger call here\\n self._compute_embeddings()\",\n \"def bulk_update_mappings(\\n self, mapper: Mapper[Any], mappings: Iterable[Dict[str, Any]]\\n ) -> None:\\n self._bulk_save_mappings(\\n mapper, mappings, True, False, False, False, False\\n )\",\n \"def batch_process(self, delete_list=[], update_list=[]):\\n self.request_url = \\\"{0}/{1}\\\".format(self.API_URL, self.USER_BULK_ENDPOINT)\\n payload = {\\n 'updated': update_list,\\n 'deleted': delete_list,\\n }\\n\\n r = self.requests.post(\\n self.request_url,\\n data=json.dumps(payload),\\n headers=self.default_headers,\\n timeout=30\\n )\\n\\n return r.status_code, r.json()\",\n \"def update_documents(self, engine_name, documents):\\n endpoint = \\\"engines/{}/documents\\\".format(engine_name)\\n data = json.dumps(documents)\\n\\n return self.swiftype_session.request('patch', endpoint, data=data)\",\n \"def do_bulk(self, args):\\n pass\",\n \"def publish_updates():\\n run_subprocess(['osg-batch-update'])\",\n \"def bulk_modify(self, file, lp, storage_id, remote_path=''):\\n client, _ = self._get_storage(remote_path, storage_id=storage_id)\\n return client.bulk_modify(lp, file)\",\n \"def refresh(self):\\n if self.is_server_process and self.cache_manager.is_refreshing():\\n raise RefreshInProgressError()\\n catalogs = MetadataManager(schemaspace=ComponentCatalogs.COMPONENT_CATALOGS_SCHEMASPACE_ID).get_all()\\n for catalog in catalogs:\\n self._insert_request(self.refresh_queue, catalog, \\\"modify\\\")\",\n \"def store(self):\\n articles = []\\n for entry in self.feed():\\n key = self.datastore_client.key(self.DATASTORE_KIND, entry['id'])\\n article = datastore.Entity(key=key)\\n article.update(entry)\\n articles.append(article)\\n self.datastore_client.put_multi(articles)\",\n \"def execute(self):\\n for coll in list(self.__bulks):\\n try:\\n bulkOp = self.__bulks[coll]\\n curr_result = Counter(bulkOp.execute())\\n self.update_results(coll, curr_result)\\n except BulkWriteError as bwe:\\n sys.stderr.write(str(bwe.details))\",\n \"def refresh():\\n DB.drop_all()\\n DB.create_all()\\n # TODO Get data from OpenAQ, make Record objects with it, and add to db\\n DB.session.commit()\\n return 'Data refreshed!'\",\n \"def refresh():\\n DB.drop_all()\\n DB.create_all()\\n # TODO Get data from OpenAQ, make Record objects with it, and add to db\\n DB.session.commit()\\n return 'Data refreshed!'\",\n \"def refresh():\\n DB.drop_all()\\n DB.create_all()\\n # TODO Get data from OpenAQ, make Record objects with it, and add to db\\n DB.session.commit()\\n return 'Data refreshed!'\",\n \"def _batch_write(self):\\n if self.to_put:\\n db.put(self.to_put)\\n self.to_put = []\\n if self.to_delete:\\n db.delete(self.to_delete)\\n self.to_delete = []\",\n \"def _update_entities(*entities):\\n futures = []\\n for entity in entities:\\n futures.append(entity.put_async())\\n Future.wait_all(futures)\\n results = []\\n for future in futures:\\n results.append(future.get_result())\\n return results\",\n \"def handle_wps_update(self, data):\\n\\n self.jobs = data\",\n \"def update_entities(self, entities: List[str]) -> None:\\n self._entities = entities or []\",\n \"def updateAll(self):\\n \\tself.idToUpdate=''\\n \\tself.newState=''\\n \\tself.save()\",\n \"def refresh():\\n DB.drop_all()\\n DB.create_all()\\n # TODO Get data from OpenAQ, make Record objects with it, and add to db\\n aq_data.add_aq_to_db()\\n DB.session.commit()\\n return 'Data refreshed!'\",\n \"def update_all_posts():\\n for post in CURRENT_POSTS:\\n update_tag(post)\",\n \"def update_project_documents(self, manifest_info):\\n\\n for proj_name, proj_info in manifest_info.projects.items():\\n # See if project document already is in the database and extract\\n # for updating if so, otherwise create a new dictionary for\\n # population\\n key_name = f'project:{proj_name}'\\n\\n try:\\n project_data = self.db.get_document(key_name)\\n except cbdatabase_db.NotFoundError:\\n project_data = dict(\\n type='project', key_=key_name, name=proj_name\\n )\\n\\n remote, repo_url = \\\\\\n manifest_info.get_project_remote_info(proj_name)\\n\\n if 'remotes' in project_data:\\n if remote in project_data['remotes']:\\n if repo_url not in project_data['remotes'][remote]:\\n project_data['remotes'][remote].append(repo_url)\\n else:\\n project_data['remotes'][remote] = [repo_url]\\n else:\\n project_data['remotes'] = {remote: [repo_url]}\\n\\n self.db.upsert_documents({key_name: project_data})\",\n \"def repopall(ctx):\\n c = ctx.obj['client']\\n if not c.login:\\n return False\\n\\n r = requests.request(\\\"GET\\\", urljoin(c.BASE_URL, '/apiproxy/JobService.js'), params={'accesskey': c.login, 'method': 'PopulateAllSearches'})\\n print(r.status_code, r.text)\\n\\n if r.status_code == 200:\\n return True\\n else:\\n return False\",\n \"def bulk_upsert(self, data, parameters=None, many_to_many_clear: bool = True):\\n elements = []\\n\\n # Skip if no identifier is set\\n if not self.unique_identifier or not data:\\n return\\n\\n for element in data:\\n if isinstance(parameters, dict):\\n element.update(parameters)\\n\\n element, created = self.upsert(data=element, many_to_many_clear=many_to_many_clear)\\n elements.append(element)\\n\\n return elements\",\n \"def do_refresh_revisions():\\n\\n rows_by_type = _get_revisions_by_type()\\n\\n for type_, rows in rows_by_type.iteritems():\\n logger.info(\\\"Updating revisions for: %s\\\", type_)\\n _fix_type_revisions(type_, rows)\",\n \"def update_archive(self, population, offsprings, generation):\\n # Store agents\\n did_store = False\\n for agent in offsprings:\\n agent['stored'] = generation\\n if self.archive.store(agent):\\n self.emitters_pop[self.emitter_idx].stored += 1\\n did_store = True\\n\\n if did_store:\\n # Update emitter distribution (This one needs to be changed if wanting to use improvement emitters)\\n solutions = [(genome, -value) for genome, value in zip(offsprings['genome'], offsprings['reward'])]\\n self.emitters_pop[self.emitter_idx].tell(solutions)\\n else:\\n # If the emitter did not store anything, we restart it from another elite\\n elite_idx = np.random.choice(self.archive.size)\\n self.emitters_pop[self.emitter_idx] = OptimizingEmitter(self.archive['genome'][elite_idx],\\n self.archive['id'][elite_idx],\\n mutation_rate=0.5,\\n bounds=self.bounds,\\n parameters=self.params)\",\n \"def test_otoroshi_controllers_adminapi_tcp_service_api_controller_bulk_update_action(self):\\n pass\",\n \"def update(self):\\n self._sync_ranges()\\n self._update_params()\",\n \"def salesforce_collection_update(self, objects):\\n for obj in objects:\\n assert obj[\\n \\\"id\\\"\\n ], \\\"Should be a list of objects with Ids returned by Salesforce Collection Insert\\\"\\n if STATUS_KEY in obj:\\n del obj[STATUS_KEY]\\n\\n assert len(objects) <= SF_COLLECTION_INSERTION_LIMIT, (\\n \\\"Cannot update more than %s objects with this keyword\\\"\\n % SF_COLLECTION_INSERTION_LIMIT\\n )\\n\\n records = self.cumulusci.sf.restful(\\n \\\"composite/sobjects\\\",\\n method=\\\"PATCH\\\",\\n json={\\\"allOrNone\\\": True, \\\"records\\\": objects},\\n )\\n\\n for record, obj in zip(records, objects):\\n obj[STATUS_KEY] = record\",\n \"def update(self, dt):\\n for obj in self.objects:\\n obj.update(dt)\",\n \"def to_db(self):\\n bulk = conn_db().initialize_ordered_bulk_op()\\n for fiction in self.fictions:\\n bulk.find({'id': fiction.id}).upsert().update({'$set': fiction.__dict__})\\n bulk.execute()\",\n \"def batch_process_async(self, delete_list=[], update_list=[]):\\n headers = update_dict(self.default_headers, {self.API_VERSION_HEADER: self.API_VERSIONS[\\\"v2\\\"]})\\n self.request_url = \\\"{0}/{1}\\\".format(self.API_URL, self.USER_BULK_ENDPOINT)\\n\\n payload = {\\n 'updated': update_list,\\n 'deleted': delete_list,\\n }\\n\\n r = self.requests.post(self.request_url, data=json.dumps(payload), headers=headers, timeout=30)\\n\\n return r.status_code, r.json()\",\n \"def do_update(services):\\n\\n global running_update\\n\\n for service in services:\\n feed = registry[service.name][0]\\n try:\\n if type(feed) is list:\\n entries = []\\n for f in feed:\\n entries.extend(f(service))\\n else:\\n entries = feed(service)\\n # TODO should be in a transaction\\n for entry in entries:\\n entry.save()\\n service.updated = datetime.utcnow()\\n service.save()\\n except:\\n logging.exception(msg='updater exception for service %s' %service.name, exception=True)\\n running_update = False\",\n \"def update_state(self, slate_documents, responses):\",\n \"def updateMasivo(self, objects):\\n retorno = 1\\n try:\\n for object in objects:\\n self.session.merge(object)\\n self.session.commit()\\n \\n except sqlalchemy.exc.DBAPIError, e:\\n if self.session is not None:\\n self.session.rollback()\\n retorno = -1\\n print(\\\"Error!\\\", e)\\n finally:\\n if self._DAOAlchemy__cerrarSesion:\\n self.session.close()\\n return retorno\",\n \"def refresh(self, parameters = {}):\\n\\n self.__enforce_connected()\\n self.collection.refresh(self, parameters = parameters)\",\n \"def update():\\n query = db.session.query(db.Series).filter_by(following=True).all()\\n output.series('Updating {} series'.format(len(query)))\\n for follow in query:\\n series = series_by_url(follow.url)\\n series.update()\\n list_new()\",\n \"def refresh(self):\\r\\n self.metadata = self.db.read(self.path).json()\",\n \"def update_list(self):\\n Asset.update_list(self, uri_keys=('ems_sys', 'list'))\",\n \"def update(self):\\n\\n\\t\\tif not self.complete:\\n\\t\\t\\tfor vasp_run in self.vasp_run_list:\\n\\t\\t\\t\\tvasp_run.update()\",\n \"def flush_batch(self, batch):\\n inserts = []\\n replacements = []\\n\\n for action_type, data in batch:\\n if action_type == processor.INSERT:\\n inserts.append(data)\\n elif action_type == processor.REPLACE:\\n replacements.append(data)\\n\\n if inserts:\\n write_rows(\\n self.clickhouse,\\n self.dist_table_name,\\n inserts\\n )\\n\\n if self.metrics:\\n self.metrics.timing('inserts', len(inserts))\\n\\n if replacements:\\n for key, replacement in replacements:\\n self.producer.produce(\\n self.replacements_topic,\\n key=six.text_type(key).encode('utf-8'),\\n value=json.dumps(replacement).encode('utf-8'),\\n on_delivery=self.delivery_callback,\\n )\\n\\n self.producer.flush()\",\n \"def _update(self):\\n self.all_params = {}\\n self._update_experiment_params()\\n self._update_preprocessing_params()\\n self._update_model_params()\",\n \"def update_users_bulk(self, users):\\n # type: (list) -> dict\\n payload = {'updated': users}\\n self.request_url = \\\"{0}/{1}\\\".format(self.API_URL, self.USER_BULK_ENDPOINT)\\n return self.__create_request(payload=payload, request_type=self.REQUEST_POST, version=\\\"v1\\\")\",\n \"def updates(self, request, version):\\n task = EtlTask.objects.get_for_model(self.queryset.model)\\n if task.last_changes:\\n offset = task.last_changes.strftime(\\\"%Y-%m-%d %H:%M\\\")\\n queryset = self.queryset.filter(last_modify_date__gte=offset)\\n else:\\n offset = \\\"none\\\"\\n queryset = self.queryset.all()\\n queryset = self.paginate_queryset(queryset)\\n serializer = self.get_serializer(queryset, many=True)\\n return self.get_paginated_response(serializer.data)\\n # return Response(serializer.data,\\n # headers={'update-date': offset})\",\n \"def bulk_write(collection, iterable, job_id=None, unsafe=False):\\n namespace = Namespace(collection.foreign_id)\\n stage = get_stage(collection, OP_INDEX, job_id=job_id)\\n entities = []\\n for item in iterable:\\n if not is_mapping(item):\\n raise InvalidData(\\\"Failed to read input data\\\", errors=item)\\n entity = model.get_proxy(item)\\n entity = namespace.apply(entity)\\n if not unsafe:\\n entity = remove_checksums(entity)\\n entities.append(entity)\\n index_entities(stage, collection, entities)\",\n \"async def reload_all(ctx):\\n await ext_manager.reload_all()\\n await ctx.send(\\\"Successfully reloaded.\\\")\",\n \"def refresh():\\r\\n DB.drop_all()\\r\\n DB.create_all()\\r\\n # TODO Get data from OpenAQ, make Record objects with it, and add to db\\r\\n for i in time_x_values():\\r\\n DB.session.add(Record(datetime=i[0], value=i[1]))\\r\\n DB.session.commit()\\r\\n return 'Data refreshed!'\",\n \"def update(self, values):\\n return self.manager.update(self, values)\",\n \"def apply_updates(self, updates):\\n raise NotImplementedError()\",\n \"def upload_entities(self, batch):\\n # TODO Include a Do Not Overwrite call\\n results = None\\n atlas_endpoint = self.endpoint_url + \\\"/entity/bulk\\\"\\n\\n payload = AtlasClient._prepare_entity_upload(batch)\\n\\n postBulkEntities = requests.post(\\n atlas_endpoint,\\n json=payload,\\n headers=self.authentication.get_authentication_headers()\\n )\\n\\n results = self._handle_response(postBulkEntities)\\n\\n return results\",\n \"def bulkSave(self, objList: List[PermissionContext], tokenData: TokenData):\",\n \"def apply(self):\\n\\n data = self._to_dict()\\n\\n # read only attributes\\n del data['Provider']\\n del data['MarkedForDelOn']\\n del data['IsDeleted']\\n del data['Database']\\n\\n data = {k: v for k, v in data.items() if v is not None}\\n\\n # remove empty arrays. Note: pylint will complain about this\\n # but zeep will complain even more :)\\n if len(data['Attributes']) == 0:\\n del data['Attributes']\\n\\n if len(data['SecondaryDomains']) == 0:\\n del data['SecondaryDomains']\\n\\n updateshopobj = self.sc.get_updateshop_obj(data)\\n\\n self.sc.update(updateshopobj)\\n\\n self.refresh()\",\n \"def flush(self):\\n self.save()\\n for obs in self.observation_set.all():\\n obs.flush();\",\n \"def merge_to_elastic(paper_authors, papers, authors, index_name):\\n columns = list(papers.columns) + ['authors']\\n for index, paper in papers.iterrows():\\n merger = paper_authors.loc[paper_authors['paper_id'] == index]\\n author_ids = merger['author_id'].values\\n author_names = [authors.loc[authors['id'] == x, 'name'].values[0] for x in author_ids]\\n paper['authors'] = author_names\\n yield {\\n \\\"_index\\\": index_name,\\n \\\"_type\\\": \\\"_doc\\\",\\n \\\"_id\\\" : f\\\"{index}\\\",\\n \\\"_source\\\": filterKeys(paper, columns),\\n }\",\n \"def update_view(self):\\n for row in self.view.obj_list:\\n for obj in row:\\n obj._update(self.model)\",\n \"def pools_refresh(self):\\n path = \\\"%s/commands/poolsRefresh\\\" % self.__base_path\\n response = self.__session.post(path)\\n self.__check_status_code(response.status_code)\\n return response.json()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.76656324","0.6743843","0.6214206","0.59055984","0.58515877","0.57441527","0.5547801","0.54766494","0.54745275","0.54507333","0.5444725","0.542627","0.53595006","0.5348997","0.5331371","0.52764916","0.527101","0.52681786","0.5237418","0.5237225","0.5230986","0.5213352","0.5195876","0.5189852","0.5159918","0.515815","0.5137841","0.51265115","0.5119004","0.5090844","0.5079043","0.5068937","0.5054568","0.50518733","0.504478","0.5031268","0.5028038","0.5025309","0.50041467","0.5000888","0.49982923","0.4989702","0.49872592","0.49785417","0.49601278","0.49572235","0.49557477","0.49524382","0.4941155","0.49405015","0.4930987","0.49290067","0.49239975","0.49214068","0.49100196","0.4908053","0.4908053","0.4908053","0.4883649","0.487883","0.48743615","0.48625854","0.48596442","0.48577398","0.48516792","0.48330256","0.48314434","0.48264906","0.48247454","0.48179412","0.4809989","0.48075843","0.47975373","0.47900942","0.47773397","0.47736093","0.4756942","0.4745793","0.47438946","0.4733899","0.47282013","0.47276178","0.47270626","0.47268492","0.4726616","0.47229195","0.4710116","0.47005188","0.46971714","0.4695512","0.4693281","0.46905023","0.46902966","0.46890056","0.46859133","0.4685558","0.46780232","0.4671876","0.46715236","0.46710223"],"string":"[\n \"0.76656324\",\n \"0.6743843\",\n \"0.6214206\",\n \"0.59055984\",\n \"0.58515877\",\n \"0.57441527\",\n \"0.5547801\",\n \"0.54766494\",\n \"0.54745275\",\n \"0.54507333\",\n \"0.5444725\",\n \"0.542627\",\n \"0.53595006\",\n \"0.5348997\",\n \"0.5331371\",\n \"0.52764916\",\n \"0.527101\",\n \"0.52681786\",\n \"0.5237418\",\n \"0.5237225\",\n \"0.5230986\",\n \"0.5213352\",\n \"0.5195876\",\n \"0.5189852\",\n \"0.5159918\",\n \"0.515815\",\n \"0.5137841\",\n \"0.51265115\",\n \"0.5119004\",\n \"0.5090844\",\n \"0.5079043\",\n \"0.5068937\",\n \"0.5054568\",\n \"0.50518733\",\n \"0.504478\",\n \"0.5031268\",\n \"0.5028038\",\n \"0.5025309\",\n \"0.50041467\",\n \"0.5000888\",\n \"0.49982923\",\n \"0.4989702\",\n \"0.49872592\",\n \"0.49785417\",\n \"0.49601278\",\n \"0.49572235\",\n \"0.49557477\",\n \"0.49524382\",\n \"0.4941155\",\n \"0.49405015\",\n \"0.4930987\",\n \"0.49290067\",\n \"0.49239975\",\n \"0.49214068\",\n \"0.49100196\",\n \"0.4908053\",\n \"0.4908053\",\n \"0.4908053\",\n \"0.4883649\",\n \"0.487883\",\n \"0.48743615\",\n \"0.48625854\",\n \"0.48596442\",\n \"0.48577398\",\n \"0.48516792\",\n \"0.48330256\",\n \"0.48314434\",\n \"0.48264906\",\n \"0.48247454\",\n \"0.48179412\",\n \"0.4809989\",\n \"0.48075843\",\n \"0.47975373\",\n \"0.47900942\",\n \"0.47773397\",\n \"0.47736093\",\n \"0.4756942\",\n \"0.4745793\",\n \"0.47438946\",\n \"0.4733899\",\n \"0.47282013\",\n \"0.47276178\",\n \"0.47270626\",\n \"0.47268492\",\n \"0.4726616\",\n \"0.47229195\",\n \"0.4710116\",\n \"0.47005188\",\n \"0.46971714\",\n \"0.4695512\",\n \"0.4693281\",\n \"0.46905023\",\n \"0.46902966\",\n \"0.46890056\",\n \"0.46859133\",\n \"0.4685558\",\n \"0.46780232\",\n \"0.4671876\",\n \"0.46715236\",\n \"0.46710223\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8021315"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":94691,"cells":{"query":{"kind":"string","value":"This will also update persuasions in bulk based on type subtype and will push it to ES API /persuasion/schedule/refresh?push_to_es=true&push_to_inflow=false&type=quality_score&sub_type=inventory_depth"},"document":{"kind":"string","value":"def schedule_refresh():\n logger.info(\"Refreshing/Updating persuasions in bulk\")\n try:\n args = request.args\n if not args.get(\"type\") or not args.get(\"sub_type\"):\n return jsonify(\n dict(status=\"failure\", message=\"Invalid type/sub_type\"))\n\n persuasions_request = PersuasionServices.get_persuasions_request_from_es(args)\n\n with concurrent.futures.ThreadPoolExecutor(max_workers=settings.MAX_WORKERS) as executor:\n {executor.submit(PersuasionServices.refresh, data, args): data for data in persuasions_request}\n\n return jsonify(\n dict(status=\"success\", message=\"Your request is in the queue, persuasion will be updated shortly\"))\n except Exception as e:\n logger.error(\"Exception while creating persuasions in bulk - \" + repr(e))\n return jsonify(dict(status=\"failure\", error=repr(e)))"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def bulk_refresh():\n logger.info(\"Refreshing/Updating persuasions in bulk\")\n try:\n request_data = json.loads(request.data)\n args = request.args\n with concurrent.futures.ThreadPoolExecutor(max_workers=settings.MAX_WORKERS) as executor:\n {executor.submit(PersuasionServices.refresh, data, args): data for data in request_data}\n\n return jsonify(\n dict(status=\"success\", message=\"Your request is in the queue, persuasion will be updated shortly\"))\n except Exception as e:\n logger.error(\"Exception while creating persuasions in bulk - \" + repr(e))\n return jsonify(dict(status=\"failure\", error=repr(e)))","def do_refresh_revisions():\n\n rows_by_type = _get_revisions_by_type()\n\n for type_, rows in rows_by_type.iteritems():\n logger.info(\"Updating revisions for: %s\", type_)\n _fix_type_revisions(type_, rows)","def updateAll(data):\n if (data.updatePositions):\n data.groups.player.update(data)\n data.groups.projectiles.update(data)\n data.groups.monsters.update(data)\n data.groups.spawners.update(data)","def mark_processed_entities(entity_type, max_date):\r\n\r\n try:\r\n \r\n # get a connection and cursor\r\n conn = ecommerce.db.getConnection()\r\n cursor = conn.cursor()\r\n \r\n # execute the query\r\n cursor.execute(\"\"\"\r\n UPDATE Stage0_Delta\r\n SET FlagUpdated = 0\r\n WHERE EntityType = ? AND\r\n FlagUpdated = 1 AND\r\n LastUpdate <= TO_DATE(?, 'YYYY-MM-DD HH24:MI:SS')\r\n \"\"\", (entity_type, max_date) )\r\n \r\n # commit changes\r\n conn.commit()\r\n except:\r\n conn.rollback()\r\n pass","def update_position():\n submissions = get_submissions_effective_date_today()\n\n if submissions:\n for submission in submissions:\n submission_changes = get_submission_changes(submission['id'])\n\n for change in submission_changes:\n execute_statement(\n f\"UPDATE position SET {change['field']} = '{change['change']}' WHERE number = '{submission['position_id']}' and isBudget = 0\"\n )\n\n # create audit trail in db\n create_audit_log('127.0.0.1', 'Server', '127.0.0.1', action='Successfully updated position {} on submission effective date'.format(\n submission['position_id']), table='position', function='UPDATE')\n else:\n # create audit trail in db\n create_audit_log('127.0.0.1', 'Server', '127.0.0.1',\n action='No positions to update today', table='position', function='')","def _batch_update(self, query, mutation):\n logger.info(\"Performing batch update on %s. Mutation: %s\", query, mutation)\n modified = 0\n for doc in self.instances.find(query):\n with lock_instance(doc['_id']):\n pre_update_doc = self.instances.find_one({'_id' : doc['_id']})\n result = self.instances.update_one({'_id': doc['_id']}, mutation)\n assert result.modified_count == 1\n modified += 1\n updated_doc = self.instances.find_one({'_id': doc['_id']})\n instance = FixtureInstance.deserialize_mongodoc(updated_doc)\n try:\n self.axdb_client.update_fixture_instance(instance.axdbdoc())\n except Exception:\n logger.exception(\"Failed to persist updates for %s. Undoing cache update\", instance)\n self.instances.replace_one({'_id' : instance.id}, pre_update_doc)\n raise\n logger.info(\"%s fixture instances modified\", modified)","def bulk_update(self, request):\n serializer = MasterySerializer(\n data=request.data,\n many=True,\n )\n if serializer.is_valid():\n serializer.save()\n return Response(serializer.data)\n else:\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)","def step070() -> None:\n logger.logMessage('Begin: elasticsearch bulk update')\n client = es.Elasticsearch(hostlist)\n\n def generate():\n with open(renumFile,'r') as f:\n line = f.readline().rstrip()\n while line != '':\n fields = line.split(';')\n oper = { '_index': fields[3], \n '_op_type': 'update',\n '_id': fields[2].rstrip(),\n '_type': 'doc',\n '_source:': {'doc': {'tsa': fields[0]}}}\n \n yield oper\n line = f.readline().rstrip()\n result = eshelp.bulk(client,generate())\n logger.logMessage('Bulk result: {0}'.format(result))\n logger.logMessage('End : elasticsearch bulk update')","def train(self):\n if self.check_if_necessary() is False:\n return\n # open a transaction\n with db as session:\n # Reset popularity score (delete and re-add column for score)\n session.execute(\n text('ALTER TABLE \"%s\" DROP COLUMN popularity_score' % self.__media__[0].tablename))\n session.execute(\n text('ALTER TABLE \"%s\" ADD COLUMN popularity_score DOUBLE PRECISION' % self.__media__[0].tablename))\n for media in self.__media__:\n st_time = datetime.utcnow()\n\n m = media(logger=self.logger)\n q_df = m.get_populars(size=1000)\n\n # open a transaction\n with db as session:\n # Set new popularity score\n for index, row in q_df.iterrows():\n session.execute(\n text(\"UPDATE %s SET popularity_score = %s WHERE %s = %s\" % (m.tablename, row[\"popularity_score\"], m.id, row[m.id])))\n self.logger.debug(\"%s popularity reloading performed in %s (%s lines)\" %\n (str(m.content_type) or \"ALL CONTENT\", datetime.utcnow()-st_time, q_df.shape[0]))\n self.store_date(m.content_type)","def update_performance_reports(update_all=True):\n from mspray.apps.main.utils import performance_report\n\n time_within = UPDATE_VISITED_MINUTES\n time_since = timezone.now() - timedelta(minutes=time_within + 1)\n\n if update_all:\n submissions = SprayDay.objects.all()\n else:\n submissions = SprayDay.objects.filter(\n Q(created_on__gte=time_since) | Q(modified_on__gte=time_since)\n )\n\n sop_queryset = (\n SprayDay.objects.filter(\n Q(created_on__gte=time_since) | Q(modified_on__gte=time_since)\n )\n .filter(spray_operator__isnull=False)\n .only(\"spray_operator\")\n .distinct(\"spray_operator\")\n )\n\n for record in sop_queryset:\n performance_report(\n record.spray_operator,\n submissions.filter(spray_operator=record.spray_operator),\n )","def updateall(self, params):\n for gauge in self.gauges:\n self.safexec(gauge.updateall, params)","def update_particles(self):\n for particle in self.particles:\n particle.update_coordinates(self.bounds)","def update(self, params):\n for gauge in self.gauges:\n self.safexec(gauge.update, params)","def update_monomer_population(self,monomer_type,update_type='remove'):\n if update_type=='remove':\n self.monomer_population[monomer_type] += -1\n self.added_set[monomer_type] += 1\n \n self.total_monomers += -1\n self.network_size += 1\n elif update_type=='add':\n self.monomer_population[monomer_type] += 1\n self.added_set[monomer_type] += -1\n \n self.total_monomers += 1\n \n self.free_monomer_fraction = self.total_monomers/float(self.total_sites)\n\n self.construct_prob_dist()\n self.compute_mean_eps_self()","def _update_coverage(self, msg, subtype, by):\n try:\n coverage = self.get_local(msg, \"coverage\")\n except KeyError:\n coverage = defaultdict(int)\n coverage[\"all\"] += by\n coverage[subtype] += by\n self.set_local(msg, \"coverage\", coverage)","def save(self):\n for t in self.ace_types:\n self.api.api_request(\"PUT\", self.url + t, data={t: self[t]})","def update(self):\n self.platform_list.update()\n self.enemy_list.update()","def update(self, es, **kwargs):\n pass","def update_members_from_preferences(self, **parameters):\n super(Sequence, self).update_members_from_preferences(**parameters)\n\n for i, item in enumerate(self.items):\n para = parameters['item_{}'.format(i)]\n item.update_members_from_preferences(**para)","def update_properties(self, prop_dict):\n ft_dict = {ft.name: ft for ft in self.get_field_types()}\n for name, val in prop_dict.items():\n ft = ft_dict[name]\n if ft.is_parameter():\n key = \"value\"\n else:\n key = \"sample\"\n if issubclass(type(val), Sequence) and ft.array:\n self.set_field_value_array(name, None, [{key: v} for v in val])\n else:\n self.set_field_value(name, None, {key: val})","def refresh_persuasion():\n try:\n request_data = json.loads(request.data)\n meta = request.args\n persuasion = PersuasionServices.refresh(request_data, meta)\n except Exception as e:\n error_msg = \"Getting persuasion details - \" + repr(e)\n logger.error(error_msg)\n return jsonify(dict(status=\"failure\", error=error_msg))\n return jsonify(dict(status=\"success\", data=persuasion if persuasion else dict()))","def process(self):\n\t\tif self.update_check():\n\t\t\tself.ingest_all()\n\t\t\tself.update_totals()\n\t\telse:\n\t\t\tlog.info('PHE cases up to date')","async def actualize(self):\r\n for guild, settings in self.bot.settings.items():\r\n # Grab the roles and their requirements\r\n guild = self.bot.get_guild(guild)\r\n base_member = settings.get(\"rank_basic_member_role_id\")\r\n base_member = guild.get_role(base_member)\r\n active_member = settings.get(\"rank_active_member_role_id\")\r\n active_member = guild.get_role(active_member)\r\n active_member_days = settings.get(\"rank_active_member_required_days\")\r\n active_member_activity = settings.get(\"rank_active_member_required_activity\")\r\n junior_mod = settings.get(\"rank_junior_mod_role_id\")\r\n junior_mod = guild.get_role(junior_mod)\r\n junior_mod_days = settings.get(\"rank_junior_mod_required_days\")\r\n junior_mod_activity = settings.get(\"rank_junior_mod_required_activity\")\r\n senior_mod = settings.get(\"rank_senior_mod_role_id\")\r\n senior_mod = guild.get_role(senior_mod)\r\n admin = settings.get(\"rank_admin_role_id\")\r\n admin = guild.get_role(admin)\r\n # Fetch the list of members eligible for each rank by their activity\r\n active_member_eligible = await util_users.fetch_users_by_days_and_activity(\r\n self.bot, guild, active_member_days, active_member_activity\r\n )\r\n active_member_eligible = [i[0] for i in active_member_eligible]\r\n junior_mod_eligible = await util_users.fetch_users_by_days_and_activity(\r\n self.bot, guild, junior_mod_days, junior_mod_activity\r\n )\r\n junior_mod_eligible = [i[0] for i in junior_mod_eligible]\r\n # Iterate over all members to edit their roles\r\n for member in [i for i in guild.members if not i.bot]:\r\n # Admin check\r\n if admin in member.roles:\r\n continue\r\n # Senior mod check\r\n elif senior_mod in member.roles:\r\n continue\r\n # Junior mod check\r\n elif junior_mod in member.roles:\r\n if member.id not in junior_mod_eligible:\r\n await member.remove_roles(junior_mod)\r\n await member.add_roles(active_member)\r\n # Active member check\r\n elif active_member in member.roles:\r\n if member.id not in active_member_eligible:\r\n await member.remove_roles(active_member)\r\n await member.add_roles(base_member)\r\n # Base member check\r\n elif base_member in member.roles:\r\n if member.id in active_member_eligible:\r\n await member.add_roles(active_member)","def update(self, events, **kwargs):\n self.collisions.update(self._collidables, self._projectiles)\n for o in list(self._updateables):\n o.update(gm=self, events=events, **kwargs)","def refresh_counterfactual_url_type(request, compound_type):\t\n\n\tnow = datetime.datetime.now()\n\tprint \"Start Time %s\" % now\n\n\tc_type \t\t= compound_type \t# compound type, o to be only and c to be compound\n\tprimeCoordinator= PrimeCoordinator() \t# initiate an PrimeCoordinator object to manipulate inputed parameters to get a counterfactual results\n\n\tnow = datetime.datetime.now()\n\tprint \"Coordinator initialised %s\" % now\n\n\t# Get variables from URL and create array\n\texposure_name \t\t= request.GET.get('exposure', '')\n\te_id \t\t\t= DBHelper.get_exposure_id_from_name(exposure_name)\n\tmean \t\t\t= request.GET.get('mean', '')\n\tsd \t\t\t= request.GET.get('standard-deviation', '')\n\tnon_rate\t\t= request.GET.get('non-rate', '')\n\texposure_sequence \t= [{'non_rate':non_rate,'mean':mean,'e_id':e_id,'sd':sd}]\n\n\tprimeCoordinator.get_counterfactual_compound_exposures(exposure_sequence)\n\n\tnow = datetime.datetime.now()\n\tprint \"Coordinator given exposures %s\" % now\n\n\t# get the data in the interface\n\tb_output_mortality \t= primeCoordinator.output_baseline_mortality # baseline mortality list for all outcomes\n\tb_output_mortality_num \t= primeCoordinator.output_baseline_mortality_num # baseline mortality sum up for each outcome\n\tb_total_mortality \t= primeCoordinator.output_baseline_mortality_total# baseline mortality sum up for all outcomes\n\n\tnow = datetime.datetime.now()\n\tprint \"Total baseline mortality calculated %s\" % now\n\n\tc_output_mortality \t= primeCoordinator.output_counterfactual_mortality# counterfactual mortality for all outcomes\n\tc_output_mortality_num \t= primeCoordinator.output_counterfactual_mortality_num# counterfactual mortality for each outcome\n\tc_total_mortality \t= primeCoordinator.output_counterfactual_mortality_total# counterfactual mortality sum up for all outcomes\n\n\tnow = datetime.datetime.now()\n\tprint \"Total counterfactual mortality calculated %s\" % now\n\n\ttotal_population\t= primeCoordinator.output_total_population\n\tall_mortality_exposure\t= primeCoordinator.output_all_mortality_exposure_outcome\n\n\tnow = datetime.datetime.now()\n\tprint \"Total deaths averted calculated %s\" % now\n\n\ttotal_death_averted\t= str(round(primeCoordinator.output_total_death_averted,0))\n\ttotal_death_baseline\t= str(primeCoordinator.output_total_death_baseline)\n\n\n\tnow = datetime.datetime.now()\n\tprint \"End Time: %s\" % now\n\n\t#transmit the parameters\n\ttemplate = loader.get_template('primemodel/index.html')\n\tpara_view = {\n\t\t\t'b_output_mortality_num':\tb_output_mortality_num,\n\t\t\t'b_total_mortality':\t\tb_total_mortality,\n\t\t\t'c_output_mortality_num':\tc_output_mortality_num,\n\t\t\t'c_total_mortality':\t\tc_total_mortality,\n\t\t\t'total_population':\t\t\ttotal_population,\n\t\t\t'total_death_averted':\t\ttotal_death_averted,\n\t\t\t'total_death_baseline':\t\ttotal_death_baseline,\n\t\t\t'all_mortality_exposure':\tall_mortality_exposure\n\t\t\t}\n\n\t#context to transmit the parameters to show\n\tcontext = Context(para_view)\n\tresponse = template.render(context)\n\treturn HttpResponse(response)","def _process(self, start_key, batch_size):\n\n query = self.MODEL.all()\n if start_key:\n query.filter('__key__ > ', start_key)\n\n try:\n entities = query.fetch(batch_size)\n\n if not entities:\n # all entities has already been processed\n return\n\n for entity in entities:\n for field in self.FIELDS_TO_UPDATE:\n old_reference = getattr(entity, field)\n\n if not old_reference:\n continue\n\n # check if the field has not been updated\n if isinstance(old_reference, self.PROFILE_MODEL):\n continue\n\n profile = _getProfileForRole(old_reference, self.PROFILE_MODEL)\n setattr(entity, field, profile)\n\n for list_property in self.LISTS_TO_UPDATE:\n l = getattr(entity, list_property)\n new_l = []\n for key in l:\n new_l.append(_getProfileKeyForRoleKey(key, self.PROFILE_MODEL))\n setattr(entity, list_property, new_l)\n\n db.put(entities)\n start_key = entities[-1].key()\n deferred.defer(self._process, start_key, batch_size)\n except DeadlineExceededError:\n # here we should probably be more careful\n deferred.defer(self._process, start_key, batch_size)","def update_entities(self):\n raise NotImplementedError()","def _update_level_data(self):\n\t\t# taxes, inhabitants\n\t\tself.tax_base = self.session.db.get_settler_tax_income(self.level)\n\t\tself.inhabitants_max = self.session.db.get_settler_inhabitants_max(self.level)\n\t\tif self.inhabitants > self.inhabitants_max: # crop settlers at level down\n\t\t\tself.inhabitants = self.inhabitants_max\n\n\t\t# consumption:\n\t\t# Settler productions are specified to be disabled by default in the db, so we can enable\n\t\t# them here per level.\n\t\tcurrent_lines = self.get_production_lines()\n\t\tfor (prod_line,) in self.session.db.get_settler_production_lines(self.level):\n\t\t\tif not self.has_production_line(prod_line):\n\t\t\t\tself.add_production_by_id(prod_line)\n\t\t\t# cross out the new lines from the current lines, so only the old ones remain\n\t\t\tif prod_line in current_lines:\n\t\t\t\tcurrent_lines.remove(prod_line)\n\t\tfor line in current_lines[:]: # iterate over copy for safe removal\n\t\t\t# all lines, that were added here but are not used due to the current level\n\t\t\tself.remove_production_by_id(line)\n\t\t# update instance graphics\n\t\tself.update_action_set_level(self.level)","def update_quality():\n global items, converted_items\n if not converted_items:\n items = convert_items(items)\n converted_items = True\n for item in items:\n item.update_q()","def setEmpireStats(self):\n totalEmpires = len(self.empires.keys())\n stats = {'Research':[], 'Fleet Size':[], 'Army Size':[], 'CR Production':[],\n 'AL Production':[],'EC Production':[],'IA Production':[]}\n \n # Calculate Research Stats\n d = {}\n for empireID, myEmpire in self.empires.iteritems():\n if empireID <> '1':\n num = 0\n for techID, myTech in myEmpire.techTree.iteritems():\n if myTech.complete == 1:\n num += 1\n d[empireID] = num\n stats['Research'] = anwp.func.funcs.sortDictByValue(d, True)\n \n # Calculate Fleet Stats\n d = {}\n for shipID, myShip in self.ships.iteritems():\n if myShip.empireID <> '1':\n (BV,CR,AL,EC,IA) = myShip.getMyValue()\n if myShip.empireID in d.keys():\n d[myShip.empireID] += BV\n else:\n d[myShip.empireID] = BV\n stats['Fleet Size'] = anwp.func.funcs.sortDictByValue(d, True)\n \n # Calculate Army Stats\n d = {}\n for regimentID, myRegiment in self.regiments.iteritems():\n if myRegiment.empireID <> '1':\n (BV,CR,AL,EC,IA) = myRegiment.getMyValue()\n if myRegiment.empireID in d.keys():\n d[myRegiment.empireID] += BV\n else:\n d[myRegiment.empireID] = BV\n stats['Army Size'] = anwp.func.funcs.sortDictByValue(d, True)\n\n # Calculate Production Stats\n for res in ['CR','AL','EC','IA']:\n d = {}\n for systemID, mySystem in self.systems.iteritems():\n if mySystem.myEmpireID <> '1':\n myValue = getattr(mySystem, 'prod%s' % res)\n if mySystem.myEmpireID in d.keys():\n d[mySystem.myEmpireID] += myValue\n else:\n d[mySystem.myEmpireID] = myValue\n myEmpire = self.empires[mySystem.myEmpireID]\n myEmpireValue = getattr(myEmpire, 'totalProd%s' % res)\n setattr(myEmpire, 'totalProd%s' % res, myEmpireValue+myValue)\n \n stats['%s Production' % res] = anwp.func.funcs.sortDictByValue(d, True)\n \n # calculate top captains\n d = {}\n for captainID, myCaptain in self.captains.iteritems():\n if myCaptain.myEmpire.id <> '1':\n myCaptain.resetData()\n d[myCaptain.id] = myCaptain.experience\n topCaptains = anwp.func.funcs.sortDictByValue(d, True)\n topCaptains = topCaptains[:2*len(self.empires.keys())]\n \n # Send out Stats to each Empire\n for empireID, myEmpire in self.empires.iteritems():\n if empireID <> '1':\n title = 'Round:%d Statistics' % self.currentRound\n body = ['%s ROUND %d STATS:' % (myEmpire.name, self.currentRound)]\n body.append('====================================================')\n for item in ['Research','Fleet Size', 'Army Size', 'CR Production',\n 'AL Production', 'EC Production', 'IA Production']:\n if empireID in stats[item]:\n body.append('You are %s in %s' % (anwp.func.funcs.getNiceNumber(stats[item].index(empireID)+1), item))\n \n # total production\n body.append('')\n body.append('TOTAL EMPIRE PRODUCTION OVER %d ROUNDS:' % self.currentRound)\n body.append('====================================================')\n for res in ['CR','AL','EC','IA']:\n body.append('Total %s Production: %d' % (res, getattr(myEmpire, 'totalProd%s' % res)))\n\n # legendary captains\n body.append('')\n body.append('TOP %d STARSHIP CAPTAINS in ROUND %d:' % ((2*len(self.empires.keys()), self.currentRound)))\n body.append('====================================================')\n for captainID in topCaptains:\n myCaptain = self.captains[captainID]\n myCaptain.promoteMe()\n body.append('%s ---> RANK:%s -- EXP:%d -- %s' % (string.upper(myCaptain.name), myCaptain.rank, myCaptain.experience, string.upper(myCaptain.myEmpire.name)))\n \n myEmpire.genMail({'fromEmpire':empireID, 'round':self.currentRound,\n 'messageType':'general', 'subject':title, 'body':body})","def update(self, data):\n self.data = data\n # extract data\n subject = data.identifier\n self.id = _uri_to_oai(subject)\n self.modified = datetime.utcnow()\n self.deleted = False\n\n itemtype, subtype = _get_itemtype(data, subject)\n\n self.metadata = {}\n\n # fixed fields:\n self.metadata['rif_key'] = _uri_to_key(subject)\n self.metadata['rif_group'] = self.provider.groupDescription\n self.metadata['rif_originatingSource'] = self.provider.originatingSource\n self.metadata['rif_object'] = {'value': itemtype,\n 'type': subtype,\n #'dateModified': '',\n }\n\n if itemtype == 'collection':\n self.updateCollection(data, subject)\n elif itemtype == 'party':\n if subtype == 'person':\n self.updatePartyPerson(data, subject)\n else:\n self.updateParty(data, subject)\n elif itemtype == 'activity':\n self.updateActivity(data, subject)\n elif itemtype == 'service':\n self.updateService(data, subject)","def _postprocessing(self):\n # (in)validate\n if len(self._var_names) == 0:\n self.invalidate()\n else:\n self.put_param('is_valid', True)\n \n # set type\n self.put_param('type', 'Generic')","def periodicUpdate(self):\n try:\n logging.info(f'{self.cn} periodicUpdate = Start')\n isHaz = JsonSettings.parseJson('settings.json','isHazelcast')\n if self.db.isDb():\n self.insertStats()\n self.insertPorts()\n if isHaz:\n self.insertHaz() \n else:\n self.db.initDb()\n self.insertSys()\n self.insertStats()\n self.insertPorts()\n if isHaz:\n self.insertHaz() \n except Exception as e:\n logging.critical(f'{self.cn} Exception: {e}')\n logging.critical(f'{self.cn} StackTrace: \\n', exc_info=1)\n finally:\n logging.info(f'{self.cn} periodicUpdate = End')","def _fix_type_revisions(type_, rows):\n model = getattr(all_models, type_, None)\n revisions_table = all_models.Revision.__table__\n if not model:\n logger.warning(\"Failed to update revisions for invalid model: %s\", type_)\n return\n\n ids = [row.resource_id for row in rows]\n objects = model.eager_query().filter(model.id.in_(ids))\n\n obj_content_map = {obj.id: obj.log_json() for obj in objects}\n\n for row in rows:\n # This if statement checks that we only update content for objects that\n # exist. If an object has been deleted via import or in some other way that\n # its delete revision was not created, this if statement will prevent a\n # false error.\n # Note: there will be other migrations that deal with adding missing\n # revisions for those deleted objects.\n if row.resource_id in obj_content_map:\n db.session.execute(\n revisions_table.update()\n .where(revisions_table.c.id == row.id)\n .values(content=obj_content_map[row.resource_id])\n )\n db.session.commit()","def task_refresh_all_stats_score(request):\n start = time.time()\n cls_name = request.POST.get('cls') or 'Day'\n destroy = int(request.POST.get('destroy', '0'))\n cursor = datastore_query.Cursor(urlsafe=request.POST.get('cursor'))\n task_count = int(request.POST.get('task_count', '0'))\n assert cls_name in ('Day', 'Multi'), cls_name\n cls = (\n models.AccountStatsDay\n if cls_name == 'Day' else models.AccountStatsMulti)\n\n # Task queues are given 10 minutes. Do it in 9 minutes chunks to protect\n # against most timeout conditions.\n timeout = 540\n updated = 0\n skipped = 0\n try:\n futures = []\n chunk_size = 10\n items = []\n more = True\n if destroy:\n options = ndb.QueryOptions(keys_only=True)\n else:\n options = ndb.QueryOptions()\n while more:\n batch, cursor, more = cls.query(default_options=options).fetch_page(\n 20, start_cursor=cursor)\n if destroy:\n futures.extend(ndb.delete_multi_async(batch))\n updated += len(batch)\n else:\n for i in batch:\n score = models.compute_score(i)\n if i.score != score:\n items.append(i)\n if len(items) == chunk_size:\n futures.extend(ndb.put_multi_async(items))\n updated += chunk_size\n items = []\n futures = [f for f in futures if not f.done()]\n else:\n skipped += 1\n if time.time() - start >= timeout:\n break\n if items:\n futures.extend(ndb.put_multi_async(items))\n updated += chunk_size\n ndb.Future.wait_all(futures)\n if not more and cls_name == 'Day':\n # Move to the Multi instances.\n more = True\n cls_name = 'Multi'\n cursor = datastore_query.Cursor()\n if more:\n taskqueue.add(\n url=reverse(task_refresh_all_stats_score),\n params={\n 'cls': cls_name,\n 'cursor': cursor.urlsafe() if cursor else '',\n 'destroy': str(destroy),\n 'task_count': str(task_count+1),\n },\n queue_name='refresh-all-stats-score')\n result = 200\n except (db.Timeout, DeadlineExceededError):\n result = 500\n out = 'Index: %d\\nType = %s\\nStored %d items\\nSkipped %d\\nIn %.1fs\\n' % (\n task_count, cls.__name__, updated, skipped, time.time() - start)\n if result == 200:\n logging.info(out)\n else:\n logging.error(out)\n return HttpTextResponse(out, status=result)","def process(self):\n\t\tif self.update_check() or self.force_update:\n\t\t\tself.district_check() #pull all local data and regions\n\t\t\tself.fix() #fix data anomalies - e.g add in Bucks.\n\t\t\tself.save_all() #store a copy of the data\n\t\t\tself.ingest() #add data to models\n\t\t\tself.update_totals() #calculate weekly data\n\t\telse:\n\t\t\tlog.info('PHE cases up to date')","def entityUpdates(self, *args):\n\t\tfor entity in self.members.values():\n\t\t\tentity.update(*args)","def _save_stats(self, msg, payload, subtype):\n\n image_id = md5(payload).hexdigest()\n\n try:\n sizes = self.get_local(msg, \"sizes\")\n except KeyError:\n sizes = defaultdict(dict)\n\n if image_id in sizes['all']:\n img = sizes['all'][image_id]\n area = img['width'] * img['height']\n self._update_coverage(msg, subtype, area)\n\n else:\n try:\n img = self._get_image_sizes(payload)\n except BadImageFile:\n self._update_invalid_counts(msg, subtype, 1)\n else:\n sizes['all'][image_id] = img\n sizes[subtype][image_id] = img\n self.set_local(msg, \"sizes\", sizes)\n area = img['width'] * img['height']\n self._update_coverage(msg, subtype, area)","def put(self, request):\n\n data = request.data\n test_sub_type_id = data['test_sub_type_id']\n data.pop(\"test_sub_type_id\")\n test_sub_type = TestSubType.objects.filter(id=test_sub_type_id)\n\n try:\n test_sub_type.update(**data)\n LOGGER.info(\"Test sub type data updated successfully\")\n return Response({\"status\": \"SUCCESS\", \"message\": \"Record updated successfully\"})\n except Exception, error:\n LOGGER.error(\"Error:%s\", str(error))\n return Response({\"status\": \"FAILED\", \"message\": str(error)})","def population_update_multi(self, parents, children, timeline=None, genUpdate=0, adoptedParents=[], mhFrac=0.0, randomParents=False):\n numFunctions = self.numObjectiveFunctions\n if self.isFunctionList == 0:\n if not hasattr(self.objective.func, '__call__'):\n raise AssertionError('Invalid \\tion handle.')\n assert self.dID != [] and np.sum(self.dID + self.xID) == len(self.discreteVals), ('A map must exist for each discrete variable. {} discrete variables, and {} maps provided.').format(np.sum(self.dID), len(self.discreteVals))\n if sum(self.dID) + sum(self.xID) != 0:\n for c in range(0, len(children)):\n children[c] = self.map_to_discretes(children[c])\n\n for p in parents:\n p.variables = self.map_to_discretes(p.variables)\n\n replace = 0\n numFunctions = self.numObjectiveFunctions\n if numFunctions == 1:\n for i in range(0, len(children), 1):\n fnew = self.objective.func(children[i])\n if fnew > self.penalty:\n self.penalty = fnew\n\n feval = 0\n for i in range(0, len(children), 1):\n if randomParents:\n j = int(rand() * len(parents))\n elif len(adoptedParents) == len(children):\n j = adoptedParents[i]\n else:\n j = i\n fnew = self.objective.func(children[i])\n for con in self.constraints:\n fnew += con.func(children[i])\n\n feval += 1\n if fnew < parents[j].fitness:\n parents[j].fitness = fnew\n parents[j].variables = cp.copy(children[i])\n parents[j].changeCount += 1\n parents[j].stallCount = 0\n replace += 1\n if parents[j].changeCount >= 25 and j >= self.population * self.fracElite:\n parents[j].variables = self.initialize(1, 'random').flatten()\n parents[j].variables = self.map_to_discretes(parents[j].variables)\n fnew = self.objective.func(parents[j].variables)\n for con in self.constraints:\n fnew += con.func(parents[j].variables)\n\n parents[j].fitness = fnew\n parents[j].changeCount = 0\n else:\n parents[j].stallCount += 1\n if parents[j].stallCount > 50000 and j != 0:\n parents[j].variables = self.initialize(1, 'random').flatten()\n parents[j].variables = self.map_to_discretes(parents[j].variables)\n fnew = self.objective.func(parents[j].variables)\n for con in self.constraints:\n fnew += con.func(parents[j].variables)\n\n parents[j].fitness = fnew\n parents[j].changeCount = 0\n parents[j].stallCount = 0\n r = int(rand() * len(parents))\n if r <= mhFrac:\n r = int(rand() * len(parents))\n if fnew < parents[r].fitness:\n parents[r].fitness = fnew\n parents[r].variables = cp.copy(children[i])\n parents[r].changeCount += 1\n parents[r].stallCount += 1\n replace += 1\n\n parents.sort(key=lambda x: x.fitness)\n else:\n numVariables = len(self.varType)\n objectivelist = self.objective\n num_cont_int_bin_variables = len(self.lb)\n var_range = []\n for k in range(0, num_cont_int_bin_variables):\n var_range += (self.lb[k], self.ub[k])\n\n num_Features = num_cont_int_bin_variables\n problem = Problem(num_of_variables=num_Features, objectives=objectivelist, variables_range=var_range)\n num_adopted_parents = len(adoptedParents)\n num_parents = len(parents)\n num_children = len(children)\n populationSize = num_parents\n num_Features = num_cont_int_bin_variables\n if timeline != None:\n if len(timeline) < 2:\n timeline.append(Event(1, feval, parents[0].fitness, parents[0].variables))\n elif parents[0].fitness < timeline[(-1)].fitness and abs((timeline[(-1)].fitness - parents[0].fitness) / parents[0].fitness) > self.convTol:\n timeline.append(Event(timeline[(-1)].generation, timeline[(-1)].evaluations + feval, parents[0].fitness, parents[0].variables))\n else:\n timeline[(-1)].generation += genUpdate\n timeline[(-1)].evaluations += feval\n if sum(self.dID) + sum(self.xID) != 0:\n for p in parents:\n p.variables = self.map_from_discretes(p.variables)\n\n if timeline != None:\n return (parents, replace, timeline)\n else:\n return (\n parents, replace)\n return","def update_all(self, request):\n\n schema = self.session.info['schema']\n\n for item in self.query().filter_by(schema=schema):\n self.session.delete(item)\n\n for item in ElectionCollection(self.session).query():\n self.update(item, request)\n\n for item in ElectionCompoundCollection(self.session).query():\n self.update(item, request)\n\n for item in VoteCollection(self.session).query():\n self.update(item, request)","def _update_all(self, criteria: Q, *args, **kwargs):\n raise NotImplementedError","def update_progress(self, argv):\n data = {}\n arg_types = [{\n \"type\": \"cognitive\",\n \"rating\": argv.c,\n \"description\": argv.cd\n },\n {\n \"type\": \"emotional\",\n \"rating\": argv.e,\n \"description\": argv.ed\n }\n , {\n \"type\": \"physical\",\n \"rating\": argv.p,\n \"description\": argv.pd\n }\n\n ]\n\n for data_type in arg_types:\n if data_type[\"rating\"]:\n if 100 >= int(data_type[\"rating\"]) > 0:\n data[data_type[\"type\"]] = {\n \"rating\": data_type[\"rating\"],\n \"description\": data_type[\"description\"],\n \"sentiment\": self.get_sentiment(data_type[\"description\"])\n }\n else:\n logging.error(\"Invalid Rating, must be 1-10\")\n exit(1)\n today = datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M\")\n insert_data = {today: {\"data\": data, \"weather\": Weather.get_weather()}}\n logging.info(\"Updating Progress\")\n self.prog_logs.insert(insert_data)","def bulk_upsert(self, request):\n data = request.data\n if not isinstance(data, list):\n data = [data]\n\n serializer = FacilityUpsertSerializer(data=data, many=True)\n serializer.is_valid(raise_exception=True)\n\n district_id = request.data[0][\"district\"]\n facilities = (\n Facility.objects.filter(district_id=district_id)\n .select_related(\"local_body\", \"district\", \"state\", \"created_by__district\", \"created_by__state\")\n .prefetch_related(\"facilitycapacity_set\")\n )\n\n facility_map = {f.name.lower(): f for f in facilities}\n facilities_to_update = []\n facilities_to_create = []\n\n for f in serializer.validated_data:\n f[\"district_id\"] = f.pop(\"district\")\n if f[\"name\"].lower() in facility_map:\n facilities_to_update.append(f)\n else:\n f[\"created_by_id\"] = request.user.id\n facilities_to_create.append(f)\n\n with transaction.atomic():\n capacity_create_objs = []\n for f in facilities_to_create:\n capacity = f.pop(\"facilitycapacity_set\")\n f_obj = Facility.objects.create(**f)\n for c in capacity:\n capacity_create_objs.append(FacilityCapacity(facility=f_obj, **c))\n for f in facilities_to_update:\n capacity = f.pop(\"facilitycapacity_set\")\n f_obj = facility_map.get(f[\"name\"].lower())\n changed = False\n for k, v in f.items():\n if getattr(f_obj, k) != v:\n setattr(f_obj, k, v)\n changed = True\n if changed:\n f_obj.save()\n capacity_map = {c.room_type: c for c in f_obj.facilitycapacity_set.all()}\n for c in capacity:\n changed = False\n if c[\"room_type\"] in capacity_map:\n c_obj = capacity_map.get(c[\"room_type\"])\n for k, v in c.items():\n if getattr(c_obj, k) != v:\n setattr(c_obj, k, v)\n changed = True\n if changed:\n c_obj.save()\n else:\n capacity_create_objs.append(FacilityCapacity(facility=f_obj, **c))\n\n bulk_create_with_history(capacity_create_objs, FacilityCapacity, batch_size=500)\n\n return Response(status=status.HTTP_204_NO_CONTENT)","def update_batch(self, *args, **kwargs):\n pass","def setupParticles(self):\n\n for ss in self.pargs['species']:\n\n # Make sure we are setting up particles, not walls (so we check for id existence)\n if 'id' in ss and 'wall' not in ss:\n if not self.rank:\n logging.info('Setting up particles for group{id}'.format(**ss))\n\n randName = np.random.randint(10**5,10**8)\n pddName = 'pdd' + '{}'.format(np.random.randint(10**5,10**8))\n\n if 'vol_lim' not in ss:\n ss['vol_lim'] = 1e-20\n\n id = ss['id'] - 1\n self.lmp.command('group group{} type {}'.format(id, ss['id']))\n\n if 'args'in ss:\n args = ss['args']\n else:\n args = ()\n\n if 'radius' in ss:\n radius = ss['radius']\n\n if not isinstance(radius, tuple):\n radius = ('constant', radius)\n\n self.lmp.command('fix {} '.format(randName) + 'group{}'.format(id) + ' particletemplate/{style} 15485867 volume_limit {vol_lim} atom_type {id} density constant {density} radius'.format(**ss) + (' {}' * len(radius)).format(*radius) \\\n + (' {}' * len(args)).format(*args))\n else:\n self.lmp.command('fix {} '.format(randName) + 'group{}'.format(id) + ' particletemplate/{style} 15485867 volume_limit {vol_lim} atom_type {id} density constant {density}'.format(**ss) + (' {}' * len(args)).format(*args))\n \n self.lmp.command('fix {} '.format(pddName) + 'group{}'.format(id) + ' particledistribution/discrete 67867967 1'.format(**ss) + ' {} 1.0'.format(randName))\n\n if ss['style'] is 'multisphere':\n itype = ss['style']\n else:\n itype = 'nve/{style}'.format(**ss)\n\n #Do NOT unfix randName! Will cause a memory corruption error\n self.pddName.append(pddName)","def update(cls, project_id, resource_type, resource_id, data):\n\n for key, value in data.items():\n cls.edit(\n resource_type=resource_type,\n resource_id=resource_id,\n project_id=project_id,\n key=key,\n value=value\n )","def _update_counts(self, msg, subtype, by):\n\n try:\n counts = self.get_local(msg, \"counts\")\n except KeyError:\n counts = defaultdict(int)\n\n counts['all'] += by\n counts[subtype] += by\n self.set_local(msg, \"counts\", counts)","def updateAllRealtime():\n for stockName in db.STOCK_MAP.keys():\n getStock(stockName, \"all\")\n\n db.UPDATING_REALTIME = False","def push_bulk(self, obj_list, doc_type=None, refresh=True):\n assert isinstance(obj_list, collections.Sequence)\n assert len(obj_list) > 0\n\n es_obj_list = []\n for obj in obj_list:\n if obj is None:\n logger.warning(\"None object in input list\")\n continue\n\n doc_type, es_repr = self._validate_doc_and_get_type_and_repr(obj, doc_type)\n metadata = {\n '_op_type': 'index',\n \"_index\": self.index_name,\n \"_type\": doc_type,\n }\n es_repr.update(**metadata)\n\n es_obj_list.append(es_repr)\n\n helpers.bulk(client=self.conn.elastic_search_client, actions=es_obj_list,\n stats_only=True, refresh=u'true' if refresh else u'false')","def _update_(self, data):\n self.heroes = dict((hero, Hero(hero, data[hero])) for hero in data)","def update(self):\n for zone in self.zones:\n self.update_sensor(\"zone\", zone[\"id\"])\n for device in self.devices:\n self.update_sensor(\"device\", device[\"id\"])","def _update(self):\n self.parametrize_beam()\n self.update_ranks()\n self._points = tf.reshape(self._endpoint, (1, 2)) * tf.reshape(self._ranks, (-1, 1))","def update_targets(self, items):\n items = list(filter(None, chain(*items)))\n items = list(filter(None, items))\n\n if len(items) > 0:\n self.logger.info(\"Updating {} thermo documents\".format(len(items)))\n bulk = self.thermo().initialize_ordered_bulk_op()\n\n for m in items:\n m[self.thermo.lu_field] = datetime.utcnow()\n bulk.find({\"material_id\": m[\"material_id\"]}).upsert().replace_one(m)\n bulk.execute()\n else:\n self.logger.info(\"No items to update\")","def _enchance_assignment(self, doc):\n\n results = self.get_archive_items_for_assignment(doc)\n if results.count() > 0:\n doc['item_ids'] = [str(item.get(config.ID_FIELD)) for item in results]\n\n self.set_type(doc, doc)","def update(probabilities, one_gene, two_genes, have_trait, p):\n for person in probabilities:\n\n # count the genes for the person\n geneCount = 0\n if person in one_gene:\n geneCount = 1\n elif person in two_genes:\n geneCount = 2\n\n # check the trait\n hasTrait = False\n if person in have_trait:\n hasTrait = True\n\n # update the probabilities\n probabilities[person][\"gene\"][geneCount] += p\n probabilities[person][\"trait\"][hasTrait] += p","def update_subcategory(self, control_type, *args):\n\n\t\tif control_type is 'intField':\n\t\t\tself.log('query intField and update others')\n\t\t\tintField_value = cmds.intField(self.grade_intField, query = True, value = True)\n\t\t\tself.log('intField is %s' % intField_value)\n\n\t\t\tself.current_grade_value = intField_value\n\t\t\tself.log('current grade is: %s' % self.current_grade_value)\n\t\t\tcmds.intSlider(self.grade_slider, edit=True, value = -intField_value)\n\t\t\tself.update_radios_default_comments(intField_value)\n\t\t\tself.update_default_comments()\n\t\t\tself.update_is_complete()\n\t\t\tself.updateFunction()\n\n\t\telif control_type is 'slider':\n\n\t\t\tself.log('query slider and update others')\n\t\t\tslider_value = abs(cmds.intSlider(self.grade_slider, query = True, value = True))\n\t\t\tself.log('intSlider is %s' % slider_value)\n\n\t\t\tself.current_grade_value = slider_value\n\t\t\tself.log('current grade is: %s' % self.current_grade_value)\n\t\t\tcmds.intField(self.grade_intField, edit = True, value = slider_value)\n\t\t\tself.update_radios_default_comments(slider_value)\n\t\t\tself.update_default_comments()\n\t\t\tself.update_is_complete()\n\t\t\tself.updateFunction()\n\n\t\telif control_type is 'radioButton':\n\t\t\tself.log('query radio collection and update others')\n\t\t\tselected = cmds.radioCollection(self.grade_radio_collection, query = True, select = True)\n\t\t\tselected_letter = cmds.radioButton(selected, query = True, label = True)\n\t\t\tselected_letter = re.sub('\\\\+', 'plus', selected_letter)\n\t\t\tself.log('selected radioButton: %s' % selected_letter)\n\n\t\t\tself.current_grade_value = int(self.grade_values.find(selected_letter).text)\n\t\t\tself.log('current grade is: %s' % self.current_grade_value)\n\t\t\tcmds.intField(self.grade_intField, edit = True, value = self.current_grade_value)\n\t\t\tcmds.intSlider(self.grade_slider, edit = True, value = -self.current_grade_value)\n\t\t\tself.log('selected_letter: %s' % selected_letter)\n\t\t\t\n\t\t\tcmds.scrollField(self.default_comments, edit = True, text = self.subcatXML.find('gradeComment').find(selected_letter).text)\n\t\t\tself.current_default_comment_text = cmds.scrollField(self.default_comments, query = True, text = True)\n\t\t\tself.log('Default Comments Updated')\n\t\t\tself.log(self.current_default_comment_text)\n\t\t\tself.update_is_complete()\n\t\t\tself.updateFunction()\n\n\t\telif control_type is 'default_comments_text':\n\t\t\tself.current_default_comment_text = cmds.scrollField(self.default_comments, query = True, text = True)\n\t\t\tself.log('Default Comments Updated')\n\t\t\tself.log(self.current_default_comment_text)\n\t\t\tself.update_is_complete()\n\n\t\telif control_type is 'example_comments_text':\n\t\t\tself.current_example_comment_text = cmds.scrollField(self.example_comments, query = True, text = True)\n\t\t\tself.log('examples updated')\n\t\t\tself.log(self.current_example_comment_text)\n\n\t\telse:\n\t\t\tself.current_comment_text = cmds.scrollField(self.comments_text_field, query = True, text = True)\n\t\t\tself.log('comments updated')\n\t\t\tself.log(self.current_comment_text)","def update(self, new_data):\n if type(new_data) is not dict:\n raise TypeError(\"Input parameter must be a dict\")\n # Update parameters\n self._type = new_data.get(\"_type\", self._type)\n self.time_units = new_data.get(\"time_units\", self.time_units)\n self.len_units = new_data.get(\"len_units\", self.len_units)\n self.pump_units = new_data.get(\"pump_units\", self.pump_units)\n self.parameters = new_data.get(\"parameters\", self.parameters)\n # Update pumping rate\n self.pumprate.update(new_data.get(\"pumprate\", self.pumprate.to_dict()))\n # Update data\n if \"wells\" in new_data:\n n = len(new_data[\"wells\"])\n if n > 1:\n self.delete_all_wells()\n for i in range(n):\n self.add_well(0, 0, new_data[\"wells\"][i][\"_type\"] - 2)\n self.wells[i].update(new_data[\"wells\"][i])\n # End Function","def updateAllEntities():\n entityKeys=list(registeredEntities.keys())\n for currKey in entityKeys:\n try:\n currEntity=registeredEntities[currKey]\n currEntity.update()\n except KeyError:\n #this should only be called if an entity is deleted (like if a rock got hit by a bullet)\n continue","def update_profit(self):\n # Acessing Redis can cause greenlet switches because new jobs. We don't\n # want to potentially switch jobs multiple times quickly, so we update\n # the profitability information all at once after the loop to avoid\n # multiple network switches\n new_price_data = {}\n for manager in self.jobmanagers.itervalues():\n currency = manager.config['currency']\n pscore = self.redis.get(\"{}_profit\".format(currency))\n\n # Deserialize\n if pscore:\n try:\n pscore = simplejson.loads(pscore, use_decimal=True)\n except Exception:\n self.logger.warn(\n \"Error parsing profit score for {}! Setting it to 0..\"\n .format(currency))\n pscore = 0\n pass\n # If no score was grabbed, pass a 0 value score\n else:\n self.logger.warn(\"Unable to grab profit info for {}!\"\n .format(currency))\n pscore = 0\n\n ratio = self.redis.get(\"{}_ratio\".format(currency)) or 1.0\n ratio = float(ratio)\n\n # Only set updated if it actually changed\n if self.price_data[currency][0] != pscore or self.price_data[currency][1] != ratio:\n new_price_data[currency] = (pscore, ratio, time.time())\n\n # If we have some new information, adjust accordingly\n if new_price_data:\n self.logger.info(\"Updated price information for {}\"\n .format(new_price_data.keys()))\n # Atomic update in gevent\n self.price_data.update(new_price_data)\n\n # Update all the profit info. No preemption, just maths\n for currency in self.jobmanagers.iterkeys():\n self.update_profitability(currency)\n\n self.logger.debug(\n \"Re-checking best network after new price data for {}\"\n .format(new_price_data.keys()))\n self.check_best()","def publish_updates():\n run_subprocess(['osg-batch-update'])","def update(self):\n\n\t\tif not self.complete:\n\t\t\tfor vasp_run in self.vasp_run_list:\n\t\t\t\tvasp_run.update()","def beam_update(self, idx, positions, beam_size):\n for e in self._all:\n a, br, d = e.size()\n sentStates = e.view(a, beam_size, br // beam_size, d)[:, :, idx]\n sentStates.data.copy_(\n sentStates.data.index_select(1, positions))","def update2(self, es, **kwargs):\n self._update_ps(es)\n raise NotImplementedError('must be implemented in a derived class')","def execute_queries():\n fetch_job_listings(engine)\n update_job_listing(engine)","def update_es_index():\n\n for job in scheduler.get_jobs():\n if 'task_type' in job.meta and job.meta['task_type'] == \"update_index\":\n scheduler.cancel(job)\n\n scheduler.schedule(\n scheduled_time=datetime.now(),\n func='haystack.management.commands.update_index.Command().handle()',\n interval=60 * 60,\n repeat=None,\n )\n\n for job in scheduler.get_jobs():\n index_job = job\n if index_job.func_name == 'haystack.management.commands.update_index.Command().handle()':\n break\n\n index_job.meta['task_type'] = \"update_index\"\n index_job.save()","def put(self):\n type_model = request.json\n\n type_model = namedtuple(\"Type\", type_model.keys())(*type_model.values())\n repository = TypeRepository(\n FLASK_APP.config[\"DBUSER\"],\n FLASK_APP.config[\"DBPASS\"],\n FLASK_APP.config[\"DBHOST\"],\n FLASK_APP.config[\"DBPORT\"],\n FLASK_APP.config[\"DBNAME\"])\n try:\n type_model = repository.update(type_model)\n Logger.Logger.create(FLASK_APP.config[\"ELASTICURL\"],\n 'Informative',\n 'Type sucessfuly updated',\n 'put()',\n str(type.__dict__),\n FLASK_APP.config[\"TYPE\"])\n return self.okResponse(\n response=type_model,\n message=\"Type sucessfuly updated.\",\n status=204), 200\n except Exception as err:\n Logger.Logger.create(FLASK_APP.config[\"ELASTICURL\"],\n 'Error',\n 'Internal server error',\n 'put()',\n str(err),\n FLASK_APP.config[\"TYPE\"])\n return self.okResponse(\n response=err,\n message=\"Internal server error: \" + str(err),\n status=500)","def update_scores(read_scores, result):\n\n\t# check that there are some results for this read\n\tassert len(result) > 0\n\t# write this read to output\n\tfor sim_match in result.keys():\n\n\t\t# get type (discordant or chimeric)\n\t\tjunc_type = sim_match.split('_')[2]\n\n\t\tfor analysis_match in result[sim_match]:\n\t\t\t\n\t\t\t# get each score type\n\t\t\tfor score_type in read_scores:\n\t\t\t\tscore = analysis_match[score_type]\n\t\t\t\tread_scores[score_type][junc_type][score] += 1","def _after_postgeneration(cls, instance, create, results=None):\n super()._after_postgeneration(instance, create, results=results)\n if results.get(\"should_publish\", False):\n for language in instance.extended_object.get_languages():\n instance.extended_object.publish(language)\n instance.refresh_from_db()","def update(self):\r\n debug.write(\"[SourceRPG] Updating all ranked positions\", 1)\r\n database.execute(\"SELECT steamid FROM Player ORDER BY level DESC,xp DESC\")\r\n results = database.cursor.fetchall()\r\n self.ranks = []\r\n for index, steamid in enumerate(results):\r\n debug.write(\"Rank: %s Steamid: %s\" % (index, steamid), 5)\r\n self.ranks.append(steamid[0])\r\n debug.write(\"[SourceRPG] All ranked positions updated\", 1)","def update_pollutant_emissions(self, ds):\n\n # Update biosphere exchanges according to GAINS emission values\n for exc in ws.biosphere(\n ds, ws.either(*[ws.contains(\"name\", x) for x in self.emissions_map])\n ):\n iam_emission_label = self.emissions_map[exc[\"name\"]]\n\n try:\n iam_emission = self.iam_data.cement_emissions.loc[\n dict(\n region=ds[\"location\"],\n pollutant=iam_emission_label\n )\n ].values.item(0)\n except KeyError:\n # TODO: fix this.\n # GAINS does not have a 'World' region, hence we use Europe as a temporary fix\n iam_emission = self.iam_data.cement_emissions.loc[\n dict(\n region=self.geo.iam_to_GAINS_region(\"World\"),\n pollutant=iam_emission_label\n )\n ].values.item(0)\n\n\n if exc[\"amount\"] == 0:\n wurst.rescale_exchange(\n exc, iam_emission / 1, remove_uncertainty=True\n )\n else:\n wurst.rescale_exchange(exc, iam_emission / exc[\"amount\"])\n return ds","def update_param_all_cells(self, landscape, params):\n cell_type = self.dict_cells[landscape]\n cell_type.update_parameters(params)","def reload(self):\n data = self.api.api_request(\"GET\", self.url)\n for t in self.ace_types:\n self[t].actors = data[t][\"actors\"]\n self[t].groups = data[t][\"groups\"]","def refresh_from_padron(self, resource_type):\n data = {'impuestos': 'afip.tax', 'actividades': 'afip.activity', 'conceptos': 'afip.concept'}\n resource_name = resource_type.capitalize().replace('tos', 'to').replace('des', 'd')\n\n model = data.get(resource_type)\n if not model:\n raise UserError(_('Resource Type %s not implemented!') % (resource_type))\n\n url = \"https://soa.afip.gob.ar/parametros/v1/%s/\" % resource_type\n res = requests.get(url=url)\n data = res.json().get('data')\n if res.status_code != 200:\n raise UserError(_('Error getting %s params from AFIP!') % resource_type)\n\n codes = []\n for item in data:\n code = item.get(\"id\" + resource_name)\n values = {'code': code,\n 'name': item.get(\"desc\" + resource_name),\n 'active': True}\n record = self.env[model].search([('code', '=', code)], limit=1)\n codes.append(code)\n if record:\n record.write(values)\n else:\n record.create(values)\n\n # deactivate the ones that are not in afip\n self.env[model].search([('code', 'not in', codes)]).write({'active': False})","def bulk_process(self):\n\n def actions():\n try:\n task = self.queue.get(block=False, timeout=None)\n\n if task['action'] == 'index':\n yield {\n '_op_type': 'index',\n '_index': self.ensure_index(task),\n '_id': task['id'],\n 'doc': task['properties']\n }\n elif task['action'] == 'delete':\n yield {\n '_op_type': 'delete',\n '_index': self.ensure_index(task),\n '_id': task['id'],\n 'doc': task['properties']\n }\n else:\n raise NotImplementedError\n\n except Empty:\n pass\n\n for success, info in streaming_bulk(self.es_client, actions()):\n if success:\n self.queue.task_done()","def _update_historical_ratings(self, pid, obs_time, rating, variance, r_type):\n\n\t\tself.historical_player_ratings[pid][obs_time][r_type + '_rating'] = rating\n\t\tself.historical_player_ratings[pid][obs_time][r_type + '_variance'] = variance","def scale_up(self):\n self.load *= 10\n for pp in self.powerplants:\n if pp[\"type\"] != \"windturbine\":\n pp[\"pmin\"] *= 10\n pp[\"pmax\"] *= 10","def update(\n self,\n filter_freq=FILTER_FREQ_DEFAULT,\n filter_stages=None,\n url_stats=\"result/matrix?show_stage_details=true&show_item_details=true\",\n url_rules=\"formula\",\n path_stats=\"data/matrix.json\",\n path_rules=\"data/formula.json\",\n gamedata_path=\"https://raw.githubusercontent.com/Kengxxiao/ArknightsGameData/master/{}/gamedata/excel/item_table.json\",\n dont_save_data=False,\n ):\n material_probs, convertion_rules = request_data(\n penguin_url + url_stats,\n penguin_url + url_rules,\n path_stats,\n path_rules,\n gamedata_path,\n dont_save_data,\n )\n self.itemdata = request_itemdata(gamedata_path)\n self.itemdata_rv = {\n lang: {v: k for k, v in dct.items()} for lang, dct in self.itemdata.items()\n }\n\n if filter_freq:\n if filter_stages is None:\n filter_stages = []\n filtered_probs = []\n for dct in material_probs[\"matrix\"]:\n if (\n dct[\"times\"] >= filter_freq\n and dct[\"stage\"][\"code\"] not in filter_stages\n ):\n filtered_probs.append(dct)\n material_probs[\"matrix\"] = filtered_probs\n\n self._set_lp_parameters(*self._pre_processing(material_probs, convertion_rules))","def update(self, context):\n session = Session()\n\n for sub in self.cart:\n offers = session.search(sub.query, self.lat, self.lon, self.radius)\n for offer in sub.handle_offers(offers):\n context.bot.send_message(self.chat_id, text=offer_text(offer))\n\n updates = sub.check_offers()\n for offer in updates['expired']:\n context.bot.send_message(self.chat_id,\n text=offer_text_expired(offer))\n for offer in updates['expiring']:\n context.bot_send_message(self.chat_id,\n text=offer_text_expiring(offer))\n self.config_updated()","async def async_update(self):\n node = self.gateway.sensors[self.node_id]\n child = node.children[self.child_id]\n set_req = self.gateway.const.SetReq\n for value_type, value in child.values.items():\n _LOGGER.debug(\n \"Entity update: %s: value_type %s, value = %s\",\n self._name,\n value_type,\n value,\n )\n if value_type in (\n set_req.V_ARMED,\n set_req.V_LIGHT,\n set_req.V_LOCK_STATUS,\n set_req.V_TRIPPED,\n ):\n self._values[value_type] = STATE_ON if int(value) == 1 else STATE_OFF\n elif value_type == set_req.V_DIMMER:\n self._values[value_type] = int(value)\n else:\n self._values[value_type] = value","def set_mass(self, type, mass):\n assert type < self.num_atom_types + 1\n assert type > 0\n self.masses[type] = mass","def handle_wps_update(self, data):\n\n self.jobs = data","def update_data(self):\n staff = Staff.objects.all()\n orgs = Organization.objects.all()\n depts = Department.objects.all()\n\n existing = self.all()\n if existing.count():\n existing.delete()\n\n if staff.count():\n for s in staff:\n record = CombinedTeledata(\n id=s.id,\n alpha=s.alpha,\n name=s.name,\n first_name=s.first_name,\n last_name=s.last_name,\n sort_name=s.sort_name,\n email=s.email,\n phone=s.phone,\n postal=s.postal,\n job_position=s.job_position,\n department=s.dept.name,\n dept_id=s.dept.id,\n organization=s.dept.org.name,\n org_id=s.dept.org.id,\n building=s.bldg.name,\n bldg_id=s.bldg.import_id,\n room=s.room,\n from_table='staff'\n )\n\n try:\n record.save(doing_import=True)\n record.keywords_combined.set(s.keywords.all())\n except Exception as e:\n logger.error(str(e))\n\n if orgs.count():\n for o in orgs:\n record = CombinedTeledata(\n id=o.id,\n name=o.name,\n sort_name=o.name,\n phone=o.phone,\n fax=o.fax,\n building=o.bldg.name,\n bldg_id=o.bldg.import_id,\n room=o.room,\n from_table='organizations'\n )\n\n try:\n record.save(doing_import=True)\n record.keywords_combined.set(o.keywords.all())\n except Exception as e:\n logger.error(str(e))\n\n if depts.count():\n for d in depts:\n record = CombinedTeledata(\n id=d.id,\n name=d.name,\n sort_name=d.name,\n phone=d.phone,\n fax=d.fax,\n organization=d.org.name,\n org_id=d.org.id,\n building=d.bldg.name,\n bldg_id=d.bldg.import_id,\n room=d.room,\n from_table='departments'\n )\n\n try:\n record.save(doing_import=True)\n record.keywords_combined.set(d.keywords.all())\n except Exception as e:\n logger.error(str(e))","def test_update_multiple_preferences_type(self):\n # This method utilises the PUT request method and will make changes to the Canvas instance. This needs consideration.\n pass","def _update(self):\n self.all_params = {}\n self._update_experiment_params()\n self._update_preprocessing_params()\n self._update_model_params()","def updateDerivedTypes(self):\n self.hasConditionals = False\n self.derivedDict = {}\n for format in self.values():\n if format.genericType:\n format.updateFromGeneric()\n generic = self[format.genericType]\n if format.genericType in self.derivedDict:\n self.derivedDict[format.genericType].append(format)\n else:\n self.derivedDict[format.genericType] = [generic, format]\n if format.conditional:\n self.hasConditionals = True\n format.conditional.setupFields(format)","def _process(proc_data: List[Dict]) -> List[Dict]:\n for item in proc_data:\n for key, val in item.items():\n if key != 'type':\n item[key] = int(val)\n\n return proc_data","def updateWeapons(self):\n self.readyWeapons = []\n self.setWeaponStatus()\n\n for myWeapon in self.activeWeapons:\n if myWeapon.preFireCheck() == 1:\n self.readyWeapons.append(myWeapon)\n self.alternateTargets = []\n\n if self.amsTargets != []:\n for myWeapon in self.amsWeapons:\n if myWeapon.preFireCheck() == 1:\n self.readyWeapons.append(myWeapon)\n self.amsTargets = []","def _update_positional_allele_dictionary(self, read_id, pos, allele, type, mapping_quality):\n if pos not in self.positional_allele_dictionary:\n self.positional_allele_dictionary[pos] = {}\n if (allele, type) not in self.positional_allele_dictionary[pos]:\n self.positional_allele_dictionary[pos][(allele, type)] = 0\n\n # increase the allele frequency of the allele at that position\n self.positional_allele_dictionary[pos][(allele, type)] += 1\n self.allele_dictionary[read_id][pos].append((allele, type))","def _postprocess_staging_data(self):\n super()._postprocess_staging_data()\n with self._conn, self._conn.cursor() as cursor:\n cursor.execute(sql.SQL(\"\"\"UPDATE {0} SET rat_bitmask = translate_bands_to_rat_bitmask(bands)\"\"\")\n .format(self._staging_tbl_identifier))","def updateSubSystems(self, data):\r\n current_subsystems_files = {}\r\n\r\n for subsystems_filename in self.versions[\"subsystems\"]:\r\n current_subsystems_files[subsystems_filename] = self.versions[\"subsystems\"][subsystems_filename]\r\n\r\n for subsystems_filename in data[\"subsystems\"]:\r\n subsystems_file = data[\"subsystems\"][subsystems_filename]\r\n if subsystems_filename == \"main\":\r\n continue\r\n try:\r\n if subsystems_filename not in current_subsystems_files.keys() or self.mission_name + \"_\" + subsystems_file[\"version\"] != current_subsystems_files[subsystems_filename][\"version\"]:\r\n downloadAndReplaceFile(subsystems_filename + \".py\", subsystems_file[\"python\"])\r\n self.versions[\"subsystems\"][subsystems_filename] = {}\r\n self.versions[\"subsystems\"][subsystems_filename][\"version\"] = self.mission_name + \"_\" + subsystems_file[\"version\"]\r\n self._logger.info(\"subsystems file \" + subsystems_filename + \" updated to version: \" + subsystems_file[\"version\"])\r\n except Exception as e:\r\n self._logger.error(\"Failed to update subsystems file \" + subsystems_filename + \" due to an exception: \" + str(e))","def execute(self):\n for coll in list(self.__bulks):\n try:\n bulkOp = self.__bulks[coll]\n curr_result = Counter(bulkOp.execute())\n self.update_results(coll, curr_result)\n except BulkWriteError as bwe:\n sys.stderr.write(str(bwe.details))","def write(self, values):\n STAGE = self.env['anytracker.stage']\n RATING = self.env['anytracker.rating']\n PRIORITY = self.env['anytracker.priority']\n IMPORTANCE = self.env['anytracker.importance']\n COMPLEXITY = self.env['anytracker.complexity']\n oldmethods = {t.id: t.method_id for t in self}\n res = super(Ticket, self).write(values)\n # change the method on subtickets as well\n if 'method_id' not in values:\n return res\n\n for ticket in self:\n children = self.search([('id', 'child_of', ticket.id),\n ('id', '!=', ticket.id)])\n new_meth_id = values['method_id']\n super(Ticket, children).write({'method_id': new_meth_id})\n\n # point subtickets to the equivalent stage in the new method\n for oldstage in oldmethods[ticket.id].stage_ids:\n # find children with this stage\n children = self.search([('id', 'child_of', ticket.id),\n ('stage_id', '=', oldstage.id)])\n if not children:\n continue\n # find a stage with the same code in the new method\n equ_stage = STAGE.search([('state', '=', oldstage.state),\n ('method_id', '=', new_meth_id)])\n # if no stage found, reset to no stage at all\n # (should display tickets on the left in the kanban)\n equ_stage = equ_stage[0].id if equ_stage else False\n self.env.cr.execute(\n 'update anytracker_ticket '\n 'set stage_id=%s where id in %s',\n (equ_stage or None, tuple(children.ids)))\n\n # point subtickets to the equivalent priority in the new method\n for oldprio in oldmethods[ticket.id].priority_ids:\n # find children with this priority\n children = self.search([('id', 'child_of', ticket.id),\n ('priority_id', '=', oldprio.id)])\n if not children:\n continue\n # find a priority with the same seq in the new method\n equ_prio = PRIORITY.search([('seq', '=', oldprio.seq),\n ('method_id', '=', new_meth_id)])\n # if no priority found, reset to no priority at all\n # (should display tickets on the left in the kanban)\n equ_prio = equ_prio[0].id if equ_prio else False\n self.env.cr.execute('update anytracker_ticket '\n 'set priority_id=%s where id in %s',\n (equ_prio or None, tuple(children.ids)))\n\n # point subtickets to the equivalent importance in the new method\n for oldimp in oldmethods[ticket.id].importance_ids:\n # find children with this importance\n children = self.search([('id', 'child_of', ticket.id),\n ('importance_id', '=', oldimp.id)])\n if not children:\n continue\n # find a importance with the same code in the new method\n equ_imp = IMPORTANCE.search([('seq', '=', oldimp.seq),\n ('method_id', '=', new_meth_id)])\n # if no importance found, reset to no importance at all\n # (should display tickets on the left in the kanban)\n equ_imp = equ_imp[0].id if equ_imp else False\n self.env.cr.execute(\n 'update anytracker_ticket set importance_id=%s'\n ' where id in %s',\n (equ_imp or None, tuple(children.ids)))\n\n # point subtickets to the equiv complexity in the new method\n for oldcplx in oldmethods[ticket.id].complexity_ids:\n # find ratings with this complexity and related to children\n ratings = RATING.search([\n ('ticket_id', 'in',\n self.search([('id', 'child_of', ticket.id)]).ids),\n ('complexity_id', '=', oldcplx.id)])\n if not ratings:\n continue\n # find a complexity with the same code in the new method\n equ_cmplx = COMPLEXITY.search(\n [('value', '=', oldcplx.value),\n ('method_id', '=', new_meth_id)])\n # if no complexity found, reset to no complexity at all\n # (should display tickets on the left in the kanban)\n equ_cmplx = equ_cmplx[0].id if equ_cmplx else False\n self.env.cr.execute(\n 'update anytracker_rating set complexity_id=%s '\n 'where id in %s',\n (equ_cmplx or None, tuple(ratings.ids)))\n\n # recompute risk and ratings\n ticket.recompute_subtickets()\n\n return res","def gbf_pub_update():\r\n LOG.info(\"Start: Update datasets in RLIDGeo warehouse.\")\r\n month_stamps = [\r\n datetime.date.today().strftime(\"%Y_%m\"),\r\n (\r\n datetime.date.today().replace(day=1)\r\n - datetime.timedelta(days=1)\r\n ).strftime(\"%Y_%m\"),\r\n ]\r\n for month_stamp in month_stamps:\r\n snapshot_db_path = SNAPSHOT_DB_PATH.format(month_stamp)\r\n if not os.path.exists(snapshot_db_path):\r\n LOG.warning(\"Snapshot database %s does not exist.\", snapshot_db_path)\r\n continue\r\n\r\n for _dataset in DATASETS:\r\n arcetl.features.update_from_dicts(\r\n dataset_path=_dataset.path(\"pub\"),\r\n update_features=source_rows(snapshot_db_path, _dataset.path(\"source\")),\r\n id_field_names=_dataset.id_field_names,\r\n field_names=_dataset.field_names,\r\n delete_missing_features=False,\r\n use_edit_session=False,\r\n )\r\n LOG.info(\"End: Update.\")","async def test_modify_schedule_type(self):\n await self.populate_test_data() # Populate data in foglamp.scheduled_processes\n\n scheduler = Scheduler(_address, _m_port)\n await scheduler.start()\n\n interval_schedule = IntervalSchedule()\n interval_schedule.name = 'sleep10'\n interval_schedule.process_name = 'sleep10'\n interval_schedule.repeat = datetime.timedelta(seconds=0)\n\n await scheduler.save_schedule(interval_schedule)\n\n manual_schedule = ManualSchedule()\n manual_schedule.schedule_id = interval_schedule.schedule_id\n manual_schedule.name = 'manual'\n manual_schedule.process_name = 'sleep10'\n manual_schedule.repeat = datetime.timedelta(seconds=0)\n\n await scheduler.save_schedule(manual_schedule)\n\n # Assert: only 1 task is running\n schedule = await scheduler.get_schedule(manual_schedule.schedule_id)\n\n assert isinstance(schedule, ManualSchedule)\n\n await self.stop_scheduler(scheduler)","def update(self, dt):\n for obj in self.objects:\n obj.update(dt)","def update(self):\n self._sync_ranges()\n self._update_params()","def update(self, **kwargs):\n for name, item in itertools.chain(\n self._cal_objs.items(),\n self._noise_objs.items()):\n logger.debug(\"update {}\".format(item))\n item.update(**kwargs)","def reindex_pidtype(pid_type):\n click.echo('Indexing pid type \"{}\"...'.format(pid_type))\n cli = create_cli()\n runner = current_app.test_cli_runner()\n runner.invoke(\n cli,\n 'index reindex --pid-type {} --yes-i-know'.format(pid_type),\n catch_exceptions=False\n )\n runner.invoke(cli, 'index run', catch_exceptions=False)\n click.echo('Indexing completed!')","def populate(self):\n\n self.create_index()\n self.check_type()\n self.create_mapping()\n\n f = open(self.csv_file, 'rU')\n\n # Read the first line for all the headers\n headers = f.readline().split(',')\n\n # Read the rest of the document\n rows = f.readlines()\n added_counter = 0\n\n actions = []\n for row in rows:\n fields = row.split(',')\n obj = {}\n for header in headers:\n # we call lower-case here because we were originally using\n # analyzed strings in elasticsearch (and they were\n # automatically converted). Code was built based on that so it's\n # easiest to convert for now\n try:\n obj[header.replace('\\n', '')] = float(fields[\n headers.index(header)].replace('\\n', '').lower())\n except ValueError:\n obj[header.replace('\\n', '')] = fields[\n headers.index(header)].replace('\\n', '').lower()\n # check afterwards to replace empty strings with None (which json.dumps hopefully writes to null)\n if obj[header.replace('\\n', '')] == '':\n obj[header.replace('\\n', '')] = None\n try:\n item = {\n '_index': self.es_main_index,\n '_type': self.es_main_type,\n '_source': obj\n }\n\n actions.append(item)\n\n added_counter += 1\n print('%s new records added' % added_counter,\n end='\\r')\n sys.stdout.flush()\n\n if added_counter % self.chunk_size == 0:\n helpers.bulk(self.es, actions)\n actions = []\n\n except ConnectionError:\n print('There was a connection error. Check your Elastic' +\n ' Search setting and make sure Elastic Search is ' +\n 'running.')\n return False\n\n # add the remaining items\n if actions:\n helpers.bulk(self.es, actions)\n\n print('The update is completed. %s new records were added.' %\n added_counter)"],"string":"[\n \"def bulk_refresh():\\n logger.info(\\\"Refreshing/Updating persuasions in bulk\\\")\\n try:\\n request_data = json.loads(request.data)\\n args = request.args\\n with concurrent.futures.ThreadPoolExecutor(max_workers=settings.MAX_WORKERS) as executor:\\n {executor.submit(PersuasionServices.refresh, data, args): data for data in request_data}\\n\\n return jsonify(\\n dict(status=\\\"success\\\", message=\\\"Your request is in the queue, persuasion will be updated shortly\\\"))\\n except Exception as e:\\n logger.error(\\\"Exception while creating persuasions in bulk - \\\" + repr(e))\\n return jsonify(dict(status=\\\"failure\\\", error=repr(e)))\",\n \"def do_refresh_revisions():\\n\\n rows_by_type = _get_revisions_by_type()\\n\\n for type_, rows in rows_by_type.iteritems():\\n logger.info(\\\"Updating revisions for: %s\\\", type_)\\n _fix_type_revisions(type_, rows)\",\n \"def updateAll(data):\\n if (data.updatePositions):\\n data.groups.player.update(data)\\n data.groups.projectiles.update(data)\\n data.groups.monsters.update(data)\\n data.groups.spawners.update(data)\",\n \"def mark_processed_entities(entity_type, max_date):\\r\\n\\r\\n try:\\r\\n \\r\\n # get a connection and cursor\\r\\n conn = ecommerce.db.getConnection()\\r\\n cursor = conn.cursor()\\r\\n \\r\\n # execute the query\\r\\n cursor.execute(\\\"\\\"\\\"\\r\\n UPDATE Stage0_Delta\\r\\n SET FlagUpdated = 0\\r\\n WHERE EntityType = ? AND\\r\\n FlagUpdated = 1 AND\\r\\n LastUpdate <= TO_DATE(?, 'YYYY-MM-DD HH24:MI:SS')\\r\\n \\\"\\\"\\\", (entity_type, max_date) )\\r\\n \\r\\n # commit changes\\r\\n conn.commit()\\r\\n except:\\r\\n conn.rollback()\\r\\n pass\",\n \"def update_position():\\n submissions = get_submissions_effective_date_today()\\n\\n if submissions:\\n for submission in submissions:\\n submission_changes = get_submission_changes(submission['id'])\\n\\n for change in submission_changes:\\n execute_statement(\\n f\\\"UPDATE position SET {change['field']} = '{change['change']}' WHERE number = '{submission['position_id']}' and isBudget = 0\\\"\\n )\\n\\n # create audit trail in db\\n create_audit_log('127.0.0.1', 'Server', '127.0.0.1', action='Successfully updated position {} on submission effective date'.format(\\n submission['position_id']), table='position', function='UPDATE')\\n else:\\n # create audit trail in db\\n create_audit_log('127.0.0.1', 'Server', '127.0.0.1',\\n action='No positions to update today', table='position', function='')\",\n \"def _batch_update(self, query, mutation):\\n logger.info(\\\"Performing batch update on %s. Mutation: %s\\\", query, mutation)\\n modified = 0\\n for doc in self.instances.find(query):\\n with lock_instance(doc['_id']):\\n pre_update_doc = self.instances.find_one({'_id' : doc['_id']})\\n result = self.instances.update_one({'_id': doc['_id']}, mutation)\\n assert result.modified_count == 1\\n modified += 1\\n updated_doc = self.instances.find_one({'_id': doc['_id']})\\n instance = FixtureInstance.deserialize_mongodoc(updated_doc)\\n try:\\n self.axdb_client.update_fixture_instance(instance.axdbdoc())\\n except Exception:\\n logger.exception(\\\"Failed to persist updates for %s. Undoing cache update\\\", instance)\\n self.instances.replace_one({'_id' : instance.id}, pre_update_doc)\\n raise\\n logger.info(\\\"%s fixture instances modified\\\", modified)\",\n \"def bulk_update(self, request):\\n serializer = MasterySerializer(\\n data=request.data,\\n many=True,\\n )\\n if serializer.is_valid():\\n serializer.save()\\n return Response(serializer.data)\\n else:\\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\",\n \"def step070() -> None:\\n logger.logMessage('Begin: elasticsearch bulk update')\\n client = es.Elasticsearch(hostlist)\\n\\n def generate():\\n with open(renumFile,'r') as f:\\n line = f.readline().rstrip()\\n while line != '':\\n fields = line.split(';')\\n oper = { '_index': fields[3], \\n '_op_type': 'update',\\n '_id': fields[2].rstrip(),\\n '_type': 'doc',\\n '_source:': {'doc': {'tsa': fields[0]}}}\\n \\n yield oper\\n line = f.readline().rstrip()\\n result = eshelp.bulk(client,generate())\\n logger.logMessage('Bulk result: {0}'.format(result))\\n logger.logMessage('End : elasticsearch bulk update')\",\n \"def train(self):\\n if self.check_if_necessary() is False:\\n return\\n # open a transaction\\n with db as session:\\n # Reset popularity score (delete and re-add column for score)\\n session.execute(\\n text('ALTER TABLE \\\"%s\\\" DROP COLUMN popularity_score' % self.__media__[0].tablename))\\n session.execute(\\n text('ALTER TABLE \\\"%s\\\" ADD COLUMN popularity_score DOUBLE PRECISION' % self.__media__[0].tablename))\\n for media in self.__media__:\\n st_time = datetime.utcnow()\\n\\n m = media(logger=self.logger)\\n q_df = m.get_populars(size=1000)\\n\\n # open a transaction\\n with db as session:\\n # Set new popularity score\\n for index, row in q_df.iterrows():\\n session.execute(\\n text(\\\"UPDATE %s SET popularity_score = %s WHERE %s = %s\\\" % (m.tablename, row[\\\"popularity_score\\\"], m.id, row[m.id])))\\n self.logger.debug(\\\"%s popularity reloading performed in %s (%s lines)\\\" %\\n (str(m.content_type) or \\\"ALL CONTENT\\\", datetime.utcnow()-st_time, q_df.shape[0]))\\n self.store_date(m.content_type)\",\n \"def update_performance_reports(update_all=True):\\n from mspray.apps.main.utils import performance_report\\n\\n time_within = UPDATE_VISITED_MINUTES\\n time_since = timezone.now() - timedelta(minutes=time_within + 1)\\n\\n if update_all:\\n submissions = SprayDay.objects.all()\\n else:\\n submissions = SprayDay.objects.filter(\\n Q(created_on__gte=time_since) | Q(modified_on__gte=time_since)\\n )\\n\\n sop_queryset = (\\n SprayDay.objects.filter(\\n Q(created_on__gte=time_since) | Q(modified_on__gte=time_since)\\n )\\n .filter(spray_operator__isnull=False)\\n .only(\\\"spray_operator\\\")\\n .distinct(\\\"spray_operator\\\")\\n )\\n\\n for record in sop_queryset:\\n performance_report(\\n record.spray_operator,\\n submissions.filter(spray_operator=record.spray_operator),\\n )\",\n \"def updateall(self, params):\\n for gauge in self.gauges:\\n self.safexec(gauge.updateall, params)\",\n \"def update_particles(self):\\n for particle in self.particles:\\n particle.update_coordinates(self.bounds)\",\n \"def update(self, params):\\n for gauge in self.gauges:\\n self.safexec(gauge.update, params)\",\n \"def update_monomer_population(self,monomer_type,update_type='remove'):\\n if update_type=='remove':\\n self.monomer_population[monomer_type] += -1\\n self.added_set[monomer_type] += 1\\n \\n self.total_monomers += -1\\n self.network_size += 1\\n elif update_type=='add':\\n self.monomer_population[monomer_type] += 1\\n self.added_set[monomer_type] += -1\\n \\n self.total_monomers += 1\\n \\n self.free_monomer_fraction = self.total_monomers/float(self.total_sites)\\n\\n self.construct_prob_dist()\\n self.compute_mean_eps_self()\",\n \"def _update_coverage(self, msg, subtype, by):\\n try:\\n coverage = self.get_local(msg, \\\"coverage\\\")\\n except KeyError:\\n coverage = defaultdict(int)\\n coverage[\\\"all\\\"] += by\\n coverage[subtype] += by\\n self.set_local(msg, \\\"coverage\\\", coverage)\",\n \"def save(self):\\n for t in self.ace_types:\\n self.api.api_request(\\\"PUT\\\", self.url + t, data={t: self[t]})\",\n \"def update(self):\\n self.platform_list.update()\\n self.enemy_list.update()\",\n \"def update(self, es, **kwargs):\\n pass\",\n \"def update_members_from_preferences(self, **parameters):\\n super(Sequence, self).update_members_from_preferences(**parameters)\\n\\n for i, item in enumerate(self.items):\\n para = parameters['item_{}'.format(i)]\\n item.update_members_from_preferences(**para)\",\n \"def update_properties(self, prop_dict):\\n ft_dict = {ft.name: ft for ft in self.get_field_types()}\\n for name, val in prop_dict.items():\\n ft = ft_dict[name]\\n if ft.is_parameter():\\n key = \\\"value\\\"\\n else:\\n key = \\\"sample\\\"\\n if issubclass(type(val), Sequence) and ft.array:\\n self.set_field_value_array(name, None, [{key: v} for v in val])\\n else:\\n self.set_field_value(name, None, {key: val})\",\n \"def refresh_persuasion():\\n try:\\n request_data = json.loads(request.data)\\n meta = request.args\\n persuasion = PersuasionServices.refresh(request_data, meta)\\n except Exception as e:\\n error_msg = \\\"Getting persuasion details - \\\" + repr(e)\\n logger.error(error_msg)\\n return jsonify(dict(status=\\\"failure\\\", error=error_msg))\\n return jsonify(dict(status=\\\"success\\\", data=persuasion if persuasion else dict()))\",\n \"def process(self):\\n\\t\\tif self.update_check():\\n\\t\\t\\tself.ingest_all()\\n\\t\\t\\tself.update_totals()\\n\\t\\telse:\\n\\t\\t\\tlog.info('PHE cases up to date')\",\n \"async def actualize(self):\\r\\n for guild, settings in self.bot.settings.items():\\r\\n # Grab the roles and their requirements\\r\\n guild = self.bot.get_guild(guild)\\r\\n base_member = settings.get(\\\"rank_basic_member_role_id\\\")\\r\\n base_member = guild.get_role(base_member)\\r\\n active_member = settings.get(\\\"rank_active_member_role_id\\\")\\r\\n active_member = guild.get_role(active_member)\\r\\n active_member_days = settings.get(\\\"rank_active_member_required_days\\\")\\r\\n active_member_activity = settings.get(\\\"rank_active_member_required_activity\\\")\\r\\n junior_mod = settings.get(\\\"rank_junior_mod_role_id\\\")\\r\\n junior_mod = guild.get_role(junior_mod)\\r\\n junior_mod_days = settings.get(\\\"rank_junior_mod_required_days\\\")\\r\\n junior_mod_activity = settings.get(\\\"rank_junior_mod_required_activity\\\")\\r\\n senior_mod = settings.get(\\\"rank_senior_mod_role_id\\\")\\r\\n senior_mod = guild.get_role(senior_mod)\\r\\n admin = settings.get(\\\"rank_admin_role_id\\\")\\r\\n admin = guild.get_role(admin)\\r\\n # Fetch the list of members eligible for each rank by their activity\\r\\n active_member_eligible = await util_users.fetch_users_by_days_and_activity(\\r\\n self.bot, guild, active_member_days, active_member_activity\\r\\n )\\r\\n active_member_eligible = [i[0] for i in active_member_eligible]\\r\\n junior_mod_eligible = await util_users.fetch_users_by_days_and_activity(\\r\\n self.bot, guild, junior_mod_days, junior_mod_activity\\r\\n )\\r\\n junior_mod_eligible = [i[0] for i in junior_mod_eligible]\\r\\n # Iterate over all members to edit their roles\\r\\n for member in [i for i in guild.members if not i.bot]:\\r\\n # Admin check\\r\\n if admin in member.roles:\\r\\n continue\\r\\n # Senior mod check\\r\\n elif senior_mod in member.roles:\\r\\n continue\\r\\n # Junior mod check\\r\\n elif junior_mod in member.roles:\\r\\n if member.id not in junior_mod_eligible:\\r\\n await member.remove_roles(junior_mod)\\r\\n await member.add_roles(active_member)\\r\\n # Active member check\\r\\n elif active_member in member.roles:\\r\\n if member.id not in active_member_eligible:\\r\\n await member.remove_roles(active_member)\\r\\n await member.add_roles(base_member)\\r\\n # Base member check\\r\\n elif base_member in member.roles:\\r\\n if member.id in active_member_eligible:\\r\\n await member.add_roles(active_member)\",\n \"def update(self, events, **kwargs):\\n self.collisions.update(self._collidables, self._projectiles)\\n for o in list(self._updateables):\\n o.update(gm=self, events=events, **kwargs)\",\n \"def refresh_counterfactual_url_type(request, compound_type):\\t\\n\\n\\tnow = datetime.datetime.now()\\n\\tprint \\\"Start Time %s\\\" % now\\n\\n\\tc_type \\t\\t= compound_type \\t# compound type, o to be only and c to be compound\\n\\tprimeCoordinator= PrimeCoordinator() \\t# initiate an PrimeCoordinator object to manipulate inputed parameters to get a counterfactual results\\n\\n\\tnow = datetime.datetime.now()\\n\\tprint \\\"Coordinator initialised %s\\\" % now\\n\\n\\t# Get variables from URL and create array\\n\\texposure_name \\t\\t= request.GET.get('exposure', '')\\n\\te_id \\t\\t\\t= DBHelper.get_exposure_id_from_name(exposure_name)\\n\\tmean \\t\\t\\t= request.GET.get('mean', '')\\n\\tsd \\t\\t\\t= request.GET.get('standard-deviation', '')\\n\\tnon_rate\\t\\t= request.GET.get('non-rate', '')\\n\\texposure_sequence \\t= [{'non_rate':non_rate,'mean':mean,'e_id':e_id,'sd':sd}]\\n\\n\\tprimeCoordinator.get_counterfactual_compound_exposures(exposure_sequence)\\n\\n\\tnow = datetime.datetime.now()\\n\\tprint \\\"Coordinator given exposures %s\\\" % now\\n\\n\\t# get the data in the interface\\n\\tb_output_mortality \\t= primeCoordinator.output_baseline_mortality # baseline mortality list for all outcomes\\n\\tb_output_mortality_num \\t= primeCoordinator.output_baseline_mortality_num # baseline mortality sum up for each outcome\\n\\tb_total_mortality \\t= primeCoordinator.output_baseline_mortality_total# baseline mortality sum up for all outcomes\\n\\n\\tnow = datetime.datetime.now()\\n\\tprint \\\"Total baseline mortality calculated %s\\\" % now\\n\\n\\tc_output_mortality \\t= primeCoordinator.output_counterfactual_mortality# counterfactual mortality for all outcomes\\n\\tc_output_mortality_num \\t= primeCoordinator.output_counterfactual_mortality_num# counterfactual mortality for each outcome\\n\\tc_total_mortality \\t= primeCoordinator.output_counterfactual_mortality_total# counterfactual mortality sum up for all outcomes\\n\\n\\tnow = datetime.datetime.now()\\n\\tprint \\\"Total counterfactual mortality calculated %s\\\" % now\\n\\n\\ttotal_population\\t= primeCoordinator.output_total_population\\n\\tall_mortality_exposure\\t= primeCoordinator.output_all_mortality_exposure_outcome\\n\\n\\tnow = datetime.datetime.now()\\n\\tprint \\\"Total deaths averted calculated %s\\\" % now\\n\\n\\ttotal_death_averted\\t= str(round(primeCoordinator.output_total_death_averted,0))\\n\\ttotal_death_baseline\\t= str(primeCoordinator.output_total_death_baseline)\\n\\n\\n\\tnow = datetime.datetime.now()\\n\\tprint \\\"End Time: %s\\\" % now\\n\\n\\t#transmit the parameters\\n\\ttemplate = loader.get_template('primemodel/index.html')\\n\\tpara_view = {\\n\\t\\t\\t'b_output_mortality_num':\\tb_output_mortality_num,\\n\\t\\t\\t'b_total_mortality':\\t\\tb_total_mortality,\\n\\t\\t\\t'c_output_mortality_num':\\tc_output_mortality_num,\\n\\t\\t\\t'c_total_mortality':\\t\\tc_total_mortality,\\n\\t\\t\\t'total_population':\\t\\t\\ttotal_population,\\n\\t\\t\\t'total_death_averted':\\t\\ttotal_death_averted,\\n\\t\\t\\t'total_death_baseline':\\t\\ttotal_death_baseline,\\n\\t\\t\\t'all_mortality_exposure':\\tall_mortality_exposure\\n\\t\\t\\t}\\n\\n\\t#context to transmit the parameters to show\\n\\tcontext = Context(para_view)\\n\\tresponse = template.render(context)\\n\\treturn HttpResponse(response)\",\n \"def _process(self, start_key, batch_size):\\n\\n query = self.MODEL.all()\\n if start_key:\\n query.filter('__key__ > ', start_key)\\n\\n try:\\n entities = query.fetch(batch_size)\\n\\n if not entities:\\n # all entities has already been processed\\n return\\n\\n for entity in entities:\\n for field in self.FIELDS_TO_UPDATE:\\n old_reference = getattr(entity, field)\\n\\n if not old_reference:\\n continue\\n\\n # check if the field has not been updated\\n if isinstance(old_reference, self.PROFILE_MODEL):\\n continue\\n\\n profile = _getProfileForRole(old_reference, self.PROFILE_MODEL)\\n setattr(entity, field, profile)\\n\\n for list_property in self.LISTS_TO_UPDATE:\\n l = getattr(entity, list_property)\\n new_l = []\\n for key in l:\\n new_l.append(_getProfileKeyForRoleKey(key, self.PROFILE_MODEL))\\n setattr(entity, list_property, new_l)\\n\\n db.put(entities)\\n start_key = entities[-1].key()\\n deferred.defer(self._process, start_key, batch_size)\\n except DeadlineExceededError:\\n # here we should probably be more careful\\n deferred.defer(self._process, start_key, batch_size)\",\n \"def update_entities(self):\\n raise NotImplementedError()\",\n \"def _update_level_data(self):\\n\\t\\t# taxes, inhabitants\\n\\t\\tself.tax_base = self.session.db.get_settler_tax_income(self.level)\\n\\t\\tself.inhabitants_max = self.session.db.get_settler_inhabitants_max(self.level)\\n\\t\\tif self.inhabitants > self.inhabitants_max: # crop settlers at level down\\n\\t\\t\\tself.inhabitants = self.inhabitants_max\\n\\n\\t\\t# consumption:\\n\\t\\t# Settler productions are specified to be disabled by default in the db, so we can enable\\n\\t\\t# them here per level.\\n\\t\\tcurrent_lines = self.get_production_lines()\\n\\t\\tfor (prod_line,) in self.session.db.get_settler_production_lines(self.level):\\n\\t\\t\\tif not self.has_production_line(prod_line):\\n\\t\\t\\t\\tself.add_production_by_id(prod_line)\\n\\t\\t\\t# cross out the new lines from the current lines, so only the old ones remain\\n\\t\\t\\tif prod_line in current_lines:\\n\\t\\t\\t\\tcurrent_lines.remove(prod_line)\\n\\t\\tfor line in current_lines[:]: # iterate over copy for safe removal\\n\\t\\t\\t# all lines, that were added here but are not used due to the current level\\n\\t\\t\\tself.remove_production_by_id(line)\\n\\t\\t# update instance graphics\\n\\t\\tself.update_action_set_level(self.level)\",\n \"def update_quality():\\n global items, converted_items\\n if not converted_items:\\n items = convert_items(items)\\n converted_items = True\\n for item in items:\\n item.update_q()\",\n \"def setEmpireStats(self):\\n totalEmpires = len(self.empires.keys())\\n stats = {'Research':[], 'Fleet Size':[], 'Army Size':[], 'CR Production':[],\\n 'AL Production':[],'EC Production':[],'IA Production':[]}\\n \\n # Calculate Research Stats\\n d = {}\\n for empireID, myEmpire in self.empires.iteritems():\\n if empireID <> '1':\\n num = 0\\n for techID, myTech in myEmpire.techTree.iteritems():\\n if myTech.complete == 1:\\n num += 1\\n d[empireID] = num\\n stats['Research'] = anwp.func.funcs.sortDictByValue(d, True)\\n \\n # Calculate Fleet Stats\\n d = {}\\n for shipID, myShip in self.ships.iteritems():\\n if myShip.empireID <> '1':\\n (BV,CR,AL,EC,IA) = myShip.getMyValue()\\n if myShip.empireID in d.keys():\\n d[myShip.empireID] += BV\\n else:\\n d[myShip.empireID] = BV\\n stats['Fleet Size'] = anwp.func.funcs.sortDictByValue(d, True)\\n \\n # Calculate Army Stats\\n d = {}\\n for regimentID, myRegiment in self.regiments.iteritems():\\n if myRegiment.empireID <> '1':\\n (BV,CR,AL,EC,IA) = myRegiment.getMyValue()\\n if myRegiment.empireID in d.keys():\\n d[myRegiment.empireID] += BV\\n else:\\n d[myRegiment.empireID] = BV\\n stats['Army Size'] = anwp.func.funcs.sortDictByValue(d, True)\\n\\n # Calculate Production Stats\\n for res in ['CR','AL','EC','IA']:\\n d = {}\\n for systemID, mySystem in self.systems.iteritems():\\n if mySystem.myEmpireID <> '1':\\n myValue = getattr(mySystem, 'prod%s' % res)\\n if mySystem.myEmpireID in d.keys():\\n d[mySystem.myEmpireID] += myValue\\n else:\\n d[mySystem.myEmpireID] = myValue\\n myEmpire = self.empires[mySystem.myEmpireID]\\n myEmpireValue = getattr(myEmpire, 'totalProd%s' % res)\\n setattr(myEmpire, 'totalProd%s' % res, myEmpireValue+myValue)\\n \\n stats['%s Production' % res] = anwp.func.funcs.sortDictByValue(d, True)\\n \\n # calculate top captains\\n d = {}\\n for captainID, myCaptain in self.captains.iteritems():\\n if myCaptain.myEmpire.id <> '1':\\n myCaptain.resetData()\\n d[myCaptain.id] = myCaptain.experience\\n topCaptains = anwp.func.funcs.sortDictByValue(d, True)\\n topCaptains = topCaptains[:2*len(self.empires.keys())]\\n \\n # Send out Stats to each Empire\\n for empireID, myEmpire in self.empires.iteritems():\\n if empireID <> '1':\\n title = 'Round:%d Statistics' % self.currentRound\\n body = ['%s ROUND %d STATS:' % (myEmpire.name, self.currentRound)]\\n body.append('====================================================')\\n for item in ['Research','Fleet Size', 'Army Size', 'CR Production',\\n 'AL Production', 'EC Production', 'IA Production']:\\n if empireID in stats[item]:\\n body.append('You are %s in %s' % (anwp.func.funcs.getNiceNumber(stats[item].index(empireID)+1), item))\\n \\n # total production\\n body.append('')\\n body.append('TOTAL EMPIRE PRODUCTION OVER %d ROUNDS:' % self.currentRound)\\n body.append('====================================================')\\n for res in ['CR','AL','EC','IA']:\\n body.append('Total %s Production: %d' % (res, getattr(myEmpire, 'totalProd%s' % res)))\\n\\n # legendary captains\\n body.append('')\\n body.append('TOP %d STARSHIP CAPTAINS in ROUND %d:' % ((2*len(self.empires.keys()), self.currentRound)))\\n body.append('====================================================')\\n for captainID in topCaptains:\\n myCaptain = self.captains[captainID]\\n myCaptain.promoteMe()\\n body.append('%s ---> RANK:%s -- EXP:%d -- %s' % (string.upper(myCaptain.name), myCaptain.rank, myCaptain.experience, string.upper(myCaptain.myEmpire.name)))\\n \\n myEmpire.genMail({'fromEmpire':empireID, 'round':self.currentRound,\\n 'messageType':'general', 'subject':title, 'body':body})\",\n \"def update(self, data):\\n self.data = data\\n # extract data\\n subject = data.identifier\\n self.id = _uri_to_oai(subject)\\n self.modified = datetime.utcnow()\\n self.deleted = False\\n\\n itemtype, subtype = _get_itemtype(data, subject)\\n\\n self.metadata = {}\\n\\n # fixed fields:\\n self.metadata['rif_key'] = _uri_to_key(subject)\\n self.metadata['rif_group'] = self.provider.groupDescription\\n self.metadata['rif_originatingSource'] = self.provider.originatingSource\\n self.metadata['rif_object'] = {'value': itemtype,\\n 'type': subtype,\\n #'dateModified': '',\\n }\\n\\n if itemtype == 'collection':\\n self.updateCollection(data, subject)\\n elif itemtype == 'party':\\n if subtype == 'person':\\n self.updatePartyPerson(data, subject)\\n else:\\n self.updateParty(data, subject)\\n elif itemtype == 'activity':\\n self.updateActivity(data, subject)\\n elif itemtype == 'service':\\n self.updateService(data, subject)\",\n \"def _postprocessing(self):\\n # (in)validate\\n if len(self._var_names) == 0:\\n self.invalidate()\\n else:\\n self.put_param('is_valid', True)\\n \\n # set type\\n self.put_param('type', 'Generic')\",\n \"def periodicUpdate(self):\\n try:\\n logging.info(f'{self.cn} periodicUpdate = Start')\\n isHaz = JsonSettings.parseJson('settings.json','isHazelcast')\\n if self.db.isDb():\\n self.insertStats()\\n self.insertPorts()\\n if isHaz:\\n self.insertHaz() \\n else:\\n self.db.initDb()\\n self.insertSys()\\n self.insertStats()\\n self.insertPorts()\\n if isHaz:\\n self.insertHaz() \\n except Exception as e:\\n logging.critical(f'{self.cn} Exception: {e}')\\n logging.critical(f'{self.cn} StackTrace: \\\\n', exc_info=1)\\n finally:\\n logging.info(f'{self.cn} periodicUpdate = End')\",\n \"def _fix_type_revisions(type_, rows):\\n model = getattr(all_models, type_, None)\\n revisions_table = all_models.Revision.__table__\\n if not model:\\n logger.warning(\\\"Failed to update revisions for invalid model: %s\\\", type_)\\n return\\n\\n ids = [row.resource_id for row in rows]\\n objects = model.eager_query().filter(model.id.in_(ids))\\n\\n obj_content_map = {obj.id: obj.log_json() for obj in objects}\\n\\n for row in rows:\\n # This if statement checks that we only update content for objects that\\n # exist. If an object has been deleted via import or in some other way that\\n # its delete revision was not created, this if statement will prevent a\\n # false error.\\n # Note: there will be other migrations that deal with adding missing\\n # revisions for those deleted objects.\\n if row.resource_id in obj_content_map:\\n db.session.execute(\\n revisions_table.update()\\n .where(revisions_table.c.id == row.id)\\n .values(content=obj_content_map[row.resource_id])\\n )\\n db.session.commit()\",\n \"def task_refresh_all_stats_score(request):\\n start = time.time()\\n cls_name = request.POST.get('cls') or 'Day'\\n destroy = int(request.POST.get('destroy', '0'))\\n cursor = datastore_query.Cursor(urlsafe=request.POST.get('cursor'))\\n task_count = int(request.POST.get('task_count', '0'))\\n assert cls_name in ('Day', 'Multi'), cls_name\\n cls = (\\n models.AccountStatsDay\\n if cls_name == 'Day' else models.AccountStatsMulti)\\n\\n # Task queues are given 10 minutes. Do it in 9 minutes chunks to protect\\n # against most timeout conditions.\\n timeout = 540\\n updated = 0\\n skipped = 0\\n try:\\n futures = []\\n chunk_size = 10\\n items = []\\n more = True\\n if destroy:\\n options = ndb.QueryOptions(keys_only=True)\\n else:\\n options = ndb.QueryOptions()\\n while more:\\n batch, cursor, more = cls.query(default_options=options).fetch_page(\\n 20, start_cursor=cursor)\\n if destroy:\\n futures.extend(ndb.delete_multi_async(batch))\\n updated += len(batch)\\n else:\\n for i in batch:\\n score = models.compute_score(i)\\n if i.score != score:\\n items.append(i)\\n if len(items) == chunk_size:\\n futures.extend(ndb.put_multi_async(items))\\n updated += chunk_size\\n items = []\\n futures = [f for f in futures if not f.done()]\\n else:\\n skipped += 1\\n if time.time() - start >= timeout:\\n break\\n if items:\\n futures.extend(ndb.put_multi_async(items))\\n updated += chunk_size\\n ndb.Future.wait_all(futures)\\n if not more and cls_name == 'Day':\\n # Move to the Multi instances.\\n more = True\\n cls_name = 'Multi'\\n cursor = datastore_query.Cursor()\\n if more:\\n taskqueue.add(\\n url=reverse(task_refresh_all_stats_score),\\n params={\\n 'cls': cls_name,\\n 'cursor': cursor.urlsafe() if cursor else '',\\n 'destroy': str(destroy),\\n 'task_count': str(task_count+1),\\n },\\n queue_name='refresh-all-stats-score')\\n result = 200\\n except (db.Timeout, DeadlineExceededError):\\n result = 500\\n out = 'Index: %d\\\\nType = %s\\\\nStored %d items\\\\nSkipped %d\\\\nIn %.1fs\\\\n' % (\\n task_count, cls.__name__, updated, skipped, time.time() - start)\\n if result == 200:\\n logging.info(out)\\n else:\\n logging.error(out)\\n return HttpTextResponse(out, status=result)\",\n \"def process(self):\\n\\t\\tif self.update_check() or self.force_update:\\n\\t\\t\\tself.district_check() #pull all local data and regions\\n\\t\\t\\tself.fix() #fix data anomalies - e.g add in Bucks.\\n\\t\\t\\tself.save_all() #store a copy of the data\\n\\t\\t\\tself.ingest() #add data to models\\n\\t\\t\\tself.update_totals() #calculate weekly data\\n\\t\\telse:\\n\\t\\t\\tlog.info('PHE cases up to date')\",\n \"def entityUpdates(self, *args):\\n\\t\\tfor entity in self.members.values():\\n\\t\\t\\tentity.update(*args)\",\n \"def _save_stats(self, msg, payload, subtype):\\n\\n image_id = md5(payload).hexdigest()\\n\\n try:\\n sizes = self.get_local(msg, \\\"sizes\\\")\\n except KeyError:\\n sizes = defaultdict(dict)\\n\\n if image_id in sizes['all']:\\n img = sizes['all'][image_id]\\n area = img['width'] * img['height']\\n self._update_coverage(msg, subtype, area)\\n\\n else:\\n try:\\n img = self._get_image_sizes(payload)\\n except BadImageFile:\\n self._update_invalid_counts(msg, subtype, 1)\\n else:\\n sizes['all'][image_id] = img\\n sizes[subtype][image_id] = img\\n self.set_local(msg, \\\"sizes\\\", sizes)\\n area = img['width'] * img['height']\\n self._update_coverage(msg, subtype, area)\",\n \"def put(self, request):\\n\\n data = request.data\\n test_sub_type_id = data['test_sub_type_id']\\n data.pop(\\\"test_sub_type_id\\\")\\n test_sub_type = TestSubType.objects.filter(id=test_sub_type_id)\\n\\n try:\\n test_sub_type.update(**data)\\n LOGGER.info(\\\"Test sub type data updated successfully\\\")\\n return Response({\\\"status\\\": \\\"SUCCESS\\\", \\\"message\\\": \\\"Record updated successfully\\\"})\\n except Exception, error:\\n LOGGER.error(\\\"Error:%s\\\", str(error))\\n return Response({\\\"status\\\": \\\"FAILED\\\", \\\"message\\\": str(error)})\",\n \"def population_update_multi(self, parents, children, timeline=None, genUpdate=0, adoptedParents=[], mhFrac=0.0, randomParents=False):\\n numFunctions = self.numObjectiveFunctions\\n if self.isFunctionList == 0:\\n if not hasattr(self.objective.func, '__call__'):\\n raise AssertionError('Invalid \\\\tion handle.')\\n assert self.dID != [] and np.sum(self.dID + self.xID) == len(self.discreteVals), ('A map must exist for each discrete variable. {} discrete variables, and {} maps provided.').format(np.sum(self.dID), len(self.discreteVals))\\n if sum(self.dID) + sum(self.xID) != 0:\\n for c in range(0, len(children)):\\n children[c] = self.map_to_discretes(children[c])\\n\\n for p in parents:\\n p.variables = self.map_to_discretes(p.variables)\\n\\n replace = 0\\n numFunctions = self.numObjectiveFunctions\\n if numFunctions == 1:\\n for i in range(0, len(children), 1):\\n fnew = self.objective.func(children[i])\\n if fnew > self.penalty:\\n self.penalty = fnew\\n\\n feval = 0\\n for i in range(0, len(children), 1):\\n if randomParents:\\n j = int(rand() * len(parents))\\n elif len(adoptedParents) == len(children):\\n j = adoptedParents[i]\\n else:\\n j = i\\n fnew = self.objective.func(children[i])\\n for con in self.constraints:\\n fnew += con.func(children[i])\\n\\n feval += 1\\n if fnew < parents[j].fitness:\\n parents[j].fitness = fnew\\n parents[j].variables = cp.copy(children[i])\\n parents[j].changeCount += 1\\n parents[j].stallCount = 0\\n replace += 1\\n if parents[j].changeCount >= 25 and j >= self.population * self.fracElite:\\n parents[j].variables = self.initialize(1, 'random').flatten()\\n parents[j].variables = self.map_to_discretes(parents[j].variables)\\n fnew = self.objective.func(parents[j].variables)\\n for con in self.constraints:\\n fnew += con.func(parents[j].variables)\\n\\n parents[j].fitness = fnew\\n parents[j].changeCount = 0\\n else:\\n parents[j].stallCount += 1\\n if parents[j].stallCount > 50000 and j != 0:\\n parents[j].variables = self.initialize(1, 'random').flatten()\\n parents[j].variables = self.map_to_discretes(parents[j].variables)\\n fnew = self.objective.func(parents[j].variables)\\n for con in self.constraints:\\n fnew += con.func(parents[j].variables)\\n\\n parents[j].fitness = fnew\\n parents[j].changeCount = 0\\n parents[j].stallCount = 0\\n r = int(rand() * len(parents))\\n if r <= mhFrac:\\n r = int(rand() * len(parents))\\n if fnew < parents[r].fitness:\\n parents[r].fitness = fnew\\n parents[r].variables = cp.copy(children[i])\\n parents[r].changeCount += 1\\n parents[r].stallCount += 1\\n replace += 1\\n\\n parents.sort(key=lambda x: x.fitness)\\n else:\\n numVariables = len(self.varType)\\n objectivelist = self.objective\\n num_cont_int_bin_variables = len(self.lb)\\n var_range = []\\n for k in range(0, num_cont_int_bin_variables):\\n var_range += (self.lb[k], self.ub[k])\\n\\n num_Features = num_cont_int_bin_variables\\n problem = Problem(num_of_variables=num_Features, objectives=objectivelist, variables_range=var_range)\\n num_adopted_parents = len(adoptedParents)\\n num_parents = len(parents)\\n num_children = len(children)\\n populationSize = num_parents\\n num_Features = num_cont_int_bin_variables\\n if timeline != None:\\n if len(timeline) < 2:\\n timeline.append(Event(1, feval, parents[0].fitness, parents[0].variables))\\n elif parents[0].fitness < timeline[(-1)].fitness and abs((timeline[(-1)].fitness - parents[0].fitness) / parents[0].fitness) > self.convTol:\\n timeline.append(Event(timeline[(-1)].generation, timeline[(-1)].evaluations + feval, parents[0].fitness, parents[0].variables))\\n else:\\n timeline[(-1)].generation += genUpdate\\n timeline[(-1)].evaluations += feval\\n if sum(self.dID) + sum(self.xID) != 0:\\n for p in parents:\\n p.variables = self.map_from_discretes(p.variables)\\n\\n if timeline != None:\\n return (parents, replace, timeline)\\n else:\\n return (\\n parents, replace)\\n return\",\n \"def update_all(self, request):\\n\\n schema = self.session.info['schema']\\n\\n for item in self.query().filter_by(schema=schema):\\n self.session.delete(item)\\n\\n for item in ElectionCollection(self.session).query():\\n self.update(item, request)\\n\\n for item in ElectionCompoundCollection(self.session).query():\\n self.update(item, request)\\n\\n for item in VoteCollection(self.session).query():\\n self.update(item, request)\",\n \"def _update_all(self, criteria: Q, *args, **kwargs):\\n raise NotImplementedError\",\n \"def update_progress(self, argv):\\n data = {}\\n arg_types = [{\\n \\\"type\\\": \\\"cognitive\\\",\\n \\\"rating\\\": argv.c,\\n \\\"description\\\": argv.cd\\n },\\n {\\n \\\"type\\\": \\\"emotional\\\",\\n \\\"rating\\\": argv.e,\\n \\\"description\\\": argv.ed\\n }\\n , {\\n \\\"type\\\": \\\"physical\\\",\\n \\\"rating\\\": argv.p,\\n \\\"description\\\": argv.pd\\n }\\n\\n ]\\n\\n for data_type in arg_types:\\n if data_type[\\\"rating\\\"]:\\n if 100 >= int(data_type[\\\"rating\\\"]) > 0:\\n data[data_type[\\\"type\\\"]] = {\\n \\\"rating\\\": data_type[\\\"rating\\\"],\\n \\\"description\\\": data_type[\\\"description\\\"],\\n \\\"sentiment\\\": self.get_sentiment(data_type[\\\"description\\\"])\\n }\\n else:\\n logging.error(\\\"Invalid Rating, must be 1-10\\\")\\n exit(1)\\n today = datetime.datetime.now().strftime(\\\"%Y-%m-%d %H:%M\\\")\\n insert_data = {today: {\\\"data\\\": data, \\\"weather\\\": Weather.get_weather()}}\\n logging.info(\\\"Updating Progress\\\")\\n self.prog_logs.insert(insert_data)\",\n \"def bulk_upsert(self, request):\\n data = request.data\\n if not isinstance(data, list):\\n data = [data]\\n\\n serializer = FacilityUpsertSerializer(data=data, many=True)\\n serializer.is_valid(raise_exception=True)\\n\\n district_id = request.data[0][\\\"district\\\"]\\n facilities = (\\n Facility.objects.filter(district_id=district_id)\\n .select_related(\\\"local_body\\\", \\\"district\\\", \\\"state\\\", \\\"created_by__district\\\", \\\"created_by__state\\\")\\n .prefetch_related(\\\"facilitycapacity_set\\\")\\n )\\n\\n facility_map = {f.name.lower(): f for f in facilities}\\n facilities_to_update = []\\n facilities_to_create = []\\n\\n for f in serializer.validated_data:\\n f[\\\"district_id\\\"] = f.pop(\\\"district\\\")\\n if f[\\\"name\\\"].lower() in facility_map:\\n facilities_to_update.append(f)\\n else:\\n f[\\\"created_by_id\\\"] = request.user.id\\n facilities_to_create.append(f)\\n\\n with transaction.atomic():\\n capacity_create_objs = []\\n for f in facilities_to_create:\\n capacity = f.pop(\\\"facilitycapacity_set\\\")\\n f_obj = Facility.objects.create(**f)\\n for c in capacity:\\n capacity_create_objs.append(FacilityCapacity(facility=f_obj, **c))\\n for f in facilities_to_update:\\n capacity = f.pop(\\\"facilitycapacity_set\\\")\\n f_obj = facility_map.get(f[\\\"name\\\"].lower())\\n changed = False\\n for k, v in f.items():\\n if getattr(f_obj, k) != v:\\n setattr(f_obj, k, v)\\n changed = True\\n if changed:\\n f_obj.save()\\n capacity_map = {c.room_type: c for c in f_obj.facilitycapacity_set.all()}\\n for c in capacity:\\n changed = False\\n if c[\\\"room_type\\\"] in capacity_map:\\n c_obj = capacity_map.get(c[\\\"room_type\\\"])\\n for k, v in c.items():\\n if getattr(c_obj, k) != v:\\n setattr(c_obj, k, v)\\n changed = True\\n if changed:\\n c_obj.save()\\n else:\\n capacity_create_objs.append(FacilityCapacity(facility=f_obj, **c))\\n\\n bulk_create_with_history(capacity_create_objs, FacilityCapacity, batch_size=500)\\n\\n return Response(status=status.HTTP_204_NO_CONTENT)\",\n \"def update_batch(self, *args, **kwargs):\\n pass\",\n \"def setupParticles(self):\\n\\n for ss in self.pargs['species']:\\n\\n # Make sure we are setting up particles, not walls (so we check for id existence)\\n if 'id' in ss and 'wall' not in ss:\\n if not self.rank:\\n logging.info('Setting up particles for group{id}'.format(**ss))\\n\\n randName = np.random.randint(10**5,10**8)\\n pddName = 'pdd' + '{}'.format(np.random.randint(10**5,10**8))\\n\\n if 'vol_lim' not in ss:\\n ss['vol_lim'] = 1e-20\\n\\n id = ss['id'] - 1\\n self.lmp.command('group group{} type {}'.format(id, ss['id']))\\n\\n if 'args'in ss:\\n args = ss['args']\\n else:\\n args = ()\\n\\n if 'radius' in ss:\\n radius = ss['radius']\\n\\n if not isinstance(radius, tuple):\\n radius = ('constant', radius)\\n\\n self.lmp.command('fix {} '.format(randName) + 'group{}'.format(id) + ' particletemplate/{style} 15485867 volume_limit {vol_lim} atom_type {id} density constant {density} radius'.format(**ss) + (' {}' * len(radius)).format(*radius) \\\\\\n + (' {}' * len(args)).format(*args))\\n else:\\n self.lmp.command('fix {} '.format(randName) + 'group{}'.format(id) + ' particletemplate/{style} 15485867 volume_limit {vol_lim} atom_type {id} density constant {density}'.format(**ss) + (' {}' * len(args)).format(*args))\\n \\n self.lmp.command('fix {} '.format(pddName) + 'group{}'.format(id) + ' particledistribution/discrete 67867967 1'.format(**ss) + ' {} 1.0'.format(randName))\\n\\n if ss['style'] is 'multisphere':\\n itype = ss['style']\\n else:\\n itype = 'nve/{style}'.format(**ss)\\n\\n #Do NOT unfix randName! Will cause a memory corruption error\\n self.pddName.append(pddName)\",\n \"def update(cls, project_id, resource_type, resource_id, data):\\n\\n for key, value in data.items():\\n cls.edit(\\n resource_type=resource_type,\\n resource_id=resource_id,\\n project_id=project_id,\\n key=key,\\n value=value\\n )\",\n \"def _update_counts(self, msg, subtype, by):\\n\\n try:\\n counts = self.get_local(msg, \\\"counts\\\")\\n except KeyError:\\n counts = defaultdict(int)\\n\\n counts['all'] += by\\n counts[subtype] += by\\n self.set_local(msg, \\\"counts\\\", counts)\",\n \"def updateAllRealtime():\\n for stockName in db.STOCK_MAP.keys():\\n getStock(stockName, \\\"all\\\")\\n\\n db.UPDATING_REALTIME = False\",\n \"def push_bulk(self, obj_list, doc_type=None, refresh=True):\\n assert isinstance(obj_list, collections.Sequence)\\n assert len(obj_list) > 0\\n\\n es_obj_list = []\\n for obj in obj_list:\\n if obj is None:\\n logger.warning(\\\"None object in input list\\\")\\n continue\\n\\n doc_type, es_repr = self._validate_doc_and_get_type_and_repr(obj, doc_type)\\n metadata = {\\n '_op_type': 'index',\\n \\\"_index\\\": self.index_name,\\n \\\"_type\\\": doc_type,\\n }\\n es_repr.update(**metadata)\\n\\n es_obj_list.append(es_repr)\\n\\n helpers.bulk(client=self.conn.elastic_search_client, actions=es_obj_list,\\n stats_only=True, refresh=u'true' if refresh else u'false')\",\n \"def _update_(self, data):\\n self.heroes = dict((hero, Hero(hero, data[hero])) for hero in data)\",\n \"def update(self):\\n for zone in self.zones:\\n self.update_sensor(\\\"zone\\\", zone[\\\"id\\\"])\\n for device in self.devices:\\n self.update_sensor(\\\"device\\\", device[\\\"id\\\"])\",\n \"def _update(self):\\n self.parametrize_beam()\\n self.update_ranks()\\n self._points = tf.reshape(self._endpoint, (1, 2)) * tf.reshape(self._ranks, (-1, 1))\",\n \"def update_targets(self, items):\\n items = list(filter(None, chain(*items)))\\n items = list(filter(None, items))\\n\\n if len(items) > 0:\\n self.logger.info(\\\"Updating {} thermo documents\\\".format(len(items)))\\n bulk = self.thermo().initialize_ordered_bulk_op()\\n\\n for m in items:\\n m[self.thermo.lu_field] = datetime.utcnow()\\n bulk.find({\\\"material_id\\\": m[\\\"material_id\\\"]}).upsert().replace_one(m)\\n bulk.execute()\\n else:\\n self.logger.info(\\\"No items to update\\\")\",\n \"def _enchance_assignment(self, doc):\\n\\n results = self.get_archive_items_for_assignment(doc)\\n if results.count() > 0:\\n doc['item_ids'] = [str(item.get(config.ID_FIELD)) for item in results]\\n\\n self.set_type(doc, doc)\",\n \"def update(probabilities, one_gene, two_genes, have_trait, p):\\n for person in probabilities:\\n\\n # count the genes for the person\\n geneCount = 0\\n if person in one_gene:\\n geneCount = 1\\n elif person in two_genes:\\n geneCount = 2\\n\\n # check the trait\\n hasTrait = False\\n if person in have_trait:\\n hasTrait = True\\n\\n # update the probabilities\\n probabilities[person][\\\"gene\\\"][geneCount] += p\\n probabilities[person][\\\"trait\\\"][hasTrait] += p\",\n \"def update_subcategory(self, control_type, *args):\\n\\n\\t\\tif control_type is 'intField':\\n\\t\\t\\tself.log('query intField and update others')\\n\\t\\t\\tintField_value = cmds.intField(self.grade_intField, query = True, value = True)\\n\\t\\t\\tself.log('intField is %s' % intField_value)\\n\\n\\t\\t\\tself.current_grade_value = intField_value\\n\\t\\t\\tself.log('current grade is: %s' % self.current_grade_value)\\n\\t\\t\\tcmds.intSlider(self.grade_slider, edit=True, value = -intField_value)\\n\\t\\t\\tself.update_radios_default_comments(intField_value)\\n\\t\\t\\tself.update_default_comments()\\n\\t\\t\\tself.update_is_complete()\\n\\t\\t\\tself.updateFunction()\\n\\n\\t\\telif control_type is 'slider':\\n\\n\\t\\t\\tself.log('query slider and update others')\\n\\t\\t\\tslider_value = abs(cmds.intSlider(self.grade_slider, query = True, value = True))\\n\\t\\t\\tself.log('intSlider is %s' % slider_value)\\n\\n\\t\\t\\tself.current_grade_value = slider_value\\n\\t\\t\\tself.log('current grade is: %s' % self.current_grade_value)\\n\\t\\t\\tcmds.intField(self.grade_intField, edit = True, value = slider_value)\\n\\t\\t\\tself.update_radios_default_comments(slider_value)\\n\\t\\t\\tself.update_default_comments()\\n\\t\\t\\tself.update_is_complete()\\n\\t\\t\\tself.updateFunction()\\n\\n\\t\\telif control_type is 'radioButton':\\n\\t\\t\\tself.log('query radio collection and update others')\\n\\t\\t\\tselected = cmds.radioCollection(self.grade_radio_collection, query = True, select = True)\\n\\t\\t\\tselected_letter = cmds.radioButton(selected, query = True, label = True)\\n\\t\\t\\tselected_letter = re.sub('\\\\\\\\+', 'plus', selected_letter)\\n\\t\\t\\tself.log('selected radioButton: %s' % selected_letter)\\n\\n\\t\\t\\tself.current_grade_value = int(self.grade_values.find(selected_letter).text)\\n\\t\\t\\tself.log('current grade is: %s' % self.current_grade_value)\\n\\t\\t\\tcmds.intField(self.grade_intField, edit = True, value = self.current_grade_value)\\n\\t\\t\\tcmds.intSlider(self.grade_slider, edit = True, value = -self.current_grade_value)\\n\\t\\t\\tself.log('selected_letter: %s' % selected_letter)\\n\\t\\t\\t\\n\\t\\t\\tcmds.scrollField(self.default_comments, edit = True, text = self.subcatXML.find('gradeComment').find(selected_letter).text)\\n\\t\\t\\tself.current_default_comment_text = cmds.scrollField(self.default_comments, query = True, text = True)\\n\\t\\t\\tself.log('Default Comments Updated')\\n\\t\\t\\tself.log(self.current_default_comment_text)\\n\\t\\t\\tself.update_is_complete()\\n\\t\\t\\tself.updateFunction()\\n\\n\\t\\telif control_type is 'default_comments_text':\\n\\t\\t\\tself.current_default_comment_text = cmds.scrollField(self.default_comments, query = True, text = True)\\n\\t\\t\\tself.log('Default Comments Updated')\\n\\t\\t\\tself.log(self.current_default_comment_text)\\n\\t\\t\\tself.update_is_complete()\\n\\n\\t\\telif control_type is 'example_comments_text':\\n\\t\\t\\tself.current_example_comment_text = cmds.scrollField(self.example_comments, query = True, text = True)\\n\\t\\t\\tself.log('examples updated')\\n\\t\\t\\tself.log(self.current_example_comment_text)\\n\\n\\t\\telse:\\n\\t\\t\\tself.current_comment_text = cmds.scrollField(self.comments_text_field, query = True, text = True)\\n\\t\\t\\tself.log('comments updated')\\n\\t\\t\\tself.log(self.current_comment_text)\",\n \"def update(self, new_data):\\n if type(new_data) is not dict:\\n raise TypeError(\\\"Input parameter must be a dict\\\")\\n # Update parameters\\n self._type = new_data.get(\\\"_type\\\", self._type)\\n self.time_units = new_data.get(\\\"time_units\\\", self.time_units)\\n self.len_units = new_data.get(\\\"len_units\\\", self.len_units)\\n self.pump_units = new_data.get(\\\"pump_units\\\", self.pump_units)\\n self.parameters = new_data.get(\\\"parameters\\\", self.parameters)\\n # Update pumping rate\\n self.pumprate.update(new_data.get(\\\"pumprate\\\", self.pumprate.to_dict()))\\n # Update data\\n if \\\"wells\\\" in new_data:\\n n = len(new_data[\\\"wells\\\"])\\n if n > 1:\\n self.delete_all_wells()\\n for i in range(n):\\n self.add_well(0, 0, new_data[\\\"wells\\\"][i][\\\"_type\\\"] - 2)\\n self.wells[i].update(new_data[\\\"wells\\\"][i])\\n # End Function\",\n \"def updateAllEntities():\\n entityKeys=list(registeredEntities.keys())\\n for currKey in entityKeys:\\n try:\\n currEntity=registeredEntities[currKey]\\n currEntity.update()\\n except KeyError:\\n #this should only be called if an entity is deleted (like if a rock got hit by a bullet)\\n continue\",\n \"def update_profit(self):\\n # Acessing Redis can cause greenlet switches because new jobs. We don't\\n # want to potentially switch jobs multiple times quickly, so we update\\n # the profitability information all at once after the loop to avoid\\n # multiple network switches\\n new_price_data = {}\\n for manager in self.jobmanagers.itervalues():\\n currency = manager.config['currency']\\n pscore = self.redis.get(\\\"{}_profit\\\".format(currency))\\n\\n # Deserialize\\n if pscore:\\n try:\\n pscore = simplejson.loads(pscore, use_decimal=True)\\n except Exception:\\n self.logger.warn(\\n \\\"Error parsing profit score for {}! Setting it to 0..\\\"\\n .format(currency))\\n pscore = 0\\n pass\\n # If no score was grabbed, pass a 0 value score\\n else:\\n self.logger.warn(\\\"Unable to grab profit info for {}!\\\"\\n .format(currency))\\n pscore = 0\\n\\n ratio = self.redis.get(\\\"{}_ratio\\\".format(currency)) or 1.0\\n ratio = float(ratio)\\n\\n # Only set updated if it actually changed\\n if self.price_data[currency][0] != pscore or self.price_data[currency][1] != ratio:\\n new_price_data[currency] = (pscore, ratio, time.time())\\n\\n # If we have some new information, adjust accordingly\\n if new_price_data:\\n self.logger.info(\\\"Updated price information for {}\\\"\\n .format(new_price_data.keys()))\\n # Atomic update in gevent\\n self.price_data.update(new_price_data)\\n\\n # Update all the profit info. No preemption, just maths\\n for currency in self.jobmanagers.iterkeys():\\n self.update_profitability(currency)\\n\\n self.logger.debug(\\n \\\"Re-checking best network after new price data for {}\\\"\\n .format(new_price_data.keys()))\\n self.check_best()\",\n \"def publish_updates():\\n run_subprocess(['osg-batch-update'])\",\n \"def update(self):\\n\\n\\t\\tif not self.complete:\\n\\t\\t\\tfor vasp_run in self.vasp_run_list:\\n\\t\\t\\t\\tvasp_run.update()\",\n \"def beam_update(self, idx, positions, beam_size):\\n for e in self._all:\\n a, br, d = e.size()\\n sentStates = e.view(a, beam_size, br // beam_size, d)[:, :, idx]\\n sentStates.data.copy_(\\n sentStates.data.index_select(1, positions))\",\n \"def update2(self, es, **kwargs):\\n self._update_ps(es)\\n raise NotImplementedError('must be implemented in a derived class')\",\n \"def execute_queries():\\n fetch_job_listings(engine)\\n update_job_listing(engine)\",\n \"def update_es_index():\\n\\n for job in scheduler.get_jobs():\\n if 'task_type' in job.meta and job.meta['task_type'] == \\\"update_index\\\":\\n scheduler.cancel(job)\\n\\n scheduler.schedule(\\n scheduled_time=datetime.now(),\\n func='haystack.management.commands.update_index.Command().handle()',\\n interval=60 * 60,\\n repeat=None,\\n )\\n\\n for job in scheduler.get_jobs():\\n index_job = job\\n if index_job.func_name == 'haystack.management.commands.update_index.Command().handle()':\\n break\\n\\n index_job.meta['task_type'] = \\\"update_index\\\"\\n index_job.save()\",\n \"def put(self):\\n type_model = request.json\\n\\n type_model = namedtuple(\\\"Type\\\", type_model.keys())(*type_model.values())\\n repository = TypeRepository(\\n FLASK_APP.config[\\\"DBUSER\\\"],\\n FLASK_APP.config[\\\"DBPASS\\\"],\\n FLASK_APP.config[\\\"DBHOST\\\"],\\n FLASK_APP.config[\\\"DBPORT\\\"],\\n FLASK_APP.config[\\\"DBNAME\\\"])\\n try:\\n type_model = repository.update(type_model)\\n Logger.Logger.create(FLASK_APP.config[\\\"ELASTICURL\\\"],\\n 'Informative',\\n 'Type sucessfuly updated',\\n 'put()',\\n str(type.__dict__),\\n FLASK_APP.config[\\\"TYPE\\\"])\\n return self.okResponse(\\n response=type_model,\\n message=\\\"Type sucessfuly updated.\\\",\\n status=204), 200\\n except Exception as err:\\n Logger.Logger.create(FLASK_APP.config[\\\"ELASTICURL\\\"],\\n 'Error',\\n 'Internal server error',\\n 'put()',\\n str(err),\\n FLASK_APP.config[\\\"TYPE\\\"])\\n return self.okResponse(\\n response=err,\\n message=\\\"Internal server error: \\\" + str(err),\\n status=500)\",\n \"def update_scores(read_scores, result):\\n\\n\\t# check that there are some results for this read\\n\\tassert len(result) > 0\\n\\t# write this read to output\\n\\tfor sim_match in result.keys():\\n\\n\\t\\t# get type (discordant or chimeric)\\n\\t\\tjunc_type = sim_match.split('_')[2]\\n\\n\\t\\tfor analysis_match in result[sim_match]:\\n\\t\\t\\t\\n\\t\\t\\t# get each score type\\n\\t\\t\\tfor score_type in read_scores:\\n\\t\\t\\t\\tscore = analysis_match[score_type]\\n\\t\\t\\t\\tread_scores[score_type][junc_type][score] += 1\",\n \"def _after_postgeneration(cls, instance, create, results=None):\\n super()._after_postgeneration(instance, create, results=results)\\n if results.get(\\\"should_publish\\\", False):\\n for language in instance.extended_object.get_languages():\\n instance.extended_object.publish(language)\\n instance.refresh_from_db()\",\n \"def update(self):\\r\\n debug.write(\\\"[SourceRPG] Updating all ranked positions\\\", 1)\\r\\n database.execute(\\\"SELECT steamid FROM Player ORDER BY level DESC,xp DESC\\\")\\r\\n results = database.cursor.fetchall()\\r\\n self.ranks = []\\r\\n for index, steamid in enumerate(results):\\r\\n debug.write(\\\"Rank: %s Steamid: %s\\\" % (index, steamid), 5)\\r\\n self.ranks.append(steamid[0])\\r\\n debug.write(\\\"[SourceRPG] All ranked positions updated\\\", 1)\",\n \"def update_pollutant_emissions(self, ds):\\n\\n # Update biosphere exchanges according to GAINS emission values\\n for exc in ws.biosphere(\\n ds, ws.either(*[ws.contains(\\\"name\\\", x) for x in self.emissions_map])\\n ):\\n iam_emission_label = self.emissions_map[exc[\\\"name\\\"]]\\n\\n try:\\n iam_emission = self.iam_data.cement_emissions.loc[\\n dict(\\n region=ds[\\\"location\\\"],\\n pollutant=iam_emission_label\\n )\\n ].values.item(0)\\n except KeyError:\\n # TODO: fix this.\\n # GAINS does not have a 'World' region, hence we use Europe as a temporary fix\\n iam_emission = self.iam_data.cement_emissions.loc[\\n dict(\\n region=self.geo.iam_to_GAINS_region(\\\"World\\\"),\\n pollutant=iam_emission_label\\n )\\n ].values.item(0)\\n\\n\\n if exc[\\\"amount\\\"] == 0:\\n wurst.rescale_exchange(\\n exc, iam_emission / 1, remove_uncertainty=True\\n )\\n else:\\n wurst.rescale_exchange(exc, iam_emission / exc[\\\"amount\\\"])\\n return ds\",\n \"def update_param_all_cells(self, landscape, params):\\n cell_type = self.dict_cells[landscape]\\n cell_type.update_parameters(params)\",\n \"def reload(self):\\n data = self.api.api_request(\\\"GET\\\", self.url)\\n for t in self.ace_types:\\n self[t].actors = data[t][\\\"actors\\\"]\\n self[t].groups = data[t][\\\"groups\\\"]\",\n \"def refresh_from_padron(self, resource_type):\\n data = {'impuestos': 'afip.tax', 'actividades': 'afip.activity', 'conceptos': 'afip.concept'}\\n resource_name = resource_type.capitalize().replace('tos', 'to').replace('des', 'd')\\n\\n model = data.get(resource_type)\\n if not model:\\n raise UserError(_('Resource Type %s not implemented!') % (resource_type))\\n\\n url = \\\"https://soa.afip.gob.ar/parametros/v1/%s/\\\" % resource_type\\n res = requests.get(url=url)\\n data = res.json().get('data')\\n if res.status_code != 200:\\n raise UserError(_('Error getting %s params from AFIP!') % resource_type)\\n\\n codes = []\\n for item in data:\\n code = item.get(\\\"id\\\" + resource_name)\\n values = {'code': code,\\n 'name': item.get(\\\"desc\\\" + resource_name),\\n 'active': True}\\n record = self.env[model].search([('code', '=', code)], limit=1)\\n codes.append(code)\\n if record:\\n record.write(values)\\n else:\\n record.create(values)\\n\\n # deactivate the ones that are not in afip\\n self.env[model].search([('code', 'not in', codes)]).write({'active': False})\",\n \"def bulk_process(self):\\n\\n def actions():\\n try:\\n task = self.queue.get(block=False, timeout=None)\\n\\n if task['action'] == 'index':\\n yield {\\n '_op_type': 'index',\\n '_index': self.ensure_index(task),\\n '_id': task['id'],\\n 'doc': task['properties']\\n }\\n elif task['action'] == 'delete':\\n yield {\\n '_op_type': 'delete',\\n '_index': self.ensure_index(task),\\n '_id': task['id'],\\n 'doc': task['properties']\\n }\\n else:\\n raise NotImplementedError\\n\\n except Empty:\\n pass\\n\\n for success, info in streaming_bulk(self.es_client, actions()):\\n if success:\\n self.queue.task_done()\",\n \"def _update_historical_ratings(self, pid, obs_time, rating, variance, r_type):\\n\\n\\t\\tself.historical_player_ratings[pid][obs_time][r_type + '_rating'] = rating\\n\\t\\tself.historical_player_ratings[pid][obs_time][r_type + '_variance'] = variance\",\n \"def scale_up(self):\\n self.load *= 10\\n for pp in self.powerplants:\\n if pp[\\\"type\\\"] != \\\"windturbine\\\":\\n pp[\\\"pmin\\\"] *= 10\\n pp[\\\"pmax\\\"] *= 10\",\n \"def update(\\n self,\\n filter_freq=FILTER_FREQ_DEFAULT,\\n filter_stages=None,\\n url_stats=\\\"result/matrix?show_stage_details=true&show_item_details=true\\\",\\n url_rules=\\\"formula\\\",\\n path_stats=\\\"data/matrix.json\\\",\\n path_rules=\\\"data/formula.json\\\",\\n gamedata_path=\\\"https://raw.githubusercontent.com/Kengxxiao/ArknightsGameData/master/{}/gamedata/excel/item_table.json\\\",\\n dont_save_data=False,\\n ):\\n material_probs, convertion_rules = request_data(\\n penguin_url + url_stats,\\n penguin_url + url_rules,\\n path_stats,\\n path_rules,\\n gamedata_path,\\n dont_save_data,\\n )\\n self.itemdata = request_itemdata(gamedata_path)\\n self.itemdata_rv = {\\n lang: {v: k for k, v in dct.items()} for lang, dct in self.itemdata.items()\\n }\\n\\n if filter_freq:\\n if filter_stages is None:\\n filter_stages = []\\n filtered_probs = []\\n for dct in material_probs[\\\"matrix\\\"]:\\n if (\\n dct[\\\"times\\\"] >= filter_freq\\n and dct[\\\"stage\\\"][\\\"code\\\"] not in filter_stages\\n ):\\n filtered_probs.append(dct)\\n material_probs[\\\"matrix\\\"] = filtered_probs\\n\\n self._set_lp_parameters(*self._pre_processing(material_probs, convertion_rules))\",\n \"def update(self, context):\\n session = Session()\\n\\n for sub in self.cart:\\n offers = session.search(sub.query, self.lat, self.lon, self.radius)\\n for offer in sub.handle_offers(offers):\\n context.bot.send_message(self.chat_id, text=offer_text(offer))\\n\\n updates = sub.check_offers()\\n for offer in updates['expired']:\\n context.bot.send_message(self.chat_id,\\n text=offer_text_expired(offer))\\n for offer in updates['expiring']:\\n context.bot_send_message(self.chat_id,\\n text=offer_text_expiring(offer))\\n self.config_updated()\",\n \"async def async_update(self):\\n node = self.gateway.sensors[self.node_id]\\n child = node.children[self.child_id]\\n set_req = self.gateway.const.SetReq\\n for value_type, value in child.values.items():\\n _LOGGER.debug(\\n \\\"Entity update: %s: value_type %s, value = %s\\\",\\n self._name,\\n value_type,\\n value,\\n )\\n if value_type in (\\n set_req.V_ARMED,\\n set_req.V_LIGHT,\\n set_req.V_LOCK_STATUS,\\n set_req.V_TRIPPED,\\n ):\\n self._values[value_type] = STATE_ON if int(value) == 1 else STATE_OFF\\n elif value_type == set_req.V_DIMMER:\\n self._values[value_type] = int(value)\\n else:\\n self._values[value_type] = value\",\n \"def set_mass(self, type, mass):\\n assert type < self.num_atom_types + 1\\n assert type > 0\\n self.masses[type] = mass\",\n \"def handle_wps_update(self, data):\\n\\n self.jobs = data\",\n \"def update_data(self):\\n staff = Staff.objects.all()\\n orgs = Organization.objects.all()\\n depts = Department.objects.all()\\n\\n existing = self.all()\\n if existing.count():\\n existing.delete()\\n\\n if staff.count():\\n for s in staff:\\n record = CombinedTeledata(\\n id=s.id,\\n alpha=s.alpha,\\n name=s.name,\\n first_name=s.first_name,\\n last_name=s.last_name,\\n sort_name=s.sort_name,\\n email=s.email,\\n phone=s.phone,\\n postal=s.postal,\\n job_position=s.job_position,\\n department=s.dept.name,\\n dept_id=s.dept.id,\\n organization=s.dept.org.name,\\n org_id=s.dept.org.id,\\n building=s.bldg.name,\\n bldg_id=s.bldg.import_id,\\n room=s.room,\\n from_table='staff'\\n )\\n\\n try:\\n record.save(doing_import=True)\\n record.keywords_combined.set(s.keywords.all())\\n except Exception as e:\\n logger.error(str(e))\\n\\n if orgs.count():\\n for o in orgs:\\n record = CombinedTeledata(\\n id=o.id,\\n name=o.name,\\n sort_name=o.name,\\n phone=o.phone,\\n fax=o.fax,\\n building=o.bldg.name,\\n bldg_id=o.bldg.import_id,\\n room=o.room,\\n from_table='organizations'\\n )\\n\\n try:\\n record.save(doing_import=True)\\n record.keywords_combined.set(o.keywords.all())\\n except Exception as e:\\n logger.error(str(e))\\n\\n if depts.count():\\n for d in depts:\\n record = CombinedTeledata(\\n id=d.id,\\n name=d.name,\\n sort_name=d.name,\\n phone=d.phone,\\n fax=d.fax,\\n organization=d.org.name,\\n org_id=d.org.id,\\n building=d.bldg.name,\\n bldg_id=d.bldg.import_id,\\n room=d.room,\\n from_table='departments'\\n )\\n\\n try:\\n record.save(doing_import=True)\\n record.keywords_combined.set(d.keywords.all())\\n except Exception as e:\\n logger.error(str(e))\",\n \"def test_update_multiple_preferences_type(self):\\n # This method utilises the PUT request method and will make changes to the Canvas instance. This needs consideration.\\n pass\",\n \"def _update(self):\\n self.all_params = {}\\n self._update_experiment_params()\\n self._update_preprocessing_params()\\n self._update_model_params()\",\n \"def updateDerivedTypes(self):\\n self.hasConditionals = False\\n self.derivedDict = {}\\n for format in self.values():\\n if format.genericType:\\n format.updateFromGeneric()\\n generic = self[format.genericType]\\n if format.genericType in self.derivedDict:\\n self.derivedDict[format.genericType].append(format)\\n else:\\n self.derivedDict[format.genericType] = [generic, format]\\n if format.conditional:\\n self.hasConditionals = True\\n format.conditional.setupFields(format)\",\n \"def _process(proc_data: List[Dict]) -> List[Dict]:\\n for item in proc_data:\\n for key, val in item.items():\\n if key != 'type':\\n item[key] = int(val)\\n\\n return proc_data\",\n \"def updateWeapons(self):\\n self.readyWeapons = []\\n self.setWeaponStatus()\\n\\n for myWeapon in self.activeWeapons:\\n if myWeapon.preFireCheck() == 1:\\n self.readyWeapons.append(myWeapon)\\n self.alternateTargets = []\\n\\n if self.amsTargets != []:\\n for myWeapon in self.amsWeapons:\\n if myWeapon.preFireCheck() == 1:\\n self.readyWeapons.append(myWeapon)\\n self.amsTargets = []\",\n \"def _update_positional_allele_dictionary(self, read_id, pos, allele, type, mapping_quality):\\n if pos not in self.positional_allele_dictionary:\\n self.positional_allele_dictionary[pos] = {}\\n if (allele, type) not in self.positional_allele_dictionary[pos]:\\n self.positional_allele_dictionary[pos][(allele, type)] = 0\\n\\n # increase the allele frequency of the allele at that position\\n self.positional_allele_dictionary[pos][(allele, type)] += 1\\n self.allele_dictionary[read_id][pos].append((allele, type))\",\n \"def _postprocess_staging_data(self):\\n super()._postprocess_staging_data()\\n with self._conn, self._conn.cursor() as cursor:\\n cursor.execute(sql.SQL(\\\"\\\"\\\"UPDATE {0} SET rat_bitmask = translate_bands_to_rat_bitmask(bands)\\\"\\\"\\\")\\n .format(self._staging_tbl_identifier))\",\n \"def updateSubSystems(self, data):\\r\\n current_subsystems_files = {}\\r\\n\\r\\n for subsystems_filename in self.versions[\\\"subsystems\\\"]:\\r\\n current_subsystems_files[subsystems_filename] = self.versions[\\\"subsystems\\\"][subsystems_filename]\\r\\n\\r\\n for subsystems_filename in data[\\\"subsystems\\\"]:\\r\\n subsystems_file = data[\\\"subsystems\\\"][subsystems_filename]\\r\\n if subsystems_filename == \\\"main\\\":\\r\\n continue\\r\\n try:\\r\\n if subsystems_filename not in current_subsystems_files.keys() or self.mission_name + \\\"_\\\" + subsystems_file[\\\"version\\\"] != current_subsystems_files[subsystems_filename][\\\"version\\\"]:\\r\\n downloadAndReplaceFile(subsystems_filename + \\\".py\\\", subsystems_file[\\\"python\\\"])\\r\\n self.versions[\\\"subsystems\\\"][subsystems_filename] = {}\\r\\n self.versions[\\\"subsystems\\\"][subsystems_filename][\\\"version\\\"] = self.mission_name + \\\"_\\\" + subsystems_file[\\\"version\\\"]\\r\\n self._logger.info(\\\"subsystems file \\\" + subsystems_filename + \\\" updated to version: \\\" + subsystems_file[\\\"version\\\"])\\r\\n except Exception as e:\\r\\n self._logger.error(\\\"Failed to update subsystems file \\\" + subsystems_filename + \\\" due to an exception: \\\" + str(e))\",\n \"def execute(self):\\n for coll in list(self.__bulks):\\n try:\\n bulkOp = self.__bulks[coll]\\n curr_result = Counter(bulkOp.execute())\\n self.update_results(coll, curr_result)\\n except BulkWriteError as bwe:\\n sys.stderr.write(str(bwe.details))\",\n \"def write(self, values):\\n STAGE = self.env['anytracker.stage']\\n RATING = self.env['anytracker.rating']\\n PRIORITY = self.env['anytracker.priority']\\n IMPORTANCE = self.env['anytracker.importance']\\n COMPLEXITY = self.env['anytracker.complexity']\\n oldmethods = {t.id: t.method_id for t in self}\\n res = super(Ticket, self).write(values)\\n # change the method on subtickets as well\\n if 'method_id' not in values:\\n return res\\n\\n for ticket in self:\\n children = self.search([('id', 'child_of', ticket.id),\\n ('id', '!=', ticket.id)])\\n new_meth_id = values['method_id']\\n super(Ticket, children).write({'method_id': new_meth_id})\\n\\n # point subtickets to the equivalent stage in the new method\\n for oldstage in oldmethods[ticket.id].stage_ids:\\n # find children with this stage\\n children = self.search([('id', 'child_of', ticket.id),\\n ('stage_id', '=', oldstage.id)])\\n if not children:\\n continue\\n # find a stage with the same code in the new method\\n equ_stage = STAGE.search([('state', '=', oldstage.state),\\n ('method_id', '=', new_meth_id)])\\n # if no stage found, reset to no stage at all\\n # (should display tickets on the left in the kanban)\\n equ_stage = equ_stage[0].id if equ_stage else False\\n self.env.cr.execute(\\n 'update anytracker_ticket '\\n 'set stage_id=%s where id in %s',\\n (equ_stage or None, tuple(children.ids)))\\n\\n # point subtickets to the equivalent priority in the new method\\n for oldprio in oldmethods[ticket.id].priority_ids:\\n # find children with this priority\\n children = self.search([('id', 'child_of', ticket.id),\\n ('priority_id', '=', oldprio.id)])\\n if not children:\\n continue\\n # find a priority with the same seq in the new method\\n equ_prio = PRIORITY.search([('seq', '=', oldprio.seq),\\n ('method_id', '=', new_meth_id)])\\n # if no priority found, reset to no priority at all\\n # (should display tickets on the left in the kanban)\\n equ_prio = equ_prio[0].id if equ_prio else False\\n self.env.cr.execute('update anytracker_ticket '\\n 'set priority_id=%s where id in %s',\\n (equ_prio or None, tuple(children.ids)))\\n\\n # point subtickets to the equivalent importance in the new method\\n for oldimp in oldmethods[ticket.id].importance_ids:\\n # find children with this importance\\n children = self.search([('id', 'child_of', ticket.id),\\n ('importance_id', '=', oldimp.id)])\\n if not children:\\n continue\\n # find a importance with the same code in the new method\\n equ_imp = IMPORTANCE.search([('seq', '=', oldimp.seq),\\n ('method_id', '=', new_meth_id)])\\n # if no importance found, reset to no importance at all\\n # (should display tickets on the left in the kanban)\\n equ_imp = equ_imp[0].id if equ_imp else False\\n self.env.cr.execute(\\n 'update anytracker_ticket set importance_id=%s'\\n ' where id in %s',\\n (equ_imp or None, tuple(children.ids)))\\n\\n # point subtickets to the equiv complexity in the new method\\n for oldcplx in oldmethods[ticket.id].complexity_ids:\\n # find ratings with this complexity and related to children\\n ratings = RATING.search([\\n ('ticket_id', 'in',\\n self.search([('id', 'child_of', ticket.id)]).ids),\\n ('complexity_id', '=', oldcplx.id)])\\n if not ratings:\\n continue\\n # find a complexity with the same code in the new method\\n equ_cmplx = COMPLEXITY.search(\\n [('value', '=', oldcplx.value),\\n ('method_id', '=', new_meth_id)])\\n # if no complexity found, reset to no complexity at all\\n # (should display tickets on the left in the kanban)\\n equ_cmplx = equ_cmplx[0].id if equ_cmplx else False\\n self.env.cr.execute(\\n 'update anytracker_rating set complexity_id=%s '\\n 'where id in %s',\\n (equ_cmplx or None, tuple(ratings.ids)))\\n\\n # recompute risk and ratings\\n ticket.recompute_subtickets()\\n\\n return res\",\n \"def gbf_pub_update():\\r\\n LOG.info(\\\"Start: Update datasets in RLIDGeo warehouse.\\\")\\r\\n month_stamps = [\\r\\n datetime.date.today().strftime(\\\"%Y_%m\\\"),\\r\\n (\\r\\n datetime.date.today().replace(day=1)\\r\\n - datetime.timedelta(days=1)\\r\\n ).strftime(\\\"%Y_%m\\\"),\\r\\n ]\\r\\n for month_stamp in month_stamps:\\r\\n snapshot_db_path = SNAPSHOT_DB_PATH.format(month_stamp)\\r\\n if not os.path.exists(snapshot_db_path):\\r\\n LOG.warning(\\\"Snapshot database %s does not exist.\\\", snapshot_db_path)\\r\\n continue\\r\\n\\r\\n for _dataset in DATASETS:\\r\\n arcetl.features.update_from_dicts(\\r\\n dataset_path=_dataset.path(\\\"pub\\\"),\\r\\n update_features=source_rows(snapshot_db_path, _dataset.path(\\\"source\\\")),\\r\\n id_field_names=_dataset.id_field_names,\\r\\n field_names=_dataset.field_names,\\r\\n delete_missing_features=False,\\r\\n use_edit_session=False,\\r\\n )\\r\\n LOG.info(\\\"End: Update.\\\")\",\n \"async def test_modify_schedule_type(self):\\n await self.populate_test_data() # Populate data in foglamp.scheduled_processes\\n\\n scheduler = Scheduler(_address, _m_port)\\n await scheduler.start()\\n\\n interval_schedule = IntervalSchedule()\\n interval_schedule.name = 'sleep10'\\n interval_schedule.process_name = 'sleep10'\\n interval_schedule.repeat = datetime.timedelta(seconds=0)\\n\\n await scheduler.save_schedule(interval_schedule)\\n\\n manual_schedule = ManualSchedule()\\n manual_schedule.schedule_id = interval_schedule.schedule_id\\n manual_schedule.name = 'manual'\\n manual_schedule.process_name = 'sleep10'\\n manual_schedule.repeat = datetime.timedelta(seconds=0)\\n\\n await scheduler.save_schedule(manual_schedule)\\n\\n # Assert: only 1 task is running\\n schedule = await scheduler.get_schedule(manual_schedule.schedule_id)\\n\\n assert isinstance(schedule, ManualSchedule)\\n\\n await self.stop_scheduler(scheduler)\",\n \"def update(self, dt):\\n for obj in self.objects:\\n obj.update(dt)\",\n \"def update(self):\\n self._sync_ranges()\\n self._update_params()\",\n \"def update(self, **kwargs):\\n for name, item in itertools.chain(\\n self._cal_objs.items(),\\n self._noise_objs.items()):\\n logger.debug(\\\"update {}\\\".format(item))\\n item.update(**kwargs)\",\n \"def reindex_pidtype(pid_type):\\n click.echo('Indexing pid type \\\"{}\\\"...'.format(pid_type))\\n cli = create_cli()\\n runner = current_app.test_cli_runner()\\n runner.invoke(\\n cli,\\n 'index reindex --pid-type {} --yes-i-know'.format(pid_type),\\n catch_exceptions=False\\n )\\n runner.invoke(cli, 'index run', catch_exceptions=False)\\n click.echo('Indexing completed!')\",\n \"def populate(self):\\n\\n self.create_index()\\n self.check_type()\\n self.create_mapping()\\n\\n f = open(self.csv_file, 'rU')\\n\\n # Read the first line for all the headers\\n headers = f.readline().split(',')\\n\\n # Read the rest of the document\\n rows = f.readlines()\\n added_counter = 0\\n\\n actions = []\\n for row in rows:\\n fields = row.split(',')\\n obj = {}\\n for header in headers:\\n # we call lower-case here because we were originally using\\n # analyzed strings in elasticsearch (and they were\\n # automatically converted). Code was built based on that so it's\\n # easiest to convert for now\\n try:\\n obj[header.replace('\\\\n', '')] = float(fields[\\n headers.index(header)].replace('\\\\n', '').lower())\\n except ValueError:\\n obj[header.replace('\\\\n', '')] = fields[\\n headers.index(header)].replace('\\\\n', '').lower()\\n # check afterwards to replace empty strings with None (which json.dumps hopefully writes to null)\\n if obj[header.replace('\\\\n', '')] == '':\\n obj[header.replace('\\\\n', '')] = None\\n try:\\n item = {\\n '_index': self.es_main_index,\\n '_type': self.es_main_type,\\n '_source': obj\\n }\\n\\n actions.append(item)\\n\\n added_counter += 1\\n print('%s new records added' % added_counter,\\n end='\\\\r')\\n sys.stdout.flush()\\n\\n if added_counter % self.chunk_size == 0:\\n helpers.bulk(self.es, actions)\\n actions = []\\n\\n except ConnectionError:\\n print('There was a connection error. Check your Elastic' +\\n ' Search setting and make sure Elastic Search is ' +\\n 'running.')\\n return False\\n\\n # add the remaining items\\n if actions:\\n helpers.bulk(self.es, actions)\\n\\n print('The update is completed. %s new records were added.' %\\n added_counter)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5894394","0.552447","0.5422689","0.51684684","0.51551306","0.50729585","0.50314814","0.495626","0.49350718","0.48938638","0.48739257","0.48735148","0.48624405","0.48345262","0.48125353","0.47565222","0.47560182","0.47403848","0.47322214","0.46917525","0.46881023","0.46878457","0.46480522","0.4646425","0.46456882","0.46377173","0.46361303","0.4632519","0.46324039","0.4630771","0.46228954","0.4617129","0.46135354","0.46124178","0.4610281","0.46080405","0.46075884","0.45996284","0.4593866","0.4573534","0.456476","0.45544326","0.454592","0.45411822","0.45402166","0.4531714","0.45267612","0.45242944","0.45142242","0.45120132","0.4510825","0.45032087","0.4492668","0.44897243","0.44885406","0.4479692","0.44752496","0.4472957","0.44685373","0.4467156","0.44625393","0.44494084","0.44449922","0.44304776","0.44287166","0.4424979","0.4424572","0.44236204","0.44182158","0.44144586","0.44134998","0.4410143","0.4408557","0.4408334","0.44070426","0.44062364","0.44043562","0.44023845","0.44013608","0.439806","0.43941742","0.43911985","0.4389549","0.4389439","0.43865544","0.43856043","0.43853626","0.43826875","0.43805185","0.43791974","0.4375671","0.43733066","0.43666795","0.43644658","0.43629995","0.43529445","0.43522617","0.4351256","0.43486255","0.4342766"],"string":"[\n \"0.5894394\",\n \"0.552447\",\n \"0.5422689\",\n \"0.51684684\",\n \"0.51551306\",\n \"0.50729585\",\n \"0.50314814\",\n \"0.495626\",\n \"0.49350718\",\n \"0.48938638\",\n \"0.48739257\",\n \"0.48735148\",\n \"0.48624405\",\n \"0.48345262\",\n \"0.48125353\",\n \"0.47565222\",\n \"0.47560182\",\n \"0.47403848\",\n \"0.47322214\",\n \"0.46917525\",\n \"0.46881023\",\n \"0.46878457\",\n \"0.46480522\",\n \"0.4646425\",\n \"0.46456882\",\n \"0.46377173\",\n \"0.46361303\",\n \"0.4632519\",\n \"0.46324039\",\n \"0.4630771\",\n \"0.46228954\",\n \"0.4617129\",\n \"0.46135354\",\n \"0.46124178\",\n \"0.4610281\",\n \"0.46080405\",\n \"0.46075884\",\n \"0.45996284\",\n \"0.4593866\",\n \"0.4573534\",\n \"0.456476\",\n \"0.45544326\",\n \"0.454592\",\n \"0.45411822\",\n \"0.45402166\",\n \"0.4531714\",\n \"0.45267612\",\n \"0.45242944\",\n \"0.45142242\",\n \"0.45120132\",\n \"0.4510825\",\n \"0.45032087\",\n \"0.4492668\",\n \"0.44897243\",\n \"0.44885406\",\n \"0.4479692\",\n \"0.44752496\",\n \"0.4472957\",\n \"0.44685373\",\n \"0.4467156\",\n \"0.44625393\",\n \"0.44494084\",\n \"0.44449922\",\n \"0.44304776\",\n \"0.44287166\",\n \"0.4424979\",\n \"0.4424572\",\n \"0.44236204\",\n \"0.44182158\",\n \"0.44144586\",\n \"0.44134998\",\n \"0.4410143\",\n \"0.4408557\",\n \"0.4408334\",\n \"0.44070426\",\n \"0.44062364\",\n \"0.44043562\",\n \"0.44023845\",\n \"0.44013608\",\n \"0.439806\",\n \"0.43941742\",\n \"0.43911985\",\n \"0.4389549\",\n \"0.4389439\",\n \"0.43865544\",\n \"0.43856043\",\n \"0.43853626\",\n \"0.43826875\",\n \"0.43805185\",\n \"0.43791974\",\n \"0.4375671\",\n \"0.43733066\",\n \"0.43666795\",\n \"0.43644658\",\n \"0.43629995\",\n \"0.43529445\",\n \"0.43522617\",\n \"0.4351256\",\n \"0.43486255\",\n \"0.4342766\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6947471"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":94692,"cells":{"query":{"kind":"string","value":"Convert any text to a fernet key for encryption."},"document":{"kind":"string","value":"def any_text_to_fernet_key(self, text):\n md5 = fingerprint.fingerprint.of_text(text)\n fernet_key = base64.b64encode(md5.encode(\"utf-8\"))\n return fernet_key"},"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 text2PublicKey(text:str):\n return RSA.importKey(b58decode(text))","def encrypt(text):\r\n\r\n cipher = fuzz(text)\r\n return hexify(cipher)","def fernet_encript(key,message):\n\tf = Fernet(key)\n\treturn f.encrypt(message)","def caesar_encryption(text):\n result = ''\n for char in text:\n if char.isdigit():\n i = (num_key.index(char) - 4) % 10\n result += num_key[i]\n elif not char.isdigit() and char.lower() in alpha_key:\n i = (alpha_key.index(char.lower()) - 4) % 26\n result += alpha_key[i]\n else:\n result += char\n return result","def _create_fernet_key(self) -> str:\n\n client = boto3.client(\"ssm\", endpoint_url=os.environ.get(\"AWS_ENDPOINT\"))\n\n try:\n response = client.get_parameter(Name=self.object_name, WithDecryption=True)\n return response[\"Parameter\"][\"Value\"]\n except client.exceptions.ParameterNotFound:\n return Fernet.generate_key().decode()","def gen_Fernet_key():\n\tkey = Fernet.generate_key()\n\treturn key","def FtEncrypt(self,text):\n \n self.text = text\n EncryptText = []\n characters = \"abcdefghijklmnopqrstuvwxyz \"\n\n #attempt to append my_list and update my_dict\n #using a random set of alphabet and a random made_key \n try:\n for char in random.sample(characters,27):\n Code_Fouad_Teniou.my_list.append(char)\n \n Code_Fouad_Teniou.my_dict.update(zip(Code_Fouad_Teniou.my_key,Code_Fouad_Teniou.my_list))\n\n for item in text.lower():\n for i in Code_Fouad_Teniou.my_dict.items():\n if item == i[1]:\n EncryptText.append(i[0])\n \n return EncryptText\n \n #Raise AttributeError if text is not a string \n except AttributeError:\n raise AttributeError, \"\\n<Please re-enter your text as a 'string'\"","def text2PrivateKey(text:str):\n return RSA.importKey(b58decode(text))","def encode(text, key):\n encrypted = []\n for i in text:\n encrypted.append(key[i])\n return encrypted","def encipher(self,string): \n string = self.remove_punctuation(string)\n ret = ''\n for (i,c) in enumerate(string):\n if i<len(self.key): offset = self.a2i(self.key[i])\n else: offset = self.a2i(string[i-len(self.key)]) \n ret += self.i2a(self.a2i(c)+offset)\n return ret","def encrypt_vigenere(plaintext: str, keyword: str) -> str:","def main():\n key, plain = get_key_plain()\n encode(key, plain)","def encryptstring(text, password):\n\n enc = []\n for i in enumerate(text):\n key_c = password[i[0] % len(password)]\n enc_c = chr((ord(i[1]) + ord(key_c)) % 256)\n enc.append(enc_c)\n return base64.urlsafe_b64encode(\"\".join(enc).encode()).decode()","def encrypt_vigenere(plaintext: str, keyword: str) -> str:\n ciphertext = \"\"\n # PUT YOUR CODE HERE\n\n key_lenght = len(keyword)\n text_lenght = len(plaintext)\n while key_lenght != text_lenght:\n keyword += keyword\n key_lenght = len(keyword)\n if key_lenght > text_lenght:\n keyword = keyword[:text_lenght]\n key_lenght = len(keyword)\n code_key = []\n ord_A = ord('A')\n ord_a = ord('a')\n\n if plaintext.islower():\n for i in range(key_lenght):\n if plaintext[i] == \" \":\n code_key.append(\" \")\n else:\n code_key.append(ord(keyword[i]) - ord_a)\n code_text = []\n for n in range(text_lenght):\n if plaintext[n] == \" \":\n code_text.append(\" \")\n else:\n code_text.append(ord(plaintext[n]) - ord_a)\n ciphertext = ''\n for u in range(len(plaintext)):\n if plaintext[u] == \" \":\n ciphertext += \" \"\n else:\n value = (code_key[u] + code_text[u]) % 26 + ord_a\n ciphertext += chr(value)\n else:\n for i in range(key_lenght):\n if plaintext[i] == \" \":\n code_key.append(\" \")\n else:\n code_key.append(ord(keyword[i]) - ord_A)\n code_text = []\n for n in range(text_lenght):\n if plaintext[n] == \" \":\n code_text.append(\" \")\n else:\n code_text.append(ord(plaintext[n]) - ord_A)\n ciphertext = ''\n for u in range(len(plaintext)):\n if plaintext[u] == \" \":\n value = ord(\" \")\n else:\n value = (code_key[u] + code_text[u]) % 26 + ord_A\n ciphertext += chr(value)\n return ciphertext","def create_crypt_key():\n\n crypt_key = Fernet.generate_key() # key is type = bytes\n\n crypt_query = 'INSERT INTO Crypt (crypt_key) VALUES (%s)'\n my_cursor.execute(crypt_query, (crypt_key,))\n pw_db.commit()","def load_Fernet_key(filename):\n\tfich = open(str(filename) +'.key', 'rb')\n\tkey = fich.read() # The key will be type bytes\n\tfich.close()\n\treturn key","def encrypt(text,key):\r\n aes = pyaes.AESModeOfOperationCTR(key)\r\n ciphertext = aes.encrypt(text)\r\n return ciphertext","def vigenere(inputString, mode, key):\n\n\t# Sanitize inputs\n\tkey = key.lower()\n\tkey.replace(\" \", \"\")\n\tif mode not in ['e', 'd']:\n\t\traise Exception(\"'mode' must be either 'e' for encryption or 'd' for decryption\")\n\n\t# Initialize local variables\n\tkeystreamCounter = 0\n\toutString = \"\"\n\n\t# Iterate on each character of the input string.\n\tfor inputChar in inputString:\n\t\t# If this character is in the alphabet, encode it\n\t\tif inputChar.lower() in alphabet:\n\t\t\t# Find the number of positions to move and the starting position\n\t\t\tkeyChar = key[keystreamCounter % len(key)]\n\t\t\tnumMoves = alphabet.index(keyChar)\n\t\t\tstartPosition = alphabet.index(inputChar.lower())\n\n\t\t\t# Encode or decode the input character by shifting the alphabet\n\t\t\tif mode == 'e': # encode\n\t\t\t\toutputChar = alphabet[(startPosition + numMoves) % len(alphabet)]\n\t\t\telif mode == 'd': # decode\n\t\t\t\toutputChar = alphabet[(startPosition - numMoves) % len(alphabet)]\n\n\t\t\t# Preserve capitalization\n\t\t\tif inputChar.isupper():\n\t\t\t\toutputChar = outputChar.upper()\n\n\t\t\t# Increment counter on each loop.\n\t\t\tkeystreamCounter += 1\n\n\t\t# If character is not in alphabet, preserve punctutation\n\t\telse:\n\t\t\toutputChar = inputChar\n\n\t\t# Append to output string\n\t\toutString += outputChar\n\n\t# Write to file\n\treturn(outString)","def MakeKey(self, string, string_1, string_2):\n ...","def run():\n key = input(\"Enter a 26 letter key: \")\n if not isValidKey(key):\n print(\"Invalid key.\")\n return\n plainText = input(\"Plain Text: \")\n cipherText = substitution(plainText, key)\n print(f\"Cipher Text: {cipherText}\")\n return","def encode(keyFile: str, string: str=\"\", inputFile: str=\"\", outputFile:str=\"\") -> str:\n print(\"Encoding message ...\")\n print(\"Is public key file ok ?\", checkKeyFile(keyFile,\"public\"))\n\n if (checkKeyFile(keyFile,\"public\")): \n f = open(keyFile)\n keyData = extractParamsFromKey(f.readlines()[1]) # read the second line of the file and extract the param\n if args.verbose : print(\"keydata (publ) :\", keyData)\n \n #open a file if the string is empty\n if(string == \"\"):\n string = str(readFile(inputFile))\n else:\n string = string\n\n # transform the ascii string into a series of numbers\n asciiToInt = \"\"\n for char in string :\n asciiToInt += str(ord(char)).zfill(3)\n if args.verbose : print(\"ascii to int\", asciiToInt)\n\n # calculate the block length\n blocklen = len(str(keyData[0])) -1\n if args.verbose : print(\"block size is\", blocklen)\n \n # split the string into blocks\n # start bu reversing the string so we can start left to right\n tmp = asciiToInt[::-1]\n # cut them\n blocks = wrap(tmp, blocklen)\n # reverse the lsit of cut\n blocks.reverse()\n # inside eecaht cut reserve the characters\n for i in range(len(blocks)):\n blocks[i] = blocks[i][::-1]\n if args.verbose : print(blocks)\n \n # make sur that every block is the corect length, overwise add padding\n for i in range(len(blocks)):\n blocks[i] = blocks[i].zfill(blocklen)\n if args.verbose : print(\"blocks after padding :\", blocks)\n \n # crypt everyblock\n tempCryptString = \"\"\n if args.verbose : print(\"encrypted blocks:\")\n for i in range(len(blocks)): \n blockEncrypted = str(calculateCrypt(blocks[i], keyData[1], keyData[0]))\n if args.verbose : print(blockEncrypted)\n blockEncrypted = blockEncrypted.zfill(blocklen+1)\n if args.verbose : print(blockEncrypted)\n tempCryptString += blockEncrypted\n if args.verbose : print(\"encrypted string :\",tempCryptString)\n \n # write the contentes to a file\n hexstr = intToHexToBase64(tempCryptString)\n if(outputFile == \"\"):\n print(\"Encrypted :\")\n print(hexstr)\n else :\n print(\"writing to file\", outputFile)\n writeToFile(outputFile, hexstr)\n return hexstr\n else: \n print(\"keyfile is incorrect\")\n return","def encrypt_string(self, raw_string):\n return self.fernet_instance.encrypt(raw_string.encode('utf-8'))","def generate_key(self):\n\n self.key = Fernet.generate_key()\n self.cryptor = Fernet(self.key)","def encrypt(key, plaintext):\n data = fk(keyGen(key)[0], ip(plaintext))\n return fp(fk(keyGen(key)[1], swapNibbles(data)))","def get_fernet_key(app: Sanic, passphrase: str) -> bytes:\n salted = (passphrase + app.secret_key).encode()\n key = hashlib.sha256(salted).digest()[:32]\n return base64.urlsafe_b64encode(key)","def de_cryptate(self):\r\n\r\n intab1 = \"abcdefghijklomnopqrstuvwxyz !,.\"\r\n outtab1 = \"?2p=o)7i(u9/y&t3%r¤5e#w1q!>*'^;)\"\r\n# Fetching from written in textbox\r\n s = self.textbox.toPlainText()\r\n a = s.lower()\r\n# Changing out the letters/numbers/etc\r\n crypted = (a.translate({ord(x): y for (y, x) in zip(intab1, outtab1)}))\r\n# Clear the textbox\r\n self.textbox.clear()\r\n# Write the Decrypted text\r\n self.textbox.setPlainText(crypted)","def tokey(*args):\n salt = '||'.join([force_text(arg) for arg in args])\n hash_ = hashlib.md5(encode(salt))\n return hash_.hexdigest()","def makeKey(text):\n key, n = {}, 0\n for i in text:\n key[i] = str(n)\n n += 1\n return key","def encrypt(string,pub):\r\n string = livingDead.utfE(string)\r\n crypto = rsa.encrypt(string, pub)\r\n return crypto","def test_encryption(e, c):\n message = input(\"Enter word to encrypt: \")\n ciphered = ''\n\n for i in range(0, len(message)):\n ciphered = f'{ciphered}{chr(endecrypt(ord(message[i]), e, c))}'\n\n print(ciphered + ' is the ciphered text')\n d = key_cracker(e, c)\n print(\"Plain text is:\")\n for i in range(0, len(ciphered)):\n print(chr(endecrypt(ord(ciphered[i]), d, c)), end='')","def cryptate(self):\r\n\r\n intab1 = \"abcdefghijklomnopqrstuvwxyz\"\r\n outtab1 = \"?2p=o)7i(u9/y&t3%r¤5e#w1q!>)\"\r\n# Fetching the writing in textbox\r\n s = self.textbox.toPlainText()\r\n a = s.lower()\r\n# The crypting process, replaces letters in intab1 with outtab1\r\n crypted = (a.translate({ord(x): y for (x, y) in zip(intab1, outtab1)}))\r\n# Clear the textbox\r\n self.textbox.clear()\r\n# Write the crypted text within textbox\r\n self.textbox.setPlainText(crypted)","def FtDecrypt(self,EncryptText):\n \n self.EncryptText = EncryptText\n characters = \"abcdefghijklmnopqrstuvwxyz \"\n DecripText = ''\n\n #attempt to decrypt the text using the made_key and EncryptText \n try:\n for item in self.EncryptText:\n DecripText += Code_Fouad_Teniou.my_dict[item]\n\n return DecripText\n \n #Raise KeyError if a different key was used to encrypt the text \n except KeyError:\n print \"\\n<Please use the right code(made_key) to decrypt your text\"","def test_from_and_to_text(self):\n rkeyring = dns.tsigkeyring.from_text(text_keyring)\n tkeyring = dns.tsigkeyring.to_text(rkeyring)\n self.assertEqual(tkeyring, text_keyring)","def keyify(text):\n text = text.lower()\n text = text.strip()\n\n text = text.replace('.', '')\n text = re.sub('[,-]', ' ', text)\n text = re.sub('\\s{2,}', ' ', text)\n\n return text","def encrypt(self,string=\"vrorqjdqgwdqnviruwkhilvk\",key=3):\r\n return \"\".join([chr((ord(ch)-key-ord('a'))%(ord('z')-ord('a')+1)+ord('a')) for ch in string])","def decrypt(self, text):\n return self.encrypt(text)","def __encrypt_text_aes__(self, text, password):\n BLOCK_SIZE = 32\n PADDING_CHAR = b'^'\n iv = Random.new().read(16)\n # key must be 32 bytes for AES-256, so the password is hashed with md5 first\n cipher = AES.new(self.__hash_md5__(password), AES.MODE_CBC, iv)\n plaintext = text.encode('utf-8')\n # plaintext must be padded to be a multiple of BLOCK_SIZE\n plaintext_padded = plaintext + (BLOCK_SIZE - len(plaintext) % BLOCK_SIZE) * PADDING_CHAR\n ciphertext = cipher.encrypt(plaintext_padded)\n return (\n base64.b64encode(iv),\n base64.b64encode(ciphertext),\n PADDING_CHAR\n )","def test_encryption(e, c):\n\n#\te = int(raw_input(\"\\nEnter e from public key\\n\"))\n#\tc = int(raw_input(\"\\nEnter c from public key\\n\"))\n\n string = raw_input(\"\\nEnter word to encrpyt\\n\")\n for i in range(0, len(string)):\n print endecrypt(ord(string[i]), e, c)","def fernet_decript(key,message):\n\tf = Fernet(key)\n\treturn f.decrypt(message)","def caesar_encode(self, text, key):\n result_list = []\n for char in text:\n if char.isalpha():\n if char.islower():\n offset = ASCII_LOWER_OFFSET\n else:\n offset = ASCII_UPPER_OFFSET\n char = chr((ord(char) - offset + key) % ALPHABET_SIZE + offset)\n result_list.append(char)\n return ''.join(result_list)","def recover_encrypt_pass(self):\n with open(self.key_path) as input_file:\n key = input_file.readlines()\n cipher_suite = Fernet(key[0])\n bin_passwd = bytes(self.password, 'utf-8')\n ciphered_text = cipher_suite.encrypt(bin_passwd)\n return ciphered_text","def Crypt(self, aString):\n kIdx = 0\n cryptStr = \"\" # empty 'crypted string to be returned\n\n # loop through the string and XOR each byte with the keyword\n # to get the 'crypted byte. Add the 'crypted byte to the\n # 'crypted string\n for x in range(len(aString)):\n cryptStr = cryptStr + \\\n chr( ord(aString[x]) ^ ord(self.key[kIdx]))\n # use the mod operator - % - to cyclically loop through\n # the keyword\n kIdx = (kIdx + 1) % len(self.key)\n\n return cryptStr","def encrypt(self, text):\n text = text.upper()\n output = []\n text_list = list(text)\n for letter in text_list:\n output.append(self.atbash_dict.get(letter, letter))\n return ''.join(output)","def encrypt_data_key(self, dataKey, token, userGroup):\n masterKey = self.retrieve_master_key(token=token, userGroup=userGroup)\n box = secret.SecretBox(masterKey)\n if isinstance(dataKey, str):\n dataKey = dataKey.encode('utf-8')\n cipherText= box.encrypt(dataKey).decode('cp855')\n return cipherText","def create_key ():","def convert_key(text):\n if text is None:\n return None\n if text == \"YESTERDAY\":\n return \"today\"\n if text == \"TODAY\":\n return \"today\"\n if text == \"MONTH TO DATE\":\n return \"month\"\n if text.startswith(\"SINCE \"):\n return text.replace(\"SINCE \", \"\").replace(\" \", \"\").lower()\n LOG.warning(\"convert_key() failed for |%s|\", text)\n return \"fail\"","def encrypt(plaintext: str, key: str) -> str:\n return \"\".join(chr(ord(p) ^ ord(k)) for (p, k) in zip(plaintext, key))","def encrypt(text, offset):\r\n\r\n return format_text(text, offset)","def encryptFromString(self, data, keyobj):\n return self.encryptByteArray(bytearray(data, 'utf-8'), keyobj)","def encrypt(key, text):\n key = _key_array(key)\n text = _text_array(text)\n aes = mxit.aes.AES()\n parts = _split(text, 16)\n encoded = []\n for part in parts:\n encoded += aes.encrypt(part, key, aes.keySize[\"SIZE_128\"])\n return encoded","def _InitFromString(self, text):\r\n # First, remove all whitespace:\r\n text = re.sub(_WHITESPACE_RE, '', text)\r\n\r\n # Parse out the period-separated components\r\n match = _KEY_RE.match(text)\r\n if not match:\r\n raise ValueError('Badly formatted key string: \"%s\"', text)\r\n\r\n private_exp = match.group('private_exp')\r\n if private_exp:\r\n private_exp = _B64ToNum(private_exp)\r\n else:\r\n private_exp = None\r\n self.keypair = Crypto.PublicKey.RSA.construct(\r\n (_B64ToNum(match.group('mod')),\r\n _B64ToNum(match.group('exp')),\r\n private_exp))","def encrypt(self, input_file, output_file):\n self.key %= 26\n ciphertext = \"\"\n with open(input_file) as plaintext:\n self.text = plaintext.read()\n for char in self.text:\n if char.isalpha():\n if 65 <= ord(char) <= 90: #char is between A and Z\n if ord(char) + self.key <= 90:\n ciphertext += chr(ord(char) + self.key)\n elif ord(char) + self.key > 90:\n ciphertext += chr(ord(char) + self.key - 26)\n if 97 <= ord(char) <= 122:\n if ord(char) + self.key <= 122:\n ciphertext += chr(ord(char) + self.key)\n elif ord(char) + self.key > 122:\n ciphertext += chr(ord(char) + self.key - 26)\n else:\n ciphertext += char\n encrypted_file = open(output_file, 'w')\n encrypted_file.write(ciphertext)\n print \"Created file: \" + output_file","def create_key(input, **kwargs):\n input = re.compile(r\"\\d+\").sub(_repl, input)\n input = input.replace(':', 'c')\n input = input.replace(';', 'c')\n input = input.replace('_', 'u')\n return re.sub('[^a-zA-Z]+', '', str(input)).lower()","def encode(encryption=None):\n\n key_to_encrypt = {'a': 'q', 'b': 'v', 'c': 'x', 'd': 'z', 'e': 'y', 'f': 'w', 'g': 'u', 'h': 't', 'i': 's',\n 'j': 'r',\n 'k': 'p', 'l': 'o', 'm': 'n', 'n': 'm', 'o': 'l', 'p': 'k', 'r': 'j', 's': 'i', 't': 'h',\n 'u': 'g', 'w': 'f',\n 'y': 'e', 'z': 'd', 'x': 'c', 'v': 'b', 'q': 'a',\n 'A': 'Q', 'B': 'V', 'C': 'X', 'D': 'Z', 'E': 'Y', 'F': 'W', 'G': 'U', 'H': 'T', 'I': 'S',\n 'J': 'R', 'K': 'P',\n 'L': 'O', 'M': 'N', 'N': 'M', 'O': 'L', 'P': 'K', 'R': 'J', 'S': 'I', 'T': 'H', 'U': 'G',\n 'W': 'F', 'Y': 'E',\n 'Z': 'D', 'X': 'C', 'V': 'B', 'Q': 'S',\n '1': '5', '2': '9', '3': '8', '4': '7', '5': '6', '6': '4', '7': '3', '8': '2', '9': '1',\n '.': ',', ',': '.', ':': ';', ';': ':', '?': '!', '!': '?', '-': '_', '_': '-', '(': ')',\n ')': '(',\n '%': '$', '$': '%', ' ': '&', '&': ' ', '+': '*', '*': '+'}\n entered_image = input(\"Image name with extension: \")\n img = Image.open(entered_image, 'r')\n\n message = input(\"Message that you want to be encoded: \")\n if (len(message) == 0):\n raise ValueError('Empty message!')\n\n e1 = monoalphabetic_encryption.Encryption(key_to_encrypt, message)\n encrypted_message = e1.encrypt()\n\n new_image = img.copy()\n putPixel(new_image, encrypted_message)\n\n new_image_name = input(\"New image name with extension: \")\n new_image.save(new_image_name, str(new_image_name.split(\".\")[1].upper()))","def weaksauce_encrypt(text, password):\n\n offset = sum([ord(x) for x in password])\n encoded = ''.join(\n chr(min(ord(x) + offset, 2**20))\n for x in text\n )\n return encoded","def plain_text(file_key, file_cipher_text):\n with open(file_key, 'r') as f:\n key = f.readline()\n with open(file_cipher_text, 'r') as f:\n cipher_text = f.readline()\n\n cipher_suite = Fernet(key)\n return cipher_suite.decrypt(cipher_text)","def encrypt_vigenere(plaintext: str, keyword: str) -> str:\n ciphertext = \"\"\n if len(keyword) < len(plaintext):\n for j in range(len(plaintext) - len(keyword)):\n keyword += keyword[j]\n for i in range(len(plaintext)):\n n = ord(plaintext[i])\n m = ord(keyword[i])\n if (n >= ord('A')) and (n <= ord('Z')):\n if (m - ord('A') + n) <= ord('Z'):\n ciphertext += chr(m - ord('А') + n)\n else:\n ciphertext += chr((m + n) % (1 + ord('Z')))\n else:\n if (m - ord('a') + n) <= ord('z'):\n ciphertext += chr(m - ord('a') + n)\n else:\n ciphertext += chr((m + n) % ord('a'))\n return ciphertext","def decrypt_vigenere(ciphertext: str, keyword: str) -> str:\n plaintext = \"\"\n # PUT YOUR CODE HERE\n key_lenght = len(keyword)\n text_lenght = len(ciphertext)\n\n while key_lenght != text_lenght:\n keyword += keyword\n key_lenght = len(keyword)\n if key_lenght > text_lenght:\n keyword = keyword[:text_lenght]\n key_lenght = len(keyword)\n code_key = []\n ord_a = ord('a')\n ord_A = ord('A')\n\n if ciphertext.islower():\n for i in range(key_lenght):\n if ciphertext[i] == \" \":\n code_key.append(\" \")\n else:\n code_key.append(ord(keyword[i]) - ord_a)\n code_text = []\n for n in range(text_lenght):\n if ciphertext[n] == \" \":\n code_text.append(\" \")\n else:\n code_text.append(ord(ciphertext[n]) - ord_a)\n for u in range(text_lenght):\n if ciphertext[u] == \" \":\n value = ord(\" \")\n else:\n\n value = ((code_text[u] - code_key[u] + 26) % 26) + ord_a\n plaintext += chr(value)\n else:\n for i in range(key_lenght):\n if ciphertext[i] == \" \":\n code_key.append(\" \")\n else:\n code_key.append(ord(keyword[i]) - ord_A)\n code_text = []\n for n in range(text_lenght):\n if ciphertext[n] == \" \":\n code_text.append(\" \")\n else:\n code_text.append(ord(ciphertext[n]) - ord_A)\n for u in range(text_lenght):\n if ciphertext[u] == \" \":\n value = ord(\" \")\n else:\n value = ((code_text[u] - code_key[u] + 26) % 26) + ord_A\n plaintext += chr(value)\n\n return plaintext","def _encrypt(self, text, **options):\n\n raise CoreNotImplementedError()","def encrypt(self, text):\n ciphertext = []\n # text = text.upper()\n for char in text:\n try:\n key = (self.a * self.characters.index(char) + self.b) % len(self.characters)\n # If character is not in set for cipher,\n # directly append it without transformation\n except ValueError:\n ciphertext.append(char)\n else:\n ciphertext.append(self.characters[key])\n return ''.join(ciphertext)","def demonstrate_string_encryption_key_based(plain_text):\n try:\n # GENERATE key\n key = AESGCM.generate_key(bit_length=256)\n\n # GENERATE random nonce (number used once)\n nonce = os.urandom(12)\n\n # ENCRYPTION\n aesgcm = AESGCM(key)\n cipher_text_bytes = aesgcm.encrypt(\n nonce=nonce,\n data=plain_text.encode('utf-8'),\n associated_data=None\n )\n # CONVERSION of raw bytes to BASE64 representation\n cipher_text = base64.urlsafe_b64encode(cipher_text_bytes)\n\n # DECRYPTION\n decrypted_cipher_text_bytes = aesgcm.decrypt(\n nonce=nonce,\n data=base64.urlsafe_b64decode(cipher_text),\n associated_data=None\n )\n decrypted_cipher_text = decrypted_cipher_text_bytes.decode('utf-8')\n\n logger.info(\"Decrypted and original plain text are the same: %s\",\n decrypted_cipher_text == plain_text)\n except InvalidTag:\n logger.exception(\"Symmetric string encryption failed\")","def encryptionSelfMadeFunction(text,index):\n s = text\n transformedChar = \"\"\n\n transformedChar = s[0:index] + s[index+1:] +s[index]\n\n print(\"Encrypted Transformed text : \" )\n return transformedChar","def HashForText (text):\n if isinstance(text, six.text_type):\n text = text.encode('utf-8')\n return __Hasher(text).hexdigest()","def generate_key():\n key = Fernet.generate_key()\n with open(\"pass.key\", \"wb\") as key_file:\n key_file.write(key)","def test_from_text(self):\n rkeyring = dns.tsigkeyring.from_text(text_keyring)\n self.assertEqual(rkeyring, rich_keyring)","def enc(text):\n if isinstance(text, str):\n return unicode(text, 'utf-8') # TODO: fix in Python 3\n elif isinstance(text, unicode):\n return text.encode('utf-8')\n else:\n raise Exception(\"Unsupported encode format.\")","def get_key_plain():\n if len(sys.argv) != 2:\n exit(\"Usage: python vigenere.py k\")\n\n # get plaintext\n user_input = input(\"plaintext: \")\n \n return sys.argv[1], user_input","def serialize_text(text):\n return serialize_plaintext(text)","def encrypt(self, text):\n output = []\n text = text.upper()\n\n for char in text:\n try:\n index = self.alpha.index(char)\n except ValueError:\n output.append(char)\n else:\n output.append(self.alpha[(index * 5 + 8) % 26])\n return \"\".join(output)","def encrypt(event=None): # event is passed by binders.\n msg = inputText.get(\"1.0\",tkinter.END)\n outText.delete('1.0', tkinter.END)\n\n f = open(myTmpDir + 'pt' + str(identity) + '.bin','wb')\n f.write(msg)\n f.close()\n\n os.popen(\"rsa.exe e \" + myTmpDir + \"pt\" + str(identity) + \".bin \"+ myTmpDir + \"locEnc\" + str(identity) + \".bin\")\n\n locEncFileName = myTmpDir + \"locEnc\" + str(identity) + \".bin\"\n with open(locEncFileName, \"rb\") as f:\n readFile = f.read()\n # Convert to hex representation\n digest = base64.encodestring(bytes(readFile))\n\n # TODO: overwirite\n outText.insert(tkinter.END, digest)","def encrypt(self, text):\n\n text = text.lower()\n encrypted_word = []\n for letter in text:\n try:\n index = self.alpha.index(letter)\n except ValueError:\n encrypted_word.append(letter)\n else:\n # Uses Affine encryption function to encrypt the word\n new_index = ((self.a*index)+self.b) % self.m\n encrypted_word.append(self.alpha[new_index])\n return \"\".join(encrypted_word)","def string_to_key(string: str):\n try:\n # Removing all characters not in alphabet and making the string lower case\n return re.compile(\"[^A-Za-z]\").sub(\"\", string).lower()\n except TypeError:\n # If string is actually None we return None\n return None","def vigenere_transform(operation, str):\n key = input(\"Passkey? \").upper()\n\n if operation == 'E':\n print(\"Encrypting {0} using Vigenere cipher with key {1}\".format(str, key))\n print(\"...\")\n return encrypt_vigenere(str, key)\n else:\n print(\"Decrypting {0} using Vigenere cipher with key {1}\".format(str, key))\n print(\"...\")\n return decrypt_vigenere(str, key)","def passwd_encryption(self):\n key = Fernet.generate_key()\n cipher_suite = Fernet(key)\n bin_passwd = bytes(self.password, 'utf-8')\n ciphered_text = cipher_suite.encrypt(bin_passwd)\n with open(self.pass_path, 'wb') as pass_output:\n pass_output.write(ciphered_text)\n with open(self.key_path, 'wb') as key_output:\n key_output.write(key)","def __encrypt(string: str) -> str:\n key = 171\n result = b\"\\0\\0\\0\" + chr(len(string)).encode('latin-1')\n for i in string.encode('latin-1'):\n a = key ^ i\n key = a\n result += chr(a).encode('latin-1')\n return result","def encrypt(self, user_input):\n # extract the message and keyword\n message = user_input[0]\n keyword = user_input[1]\n\n # generate encryption key\n cipher_key = self.__generate_key(keyword, \"encrypt\")\n\n # process and return text\n return self.__process_text(cipher_key, message)","def encrypt(self, text):\n\t\tclean_text = message2num(text)\n\t\tencrypted = []\n\t\tplug = self._enc_plugboard(clean_text)\n\n\t\tfor letter in plug:\n\t\t\tl = self._forward(letter)\n\t\t\tl = self._reflect(self.reflector, l)\n\t\t\tl = self._backwards(l)\n\t\t\tencrypted.append(l)\n\n\t\tencrypted = self._enc_plugboard(encrypted)\n\t\tencrypted = \"\".join(POS2L[l] for l in encrypted)\n\n\t\treturn encrypted","def generate_symmetric_key():\n return Fernet.generate_key()","def store_Fernet_key(key,filename):\n\tfich = open(str(filename) + '.key', 'wb')\n\tfich.write(key) # The key is type bytes still\n\tfich.close()","def crypt(text, passphrase, which):\n text = scrub_string(text)\n passphrase = scrub_string(passphrase)\n letters = (\n shift_string_by_letter(ch, passphrase[i % len(passphrase)], which)\n for i, ch in enumerate(text)\n )\n return \"\".join(letters)","def test_to_text(self):\n tkeyring = dns.tsigkeyring.to_text(rich_keyring)\n self.assertEqual(tkeyring, text_keyring)","def encipher(self):\n ciphertext = \"\"\n for pt, key_char in zip(self.text, self.key):\n char_index = self.char_block.alphabet.index(pt)\n ciphertext += self.char_block.rows[key_char][char_index]\n print(ciphertext)","def _encrypted_user_photo_key_str(self):\r\n face_aes_key_str = settings.VERIFY_STUDENT[\"SOFTWARE_SECURE\"][\"FACE_IMAGE_AES_KEY\"]\r\n face_aes_key = face_aes_key_str.decode(\"hex\")\r\n rsa_key_str = settings.VERIFY_STUDENT[\"SOFTWARE_SECURE\"][\"RSA_PUBLIC_KEY\"]\r\n rsa_encrypted_face_aes_key = rsa_encrypt(face_aes_key, rsa_key_str)\r\n\r\n return rsa_encrypted_face_aes_key.encode(\"base64\")","def encrypt(text, offset):\n encrypted_text = \"\"\n for char in text:\n if ord(char) <= 64:\n encrypted_character = chr(ord(char))\n elif ord(char) < 90:\n encrypted_character = ord(char) + offset\n if encrypted_character > 90:\n encrypted_character -= 26\n encrypted_character = chr(encrypted_character)\n else:\n encrypted_character = ord(char) + offset\n if encrypted_character > 122:\n encrypted_character -= 26\n encrypted_character = chr(encrypted_character)\n encrypted_text += encrypted_character\n\n return encrypted_text","def applyCoder(text, coder):\n ciphertext = str()\n #for each letter in the text find it, and grab shifted letter\n for letter in text:\n ciphertext += coder.get(letter, letter)\n return ciphertext","def generate_key():\n key = Fernet.generate_key()\n with open(\"Secret.key\",\"wb\")as key_file:\n key_file.write(key)","def encryptor(text: bytes, IV: bytes, key: bytes) -> bytes:\n \n # Given\n prepend_string = \"comment1=cooking%20MCs;userdata=\"\n append_string = \";comment2=%20like%20a%20pound%20of%20bacon\"\n\t\n plaintext = text.replace(b';', b'\";\"').replace(b'=', b'\"=\"')\n ciphertext = AES_CBC_encrypt(PKCS7_pad(plaintext, len(key)), IV, key)\n return ciphertext","def encrypt_vigenere(plaintext, keyword):\n ciphertext = ''\n new_keyword = ''\n for i in range(len(plaintext)):\n num_word = ord(plaintext[i])\n while len(plaintext) > len(new_keyword):\n new_keyword += keyword\n num_key = ord(new_keyword[i])\n if num_key <= 90:\n num_key -= 65\n elif num_key >= 97:\n num_key -= 97\n if num_word <= 90:\n if (num_key+num_word) > 90:\n num_word -= 26\n else:\n if (num_key+num_word) > 122:\n num_word -= 26\n ciphertext += chr(num_word+num_key)\n return ciphertext","def generate_key():\r\n # generating key\r\n key = Fernet.generate_key()\r\n\r\n key_dir = os.path.join(os.path.dirname(__file__), \"resources/key\")\r\n\r\n # writing key in file\r\n with open(key_dir, \"wb\") as keyFile:\r\n keyFile.write(key)","def encodeVigenere(self, key):\n\n key = key.upper().replace(\" \", \"\")\n encode = Vig(key)\n cipherText = encode.encode(self.planeText)\n \n if (self.verbose == 1):\n print(cipherText)\n \n return(cipherText)","def key(self, value=None):\n if self.crypt_method == 'C':\n key_type = \"number\"\n else:\n key_type = \"string\"\n\n input_message = f\"Please enter a {key_type} as a \" \\\n f\"{self.crypt_type}ion key\\n>> \"\n if value is None:\n key = input(input_message)\n else:\n key = value\n\n is_valid_key, key = Check.is_valid_key(key, self.crypt_method)\n if is_valid_key:\n self._key = key\n else:\n raise ValueError(f\"Key{key} is invalid\")","def makekey(function, *args, **kwargs) -> str:\n arguments = str((function.__name__, args, kwargs)).strip()\n arguments = arguments.translate(\n str.maketrans('', '', string.punctuation+string.whitespace)\n )\n key = codecs.encode(pickle.dumps(arguments, protocol=0), \"base64\").decode().strip()\n return key","def encode(text, password):\r\n\tstep_index = 0\r\n\tencoded_text = ''\r\n\tfor letter in text:\r\n\t\tencoded_text += next_letter(letter, to_int(password[step_index]))\r\n\t\tstep_index += 1\r\n\t\tif step_index > len(password)-1:\r\n\t\t\tstep_index = 0\r\n\treturn encoded_text","def __generate_key(self, keyword, action):\n encryption_key = {}\n\n # get separate modified & unmodified alpha objects\n plain_alpha = self.helpers.alphabet(\"list\")\n cipher_alpha = self.helpers.alphabet(\"list\")\n\n # remove duplicate letters from the keyword\n keyword_stripped = self.helpers.unique(keyword)\n\n # remove keyword letters from cipher alpha\n for i in range(len(keyword_stripped)):\n cipher_alpha.remove(keyword_stripped[i])\n\n # append keyword letters to cipher alpha\n cipher_alpha = keyword_stripped + cipher_alpha\n\n if action == \"encrypt\":\n # generate encryption key\n for i in range(len(plain_alpha)):\n encryption_key[plain_alpha[i]] = cipher_alpha[i]\n elif action == \"decrypt\":\n # generate decryption key\n for i in range(len(plain_alpha)):\n encryption_key[cipher_alpha[i]] = plain_alpha[i]\n else:\n raise ValueError(\"Cheatin', uh?\")\n\n return encryption_key","def get_key(name):\n import os\n salt = os.urandom(16)\n name = name.encode()\n from cryptography.hazmat.primitives.kdf.pbkdf2 import PBKDF2HMAC\n from cryptography.hazmat.primitives import hashes\n from cryptography.hazmat.backends import default_backend\n kdf = PBKDF2HMAC(algorithm=hashes.SHA256(),\n length=32,\n salt=salt,\n iterations=100000,\n backend=default_backend())\n import base64\n key = base64.urlsafe_b64encode(kdf.derive(name))\n return key","def encode_email(email, key):\n return","def find_key(plaintext1, plaintext2):\n\n # if plain1 XOR cipher1 == plain2 XOR cipher2\n xor1 = strings_xor(list(plaintext1.encode()), list(CIPHERTEXT1))\n xor2 = strings_xor(list(plaintext2.encode()), list(CIPHERTEXT2))\n if xor1 == xor2:\n return xor1.encode()\n\n # if plain1 XOR cipher2 == plain2 XOR cipher1\n xor1 = strings_xor(list(plaintext1.encode()), list(CIPHERTEXT2))\n xor2 = strings_xor(list(plaintext2.encode()), list(CIPHERTEXT1))\n if xor1 == xor2:\n return xor1.encode()\n\n return None","def generate_key():\n key = Fernet.generate_key()\n with open(\"secret.key\", \"wb\") as key_file:\n key_file.write(key)","def setup_key_encrypt(self):\r\n\t\tself.max_key = math.floor(len(self.message) / 2)\r\n\t\twhile True:\r\n\t\t\tkey = input(f\"Please enter a key value less than or equal to {self.max_key}. --> \")\r\n\t\t\ttry:\r\n\t\t\t\tself.key = int(key)\r\n\t\t\texcept ValueError:\r\n\t\t\t\tprint(\"Key needs to be a number.\")\r\n\t\t\t\tcontinue\r\n\t\t\tif self.key > self.max_key: \t\t\t\r\n\t\t\t\tprint(f\"{key} is too big of a number.\")\t\r\n\t\t\telif self.key == 0:\r\n\t\t\t\tprint(\"0 cannot be a key\")\t\t\t\r\n\t\t\telse:\t\t\t\r\n\t\t\t\tbreak","def encrypt(cls, plaintext, aad, key, iv):"],"string":"[\n \"def text2PublicKey(text:str):\\n return RSA.importKey(b58decode(text))\",\n \"def encrypt(text):\\r\\n\\r\\n cipher = fuzz(text)\\r\\n return hexify(cipher)\",\n \"def fernet_encript(key,message):\\n\\tf = Fernet(key)\\n\\treturn f.encrypt(message)\",\n \"def caesar_encryption(text):\\n result = ''\\n for char in text:\\n if char.isdigit():\\n i = (num_key.index(char) - 4) % 10\\n result += num_key[i]\\n elif not char.isdigit() and char.lower() in alpha_key:\\n i = (alpha_key.index(char.lower()) - 4) % 26\\n result += alpha_key[i]\\n else:\\n result += char\\n return result\",\n \"def _create_fernet_key(self) -> str:\\n\\n client = boto3.client(\\\"ssm\\\", endpoint_url=os.environ.get(\\\"AWS_ENDPOINT\\\"))\\n\\n try:\\n response = client.get_parameter(Name=self.object_name, WithDecryption=True)\\n return response[\\\"Parameter\\\"][\\\"Value\\\"]\\n except client.exceptions.ParameterNotFound:\\n return Fernet.generate_key().decode()\",\n \"def gen_Fernet_key():\\n\\tkey = Fernet.generate_key()\\n\\treturn key\",\n \"def FtEncrypt(self,text):\\n \\n self.text = text\\n EncryptText = []\\n characters = \\\"abcdefghijklmnopqrstuvwxyz \\\"\\n\\n #attempt to append my_list and update my_dict\\n #using a random set of alphabet and a random made_key \\n try:\\n for char in random.sample(characters,27):\\n Code_Fouad_Teniou.my_list.append(char)\\n \\n Code_Fouad_Teniou.my_dict.update(zip(Code_Fouad_Teniou.my_key,Code_Fouad_Teniou.my_list))\\n\\n for item in text.lower():\\n for i in Code_Fouad_Teniou.my_dict.items():\\n if item == i[1]:\\n EncryptText.append(i[0])\\n \\n return EncryptText\\n \\n #Raise AttributeError if text is not a string \\n except AttributeError:\\n raise AttributeError, \\\"\\\\n<Please re-enter your text as a 'string'\\\"\",\n \"def text2PrivateKey(text:str):\\n return RSA.importKey(b58decode(text))\",\n \"def encode(text, key):\\n encrypted = []\\n for i in text:\\n encrypted.append(key[i])\\n return encrypted\",\n \"def encipher(self,string): \\n string = self.remove_punctuation(string)\\n ret = ''\\n for (i,c) in enumerate(string):\\n if i<len(self.key): offset = self.a2i(self.key[i])\\n else: offset = self.a2i(string[i-len(self.key)]) \\n ret += self.i2a(self.a2i(c)+offset)\\n return ret\",\n \"def encrypt_vigenere(plaintext: str, keyword: str) -> str:\",\n \"def main():\\n key, plain = get_key_plain()\\n encode(key, plain)\",\n \"def encryptstring(text, password):\\n\\n enc = []\\n for i in enumerate(text):\\n key_c = password[i[0] % len(password)]\\n enc_c = chr((ord(i[1]) + ord(key_c)) % 256)\\n enc.append(enc_c)\\n return base64.urlsafe_b64encode(\\\"\\\".join(enc).encode()).decode()\",\n \"def encrypt_vigenere(plaintext: str, keyword: str) -> str:\\n ciphertext = \\\"\\\"\\n # PUT YOUR CODE HERE\\n\\n key_lenght = len(keyword)\\n text_lenght = len(plaintext)\\n while key_lenght != text_lenght:\\n keyword += keyword\\n key_lenght = len(keyword)\\n if key_lenght > text_lenght:\\n keyword = keyword[:text_lenght]\\n key_lenght = len(keyword)\\n code_key = []\\n ord_A = ord('A')\\n ord_a = ord('a')\\n\\n if plaintext.islower():\\n for i in range(key_lenght):\\n if plaintext[i] == \\\" \\\":\\n code_key.append(\\\" \\\")\\n else:\\n code_key.append(ord(keyword[i]) - ord_a)\\n code_text = []\\n for n in range(text_lenght):\\n if plaintext[n] == \\\" \\\":\\n code_text.append(\\\" \\\")\\n else:\\n code_text.append(ord(plaintext[n]) - ord_a)\\n ciphertext = ''\\n for u in range(len(plaintext)):\\n if plaintext[u] == \\\" \\\":\\n ciphertext += \\\" \\\"\\n else:\\n value = (code_key[u] + code_text[u]) % 26 + ord_a\\n ciphertext += chr(value)\\n else:\\n for i in range(key_lenght):\\n if plaintext[i] == \\\" \\\":\\n code_key.append(\\\" \\\")\\n else:\\n code_key.append(ord(keyword[i]) - ord_A)\\n code_text = []\\n for n in range(text_lenght):\\n if plaintext[n] == \\\" \\\":\\n code_text.append(\\\" \\\")\\n else:\\n code_text.append(ord(plaintext[n]) - ord_A)\\n ciphertext = ''\\n for u in range(len(plaintext)):\\n if plaintext[u] == \\\" \\\":\\n value = ord(\\\" \\\")\\n else:\\n value = (code_key[u] + code_text[u]) % 26 + ord_A\\n ciphertext += chr(value)\\n return ciphertext\",\n \"def create_crypt_key():\\n\\n crypt_key = Fernet.generate_key() # key is type = bytes\\n\\n crypt_query = 'INSERT INTO Crypt (crypt_key) VALUES (%s)'\\n my_cursor.execute(crypt_query, (crypt_key,))\\n pw_db.commit()\",\n \"def load_Fernet_key(filename):\\n\\tfich = open(str(filename) +'.key', 'rb')\\n\\tkey = fich.read() # The key will be type bytes\\n\\tfich.close()\\n\\treturn key\",\n \"def encrypt(text,key):\\r\\n aes = pyaes.AESModeOfOperationCTR(key)\\r\\n ciphertext = aes.encrypt(text)\\r\\n return ciphertext\",\n \"def vigenere(inputString, mode, key):\\n\\n\\t# Sanitize inputs\\n\\tkey = key.lower()\\n\\tkey.replace(\\\" \\\", \\\"\\\")\\n\\tif mode not in ['e', 'd']:\\n\\t\\traise Exception(\\\"'mode' must be either 'e' for encryption or 'd' for decryption\\\")\\n\\n\\t# Initialize local variables\\n\\tkeystreamCounter = 0\\n\\toutString = \\\"\\\"\\n\\n\\t# Iterate on each character of the input string.\\n\\tfor inputChar in inputString:\\n\\t\\t# If this character is in the alphabet, encode it\\n\\t\\tif inputChar.lower() in alphabet:\\n\\t\\t\\t# Find the number of positions to move and the starting position\\n\\t\\t\\tkeyChar = key[keystreamCounter % len(key)]\\n\\t\\t\\tnumMoves = alphabet.index(keyChar)\\n\\t\\t\\tstartPosition = alphabet.index(inputChar.lower())\\n\\n\\t\\t\\t# Encode or decode the input character by shifting the alphabet\\n\\t\\t\\tif mode == 'e': # encode\\n\\t\\t\\t\\toutputChar = alphabet[(startPosition + numMoves) % len(alphabet)]\\n\\t\\t\\telif mode == 'd': # decode\\n\\t\\t\\t\\toutputChar = alphabet[(startPosition - numMoves) % len(alphabet)]\\n\\n\\t\\t\\t# Preserve capitalization\\n\\t\\t\\tif inputChar.isupper():\\n\\t\\t\\t\\toutputChar = outputChar.upper()\\n\\n\\t\\t\\t# Increment counter on each loop.\\n\\t\\t\\tkeystreamCounter += 1\\n\\n\\t\\t# If character is not in alphabet, preserve punctutation\\n\\t\\telse:\\n\\t\\t\\toutputChar = inputChar\\n\\n\\t\\t# Append to output string\\n\\t\\toutString += outputChar\\n\\n\\t# Write to file\\n\\treturn(outString)\",\n \"def MakeKey(self, string, string_1, string_2):\\n ...\",\n \"def run():\\n key = input(\\\"Enter a 26 letter key: \\\")\\n if not isValidKey(key):\\n print(\\\"Invalid key.\\\")\\n return\\n plainText = input(\\\"Plain Text: \\\")\\n cipherText = substitution(plainText, key)\\n print(f\\\"Cipher Text: {cipherText}\\\")\\n return\",\n \"def encode(keyFile: str, string: str=\\\"\\\", inputFile: str=\\\"\\\", outputFile:str=\\\"\\\") -> str:\\n print(\\\"Encoding message ...\\\")\\n print(\\\"Is public key file ok ?\\\", checkKeyFile(keyFile,\\\"public\\\"))\\n\\n if (checkKeyFile(keyFile,\\\"public\\\")): \\n f = open(keyFile)\\n keyData = extractParamsFromKey(f.readlines()[1]) # read the second line of the file and extract the param\\n if args.verbose : print(\\\"keydata (publ) :\\\", keyData)\\n \\n #open a file if the string is empty\\n if(string == \\\"\\\"):\\n string = str(readFile(inputFile))\\n else:\\n string = string\\n\\n # transform the ascii string into a series of numbers\\n asciiToInt = \\\"\\\"\\n for char in string :\\n asciiToInt += str(ord(char)).zfill(3)\\n if args.verbose : print(\\\"ascii to int\\\", asciiToInt)\\n\\n # calculate the block length\\n blocklen = len(str(keyData[0])) -1\\n if args.verbose : print(\\\"block size is\\\", blocklen)\\n \\n # split the string into blocks\\n # start bu reversing the string so we can start left to right\\n tmp = asciiToInt[::-1]\\n # cut them\\n blocks = wrap(tmp, blocklen)\\n # reverse the lsit of cut\\n blocks.reverse()\\n # inside eecaht cut reserve the characters\\n for i in range(len(blocks)):\\n blocks[i] = blocks[i][::-1]\\n if args.verbose : print(blocks)\\n \\n # make sur that every block is the corect length, overwise add padding\\n for i in range(len(blocks)):\\n blocks[i] = blocks[i].zfill(blocklen)\\n if args.verbose : print(\\\"blocks after padding :\\\", blocks)\\n \\n # crypt everyblock\\n tempCryptString = \\\"\\\"\\n if args.verbose : print(\\\"encrypted blocks:\\\")\\n for i in range(len(blocks)): \\n blockEncrypted = str(calculateCrypt(blocks[i], keyData[1], keyData[0]))\\n if args.verbose : print(blockEncrypted)\\n blockEncrypted = blockEncrypted.zfill(blocklen+1)\\n if args.verbose : print(blockEncrypted)\\n tempCryptString += blockEncrypted\\n if args.verbose : print(\\\"encrypted string :\\\",tempCryptString)\\n \\n # write the contentes to a file\\n hexstr = intToHexToBase64(tempCryptString)\\n if(outputFile == \\\"\\\"):\\n print(\\\"Encrypted :\\\")\\n print(hexstr)\\n else :\\n print(\\\"writing to file\\\", outputFile)\\n writeToFile(outputFile, hexstr)\\n return hexstr\\n else: \\n print(\\\"keyfile is incorrect\\\")\\n return\",\n \"def encrypt_string(self, raw_string):\\n return self.fernet_instance.encrypt(raw_string.encode('utf-8'))\",\n \"def generate_key(self):\\n\\n self.key = Fernet.generate_key()\\n self.cryptor = Fernet(self.key)\",\n \"def encrypt(key, plaintext):\\n data = fk(keyGen(key)[0], ip(plaintext))\\n return fp(fk(keyGen(key)[1], swapNibbles(data)))\",\n \"def get_fernet_key(app: Sanic, passphrase: str) -> bytes:\\n salted = (passphrase + app.secret_key).encode()\\n key = hashlib.sha256(salted).digest()[:32]\\n return base64.urlsafe_b64encode(key)\",\n \"def de_cryptate(self):\\r\\n\\r\\n intab1 = \\\"abcdefghijklomnopqrstuvwxyz !,.\\\"\\r\\n outtab1 = \\\"?2p=o)7i(u9/y&t3%r¤5e#w1q!>*'^;)\\\"\\r\\n# Fetching from written in textbox\\r\\n s = self.textbox.toPlainText()\\r\\n a = s.lower()\\r\\n# Changing out the letters/numbers/etc\\r\\n crypted = (a.translate({ord(x): y for (y, x) in zip(intab1, outtab1)}))\\r\\n# Clear the textbox\\r\\n self.textbox.clear()\\r\\n# Write the Decrypted text\\r\\n self.textbox.setPlainText(crypted)\",\n \"def tokey(*args):\\n salt = '||'.join([force_text(arg) for arg in args])\\n hash_ = hashlib.md5(encode(salt))\\n return hash_.hexdigest()\",\n \"def makeKey(text):\\n key, n = {}, 0\\n for i in text:\\n key[i] = str(n)\\n n += 1\\n return key\",\n \"def encrypt(string,pub):\\r\\n string = livingDead.utfE(string)\\r\\n crypto = rsa.encrypt(string, pub)\\r\\n return crypto\",\n \"def test_encryption(e, c):\\n message = input(\\\"Enter word to encrypt: \\\")\\n ciphered = ''\\n\\n for i in range(0, len(message)):\\n ciphered = f'{ciphered}{chr(endecrypt(ord(message[i]), e, c))}'\\n\\n print(ciphered + ' is the ciphered text')\\n d = key_cracker(e, c)\\n print(\\\"Plain text is:\\\")\\n for i in range(0, len(ciphered)):\\n print(chr(endecrypt(ord(ciphered[i]), d, c)), end='')\",\n \"def cryptate(self):\\r\\n\\r\\n intab1 = \\\"abcdefghijklomnopqrstuvwxyz\\\"\\r\\n outtab1 = \\\"?2p=o)7i(u9/y&t3%r¤5e#w1q!>)\\\"\\r\\n# Fetching the writing in textbox\\r\\n s = self.textbox.toPlainText()\\r\\n a = s.lower()\\r\\n# The crypting process, replaces letters in intab1 with outtab1\\r\\n crypted = (a.translate({ord(x): y for (x, y) in zip(intab1, outtab1)}))\\r\\n# Clear the textbox\\r\\n self.textbox.clear()\\r\\n# Write the crypted text within textbox\\r\\n self.textbox.setPlainText(crypted)\",\n \"def FtDecrypt(self,EncryptText):\\n \\n self.EncryptText = EncryptText\\n characters = \\\"abcdefghijklmnopqrstuvwxyz \\\"\\n DecripText = ''\\n\\n #attempt to decrypt the text using the made_key and EncryptText \\n try:\\n for item in self.EncryptText:\\n DecripText += Code_Fouad_Teniou.my_dict[item]\\n\\n return DecripText\\n \\n #Raise KeyError if a different key was used to encrypt the text \\n except KeyError:\\n print \\\"\\\\n<Please use the right code(made_key) to decrypt your text\\\"\",\n \"def test_from_and_to_text(self):\\n rkeyring = dns.tsigkeyring.from_text(text_keyring)\\n tkeyring = dns.tsigkeyring.to_text(rkeyring)\\n self.assertEqual(tkeyring, text_keyring)\",\n \"def keyify(text):\\n text = text.lower()\\n text = text.strip()\\n\\n text = text.replace('.', '')\\n text = re.sub('[,-]', ' ', text)\\n text = re.sub('\\\\s{2,}', ' ', text)\\n\\n return text\",\n \"def encrypt(self,string=\\\"vrorqjdqgwdqnviruwkhilvk\\\",key=3):\\r\\n return \\\"\\\".join([chr((ord(ch)-key-ord('a'))%(ord('z')-ord('a')+1)+ord('a')) for ch in string])\",\n \"def decrypt(self, text):\\n return self.encrypt(text)\",\n \"def __encrypt_text_aes__(self, text, password):\\n BLOCK_SIZE = 32\\n PADDING_CHAR = b'^'\\n iv = Random.new().read(16)\\n # key must be 32 bytes for AES-256, so the password is hashed with md5 first\\n cipher = AES.new(self.__hash_md5__(password), AES.MODE_CBC, iv)\\n plaintext = text.encode('utf-8')\\n # plaintext must be padded to be a multiple of BLOCK_SIZE\\n plaintext_padded = plaintext + (BLOCK_SIZE - len(plaintext) % BLOCK_SIZE) * PADDING_CHAR\\n ciphertext = cipher.encrypt(plaintext_padded)\\n return (\\n base64.b64encode(iv),\\n base64.b64encode(ciphertext),\\n PADDING_CHAR\\n )\",\n \"def test_encryption(e, c):\\n\\n#\\te = int(raw_input(\\\"\\\\nEnter e from public key\\\\n\\\"))\\n#\\tc = int(raw_input(\\\"\\\\nEnter c from public key\\\\n\\\"))\\n\\n string = raw_input(\\\"\\\\nEnter word to encrpyt\\\\n\\\")\\n for i in range(0, len(string)):\\n print endecrypt(ord(string[i]), e, c)\",\n \"def fernet_decript(key,message):\\n\\tf = Fernet(key)\\n\\treturn f.decrypt(message)\",\n \"def caesar_encode(self, text, key):\\n result_list = []\\n for char in text:\\n if char.isalpha():\\n if char.islower():\\n offset = ASCII_LOWER_OFFSET\\n else:\\n offset = ASCII_UPPER_OFFSET\\n char = chr((ord(char) - offset + key) % ALPHABET_SIZE + offset)\\n result_list.append(char)\\n return ''.join(result_list)\",\n \"def recover_encrypt_pass(self):\\n with open(self.key_path) as input_file:\\n key = input_file.readlines()\\n cipher_suite = Fernet(key[0])\\n bin_passwd = bytes(self.password, 'utf-8')\\n ciphered_text = cipher_suite.encrypt(bin_passwd)\\n return ciphered_text\",\n \"def Crypt(self, aString):\\n kIdx = 0\\n cryptStr = \\\"\\\" # empty 'crypted string to be returned\\n\\n # loop through the string and XOR each byte with the keyword\\n # to get the 'crypted byte. Add the 'crypted byte to the\\n # 'crypted string\\n for x in range(len(aString)):\\n cryptStr = cryptStr + \\\\\\n chr( ord(aString[x]) ^ ord(self.key[kIdx]))\\n # use the mod operator - % - to cyclically loop through\\n # the keyword\\n kIdx = (kIdx + 1) % len(self.key)\\n\\n return cryptStr\",\n \"def encrypt(self, text):\\n text = text.upper()\\n output = []\\n text_list = list(text)\\n for letter in text_list:\\n output.append(self.atbash_dict.get(letter, letter))\\n return ''.join(output)\",\n \"def encrypt_data_key(self, dataKey, token, userGroup):\\n masterKey = self.retrieve_master_key(token=token, userGroup=userGroup)\\n box = secret.SecretBox(masterKey)\\n if isinstance(dataKey, str):\\n dataKey = dataKey.encode('utf-8')\\n cipherText= box.encrypt(dataKey).decode('cp855')\\n return cipherText\",\n \"def create_key ():\",\n \"def convert_key(text):\\n if text is None:\\n return None\\n if text == \\\"YESTERDAY\\\":\\n return \\\"today\\\"\\n if text == \\\"TODAY\\\":\\n return \\\"today\\\"\\n if text == \\\"MONTH TO DATE\\\":\\n return \\\"month\\\"\\n if text.startswith(\\\"SINCE \\\"):\\n return text.replace(\\\"SINCE \\\", \\\"\\\").replace(\\\" \\\", \\\"\\\").lower()\\n LOG.warning(\\\"convert_key() failed for |%s|\\\", text)\\n return \\\"fail\\\"\",\n \"def encrypt(plaintext: str, key: str) -> str:\\n return \\\"\\\".join(chr(ord(p) ^ ord(k)) for (p, k) in zip(plaintext, key))\",\n \"def encrypt(text, offset):\\r\\n\\r\\n return format_text(text, offset)\",\n \"def encryptFromString(self, data, keyobj):\\n return self.encryptByteArray(bytearray(data, 'utf-8'), keyobj)\",\n \"def encrypt(key, text):\\n key = _key_array(key)\\n text = _text_array(text)\\n aes = mxit.aes.AES()\\n parts = _split(text, 16)\\n encoded = []\\n for part in parts:\\n encoded += aes.encrypt(part, key, aes.keySize[\\\"SIZE_128\\\"])\\n return encoded\",\n \"def _InitFromString(self, text):\\r\\n # First, remove all whitespace:\\r\\n text = re.sub(_WHITESPACE_RE, '', text)\\r\\n\\r\\n # Parse out the period-separated components\\r\\n match = _KEY_RE.match(text)\\r\\n if not match:\\r\\n raise ValueError('Badly formatted key string: \\\"%s\\\"', text)\\r\\n\\r\\n private_exp = match.group('private_exp')\\r\\n if private_exp:\\r\\n private_exp = _B64ToNum(private_exp)\\r\\n else:\\r\\n private_exp = None\\r\\n self.keypair = Crypto.PublicKey.RSA.construct(\\r\\n (_B64ToNum(match.group('mod')),\\r\\n _B64ToNum(match.group('exp')),\\r\\n private_exp))\",\n \"def encrypt(self, input_file, output_file):\\n self.key %= 26\\n ciphertext = \\\"\\\"\\n with open(input_file) as plaintext:\\n self.text = plaintext.read()\\n for char in self.text:\\n if char.isalpha():\\n if 65 <= ord(char) <= 90: #char is between A and Z\\n if ord(char) + self.key <= 90:\\n ciphertext += chr(ord(char) + self.key)\\n elif ord(char) + self.key > 90:\\n ciphertext += chr(ord(char) + self.key - 26)\\n if 97 <= ord(char) <= 122:\\n if ord(char) + self.key <= 122:\\n ciphertext += chr(ord(char) + self.key)\\n elif ord(char) + self.key > 122:\\n ciphertext += chr(ord(char) + self.key - 26)\\n else:\\n ciphertext += char\\n encrypted_file = open(output_file, 'w')\\n encrypted_file.write(ciphertext)\\n print \\\"Created file: \\\" + output_file\",\n \"def create_key(input, **kwargs):\\n input = re.compile(r\\\"\\\\d+\\\").sub(_repl, input)\\n input = input.replace(':', 'c')\\n input = input.replace(';', 'c')\\n input = input.replace('_', 'u')\\n return re.sub('[^a-zA-Z]+', '', str(input)).lower()\",\n \"def encode(encryption=None):\\n\\n key_to_encrypt = {'a': 'q', 'b': 'v', 'c': 'x', 'd': 'z', 'e': 'y', 'f': 'w', 'g': 'u', 'h': 't', 'i': 's',\\n 'j': 'r',\\n 'k': 'p', 'l': 'o', 'm': 'n', 'n': 'm', 'o': 'l', 'p': 'k', 'r': 'j', 's': 'i', 't': 'h',\\n 'u': 'g', 'w': 'f',\\n 'y': 'e', 'z': 'd', 'x': 'c', 'v': 'b', 'q': 'a',\\n 'A': 'Q', 'B': 'V', 'C': 'X', 'D': 'Z', 'E': 'Y', 'F': 'W', 'G': 'U', 'H': 'T', 'I': 'S',\\n 'J': 'R', 'K': 'P',\\n 'L': 'O', 'M': 'N', 'N': 'M', 'O': 'L', 'P': 'K', 'R': 'J', 'S': 'I', 'T': 'H', 'U': 'G',\\n 'W': 'F', 'Y': 'E',\\n 'Z': 'D', 'X': 'C', 'V': 'B', 'Q': 'S',\\n '1': '5', '2': '9', '3': '8', '4': '7', '5': '6', '6': '4', '7': '3', '8': '2', '9': '1',\\n '.': ',', ',': '.', ':': ';', ';': ':', '?': '!', '!': '?', '-': '_', '_': '-', '(': ')',\\n ')': '(',\\n '%': '$', '$': '%', ' ': '&', '&': ' ', '+': '*', '*': '+'}\\n entered_image = input(\\\"Image name with extension: \\\")\\n img = Image.open(entered_image, 'r')\\n\\n message = input(\\\"Message that you want to be encoded: \\\")\\n if (len(message) == 0):\\n raise ValueError('Empty message!')\\n\\n e1 = monoalphabetic_encryption.Encryption(key_to_encrypt, message)\\n encrypted_message = e1.encrypt()\\n\\n new_image = img.copy()\\n putPixel(new_image, encrypted_message)\\n\\n new_image_name = input(\\\"New image name with extension: \\\")\\n new_image.save(new_image_name, str(new_image_name.split(\\\".\\\")[1].upper()))\",\n \"def weaksauce_encrypt(text, password):\\n\\n offset = sum([ord(x) for x in password])\\n encoded = ''.join(\\n chr(min(ord(x) + offset, 2**20))\\n for x in text\\n )\\n return encoded\",\n \"def plain_text(file_key, file_cipher_text):\\n with open(file_key, 'r') as f:\\n key = f.readline()\\n with open(file_cipher_text, 'r') as f:\\n cipher_text = f.readline()\\n\\n cipher_suite = Fernet(key)\\n return cipher_suite.decrypt(cipher_text)\",\n \"def encrypt_vigenere(plaintext: str, keyword: str) -> str:\\n ciphertext = \\\"\\\"\\n if len(keyword) < len(plaintext):\\n for j in range(len(plaintext) - len(keyword)):\\n keyword += keyword[j]\\n for i in range(len(plaintext)):\\n n = ord(plaintext[i])\\n m = ord(keyword[i])\\n if (n >= ord('A')) and (n <= ord('Z')):\\n if (m - ord('A') + n) <= ord('Z'):\\n ciphertext += chr(m - ord('А') + n)\\n else:\\n ciphertext += chr((m + n) % (1 + ord('Z')))\\n else:\\n if (m - ord('a') + n) <= ord('z'):\\n ciphertext += chr(m - ord('a') + n)\\n else:\\n ciphertext += chr((m + n) % ord('a'))\\n return ciphertext\",\n \"def decrypt_vigenere(ciphertext: str, keyword: str) -> str:\\n plaintext = \\\"\\\"\\n # PUT YOUR CODE HERE\\n key_lenght = len(keyword)\\n text_lenght = len(ciphertext)\\n\\n while key_lenght != text_lenght:\\n keyword += keyword\\n key_lenght = len(keyword)\\n if key_lenght > text_lenght:\\n keyword = keyword[:text_lenght]\\n key_lenght = len(keyword)\\n code_key = []\\n ord_a = ord('a')\\n ord_A = ord('A')\\n\\n if ciphertext.islower():\\n for i in range(key_lenght):\\n if ciphertext[i] == \\\" \\\":\\n code_key.append(\\\" \\\")\\n else:\\n code_key.append(ord(keyword[i]) - ord_a)\\n code_text = []\\n for n in range(text_lenght):\\n if ciphertext[n] == \\\" \\\":\\n code_text.append(\\\" \\\")\\n else:\\n code_text.append(ord(ciphertext[n]) - ord_a)\\n for u in range(text_lenght):\\n if ciphertext[u] == \\\" \\\":\\n value = ord(\\\" \\\")\\n else:\\n\\n value = ((code_text[u] - code_key[u] + 26) % 26) + ord_a\\n plaintext += chr(value)\\n else:\\n for i in range(key_lenght):\\n if ciphertext[i] == \\\" \\\":\\n code_key.append(\\\" \\\")\\n else:\\n code_key.append(ord(keyword[i]) - ord_A)\\n code_text = []\\n for n in range(text_lenght):\\n if ciphertext[n] == \\\" \\\":\\n code_text.append(\\\" \\\")\\n else:\\n code_text.append(ord(ciphertext[n]) - ord_A)\\n for u in range(text_lenght):\\n if ciphertext[u] == \\\" \\\":\\n value = ord(\\\" \\\")\\n else:\\n value = ((code_text[u] - code_key[u] + 26) % 26) + ord_A\\n plaintext += chr(value)\\n\\n return plaintext\",\n \"def _encrypt(self, text, **options):\\n\\n raise CoreNotImplementedError()\",\n \"def encrypt(self, text):\\n ciphertext = []\\n # text = text.upper()\\n for char in text:\\n try:\\n key = (self.a * self.characters.index(char) + self.b) % len(self.characters)\\n # If character is not in set for cipher,\\n # directly append it without transformation\\n except ValueError:\\n ciphertext.append(char)\\n else:\\n ciphertext.append(self.characters[key])\\n return ''.join(ciphertext)\",\n \"def demonstrate_string_encryption_key_based(plain_text):\\n try:\\n # GENERATE key\\n key = AESGCM.generate_key(bit_length=256)\\n\\n # GENERATE random nonce (number used once)\\n nonce = os.urandom(12)\\n\\n # ENCRYPTION\\n aesgcm = AESGCM(key)\\n cipher_text_bytes = aesgcm.encrypt(\\n nonce=nonce,\\n data=plain_text.encode('utf-8'),\\n associated_data=None\\n )\\n # CONVERSION of raw bytes to BASE64 representation\\n cipher_text = base64.urlsafe_b64encode(cipher_text_bytes)\\n\\n # DECRYPTION\\n decrypted_cipher_text_bytes = aesgcm.decrypt(\\n nonce=nonce,\\n data=base64.urlsafe_b64decode(cipher_text),\\n associated_data=None\\n )\\n decrypted_cipher_text = decrypted_cipher_text_bytes.decode('utf-8')\\n\\n logger.info(\\\"Decrypted and original plain text are the same: %s\\\",\\n decrypted_cipher_text == plain_text)\\n except InvalidTag:\\n logger.exception(\\\"Symmetric string encryption failed\\\")\",\n \"def encryptionSelfMadeFunction(text,index):\\n s = text\\n transformedChar = \\\"\\\"\\n\\n transformedChar = s[0:index] + s[index+1:] +s[index]\\n\\n print(\\\"Encrypted Transformed text : \\\" )\\n return transformedChar\",\n \"def HashForText (text):\\n if isinstance(text, six.text_type):\\n text = text.encode('utf-8')\\n return __Hasher(text).hexdigest()\",\n \"def generate_key():\\n key = Fernet.generate_key()\\n with open(\\\"pass.key\\\", \\\"wb\\\") as key_file:\\n key_file.write(key)\",\n \"def test_from_text(self):\\n rkeyring = dns.tsigkeyring.from_text(text_keyring)\\n self.assertEqual(rkeyring, rich_keyring)\",\n \"def enc(text):\\n if isinstance(text, str):\\n return unicode(text, 'utf-8') # TODO: fix in Python 3\\n elif isinstance(text, unicode):\\n return text.encode('utf-8')\\n else:\\n raise Exception(\\\"Unsupported encode format.\\\")\",\n \"def get_key_plain():\\n if len(sys.argv) != 2:\\n exit(\\\"Usage: python vigenere.py k\\\")\\n\\n # get plaintext\\n user_input = input(\\\"plaintext: \\\")\\n \\n return sys.argv[1], user_input\",\n \"def serialize_text(text):\\n return serialize_plaintext(text)\",\n \"def encrypt(self, text):\\n output = []\\n text = text.upper()\\n\\n for char in text:\\n try:\\n index = self.alpha.index(char)\\n except ValueError:\\n output.append(char)\\n else:\\n output.append(self.alpha[(index * 5 + 8) % 26])\\n return \\\"\\\".join(output)\",\n \"def encrypt(event=None): # event is passed by binders.\\n msg = inputText.get(\\\"1.0\\\",tkinter.END)\\n outText.delete('1.0', tkinter.END)\\n\\n f = open(myTmpDir + 'pt' + str(identity) + '.bin','wb')\\n f.write(msg)\\n f.close()\\n\\n os.popen(\\\"rsa.exe e \\\" + myTmpDir + \\\"pt\\\" + str(identity) + \\\".bin \\\"+ myTmpDir + \\\"locEnc\\\" + str(identity) + \\\".bin\\\")\\n\\n locEncFileName = myTmpDir + \\\"locEnc\\\" + str(identity) + \\\".bin\\\"\\n with open(locEncFileName, \\\"rb\\\") as f:\\n readFile = f.read()\\n # Convert to hex representation\\n digest = base64.encodestring(bytes(readFile))\\n\\n # TODO: overwirite\\n outText.insert(tkinter.END, digest)\",\n \"def encrypt(self, text):\\n\\n text = text.lower()\\n encrypted_word = []\\n for letter in text:\\n try:\\n index = self.alpha.index(letter)\\n except ValueError:\\n encrypted_word.append(letter)\\n else:\\n # Uses Affine encryption function to encrypt the word\\n new_index = ((self.a*index)+self.b) % self.m\\n encrypted_word.append(self.alpha[new_index])\\n return \\\"\\\".join(encrypted_word)\",\n \"def string_to_key(string: str):\\n try:\\n # Removing all characters not in alphabet and making the string lower case\\n return re.compile(\\\"[^A-Za-z]\\\").sub(\\\"\\\", string).lower()\\n except TypeError:\\n # If string is actually None we return None\\n return None\",\n \"def vigenere_transform(operation, str):\\n key = input(\\\"Passkey? \\\").upper()\\n\\n if operation == 'E':\\n print(\\\"Encrypting {0} using Vigenere cipher with key {1}\\\".format(str, key))\\n print(\\\"...\\\")\\n return encrypt_vigenere(str, key)\\n else:\\n print(\\\"Decrypting {0} using Vigenere cipher with key {1}\\\".format(str, key))\\n print(\\\"...\\\")\\n return decrypt_vigenere(str, key)\",\n \"def passwd_encryption(self):\\n key = Fernet.generate_key()\\n cipher_suite = Fernet(key)\\n bin_passwd = bytes(self.password, 'utf-8')\\n ciphered_text = cipher_suite.encrypt(bin_passwd)\\n with open(self.pass_path, 'wb') as pass_output:\\n pass_output.write(ciphered_text)\\n with open(self.key_path, 'wb') as key_output:\\n key_output.write(key)\",\n \"def __encrypt(string: str) -> str:\\n key = 171\\n result = b\\\"\\\\0\\\\0\\\\0\\\" + chr(len(string)).encode('latin-1')\\n for i in string.encode('latin-1'):\\n a = key ^ i\\n key = a\\n result += chr(a).encode('latin-1')\\n return result\",\n \"def encrypt(self, user_input):\\n # extract the message and keyword\\n message = user_input[0]\\n keyword = user_input[1]\\n\\n # generate encryption key\\n cipher_key = self.__generate_key(keyword, \\\"encrypt\\\")\\n\\n # process and return text\\n return self.__process_text(cipher_key, message)\",\n \"def encrypt(self, text):\\n\\t\\tclean_text = message2num(text)\\n\\t\\tencrypted = []\\n\\t\\tplug = self._enc_plugboard(clean_text)\\n\\n\\t\\tfor letter in plug:\\n\\t\\t\\tl = self._forward(letter)\\n\\t\\t\\tl = self._reflect(self.reflector, l)\\n\\t\\t\\tl = self._backwards(l)\\n\\t\\t\\tencrypted.append(l)\\n\\n\\t\\tencrypted = self._enc_plugboard(encrypted)\\n\\t\\tencrypted = \\\"\\\".join(POS2L[l] for l in encrypted)\\n\\n\\t\\treturn encrypted\",\n \"def generate_symmetric_key():\\n return Fernet.generate_key()\",\n \"def store_Fernet_key(key,filename):\\n\\tfich = open(str(filename) + '.key', 'wb')\\n\\tfich.write(key) # The key is type bytes still\\n\\tfich.close()\",\n \"def crypt(text, passphrase, which):\\n text = scrub_string(text)\\n passphrase = scrub_string(passphrase)\\n letters = (\\n shift_string_by_letter(ch, passphrase[i % len(passphrase)], which)\\n for i, ch in enumerate(text)\\n )\\n return \\\"\\\".join(letters)\",\n \"def test_to_text(self):\\n tkeyring = dns.tsigkeyring.to_text(rich_keyring)\\n self.assertEqual(tkeyring, text_keyring)\",\n \"def encipher(self):\\n ciphertext = \\\"\\\"\\n for pt, key_char in zip(self.text, self.key):\\n char_index = self.char_block.alphabet.index(pt)\\n ciphertext += self.char_block.rows[key_char][char_index]\\n print(ciphertext)\",\n \"def _encrypted_user_photo_key_str(self):\\r\\n face_aes_key_str = settings.VERIFY_STUDENT[\\\"SOFTWARE_SECURE\\\"][\\\"FACE_IMAGE_AES_KEY\\\"]\\r\\n face_aes_key = face_aes_key_str.decode(\\\"hex\\\")\\r\\n rsa_key_str = settings.VERIFY_STUDENT[\\\"SOFTWARE_SECURE\\\"][\\\"RSA_PUBLIC_KEY\\\"]\\r\\n rsa_encrypted_face_aes_key = rsa_encrypt(face_aes_key, rsa_key_str)\\r\\n\\r\\n return rsa_encrypted_face_aes_key.encode(\\\"base64\\\")\",\n \"def encrypt(text, offset):\\n encrypted_text = \\\"\\\"\\n for char in text:\\n if ord(char) <= 64:\\n encrypted_character = chr(ord(char))\\n elif ord(char) < 90:\\n encrypted_character = ord(char) + offset\\n if encrypted_character > 90:\\n encrypted_character -= 26\\n encrypted_character = chr(encrypted_character)\\n else:\\n encrypted_character = ord(char) + offset\\n if encrypted_character > 122:\\n encrypted_character -= 26\\n encrypted_character = chr(encrypted_character)\\n encrypted_text += encrypted_character\\n\\n return encrypted_text\",\n \"def applyCoder(text, coder):\\n ciphertext = str()\\n #for each letter in the text find it, and grab shifted letter\\n for letter in text:\\n ciphertext += coder.get(letter, letter)\\n return ciphertext\",\n \"def generate_key():\\n key = Fernet.generate_key()\\n with open(\\\"Secret.key\\\",\\\"wb\\\")as key_file:\\n key_file.write(key)\",\n \"def encryptor(text: bytes, IV: bytes, key: bytes) -> bytes:\\n \\n # Given\\n prepend_string = \\\"comment1=cooking%20MCs;userdata=\\\"\\n append_string = \\\";comment2=%20like%20a%20pound%20of%20bacon\\\"\\n\\t\\n plaintext = text.replace(b';', b'\\\";\\\"').replace(b'=', b'\\\"=\\\"')\\n ciphertext = AES_CBC_encrypt(PKCS7_pad(plaintext, len(key)), IV, key)\\n return ciphertext\",\n \"def encrypt_vigenere(plaintext, keyword):\\n ciphertext = ''\\n new_keyword = ''\\n for i in range(len(plaintext)):\\n num_word = ord(plaintext[i])\\n while len(plaintext) > len(new_keyword):\\n new_keyword += keyword\\n num_key = ord(new_keyword[i])\\n if num_key <= 90:\\n num_key -= 65\\n elif num_key >= 97:\\n num_key -= 97\\n if num_word <= 90:\\n if (num_key+num_word) > 90:\\n num_word -= 26\\n else:\\n if (num_key+num_word) > 122:\\n num_word -= 26\\n ciphertext += chr(num_word+num_key)\\n return ciphertext\",\n \"def generate_key():\\r\\n # generating key\\r\\n key = Fernet.generate_key()\\r\\n\\r\\n key_dir = os.path.join(os.path.dirname(__file__), \\\"resources/key\\\")\\r\\n\\r\\n # writing key in file\\r\\n with open(key_dir, \\\"wb\\\") as keyFile:\\r\\n keyFile.write(key)\",\n \"def encodeVigenere(self, key):\\n\\n key = key.upper().replace(\\\" \\\", \\\"\\\")\\n encode = Vig(key)\\n cipherText = encode.encode(self.planeText)\\n \\n if (self.verbose == 1):\\n print(cipherText)\\n \\n return(cipherText)\",\n \"def key(self, value=None):\\n if self.crypt_method == 'C':\\n key_type = \\\"number\\\"\\n else:\\n key_type = \\\"string\\\"\\n\\n input_message = f\\\"Please enter a {key_type} as a \\\" \\\\\\n f\\\"{self.crypt_type}ion key\\\\n>> \\\"\\n if value is None:\\n key = input(input_message)\\n else:\\n key = value\\n\\n is_valid_key, key = Check.is_valid_key(key, self.crypt_method)\\n if is_valid_key:\\n self._key = key\\n else:\\n raise ValueError(f\\\"Key{key} is invalid\\\")\",\n \"def makekey(function, *args, **kwargs) -> str:\\n arguments = str((function.__name__, args, kwargs)).strip()\\n arguments = arguments.translate(\\n str.maketrans('', '', string.punctuation+string.whitespace)\\n )\\n key = codecs.encode(pickle.dumps(arguments, protocol=0), \\\"base64\\\").decode().strip()\\n return key\",\n \"def encode(text, password):\\r\\n\\tstep_index = 0\\r\\n\\tencoded_text = ''\\r\\n\\tfor letter in text:\\r\\n\\t\\tencoded_text += next_letter(letter, to_int(password[step_index]))\\r\\n\\t\\tstep_index += 1\\r\\n\\t\\tif step_index > len(password)-1:\\r\\n\\t\\t\\tstep_index = 0\\r\\n\\treturn encoded_text\",\n \"def __generate_key(self, keyword, action):\\n encryption_key = {}\\n\\n # get separate modified & unmodified alpha objects\\n plain_alpha = self.helpers.alphabet(\\\"list\\\")\\n cipher_alpha = self.helpers.alphabet(\\\"list\\\")\\n\\n # remove duplicate letters from the keyword\\n keyword_stripped = self.helpers.unique(keyword)\\n\\n # remove keyword letters from cipher alpha\\n for i in range(len(keyword_stripped)):\\n cipher_alpha.remove(keyword_stripped[i])\\n\\n # append keyword letters to cipher alpha\\n cipher_alpha = keyword_stripped + cipher_alpha\\n\\n if action == \\\"encrypt\\\":\\n # generate encryption key\\n for i in range(len(plain_alpha)):\\n encryption_key[plain_alpha[i]] = cipher_alpha[i]\\n elif action == \\\"decrypt\\\":\\n # generate decryption key\\n for i in range(len(plain_alpha)):\\n encryption_key[cipher_alpha[i]] = plain_alpha[i]\\n else:\\n raise ValueError(\\\"Cheatin', uh?\\\")\\n\\n return encryption_key\",\n \"def get_key(name):\\n import os\\n salt = os.urandom(16)\\n name = name.encode()\\n from cryptography.hazmat.primitives.kdf.pbkdf2 import PBKDF2HMAC\\n from cryptography.hazmat.primitives import hashes\\n from cryptography.hazmat.backends import default_backend\\n kdf = PBKDF2HMAC(algorithm=hashes.SHA256(),\\n length=32,\\n salt=salt,\\n iterations=100000,\\n backend=default_backend())\\n import base64\\n key = base64.urlsafe_b64encode(kdf.derive(name))\\n return key\",\n \"def encode_email(email, key):\\n return\",\n \"def find_key(plaintext1, plaintext2):\\n\\n # if plain1 XOR cipher1 == plain2 XOR cipher2\\n xor1 = strings_xor(list(plaintext1.encode()), list(CIPHERTEXT1))\\n xor2 = strings_xor(list(plaintext2.encode()), list(CIPHERTEXT2))\\n if xor1 == xor2:\\n return xor1.encode()\\n\\n # if plain1 XOR cipher2 == plain2 XOR cipher1\\n xor1 = strings_xor(list(plaintext1.encode()), list(CIPHERTEXT2))\\n xor2 = strings_xor(list(plaintext2.encode()), list(CIPHERTEXT1))\\n if xor1 == xor2:\\n return xor1.encode()\\n\\n return None\",\n \"def generate_key():\\n key = Fernet.generate_key()\\n with open(\\\"secret.key\\\", \\\"wb\\\") as key_file:\\n key_file.write(key)\",\n \"def setup_key_encrypt(self):\\r\\n\\t\\tself.max_key = math.floor(len(self.message) / 2)\\r\\n\\t\\twhile True:\\r\\n\\t\\t\\tkey = input(f\\\"Please enter a key value less than or equal to {self.max_key}. --> \\\")\\r\\n\\t\\t\\ttry:\\r\\n\\t\\t\\t\\tself.key = int(key)\\r\\n\\t\\t\\texcept ValueError:\\r\\n\\t\\t\\t\\tprint(\\\"Key needs to be a number.\\\")\\r\\n\\t\\t\\t\\tcontinue\\r\\n\\t\\t\\tif self.key > self.max_key: \\t\\t\\t\\r\\n\\t\\t\\t\\tprint(f\\\"{key} is too big of a number.\\\")\\t\\r\\n\\t\\t\\telif self.key == 0:\\r\\n\\t\\t\\t\\tprint(\\\"0 cannot be a key\\\")\\t\\t\\t\\r\\n\\t\\t\\telse:\\t\\t\\t\\r\\n\\t\\t\\t\\tbreak\",\n \"def encrypt(cls, plaintext, aad, key, iv):\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6408409","0.6371177","0.63681877","0.6212298","0.61476415","0.60205185","0.60133415","0.60062563","0.6004446","0.5816517","0.5788318","0.5727161","0.56803447","0.56624347","0.5623659","0.56033343","0.560295","0.55829906","0.5578667","0.5558498","0.5555277","0.5529817","0.55219585","0.55100524","0.55099773","0.5496666","0.546912","0.5459327","0.5457194","0.54402053","0.5433752","0.5416148","0.54044837","0.53962094","0.5384977","0.5374268","0.5373688","0.53684396","0.53620696","0.5357686","0.5350703","0.5341708","0.53390324","0.53321403","0.53226244","0.53205097","0.529828","0.5294416","0.5293679","0.5292488","0.52849376","0.52752256","0.5271905","0.5270045","0.5265118","0.52586555","0.5254675","0.5249991","0.5243344","0.5235718","0.52249676","0.5223514","0.5194058","0.5192591","0.519175","0.5191739","0.5179525","0.51780415","0.51703864","0.51647353","0.51549566","0.51525","0.5147639","0.5145504","0.51429904","0.5137442","0.5135708","0.5134955","0.51326174","0.5124815","0.51186","0.5114379","0.5103466","0.5097508","0.50922596","0.5090383","0.5085826","0.5074838","0.5073431","0.5072858","0.5070852","0.5062184","0.505437","0.50396746","0.50387615","0.50337076","0.50261676","0.50237846","0.50227135","0.5017928"],"string":"[\n \"0.6408409\",\n \"0.6371177\",\n \"0.63681877\",\n \"0.6212298\",\n \"0.61476415\",\n \"0.60205185\",\n \"0.60133415\",\n \"0.60062563\",\n \"0.6004446\",\n \"0.5816517\",\n \"0.5788318\",\n \"0.5727161\",\n \"0.56803447\",\n \"0.56624347\",\n \"0.5623659\",\n \"0.56033343\",\n \"0.560295\",\n \"0.55829906\",\n \"0.5578667\",\n \"0.5558498\",\n \"0.5555277\",\n \"0.5529817\",\n \"0.55219585\",\n \"0.55100524\",\n \"0.55099773\",\n \"0.5496666\",\n \"0.546912\",\n \"0.5459327\",\n \"0.5457194\",\n \"0.54402053\",\n \"0.5433752\",\n \"0.5416148\",\n \"0.54044837\",\n \"0.53962094\",\n \"0.5384977\",\n \"0.5374268\",\n \"0.5373688\",\n \"0.53684396\",\n \"0.53620696\",\n \"0.5357686\",\n \"0.5350703\",\n \"0.5341708\",\n \"0.53390324\",\n \"0.53321403\",\n \"0.53226244\",\n \"0.53205097\",\n \"0.529828\",\n \"0.5294416\",\n \"0.5293679\",\n \"0.5292488\",\n \"0.52849376\",\n \"0.52752256\",\n \"0.5271905\",\n \"0.5270045\",\n \"0.5265118\",\n \"0.52586555\",\n \"0.5254675\",\n \"0.5249991\",\n \"0.5243344\",\n \"0.5235718\",\n \"0.52249676\",\n \"0.5223514\",\n \"0.5194058\",\n \"0.5192591\",\n \"0.519175\",\n \"0.5191739\",\n \"0.5179525\",\n \"0.51780415\",\n \"0.51703864\",\n \"0.51647353\",\n \"0.51549566\",\n \"0.51525\",\n \"0.5147639\",\n \"0.5145504\",\n \"0.51429904\",\n \"0.5137442\",\n \"0.5135708\",\n \"0.5134955\",\n \"0.51326174\",\n \"0.5124815\",\n \"0.51186\",\n \"0.5114379\",\n \"0.5103466\",\n \"0.5097508\",\n \"0.50922596\",\n \"0.5090383\",\n \"0.5085826\",\n \"0.5074838\",\n \"0.5073431\",\n \"0.5072858\",\n \"0.5070852\",\n \"0.5062184\",\n \"0.505437\",\n \"0.50396746\",\n \"0.50387615\",\n \"0.50337076\",\n \"0.50261676\",\n \"0.50237846\",\n \"0.50227135\",\n \"0.5017928\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.85045666"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":94693,"cells":{"query":{"kind":"string","value":"Manually enter a password for encryption on keyboard."},"document":{"kind":"string","value":"def input_password(self): # pragma: no cover\n password = input(\"Please enter your secret key (case sensitive): \")\n self.set_password(password)"},"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 enter_password(self):","def enter_password(self):\n self.password.clear()\n self.password.click()\n self.password.send_keys(TestData.PASSWORD)\n sleep(TestData.DELAY)\n return self.password","async def password(self, ctx):\n pass","def prompt_pass():\n msg = \"Enter Password: \"\n password = getpass.getpass(msg)\n return password","def input_user_pass(self, user_pass):\n self.locate_element_by_css_selector(PASSWORD_SELECTOR).send_keys(user_pass)","def OnTextCtrlPasswordText(self, event):\r\n\t\tself._password = event.GetString()","def ask_password(self, prompt: str) -> str:\n raise NotImplementedError","def password(self) -> str:","def set_Password(self, value):\n super(DownloadDocumentInputSet, self)._set_input('Password', value)","def setpassword(self, pwd):\n pass","def focus_password(self, **kws):\r\n self.password_box.focus()","def ask_password(db_params: Dict[str, str]) -> None:\r\n db_params[Toml.PASSWORD] = getpass('DB Password: ')","def new_password():\n new_pass = generate_password()\n entry_pass.delete(0, END)\n entry_pass.insert(0, new_pass)","def take_pass(text_to_prompt):\r\n return prompt(text_to_prompt, is_password=True)","def AskPassword(prompt, default='', id=264, ok=None, cancel=None):\n raise NotImplementedError(\"AskPassword\")","def password(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"password\")","def password(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"password\")","def password(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"password\")","def password(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"password\")","def password(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"password\")","def password(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"password\")","def password(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"password\")","def password(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"password\")","def password(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"password\")","def password(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"password\")","def password(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"password\")","def password(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"password\")","def password(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"password\")","def password(caller, input):\n caller.msg(echo=True)\n input = input.strip()\n text = \"\"\n options = (\n {\n \"key\": \"_default\",\n \"desc\": \"Enter your password.\",\n \"goto\": \"password\",\n },\n )\n\n # Check the password\n player = caller.db._player\n # If the account is locked, the user has to wait (maximum\n # 3 seconds) before retrying\n if player.db._locked:\n text = \"|gPlease wait, you cannot enter your password yet.|n\"\n return text, options\n\n caller.msg(echo=True)\n bans = ServerConfig.objects.conf(\"server_bans\")\n banned = bans and (any(tup[0] == player.name.lower() for tup in bans) \\\n or any(tup[2].match(caller.address) for tup in bans if tup[2]))\n\n if not player.check_password(input):\n caller.msg(echo=False)\n text = dedent(\"\"\"\n |rIncorrect password.|n\n Type |yb|n to go back to the login screen.\n Or wait 3 seconds before trying a new password.\n \"\"\".strip(\"\\n\"))\n\n # Loops on the same node\n player.scripts.add(WrongPassword)\n scripts = player.scripts.get(\"wrong_password\")\n if scripts:\n script = scripts[0]\n script.db.session = caller\n else:\n print \"Cannot retrieve the 'wrong_password' script.\"\n\n options = (\n {\n \"key\": \"b\",\n \"desc\": \"Go back to the login screen.\",\n \"goto\": \"start\",\n },\n {\n \"key\": \"_default\",\n \"desc\": \"Enter your password again.\",\n \"goto\": \"password\",\n },\n )\n elif banned:\n # This is a banned IP or name!\n string = dedent(\"\"\"\n |rYou have been banned and cannot continue from here.|n\n If you feel this ban is in error, please email an admin.\n \"\"\".strip(\"\\n\"))\n caller.msg(string)\n caller.sessionhandler.disconnect(\n caller, \"Good bye! Disconnecting...\")\n else:\n # The password is correct, we can log into the player.\n if not player.email:\n # Redirects to the node to set an e-mail address\n text = text_email_address(player)\n options = (\n {\n \"key\": \"_default\",\n \"desc\": \"Enter your e-mail address.\",\n \"goto\": \"email_address\",\n },\n )\n elif not player.db.valid:\n # Redirects to the node for the validation code\n text = \"Enter your received validation code.\"\n options = (\n {\n \"key\": \"_default\",\n \"desc\": \"Enter your validation code.\",\n \"goto\": \"validate_account\",\n },\n )\n else:\n _login(caller, player)\n text = \"\"\n options = _options_choose_characters(player)\n\n return text, options","def generic_input_password(self, element_id, password):\n self._generic_input(element_id, password, False)","def prompt_encrypt(self):\r\n print(\"Please copy/paste key and secret from MtGox and\")\r\n print(\"then provide a password to encrypt them.\")\r\n print(\"\")\r\n\r\n\r\n key = input(\" key: \").strip()\r\n secret = input(\" secret: \").strip()\r\n while True:\r\n password1 = getpass.getpass(\" password: \").strip()\r\n if password1 == \"\":\r\n print(\"aborting\")\r\n return\r\n password2 = getpass.getpass(\"password (again): \").strip()\r\n if password1 != password2:\r\n print(\"you had a typo in the password. try again...\")\r\n else:\r\n break\r\n\r\n # pylint: disable=E1101\r\n hashed_pass = hashlib.sha512(password1.encode(\"utf-8\")).digest()\r\n crypt_key = hashed_pass[:32]\r\n crypt_ini = hashed_pass[-16:]\r\n aes = AES.new(crypt_key, AES.MODE_OFB, crypt_ini)\r\n\r\n # since the secret is a base64 string we can just just pad it with\r\n # spaces which can easily be stripped again after decryping\r\n print(len(secret))\r\n secret += \" \" * (16 - len(secret) % 16)\r\n print(len(secret))\r\n secret = base64.b64encode(aes.encrypt(secret)).decode(\"ascii\")\r\n\r\n self.config.set(\"gox\", \"secret_key\", key)\r\n self.config.set(\"gox\", \"secret_secret\", secret)\r\n self.config.save()\r\n\r\n print(\"encrypted secret has been saved in %s\" % self.config.filename)","def ask_password(ip):\n password_question = [\n {\n 'type': 'password',\n 'message': 'Enter the password of the machine with the IP address ' + ip + ':',\n 'name': 'password'\n }\n ]\n\n password_answer = prompt(password_question, style=style)\n pwd = password_answer[\"password\"]\n return pwd","def _prompt_for_password(self, args):\n if not args.password:\n args.password = getpass.getpass(\n prompt='\"--password\" not provided! Please enter password for host %s and user %s: '\n % (args.host, args.user))\n return args","def aisappium_input_password(self, locator, text, oAppiumInfo=None):\n self._info(\"Typing password into text field '%s'\" % locator)\n if oAppiumInfo is not None:\n self._element_input_text_by_locator_atlas(locator, text, oAppiumInfo.driver)\n else:\n self._element_input_text_by_locator(locator, text)","def prompt_for_password(args):\n if not args.password:\n args.password = getpass.getpass(\n prompt='Enter password for host %s and user %s: ' %\n (args.host, args.user))\n return args","def ask_input(prompt = '', is_password = False):\n\n while True:\n answer = getpass.getpass() if is_password == True else input(prompt)\n if answer is not '':\n return answer","def set_Password(self, value):\n super(AddressValidationInputSet, self)._set_input('Password', value)","def change_pwd(self):\r\n if self.field_pwd.text() == \"\":\r\n self.label_chg_pwd.setText(\"Password cannot be empty\")\r\n return None\r\n self.encryptor.set_key_from_password(self.field_pwd.text())\r\n self.label_chg_pwd.setText(\"Password typed\")\r\n self.label_chg_pwd.setStyleSheet(\"color:#01ac2d\")\r\n self.label_chg_key.clear()\r\n self.field_key.clear()\r\n QtWidgets.QMessageBox.information(self, \"Password Change\", \r\n (\"Your password has been successfully changed.\\n\\n\"\r\n \"You can now encrypt / decrypt files.\"))","def getpass(self, prompt):\r\n return getpass.getpass(prompt)","def get_user_password(text):\n return getpass.getpass(text)","def password(self, password):\n self.password_hash = generate_password_hash(password)","def store_passwd(self, clr_passwd):\n aes_cipher = AESCipher()\n self.__aes_key = aes_cipher.AES_KEY\n self.__password = aes_cipher.encrypt(clr_passwd)","def on_encryptionKeyEdit_textChanged(self, txt):\n self.passwordMeter.checkPasswordStrength(txt)\n self.__updateUI()","def password(self, password):\n\n self._password = password","def password(self, password):\n\n self._password = password","def password(self, password):\n\n self._password = password","def password(self, password):\n\n self._password = password","def encrypt_password(pass_to_encrypt):\n\n temp_key = get_crypt_key()\n tk = Fernet(temp_key)\n\n pass_to_encrypt = pass_to_encrypt.encode(\"UTF-8\")\n return tk.encrypt(pass_to_encrypt)","def set_password(self, password):\n self.password = password","def set_password(self, password):\n from kalon.auth import encrypt_password\n self.document.password = encrypt_password(password)","def password_error(self, msg):\n print('\\nBad password: %s' % msg, file=self.console)\n import getpass\n self.password = getpass.getpass('Password: ')\n self.verifiedpassword = getpass.getpass('Password again: ')","def get_password(self,text):\r\n return self.driver.find_element(*SinginPage.password).send_keys(text)","def main():\n password = input(\"Enter password that contains {} or more characters: \".format(MIN_LENGTH))\n while not is_valid_password(password):\n print(\"Invalid password!\")\n password = input(\"Enter password that contains {} or more characters: \".format(MIN_LENGTH))\n print(\"*\" * len(password))","def GetPassword(self):\n pass","def password_request():\n while True:\n print(\"You must input the 'password' to authorize the update.\")\n print(\"***NOTE: the password is case sensitive.***\\n\")\n\n password = pwinput.pwinput(\"Please enter your password: \\n\")\n\n if validate_password(password):\n print(\"Loading systems...\")\n break","def set_password(self, password):\n self.PASS = password","def password(self, password: str):\n\n self._password = password","def set_pass(self, pw):\n\t\tself.passhash = generate_password_hash(pw)","def set_password(self, password):\n self.PASSWORD = password","def acceptsPassword(self):\r\n raise NotImplementedError()","def askPass( user, host ):\n prompt = \"Password for user {} on host {}: \".format( user, host )\n password = getpass.getpass( prompt )\n return password","def do_masterpassword(self, masterpassword):\n if masterpassword:\n if self.taskstarted == True:\n self.masterpassword = masterpassword\n else:\n if self.taskstarted == False:\n print(self.cl.red(\"[!] <ERROR> You need to start a new KeePass Interaction.\"))\n print(self.cl.red(\"[!] <ERROR> Start this with 'new' from the menu.\"))\n print(\"[!] <ERROR> You need to supply the command for typing\")","def password(self):\n return self._password()","def set_password(ctx, new_password, remember):\n ensure_validated(ctx, prompt='Enter your current password')\n if not new_password:\n new_password = click.prompt(\n 'Enter your new password',\n hide_input=True,\n confirmation_prompt=True,\n err=True)\n\n controller = ctx.obj['controller']\n settings = ctx.obj['settings']\n keys = settings.setdefault('keys', {})\n key = controller.set_password(new_password)\n click.echo('Password updated.')\n if remember:\n keys[controller.id] = b2a_hex(key).decode()\n settings.write()\n click.echo('Password remembered')\n elif controller.id in keys:\n del keys[controller.id]\n settings.write()","async def handle_password_prompt(self):\n self.applog.info(\"sending password %s\" % self.password)\n self._flush_buffer()\n self.pexpect_child.sendline(self.password)\n ret = self.pexpect_child.expect_exact(\n [self.cmd_prompt, pexpect.TIMEOUT, pexpect.EOF], timeout=10\n )\n if ret == 0:\n return True\n self.applog.info(\"handle_password_prompt failed with %s\" % ret)\n return False","def user_encrypt_password(data=None, **kw):\n if 'password' in data:\n data['password'] = encrypt_password(data['password'])","def create_password(self):\r\n alphabet = string.ascii_letters + string.digits\r\n password = ''.join(secrets.choice(alphabet) for i in range(30))\r\n\r\n QtWidgets.QMessageBox.information(self, \"Password generated\", \r\n \"{}\".format(password))","def password_listener(target, value, oldvalue, initiator):\n return pwd_context.encrypt(value)","def fake_login(self):\n self.username = self.console.input('Please type your username here:')\n self.password = self.console.input('Please type your password here:')","def passphrase(self):\n password = self.entry.password\n if password:\n return self.entry.password.encode('UTF-8')\n else:\n return None","def passwd_encryption(self):\n key = Fernet.generate_key()\n cipher_suite = Fernet(key)\n bin_passwd = bytes(self.password, 'utf-8')\n ciphered_text = cipher_suite.encrypt(bin_passwd)\n with open(self.pass_path, 'wb') as pass_output:\n pass_output.write(ciphered_text)\n with open(self.key_path, 'wb') as key_output:\n key_output.write(key)","def password(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"password\")","def password(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"password\")","def password(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"password\")","def password(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"password\")","def password(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"password\")","def password(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"password\")","def password(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"password\")","def password(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"password\")","def password(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"password\")","def password(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"password\")","def password(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"password\")","def password(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"password\")","def password(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"password\")","def login(self):\n def twobytwo(s):\n i = 0\n while i < len(s):\n yield s[i:i + 2]\n i += 2\n sha1_pass_bytes = sha1(self._password).hexdigest()\n self.command('PASS: \"%s\"' % ' '.join(twobytwo(sha1_pass_bytes.upper())))","def set_password() -> \"Function\":\n password = \"\"\n\n def inner():\n nonlocal password\n if password == \"\":\n password = get_password()\n return password\n\n return inner","def password(self, value):\n self.password_hashed = func.crypt(value, func.gen_salt('bf'))","def create_pwd_login_internal(password, re_enter_password):\r\n if g.platform == 'android':\r\n 'Enter the password in Create password text box'\r\n flag1 = ui_controls.text_box(get_obj_identifier('login_createPassword_txt'), value=password)\r\n\r\n 'Checks for the keyboard presence and hides it'\r\n flag2 = ui_controls.hide_keyboard()\r\n\r\n 'Enter the confirm password in Confirm Password text box'\r\n flag3 = ui_controls.text_box(get_obj_identifier('login_confirmPassword_txt'), value=re_enter_password)\r\n\r\n 'Checks for the keyboard presence and hides it'\r\n flag4 = ui_controls.hide_keyboard()\r\n\r\n 'Enter password hint in Password Hint text box'\r\n #flag5 = ui_controls.text_box(get_obj_identifier('login_passwordHint_txt'), value=hint)\r\n\r\n 'Checks for the keyboard presence and hides it'\r\n #flag6 = ui_controls.hide_keyboard()\r\n\r\n 'Click on create password login button'\r\n flag7 = ui_controls.button(get_obj_identifier('login_createPasswordLogin_btn'))\r\n\r\n status = False if not (flag1 and flag2 and flag3 and flag4 and flag7) else True\r\n else:\r\n 'Setting values on Create Password text box in ios. Using set value as send_keys failing here'\r\n flag1 = ui_controls.setValue(get_obj_identifier('login_createPassword_txt'), password)\r\n sleep(3)\r\n\r\n 'Setting values on Confirm Password text box in ios.'\r\n flag2 = ui_controls.setValue(get_obj_identifier('login_confirmPassword_txt'), value=re_enter_password)\r\n\r\n 'Setting values on Password Hint text box in ios.'\r\n # flag3 = ui_controls.setValue(get_obj_identifier('login_passwordHint_txt'), hint)\r\n sleep(3)\r\n\r\n 'Click on Create Password button in IOS'\r\n flag4 = ui_controls.button(get_obj_identifier('login_createPasswordLogin_btn'))\r\n\r\n status = False if not (flag1 and flag2 and flag4) else True\r\n return status","def get_password_from_user():\n pwd = ''\n keyboard = xbmc.Keyboard('', ADDON_NAME + ': ' + localise(32022), True)\n keyboard.doModal()\n if keyboard.isConfirmed():\n pwd = keyboard.getText()\n return pwd","def _password_prompt(question: str, console: io.IO) -> str:\n console.tell(question)\n while True:\n password1 = console.getpass('Password: ')\n try:\n _password_validate(password1)\n except ValueError as e:\n console.error(e)\n continue\n password2 = console.getpass('Password (again): ')\n if password1 != password2:\n console.error('Passwords do not match, please try again')\n continue\n return password1","def _set_password(self, cr, uid, id, password, context=None):\n encrypted = self._crypt_context(\n cr, uid, id, context=context).encrypt(password)\n print(password)\n print(encrypted)\n self._set_encrypted_password(cr, uid, id, encrypted, context=context)\n self._set_password_again(cr, uid, id, password, context=context)","def switchPassword(password):\n bits = base64.b64decode(password)\n bits = bytes([x ^ y for (x, y) in zip(settings.BITMASK, bits)])\n return base64.b64encode(bits).decode()","def islandPassword(self, click, password):\n\t\tpos = mouse.get_pos()\n\t\tdef msg(text, length):\n\t\t\t\"\"\" Render message \"\"\"\n\t\t\tself.screen.blit(transform.scale(self.message.background, (600,150)), (259,30))\n\t\t\tself.screen.blit(self.message.font.render(text, True, (0,0,0)), (275,59))\n\t\t\tself.treasure.render(True, False, False, False, self.message)\n\t\t\t# Render and pause\n\t\t\tdisplay.flip()\n\t\t\ttime.wait(length)\n\n\t\t# Blit background\n\t\t# self.screen.blit(transform.scale(self.message.background, (600,150)), (259,30))\n\n\t\tkeysDict = {\n\t\t\t\"1\": Rect(96, 72, 170, 90),\n\t\t\t\"2\": Rect(315, 72, 170, 90),\n\t\t\t\"3\": Rect(531, 72, 170, 90),\n\t\t\t\"4\": Rect(96, 200, 170, 90),\n\t\t\t\"5\": Rect(315, 200, 170, 90),\n\t\t\t\"6\": Rect(531, 200, 170, 90),\n\t\t\t\"7\": Rect(96, 322, 170, 90),\n\t\t\t\"8\": Rect(315, 322, 170, 90),\n\t\t\t\"9\": Rect(531, 322, 170, 90),\n\t\t\t\"0\": Rect(315, 451, 170, 90), \n\t\t\t\"cancel\": Rect(803, 72, 170, 90),\n\t\t\t\"clear\": Rect(803, 200, 170, 90),\n\t\t\t\"enter\": Rect(803, 322, 170, 90)\n\t\t}\n\t\tif not self.passwordMsgFinished:\n\t\t\tmsg(\"Enter Password\", 1000)\n\t\t\tself.passwordMsgFinished=True\n\t\tcorrect = [\"6\", \"3\", \"8\", \"5\", \"4\", \"6\"]#original password\n\t\tfor key in keysDict:\n\t\t\tif keysDict[key].collidepoint(pos):\n\t\t\t\tif click:\n\t\t\t\t\t# print(\"Pressed\", key)\n\t\t\t\t\tif key!=\"cancel\" and key!=\"clear\" and key!=\"enter\":\n\t\t\t\t\t\tpassword.append(key)\n\t\t\t\t\tif key== \"cancel\":\n\t\t\t\t\t\tself.fade.fadeDark(self.maps.allScenes[\"finalisland\"][0], self.screen, (0, 0))\n\t\t\t\t\t\t# Create new scene\t\n\t\t\t\t\t\tself.maps.newScene(\"finalisland\")\n\t\t\t\t\t\t# Set player coordinates\n\t\t\t\t\t\tself.player.x = 510\n\t\t\t\t\t\tself.player.y = 46\n\t\t\t\t\t\t# Reset fade\n\t\t\t\t\t\tself.fade.reset()\n\t\t\t\t\tif key==\"clear\":\n\t\t\t\t\t\tpassword.clear()\n\t\t\t\t\tif key == \"enter\":\n\t\t\t\t\t\tif not (\"key\" in self.treasure.collectedItems):\n\t\t\t\t\t\t\tmsg(\"Key Missing\", 1500)\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tif password == correct:\n\t\t\t\t\t\t\t\t#print(\"correct password\")\n\t\t\t\t\t\t\t\tself.fade.fadeDark(self.maps.allScenes[\"finalisland\"][0], self.screen, (0, 0))\n\t\t\t\t\t\t\t\t# Create new scene\t\n\t\t\t\t\t\t\t\tself.maps.newScene(\"finalisland\")\n\t\t\t\t\t\t\t\t# Set player coordinates\n\t\t\t\t\t\t\t\tself.player.x = 516\n\t\t\t\t\t\t\t\tself.player.y = 46\n\t\t\t\t\t\t\t\t# Reset fade\n\t\t\t\t\t\t\t\tself.fade.reset()\n\t\t\t\t\t\t\t\tmsg(\"You have succesfully opened the safe.\", 3000)\n\t\t\t\t\t\t\t\tself.gameWon=True\n\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\tmsg(\"Wrong password. Press clear and try again.\", 1500)","def log_in_password(self, password):\n waiter.find_write(self.driver, 'password', password, by=NAME)","def on_passwordCheckBox_toggled(self, isOn):\n if isOn:\n self.input.setEchoMode(QLineEdit.Password)\n else:\n self.input.setEchoMode(QLineEdit.Normal)","def passsword(self, password):\n self.passwor_harsh = generate_password_hash(password)","def add_password_input(\n self,\n name: str,\n label: Optional[str] = None,\n placeholder: Optional[str] = None,\n ) -> None:\n self._client.add_element(\n name=name, element=TextField(label=label, value=placeholder, password=True)\n )","def onPasswordFocusIn(self,event):\n if self.obj2.get() == \"New Password\":\n self.obj2.delete(0,END)","def text_typing_block(self):\n\t # open the database using the masterpassword\n typing_text = 'Send({})\\n'.format(self.masterpassword)\n\n # add in exit - this is achieved using CTRL + q\n typing_text += 'Sleep(15677)\\n'\n typing_text += \"SendKeepActive('KeePass')\\n\"\n typing_text += 'Send(\"^q\")\\n'\n typing_text += \"; Reset Focus\\n\"\n typing_text += 'SendKeepActive(\"\")'\n\n return textwrap.indent(typing_text, self.indent_space)","def passwd_prompt():\n\n print(\"Passwords MUST contain AT LEAST: one lower-case letter,\" \n \"one number, one symbol, and be a MINIMUM of 8 characters in length,\"\n \"e.g. r!ght2oE\")\n\n while True:\n\n passy = getpass.getpass(prompt=\"Enter password for user: \")\n confirm_passy = getpass.getpass(prompt=\"To confirm, \" \\\n \"re-enter password: \")\n\n # check for the following conditions: \n # user input matches\n # length of input is at least 8 characters\n # input contains at least 1 number \n # input contains at least 1 letter \n # input contains at least 1 symbol \n \n if passy != confirm_passy \\\n or len(passy) <8 \\\n or not re.search('\\d', passy) \\\n or not re.search(r\"[a-z]\",passy) \\\n or not re.search(r\"[ !#$%&'()*+,-./[\\\\\\]^_`{|}~\"+r'\"]', passy): \n \n print(TRY_AGAIN)\n continue \n \n else:\n print(\"Password meets complexity requirement. Continuing...\") \n return passy"],"string":"[\n \"def enter_password(self):\",\n \"def enter_password(self):\\n self.password.clear()\\n self.password.click()\\n self.password.send_keys(TestData.PASSWORD)\\n sleep(TestData.DELAY)\\n return self.password\",\n \"async def password(self, ctx):\\n pass\",\n \"def prompt_pass():\\n msg = \\\"Enter Password: \\\"\\n password = getpass.getpass(msg)\\n return password\",\n \"def input_user_pass(self, user_pass):\\n self.locate_element_by_css_selector(PASSWORD_SELECTOR).send_keys(user_pass)\",\n \"def OnTextCtrlPasswordText(self, event):\\r\\n\\t\\tself._password = event.GetString()\",\n \"def ask_password(self, prompt: str) -> str:\\n raise NotImplementedError\",\n \"def password(self) -> str:\",\n \"def set_Password(self, value):\\n super(DownloadDocumentInputSet, self)._set_input('Password', value)\",\n \"def setpassword(self, pwd):\\n pass\",\n \"def focus_password(self, **kws):\\r\\n self.password_box.focus()\",\n \"def ask_password(db_params: Dict[str, str]) -> None:\\r\\n db_params[Toml.PASSWORD] = getpass('DB Password: ')\",\n \"def new_password():\\n new_pass = generate_password()\\n entry_pass.delete(0, END)\\n entry_pass.insert(0, new_pass)\",\n \"def take_pass(text_to_prompt):\\r\\n return prompt(text_to_prompt, is_password=True)\",\n \"def AskPassword(prompt, default='', id=264, ok=None, cancel=None):\\n raise NotImplementedError(\\\"AskPassword\\\")\",\n \"def password(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"password\\\")\",\n \"def password(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"password\\\")\",\n \"def password(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"password\\\")\",\n \"def password(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"password\\\")\",\n \"def password(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"password\\\")\",\n \"def password(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"password\\\")\",\n \"def password(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"password\\\")\",\n \"def password(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"password\\\")\",\n \"def password(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"password\\\")\",\n \"def password(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"password\\\")\",\n \"def password(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"password\\\")\",\n \"def password(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"password\\\")\",\n \"def password(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"password\\\")\",\n \"def password(caller, input):\\n caller.msg(echo=True)\\n input = input.strip()\\n text = \\\"\\\"\\n options = (\\n {\\n \\\"key\\\": \\\"_default\\\",\\n \\\"desc\\\": \\\"Enter your password.\\\",\\n \\\"goto\\\": \\\"password\\\",\\n },\\n )\\n\\n # Check the password\\n player = caller.db._player\\n # If the account is locked, the user has to wait (maximum\\n # 3 seconds) before retrying\\n if player.db._locked:\\n text = \\\"|gPlease wait, you cannot enter your password yet.|n\\\"\\n return text, options\\n\\n caller.msg(echo=True)\\n bans = ServerConfig.objects.conf(\\\"server_bans\\\")\\n banned = bans and (any(tup[0] == player.name.lower() for tup in bans) \\\\\\n or any(tup[2].match(caller.address) for tup in bans if tup[2]))\\n\\n if not player.check_password(input):\\n caller.msg(echo=False)\\n text = dedent(\\\"\\\"\\\"\\n |rIncorrect password.|n\\n Type |yb|n to go back to the login screen.\\n Or wait 3 seconds before trying a new password.\\n \\\"\\\"\\\".strip(\\\"\\\\n\\\"))\\n\\n # Loops on the same node\\n player.scripts.add(WrongPassword)\\n scripts = player.scripts.get(\\\"wrong_password\\\")\\n if scripts:\\n script = scripts[0]\\n script.db.session = caller\\n else:\\n print \\\"Cannot retrieve the 'wrong_password' script.\\\"\\n\\n options = (\\n {\\n \\\"key\\\": \\\"b\\\",\\n \\\"desc\\\": \\\"Go back to the login screen.\\\",\\n \\\"goto\\\": \\\"start\\\",\\n },\\n {\\n \\\"key\\\": \\\"_default\\\",\\n \\\"desc\\\": \\\"Enter your password again.\\\",\\n \\\"goto\\\": \\\"password\\\",\\n },\\n )\\n elif banned:\\n # This is a banned IP or name!\\n string = dedent(\\\"\\\"\\\"\\n |rYou have been banned and cannot continue from here.|n\\n If you feel this ban is in error, please email an admin.\\n \\\"\\\"\\\".strip(\\\"\\\\n\\\"))\\n caller.msg(string)\\n caller.sessionhandler.disconnect(\\n caller, \\\"Good bye! Disconnecting...\\\")\\n else:\\n # The password is correct, we can log into the player.\\n if not player.email:\\n # Redirects to the node to set an e-mail address\\n text = text_email_address(player)\\n options = (\\n {\\n \\\"key\\\": \\\"_default\\\",\\n \\\"desc\\\": \\\"Enter your e-mail address.\\\",\\n \\\"goto\\\": \\\"email_address\\\",\\n },\\n )\\n elif not player.db.valid:\\n # Redirects to the node for the validation code\\n text = \\\"Enter your received validation code.\\\"\\n options = (\\n {\\n \\\"key\\\": \\\"_default\\\",\\n \\\"desc\\\": \\\"Enter your validation code.\\\",\\n \\\"goto\\\": \\\"validate_account\\\",\\n },\\n )\\n else:\\n _login(caller, player)\\n text = \\\"\\\"\\n options = _options_choose_characters(player)\\n\\n return text, options\",\n \"def generic_input_password(self, element_id, password):\\n self._generic_input(element_id, password, False)\",\n \"def prompt_encrypt(self):\\r\\n print(\\\"Please copy/paste key and secret from MtGox and\\\")\\r\\n print(\\\"then provide a password to encrypt them.\\\")\\r\\n print(\\\"\\\")\\r\\n\\r\\n\\r\\n key = input(\\\" key: \\\").strip()\\r\\n secret = input(\\\" secret: \\\").strip()\\r\\n while True:\\r\\n password1 = getpass.getpass(\\\" password: \\\").strip()\\r\\n if password1 == \\\"\\\":\\r\\n print(\\\"aborting\\\")\\r\\n return\\r\\n password2 = getpass.getpass(\\\"password (again): \\\").strip()\\r\\n if password1 != password2:\\r\\n print(\\\"you had a typo in the password. try again...\\\")\\r\\n else:\\r\\n break\\r\\n\\r\\n # pylint: disable=E1101\\r\\n hashed_pass = hashlib.sha512(password1.encode(\\\"utf-8\\\")).digest()\\r\\n crypt_key = hashed_pass[:32]\\r\\n crypt_ini = hashed_pass[-16:]\\r\\n aes = AES.new(crypt_key, AES.MODE_OFB, crypt_ini)\\r\\n\\r\\n # since the secret is a base64 string we can just just pad it with\\r\\n # spaces which can easily be stripped again after decryping\\r\\n print(len(secret))\\r\\n secret += \\\" \\\" * (16 - len(secret) % 16)\\r\\n print(len(secret))\\r\\n secret = base64.b64encode(aes.encrypt(secret)).decode(\\\"ascii\\\")\\r\\n\\r\\n self.config.set(\\\"gox\\\", \\\"secret_key\\\", key)\\r\\n self.config.set(\\\"gox\\\", \\\"secret_secret\\\", secret)\\r\\n self.config.save()\\r\\n\\r\\n print(\\\"encrypted secret has been saved in %s\\\" % self.config.filename)\",\n \"def ask_password(ip):\\n password_question = [\\n {\\n 'type': 'password',\\n 'message': 'Enter the password of the machine with the IP address ' + ip + ':',\\n 'name': 'password'\\n }\\n ]\\n\\n password_answer = prompt(password_question, style=style)\\n pwd = password_answer[\\\"password\\\"]\\n return pwd\",\n \"def _prompt_for_password(self, args):\\n if not args.password:\\n args.password = getpass.getpass(\\n prompt='\\\"--password\\\" not provided! Please enter password for host %s and user %s: '\\n % (args.host, args.user))\\n return args\",\n \"def aisappium_input_password(self, locator, text, oAppiumInfo=None):\\n self._info(\\\"Typing password into text field '%s'\\\" % locator)\\n if oAppiumInfo is not None:\\n self._element_input_text_by_locator_atlas(locator, text, oAppiumInfo.driver)\\n else:\\n self._element_input_text_by_locator(locator, text)\",\n \"def prompt_for_password(args):\\n if not args.password:\\n args.password = getpass.getpass(\\n prompt='Enter password for host %s and user %s: ' %\\n (args.host, args.user))\\n return args\",\n \"def ask_input(prompt = '', is_password = False):\\n\\n while True:\\n answer = getpass.getpass() if is_password == True else input(prompt)\\n if answer is not '':\\n return answer\",\n \"def set_Password(self, value):\\n super(AddressValidationInputSet, self)._set_input('Password', value)\",\n \"def change_pwd(self):\\r\\n if self.field_pwd.text() == \\\"\\\":\\r\\n self.label_chg_pwd.setText(\\\"Password cannot be empty\\\")\\r\\n return None\\r\\n self.encryptor.set_key_from_password(self.field_pwd.text())\\r\\n self.label_chg_pwd.setText(\\\"Password typed\\\")\\r\\n self.label_chg_pwd.setStyleSheet(\\\"color:#01ac2d\\\")\\r\\n self.label_chg_key.clear()\\r\\n self.field_key.clear()\\r\\n QtWidgets.QMessageBox.information(self, \\\"Password Change\\\", \\r\\n (\\\"Your password has been successfully changed.\\\\n\\\\n\\\"\\r\\n \\\"You can now encrypt / decrypt files.\\\"))\",\n \"def getpass(self, prompt):\\r\\n return getpass.getpass(prompt)\",\n \"def get_user_password(text):\\n return getpass.getpass(text)\",\n \"def password(self, password):\\n self.password_hash = generate_password_hash(password)\",\n \"def store_passwd(self, clr_passwd):\\n aes_cipher = AESCipher()\\n self.__aes_key = aes_cipher.AES_KEY\\n self.__password = aes_cipher.encrypt(clr_passwd)\",\n \"def on_encryptionKeyEdit_textChanged(self, txt):\\n self.passwordMeter.checkPasswordStrength(txt)\\n self.__updateUI()\",\n \"def password(self, password):\\n\\n self._password = password\",\n \"def password(self, password):\\n\\n self._password = password\",\n \"def password(self, password):\\n\\n self._password = password\",\n \"def password(self, password):\\n\\n self._password = password\",\n \"def encrypt_password(pass_to_encrypt):\\n\\n temp_key = get_crypt_key()\\n tk = Fernet(temp_key)\\n\\n pass_to_encrypt = pass_to_encrypt.encode(\\\"UTF-8\\\")\\n return tk.encrypt(pass_to_encrypt)\",\n \"def set_password(self, password):\\n self.password = password\",\n \"def set_password(self, password):\\n from kalon.auth import encrypt_password\\n self.document.password = encrypt_password(password)\",\n \"def password_error(self, msg):\\n print('\\\\nBad password: %s' % msg, file=self.console)\\n import getpass\\n self.password = getpass.getpass('Password: ')\\n self.verifiedpassword = getpass.getpass('Password again: ')\",\n \"def get_password(self,text):\\r\\n return self.driver.find_element(*SinginPage.password).send_keys(text)\",\n \"def main():\\n password = input(\\\"Enter password that contains {} or more characters: \\\".format(MIN_LENGTH))\\n while not is_valid_password(password):\\n print(\\\"Invalid password!\\\")\\n password = input(\\\"Enter password that contains {} or more characters: \\\".format(MIN_LENGTH))\\n print(\\\"*\\\" * len(password))\",\n \"def GetPassword(self):\\n pass\",\n \"def password_request():\\n while True:\\n print(\\\"You must input the 'password' to authorize the update.\\\")\\n print(\\\"***NOTE: the password is case sensitive.***\\\\n\\\")\\n\\n password = pwinput.pwinput(\\\"Please enter your password: \\\\n\\\")\\n\\n if validate_password(password):\\n print(\\\"Loading systems...\\\")\\n break\",\n \"def set_password(self, password):\\n self.PASS = password\",\n \"def password(self, password: str):\\n\\n self._password = password\",\n \"def set_pass(self, pw):\\n\\t\\tself.passhash = generate_password_hash(pw)\",\n \"def set_password(self, password):\\n self.PASSWORD = password\",\n \"def acceptsPassword(self):\\r\\n raise NotImplementedError()\",\n \"def askPass( user, host ):\\n prompt = \\\"Password for user {} on host {}: \\\".format( user, host )\\n password = getpass.getpass( prompt )\\n return password\",\n \"def do_masterpassword(self, masterpassword):\\n if masterpassword:\\n if self.taskstarted == True:\\n self.masterpassword = masterpassword\\n else:\\n if self.taskstarted == False:\\n print(self.cl.red(\\\"[!] <ERROR> You need to start a new KeePass Interaction.\\\"))\\n print(self.cl.red(\\\"[!] <ERROR> Start this with 'new' from the menu.\\\"))\\n print(\\\"[!] <ERROR> You need to supply the command for typing\\\")\",\n \"def password(self):\\n return self._password()\",\n \"def set_password(ctx, new_password, remember):\\n ensure_validated(ctx, prompt='Enter your current password')\\n if not new_password:\\n new_password = click.prompt(\\n 'Enter your new password',\\n hide_input=True,\\n confirmation_prompt=True,\\n err=True)\\n\\n controller = ctx.obj['controller']\\n settings = ctx.obj['settings']\\n keys = settings.setdefault('keys', {})\\n key = controller.set_password(new_password)\\n click.echo('Password updated.')\\n if remember:\\n keys[controller.id] = b2a_hex(key).decode()\\n settings.write()\\n click.echo('Password remembered')\\n elif controller.id in keys:\\n del keys[controller.id]\\n settings.write()\",\n \"async def handle_password_prompt(self):\\n self.applog.info(\\\"sending password %s\\\" % self.password)\\n self._flush_buffer()\\n self.pexpect_child.sendline(self.password)\\n ret = self.pexpect_child.expect_exact(\\n [self.cmd_prompt, pexpect.TIMEOUT, pexpect.EOF], timeout=10\\n )\\n if ret == 0:\\n return True\\n self.applog.info(\\\"handle_password_prompt failed with %s\\\" % ret)\\n return False\",\n \"def user_encrypt_password(data=None, **kw):\\n if 'password' in data:\\n data['password'] = encrypt_password(data['password'])\",\n \"def create_password(self):\\r\\n alphabet = string.ascii_letters + string.digits\\r\\n password = ''.join(secrets.choice(alphabet) for i in range(30))\\r\\n\\r\\n QtWidgets.QMessageBox.information(self, \\\"Password generated\\\", \\r\\n \\\"{}\\\".format(password))\",\n \"def password_listener(target, value, oldvalue, initiator):\\n return pwd_context.encrypt(value)\",\n \"def fake_login(self):\\n self.username = self.console.input('Please type your username here:')\\n self.password = self.console.input('Please type your password here:')\",\n \"def passphrase(self):\\n password = self.entry.password\\n if password:\\n return self.entry.password.encode('UTF-8')\\n else:\\n return None\",\n \"def passwd_encryption(self):\\n key = Fernet.generate_key()\\n cipher_suite = Fernet(key)\\n bin_passwd = bytes(self.password, 'utf-8')\\n ciphered_text = cipher_suite.encrypt(bin_passwd)\\n with open(self.pass_path, 'wb') as pass_output:\\n pass_output.write(ciphered_text)\\n with open(self.key_path, 'wb') as key_output:\\n key_output.write(key)\",\n \"def password(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"password\\\")\",\n \"def password(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"password\\\")\",\n \"def password(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"password\\\")\",\n \"def password(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"password\\\")\",\n \"def password(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"password\\\")\",\n \"def password(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"password\\\")\",\n \"def password(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"password\\\")\",\n \"def password(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"password\\\")\",\n \"def password(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"password\\\")\",\n \"def password(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"password\\\")\",\n \"def password(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"password\\\")\",\n \"def password(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"password\\\")\",\n \"def password(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"password\\\")\",\n \"def login(self):\\n def twobytwo(s):\\n i = 0\\n while i < len(s):\\n yield s[i:i + 2]\\n i += 2\\n sha1_pass_bytes = sha1(self._password).hexdigest()\\n self.command('PASS: \\\"%s\\\"' % ' '.join(twobytwo(sha1_pass_bytes.upper())))\",\n \"def set_password() -> \\\"Function\\\":\\n password = \\\"\\\"\\n\\n def inner():\\n nonlocal password\\n if password == \\\"\\\":\\n password = get_password()\\n return password\\n\\n return inner\",\n \"def password(self, value):\\n self.password_hashed = func.crypt(value, func.gen_salt('bf'))\",\n \"def create_pwd_login_internal(password, re_enter_password):\\r\\n if g.platform == 'android':\\r\\n 'Enter the password in Create password text box'\\r\\n flag1 = ui_controls.text_box(get_obj_identifier('login_createPassword_txt'), value=password)\\r\\n\\r\\n 'Checks for the keyboard presence and hides it'\\r\\n flag2 = ui_controls.hide_keyboard()\\r\\n\\r\\n 'Enter the confirm password in Confirm Password text box'\\r\\n flag3 = ui_controls.text_box(get_obj_identifier('login_confirmPassword_txt'), value=re_enter_password)\\r\\n\\r\\n 'Checks for the keyboard presence and hides it'\\r\\n flag4 = ui_controls.hide_keyboard()\\r\\n\\r\\n 'Enter password hint in Password Hint text box'\\r\\n #flag5 = ui_controls.text_box(get_obj_identifier('login_passwordHint_txt'), value=hint)\\r\\n\\r\\n 'Checks for the keyboard presence and hides it'\\r\\n #flag6 = ui_controls.hide_keyboard()\\r\\n\\r\\n 'Click on create password login button'\\r\\n flag7 = ui_controls.button(get_obj_identifier('login_createPasswordLogin_btn'))\\r\\n\\r\\n status = False if not (flag1 and flag2 and flag3 and flag4 and flag7) else True\\r\\n else:\\r\\n 'Setting values on Create Password text box in ios. Using set value as send_keys failing here'\\r\\n flag1 = ui_controls.setValue(get_obj_identifier('login_createPassword_txt'), password)\\r\\n sleep(3)\\r\\n\\r\\n 'Setting values on Confirm Password text box in ios.'\\r\\n flag2 = ui_controls.setValue(get_obj_identifier('login_confirmPassword_txt'), value=re_enter_password)\\r\\n\\r\\n 'Setting values on Password Hint text box in ios.'\\r\\n # flag3 = ui_controls.setValue(get_obj_identifier('login_passwordHint_txt'), hint)\\r\\n sleep(3)\\r\\n\\r\\n 'Click on Create Password button in IOS'\\r\\n flag4 = ui_controls.button(get_obj_identifier('login_createPasswordLogin_btn'))\\r\\n\\r\\n status = False if not (flag1 and flag2 and flag4) else True\\r\\n return status\",\n \"def get_password_from_user():\\n pwd = ''\\n keyboard = xbmc.Keyboard('', ADDON_NAME + ': ' + localise(32022), True)\\n keyboard.doModal()\\n if keyboard.isConfirmed():\\n pwd = keyboard.getText()\\n return pwd\",\n \"def _password_prompt(question: str, console: io.IO) -> str:\\n console.tell(question)\\n while True:\\n password1 = console.getpass('Password: ')\\n try:\\n _password_validate(password1)\\n except ValueError as e:\\n console.error(e)\\n continue\\n password2 = console.getpass('Password (again): ')\\n if password1 != password2:\\n console.error('Passwords do not match, please try again')\\n continue\\n return password1\",\n \"def _set_password(self, cr, uid, id, password, context=None):\\n encrypted = self._crypt_context(\\n cr, uid, id, context=context).encrypt(password)\\n print(password)\\n print(encrypted)\\n self._set_encrypted_password(cr, uid, id, encrypted, context=context)\\n self._set_password_again(cr, uid, id, password, context=context)\",\n \"def switchPassword(password):\\n bits = base64.b64decode(password)\\n bits = bytes([x ^ y for (x, y) in zip(settings.BITMASK, bits)])\\n return base64.b64encode(bits).decode()\",\n \"def islandPassword(self, click, password):\\n\\t\\tpos = mouse.get_pos()\\n\\t\\tdef msg(text, length):\\n\\t\\t\\t\\\"\\\"\\\" Render message \\\"\\\"\\\"\\n\\t\\t\\tself.screen.blit(transform.scale(self.message.background, (600,150)), (259,30))\\n\\t\\t\\tself.screen.blit(self.message.font.render(text, True, (0,0,0)), (275,59))\\n\\t\\t\\tself.treasure.render(True, False, False, False, self.message)\\n\\t\\t\\t# Render and pause\\n\\t\\t\\tdisplay.flip()\\n\\t\\t\\ttime.wait(length)\\n\\n\\t\\t# Blit background\\n\\t\\t# self.screen.blit(transform.scale(self.message.background, (600,150)), (259,30))\\n\\n\\t\\tkeysDict = {\\n\\t\\t\\t\\\"1\\\": Rect(96, 72, 170, 90),\\n\\t\\t\\t\\\"2\\\": Rect(315, 72, 170, 90),\\n\\t\\t\\t\\\"3\\\": Rect(531, 72, 170, 90),\\n\\t\\t\\t\\\"4\\\": Rect(96, 200, 170, 90),\\n\\t\\t\\t\\\"5\\\": Rect(315, 200, 170, 90),\\n\\t\\t\\t\\\"6\\\": Rect(531, 200, 170, 90),\\n\\t\\t\\t\\\"7\\\": Rect(96, 322, 170, 90),\\n\\t\\t\\t\\\"8\\\": Rect(315, 322, 170, 90),\\n\\t\\t\\t\\\"9\\\": Rect(531, 322, 170, 90),\\n\\t\\t\\t\\\"0\\\": Rect(315, 451, 170, 90), \\n\\t\\t\\t\\\"cancel\\\": Rect(803, 72, 170, 90),\\n\\t\\t\\t\\\"clear\\\": Rect(803, 200, 170, 90),\\n\\t\\t\\t\\\"enter\\\": Rect(803, 322, 170, 90)\\n\\t\\t}\\n\\t\\tif not self.passwordMsgFinished:\\n\\t\\t\\tmsg(\\\"Enter Password\\\", 1000)\\n\\t\\t\\tself.passwordMsgFinished=True\\n\\t\\tcorrect = [\\\"6\\\", \\\"3\\\", \\\"8\\\", \\\"5\\\", \\\"4\\\", \\\"6\\\"]#original password\\n\\t\\tfor key in keysDict:\\n\\t\\t\\tif keysDict[key].collidepoint(pos):\\n\\t\\t\\t\\tif click:\\n\\t\\t\\t\\t\\t# print(\\\"Pressed\\\", key)\\n\\t\\t\\t\\t\\tif key!=\\\"cancel\\\" and key!=\\\"clear\\\" and key!=\\\"enter\\\":\\n\\t\\t\\t\\t\\t\\tpassword.append(key)\\n\\t\\t\\t\\t\\tif key== \\\"cancel\\\":\\n\\t\\t\\t\\t\\t\\tself.fade.fadeDark(self.maps.allScenes[\\\"finalisland\\\"][0], self.screen, (0, 0))\\n\\t\\t\\t\\t\\t\\t# Create new scene\\t\\n\\t\\t\\t\\t\\t\\tself.maps.newScene(\\\"finalisland\\\")\\n\\t\\t\\t\\t\\t\\t# Set player coordinates\\n\\t\\t\\t\\t\\t\\tself.player.x = 510\\n\\t\\t\\t\\t\\t\\tself.player.y = 46\\n\\t\\t\\t\\t\\t\\t# Reset fade\\n\\t\\t\\t\\t\\t\\tself.fade.reset()\\n\\t\\t\\t\\t\\tif key==\\\"clear\\\":\\n\\t\\t\\t\\t\\t\\tpassword.clear()\\n\\t\\t\\t\\t\\tif key == \\\"enter\\\":\\n\\t\\t\\t\\t\\t\\tif not (\\\"key\\\" in self.treasure.collectedItems):\\n\\t\\t\\t\\t\\t\\t\\tmsg(\\\"Key Missing\\\", 1500)\\n\\t\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\t\\tif password == correct:\\n\\t\\t\\t\\t\\t\\t\\t\\t#print(\\\"correct password\\\")\\n\\t\\t\\t\\t\\t\\t\\t\\tself.fade.fadeDark(self.maps.allScenes[\\\"finalisland\\\"][0], self.screen, (0, 0))\\n\\t\\t\\t\\t\\t\\t\\t\\t# Create new scene\\t\\n\\t\\t\\t\\t\\t\\t\\t\\tself.maps.newScene(\\\"finalisland\\\")\\n\\t\\t\\t\\t\\t\\t\\t\\t# Set player coordinates\\n\\t\\t\\t\\t\\t\\t\\t\\tself.player.x = 516\\n\\t\\t\\t\\t\\t\\t\\t\\tself.player.y = 46\\n\\t\\t\\t\\t\\t\\t\\t\\t# Reset fade\\n\\t\\t\\t\\t\\t\\t\\t\\tself.fade.reset()\\n\\t\\t\\t\\t\\t\\t\\t\\tmsg(\\\"You have succesfully opened the safe.\\\", 3000)\\n\\t\\t\\t\\t\\t\\t\\t\\tself.gameWon=True\\n\\n\\t\\t\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\t\\t\\tmsg(\\\"Wrong password. Press clear and try again.\\\", 1500)\",\n \"def log_in_password(self, password):\\n waiter.find_write(self.driver, 'password', password, by=NAME)\",\n \"def on_passwordCheckBox_toggled(self, isOn):\\n if isOn:\\n self.input.setEchoMode(QLineEdit.Password)\\n else:\\n self.input.setEchoMode(QLineEdit.Normal)\",\n \"def passsword(self, password):\\n self.passwor_harsh = generate_password_hash(password)\",\n \"def add_password_input(\\n self,\\n name: str,\\n label: Optional[str] = None,\\n placeholder: Optional[str] = None,\\n ) -> None:\\n self._client.add_element(\\n name=name, element=TextField(label=label, value=placeholder, password=True)\\n )\",\n \"def onPasswordFocusIn(self,event):\\n if self.obj2.get() == \\\"New Password\\\":\\n self.obj2.delete(0,END)\",\n \"def text_typing_block(self):\\n\\t # open the database using the masterpassword\\n typing_text = 'Send({})\\\\n'.format(self.masterpassword)\\n\\n # add in exit - this is achieved using CTRL + q\\n typing_text += 'Sleep(15677)\\\\n'\\n typing_text += \\\"SendKeepActive('KeePass')\\\\n\\\"\\n typing_text += 'Send(\\\"^q\\\")\\\\n'\\n typing_text += \\\"; Reset Focus\\\\n\\\"\\n typing_text += 'SendKeepActive(\\\"\\\")'\\n\\n return textwrap.indent(typing_text, self.indent_space)\",\n \"def passwd_prompt():\\n\\n print(\\\"Passwords MUST contain AT LEAST: one lower-case letter,\\\" \\n \\\"one number, one symbol, and be a MINIMUM of 8 characters in length,\\\"\\n \\\"e.g. r!ght2oE\\\")\\n\\n while True:\\n\\n passy = getpass.getpass(prompt=\\\"Enter password for user: \\\")\\n confirm_passy = getpass.getpass(prompt=\\\"To confirm, \\\" \\\\\\n \\\"re-enter password: \\\")\\n\\n # check for the following conditions: \\n # user input matches\\n # length of input is at least 8 characters\\n # input contains at least 1 number \\n # input contains at least 1 letter \\n # input contains at least 1 symbol \\n \\n if passy != confirm_passy \\\\\\n or len(passy) <8 \\\\\\n or not re.search('\\\\d', passy) \\\\\\n or not re.search(r\\\"[a-z]\\\",passy) \\\\\\n or not re.search(r\\\"[ !#$%&'()*+,-./[\\\\\\\\\\\\]^_`{|}~\\\"+r'\\\"]', passy): \\n \\n print(TRY_AGAIN)\\n continue \\n \\n else:\\n print(\\\"Password meets complexity requirement. Continuing...\\\") \\n return passy\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.81982946","0.7473501","0.7234966","0.7115085","0.70761955","0.7038702","0.693298","0.68611926","0.68122035","0.67915016","0.67278564","0.67168885","0.6711128","0.67014295","0.66786796","0.66681784","0.66681784","0.66681784","0.66681784","0.66681784","0.66681784","0.66681784","0.66681784","0.66681784","0.66681784","0.66681784","0.66681784","0.66681784","0.6648841","0.6580645","0.6552636","0.65179044","0.65161204","0.65135264","0.6452248","0.64482844","0.6428498","0.63997483","0.6386326","0.6382127","0.63622737","0.6355577","0.63357073","0.632049","0.632049","0.632049","0.632049","0.6319766","0.6314133","0.62859684","0.62633157","0.625626","0.6249111","0.6247365","0.62414","0.6236894","0.6236136","0.62324154","0.6204806","0.62038016","0.6200925","0.61840296","0.61821663","0.61785656","0.6177012","0.6163865","0.61490643","0.6146791","0.6143447","0.6140038","0.6138022","0.61375594","0.61375594","0.61375594","0.61375594","0.61375594","0.61375594","0.61375594","0.61375594","0.61375594","0.61375594","0.61375594","0.61375594","0.61375594","0.6129404","0.61287224","0.61244535","0.6115429","0.6109917","0.61036074","0.6101498","0.61009014","0.60924566","0.6081051","0.6072906","0.6072464","0.6072282","0.6066762","0.60654116","0.6063046"],"string":"[\n \"0.81982946\",\n \"0.7473501\",\n \"0.7234966\",\n \"0.7115085\",\n \"0.70761955\",\n \"0.7038702\",\n \"0.693298\",\n \"0.68611926\",\n \"0.68122035\",\n \"0.67915016\",\n \"0.67278564\",\n \"0.67168885\",\n \"0.6711128\",\n \"0.67014295\",\n \"0.66786796\",\n \"0.66681784\",\n \"0.66681784\",\n \"0.66681784\",\n \"0.66681784\",\n \"0.66681784\",\n \"0.66681784\",\n \"0.66681784\",\n \"0.66681784\",\n \"0.66681784\",\n \"0.66681784\",\n \"0.66681784\",\n \"0.66681784\",\n \"0.66681784\",\n \"0.6648841\",\n \"0.6580645\",\n \"0.6552636\",\n \"0.65179044\",\n \"0.65161204\",\n \"0.65135264\",\n \"0.6452248\",\n \"0.64482844\",\n \"0.6428498\",\n \"0.63997483\",\n \"0.6386326\",\n \"0.6382127\",\n \"0.63622737\",\n \"0.6355577\",\n \"0.63357073\",\n \"0.632049\",\n \"0.632049\",\n \"0.632049\",\n \"0.632049\",\n \"0.6319766\",\n \"0.6314133\",\n \"0.62859684\",\n \"0.62633157\",\n \"0.625626\",\n \"0.6249111\",\n \"0.6247365\",\n \"0.62414\",\n \"0.6236894\",\n \"0.6236136\",\n \"0.62324154\",\n \"0.6204806\",\n \"0.62038016\",\n \"0.6200925\",\n \"0.61840296\",\n \"0.61821663\",\n \"0.61785656\",\n \"0.6177012\",\n \"0.6163865\",\n \"0.61490643\",\n \"0.6146791\",\n \"0.6143447\",\n \"0.6140038\",\n \"0.6138022\",\n \"0.61375594\",\n \"0.61375594\",\n \"0.61375594\",\n \"0.61375594\",\n \"0.61375594\",\n \"0.61375594\",\n \"0.61375594\",\n \"0.61375594\",\n \"0.61375594\",\n \"0.61375594\",\n \"0.61375594\",\n \"0.61375594\",\n \"0.61375594\",\n \"0.6129404\",\n \"0.61287224\",\n \"0.61244535\",\n \"0.6115429\",\n \"0.6109917\",\n \"0.61036074\",\n \"0.6101498\",\n \"0.61009014\",\n \"0.60924566\",\n \"0.6081051\",\n \"0.6072906\",\n \"0.6072464\",\n \"0.6072282\",\n \"0.6066762\",\n \"0.60654116\",\n \"0.6063046\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.78404224"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":94694,"cells":{"query":{"kind":"string","value":"Set a new password for encryption."},"document":{"kind":"string","value":"def set_password(self, password):\n self.__init__(password=password)"},"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 setpassword(self, pwd):\n pass","def set_password(self, value):\n # Salt need to be generated before set password\n m = hashlib.sha256()\n m.update('-'.join([\n str(datetime.now()),\n config.get('security.password_salt')\n ]))\n self.salt = m.hexdigest()\n self.password_pending = False\n self.password = self.__encrypt(value)","def set_password(self, password):\n self.password = password","def set_password(self, password):\n self.password = md5crypt(password, gen_salt())","def _set_password(self, cr, uid, id, password, context=None):\n encrypted = self._crypt_context(\n cr, uid, id, context=context).encrypt(password)\n print(password)\n print(encrypted)\n self._set_encrypted_password(cr, uid, id, encrypted, context=context)\n self._set_password_again(cr, uid, id, password, context=context)","def set_new_password(self, new_password):\n self.password = new_password","def set_password(self, password):\n self.PASS = password","def set_password(self, password):\n from kalon.auth import encrypt_password\n self.document.password = encrypt_password(password)","def set_password(self, password):\n self.PASSWORD = password","def set_password(self, password):\n self.password = generate_password_hash(password, method='pbkdf2:sha256')","def set_password(self, new_password):\n super(Mafiasi, self).set_password(new_password)\n self.new_password = new_password","def set_password(self, password):\n self.password = generate_password_hash(password)","def _set_password(self, password):\n self._password = generate_password_hash(password)","def set_password(self, password):\n self.password = self.hash_password(password)","def set_password(self, password):\n self.cloudserver.change_password(password)","def password(self, password):\n\n self._password = password","def password(self, password):\n\n self._password = password","def password(self, password):\n\n self._password = password","def password(self, password):\n\n self._password = password","def set_password(self, password):\n self.password_hash = generate_password_hash(str(password))","def set_password(self, password):\n self.password_hash = generate_password_hash(password)","def set_password(self, password):\n self.password_hash = generate_password_hash(password)","def set_password(self, password):\n self.password_hash = generate_password_hash(f\"{password}{self.user_salt}\")","def set_password(self, value):\n hashed = bcrypt.encode(value)\n self._password = unicode(hashed)","def set_password(self, password):\n self.authentication.password = password","def password(self, password):\n self.password_hash = generate_password_hash(password)\n self.password_set = True","def password(self, password: str):\n\n self._password = password","def set_password(self, password):\n\n self.password = bcrypt.generate_password_hash(password)","def set_pass(self, pw):\n\t\tself.passhash = generate_password_hash(pw)","def set_password(ctx, new_password, remember):\n ensure_validated(ctx, prompt='Enter your current password')\n if not new_password:\n new_password = click.prompt(\n 'Enter your new password',\n hide_input=True,\n confirmation_prompt=True,\n err=True)\n\n controller = ctx.obj['controller']\n settings = ctx.obj['settings']\n keys = settings.setdefault('keys', {})\n key = controller.set_password(new_password)\n click.echo('Password updated.')\n if remember:\n keys[controller.id] = b2a_hex(key).decode()\n settings.write()\n click.echo('Password remembered')\n elif controller.id in keys:\n del keys[controller.id]\n settings.write()","def set_password(self, raw_password: str):\n self.new_password = raw_password","def password(self, password) :\n\t\ttry :\n\t\t\tself._password = password\n\t\texcept Exception as e:\n\t\t\traise e","def set_encryption_password(session, password, old_password=None,\n return_type=None, **kwargs):\n verify_not_none(password, \"password\")\n\n body_values = {'encryption_pwd': password}\n if old_password:\n body_values['old_encryption_pwd'] = old_password\n\n path = '/api/settings/encryption.json'\n\n return session.post_api(path=path, body=body_values,\n return_type=return_type, **kwargs)","def set_password(self, service, username, password):\n segments = range(0, len(password), self._max_password_size)\n password_parts = [password[i : i + self._max_password_size] for i in segments]\n for i, password_part in enumerate(password_parts):\n curr_username = username\n if i > 0:\n curr_username += '{{part_%d}}' % i\n self._keyring.set_password(service, curr_username, password_part)","def password(self, password):\n self._password = password\n return self","def passwd(self, plaintext):\n self._password = bcrypt.generate_password_hash(plaintext.encode('utf8')).decode('utf8')","def set_password(self, password):\n self._java_ref.setPassword(password)","def set_admin_password(self, instance, new_pass):\n raise NotImplementedError()","def set_admin_password(self, instance, new_pass):\n pass","def set_password(self, raw_password):\n self.password = security.generate_password_hash(raw_password, length=12)","def set_password(self, raw_password):\n self.password = security.generate_password_hash(raw_password, length=12)","def password(self, password):\n self.password_hash = generate_password_hash(password)","def password(self, password):\n self.password_hash = generate_password_hash(password)","def password(self, password):\n self.password_hash = generate_password_hash(password)","def password(self, password):\n self.password_hash = generate_password_hash(password)","def password(self, password):\n self.password_hash = generate_password_hash(password)","def password(self, password):\n\n self.password_hash = generate_password_hash(password)","def password(self, value):\n self.password_hashed = func.crypt(value, func.gen_salt('bf'))","def set_Password(self, value):\n super(DownloadDocumentInputSet, self)._set_input('Password', value)","def setUserPassword(self,value):\n self.PDFreactorConfiguration.in1[\"userPassword\"] = value","def change_password(self, password, newpassword):\n cred = {\"newpasswd\": newpassword, \"passwd\": password}\n return self.put(\"passwd\", cred)","def change_password(self, new_password):\n dev = self.nearest_pandevice()\n self.password_hash = dev.request_password_hash(new_password)\n self.update(\"password_hash\")","def update_password(self, user, password):\n user.password = hashers.make_password(password)","def password(self, password):\n if password is None:\n self._password = None\n else:\n self._password = generate_password_hash(password)","def _set_password(self, password):\r\n hashed_password = password\r\n\r\n if isinstance(password, unicode):\r\n password_8bit = password.encode('UTF-8')\r\n else:\r\n password_8bit = password\r\n\r\n # Hash a password for the first time, with a randomly-generated salt\r\n salt = bcrypt.gensalt(10)\r\n hashed_password = bcrypt.hashpw(password_8bit, salt)\r\n\r\n # Make sure the hased password is an UTF-8 object at the end of the\r\n # process because SQLAlchemy _wants_ a unicode object for Unicode\r\n # fields\r\n if not isinstance(hashed_password, unicode):\r\n hashed_password = hashed_password.decode('UTF-8')\r\n\r\n self._password = hashed_password","def set_password(self, raw_password):\n if raw_password is None:\n self.set_unusable_password()\n else:\n import random\n algo = PASSWORD_ALGO\n salt = get_hexdigest(algo, str(random.random()), str(random.random()))[:5]\n hsh = get_hexdigest(algo, salt, raw_password)\n self.password = '%s$%s$%s' % (algo, salt, hsh)","def store_passwd(self, clr_passwd):\n aes_cipher = AESCipher()\n self.__aes_key = aes_cipher.AES_KEY\n self.__password = aes_cipher.encrypt(clr_passwd)","def setPassword(self, unhashPass):\n\t\tself.passHash = generate_password_hash(unhashPass)","def update_password(self, username, password):\n self.update(('Password', password), username)","def password(self, password):\n if password is not None and len(password) > 30:\n raise ValueError(\"Invalid value for `password`, length must be less than or equal to `30`\")\n\n self._password = password","def setpass(self, type, key):\n self.data.passwords[type] = key\n self.save()","def new_password():\n new_pass = generate_password()\n entry_pass.delete(0, END)\n entry_pass.insert(0, new_pass)","def hash_password(self, password):\n self.password = pwd_context.encrypt(password)","def SetPassword(self, request, context):\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')","def change_password(self, new_pass):\n self.manager.change_user_password(self, new_pass)","def set_Password(self, value):\n super(AddressValidationInputSet, self)._set_input('Password', value)","def password(self):\n raise RuntimeError(\"Password can not be read, only set\")","def passsword(self, password):\n self.passwor_harsh = generate_password_hash(password)","def update_password(self, new_password=None):\n\n self.password = generate_password_hash(new_password)\n\n if self.save(verbose=False):\n self.logger.warn('Updated password! %s' % self)\n else:\n raise AttributeError('Password update failed!')","def set_password(username, new_password):\n if not validate_password(new_password):\n return \"salasana on väärää muotoa\"\n new_password_hash = generate_password_hash(new_password)\n sql = \"UPDATE users \" \\\n \"SET password=:new_pw \" \\\n \"WHERE username=:username\"\n db.session.execute(sql, {\"new_pw\": new_password_hash, \"username\": username})\n db.session.commit()\n return \"ok\"","def update_password(self, pwd):\n self.password = bcrypt.generate_password_hash(pwd).decode('utf8')","def save_password(self, new_password):\n # 55 iterations takes about 100 ms on a Netgear WNDR3800 or about 8ms on a\n # Core2 Duo at 1200 MHz.\n hashed = pbkdf2.crypt(new_password, iterations=55)\n self.write(self.password_filename, hashed)","def change_password(self):\n self.test_user.set_password(self.create_user_data()['password1'])\n self.test_user.save()","def change_password(change_account):\n change_data(change_account, changed_data='password')","def set_session_password(self, pwd):\n\n if (self.__rootpwd == None):\n self.__rootpwd = hashlib.md5(pwd).hexdigest()\n else:\n self.__change_password(pwd)","def wifi_password(self):\n raise RuntimeError(\"Password can not be read, only set\")","def set_password_hash(self, password):\n salt = bcrypt.gensalt()\n self.password_hash = bcrypt.hashpw(password.encode(), salt)","def setPassword(self, password, hashed=False):\n if hashed or self.hashed:\n self.hashed = True\n self.password = utils.saltHash(password)\n else:\n self.password = password","def passwd_encryption(self):\n key = Fernet.generate_key()\n cipher_suite = Fernet(key)\n bin_passwd = bytes(self.password, 'utf-8')\n ciphered_text = cipher_suite.encrypt(bin_passwd)\n with open(self.pass_path, 'wb') as pass_output:\n pass_output.write(ciphered_text)\n with open(self.key_path, 'wb') as key_output:\n key_output.write(key)","def store_lc_passwd(self, clr_passwd):\n aes_cipher = AESCipher()\n self.__aes_key = aes_cipher.AES_KEY\n self.__lc_password = aes_cipher.encrypt(clr_passwd)","def new_password(self):\n # create new password\n return password_generator.create_password()\n # have password reset","def store_password_in_keyring(username, password):\n return keyring.set_password(KEYRING_SYSTEM, username, password,)","def hash_password(self):\n self.__password = self.str_to_hash(self.__password)","def auth_password(self, auth_password):\n\n self._auth_password = auth_password","def set_password(self,\n\t password,\n\t user='',\n\t shutit_pexpect_child=None,\n\t note=None):\n\t\tshutit_global.shutit_global_object.yield_to_draw()\n\t\tshutit_pexpect_child = shutit_pexpect_child or self.get_current_shutit_pexpect_session().pexpect_child\n\t\tshutit_pexpect_session = self.get_shutit_pexpect_session_from_child(shutit_pexpect_child)\n\t\treturn shutit_pexpect_session.set_password(password,user=user,note=note)","def test_password_set(self):\r\n tst = User()\r\n tst.password = self.test_password\r\n\r\n self.assertEqual(\r\n len(tst.password),\r\n 60,\r\n \"Hashed should be 60 char long: \" + tst.password)\r\n self.assertEqual(\r\n '$2a$',\r\n tst.password[:4],\r\n \"Hash should start with the right complexity: \" + tst.password[:4])","def set_password(self, user, password):\n hashed_password = self.hash_password(password)\n server_name = self.get_server_name()\n hookenv.log(\"Storing hash: {}\".format(hashed_password), hookenv.DEBUG)\n result = self.pgsql_query(\n \"UPDATE users SET password_hash = '{}' WHERE name = '@{}:{}';\".format(\n hashed_password, user, server_name\n )\n )\n return result","def password(self):\n raise AttributeError(\"password: write-only field\")","def user_encrypt_password(data=None, **kw):\n if 'password' in data:\n data['password'] = encrypt_password(data['password'])","def reset_password(self):\n self.password = passwordResetter(self.user_id, self.password)","def set_password(name, password):\n s_file = \"/etc/shadow\"\n ret = {}\n if not os.path.isfile(s_file):\n return ret\n lines = []\n with salt.utils.files.fopen(s_file, \"r\") as ifile:\n for line in ifile:\n comps = line.strip().split(\":\")\n if comps[0] != name:\n lines.append(line)\n continue\n comps[1] = password\n line = \":\".join(comps)\n lines.append(\"{}\\n\".format(line))\n with salt.utils.files.fopen(s_file, \"w+\") as ofile:\n lines = [salt.utils.stringutils.to_str(_l) for _l in lines]\n ofile.writelines(lines)\n uinfo = info(name)\n return uinfo[\"passwd\"] == password","def hash_password(self, original_password):\n self.password = generate_password_hash(original_password)","async def password(self, ctx):\n pass","def set_password(self, password: Optional[str]) -> None:\n if password is None:\n self.set_unusable_password()\n return\n\n if not check_password_strength(password):\n raise PasswordTooWeakError\n\n super().set_password(password)","def change_password(host, username, password):\r\n # type: (Docker, str, str) -> None\r\n host.cmd(\"echo '%s:%s' | chpasswd\" % (username, password))","def update_password(self, password):\n self.password = scryptsalsa208sha256_str(password.encode('utf-8')).decode('utf-8')\n return True","def changePassword(self, loginName, password, newPassword):\n return self.talk(\n 'purchase',\n data=self.__makeLoginDict(loginName, password,\n {'newPassword': newPassword}))","def _encryptDBPass():\n #run encrypt tool on user given password\n controller.CONF[\"ENCRYPTED_DB_PASS\"] = utils.encryptEngineDBPass(password=controller.CONF[\"DB_PASS\"],\n maskList=masked_value_set)","def encrypt_password(pass_to_encrypt):\n\n temp_key = get_crypt_key()\n tk = Fernet(temp_key)\n\n pass_to_encrypt = pass_to_encrypt.encode(\"UTF-8\")\n return tk.encrypt(pass_to_encrypt)","def test_setPassword(self):\n self.realm._txCryptContext, perform = getTestContext()\n account = self.realm.addAccount(\n self.localpart, self.domain, self.password)\n username = u'%s@%s' % (self.localpart, self.domain)\n d = account.setPassword(u'blahblah')\n perform()\n self.successResultOf(d)\n d = self._requestAvatarId(UsernamePassword(username, u'blahblah'))\n perform()\n self.assertEquals(self.successResultOf(d), account.storeID)\n d = self._requestAvatarId(UsernamePassword(username, self.password))\n perform()\n self.failureResultOf(d, UnauthorizedLogin)\n d = self._requestAvatarId(UsernamePassword(username, account.passwordHash))\n perform()\n self.failureResultOf(d, UnauthorizedLogin)"],"string":"[\n \"def setpassword(self, pwd):\\n pass\",\n \"def set_password(self, value):\\n # Salt need to be generated before set password\\n m = hashlib.sha256()\\n m.update('-'.join([\\n str(datetime.now()),\\n config.get('security.password_salt')\\n ]))\\n self.salt = m.hexdigest()\\n self.password_pending = False\\n self.password = self.__encrypt(value)\",\n \"def set_password(self, password):\\n self.password = password\",\n \"def set_password(self, password):\\n self.password = md5crypt(password, gen_salt())\",\n \"def _set_password(self, cr, uid, id, password, context=None):\\n encrypted = self._crypt_context(\\n cr, uid, id, context=context).encrypt(password)\\n print(password)\\n print(encrypted)\\n self._set_encrypted_password(cr, uid, id, encrypted, context=context)\\n self._set_password_again(cr, uid, id, password, context=context)\",\n \"def set_new_password(self, new_password):\\n self.password = new_password\",\n \"def set_password(self, password):\\n self.PASS = password\",\n \"def set_password(self, password):\\n from kalon.auth import encrypt_password\\n self.document.password = encrypt_password(password)\",\n \"def set_password(self, password):\\n self.PASSWORD = password\",\n \"def set_password(self, password):\\n self.password = generate_password_hash(password, method='pbkdf2:sha256')\",\n \"def set_password(self, new_password):\\n super(Mafiasi, self).set_password(new_password)\\n self.new_password = new_password\",\n \"def set_password(self, password):\\n self.password = generate_password_hash(password)\",\n \"def _set_password(self, password):\\n self._password = generate_password_hash(password)\",\n \"def set_password(self, password):\\n self.password = self.hash_password(password)\",\n \"def set_password(self, password):\\n self.cloudserver.change_password(password)\",\n \"def password(self, password):\\n\\n self._password = password\",\n \"def password(self, password):\\n\\n self._password = password\",\n \"def password(self, password):\\n\\n self._password = password\",\n \"def password(self, password):\\n\\n self._password = password\",\n \"def set_password(self, password):\\n self.password_hash = generate_password_hash(str(password))\",\n \"def set_password(self, password):\\n self.password_hash = generate_password_hash(password)\",\n \"def set_password(self, password):\\n self.password_hash = generate_password_hash(password)\",\n \"def set_password(self, password):\\n self.password_hash = generate_password_hash(f\\\"{password}{self.user_salt}\\\")\",\n \"def set_password(self, value):\\n hashed = bcrypt.encode(value)\\n self._password = unicode(hashed)\",\n \"def set_password(self, password):\\n self.authentication.password = password\",\n \"def password(self, password):\\n self.password_hash = generate_password_hash(password)\\n self.password_set = True\",\n \"def password(self, password: str):\\n\\n self._password = password\",\n \"def set_password(self, password):\\n\\n self.password = bcrypt.generate_password_hash(password)\",\n \"def set_pass(self, pw):\\n\\t\\tself.passhash = generate_password_hash(pw)\",\n \"def set_password(ctx, new_password, remember):\\n ensure_validated(ctx, prompt='Enter your current password')\\n if not new_password:\\n new_password = click.prompt(\\n 'Enter your new password',\\n hide_input=True,\\n confirmation_prompt=True,\\n err=True)\\n\\n controller = ctx.obj['controller']\\n settings = ctx.obj['settings']\\n keys = settings.setdefault('keys', {})\\n key = controller.set_password(new_password)\\n click.echo('Password updated.')\\n if remember:\\n keys[controller.id] = b2a_hex(key).decode()\\n settings.write()\\n click.echo('Password remembered')\\n elif controller.id in keys:\\n del keys[controller.id]\\n settings.write()\",\n \"def set_password(self, raw_password: str):\\n self.new_password = raw_password\",\n \"def password(self, password) :\\n\\t\\ttry :\\n\\t\\t\\tself._password = password\\n\\t\\texcept Exception as e:\\n\\t\\t\\traise e\",\n \"def set_encryption_password(session, password, old_password=None,\\n return_type=None, **kwargs):\\n verify_not_none(password, \\\"password\\\")\\n\\n body_values = {'encryption_pwd': password}\\n if old_password:\\n body_values['old_encryption_pwd'] = old_password\\n\\n path = '/api/settings/encryption.json'\\n\\n return session.post_api(path=path, body=body_values,\\n return_type=return_type, **kwargs)\",\n \"def set_password(self, service, username, password):\\n segments = range(0, len(password), self._max_password_size)\\n password_parts = [password[i : i + self._max_password_size] for i in segments]\\n for i, password_part in enumerate(password_parts):\\n curr_username = username\\n if i > 0:\\n curr_username += '{{part_%d}}' % i\\n self._keyring.set_password(service, curr_username, password_part)\",\n \"def password(self, password):\\n self._password = password\\n return self\",\n \"def passwd(self, plaintext):\\n self._password = bcrypt.generate_password_hash(plaintext.encode('utf8')).decode('utf8')\",\n \"def set_password(self, password):\\n self._java_ref.setPassword(password)\",\n \"def set_admin_password(self, instance, new_pass):\\n raise NotImplementedError()\",\n \"def set_admin_password(self, instance, new_pass):\\n pass\",\n \"def set_password(self, raw_password):\\n self.password = security.generate_password_hash(raw_password, length=12)\",\n \"def set_password(self, raw_password):\\n self.password = security.generate_password_hash(raw_password, length=12)\",\n \"def password(self, password):\\n self.password_hash = generate_password_hash(password)\",\n \"def password(self, password):\\n self.password_hash = generate_password_hash(password)\",\n \"def password(self, password):\\n self.password_hash = generate_password_hash(password)\",\n \"def password(self, password):\\n self.password_hash = generate_password_hash(password)\",\n \"def password(self, password):\\n self.password_hash = generate_password_hash(password)\",\n \"def password(self, password):\\n\\n self.password_hash = generate_password_hash(password)\",\n \"def password(self, value):\\n self.password_hashed = func.crypt(value, func.gen_salt('bf'))\",\n \"def set_Password(self, value):\\n super(DownloadDocumentInputSet, self)._set_input('Password', value)\",\n \"def setUserPassword(self,value):\\n self.PDFreactorConfiguration.in1[\\\"userPassword\\\"] = value\",\n \"def change_password(self, password, newpassword):\\n cred = {\\\"newpasswd\\\": newpassword, \\\"passwd\\\": password}\\n return self.put(\\\"passwd\\\", cred)\",\n \"def change_password(self, new_password):\\n dev = self.nearest_pandevice()\\n self.password_hash = dev.request_password_hash(new_password)\\n self.update(\\\"password_hash\\\")\",\n \"def update_password(self, user, password):\\n user.password = hashers.make_password(password)\",\n \"def password(self, password):\\n if password is None:\\n self._password = None\\n else:\\n self._password = generate_password_hash(password)\",\n \"def _set_password(self, password):\\r\\n hashed_password = password\\r\\n\\r\\n if isinstance(password, unicode):\\r\\n password_8bit = password.encode('UTF-8')\\r\\n else:\\r\\n password_8bit = password\\r\\n\\r\\n # Hash a password for the first time, with a randomly-generated salt\\r\\n salt = bcrypt.gensalt(10)\\r\\n hashed_password = bcrypt.hashpw(password_8bit, salt)\\r\\n\\r\\n # Make sure the hased password is an UTF-8 object at the end of the\\r\\n # process because SQLAlchemy _wants_ a unicode object for Unicode\\r\\n # fields\\r\\n if not isinstance(hashed_password, unicode):\\r\\n hashed_password = hashed_password.decode('UTF-8')\\r\\n\\r\\n self._password = hashed_password\",\n \"def set_password(self, raw_password):\\n if raw_password is None:\\n self.set_unusable_password()\\n else:\\n import random\\n algo = PASSWORD_ALGO\\n salt = get_hexdigest(algo, str(random.random()), str(random.random()))[:5]\\n hsh = get_hexdigest(algo, salt, raw_password)\\n self.password = '%s$%s$%s' % (algo, salt, hsh)\",\n \"def store_passwd(self, clr_passwd):\\n aes_cipher = AESCipher()\\n self.__aes_key = aes_cipher.AES_KEY\\n self.__password = aes_cipher.encrypt(clr_passwd)\",\n \"def setPassword(self, unhashPass):\\n\\t\\tself.passHash = generate_password_hash(unhashPass)\",\n \"def update_password(self, username, password):\\n self.update(('Password', password), username)\",\n \"def password(self, password):\\n if password is not None and len(password) > 30:\\n raise ValueError(\\\"Invalid value for `password`, length must be less than or equal to `30`\\\")\\n\\n self._password = password\",\n \"def setpass(self, type, key):\\n self.data.passwords[type] = key\\n self.save()\",\n \"def new_password():\\n new_pass = generate_password()\\n entry_pass.delete(0, END)\\n entry_pass.insert(0, new_pass)\",\n \"def hash_password(self, password):\\n self.password = pwd_context.encrypt(password)\",\n \"def SetPassword(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 change_password(self, new_pass):\\n self.manager.change_user_password(self, new_pass)\",\n \"def set_Password(self, value):\\n super(AddressValidationInputSet, self)._set_input('Password', value)\",\n \"def password(self):\\n raise RuntimeError(\\\"Password can not be read, only set\\\")\",\n \"def passsword(self, password):\\n self.passwor_harsh = generate_password_hash(password)\",\n \"def update_password(self, new_password=None):\\n\\n self.password = generate_password_hash(new_password)\\n\\n if self.save(verbose=False):\\n self.logger.warn('Updated password! %s' % self)\\n else:\\n raise AttributeError('Password update failed!')\",\n \"def set_password(username, new_password):\\n if not validate_password(new_password):\\n return \\\"salasana on väärää muotoa\\\"\\n new_password_hash = generate_password_hash(new_password)\\n sql = \\\"UPDATE users \\\" \\\\\\n \\\"SET password=:new_pw \\\" \\\\\\n \\\"WHERE username=:username\\\"\\n db.session.execute(sql, {\\\"new_pw\\\": new_password_hash, \\\"username\\\": username})\\n db.session.commit()\\n return \\\"ok\\\"\",\n \"def update_password(self, pwd):\\n self.password = bcrypt.generate_password_hash(pwd).decode('utf8')\",\n \"def save_password(self, new_password):\\n # 55 iterations takes about 100 ms on a Netgear WNDR3800 or about 8ms on a\\n # Core2 Duo at 1200 MHz.\\n hashed = pbkdf2.crypt(new_password, iterations=55)\\n self.write(self.password_filename, hashed)\",\n \"def change_password(self):\\n self.test_user.set_password(self.create_user_data()['password1'])\\n self.test_user.save()\",\n \"def change_password(change_account):\\n change_data(change_account, changed_data='password')\",\n \"def set_session_password(self, pwd):\\n\\n if (self.__rootpwd == None):\\n self.__rootpwd = hashlib.md5(pwd).hexdigest()\\n else:\\n self.__change_password(pwd)\",\n \"def wifi_password(self):\\n raise RuntimeError(\\\"Password can not be read, only set\\\")\",\n \"def set_password_hash(self, password):\\n salt = bcrypt.gensalt()\\n self.password_hash = bcrypt.hashpw(password.encode(), salt)\",\n \"def setPassword(self, password, hashed=False):\\n if hashed or self.hashed:\\n self.hashed = True\\n self.password = utils.saltHash(password)\\n else:\\n self.password = password\",\n \"def passwd_encryption(self):\\n key = Fernet.generate_key()\\n cipher_suite = Fernet(key)\\n bin_passwd = bytes(self.password, 'utf-8')\\n ciphered_text = cipher_suite.encrypt(bin_passwd)\\n with open(self.pass_path, 'wb') as pass_output:\\n pass_output.write(ciphered_text)\\n with open(self.key_path, 'wb') as key_output:\\n key_output.write(key)\",\n \"def store_lc_passwd(self, clr_passwd):\\n aes_cipher = AESCipher()\\n self.__aes_key = aes_cipher.AES_KEY\\n self.__lc_password = aes_cipher.encrypt(clr_passwd)\",\n \"def new_password(self):\\n # create new password\\n return password_generator.create_password()\\n # have password reset\",\n \"def store_password_in_keyring(username, password):\\n return keyring.set_password(KEYRING_SYSTEM, username, password,)\",\n \"def hash_password(self):\\n self.__password = self.str_to_hash(self.__password)\",\n \"def auth_password(self, auth_password):\\n\\n self._auth_password = auth_password\",\n \"def set_password(self,\\n\\t password,\\n\\t user='',\\n\\t shutit_pexpect_child=None,\\n\\t note=None):\\n\\t\\tshutit_global.shutit_global_object.yield_to_draw()\\n\\t\\tshutit_pexpect_child = shutit_pexpect_child or self.get_current_shutit_pexpect_session().pexpect_child\\n\\t\\tshutit_pexpect_session = self.get_shutit_pexpect_session_from_child(shutit_pexpect_child)\\n\\t\\treturn shutit_pexpect_session.set_password(password,user=user,note=note)\",\n \"def test_password_set(self):\\r\\n tst = User()\\r\\n tst.password = self.test_password\\r\\n\\r\\n self.assertEqual(\\r\\n len(tst.password),\\r\\n 60,\\r\\n \\\"Hashed should be 60 char long: \\\" + tst.password)\\r\\n self.assertEqual(\\r\\n '$2a$',\\r\\n tst.password[:4],\\r\\n \\\"Hash should start with the right complexity: \\\" + tst.password[:4])\",\n \"def set_password(self, user, password):\\n hashed_password = self.hash_password(password)\\n server_name = self.get_server_name()\\n hookenv.log(\\\"Storing hash: {}\\\".format(hashed_password), hookenv.DEBUG)\\n result = self.pgsql_query(\\n \\\"UPDATE users SET password_hash = '{}' WHERE name = '@{}:{}';\\\".format(\\n hashed_password, user, server_name\\n )\\n )\\n return result\",\n \"def password(self):\\n raise AttributeError(\\\"password: write-only field\\\")\",\n \"def user_encrypt_password(data=None, **kw):\\n if 'password' in data:\\n data['password'] = encrypt_password(data['password'])\",\n \"def reset_password(self):\\n self.password = passwordResetter(self.user_id, self.password)\",\n \"def set_password(name, password):\\n s_file = \\\"/etc/shadow\\\"\\n ret = {}\\n if not os.path.isfile(s_file):\\n return ret\\n lines = []\\n with salt.utils.files.fopen(s_file, \\\"r\\\") as ifile:\\n for line in ifile:\\n comps = line.strip().split(\\\":\\\")\\n if comps[0] != name:\\n lines.append(line)\\n continue\\n comps[1] = password\\n line = \\\":\\\".join(comps)\\n lines.append(\\\"{}\\\\n\\\".format(line))\\n with salt.utils.files.fopen(s_file, \\\"w+\\\") as ofile:\\n lines = [salt.utils.stringutils.to_str(_l) for _l in lines]\\n ofile.writelines(lines)\\n uinfo = info(name)\\n return uinfo[\\\"passwd\\\"] == password\",\n \"def hash_password(self, original_password):\\n self.password = generate_password_hash(original_password)\",\n \"async def password(self, ctx):\\n pass\",\n \"def set_password(self, password: Optional[str]) -> None:\\n if password is None:\\n self.set_unusable_password()\\n return\\n\\n if not check_password_strength(password):\\n raise PasswordTooWeakError\\n\\n super().set_password(password)\",\n \"def change_password(host, username, password):\\r\\n # type: (Docker, str, str) -> None\\r\\n host.cmd(\\\"echo '%s:%s' | chpasswd\\\" % (username, password))\",\n \"def update_password(self, password):\\n self.password = scryptsalsa208sha256_str(password.encode('utf-8')).decode('utf-8')\\n return True\",\n \"def changePassword(self, loginName, password, newPassword):\\n return self.talk(\\n 'purchase',\\n data=self.__makeLoginDict(loginName, password,\\n {'newPassword': newPassword}))\",\n \"def _encryptDBPass():\\n #run encrypt tool on user given password\\n controller.CONF[\\\"ENCRYPTED_DB_PASS\\\"] = utils.encryptEngineDBPass(password=controller.CONF[\\\"DB_PASS\\\"],\\n maskList=masked_value_set)\",\n \"def encrypt_password(pass_to_encrypt):\\n\\n temp_key = get_crypt_key()\\n tk = Fernet(temp_key)\\n\\n pass_to_encrypt = pass_to_encrypt.encode(\\\"UTF-8\\\")\\n return tk.encrypt(pass_to_encrypt)\",\n \"def test_setPassword(self):\\n self.realm._txCryptContext, perform = getTestContext()\\n account = self.realm.addAccount(\\n self.localpart, self.domain, self.password)\\n username = u'%s@%s' % (self.localpart, self.domain)\\n d = account.setPassword(u'blahblah')\\n perform()\\n self.successResultOf(d)\\n d = self._requestAvatarId(UsernamePassword(username, u'blahblah'))\\n perform()\\n self.assertEquals(self.successResultOf(d), account.storeID)\\n d = self._requestAvatarId(UsernamePassword(username, self.password))\\n perform()\\n self.failureResultOf(d, UnauthorizedLogin)\\n d = self._requestAvatarId(UsernamePassword(username, account.passwordHash))\\n perform()\\n self.failureResultOf(d, UnauthorizedLogin)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8377578","0.8145898","0.8129737","0.80971813","0.8089785","0.80798155","0.80655986","0.8056996","0.8050682","0.7988545","0.7963893","0.7942522","0.7855977","0.7843381","0.7838417","0.78155446","0.78155446","0.78155446","0.78155446","0.7810031","0.7780505","0.7780505","0.7763531","0.7751662","0.7748675","0.773308","0.772262","0.76941186","0.7692444","0.76771563","0.7667029","0.75896794","0.7550105","0.75218284","0.75177276","0.7505668","0.7494208","0.74807173","0.7468678","0.7435102","0.7435102","0.7417522","0.73941946","0.73941946","0.73941946","0.73941946","0.7359088","0.7324293","0.7284486","0.7275708","0.72734904","0.72678626","0.7261777","0.7204337","0.71910566","0.71672606","0.7155414","0.7147419","0.7144916","0.7068454","0.705407","0.7021587","0.69880855","0.6953109","0.69441915","0.69401765","0.6939661","0.69266814","0.6912347","0.6911378","0.6906269","0.6898913","0.6897689","0.68754613","0.68440706","0.6798301","0.6774968","0.67546016","0.6744525","0.67412746","0.67323625","0.67296183","0.6727924","0.670346","0.66879374","0.667256","0.66658","0.66365206","0.66325676","0.66284204","0.66277254","0.66275716","0.6617518","0.6613581","0.66105926","0.66073674","0.6600977","0.65998894","0.657383","0.6572627"],"string":"[\n \"0.8377578\",\n \"0.8145898\",\n \"0.8129737\",\n \"0.80971813\",\n \"0.8089785\",\n \"0.80798155\",\n \"0.80655986\",\n \"0.8056996\",\n \"0.8050682\",\n \"0.7988545\",\n \"0.7963893\",\n \"0.7942522\",\n \"0.7855977\",\n \"0.7843381\",\n \"0.7838417\",\n \"0.78155446\",\n \"0.78155446\",\n \"0.78155446\",\n \"0.78155446\",\n \"0.7810031\",\n \"0.7780505\",\n \"0.7780505\",\n \"0.7763531\",\n \"0.7751662\",\n \"0.7748675\",\n \"0.773308\",\n \"0.772262\",\n \"0.76941186\",\n \"0.7692444\",\n \"0.76771563\",\n \"0.7667029\",\n \"0.75896794\",\n \"0.7550105\",\n \"0.75218284\",\n \"0.75177276\",\n \"0.7505668\",\n \"0.7494208\",\n \"0.74807173\",\n \"0.7468678\",\n \"0.7435102\",\n \"0.7435102\",\n \"0.7417522\",\n \"0.73941946\",\n \"0.73941946\",\n \"0.73941946\",\n \"0.73941946\",\n \"0.7359088\",\n \"0.7324293\",\n \"0.7284486\",\n \"0.7275708\",\n \"0.72734904\",\n \"0.72678626\",\n \"0.7261777\",\n \"0.7204337\",\n \"0.71910566\",\n \"0.71672606\",\n \"0.7155414\",\n \"0.7147419\",\n \"0.7144916\",\n \"0.7068454\",\n \"0.705407\",\n \"0.7021587\",\n \"0.69880855\",\n \"0.6953109\",\n \"0.69441915\",\n \"0.69401765\",\n \"0.6939661\",\n \"0.69266814\",\n \"0.6912347\",\n \"0.6911378\",\n \"0.6906269\",\n \"0.6898913\",\n \"0.6897689\",\n \"0.68754613\",\n \"0.68440706\",\n \"0.6798301\",\n \"0.6774968\",\n \"0.67546016\",\n \"0.6744525\",\n \"0.67412746\",\n \"0.67323625\",\n \"0.67296183\",\n \"0.6727924\",\n \"0.670346\",\n \"0.66879374\",\n \"0.667256\",\n \"0.66658\",\n \"0.66365206\",\n \"0.66325676\",\n \"0.66284204\",\n \"0.66277254\",\n \"0.66275716\",\n \"0.6617518\",\n \"0.6613581\",\n \"0.66105926\",\n \"0.66073674\",\n \"0.6600977\",\n \"0.65998894\",\n \"0.657383\",\n \"0.6572627\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7456274"},"document_rank":{"kind":"string","value":"39"}}},{"rowIdx":94695,"cells":{"query":{"kind":"string","value":"Open the deletion modal for an address row."},"document":{"kind":"string","value":"def delete_address(self) -> object:\n self.delete_button.click()\n\n return DeletionModal(self).wait_for_component_to_be_present()"},"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_address(self, address: dict) -> None:\n row = self.addresses_list.surface_address_row(address)\n\n row.open_kebab_menu()\n row.kebab_menu.delete_address()\n\n self.deletion_modal.confirm_address_deletion()","def DeleteRow(self, entry):\n for a_link in entry.link:\n if a_link.rel == 'edit':\n return self.Delete(a_link.href)","def open_edit_address(self, address: dict) -> None:\n row = self.addresses_list.surface_address_row(address)\n\n row.open_kebab_menu()\n row.kebab_menu.edit_address()","def _open_delete_dialog():\n import hiero\n hiero.ui.findMenuAction('foundry.project.clearlocalcacheNuke').trigger()","def delete_record():\n global data_base, table, choice, res, confirmation, output1, place_for_enter, choice_row\n choice_row = choice.get()\n sqlite3_simple_delete_record(data_base, table, choice_row, res)\n output1.delete(1.0, END)\n confirmation.after(1, confirmation.destroy)\n place_for_enter.delete(0, END)","def delete(self, request, *args, **kwargs):\n # validate address id and get object\n instance = self.get_object()\n\n # get last transaction save point id\n sid = transaction.savepoint()\n\n try:\n # soft delete address\n instance.delete_address(request.user)\n except Exception as err:\n logger.error(\"Unexpected error occurred : %s.\", err)\n # roll back transaction if any exception occur while delete address\n transaction.savepoint_rollback(sid)\n return APIResponse({\"message\": err.args[0]}, HTTP_400_BAD_REQUEST)\n\n return APIResponse({\"message\": DELETE_ADDRESS}, HTTP_OK)","def delete_employee():\r\n id = request.args.get('id', \"\")\r\n return render_template(\"delete_employee.html\", id=id)","def onAddRecord(self, event):\r\n dbItem = getattr(db, self.modelName) ()\r\n dlg = dPerson.Person(self.view, dbItem).view\r\n dlg.ShowModal()\r\n dlg.Destroy()","def delete():\n add_book_tk = DeleteBookDialog()\n entries_args = [\n (\"Book ID : \", 0.5),\n ]\n add_book_tk.create_components(entries_args)\n add_book_tk.mainloop()","def delete_row(self, pk):\n return self.execute(self.commands.delete_row(self.name, self.primary_key_column, pk))","def delete_item(self):\n self.df_user.drop(self.index_select_number, inplace=True)\n self.df_user.to_csv(\"user_items.csv\", index=False)\n self.update_treeview()\n self.changing_item_label.config(text=\"Please double click on the item you want to edit.\")\n self.delete_but.destroy()\n self.serv_drop.destroy()\n self.serv_but.destroy()\n self.pop_up_del.destroy()","def test_client_address_delete(self):\n pass","def on_delete_event(self, widget, event):\n\n self.hide()\n return True","def click_map_line_items_delete_button(self):\n self.click_element(self.map_line_items_delete_button_locator, True, True)\n try:\n self.wait().until(EC.visibility_of_element_located(self.delete_mappings_confirmation_message_locator), 'delete mappings confirmation message locator not found before specified time out')\n self.click_element(self.ok_button_locator)\n except:\n raise","def cmd_delete_employee():\r\n id = request.form.get('id', \"\")\r\n confirm = request.form.get(\"confirm\", \"\")\r\n if confirm != \"DELETE\":\r\n flash(f\"Contact '{id}' NOT deleted. Please enter DELETE in the confirm field.\")\r\n return redirect(url_for('main.jobs'))\r\n \r\n index = get_employee_by_id(id)\r\n User.query.filter(User.id == id).delete()\r\n db.session.commit()\r\n\r\n\r\n if index != None:\r\n flash(f\"Employee '{id}' was succesfully deleted!\")\r\n return redirect(url_for('main.employees'))\r\n else:\r\n flash(f\"Employee '{id}' was not found\")\r\n return redirect(url_for('main.employees'))","def delete(request, id, template='contacts/regtaller/delete.html'):\n if not request.user.is_authenticated():\n return HttpResponseRedirect('/login/?next=%s' % request.path)\n\n user = request.user\n if not user.has_perm('delete_regtaller'):\n return HttpResponseForbidden()\n\n try:\n regtaller = TallerRegistration.objects.get(id=id)\n except TallerRegistration.DoesNotExist:\n raise Http404\n\n if request.method == 'POST':\n new_data = request.POST.copy()\n if new_data['delete_regtaller'] == 'Yes':\n regtaller.delete()\n return HttpResponseRedirect(reverse('contacts_regtaller_list'))\n else:\n return HttpResponseRedirect(regtaller.get_absolute_url())\n\n kwvars = {\n 'object': regtaller\n }\n\n return render_to_response(template, kwvars, RequestContext(request))","def test_delete__DeleteForm__4(address_book, browser, role):\n browser.login(role)\n browser.assert_forbidden(browser.SEARCH_DELETE_URL)","def delete(self, request, pk):\n employee = EmployeeDetail.objects.get(pk=pk)\n employee.delete()\n return Response(\n data=' Entry deleted',\n status=status.HTTP_400_BAD_REQUEST\n )","def customer_delete(request, slug,id):\n \n company =get_object_or_404(Company,slug=slug)\n edit = validate_user_company_access_or_redirect(request,company)\n\n if request.method == 'POST':\n return HttpResponseRedirect('/company/'+str(slug))\n else: \n #verifies if the company exists if not returns a 404 page\n customer_reference = get_object_or_404(Customer, id=id,company=company)\n\n #deletes the view and redirects to the page.\n customer_reference.delete()\n return HttpResponseRedirect('/company/'+str(slug))","def del_row(self, row_index):\n ...","def delete(no):\n\n conn = sqlite3.connect(\"person_database.bd\")\n c = conn.cursor()\n\n # delete a record\n c.execute(f\"DELETE from person_info WHERE oid= \" + str(no))\n\n conn.commit()\n conn.close()","def delete():\n click.echo('delete was called.')","def delete(self, source_index):\r\n click_css(self, 'a.delete-button', source_index, require_notification=False)\r\n # Click the confirmation dialog button\r\n click_css(self, 'a.button.action-primary', 0)","def dataGridView_UserDeletedRow(self, send, eventArgs):\r\n name = eventArgs.Row.Cells[0].Value\r\n Application.DeleteObj(name)","def delete_entry(self, scenario_info):\n sql = self.delete(\"id\")\n self.cur.execute(sql, (scenario_info[\"id\"],))","def delete_address(address_id: int):\n try:\n current_app.logger.info(\"delete address : address_id: %s\", address_id)\n return jsonify({\n \"status\": address_service.delete_address_by_id(address_id)\n }), 200\n except SQLCustomError as error:\n current_app.logger.error(\"fail to delete address : address_id: %s\", address_id)\n return jsonify({\n \"errors\": {\n \"error\": error.__dict__\n }\n }), 400","def get_delete_confirmation_form(self, data):\n self.add_success(data)\n rv = self.get((data[self.id_field], self.delete_url))\n assert not is_404(rv)\n assert in_response(rv, 'Delete {}'.format(data[self.name_field]))\n return rv","def delete_button(self):\n self.pop_up_del = Toplevel(master)\n self.pop_up_del.geometry(\"500x50\")\n\n self.del_label = Label(self.pop_up_del, text=\"Are you sure you want to delete this item?\", font=\"roboto 12\")\n self.del_label.place(relx=0.5, rely=0.01, anchor=\"n\")\n\n self.del_button = Button(self.pop_up_del, text=\"DELETE\", command=self.delete_item)\n self.del_button.place(relx=0.4, rely=0.5, anchor=\"n\")\n\n self.keep_button = Button(self.pop_up_del, text=\"CANCEL\", command=self.close_1)\n self.keep_button.place(relx=0.6, rely=0.5, anchor=\"n\")","def test_delete__DeleteForm__1(search_data, UserFactory, browser):\n address_book = search_data\n # Create a user -- the person of a user cannot be deleted using this search\n # result handler.\n UserFactory(address_book, u'Ben', u'Utzer', u'ben@example.com',\n u'12345678', [], keywords=[u'church'])\n browser.login('mgr')\n browser.keyword_search('church')\n # Only the selected persons get deleted. Deselected persons will not:\n browser.getControl(name='persons:list').getControl(\n value=\"Person-2\").selected = False # This this the person named \"Koch\"\n browser.getControl('Apply on selected persons').displayValue = [\n 'Delete']\n browser.getControl(name='form.buttons.apply').click()\n # The number of persons for deletion is shown on the question screen:\n # (There are 3 persons with the church keyword in the fixture, one got\n # deselected but there is additionally a newly created user.\n assert ['3'] == browser.etree.xpath(\n '//span[@id=\"form-widgets-count\"]/text()')\n assert ('You are not able to delete a person who is referenced.'\n in browser.contents)\n assert browser.SEARCH_DELETE_URL == browser.url\n browser.getControl('Yes, delete').click()\n assert 'Selected persons deleted: 2' == browser.message\n assert browser.PERSONS_LIST_URL == browser.url\n # Only the two non-users got deleted:\n assert 'Koch' in browser.contents\n assert 'Utzer' in browser.contents\n assert 'Liebig' not in browser.contents\n assert 'Velleuer' not in browser.contents","def test_delete__DeleteForm__3(search_data, browser, role):\n browser.login(role)\n browser.keyword_search('church')\n # There is no delete option which can be applied:\n assert ([\n 'XLS export main (Exports person data and main addresses resp. '\n 'phone numbers.)',\n 'XLS export complete (Exports person data and all addresses resp. '\n 'phone numbers.)',\n 'E-Mail (Creates a link to send e-mails.)',\n 'Names (Comma separated list of person names.)',\n 'Checklist (List of person names with check-boxes.)',\n \"iCalendar export birthday (Export person's birthdays as \"\n \".ics file.)\",\n 'Birthday list (Person names sorted by birthday.)',\n ] == browser.getControl('Apply on selected persons').displayOptions)\n browser.assert_forbidden(browser.SEARCH_DELETE_URL)","def delete_place(place_id):\n place_db = mongo.db.places.find_one_or_404({'_id': ObjectId(place_id)})\n if request.method == 'GET':\n form = ConfirmDelete(data=place_db)\n return render_template('delete_restaurant.html', title=\"Delete Restaurant\", form=form)\n form = ConfirmDelete(request.form)\n if form.validate_on_submit():\n places_db = mongo.db.places\n places_db.delete_one({\n '_id': ObjectId(place_id),\n })\n return redirect(url_for('home'))\n return render_template('delete_restaurant.html', place=place_db, form=form)","def open_download_delete_file(self):\n self._unable_open_option()\n self._tap_on_confirm_button(yes=True, msg=\"Delete button\")\n self._tap_on_confirm_button(yes=True, msg=\"Confirm Delete button\")","def on_pushButton_delete_clicked(self):\n row = self.tableWidget.currentIndex().row()\n # 找到对于行的第一项（XX编码项）\n xxbm = unicode(self.tableWidget.takeItem(row, 0).text())\n self.tableWidget.setItem(row, 0, QTableWidgetItem(xxbm))\n content = unicode(self.comboBox.currentText())\n if content == \"职称表\":\n result_signal = self.sql_client.delete_zc_info(xxbm)\n elif content == \"文化表\":\n result_signal = self.sql_client.delete_wh_info(xxbm)\n else:\n result_signal = self.sql_client.delete_bm_info(xxbm)\n\n QMessageBox.information(self, 'Message', \"删除成功！\", QMessageBox.Yes)\n self.tableWidget.removeRow(row)","def delete_deals(self):\n if not self._parent.connected():\n return\n selected = self.deals_view.get_selected_rows()\n if len(selected) > 0:\n self._parent.model.taremove_deal(map(lambda dl: dl[0], selected))\n self._parent.call_update_callback()","def test_destroy_address(self):\n address = ProfileAddressFactory.create(city=\"city_destroyed\")\n \n url = reverse('v1:addresses-detail', args=[1, address.id])\n self.assertEqual(len(ProfileAddress.objects.all()), 1)\n\n # Check Anonymous User should return 403\n response = self.client.delete(url, format='json')\n self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)\n\n # Profile Owner User\n self.client.credentials(Authorization='Bearer ' + 'regularusertoken')\n response = self.client.delete(url, format='json')\n self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT)\n \n self.assertEqual(len(ProfileAddress.objects.all()), 0)","def delete(request):\n return render(request, 'modify.html')","def delete_actor(self):\n\t\tif(self.ui.lista_act.currentItem()):\n\t\t\tmsgBox = QtGui.QMessageBox.question(self, \"Borrar registro\",\"¿Estas seguro de eliminar esta columna?\",\n\t\t\t\t\t\t\t\t\t\t\t\tQtGui.QMessageBox.No | QtGui.QMessageBox.Yes)\n\t\t\tif msgBox == QtGui.QMessageBox.Yes:\n\t\t\t\tname = self.ui.lista_act.currentItem().text()\n\t\t\t\tcontroller.delete_actor(name)\n\t\t\t\tself.load_data()\n\t\t\telse:\n\t\t\t\treturn False\n\t\telse:\n\t\t\terrorMessageBox = QtGui.QMessageBox.warning(self,\"Error\",\"Debe seleccionar un elemento\")\n\t\t\treturn False","def delete_entry(entry):\n\n # Llama a view_entries despues de haber añadido nueva funcionalidad\n\n response = input(\"Estás seguro? [yN]\").lower()\n\n if response == 'y':\n entry.delete_instance()\n print('Entrada borrada.')","def delete_rows(self, table_model, row, count):\n self.undostack.push(DeleteRowCommand(table_model, row, table_model.get_rows(row, count=count)))","def deleteJournal(self):\n\n x = self.tableWidget_journals.currentRow()\n journalName = self.journals[x]['name']\n #print((\"Delete row: \" + str(x)))\n Dialog_confirmDelete = QtGui.QDialog()\n ui = Ui_Dialog_confirmDelete(self.journals[x]['name'])\n ui.setupUi(Dialog_confirmDelete)\n ok = Dialog_confirmDelete.exec_()\n\n if ok:\n cur = self.settings['conn'].cursor()\n cur.execute(\"delete from journal where name = ?\", [journalName])\n self.settings['conn'].commit()\n for item in self.journals:\n if item['name'] == journalName:\n self.journals.remove(item)\n self.tableWidget_journals.removeRow(x)\n self.log += \"Journal \" + journalName + \" deleted\\n\"","def delete(self, _id):","def delete_row(self, pos):\n del self._grid[pos]","def office_delete(request, slug,id):\n \n company =get_object_or_404(Company,slug=slug)\n edit = validate_user_company_access_or_redirect(request,company)\n\n if request.method == 'POST':\n return HttpResponseRedirect('/company/'+str(slug))\n else: \n #verifies if the company exists if not returns a 404 page\n office_reference = get_object_or_404(Office, id=id,company=company)\n\n #deletes the view and redirects to the page.\n office_reference.delete()\n return HttpResponseRedirect('/company/'+str(slug))","def delete_entry():\n u_id = request.args(0) or redirect(URL('moderation', 'new_entries'))\n db(db.lioli_main.unique_id == u_id).delete()\n redirect(URL('new_entries'))\n return dict()","def deleteRecord(self):\n selectedData = self.controller.chooseRecord(\"Enter the record number: \") - 1\n if selectedData >= (len(self.dto.getRecord())):\n print(\"Please choose number within the number of records.\")\n else:\n print(self.dto.getRecord()[selectedData].__dict__)\n if self.controller.confirmMsg(\"Do you want to delete this data? (y/n): \") == \"y\":\n self.dto.getRecord().remove(self.dto.getRecord()[selectedData])\n print(\"Record deleted.\")","def taskdetail_delete(td):\n return IMPL.taskdetail_delete(td)","def delete_view(self, request, object_id):\r\n obj = self.get_object(unquote(object_id))\r\n obj.delete()\r\n return HttpResponse(\"Deleted\")","def admindelete(object, id):\n db = get_db()\n execute_str = 'DELETE FROM ' + object + ' WHERE id = ' + str(id)\n db.execute(execute_str)\n db.commit()\n return redirect(url_for(\"adminview\", object=object))","def test_delete__DeleteForm__2(search_data, browser):\n browser.login('mgr')\n browser.keyword_search('church', 'Delete')\n # Seleting the `cancel` button leads to the person list without deleting\n # anybody:\n browser.getControl('No, cancel').click()\n assert 'Deletion canceled.' == browser.message\n assert browser.PERSONS_LIST_URL == browser.url\n assert 'Koch' in browser.contents\n assert 'Liebig' in browser.contents\n assert 'Velleuer' in browser.contents","def delete_event(self,widget=None):\n self.on_device_dialog_cancel_clicked()\n return True","def competitors_delete(request, slug,id):\n company =get_object_or_404(Company,slug=slug)\n edit = validate_user_company_access_or_redirect(request,company)\n\n if request.method == 'POST':\n return HttpResponseRedirect('/company/'+str(slug))\n else: \n #verifies if the company exists if not returns a 404 page\n competitors_reference = get_object_or_404(Competitors, id=id,company=company)\n\n #deletes the view and redirects to the page.\n competitors_reference.delete()\n return HttpResponseRedirect('/company/'+str(slug))","def delete():\n return render_template('layout.html')","def delete_row(self, identifier, rowid, datastore):\n # Get dataset. Raise exception if dataset is unknown.\n dataset = datastore.get_dataset(identifier)\n if dataset is None:\n raise ValueError(\"unknown dataset '{}'\".format(identifier))\n # Delete the row at the given index position\n df = vizual.delete_rows(dataset.to_dataframe(), rowids=[rowid])\n # Store updated dataset to get new identifier.\n ds = datastore.update_dataset(\n origin=dataset,\n df=df,\n annotations=dataset.annotations.filter(rows=list(df.index))\n )\n return VizualApiResult(ds)","def delete(self, *args, **kwargs):\n self.request(\"delete\", *args, **kwargs)","def get_absolute_url(self):\n return reverse('order_items:order_item_delete', args=[str(self.id)])","def delete_loc(lid):\r\n\r\n db = get_db()\r\n b_id = session.get(\"user_id\")\r\n db.execute(\r\n \"DELETE FROM location WHERE location_id = ? AND for_business = ?\", (lid, b_id,)\r\n )\r\n db.commit()\r\n db.execute(\"DELETE FROM warehouse WHERE loc_id = ? AND b_id = ?\", (lid, b_id,))\r\n db.commit()\r\n return redirect(url_for(\"main.locations\"))","def handle_delete(self, peer, row, col):\n if peer.hasSelection():\n \n peer.deleteSelection()\n \n else:\n\n self.delete(\"{}.{}\".format(row, col))\n \n # peer.move(row, col)\n\n return","def delete_location(self, location_id):","def __editDelete(self):\n if QApplication.focusWidget() == e5App().getObject(\"Shell\"):\n e5App().getObject(\"Shell\").clear()\n else:\n self.activeWindow().clear()","def do_deluser(self, line):\n\t\tif isinstance(self.cl, Book):\n\t\t\ttry:\n\t\t\t\tself.cl.del_contact(line)\n\t\t\texcept ValueError:\n\t\t\t\tprint(\"Wrong syntax! Type 'help delete'\")\n\t\telse:\n\t\t\tprint(\"To delete contacts you need to open or create a book.\")","def mock_datatable_delete_row(self, request):\n\n data = self.mock_success_delete\n\n return create_response(request,\n status_code=200,\n content=b(dumps(data)))","def delete_donation():\n print()\n print('Welcome to the Delete a a Donor Donation Menu')\n print()\n donor_name = get_name_input()\n single_donor_print(donor_name)\n print('See the donation you want to delete in the report? Follow the prompts to enter donation to delete')\n donation_delete = check_number_input()\n print()\n delete_donation_from_db(donor_name,donation_delete)\n print('Donation has been deleted. See report below for verification')\n single_donor_print(donor_name)","def delete(self):\n if self.id_:\n GsSession.current._delete(f'{API}/{self.id_}', request_headers=DATAGRID_HEADERS)\n else:\n raise MqValueError('DataGrid has not been persisted.')","def delete_entry(id):\n if not session.get('logged_in'):\n abort(401)\n\n db = get_db()\n cur = db.execute('select id, title from entries where id = ?',\n [id.strip()])\n entries = cur.fetchall()\n title = entries[0]['title']\n db = get_db()\n db.execute('delete from entries where id = ?', [id.strip()])\n db.commit()\n flash('Recipe, ' + escape(title) + ', has been deleted', 'success')\n return redirect(url_for('show_entries'))","def delete(self, request , pk=None): \n return Response({'message':'DELETE'})","def delete_row(self):\n return exclusions.closed()","def view_delete():\n\n return jsonify(\n get_dict(\"url\", \"args\", \"form\", \"data\", \"origin\", \"headers\", \"files\", \"json\")\n )","def delete():\n if request.method == 'POST':\n table_name = request.form['table']\n table = get_table_by_name(table_name)\n if 'id' not in request.form:\n user_data = get_data_from_table(table)\n\n return render_template(\n 'delete.html', table_name=table_name, user_data=user_data\n )\n\n if table is None:\n return 'Table not chosen?'\n\n success, reason = utils.delete_user(request.form['id'], table_name)\n\n if not success:\n return f'Error occurred trying to delete - {reason}'\n\n log(\n f\"<code>{current_user.name}</code> has deleted <code>{request.form['id']}</code> from {table_name}\"\n )\n return (\n f\"<code>{request.form['id']}</code> has been deleted from from {table_name}\"\n )\n return render_template('events.html', events=get_accessible_tables())","def del_history_to_address(address):\n \n result = delete_transaction_observation_to_address(address)\n\n # if successfully deleted from observation list, return a plain 200\n if \"error\" in result:\n return make_response(jsonify(build_error(result[\"error\"])), result[\"status\"])\n else:\n return \"\"","def test_delete_shipping_address(self):\n self.cim.delete_shipping_address(\n customer_profile_id=u\"123\",\n customer_address_id=u\"543\"\n )","def delete_command():\n global selected_tuple\n backend.delete(selected_tuple[0])","def delete(self, *args, **kwargs):\n pass","def delete(self, *args, **kwargs):\n pass","def delete(self):\n ...","def remove_row(self, row_id):","def delete_model(self, request, obj):\n obj.post.comNumDrease()\n obj.delete()","def click_delete_button(self):\n self.number_of_rows = self.get_inbound_statement_grid_number_of_rows()\n self.click_element(self.delete_button_locator, True)\n try:\n self.wait().until(EC.visibility_of_element_located(self.statement_delete_confirmation_locator), 'statement delete confirmation locator not found before specified time out')\n ok_button_element = self.wait().until(EC.element_to_be_clickable(self.ok_button_locator), 'ok button locator not found before specified time out')\n self.wait_for_ajax_spinner_load()\n ok_button_element.click()\n self.wait_for_ajax_spinner_load()\n self.wait().until(EC.visibility_of_element_located(self.statement_delete_success_message_locator), 'statement delete success message locator not found before specified time out')\n ok_button_element = self.wait().until(EC.element_to_be_clickable(self.ok_button_locator), 'ok button locator not found before specified time out')\n self.wait_for_ajax_spinner_load()\n ok_button_element.click()\n except:\n raise\n self.wait_for_ajax_spinner_load()","def _delete(self, pk, user=None):\n request = self.factory.delete(self.detail_url(pk), format='json')\n force_authenticate(request, user)\n resp = self.detail_view(request, pk=pk)\n resp.render()\n return resp","def transaction_delete(request, transaction_id, model_class=Transaction, template_name='budget/transactions/delete.html'):\n transaction = get_object_or_404(Transaction.active.all(), pk=transaction_id)\n if request.POST:\n if request.POST.get('confirmed'):\n transaction.delete()\n return HttpResponseRedirect(reverse('budget_transaction_list'))\n return render_to_response(template_name, {\n 'transaction': transaction,\n }, context_instance=RequestContext(request))","def cmd_delete_job():\r\n id = request.form.get('id', \"\")\r\n confirm = request.form.get(\"confirm\", \"\")\r\n if confirm != \"DELETE\":\r\n flash(f\"Contact '{id}' NOT deleted. Please enter DELETE in the confirm field.\")\r\n return redirect(url_for('main.jobs'))\r\n \r\n index = get_job_by_id(id)\r\n Job.query.filter(Job.id == id).delete()\r\n db.session.commit()\r\n\r\n\r\n if index != None:\r\n flash(f\"Job '{id}' was succesfully deleted!\")\r\n return redirect(url_for('main.jobs'))\r\n else:\r\n flash(f\"Job '{id}' was not found\")\r\n return redirect(url_for('main.jobs'))","def delete_people(pid):\n db_conn = get_connection()\n cur = db_conn.cursor()\n _sql = \"\"\"SELECT \n id,firstname,lastname,address,country\n FROM peoples where id = ?\"\"\"\n cur.execute(_sql, (pid,))\n record = cur.fetchone()\n\n if record:\n delete_sql = \"\"\"DELETE FROM peoples where id = ?\"\"\"\n cur.execute(delete_sql, (pid,))\n db_conn.commit()\n flash(\"Record with id {} deleted successfully!!!\".format(pid))\n return redirect(url_for('list_people'))\n\n else:\n flash(\"Sorry! No user with id {} found to delete\".format(pid))\n return redirect(url_for(\"list_people\"))","def __render_deletion_cascade(self, r, method, row):\n \n r.line('// %s' % self.get_debug_name())\n self.local_index.render_deletion(r,\n self.project_key_foreign_to_local(row))\n return","def showEditContact(self):","def deleteRow(self):\r\n self.conn = connect('database.sqlite')\r\n self.cur = self.conn.cursor()\r\n self.cur.execute('''\r\n DELETE FROM Clients WHERE id=(SELECT MAX(id) FROM Clients)\r\n ''')\r\n self.conn.commit()\r\n self.cur.close()","def help_delete(self):\n print(DELETE)","def on_deleteButton_clicked(self):\n for itm in self.historyList.selectedItems():\n ditm = self.historyList.takeItem(self.historyList.row(itm))\n del ditm\n self.historyList.scrollToItem(self.historyList.currentItem())\n self.historyList.setFocus()","def delete_entry(self, id, **args):\n args.update(id=id)\n return self.fetch(\"/entry/delete\", post_args=args)","def del_accomment(request, pk):\n\n comment = get_object_or_404(ActorComment, pk=pk)\n comment.delete()\n actor = comment.actor\n url = '../../' + str(comment.actor.pk)\n return redirect(url)","async def delete_one(self, where):\n\n pass","def delete_args():\n return {\"id\": fields.UUID(required=True, location=\"view_args\")}","def management_delete(request, slug,id):\n \n company =get_object_or_404(Company,slug=slug)\n edit = validate_user_company_access_or_redirect(request,company)\n\n if request.method == 'POST':\n return HttpResponseRedirect('/company/'+str(slug))\n else: \n #verifies if the company exists if not returns a 404 page\n management_reference = get_object_or_404(Management, id=id,company=company)\n\n #deletes the view and redirects to the page.\n management_reference.delete()\n return HttpResponseRedirect('/company/'+str(slug))","def award_delete(request, slug,id):\n \n company =get_object_or_404(Company,slug=slug)\n edit = validate_user_company_access_or_redirect(request,company)\n\n if request.method == 'POST':\n return HttpResponseRedirect('/company/'+str(slug))\n else: \n #verifies if the company exists if not returns a 404 page\n award_reference = get_object_or_404(Award, id=id,company=company)\n\n #deletes the view and redirects to the page.\n award_reference.delete()\n return HttpResponseRedirect('/company/'+str(slug))","def del_history_from_address(address):\n \n result = delete_transaction_observation_from_address(address)\n\n # if successfully deleted from observation list, return a plain 200\n if \"error\" in result:\n return make_response(jsonify(build_error(result[\"error\"])), result[\"status\"])\n else:\n return \"\"","def delete(id):\n get_autor(id)\n try:\n db.insert_bd('DELETE FROM autor WHERE id = %d' % id)\n return redirect(url_for('autor.index'))\n except:\n return render_template('404.html')","def create_delete_entry(columns: list, win, x: int, y: int):\n global choice, search_delete_enter\n window_for_search = Frame(win)\n window_for_search.place(x=x, y=y)\n search_delete_enter = ttk.Combobox(window_for_search, values=columns, height=3)\n search_delete_enter.set(u'Выбор параметра')\n search_delete_enter.grid(column=0, row=0)\n choice = search_delete_enter\n return choice","def post_delete(self, *args, **kw):\n #obtenemos el id de la fase para hacer el filtrado despues de la redireccion\n item_to_del = DBSession.query(Item).filter_by(id_item=args[0]).one()\n fid = item_to_del.id_fase_fk\n pks = self.provider.get_primary_fields(self.model)\n d = {}\n for i, arg in enumerate(args):\n d[pks[i]] = arg\n self.provider.delete(self.model, d)\n\n path = './' + '../' * (len(pks) - 1) + '?fid=' + str(fid)\n\n redirect(path)","def delete():\n id_num = int(input('Enter the ID number of the item you wish to delete\\n'))\n db_actions.remove(id_num)","def funding_delete(request, slug,id):\n \n company =get_object_or_404(Company,slug=slug)\n edit = validate_user_company_access_or_redirect(request,company)\n\n if request.method == 'POST':\n return HttpResponseRedirect('/company/'+str(slug))\n else: \n #verifies if the company exists if not returns a 404 page\n funding_reference = get_object_or_404(Funding, id=id,company=company)\n\n #deletes the view and redirects to the page.\n funding_reference.delete()\n return HttpResponseRedirect('/company/'+str(slug))","def view_delete(\n request: HttpRequest,\n pk: Optional[int] = None,\n workflow: Optional[Workflow] = None,\n view: Optional[View] = None,\n) -> JsonResponse:\n if request.method == 'POST':\n # Log the event\n Log.objects.register(\n request.user,\n Log.VIEW_DELETE,\n view.workflow,\n {\n 'id': view.id,\n 'name': view.name,\n 'workflow_name': view.workflow.name,\n 'workflow_id': view.workflow.id})\n\n # Perform the delete operation\n view.delete()\n\n # In this case, the form is valid anyway\n return JsonResponse({'html_redirect': reverse('table:view_index')})\n\n return JsonResponse({\n 'html_form': render_to_string(\n 'table/includes/partial_view_delete.html',\n {'view': view},\n request=request),\n })","def post_delete(self, *args, **kw):\n id_atributo = int(args[0])\n transaction.begin()\n attr = AtributosPorTipoItem.por_id(id_atributo)\n DBSession.delete(attr)\n transaction.commit()\n flash(\"Atributo Eliminado\")\n redirect(\"./\")"],"string":"[\n \"def delete_address(self, address: dict) -> None:\\n row = self.addresses_list.surface_address_row(address)\\n\\n row.open_kebab_menu()\\n row.kebab_menu.delete_address()\\n\\n self.deletion_modal.confirm_address_deletion()\",\n \"def DeleteRow(self, entry):\\n for a_link in entry.link:\\n if a_link.rel == 'edit':\\n return self.Delete(a_link.href)\",\n \"def open_edit_address(self, address: dict) -> None:\\n row = self.addresses_list.surface_address_row(address)\\n\\n row.open_kebab_menu()\\n row.kebab_menu.edit_address()\",\n \"def _open_delete_dialog():\\n import hiero\\n hiero.ui.findMenuAction('foundry.project.clearlocalcacheNuke').trigger()\",\n \"def delete_record():\\n global data_base, table, choice, res, confirmation, output1, place_for_enter, choice_row\\n choice_row = choice.get()\\n sqlite3_simple_delete_record(data_base, table, choice_row, res)\\n output1.delete(1.0, END)\\n confirmation.after(1, confirmation.destroy)\\n place_for_enter.delete(0, END)\",\n \"def delete(self, request, *args, **kwargs):\\n # validate address id and get object\\n instance = self.get_object()\\n\\n # get last transaction save point id\\n sid = transaction.savepoint()\\n\\n try:\\n # soft delete address\\n instance.delete_address(request.user)\\n except Exception as err:\\n logger.error(\\\"Unexpected error occurred : %s.\\\", err)\\n # roll back transaction if any exception occur while delete address\\n transaction.savepoint_rollback(sid)\\n return APIResponse({\\\"message\\\": err.args[0]}, HTTP_400_BAD_REQUEST)\\n\\n return APIResponse({\\\"message\\\": DELETE_ADDRESS}, HTTP_OK)\",\n \"def delete_employee():\\r\\n id = request.args.get('id', \\\"\\\")\\r\\n return render_template(\\\"delete_employee.html\\\", id=id)\",\n \"def onAddRecord(self, event):\\r\\n dbItem = getattr(db, self.modelName) ()\\r\\n dlg = dPerson.Person(self.view, dbItem).view\\r\\n dlg.ShowModal()\\r\\n dlg.Destroy()\",\n \"def delete():\\n add_book_tk = DeleteBookDialog()\\n entries_args = [\\n (\\\"Book ID : \\\", 0.5),\\n ]\\n add_book_tk.create_components(entries_args)\\n add_book_tk.mainloop()\",\n \"def delete_row(self, pk):\\n return self.execute(self.commands.delete_row(self.name, self.primary_key_column, pk))\",\n \"def delete_item(self):\\n self.df_user.drop(self.index_select_number, inplace=True)\\n self.df_user.to_csv(\\\"user_items.csv\\\", index=False)\\n self.update_treeview()\\n self.changing_item_label.config(text=\\\"Please double click on the item you want to edit.\\\")\\n self.delete_but.destroy()\\n self.serv_drop.destroy()\\n self.serv_but.destroy()\\n self.pop_up_del.destroy()\",\n \"def test_client_address_delete(self):\\n pass\",\n \"def on_delete_event(self, widget, event):\\n\\n self.hide()\\n return True\",\n \"def click_map_line_items_delete_button(self):\\n self.click_element(self.map_line_items_delete_button_locator, True, True)\\n try:\\n self.wait().until(EC.visibility_of_element_located(self.delete_mappings_confirmation_message_locator), 'delete mappings confirmation message locator not found before specified time out')\\n self.click_element(self.ok_button_locator)\\n except:\\n raise\",\n \"def cmd_delete_employee():\\r\\n id = request.form.get('id', \\\"\\\")\\r\\n confirm = request.form.get(\\\"confirm\\\", \\\"\\\")\\r\\n if confirm != \\\"DELETE\\\":\\r\\n flash(f\\\"Contact '{id}' NOT deleted. Please enter DELETE in the confirm field.\\\")\\r\\n return redirect(url_for('main.jobs'))\\r\\n \\r\\n index = get_employee_by_id(id)\\r\\n User.query.filter(User.id == id).delete()\\r\\n db.session.commit()\\r\\n\\r\\n\\r\\n if index != None:\\r\\n flash(f\\\"Employee '{id}' was succesfully deleted!\\\")\\r\\n return redirect(url_for('main.employees'))\\r\\n else:\\r\\n flash(f\\\"Employee '{id}' was not found\\\")\\r\\n return redirect(url_for('main.employees'))\",\n \"def delete(request, id, template='contacts/regtaller/delete.html'):\\n if not request.user.is_authenticated():\\n return HttpResponseRedirect('/login/?next=%s' % request.path)\\n\\n user = request.user\\n if not user.has_perm('delete_regtaller'):\\n return HttpResponseForbidden()\\n\\n try:\\n regtaller = TallerRegistration.objects.get(id=id)\\n except TallerRegistration.DoesNotExist:\\n raise Http404\\n\\n if request.method == 'POST':\\n new_data = request.POST.copy()\\n if new_data['delete_regtaller'] == 'Yes':\\n regtaller.delete()\\n return HttpResponseRedirect(reverse('contacts_regtaller_list'))\\n else:\\n return HttpResponseRedirect(regtaller.get_absolute_url())\\n\\n kwvars = {\\n 'object': regtaller\\n }\\n\\n return render_to_response(template, kwvars, RequestContext(request))\",\n \"def test_delete__DeleteForm__4(address_book, browser, role):\\n browser.login(role)\\n browser.assert_forbidden(browser.SEARCH_DELETE_URL)\",\n \"def delete(self, request, pk):\\n employee = EmployeeDetail.objects.get(pk=pk)\\n employee.delete()\\n return Response(\\n data=' Entry deleted',\\n status=status.HTTP_400_BAD_REQUEST\\n )\",\n \"def customer_delete(request, slug,id):\\n \\n company =get_object_or_404(Company,slug=slug)\\n edit = validate_user_company_access_or_redirect(request,company)\\n\\n if request.method == 'POST':\\n return HttpResponseRedirect('/company/'+str(slug))\\n else: \\n #verifies if the company exists if not returns a 404 page\\n customer_reference = get_object_or_404(Customer, id=id,company=company)\\n\\n #deletes the view and redirects to the page.\\n customer_reference.delete()\\n return HttpResponseRedirect('/company/'+str(slug))\",\n \"def del_row(self, row_index):\\n ...\",\n \"def delete(no):\\n\\n conn = sqlite3.connect(\\\"person_database.bd\\\")\\n c = conn.cursor()\\n\\n # delete a record\\n c.execute(f\\\"DELETE from person_info WHERE oid= \\\" + str(no))\\n\\n conn.commit()\\n conn.close()\",\n \"def delete():\\n click.echo('delete was called.')\",\n \"def delete(self, source_index):\\r\\n click_css(self, 'a.delete-button', source_index, require_notification=False)\\r\\n # Click the confirmation dialog button\\r\\n click_css(self, 'a.button.action-primary', 0)\",\n \"def dataGridView_UserDeletedRow(self, send, eventArgs):\\r\\n name = eventArgs.Row.Cells[0].Value\\r\\n Application.DeleteObj(name)\",\n \"def delete_entry(self, scenario_info):\\n sql = self.delete(\\\"id\\\")\\n self.cur.execute(sql, (scenario_info[\\\"id\\\"],))\",\n \"def delete_address(address_id: int):\\n try:\\n current_app.logger.info(\\\"delete address : address_id: %s\\\", address_id)\\n return jsonify({\\n \\\"status\\\": address_service.delete_address_by_id(address_id)\\n }), 200\\n except SQLCustomError as error:\\n current_app.logger.error(\\\"fail to delete address : address_id: %s\\\", address_id)\\n return jsonify({\\n \\\"errors\\\": {\\n \\\"error\\\": error.__dict__\\n }\\n }), 400\",\n \"def get_delete_confirmation_form(self, data):\\n self.add_success(data)\\n rv = self.get((data[self.id_field], self.delete_url))\\n assert not is_404(rv)\\n assert in_response(rv, 'Delete {}'.format(data[self.name_field]))\\n return rv\",\n \"def delete_button(self):\\n self.pop_up_del = Toplevel(master)\\n self.pop_up_del.geometry(\\\"500x50\\\")\\n\\n self.del_label = Label(self.pop_up_del, text=\\\"Are you sure you want to delete this item?\\\", font=\\\"roboto 12\\\")\\n self.del_label.place(relx=0.5, rely=0.01, anchor=\\\"n\\\")\\n\\n self.del_button = Button(self.pop_up_del, text=\\\"DELETE\\\", command=self.delete_item)\\n self.del_button.place(relx=0.4, rely=0.5, anchor=\\\"n\\\")\\n\\n self.keep_button = Button(self.pop_up_del, text=\\\"CANCEL\\\", command=self.close_1)\\n self.keep_button.place(relx=0.6, rely=0.5, anchor=\\\"n\\\")\",\n \"def test_delete__DeleteForm__1(search_data, UserFactory, browser):\\n address_book = search_data\\n # Create a user -- the person of a user cannot be deleted using this search\\n # result handler.\\n UserFactory(address_book, u'Ben', u'Utzer', u'ben@example.com',\\n u'12345678', [], keywords=[u'church'])\\n browser.login('mgr')\\n browser.keyword_search('church')\\n # Only the selected persons get deleted. Deselected persons will not:\\n browser.getControl(name='persons:list').getControl(\\n value=\\\"Person-2\\\").selected = False # This this the person named \\\"Koch\\\"\\n browser.getControl('Apply on selected persons').displayValue = [\\n 'Delete']\\n browser.getControl(name='form.buttons.apply').click()\\n # The number of persons for deletion is shown on the question screen:\\n # (There are 3 persons with the church keyword in the fixture, one got\\n # deselected but there is additionally a newly created user.\\n assert ['3'] == browser.etree.xpath(\\n '//span[@id=\\\"form-widgets-count\\\"]/text()')\\n assert ('You are not able to delete a person who is referenced.'\\n in browser.contents)\\n assert browser.SEARCH_DELETE_URL == browser.url\\n browser.getControl('Yes, delete').click()\\n assert 'Selected persons deleted: 2' == browser.message\\n assert browser.PERSONS_LIST_URL == browser.url\\n # Only the two non-users got deleted:\\n assert 'Koch' in browser.contents\\n assert 'Utzer' in browser.contents\\n assert 'Liebig' not in browser.contents\\n assert 'Velleuer' not in browser.contents\",\n \"def test_delete__DeleteForm__3(search_data, browser, role):\\n browser.login(role)\\n browser.keyword_search('church')\\n # There is no delete option which can be applied:\\n assert ([\\n 'XLS export main (Exports person data and main addresses resp. '\\n 'phone numbers.)',\\n 'XLS export complete (Exports person data and all addresses resp. '\\n 'phone numbers.)',\\n 'E-Mail (Creates a link to send e-mails.)',\\n 'Names (Comma separated list of person names.)',\\n 'Checklist (List of person names with check-boxes.)',\\n \\\"iCalendar export birthday (Export person's birthdays as \\\"\\n \\\".ics file.)\\\",\\n 'Birthday list (Person names sorted by birthday.)',\\n ] == browser.getControl('Apply on selected persons').displayOptions)\\n browser.assert_forbidden(browser.SEARCH_DELETE_URL)\",\n \"def delete_place(place_id):\\n place_db = mongo.db.places.find_one_or_404({'_id': ObjectId(place_id)})\\n if request.method == 'GET':\\n form = ConfirmDelete(data=place_db)\\n return render_template('delete_restaurant.html', title=\\\"Delete Restaurant\\\", form=form)\\n form = ConfirmDelete(request.form)\\n if form.validate_on_submit():\\n places_db = mongo.db.places\\n places_db.delete_one({\\n '_id': ObjectId(place_id),\\n })\\n return redirect(url_for('home'))\\n return render_template('delete_restaurant.html', place=place_db, form=form)\",\n \"def open_download_delete_file(self):\\n self._unable_open_option()\\n self._tap_on_confirm_button(yes=True, msg=\\\"Delete button\\\")\\n self._tap_on_confirm_button(yes=True, msg=\\\"Confirm Delete button\\\")\",\n \"def on_pushButton_delete_clicked(self):\\n row = self.tableWidget.currentIndex().row()\\n # 找到对于行的第一项（XX编码项）\\n xxbm = unicode(self.tableWidget.takeItem(row, 0).text())\\n self.tableWidget.setItem(row, 0, QTableWidgetItem(xxbm))\\n content = unicode(self.comboBox.currentText())\\n if content == \\\"职称表\\\":\\n result_signal = self.sql_client.delete_zc_info(xxbm)\\n elif content == \\\"文化表\\\":\\n result_signal = self.sql_client.delete_wh_info(xxbm)\\n else:\\n result_signal = self.sql_client.delete_bm_info(xxbm)\\n\\n QMessageBox.information(self, 'Message', \\\"删除成功！\\\", QMessageBox.Yes)\\n self.tableWidget.removeRow(row)\",\n \"def delete_deals(self):\\n if not self._parent.connected():\\n return\\n selected = self.deals_view.get_selected_rows()\\n if len(selected) > 0:\\n self._parent.model.taremove_deal(map(lambda dl: dl[0], selected))\\n self._parent.call_update_callback()\",\n \"def test_destroy_address(self):\\n address = ProfileAddressFactory.create(city=\\\"city_destroyed\\\")\\n \\n url = reverse('v1:addresses-detail', args=[1, address.id])\\n self.assertEqual(len(ProfileAddress.objects.all()), 1)\\n\\n # Check Anonymous User should return 403\\n response = self.client.delete(url, format='json')\\n self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)\\n\\n # Profile Owner User\\n self.client.credentials(Authorization='Bearer ' + 'regularusertoken')\\n response = self.client.delete(url, format='json')\\n self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT)\\n \\n self.assertEqual(len(ProfileAddress.objects.all()), 0)\",\n \"def delete(request):\\n return render(request, 'modify.html')\",\n \"def delete_actor(self):\\n\\t\\tif(self.ui.lista_act.currentItem()):\\n\\t\\t\\tmsgBox = QtGui.QMessageBox.question(self, \\\"Borrar registro\\\",\\\"¿Estas seguro de eliminar esta columna?\\\",\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tQtGui.QMessageBox.No | QtGui.QMessageBox.Yes)\\n\\t\\t\\tif msgBox == QtGui.QMessageBox.Yes:\\n\\t\\t\\t\\tname = self.ui.lista_act.currentItem().text()\\n\\t\\t\\t\\tcontroller.delete_actor(name)\\n\\t\\t\\t\\tself.load_data()\\n\\t\\t\\telse:\\n\\t\\t\\t\\treturn False\\n\\t\\telse:\\n\\t\\t\\terrorMessageBox = QtGui.QMessageBox.warning(self,\\\"Error\\\",\\\"Debe seleccionar un elemento\\\")\\n\\t\\t\\treturn False\",\n \"def delete_entry(entry):\\n\\n # Llama a view_entries despues de haber añadido nueva funcionalidad\\n\\n response = input(\\\"Estás seguro? [yN]\\\").lower()\\n\\n if response == 'y':\\n entry.delete_instance()\\n print('Entrada borrada.')\",\n \"def delete_rows(self, table_model, row, count):\\n self.undostack.push(DeleteRowCommand(table_model, row, table_model.get_rows(row, count=count)))\",\n \"def deleteJournal(self):\\n\\n x = self.tableWidget_journals.currentRow()\\n journalName = self.journals[x]['name']\\n #print((\\\"Delete row: \\\" + str(x)))\\n Dialog_confirmDelete = QtGui.QDialog()\\n ui = Ui_Dialog_confirmDelete(self.journals[x]['name'])\\n ui.setupUi(Dialog_confirmDelete)\\n ok = Dialog_confirmDelete.exec_()\\n\\n if ok:\\n cur = self.settings['conn'].cursor()\\n cur.execute(\\\"delete from journal where name = ?\\\", [journalName])\\n self.settings['conn'].commit()\\n for item in self.journals:\\n if item['name'] == journalName:\\n self.journals.remove(item)\\n self.tableWidget_journals.removeRow(x)\\n self.log += \\\"Journal \\\" + journalName + \\\" deleted\\\\n\\\"\",\n \"def delete(self, _id):\",\n \"def delete_row(self, pos):\\n del self._grid[pos]\",\n \"def office_delete(request, slug,id):\\n \\n company =get_object_or_404(Company,slug=slug)\\n edit = validate_user_company_access_or_redirect(request,company)\\n\\n if request.method == 'POST':\\n return HttpResponseRedirect('/company/'+str(slug))\\n else: \\n #verifies if the company exists if not returns a 404 page\\n office_reference = get_object_or_404(Office, id=id,company=company)\\n\\n #deletes the view and redirects to the page.\\n office_reference.delete()\\n return HttpResponseRedirect('/company/'+str(slug))\",\n \"def delete_entry():\\n u_id = request.args(0) or redirect(URL('moderation', 'new_entries'))\\n db(db.lioli_main.unique_id == u_id).delete()\\n redirect(URL('new_entries'))\\n return dict()\",\n \"def deleteRecord(self):\\n selectedData = self.controller.chooseRecord(\\\"Enter the record number: \\\") - 1\\n if selectedData >= (len(self.dto.getRecord())):\\n print(\\\"Please choose number within the number of records.\\\")\\n else:\\n print(self.dto.getRecord()[selectedData].__dict__)\\n if self.controller.confirmMsg(\\\"Do you want to delete this data? (y/n): \\\") == \\\"y\\\":\\n self.dto.getRecord().remove(self.dto.getRecord()[selectedData])\\n print(\\\"Record deleted.\\\")\",\n \"def taskdetail_delete(td):\\n return IMPL.taskdetail_delete(td)\",\n \"def delete_view(self, request, object_id):\\r\\n obj = self.get_object(unquote(object_id))\\r\\n obj.delete()\\r\\n return HttpResponse(\\\"Deleted\\\")\",\n \"def admindelete(object, id):\\n db = get_db()\\n execute_str = 'DELETE FROM ' + object + ' WHERE id = ' + str(id)\\n db.execute(execute_str)\\n db.commit()\\n return redirect(url_for(\\\"adminview\\\", object=object))\",\n \"def test_delete__DeleteForm__2(search_data, browser):\\n browser.login('mgr')\\n browser.keyword_search('church', 'Delete')\\n # Seleting the `cancel` button leads to the person list without deleting\\n # anybody:\\n browser.getControl('No, cancel').click()\\n assert 'Deletion canceled.' == browser.message\\n assert browser.PERSONS_LIST_URL == browser.url\\n assert 'Koch' in browser.contents\\n assert 'Liebig' in browser.contents\\n assert 'Velleuer' in browser.contents\",\n \"def delete_event(self,widget=None):\\n self.on_device_dialog_cancel_clicked()\\n return True\",\n \"def competitors_delete(request, slug,id):\\n company =get_object_or_404(Company,slug=slug)\\n edit = validate_user_company_access_or_redirect(request,company)\\n\\n if request.method == 'POST':\\n return HttpResponseRedirect('/company/'+str(slug))\\n else: \\n #verifies if the company exists if not returns a 404 page\\n competitors_reference = get_object_or_404(Competitors, id=id,company=company)\\n\\n #deletes the view and redirects to the page.\\n competitors_reference.delete()\\n return HttpResponseRedirect('/company/'+str(slug))\",\n \"def delete():\\n return render_template('layout.html')\",\n \"def delete_row(self, identifier, rowid, datastore):\\n # Get dataset. Raise exception if dataset is unknown.\\n dataset = datastore.get_dataset(identifier)\\n if dataset is None:\\n raise ValueError(\\\"unknown dataset '{}'\\\".format(identifier))\\n # Delete the row at the given index position\\n df = vizual.delete_rows(dataset.to_dataframe(), rowids=[rowid])\\n # Store updated dataset to get new identifier.\\n ds = datastore.update_dataset(\\n origin=dataset,\\n df=df,\\n annotations=dataset.annotations.filter(rows=list(df.index))\\n )\\n return VizualApiResult(ds)\",\n \"def delete(self, *args, **kwargs):\\n self.request(\\\"delete\\\", *args, **kwargs)\",\n \"def get_absolute_url(self):\\n return reverse('order_items:order_item_delete', args=[str(self.id)])\",\n \"def delete_loc(lid):\\r\\n\\r\\n db = get_db()\\r\\n b_id = session.get(\\\"user_id\\\")\\r\\n db.execute(\\r\\n \\\"DELETE FROM location WHERE location_id = ? AND for_business = ?\\\", (lid, b_id,)\\r\\n )\\r\\n db.commit()\\r\\n db.execute(\\\"DELETE FROM warehouse WHERE loc_id = ? AND b_id = ?\\\", (lid, b_id,))\\r\\n db.commit()\\r\\n return redirect(url_for(\\\"main.locations\\\"))\",\n \"def handle_delete(self, peer, row, col):\\n if peer.hasSelection():\\n \\n peer.deleteSelection()\\n \\n else:\\n\\n self.delete(\\\"{}.{}\\\".format(row, col))\\n \\n # peer.move(row, col)\\n\\n return\",\n \"def delete_location(self, location_id):\",\n \"def __editDelete(self):\\n if QApplication.focusWidget() == e5App().getObject(\\\"Shell\\\"):\\n e5App().getObject(\\\"Shell\\\").clear()\\n else:\\n self.activeWindow().clear()\",\n \"def do_deluser(self, line):\\n\\t\\tif isinstance(self.cl, Book):\\n\\t\\t\\ttry:\\n\\t\\t\\t\\tself.cl.del_contact(line)\\n\\t\\t\\texcept ValueError:\\n\\t\\t\\t\\tprint(\\\"Wrong syntax! Type 'help delete'\\\")\\n\\t\\telse:\\n\\t\\t\\tprint(\\\"To delete contacts you need to open or create a book.\\\")\",\n \"def mock_datatable_delete_row(self, request):\\n\\n data = self.mock_success_delete\\n\\n return create_response(request,\\n status_code=200,\\n content=b(dumps(data)))\",\n \"def delete_donation():\\n print()\\n print('Welcome to the Delete a a Donor Donation Menu')\\n print()\\n donor_name = get_name_input()\\n single_donor_print(donor_name)\\n print('See the donation you want to delete in the report? Follow the prompts to enter donation to delete')\\n donation_delete = check_number_input()\\n print()\\n delete_donation_from_db(donor_name,donation_delete)\\n print('Donation has been deleted. See report below for verification')\\n single_donor_print(donor_name)\",\n \"def delete(self):\\n if self.id_:\\n GsSession.current._delete(f'{API}/{self.id_}', request_headers=DATAGRID_HEADERS)\\n else:\\n raise MqValueError('DataGrid has not been persisted.')\",\n \"def delete_entry(id):\\n if not session.get('logged_in'):\\n abort(401)\\n\\n db = get_db()\\n cur = db.execute('select id, title from entries where id = ?',\\n [id.strip()])\\n entries = cur.fetchall()\\n title = entries[0]['title']\\n db = get_db()\\n db.execute('delete from entries where id = ?', [id.strip()])\\n db.commit()\\n flash('Recipe, ' + escape(title) + ', has been deleted', 'success')\\n return redirect(url_for('show_entries'))\",\n \"def delete(self, request , pk=None): \\n return Response({'message':'DELETE'})\",\n \"def delete_row(self):\\n return exclusions.closed()\",\n \"def view_delete():\\n\\n return jsonify(\\n get_dict(\\\"url\\\", \\\"args\\\", \\\"form\\\", \\\"data\\\", \\\"origin\\\", \\\"headers\\\", \\\"files\\\", \\\"json\\\")\\n )\",\n \"def delete():\\n if request.method == 'POST':\\n table_name = request.form['table']\\n table = get_table_by_name(table_name)\\n if 'id' not in request.form:\\n user_data = get_data_from_table(table)\\n\\n return render_template(\\n 'delete.html', table_name=table_name, user_data=user_data\\n )\\n\\n if table is None:\\n return 'Table not chosen?'\\n\\n success, reason = utils.delete_user(request.form['id'], table_name)\\n\\n if not success:\\n return f'Error occurred trying to delete - {reason}'\\n\\n log(\\n f\\\"<code>{current_user.name}</code> has deleted <code>{request.form['id']}</code> from {table_name}\\\"\\n )\\n return (\\n f\\\"<code>{request.form['id']}</code> has been deleted from from {table_name}\\\"\\n )\\n return render_template('events.html', events=get_accessible_tables())\",\n \"def del_history_to_address(address):\\n \\n result = delete_transaction_observation_to_address(address)\\n\\n # if successfully deleted from observation list, return a plain 200\\n if \\\"error\\\" in result:\\n return make_response(jsonify(build_error(result[\\\"error\\\"])), result[\\\"status\\\"])\\n else:\\n return \\\"\\\"\",\n \"def test_delete_shipping_address(self):\\n self.cim.delete_shipping_address(\\n customer_profile_id=u\\\"123\\\",\\n customer_address_id=u\\\"543\\\"\\n )\",\n \"def delete_command():\\n global selected_tuple\\n backend.delete(selected_tuple[0])\",\n \"def delete(self, *args, **kwargs):\\n pass\",\n \"def delete(self, *args, **kwargs):\\n pass\",\n \"def delete(self):\\n ...\",\n \"def remove_row(self, row_id):\",\n \"def delete_model(self, request, obj):\\n obj.post.comNumDrease()\\n obj.delete()\",\n \"def click_delete_button(self):\\n self.number_of_rows = self.get_inbound_statement_grid_number_of_rows()\\n self.click_element(self.delete_button_locator, True)\\n try:\\n self.wait().until(EC.visibility_of_element_located(self.statement_delete_confirmation_locator), 'statement delete confirmation locator not found before specified time out')\\n ok_button_element = self.wait().until(EC.element_to_be_clickable(self.ok_button_locator), 'ok button locator not found before specified time out')\\n self.wait_for_ajax_spinner_load()\\n ok_button_element.click()\\n self.wait_for_ajax_spinner_load()\\n self.wait().until(EC.visibility_of_element_located(self.statement_delete_success_message_locator), 'statement delete success message locator not found before specified time out')\\n ok_button_element = self.wait().until(EC.element_to_be_clickable(self.ok_button_locator), 'ok button locator not found before specified time out')\\n self.wait_for_ajax_spinner_load()\\n ok_button_element.click()\\n except:\\n raise\\n self.wait_for_ajax_spinner_load()\",\n \"def _delete(self, pk, user=None):\\n request = self.factory.delete(self.detail_url(pk), format='json')\\n force_authenticate(request, user)\\n resp = self.detail_view(request, pk=pk)\\n resp.render()\\n return resp\",\n \"def transaction_delete(request, transaction_id, model_class=Transaction, template_name='budget/transactions/delete.html'):\\n transaction = get_object_or_404(Transaction.active.all(), pk=transaction_id)\\n if request.POST:\\n if request.POST.get('confirmed'):\\n transaction.delete()\\n return HttpResponseRedirect(reverse('budget_transaction_list'))\\n return render_to_response(template_name, {\\n 'transaction': transaction,\\n }, context_instance=RequestContext(request))\",\n \"def cmd_delete_job():\\r\\n id = request.form.get('id', \\\"\\\")\\r\\n confirm = request.form.get(\\\"confirm\\\", \\\"\\\")\\r\\n if confirm != \\\"DELETE\\\":\\r\\n flash(f\\\"Contact '{id}' NOT deleted. Please enter DELETE in the confirm field.\\\")\\r\\n return redirect(url_for('main.jobs'))\\r\\n \\r\\n index = get_job_by_id(id)\\r\\n Job.query.filter(Job.id == id).delete()\\r\\n db.session.commit()\\r\\n\\r\\n\\r\\n if index != None:\\r\\n flash(f\\\"Job '{id}' was succesfully deleted!\\\")\\r\\n return redirect(url_for('main.jobs'))\\r\\n else:\\r\\n flash(f\\\"Job '{id}' was not found\\\")\\r\\n return redirect(url_for('main.jobs'))\",\n \"def delete_people(pid):\\n db_conn = get_connection()\\n cur = db_conn.cursor()\\n _sql = \\\"\\\"\\\"SELECT \\n id,firstname,lastname,address,country\\n FROM peoples where id = ?\\\"\\\"\\\"\\n cur.execute(_sql, (pid,))\\n record = cur.fetchone()\\n\\n if record:\\n delete_sql = \\\"\\\"\\\"DELETE FROM peoples where id = ?\\\"\\\"\\\"\\n cur.execute(delete_sql, (pid,))\\n db_conn.commit()\\n flash(\\\"Record with id {} deleted successfully!!!\\\".format(pid))\\n return redirect(url_for('list_people'))\\n\\n else:\\n flash(\\\"Sorry! No user with id {} found to delete\\\".format(pid))\\n return redirect(url_for(\\\"list_people\\\"))\",\n \"def __render_deletion_cascade(self, r, method, row):\\n \\n r.line('// %s' % self.get_debug_name())\\n self.local_index.render_deletion(r,\\n self.project_key_foreign_to_local(row))\\n return\",\n \"def showEditContact(self):\",\n \"def deleteRow(self):\\r\\n self.conn = connect('database.sqlite')\\r\\n self.cur = self.conn.cursor()\\r\\n self.cur.execute('''\\r\\n DELETE FROM Clients WHERE id=(SELECT MAX(id) FROM Clients)\\r\\n ''')\\r\\n self.conn.commit()\\r\\n self.cur.close()\",\n \"def help_delete(self):\\n print(DELETE)\",\n \"def on_deleteButton_clicked(self):\\n for itm in self.historyList.selectedItems():\\n ditm = self.historyList.takeItem(self.historyList.row(itm))\\n del ditm\\n self.historyList.scrollToItem(self.historyList.currentItem())\\n self.historyList.setFocus()\",\n \"def delete_entry(self, id, **args):\\n args.update(id=id)\\n return self.fetch(\\\"/entry/delete\\\", post_args=args)\",\n \"def del_accomment(request, pk):\\n\\n comment = get_object_or_404(ActorComment, pk=pk)\\n comment.delete()\\n actor = comment.actor\\n url = '../../' + str(comment.actor.pk)\\n return redirect(url)\",\n \"async def delete_one(self, where):\\n\\n pass\",\n \"def delete_args():\\n return {\\\"id\\\": fields.UUID(required=True, location=\\\"view_args\\\")}\",\n \"def management_delete(request, slug,id):\\n \\n company =get_object_or_404(Company,slug=slug)\\n edit = validate_user_company_access_or_redirect(request,company)\\n\\n if request.method == 'POST':\\n return HttpResponseRedirect('/company/'+str(slug))\\n else: \\n #verifies if the company exists if not returns a 404 page\\n management_reference = get_object_or_404(Management, id=id,company=company)\\n\\n #deletes the view and redirects to the page.\\n management_reference.delete()\\n return HttpResponseRedirect('/company/'+str(slug))\",\n \"def award_delete(request, slug,id):\\n \\n company =get_object_or_404(Company,slug=slug)\\n edit = validate_user_company_access_or_redirect(request,company)\\n\\n if request.method == 'POST':\\n return HttpResponseRedirect('/company/'+str(slug))\\n else: \\n #verifies if the company exists if not returns a 404 page\\n award_reference = get_object_or_404(Award, id=id,company=company)\\n\\n #deletes the view and redirects to the page.\\n award_reference.delete()\\n return HttpResponseRedirect('/company/'+str(slug))\",\n \"def del_history_from_address(address):\\n \\n result = delete_transaction_observation_from_address(address)\\n\\n # if successfully deleted from observation list, return a plain 200\\n if \\\"error\\\" in result:\\n return make_response(jsonify(build_error(result[\\\"error\\\"])), result[\\\"status\\\"])\\n else:\\n return \\\"\\\"\",\n \"def delete(id):\\n get_autor(id)\\n try:\\n db.insert_bd('DELETE FROM autor WHERE id = %d' % id)\\n return redirect(url_for('autor.index'))\\n except:\\n return render_template('404.html')\",\n \"def create_delete_entry(columns: list, win, x: int, y: int):\\n global choice, search_delete_enter\\n window_for_search = Frame(win)\\n window_for_search.place(x=x, y=y)\\n search_delete_enter = ttk.Combobox(window_for_search, values=columns, height=3)\\n search_delete_enter.set(u'Выбор параметра')\\n search_delete_enter.grid(column=0, row=0)\\n choice = search_delete_enter\\n return choice\",\n \"def post_delete(self, *args, **kw):\\n #obtenemos el id de la fase para hacer el filtrado despues de la redireccion\\n item_to_del = DBSession.query(Item).filter_by(id_item=args[0]).one()\\n fid = item_to_del.id_fase_fk\\n pks = self.provider.get_primary_fields(self.model)\\n d = {}\\n for i, arg in enumerate(args):\\n d[pks[i]] = arg\\n self.provider.delete(self.model, d)\\n\\n path = './' + '../' * (len(pks) - 1) + '?fid=' + str(fid)\\n\\n redirect(path)\",\n \"def delete():\\n id_num = int(input('Enter the ID number of the item you wish to delete\\\\n'))\\n db_actions.remove(id_num)\",\n \"def funding_delete(request, slug,id):\\n \\n company =get_object_or_404(Company,slug=slug)\\n edit = validate_user_company_access_or_redirect(request,company)\\n\\n if request.method == 'POST':\\n return HttpResponseRedirect('/company/'+str(slug))\\n else: \\n #verifies if the company exists if not returns a 404 page\\n funding_reference = get_object_or_404(Funding, id=id,company=company)\\n\\n #deletes the view and redirects to the page.\\n funding_reference.delete()\\n return HttpResponseRedirect('/company/'+str(slug))\",\n \"def view_delete(\\n request: HttpRequest,\\n pk: Optional[int] = None,\\n workflow: Optional[Workflow] = None,\\n view: Optional[View] = None,\\n) -> JsonResponse:\\n if request.method == 'POST':\\n # Log the event\\n Log.objects.register(\\n request.user,\\n Log.VIEW_DELETE,\\n view.workflow,\\n {\\n 'id': view.id,\\n 'name': view.name,\\n 'workflow_name': view.workflow.name,\\n 'workflow_id': view.workflow.id})\\n\\n # Perform the delete operation\\n view.delete()\\n\\n # In this case, the form is valid anyway\\n return JsonResponse({'html_redirect': reverse('table:view_index')})\\n\\n return JsonResponse({\\n 'html_form': render_to_string(\\n 'table/includes/partial_view_delete.html',\\n {'view': view},\\n request=request),\\n })\",\n \"def post_delete(self, *args, **kw):\\n id_atributo = int(args[0])\\n transaction.begin()\\n attr = AtributosPorTipoItem.por_id(id_atributo)\\n DBSession.delete(attr)\\n transaction.commit()\\n flash(\\\"Atributo Eliminado\\\")\\n redirect(\\\"./\\\")\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.70819616","0.61636835","0.6088303","0.5755534","0.5709963","0.55882573","0.55848145","0.5560167","0.55589795","0.55150014","0.5407896","0.5399962","0.5394524","0.53886646","0.5371516","0.5349296","0.5309787","0.53002477","0.529465","0.52785975","0.5267689","0.52535003","0.5235827","0.5234687","0.52154475","0.52142286","0.52094084","0.5200737","0.5193239","0.5184428","0.5179076","0.5164752","0.51585364","0.51459754","0.51252663","0.5115147","0.5110028","0.5106845","0.5105958","0.5103508","0.5088271","0.5086246","0.5079797","0.50732964","0.50702447","0.50586593","0.50523764","0.50448316","0.50430036","0.5034576","0.50332683","0.50167614","0.50108457","0.49828774","0.4980904","0.4977461","0.49648088","0.49636167","0.49478373","0.49364144","0.49316856","0.49308982","0.49305663","0.4921388","0.49104849","0.4907485","0.48972702","0.48906982","0.48890457","0.4873864","0.48722067","0.48683217","0.48683217","0.4868106","0.48679993","0.48665205","0.48658377","0.4865804","0.48634997","0.4859881","0.48598295","0.4858247","0.48565552","0.4856375","0.48535487","0.4853115","0.485306","0.4852368","0.484801","0.48462522","0.48438928","0.4837866","0.48375046","0.4823","0.4819474","0.48154545","0.48145008","0.4813918","0.48127896","0.4811962"],"string":"[\n \"0.70819616\",\n \"0.61636835\",\n \"0.6088303\",\n \"0.5755534\",\n \"0.5709963\",\n \"0.55882573\",\n \"0.55848145\",\n \"0.5560167\",\n \"0.55589795\",\n \"0.55150014\",\n \"0.5407896\",\n \"0.5399962\",\n \"0.5394524\",\n \"0.53886646\",\n \"0.5371516\",\n \"0.5349296\",\n \"0.5309787\",\n \"0.53002477\",\n \"0.529465\",\n \"0.52785975\",\n \"0.5267689\",\n \"0.52535003\",\n \"0.5235827\",\n \"0.5234687\",\n \"0.52154475\",\n \"0.52142286\",\n \"0.52094084\",\n \"0.5200737\",\n \"0.5193239\",\n \"0.5184428\",\n \"0.5179076\",\n \"0.5164752\",\n \"0.51585364\",\n \"0.51459754\",\n \"0.51252663\",\n \"0.5115147\",\n \"0.5110028\",\n \"0.5106845\",\n \"0.5105958\",\n \"0.5103508\",\n \"0.5088271\",\n \"0.5086246\",\n \"0.5079797\",\n \"0.50732964\",\n \"0.50702447\",\n \"0.50586593\",\n \"0.50523764\",\n \"0.50448316\",\n \"0.50430036\",\n \"0.5034576\",\n \"0.50332683\",\n \"0.50167614\",\n \"0.50108457\",\n \"0.49828774\",\n \"0.4980904\",\n \"0.4977461\",\n \"0.49648088\",\n \"0.49636167\",\n \"0.49478373\",\n \"0.49364144\",\n \"0.49316856\",\n \"0.49308982\",\n \"0.49305663\",\n \"0.4921388\",\n \"0.49104849\",\n \"0.4907485\",\n \"0.48972702\",\n \"0.48906982\",\n \"0.48890457\",\n \"0.4873864\",\n \"0.48722067\",\n \"0.48683217\",\n \"0.48683217\",\n \"0.4868106\",\n \"0.48679993\",\n \"0.48665205\",\n \"0.48658377\",\n \"0.4865804\",\n \"0.48634997\",\n \"0.4859881\",\n \"0.48598295\",\n \"0.4858247\",\n \"0.48565552\",\n \"0.4856375\",\n \"0.48535487\",\n \"0.4853115\",\n \"0.485306\",\n \"0.4852368\",\n \"0.484801\",\n \"0.48462522\",\n \"0.48438928\",\n \"0.4837866\",\n \"0.48375046\",\n \"0.4823\",\n \"0.4819474\",\n \"0.48154545\",\n \"0.48145008\",\n \"0.4813918\",\n \"0.48127896\",\n \"0.4811962\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.77717626"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":94696,"cells":{"query":{"kind":"string","value":"Open the edit address form for an address row. A conditional check for whether the URL contains 'admin' allows this method to be used in both OnDemand Web and OnDemand Admin."},"document":{"kind":"string","value":"def edit_address(self) -> object:\n self.edit_button.click()\n\n if 'admin' not in self.driver.current_url:\n return WebAddressForm(self).wait_for_component_to_be_present()\n return AdminAddressForm(self).wait_for_component_to_be_present()"},"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 open_edit_address(self, address: dict) -> None:\n row = self.addresses_list.surface_address_row(address)\n\n row.open_kebab_menu()\n row.kebab_menu.edit_address()","def __editAddress(self):\n idx = self.bookmarksTree.currentIndex()\n idx = idx.sibling(idx.row(), 1)\n self.bookmarksTree.edit(idx)","def edit_form():\n return template (\"edit\")","def edit_address(self, new_label: str) -> None:\n self.address_form.label_input.fill(new_label)\n self.address_form.save_button.click()","def activate_external_editing(self, new_doc):\n new_doc.setup_external_edit_redirect(self.request, action=\"oneoffixx\")","def test_phone_address_view_with_url(self):\n url = reverse('profiles:update_phone_address')\n response = self.client.get(url)\n self.assertEqual(response.status_code, 200)\n self.assertTemplateUsed(\n response,\n 'profiles/update_phone_address.html'\n )","def get(self, request, *args, **kwargs):\n\n self.object = self.get_object()\n formclass = self.get_form_class()\n opform = self.get_form(formclass)\n if not request.user.is_superuser:\n del opform.fields['administrator']\n addrform = AddressForm(instance=self.object.address)\n return self.render_to_response(\n self.get_context_data(\n form=opform,\n address_form=addrform,\n )\n )","def showEditContact(self):","def show_edit_form(user_id):\n user = User.query.get_or_404(user_id)\n return render_template('edit.html', user=user)","def getEditForm( self ):\n return \"listc_edit\"","def set_show_in_edit_form(self, flag):\n qry = ServiceOperationQuery(self, \"setShowInEditForm\", [flag])\n self.context.add_query(qry)","def edit_link(db_object, text=None):\n if text is None:\n text = 'edit'\n return _make_link(db_object.update_url(), text)","def lineEdit( self ):\n return self._urlEdit","def edit(self, **kwargs):\n ...","def open_ride_edit_form(self) -> object:\n self.container.find_element(*self._ride_edit).click()\n\n return EditForm(self).wait_for_component_to_be_present()","def url_to_edit(obj):\n return reverse(\n 'admin:%s_%s_change' % (obj._meta.app_label, obj._meta.model_name),\n args=[obj.id]\n )","def edit_redirect_url(self):\n return url_for(self.edit_redirect_to_view)","def process_address():\n #get address info from form\n user_details = request.form\n #validate address with google geocoding\n update_details = apiapijoyjoy.validate_address(user_details)\n #update ino in db\n dbwrangler.newaddress(update_details)\n \n return redirect(\"/\")","def edit_link(instance):\n\n try:\n content_type = ContentType.objects.get_for_model(instance.__class__)\n except AttributeError:\n raise ValueError('Passed value must be registered model instance')\n else:\n model_admin_change_link = 'admin:{app}_{model}_change'.format(\n app=content_type.app_label,\n model=content_type.model\n )\n return reverse(model_admin_change_link, args=(instance.id,))","def test_the_data_edit_url(self):\n\n my_instance = Contact.objects.first()\n info_url = resolve('/to_form/%s/' % my_instance.id)\n self.assertEqual(info_url.func.__name__, 'my_edit_data')\n self.assertEqual(self.response.status_code, 200)","def choose_new_address(self) -> \"CheckoutPage\":\n self.accordion = BillingDetailsUser(self.driver)\n self.accordion.btn_new_address.click()\n return self","def edit(self):\n\n pass","def show_admin_edit_admins():\n return render_admin_page(\"admin-ea.html\")","def get_edit_form(self, data):\n self.add_success(data)\n rv = self.get((data[self.id_field], self.edit_url))\n assert not is_404(rv)\n assert in_response(rv, 'Edit {}'.format(data[self.name_field]))\n for field, name in self.fields:\n assert in_response(rv, name)\n return rv","def show_admin_edit_users():\n return render_admin_page(\"admin-eu.html\")","def show_edit_form(user_id):\n\n user = User.query.get_or_404(user_id)\n\n return render_template(\"users/edit_user.html\", user=user)","def OnToggleEdit(self, event):\n\t\ttoggle = self.btn_edit.GetToggle()\n\t\tif not toggle:\n\t\t\tif not util.gAuthen.Authenticate(util.ADMIN):\n\t\t\t\tself.btn_edit.SetToggle(not toggle)\n\t\t\t\treturn \n\t\tself.UpdateToggle()","def register_edit_view(self, blueprint):\n view = apply_decorators(self.edit_view, self.edit_decorators)\n blueprint.add_url_rule(\n self.edit_rule, self.edit_endpoint, view, methods=['GET', 'POST'])","def edit():","def link_edit_callback(self):\n pass","def the_user_edit_page_should_open(driver):\n assert wait_on_element(driver, 7, xpaths.add_User.edit_Title)\n time.sleep(0.5)","def edit_employee(employee_id):\n\n if not g.user:\n flash(\"Please login to access\", \"danger\")\n return redirect(\"/\")\n \n if g.user.is_admin == False:\n flash (\"Unauthorized\", \"danger\")\n return redirect(\"/login\")\n\n employee = Employee.query.get_or_404(employee_id)\n form = Edit_User_Form(obj = employee)\n \n #form.location.choices = db.session.query(Location.id, Location.site_name).all()\n \n #form.certs.choices = db.session.query(Certs.id , Certs.cert_name).all()\n\n if form.validate_on_submit():\n \n employee.email = form.email.data, \n employee.first_name = form.first_name.data,\n employee.last_name = form.last_name.data,\n employee.hire_date = form.hire_date.data, \n employee.is_admin = form.is_admin.data\n\n \n db.session.commit()\n \n flash(f\"{employee.first_name} {employee.last_name} has been saved\", \"success\")\n return redirect(\"/administrator\")\n else:\n\n return render_template(\"/admin/edit_user.html\", employee = employee, form = form)","def manage_edit_save(self, REQUEST):\n self._config.update(ldap_config.read_form(REQUEST.form, edit=True))\n REQUEST.RESPONSE.redirect(self.absolute_url() + '/manage_edit')","def edit_agency(self) -> object:\n self.edit_agency_button.click()\n\n return AgencySelectModal(self).wait_for_component_to_be_present()","def edit():\n database.ask(mode='single')\n F = database.check(single=True)\n if F and hasattr(F,'edit'):\n name = database[0]\n F.edit(name)","def edit_employee_locations(employee_id):\n\n if not g.user:\n flash(\"Please login to access\", \"danger\")\n return redirect(\"/\")\n \n if g.user.is_admin == False:\n flash (\"Unauthorized\", \"danger\")\n return redirect(\"/login\")\n\n employee = Employee.query.get_or_404(employee_id)\n \n form = Add_Loc_Form(obj = employee)\n \n form.location.choices = db.session.query(Location.id, Location.site_name).all()\n \n \n if form.validate_on_submit():\n \n location = Location.query.get(form.location.data) \n employee.locations.append(location)\n db.session.add(employee)\n \n db.session.commit()\n\n \n\n flash(f\"{employee.first_name} {employee.last_name} has been saved\", \"success\")\n return redirect(\"/administrator\")\n else:\n \n return render_template(\"/admin/employee_cert.html\", employee = employee, form = form)","def editPage(request, title):\n entry = util.get_entry(title)\n if request.method == \"POST\":\n # check if the data is valid then save/replace old data\n form = editPageForm(request.POST)\n if form.is_valid():\n title = form.cleaned_data[\"editTitle\"]\n content = form.cleaned_data[\"editBody\"]\n\n util.save_entry(title, content)\n\n # take user to their editted page\n return HttpResponseRedirect(reverse(\"entry\", kwargs={\n \"title\": title\n }))\n # give user a editting form with existing data filled in by defult. \n else:\n editForm = editPageForm(initial={\n \"editTitle\": title,\n \"editBody\": entry\n })\n editFormTitle = editForm[\"editTitle\"]\n editFormBody = editForm[\"editBody\"]\n return render(request, \"encyclopedia/editPage.html\", {\n \"formTitle\": editFormTitle,\n \"formBody\": editFormBody\n })","def test_address_page(self):\n tester = app.test_client(self)\n response = tester.get('/', content_type = \"html_text\")\n self.assertTrue(b'Address Locator' in response.data)","def new_address():\n user = dbwrangler.get_current_user()\n if user:\n return render_template(\"new_user_more.html\")\n else:\n return redirect(\"/\")","def edit_form_entry_edit_option_html(cls):\n return \"\"\"\n <li><a href=\"{edit_url}\">\n <span class=\"{edit_option_class}\"></span> {edit_text}</a>\n </li>\n \"\"\".format(\n edit_url=\"{edit_url}\",\n edit_option_class=cls.form_edit_form_entry_option_class,\n edit_text=\"{edit_text}\",\n )","def restaurants_edit(restaurant_id):\n # If the user isn't logged in, send to the login page\n if helper.handle_login(login_session) is False:\n return redirect('/login')\n # Find the restaurant\n restaurant = session.query(Restaurant).filter_by(id=restaurant_id).one()\n if request.method == 'POST':\n # Only edit if the entry was re-written\n if len(request.form['address']) > 0:\n restaurant.address = request.form['address']\n if len(request.form['phone']) > 0:\n restaurant.phone = request.form['phone']\n if len(request.form['web']) > 0:\n restaurant.web = helper.check_restaurant_URL(request.form['web'])\n if len(request.form['tag_line']) > 0:\n tag_line = request.form['tag_line']\n tag_list = tag_line.split(',')\n helper.delete_restaurant_tag_pairs(restaurant.id)\n for tag in tag_list:\n helper.add_tag_if_not_exists(tag, restaurant.id)\n if len(request.form['description']) > 0:\n restaurant.description = request.form['description']\n\n restaurant.last_update = datetime.utcnow()\n\n session.add(restaurant)\n session.commit()\n flash(\"Restaurant {} edited!\".format(restaurant.name))\n return redirect(url_for('restaurants_page'))\n else:\n # Get user info if the user is signed in to render edit form\n user_info = helper.get_user_if_exists(login_session)\n tag_rest_list = session.query(RestaurantTags).filter_by(restaurant_id=restaurant.id).all()\n tag_line = ''\n # Create a tag line - by compiling the string tag_name for each tag\n for pair in tag_rest_list:\n tag = session.query(Tags).filter_by(id=pair.tag_id).first()\n tag_line += tag.tag_name + ', '\n return render_template('editrestaurant.html',\n restaurant=restaurant,\n tag_line=tag_line,\n user_info=user_info)","def update(self, addr, replace=False):\n if self.ignorer.ignore_address(addr[1]):\n return False\n try:\n with self.connect() as c:\n cur = c.cursor()\n if replace:\n present = cur.execute(\"SELECT 1 FROM AddressBook WHERE address = ?\", [addr[1]])\n if present:\n cur.execute(\"UPDATE AddressBook SET name = ? WHERE address = ?\", addr)\n else:\n cur.execute(\"INSERT INTO AddressBookView VALUES(?,?)\", addr)\n else:\n cur.execute(\"INSERT INTO AddressBookView VALUES(?,?)\", addr)\n return True\n except sqlite3.IntegrityError:\n return False","def edit(user_id):\n if user_id != current_user.id:\n return abort(403)\n\n user = get_user(user_id)\n form = EditForm(obj=user)\n form.email.data = user.email\n\n if form.validate_on_submit():\n password = form.password.data\n username = form.username.data\n\n save_result = edit_user(user_id, password, username, user.active)\n user = save_result['entry']\n form = EditForm(request.form, obj=save_result['entry'])\n form.email.data = user.email\n return redirect(url_for('.index'))\n \n return render_template('users/edit.html'\n ,form=form\n ,user=user\n ,t=t\n ,m=m)","def edit_user(user_id):\n user = User.query.get_or_404(user_id)\n return render_template('/users/edit_page.html', user=user)","def home_edituser():\n\tpass","def click_the_edit_button_that_appears(driver):\n driver.find_element_by_xpath(xpaths.users.eric_Edit_Button).click()","def __edit(self):\n from .BookmarkPropertiesDialog import BookmarkPropertiesDialog\n \n idx = self.bookmarksTree.currentIndex()\n sourceIndex = self.__proxyModel.mapToSource(idx)\n node = self.__bookmarksModel.node(sourceIndex)\n dlg = BookmarkPropertiesDialog(node)\n dlg.exec_()","def edit_place(place_id):\n place_db = mongo.db.places.find_one_or_404({'_id': ObjectId(place_id)})\n if request.method == 'GET':\n form = EditPlacesForm(data=place_db)\n return render_template('edit_restaurant.html', recipe=place_db, form=form)\n form = EditPlacesForm(request.form)\n if form.validate_on_submit():\n places_db = mongo.db.places\n places_db.update_one({\n '_id': ObjectId(place_id),\n }, {\n '$set': {\n 'name': request.form['name'],\n 'city': request.form['city'],\n 'added_by': session['username'],\n 'description': request.form['description'],\n 'tags': request.form['tags'],\n 'image': request.form['image'],\n }\n })\n return redirect(url_for('home'))\n return render_template('edit_restaurant.html', place=place_db, form=form)","def _enter_edit_mode(self):\n edit_mode = self.UTILS.element.getElement(DOM.DownloadManager.download_edit_button,\n \"Download edit button\", True, 10)\n edit_mode.tap()\n self.UTILS.element.waitForElements(DOM.DownloadManager.downloads_edit_header_title,\n \"Edit downloads header\")","def show_edit_user_form(user_id):\n\n user = User.query.get_or_404(user_id)\n return render_template('edit_user.html', user=user)","def edit_restaurant(restaurant_id):\n user_id = login_session['user_id']\n r = read_restaurants(restaurant_id, user_id)\n if r[1] is True: # Means if user is owner\n if request.method == 'GET':\n return render_template('restaurants/editrestaurant.html',\n restaurant=r[0][0])\n elif request.method == 'POST':\n # Got post request -> First we get the request arguemnts\n name = request.form['name']\n address = request.form['address']\n city = request.form['city']\n state = request.form['state']\n zipCode = request.form['zipCode']\n # Next we do the db edit\n update_restaurant(restaurant_id, name, address,\n city, state, zipCode)\n # Finally we return the success html\n flash(\"Edited your restaurant\")\n return render_template(\"submitted.html\")\n else:\n return \"Invalid http\"\n else:\n flash(\"You need to be the owner of the restaurant to edit\")\n return redirect(url_for('site.show_restaurants',\n restaurant_id=restaurant_id))","def show_edit_form(self, obj_pk=None):\n obj = self.model.objects.get(pk=obj_pk)\n # if there is no edit permission then does not show the form\n if not self.has_view_permissions(obj): return\n\n\n # create the edit form a add it to the empty widget details\n # override the function hide_form to make sure the list is shown after the user close the edition form\n params = {\n 'title':'Edit',\n 'model':self.model,\n 'pk':obj.pk,\n 'parent_model':self.parent_model,\n 'parent_pk':self.parent_pk,\n 'parent_win': self\n }\n\n if self.INLINES: params.update({'inlines': self.INLINES} )\n if self.FIELDSETS: params.update({'fieldsets':self.FIELDSETS})\n if self.READ_ONLY: params.update({'readonly': self.READ_ONLY})\n\n editmodel_class = self.get_editmodel_class(obj)\n editform = editmodel_class(**params)\n\n if hasattr(self, '_details') and self.USE_DETAILS_TO_EDIT:\n self._details.value = editform\n self._list.hide()\n self._details.show()\n\n # only if the button exists:\n toolbar = [self.toolbar] if isinstance(self.toolbar, str) else self.toolbar\n if toolbar:\n for o in toolbar:\n if o and hasattr(self, o): getattr(self, o).hide()\n\n else:\n self._list.show()\n if hasattr(self, '_details'):\n self._details.hide()","def edit(request, address):\n if address.startswith(\"/\"):\n address = address[1:]\n if address.endswith(\"/\"):\n address = address[:-1]\n\n # we try to find the parent. Creating a page without parent isn't possible.\n parent = None\n if \"/\" in address:\n parent = address.rsplit(\"/\", 1)[0]\n else:\n parent = \"\"\n\n try:\n parent = Page.objects.get(address=parent)\n except Page.DoesNotExist:\n parent = None\n\n # try to get the page itself, which might exist\n try:\n page = Page.objects.get(address=address)\n except Page.DoesNotExist:\n page = None\n\n initial = {}\n if page:\n initial[\"title\"] = page.title\n initial[\"content\"] = page.content\n\n if request.method == 'POST':\n # the form has been sent, use the different access rights\n form = PageForm(request.POST, initial=initial)\n if form.is_valid():\n title = form.cleaned_data[\"title\"]\n content = form.cleaned_data[\"content\"]\n user = request.user\n user = user if user.is_authenticated else None\n can = False\n if user and user.is_superuser:\n # the superuser can do it all\n can = True\n elif parent and page is None and parent.access(user, \"write\"):\n # the page doesn't exist, but the parent does, and the user can edit it\n can = True\n elif page and page.access(user, \"write\"):\n # the page already exist and the user can edit it\n can = True\n\n if can:\n new_page = Page.objects.create_or_update_content(address, user, content)\n new_page.title = title\n if parent is not None and page is None:\n new_page.can_write = parent.can_write\n new_page.can_read = parent.can_read\n new_page.save()\n\n return HttpResponseRedirect('/wiki/' + address)\n else:\n form = PageForm(initial=initial)\n\n return render(request, \"wiki/edit.html\", {'form': form, 'address': address, \"page\": page, \"parent\": parent})","def show_employee_edit_form(self, staff_ob, number):\n\n print(self.LENGTH_STAR * \"*\")\n print(f\"EDIT {staff_ob.role.upper()}\\n\")\n\n if number == 1:\n print(self.LENGTH_STAR * \"*\")\n print(f\"You are changing {staff_ob.name}´s address\\nThe current address is: {staff_ob.address}\")\n new_address = self.get_address()\n while new_address == False:\n new_address = self.get_address()\n self.check_action_edit_form(staff_ob, number, new_address)\n\n elif number == 2:\n print(self.LENGTH_STAR * \"*\")\n print(f\"You are changing {staff_ob.name}´s mobile number\\nThe current mobile number is: {staff_ob.mobile_number}\")\n new_mobile_number = self.get_mobile_number()\n while new_mobile_number == False:\n new_mobile_number = self.get_mobile_number\n self.check_action_edit_form(staff_ob, number, new_mobile_number)\n \n elif number == 3:\n print(self.LENGTH_STAR * \"*\")\n print(f\"You are changing {staff_ob.name}´s email\\nThe current the email is: {staff_ob.email}\")\n new_email = self.get_email()\n while new_email == False:\n new_email = self.get_email()\n self.check_action_edit_form(staff_ob, number, new_email)\n \n print(f\"\\n{staff_ob.name}'s information successfully changed!\\n\")\n \n return","def edit(request, pageName):\n \n if request.method == \"POST\":\n form = EditForm(request.POST)\n \n if form.is_valid(): \n content = form.cleaned_data[\"content\"]\n title = form.cleaned_data[\"title\"]\n \n util.save_entry(title, content)\n return HttpResponseRedirect(reverse(\"encyclopedia:visit_entry\", args=(title, )))\n \n else:\n\n form = EditForm({'title': pageName, 'content': util.get_entry(pageName) })\n return render(request, \"encyclopedia/edit_page.html\", {\n \"form\": EditForm(),\n \"pageName\": pageName\n })\n \n \n return render(request, \"encyclopedia/edit_page.html\", {\n \"form\": EditForm({'title': pageName, 'content': util.get_entry(pageName) }),\n \"pageName\": pageName\n })","def show_edit_user_form(user_id):\r\n user = User.query.get_or_404(user_id)\r\n\r\n return render_template('edit-user.html', user=user)","def click_bulk_edit_button(self):\n self.click_element(self.bulk_edit_button_locator, True)","def edit_locations(location_id):\n\n if not g.user:\n flash(\"Please login to access\", \"danger\")\n return redirect(\"/\")\n \n if g.user.is_admin == False:\n flash (\"Unauthorized\", \"danger\")\n return redirect(\"/login\")\n\n location = Location.query.get_or_404(location_id)\n form = Location_Form(obj = location)\n\n if form.validate_on_submit():\n location.site_name = form.site_name.data,\n location.city = form.city.data,\n location.state = form.state.data\n \n db.session.commit()\n flash(f\"Location {site_name} has been updated\")\n return redirect(\"/administrator\")\n else:\n return render_template(\"/admin/edit_location.html\", form = form, location = location)","def add_new_address(self, address: dict) -> None:\n self.new_address_button.click()\n\n self.address_form.select_location(address['address'])\n self.address_form.label_input.fill(address['name'])\n\n self.address_form.save_button.click()","def _edit( self, remote_url ):\n # strip off scheme and machine from URL if present\n tokens = urlparse.urlparse( remote_url, 'http' )\n if tokens[1]:\n # There is a nethost, remove it\n t=('', '') + tokens[2:]\n remote_url=urlparse.urlunparse(t)\n # if URL begins with site URL, remove site URL\n utool = queryUtility(IURLTool)\n if utool is None:\n # fallback for bootstrap\n utool = aq_get(self, 'portal_url', None)\n portal_url = utool.getPortalPath()\n i = remote_url.find(portal_url)\n if i==0:\n remote_url=remote_url[len(portal_url):]\n # if site is still absolute, make it relative\n if remote_url[:1]=='/':\n remote_url=remote_url[1:]\n self.remote_url=remote_url\n\n # save unique id of favorite\n self.remote_uid = self._getUidByUrl()","def edit_person(self, pk):","def create_or_update_address(address, customer):\n\tname = frappe.db.get_value('Address', { 'entity_id': address.get('entity_id') })\n\tif not name:\n\t\taddr = frappe.new_doc('Address')\n\t\taddr.address_title = \"{} {} {}\".format(\n\t\t\taddress.get(\"firstname\"),\n\t\t\taddress.get(\"lastname\"),\n\t\t\taddress.get(\"entity_id\")\n\t\t)\n\telse:\n\t\taddr = frappe.get_doc(\"Address\", name)\n\n\taddr.address_type = get_address_type(address).get('type')\n\taddr.entity_id = address.get('entity_id')\n\taddr.address_line1 = address.get('street')[0]\n\taddr.address_line2 = address.get('street')[1] if len(address.get('street')) > 1 else \"\"\n\taddr.city = address.get('city')\n\taddr.country = frappe.db.get_value('Country', { 'code': address.get('country_id') })\n\taddr.state = address.get('region')\n\taddr.pincode = address.get('postcode')\n\taddr.phone = address.get('telephone') or '00000'\n\taddr.fax = address.get('fax')\n\taddr.customer = customer\n\taddr.customer_name = address.get('firstname')+' '+address.get('lastname')\n\taddr.is_primary_address = get_address_type(address).get('is_primary_address')\n\taddr.is_shipping_address = get_address_type(address).get('is_shipping_address')\n\n\taddr.save(ignore_permissions=True)","def adminedit(object, id):\n\n db = get_db()\n\n if request.method == \"POST\":\n execute_string = 'UPDATE ' + object.title() + \" SET \"\n\n if object == 'post':\n execute_string += 'title = \"' + request.form['title'] + '\", content = \"' + request.form['content'] + '\", authorId = ' + request.form[\"authorid\"] + ', categoryId = ' + request.form[\"categoryid\"] + ''\n elif object == 'author':\n execute_string += 'name = \"' + request.form['name'] + '\"'\n elif object == 'category':\n execute_string += 'name = \"' + request.form['name'] + '\", description = \"' + request.form['description'] + '\"'\n\n execute_string += \" WHERE id = \" + str(id)\n db.execute(execute_string)\n db.commit()\n return redirect(url_for(\"adminview\", object=object))\n\n execute_string = \"SELECT * FROM \" + object.title() + \" WHERE id = \" + str(id)\n item = db.execute(execute_string).fetchone()\n\n return render_template(\"new.html\", object=object, item=item)","def admin(self, view):\n view.admin = True\n return view","def enableAddressLayer(self):\n\n self._deladdressaction.setEnabled(True)\n self._createnewaddressaction.setEnabled(True)\n self._moveaddressaction.setEnabled(True)\n self._updateaddressaction.setEnabled(True)\n self._highlightaction.setEnabled(True)","def edit_view(request, title, modelform, instance=None, **kwargs):\n instance_form = modelform(request.POST or None, instance=instance)\n if instance_form.is_valid():\n instance = instance_form.save()\n messages.success(request, _(\"%s was edited.\") % instance)\n return redirect(instance.get_absolute_url())\n return form(\n {**kwargs, \"form\": instance_form, \"action_name\": _(\"Edit\"), \"title\": title},\n \"deployments/form.html\",\n request,\n )","def test_edit_has_populated_form(testapp, fill_the_db, login_testcase):\n response = testapp.get('/journal/1/edit-entry', status=200)\n title = response.html.find_all('input')[1]['value']\n body = response.html.form.textarea.contents[0]\n assert title == ENTRIES[0]['title']\n assert body == ENTRIES[0]['body']","def edit_entry(self):\r\n self.session = tk.Toplevel(self.master, **jt.bframe_style)\r\n jd.Page(self.session, self.source)","def edit_contact(self):\n edit_data = input(\"Enter the first name of user you want to edit\\n\")\n\n for contact in self.contact_list:\n if contact.first_name == edit_data:\n user_input = int(input(\n \"Enter the number that you want to edit field in details\"\n \" \\n 1. First Name 2. Last name 3. Address 4. city 5. state 6.zip 7. Phone number 8.Email \\n\"))\n if user_input == 1:\n first_name = input(\"Enter new first name\\n\")\n contact.first_name = first_name\n elif user_input == 2:\n last_name = input(\"Enter new last name\\n\")\n contact.last_name = last_name\n elif user_input == 3:\n address = input(\"Enter new address\\n\")\n contact.address = address\n elif user_input == 4:\n city = input(\"Enter new city\\n\")\n contact.city = city\n elif user_input == 5:\n state = input(\"Enter new state\\n\")\n contact.state = state\n elif user_input == 6:\n zip = input(\"Enter new zip\\n\")\n contact.zip = zip\n elif user_input == 7:\n phone_number = input(\"Enter new phone number\\n\")\n contact.phone_number = phone_number\n elif user_input == 8:\n email = input(\"Enter new email\\n\")\n contact.email = email\n else:\n print(\"Please enter a valid input\")\n else:\n print(\"Please enter a valid name\")","def open_attr_req_editing(self, element) -> None:\n pass","def edit_entry(self, id, body=None, link=None, **args):\n args.update(id=id)\n if body: args.update(body=body)\n if link: args.update(link=link)\n return self.fetch(\"/entry\", post_args=args)","def edit_item(self):\n for item in self.selection():\n origin_name = self.set(item)[\"1\"]\n origin_url = self.set(item)[\"2\"]\n popup = GetItemURLDialogue(self, \"Edit Item\", origin_name, origin_url)\n\n self.item(item, values=(popup.name, popup.url, self.set(item)[\"3\"]))\n self.set(item)[\"2\"] = popup.url\n\n # Edit the item - backend\n for row in s.item:\n if row[\"item\"] == origin_name and row[\"url\"] == origin_url:\n s.item.remove(row)\n s.updateItem({\"item\": popup.name, \"url\": popup.url})","def show_orion_admin_url(self, obj):\n return obj.orion_admin_url","def test_edit_view(self):\n target_url = url_for('content.edit_content')\n redirect_url = url_for('users.login', next=target_url)\n response = self.client.get(target_url)\n self.assertEqual(response.status_code, 302)\n self.assertRedirects(response, redirect_url)","def editChange(self,editBtn):\n if self.edit ==True:\n self.updateDetails(\"normal\") #update details column\n self.edit = False #switch boolean\n self.editBtn.setText(\"Edit\") #update button text\n else:\n self.updateDetails(\"edit\") #update details column\n self.edit= True #switch boolean\n self.editBtn.setText(\"Stop Editing\") #update button text","def editarEndereco(self, ide, rua = None, num = None, bairro = None, cidade = None, uf = None, cep = None, complemento = None):\r\n try:\r\n if ide is None:\r\n return False\r\n else:\r\n if rua != None:\r\n self.cursor.execute(\"UPDATE ENDERECO SET RUA = '%s' WHERE ID = %s;\" %(rua, ide))\r\n if num != None:\r\n self.cursor.execute(\"UPDATE ENDERECO SET NUM = %s WHERE ID = %s;\" %(num, ide))\r\n if bairro != None:\r\n self.cursor.execute(\"UPDATE ENDERECO SET BAIRRO = '%s' WHERE ID = %s;\" %(bairro, ide))\r\n if cidade != None:\r\n self.cursor.execute(\"UPDATE ENDERECO SET CIDADE = '%s' WHERE ID = %s;\" %(cidade, ide))\r\n if uf != None:\r\n self.cursor.execute(\"UPDATE ENDERECO SET UF = '%s' WHERE ID = %s;\" %(uf, ide))\r\n if cep != None:\r\n self.cursor.execute(\"UPDATE ENDERECO SET CEP = '%s' WHERE ID = %s;\" %(cep, ide))\r\n if complemento != None:\r\n self.cursor.execute(\"UPDATE ENDERECO SET COMPLEMENTO = '%s' WHERE ID = %s;\" %(complemento, ide))\r\n return True\r\n except:\r\n return False","def __get_redirect_url(self):\n if self.get_submit_save_and_continue_edititing_button_name() not in self.request.POST:\n return self.request.cradmin_app.reverse_appindexurl()\n return self.request.cradmin_app.reverse_appurl(\n 'groupcomment-edit',\n args=self.args,\n kwargs=self.kwargs)","def edit_register(id):\n add_employee = False\n\n employee = Employee.query.get_or_404(id) #from table\n print('----update 1----')\n form = UpdateForm(obj=employee) #if not 404\n print('----update 2----')\n if form.validate_on_submit():\n employee.email = email=form.email.data\n employee.username=form.username.data\n employee.glad_id=form.glad_id.data\n employee.tel_no=form.tel_no.data\n employee.role_id=form.role_id.data\n employee.password=form.password.data\n\n # UPDATE employee to the database\n print('----update----',employee.role_id)\n db.session.commit()\n flash('You have successfully updated! ')\n\n # # redirect to the login page\n # return redirect(url_for('auth.login'))\n\n # load registration template\n return render_template('auth/register.html', form=form, title='Update')","def admin_edit_admins():\n return user_management_handler(\"show_admin_edit_admins\", \"new_admins\", True)","def save_edit(self):\r\n self.driver.find_element_by_xpath('//*[@id=\"vnos\"]/span[1]/div/a').click()","def edit_user(user_id):\n\n user = User.query.get_or_404(user_id)\n return render_template('users/edit.html', user=user)","def is_edit(self):\n return self._tag == 'edit'","def admin_update_preview():\n return user_management_handler(\"show_admin\", \"\", False)","def form_valid(self, form):\n address_form = AddressInlineFormSet(self.request.POST, instance = self.object)\n if form.is_valid() and address_form.is_valid():\n form.save()\n address_form.save()\n return HttpResponseRedirect(self.get_success_url())\n else:\n context = {\n 'form' : form,\n 'address_form' : address_form\n }\n return self.render_to_response(context)","def isEditLink(self, rel, type = None): #$NON-NLS-1$\r\n return self._isInRelList(rel, ZAtomRelTypes.ATOM_EDIT_LINK_REL_LIST)","def admin_user_area():\n session_id = request.args.get('session-id', None)\n user_id = request.args.get('user-id', None)\n edit = request.args.get('edit', None)\n today = datetime.date.today()\n reservations_list = get_user_reservations_list(user_id)\n cars_reservations_list = get_cars_user_reservations_list(reservations_list)\n reservations_status_list = get_reservations_status_list(reservations_list)\n if edit == \"true\":\n edit_mode = True\n else:\n edit_mode = False\n user = get_user_by_id(user_id)\n if check_authentication(session_id, user_id) and is_admin_user(user_id):\n return render_template('user_area.html', user=user_id, session_id=session_id, edit_mode=edit_mode,\n surname=user.surname, name=user.name, birthdate=user.birthdate, today=today,\n reservations_list=reservations_list, cars_reservations_list=cars_reservations_list,\n reservations_status_list=reservations_status_list, admin=True)\n else:\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__(), del_session_cookie=True)","def _editClickedSlot(self):\r\n\r\n index = self.propertiesTableView.selectionModel().currentIndex()\r\n if index.isValid():\r\n self.propertiesTableView.edit(index)","def model_admin_url(self, instance: BaseModel, name: str = None) -> str:\n url = resolve_url(\n admin_urlname(instance._meta, SafeText(\"change\")), instance.pk\n )\n return format_html('<a href=\"{}\">{}</a>', url, name or str(instance))","def _get_address_bar(parent_handle):\n childwins = []\n win32gui.EnumChildWindows(parent_handle, _enumWindowsCallback,\n childwins)\n for win in childwins:\n child_handle = win[0]\n class_name = win[1]\n if 'Edit' in class_name:\n edit_text = _get_edit_text(child_handle)\n if 'http://' in edit_text or 'https://' in edit_text:\n return child_handle # then this must be it...","def admin_edit_users():\n return user_management_handler(\"show_admin_edit_users\", \"new_users\", False)","def show_fresh_address(self):\n\t\treturn self.__fresh_account()[\"address\"]","def edit(tesserae, tessera_id):\n try:\n return tesserae.edit(tessera_id)\n except TesseraError, e:\n sys.stderr.write(\"Error: %s\\n\", str(e))\n return False","def editar_empresa(id):\n cadastrando_empresa = False\n\n empresa = Empresa.query.get_or_404(id)\n form = EditarEmpresaForm(obj=empresa)\n\n if form.validate_on_submit():\n empresa.nome = form.nome.data\n empresa.simbolo = form.simbolo.data\n empresa.regiao = form.regiao.data\n empresa.tipo = form.tipo.data\n empresa.abertura = form.abertura.data\n empresa.fechamento = form.fechamento.data\n empresa.zona = form.zona.data\n empresa.moeda = form.moeda.data\n db.session.commit()\n flash('Empresa editada com sucesso!')\n\n return redirect(url_for('home.listar_empresas'))\n\n form.nome.data = empresa.nome\n form.simbolo.data = empresa.abertura \n form.regiao.data = empresa.regiao\n form.tipo.data = empresa.tipo\n form.abertura = empresa.abertura\n form.fechamento = empresa.fechamento\n form.zona.data = empresa.zona\n form.moeda.data = empresa.moeda\n\n\n return render_template('home/empresa.html', action=\"Edit\",\n cadastrando_empresa=cadastrando_empresa, form=form,\n empresa=empresa, title=\"Editar empresa\")","def test_edit_view(self):\n self.client.post(reverse('misago:admin:users:bans:new'), data={\n 'check_type': '0',\n 'banned_value': 'Admin',\n })\n\n test_ban = Ban.objects.get(banned_value='admin')\n form_link = reverse('misago:admin:users:bans:edit', kwargs={'pk': test_ban.pk})\n\n response = self.client.post(form_link, data={\n 'check_type': '1',\n 'banned_value': 'test@test.com',\n 'user_message': 'Lorem ipsum dolor met',\n 'staff_message': 'Sit amet elit',\n 'expires_on': '',\n })\n self.assertEqual(response.status_code, 302)\n\n response = self.client.get(reverse('misago:admin:users:bans:index'))\n response = self.client.get(response['location'])\n self.assertEqual(response.status_code, 200)\n self.assertContains(response, 'test@test.com')","def edit(self, *args, **kw):\n\t\t\ttmpl_context.widget = self.edit_form\n\t\t\tpks \t\t= self.provider.get_primary_fields(self.model)\n\t\t\tkw \t\t\t= {}\n\n\t\t\tfor i, pk in enumerate(pks):\n\t\t\t\tkw[pk] \t\t= args[i]\n\n\t\t\tvalue \t\t= self.edit_filler.get_value(kw)\n\t\t\tvalue['_method'] \t= 'PUT'\n\n\t\t\treturn dict(value = value, model = self.model.__name__, pk_count = len(pks))","def allow_to_edit(user):\n return allow_to_edit_well(user)","def office_view(request, slug, id):\n company =get_object_or_404(Company,slug=slug)\n edit = validate_user_company_access_or_redirect(request,company)\n office_reference = get_object_or_404(Office, id=id,company=company)\n\n return render_to_response('office_form.html', \n {'details': office_reference,'info':office_reference},\n context_instance=RequestContext(request))","def test_update_route_has_populated_form(testapp, fill_the_db):\n response = testapp.get('/journal/1/edit-entry', status=200)\n title = response.html.form.input[\"value\"]\n body = response.html.form.textarea.contents[0]\n assert title == ENTRIES[0][\"title\"]\n assert body == ENTRIES[0][\"body\"]","def startNewAddressTool(self):\n self.iface.mapCanvas().setMapTool(self._createnewaddresstool)\n self._createnewaddresstool.setEnabled(True)","def edit(self,item=None):\r\n raise AbstractError\r\n return False"],"string":"[\n \"def open_edit_address(self, address: dict) -> None:\\n row = self.addresses_list.surface_address_row(address)\\n\\n row.open_kebab_menu()\\n row.kebab_menu.edit_address()\",\n \"def __editAddress(self):\\n idx = self.bookmarksTree.currentIndex()\\n idx = idx.sibling(idx.row(), 1)\\n self.bookmarksTree.edit(idx)\",\n \"def edit_form():\\n return template (\\\"edit\\\")\",\n \"def edit_address(self, new_label: str) -> None:\\n self.address_form.label_input.fill(new_label)\\n self.address_form.save_button.click()\",\n \"def activate_external_editing(self, new_doc):\\n new_doc.setup_external_edit_redirect(self.request, action=\\\"oneoffixx\\\")\",\n \"def test_phone_address_view_with_url(self):\\n url = reverse('profiles:update_phone_address')\\n response = self.client.get(url)\\n self.assertEqual(response.status_code, 200)\\n self.assertTemplateUsed(\\n response,\\n 'profiles/update_phone_address.html'\\n )\",\n \"def get(self, request, *args, **kwargs):\\n\\n self.object = self.get_object()\\n formclass = self.get_form_class()\\n opform = self.get_form(formclass)\\n if not request.user.is_superuser:\\n del opform.fields['administrator']\\n addrform = AddressForm(instance=self.object.address)\\n return self.render_to_response(\\n self.get_context_data(\\n form=opform,\\n address_form=addrform,\\n )\\n )\",\n \"def showEditContact(self):\",\n \"def show_edit_form(user_id):\\n user = User.query.get_or_404(user_id)\\n return render_template('edit.html', user=user)\",\n \"def getEditForm( self ):\\n return \\\"listc_edit\\\"\",\n \"def set_show_in_edit_form(self, flag):\\n qry = ServiceOperationQuery(self, \\\"setShowInEditForm\\\", [flag])\\n self.context.add_query(qry)\",\n \"def edit_link(db_object, text=None):\\n if text is None:\\n text = 'edit'\\n return _make_link(db_object.update_url(), text)\",\n \"def lineEdit( self ):\\n return self._urlEdit\",\n \"def edit(self, **kwargs):\\n ...\",\n \"def open_ride_edit_form(self) -> object:\\n self.container.find_element(*self._ride_edit).click()\\n\\n return EditForm(self).wait_for_component_to_be_present()\",\n \"def url_to_edit(obj):\\n return reverse(\\n 'admin:%s_%s_change' % (obj._meta.app_label, obj._meta.model_name),\\n args=[obj.id]\\n )\",\n \"def edit_redirect_url(self):\\n return url_for(self.edit_redirect_to_view)\",\n \"def process_address():\\n #get address info from form\\n user_details = request.form\\n #validate address with google geocoding\\n update_details = apiapijoyjoy.validate_address(user_details)\\n #update ino in db\\n dbwrangler.newaddress(update_details)\\n \\n return redirect(\\\"/\\\")\",\n \"def edit_link(instance):\\n\\n try:\\n content_type = ContentType.objects.get_for_model(instance.__class__)\\n except AttributeError:\\n raise ValueError('Passed value must be registered model instance')\\n else:\\n model_admin_change_link = 'admin:{app}_{model}_change'.format(\\n app=content_type.app_label,\\n model=content_type.model\\n )\\n return reverse(model_admin_change_link, args=(instance.id,))\",\n \"def test_the_data_edit_url(self):\\n\\n my_instance = Contact.objects.first()\\n info_url = resolve('/to_form/%s/' % my_instance.id)\\n self.assertEqual(info_url.func.__name__, 'my_edit_data')\\n self.assertEqual(self.response.status_code, 200)\",\n \"def choose_new_address(self) -> \\\"CheckoutPage\\\":\\n self.accordion = BillingDetailsUser(self.driver)\\n self.accordion.btn_new_address.click()\\n return self\",\n \"def edit(self):\\n\\n pass\",\n \"def show_admin_edit_admins():\\n return render_admin_page(\\\"admin-ea.html\\\")\",\n \"def get_edit_form(self, data):\\n self.add_success(data)\\n rv = self.get((data[self.id_field], self.edit_url))\\n assert not is_404(rv)\\n assert in_response(rv, 'Edit {}'.format(data[self.name_field]))\\n for field, name in self.fields:\\n assert in_response(rv, name)\\n return rv\",\n \"def show_admin_edit_users():\\n return render_admin_page(\\\"admin-eu.html\\\")\",\n \"def show_edit_form(user_id):\\n\\n user = User.query.get_or_404(user_id)\\n\\n return render_template(\\\"users/edit_user.html\\\", user=user)\",\n \"def OnToggleEdit(self, event):\\n\\t\\ttoggle = self.btn_edit.GetToggle()\\n\\t\\tif not toggle:\\n\\t\\t\\tif not util.gAuthen.Authenticate(util.ADMIN):\\n\\t\\t\\t\\tself.btn_edit.SetToggle(not toggle)\\n\\t\\t\\t\\treturn \\n\\t\\tself.UpdateToggle()\",\n \"def register_edit_view(self, blueprint):\\n view = apply_decorators(self.edit_view, self.edit_decorators)\\n blueprint.add_url_rule(\\n self.edit_rule, self.edit_endpoint, view, methods=['GET', 'POST'])\",\n \"def edit():\",\n \"def link_edit_callback(self):\\n pass\",\n \"def the_user_edit_page_should_open(driver):\\n assert wait_on_element(driver, 7, xpaths.add_User.edit_Title)\\n time.sleep(0.5)\",\n \"def edit_employee(employee_id):\\n\\n if not g.user:\\n flash(\\\"Please login to access\\\", \\\"danger\\\")\\n return redirect(\\\"/\\\")\\n \\n if g.user.is_admin == False:\\n flash (\\\"Unauthorized\\\", \\\"danger\\\")\\n return redirect(\\\"/login\\\")\\n\\n employee = Employee.query.get_or_404(employee_id)\\n form = Edit_User_Form(obj = employee)\\n \\n #form.location.choices = db.session.query(Location.id, Location.site_name).all()\\n \\n #form.certs.choices = db.session.query(Certs.id , Certs.cert_name).all()\\n\\n if form.validate_on_submit():\\n \\n employee.email = form.email.data, \\n employee.first_name = form.first_name.data,\\n employee.last_name = form.last_name.data,\\n employee.hire_date = form.hire_date.data, \\n employee.is_admin = form.is_admin.data\\n\\n \\n db.session.commit()\\n \\n flash(f\\\"{employee.first_name} {employee.last_name} has been saved\\\", \\\"success\\\")\\n return redirect(\\\"/administrator\\\")\\n else:\\n\\n return render_template(\\\"/admin/edit_user.html\\\", employee = employee, form = form)\",\n \"def manage_edit_save(self, REQUEST):\\n self._config.update(ldap_config.read_form(REQUEST.form, edit=True))\\n REQUEST.RESPONSE.redirect(self.absolute_url() + '/manage_edit')\",\n \"def edit_agency(self) -> object:\\n self.edit_agency_button.click()\\n\\n return AgencySelectModal(self).wait_for_component_to_be_present()\",\n \"def edit():\\n database.ask(mode='single')\\n F = database.check(single=True)\\n if F and hasattr(F,'edit'):\\n name = database[0]\\n F.edit(name)\",\n \"def edit_employee_locations(employee_id):\\n\\n if not g.user:\\n flash(\\\"Please login to access\\\", \\\"danger\\\")\\n return redirect(\\\"/\\\")\\n \\n if g.user.is_admin == False:\\n flash (\\\"Unauthorized\\\", \\\"danger\\\")\\n return redirect(\\\"/login\\\")\\n\\n employee = Employee.query.get_or_404(employee_id)\\n \\n form = Add_Loc_Form(obj = employee)\\n \\n form.location.choices = db.session.query(Location.id, Location.site_name).all()\\n \\n \\n if form.validate_on_submit():\\n \\n location = Location.query.get(form.location.data) \\n employee.locations.append(location)\\n db.session.add(employee)\\n \\n db.session.commit()\\n\\n \\n\\n flash(f\\\"{employee.first_name} {employee.last_name} has been saved\\\", \\\"success\\\")\\n return redirect(\\\"/administrator\\\")\\n else:\\n \\n return render_template(\\\"/admin/employee_cert.html\\\", employee = employee, form = form)\",\n \"def editPage(request, title):\\n entry = util.get_entry(title)\\n if request.method == \\\"POST\\\":\\n # check if the data is valid then save/replace old data\\n form = editPageForm(request.POST)\\n if form.is_valid():\\n title = form.cleaned_data[\\\"editTitle\\\"]\\n content = form.cleaned_data[\\\"editBody\\\"]\\n\\n util.save_entry(title, content)\\n\\n # take user to their editted page\\n return HttpResponseRedirect(reverse(\\\"entry\\\", kwargs={\\n \\\"title\\\": title\\n }))\\n # give user a editting form with existing data filled in by defult. \\n else:\\n editForm = editPageForm(initial={\\n \\\"editTitle\\\": title,\\n \\\"editBody\\\": entry\\n })\\n editFormTitle = editForm[\\\"editTitle\\\"]\\n editFormBody = editForm[\\\"editBody\\\"]\\n return render(request, \\\"encyclopedia/editPage.html\\\", {\\n \\\"formTitle\\\": editFormTitle,\\n \\\"formBody\\\": editFormBody\\n })\",\n \"def test_address_page(self):\\n tester = app.test_client(self)\\n response = tester.get('/', content_type = \\\"html_text\\\")\\n self.assertTrue(b'Address Locator' in response.data)\",\n \"def new_address():\\n user = dbwrangler.get_current_user()\\n if user:\\n return render_template(\\\"new_user_more.html\\\")\\n else:\\n return redirect(\\\"/\\\")\",\n \"def edit_form_entry_edit_option_html(cls):\\n return \\\"\\\"\\\"\\n <li><a href=\\\"{edit_url}\\\">\\n <span class=\\\"{edit_option_class}\\\"></span> {edit_text}</a>\\n </li>\\n \\\"\\\"\\\".format(\\n edit_url=\\\"{edit_url}\\\",\\n edit_option_class=cls.form_edit_form_entry_option_class,\\n edit_text=\\\"{edit_text}\\\",\\n )\",\n \"def restaurants_edit(restaurant_id):\\n # If the user isn't logged in, send to the login page\\n if helper.handle_login(login_session) is False:\\n return redirect('/login')\\n # Find the restaurant\\n restaurant = session.query(Restaurant).filter_by(id=restaurant_id).one()\\n if request.method == 'POST':\\n # Only edit if the entry was re-written\\n if len(request.form['address']) > 0:\\n restaurant.address = request.form['address']\\n if len(request.form['phone']) > 0:\\n restaurant.phone = request.form['phone']\\n if len(request.form['web']) > 0:\\n restaurant.web = helper.check_restaurant_URL(request.form['web'])\\n if len(request.form['tag_line']) > 0:\\n tag_line = request.form['tag_line']\\n tag_list = tag_line.split(',')\\n helper.delete_restaurant_tag_pairs(restaurant.id)\\n for tag in tag_list:\\n helper.add_tag_if_not_exists(tag, restaurant.id)\\n if len(request.form['description']) > 0:\\n restaurant.description = request.form['description']\\n\\n restaurant.last_update = datetime.utcnow()\\n\\n session.add(restaurant)\\n session.commit()\\n flash(\\\"Restaurant {} edited!\\\".format(restaurant.name))\\n return redirect(url_for('restaurants_page'))\\n else:\\n # Get user info if the user is signed in to render edit form\\n user_info = helper.get_user_if_exists(login_session)\\n tag_rest_list = session.query(RestaurantTags).filter_by(restaurant_id=restaurant.id).all()\\n tag_line = ''\\n # Create a tag line - by compiling the string tag_name for each tag\\n for pair in tag_rest_list:\\n tag = session.query(Tags).filter_by(id=pair.tag_id).first()\\n tag_line += tag.tag_name + ', '\\n return render_template('editrestaurant.html',\\n restaurant=restaurant,\\n tag_line=tag_line,\\n user_info=user_info)\",\n \"def update(self, addr, replace=False):\\n if self.ignorer.ignore_address(addr[1]):\\n return False\\n try:\\n with self.connect() as c:\\n cur = c.cursor()\\n if replace:\\n present = cur.execute(\\\"SELECT 1 FROM AddressBook WHERE address = ?\\\", [addr[1]])\\n if present:\\n cur.execute(\\\"UPDATE AddressBook SET name = ? WHERE address = ?\\\", addr)\\n else:\\n cur.execute(\\\"INSERT INTO AddressBookView VALUES(?,?)\\\", addr)\\n else:\\n cur.execute(\\\"INSERT INTO AddressBookView VALUES(?,?)\\\", addr)\\n return True\\n except sqlite3.IntegrityError:\\n return False\",\n \"def edit(user_id):\\n if user_id != current_user.id:\\n return abort(403)\\n\\n user = get_user(user_id)\\n form = EditForm(obj=user)\\n form.email.data = user.email\\n\\n if form.validate_on_submit():\\n password = form.password.data\\n username = form.username.data\\n\\n save_result = edit_user(user_id, password, username, user.active)\\n user = save_result['entry']\\n form = EditForm(request.form, obj=save_result['entry'])\\n form.email.data = user.email\\n return redirect(url_for('.index'))\\n \\n return render_template('users/edit.html'\\n ,form=form\\n ,user=user\\n ,t=t\\n ,m=m)\",\n \"def edit_user(user_id):\\n user = User.query.get_or_404(user_id)\\n return render_template('/users/edit_page.html', user=user)\",\n \"def home_edituser():\\n\\tpass\",\n \"def click_the_edit_button_that_appears(driver):\\n driver.find_element_by_xpath(xpaths.users.eric_Edit_Button).click()\",\n \"def __edit(self):\\n from .BookmarkPropertiesDialog import BookmarkPropertiesDialog\\n \\n idx = self.bookmarksTree.currentIndex()\\n sourceIndex = self.__proxyModel.mapToSource(idx)\\n node = self.__bookmarksModel.node(sourceIndex)\\n dlg = BookmarkPropertiesDialog(node)\\n dlg.exec_()\",\n \"def edit_place(place_id):\\n place_db = mongo.db.places.find_one_or_404({'_id': ObjectId(place_id)})\\n if request.method == 'GET':\\n form = EditPlacesForm(data=place_db)\\n return render_template('edit_restaurant.html', recipe=place_db, form=form)\\n form = EditPlacesForm(request.form)\\n if form.validate_on_submit():\\n places_db = mongo.db.places\\n places_db.update_one({\\n '_id': ObjectId(place_id),\\n }, {\\n '$set': {\\n 'name': request.form['name'],\\n 'city': request.form['city'],\\n 'added_by': session['username'],\\n 'description': request.form['description'],\\n 'tags': request.form['tags'],\\n 'image': request.form['image'],\\n }\\n })\\n return redirect(url_for('home'))\\n return render_template('edit_restaurant.html', place=place_db, form=form)\",\n \"def _enter_edit_mode(self):\\n edit_mode = self.UTILS.element.getElement(DOM.DownloadManager.download_edit_button,\\n \\\"Download edit button\\\", True, 10)\\n edit_mode.tap()\\n self.UTILS.element.waitForElements(DOM.DownloadManager.downloads_edit_header_title,\\n \\\"Edit downloads header\\\")\",\n \"def show_edit_user_form(user_id):\\n\\n user = User.query.get_or_404(user_id)\\n return render_template('edit_user.html', user=user)\",\n \"def edit_restaurant(restaurant_id):\\n user_id = login_session['user_id']\\n r = read_restaurants(restaurant_id, user_id)\\n if r[1] is True: # Means if user is owner\\n if request.method == 'GET':\\n return render_template('restaurants/editrestaurant.html',\\n restaurant=r[0][0])\\n elif request.method == 'POST':\\n # Got post request -> First we get the request arguemnts\\n name = request.form['name']\\n address = request.form['address']\\n city = request.form['city']\\n state = request.form['state']\\n zipCode = request.form['zipCode']\\n # Next we do the db edit\\n update_restaurant(restaurant_id, name, address,\\n city, state, zipCode)\\n # Finally we return the success html\\n flash(\\\"Edited your restaurant\\\")\\n return render_template(\\\"submitted.html\\\")\\n else:\\n return \\\"Invalid http\\\"\\n else:\\n flash(\\\"You need to be the owner of the restaurant to edit\\\")\\n return redirect(url_for('site.show_restaurants',\\n restaurant_id=restaurant_id))\",\n \"def show_edit_form(self, obj_pk=None):\\n obj = self.model.objects.get(pk=obj_pk)\\n # if there is no edit permission then does not show the form\\n if not self.has_view_permissions(obj): return\\n\\n\\n # create the edit form a add it to the empty widget details\\n # override the function hide_form to make sure the list is shown after the user close the edition form\\n params = {\\n 'title':'Edit',\\n 'model':self.model,\\n 'pk':obj.pk,\\n 'parent_model':self.parent_model,\\n 'parent_pk':self.parent_pk,\\n 'parent_win': self\\n }\\n\\n if self.INLINES: params.update({'inlines': self.INLINES} )\\n if self.FIELDSETS: params.update({'fieldsets':self.FIELDSETS})\\n if self.READ_ONLY: params.update({'readonly': self.READ_ONLY})\\n\\n editmodel_class = self.get_editmodel_class(obj)\\n editform = editmodel_class(**params)\\n\\n if hasattr(self, '_details') and self.USE_DETAILS_TO_EDIT:\\n self._details.value = editform\\n self._list.hide()\\n self._details.show()\\n\\n # only if the button exists:\\n toolbar = [self.toolbar] if isinstance(self.toolbar, str) else self.toolbar\\n if toolbar:\\n for o in toolbar:\\n if o and hasattr(self, o): getattr(self, o).hide()\\n\\n else:\\n self._list.show()\\n if hasattr(self, '_details'):\\n self._details.hide()\",\n \"def edit(request, address):\\n if address.startswith(\\\"/\\\"):\\n address = address[1:]\\n if address.endswith(\\\"/\\\"):\\n address = address[:-1]\\n\\n # we try to find the parent. Creating a page without parent isn't possible.\\n parent = None\\n if \\\"/\\\" in address:\\n parent = address.rsplit(\\\"/\\\", 1)[0]\\n else:\\n parent = \\\"\\\"\\n\\n try:\\n parent = Page.objects.get(address=parent)\\n except Page.DoesNotExist:\\n parent = None\\n\\n # try to get the page itself, which might exist\\n try:\\n page = Page.objects.get(address=address)\\n except Page.DoesNotExist:\\n page = None\\n\\n initial = {}\\n if page:\\n initial[\\\"title\\\"] = page.title\\n initial[\\\"content\\\"] = page.content\\n\\n if request.method == 'POST':\\n # the form has been sent, use the different access rights\\n form = PageForm(request.POST, initial=initial)\\n if form.is_valid():\\n title = form.cleaned_data[\\\"title\\\"]\\n content = form.cleaned_data[\\\"content\\\"]\\n user = request.user\\n user = user if user.is_authenticated else None\\n can = False\\n if user and user.is_superuser:\\n # the superuser can do it all\\n can = True\\n elif parent and page is None and parent.access(user, \\\"write\\\"):\\n # the page doesn't exist, but the parent does, and the user can edit it\\n can = True\\n elif page and page.access(user, \\\"write\\\"):\\n # the page already exist and the user can edit it\\n can = True\\n\\n if can:\\n new_page = Page.objects.create_or_update_content(address, user, content)\\n new_page.title = title\\n if parent is not None and page is None:\\n new_page.can_write = parent.can_write\\n new_page.can_read = parent.can_read\\n new_page.save()\\n\\n return HttpResponseRedirect('/wiki/' + address)\\n else:\\n form = PageForm(initial=initial)\\n\\n return render(request, \\\"wiki/edit.html\\\", {'form': form, 'address': address, \\\"page\\\": page, \\\"parent\\\": parent})\",\n \"def show_employee_edit_form(self, staff_ob, number):\\n\\n print(self.LENGTH_STAR * \\\"*\\\")\\n print(f\\\"EDIT {staff_ob.role.upper()}\\\\n\\\")\\n\\n if number == 1:\\n print(self.LENGTH_STAR * \\\"*\\\")\\n print(f\\\"You are changing {staff_ob.name}´s address\\\\nThe current address is: {staff_ob.address}\\\")\\n new_address = self.get_address()\\n while new_address == False:\\n new_address = self.get_address()\\n self.check_action_edit_form(staff_ob, number, new_address)\\n\\n elif number == 2:\\n print(self.LENGTH_STAR * \\\"*\\\")\\n print(f\\\"You are changing {staff_ob.name}´s mobile number\\\\nThe current mobile number is: {staff_ob.mobile_number}\\\")\\n new_mobile_number = self.get_mobile_number()\\n while new_mobile_number == False:\\n new_mobile_number = self.get_mobile_number\\n self.check_action_edit_form(staff_ob, number, new_mobile_number)\\n \\n elif number == 3:\\n print(self.LENGTH_STAR * \\\"*\\\")\\n print(f\\\"You are changing {staff_ob.name}´s email\\\\nThe current the email is: {staff_ob.email}\\\")\\n new_email = self.get_email()\\n while new_email == False:\\n new_email = self.get_email()\\n self.check_action_edit_form(staff_ob, number, new_email)\\n \\n print(f\\\"\\\\n{staff_ob.name}'s information successfully changed!\\\\n\\\")\\n \\n return\",\n \"def edit(request, pageName):\\n \\n if request.method == \\\"POST\\\":\\n form = EditForm(request.POST)\\n \\n if form.is_valid(): \\n content = form.cleaned_data[\\\"content\\\"]\\n title = form.cleaned_data[\\\"title\\\"]\\n \\n util.save_entry(title, content)\\n return HttpResponseRedirect(reverse(\\\"encyclopedia:visit_entry\\\", args=(title, )))\\n \\n else:\\n\\n form = EditForm({'title': pageName, 'content': util.get_entry(pageName) })\\n return render(request, \\\"encyclopedia/edit_page.html\\\", {\\n \\\"form\\\": EditForm(),\\n \\\"pageName\\\": pageName\\n })\\n \\n \\n return render(request, \\\"encyclopedia/edit_page.html\\\", {\\n \\\"form\\\": EditForm({'title': pageName, 'content': util.get_entry(pageName) }),\\n \\\"pageName\\\": pageName\\n })\",\n \"def show_edit_user_form(user_id):\\r\\n user = User.query.get_or_404(user_id)\\r\\n\\r\\n return render_template('edit-user.html', user=user)\",\n \"def click_bulk_edit_button(self):\\n self.click_element(self.bulk_edit_button_locator, True)\",\n \"def edit_locations(location_id):\\n\\n if not g.user:\\n flash(\\\"Please login to access\\\", \\\"danger\\\")\\n return redirect(\\\"/\\\")\\n \\n if g.user.is_admin == False:\\n flash (\\\"Unauthorized\\\", \\\"danger\\\")\\n return redirect(\\\"/login\\\")\\n\\n location = Location.query.get_or_404(location_id)\\n form = Location_Form(obj = location)\\n\\n if form.validate_on_submit():\\n location.site_name = form.site_name.data,\\n location.city = form.city.data,\\n location.state = form.state.data\\n \\n db.session.commit()\\n flash(f\\\"Location {site_name} has been updated\\\")\\n return redirect(\\\"/administrator\\\")\\n else:\\n return render_template(\\\"/admin/edit_location.html\\\", form = form, location = location)\",\n \"def add_new_address(self, address: dict) -> None:\\n self.new_address_button.click()\\n\\n self.address_form.select_location(address['address'])\\n self.address_form.label_input.fill(address['name'])\\n\\n self.address_form.save_button.click()\",\n \"def _edit( self, remote_url ):\\n # strip off scheme and machine from URL if present\\n tokens = urlparse.urlparse( remote_url, 'http' )\\n if tokens[1]:\\n # There is a nethost, remove it\\n t=('', '') + tokens[2:]\\n remote_url=urlparse.urlunparse(t)\\n # if URL begins with site URL, remove site URL\\n utool = queryUtility(IURLTool)\\n if utool is None:\\n # fallback for bootstrap\\n utool = aq_get(self, 'portal_url', None)\\n portal_url = utool.getPortalPath()\\n i = remote_url.find(portal_url)\\n if i==0:\\n remote_url=remote_url[len(portal_url):]\\n # if site is still absolute, make it relative\\n if remote_url[:1]=='/':\\n remote_url=remote_url[1:]\\n self.remote_url=remote_url\\n\\n # save unique id of favorite\\n self.remote_uid = self._getUidByUrl()\",\n \"def edit_person(self, pk):\",\n \"def create_or_update_address(address, customer):\\n\\tname = frappe.db.get_value('Address', { 'entity_id': address.get('entity_id') })\\n\\tif not name:\\n\\t\\taddr = frappe.new_doc('Address')\\n\\t\\taddr.address_title = \\\"{} {} {}\\\".format(\\n\\t\\t\\taddress.get(\\\"firstname\\\"),\\n\\t\\t\\taddress.get(\\\"lastname\\\"),\\n\\t\\t\\taddress.get(\\\"entity_id\\\")\\n\\t\\t)\\n\\telse:\\n\\t\\taddr = frappe.get_doc(\\\"Address\\\", name)\\n\\n\\taddr.address_type = get_address_type(address).get('type')\\n\\taddr.entity_id = address.get('entity_id')\\n\\taddr.address_line1 = address.get('street')[0]\\n\\taddr.address_line2 = address.get('street')[1] if len(address.get('street')) > 1 else \\\"\\\"\\n\\taddr.city = address.get('city')\\n\\taddr.country = frappe.db.get_value('Country', { 'code': address.get('country_id') })\\n\\taddr.state = address.get('region')\\n\\taddr.pincode = address.get('postcode')\\n\\taddr.phone = address.get('telephone') or '00000'\\n\\taddr.fax = address.get('fax')\\n\\taddr.customer = customer\\n\\taddr.customer_name = address.get('firstname')+' '+address.get('lastname')\\n\\taddr.is_primary_address = get_address_type(address).get('is_primary_address')\\n\\taddr.is_shipping_address = get_address_type(address).get('is_shipping_address')\\n\\n\\taddr.save(ignore_permissions=True)\",\n \"def adminedit(object, id):\\n\\n db = get_db()\\n\\n if request.method == \\\"POST\\\":\\n execute_string = 'UPDATE ' + object.title() + \\\" SET \\\"\\n\\n if object == 'post':\\n execute_string += 'title = \\\"' + request.form['title'] + '\\\", content = \\\"' + request.form['content'] + '\\\", authorId = ' + request.form[\\\"authorid\\\"] + ', categoryId = ' + request.form[\\\"categoryid\\\"] + ''\\n elif object == 'author':\\n execute_string += 'name = \\\"' + request.form['name'] + '\\\"'\\n elif object == 'category':\\n execute_string += 'name = \\\"' + request.form['name'] + '\\\", description = \\\"' + request.form['description'] + '\\\"'\\n\\n execute_string += \\\" WHERE id = \\\" + str(id)\\n db.execute(execute_string)\\n db.commit()\\n return redirect(url_for(\\\"adminview\\\", object=object))\\n\\n execute_string = \\\"SELECT * FROM \\\" + object.title() + \\\" WHERE id = \\\" + str(id)\\n item = db.execute(execute_string).fetchone()\\n\\n return render_template(\\\"new.html\\\", object=object, item=item)\",\n \"def admin(self, view):\\n view.admin = True\\n return view\",\n \"def enableAddressLayer(self):\\n\\n self._deladdressaction.setEnabled(True)\\n self._createnewaddressaction.setEnabled(True)\\n self._moveaddressaction.setEnabled(True)\\n self._updateaddressaction.setEnabled(True)\\n self._highlightaction.setEnabled(True)\",\n \"def edit_view(request, title, modelform, instance=None, **kwargs):\\n instance_form = modelform(request.POST or None, instance=instance)\\n if instance_form.is_valid():\\n instance = instance_form.save()\\n messages.success(request, _(\\\"%s was edited.\\\") % instance)\\n return redirect(instance.get_absolute_url())\\n return form(\\n {**kwargs, \\\"form\\\": instance_form, \\\"action_name\\\": _(\\\"Edit\\\"), \\\"title\\\": title},\\n \\\"deployments/form.html\\\",\\n request,\\n )\",\n \"def test_edit_has_populated_form(testapp, fill_the_db, login_testcase):\\n response = testapp.get('/journal/1/edit-entry', status=200)\\n title = response.html.find_all('input')[1]['value']\\n body = response.html.form.textarea.contents[0]\\n assert title == ENTRIES[0]['title']\\n assert body == ENTRIES[0]['body']\",\n \"def edit_entry(self):\\r\\n self.session = tk.Toplevel(self.master, **jt.bframe_style)\\r\\n jd.Page(self.session, self.source)\",\n \"def edit_contact(self):\\n edit_data = input(\\\"Enter the first name of user you want to edit\\\\n\\\")\\n\\n for contact in self.contact_list:\\n if contact.first_name == edit_data:\\n user_input = int(input(\\n \\\"Enter the number that you want to edit field in details\\\"\\n \\\" \\\\n 1. First Name 2. Last name 3. Address 4. city 5. state 6.zip 7. Phone number 8.Email \\\\n\\\"))\\n if user_input == 1:\\n first_name = input(\\\"Enter new first name\\\\n\\\")\\n contact.first_name = first_name\\n elif user_input == 2:\\n last_name = input(\\\"Enter new last name\\\\n\\\")\\n contact.last_name = last_name\\n elif user_input == 3:\\n address = input(\\\"Enter new address\\\\n\\\")\\n contact.address = address\\n elif user_input == 4:\\n city = input(\\\"Enter new city\\\\n\\\")\\n contact.city = city\\n elif user_input == 5:\\n state = input(\\\"Enter new state\\\\n\\\")\\n contact.state = state\\n elif user_input == 6:\\n zip = input(\\\"Enter new zip\\\\n\\\")\\n contact.zip = zip\\n elif user_input == 7:\\n phone_number = input(\\\"Enter new phone number\\\\n\\\")\\n contact.phone_number = phone_number\\n elif user_input == 8:\\n email = input(\\\"Enter new email\\\\n\\\")\\n contact.email = email\\n else:\\n print(\\\"Please enter a valid input\\\")\\n else:\\n print(\\\"Please enter a valid name\\\")\",\n \"def open_attr_req_editing(self, element) -> None:\\n pass\",\n \"def edit_entry(self, id, body=None, link=None, **args):\\n args.update(id=id)\\n if body: args.update(body=body)\\n if link: args.update(link=link)\\n return self.fetch(\\\"/entry\\\", post_args=args)\",\n \"def edit_item(self):\\n for item in self.selection():\\n origin_name = self.set(item)[\\\"1\\\"]\\n origin_url = self.set(item)[\\\"2\\\"]\\n popup = GetItemURLDialogue(self, \\\"Edit Item\\\", origin_name, origin_url)\\n\\n self.item(item, values=(popup.name, popup.url, self.set(item)[\\\"3\\\"]))\\n self.set(item)[\\\"2\\\"] = popup.url\\n\\n # Edit the item - backend\\n for row in s.item:\\n if row[\\\"item\\\"] == origin_name and row[\\\"url\\\"] == origin_url:\\n s.item.remove(row)\\n s.updateItem({\\\"item\\\": popup.name, \\\"url\\\": popup.url})\",\n \"def show_orion_admin_url(self, obj):\\n return obj.orion_admin_url\",\n \"def test_edit_view(self):\\n target_url = url_for('content.edit_content')\\n redirect_url = url_for('users.login', next=target_url)\\n response = self.client.get(target_url)\\n self.assertEqual(response.status_code, 302)\\n self.assertRedirects(response, redirect_url)\",\n \"def editChange(self,editBtn):\\n if self.edit ==True:\\n self.updateDetails(\\\"normal\\\") #update details column\\n self.edit = False #switch boolean\\n self.editBtn.setText(\\\"Edit\\\") #update button text\\n else:\\n self.updateDetails(\\\"edit\\\") #update details column\\n self.edit= True #switch boolean\\n self.editBtn.setText(\\\"Stop Editing\\\") #update button text\",\n \"def editarEndereco(self, ide, rua = None, num = None, bairro = None, cidade = None, uf = None, cep = None, complemento = None):\\r\\n try:\\r\\n if ide is None:\\r\\n return False\\r\\n else:\\r\\n if rua != None:\\r\\n self.cursor.execute(\\\"UPDATE ENDERECO SET RUA = '%s' WHERE ID = %s;\\\" %(rua, ide))\\r\\n if num != None:\\r\\n self.cursor.execute(\\\"UPDATE ENDERECO SET NUM = %s WHERE ID = %s;\\\" %(num, ide))\\r\\n if bairro != None:\\r\\n self.cursor.execute(\\\"UPDATE ENDERECO SET BAIRRO = '%s' WHERE ID = %s;\\\" %(bairro, ide))\\r\\n if cidade != None:\\r\\n self.cursor.execute(\\\"UPDATE ENDERECO SET CIDADE = '%s' WHERE ID = %s;\\\" %(cidade, ide))\\r\\n if uf != None:\\r\\n self.cursor.execute(\\\"UPDATE ENDERECO SET UF = '%s' WHERE ID = %s;\\\" %(uf, ide))\\r\\n if cep != None:\\r\\n self.cursor.execute(\\\"UPDATE ENDERECO SET CEP = '%s' WHERE ID = %s;\\\" %(cep, ide))\\r\\n if complemento != None:\\r\\n self.cursor.execute(\\\"UPDATE ENDERECO SET COMPLEMENTO = '%s' WHERE ID = %s;\\\" %(complemento, ide))\\r\\n return True\\r\\n except:\\r\\n return False\",\n \"def __get_redirect_url(self):\\n if self.get_submit_save_and_continue_edititing_button_name() not in self.request.POST:\\n return self.request.cradmin_app.reverse_appindexurl()\\n return self.request.cradmin_app.reverse_appurl(\\n 'groupcomment-edit',\\n args=self.args,\\n kwargs=self.kwargs)\",\n \"def edit_register(id):\\n add_employee = False\\n\\n employee = Employee.query.get_or_404(id) #from table\\n print('----update 1----')\\n form = UpdateForm(obj=employee) #if not 404\\n print('----update 2----')\\n if form.validate_on_submit():\\n employee.email = email=form.email.data\\n employee.username=form.username.data\\n employee.glad_id=form.glad_id.data\\n employee.tel_no=form.tel_no.data\\n employee.role_id=form.role_id.data\\n employee.password=form.password.data\\n\\n # UPDATE employee to the database\\n print('----update----',employee.role_id)\\n db.session.commit()\\n flash('You have successfully updated! ')\\n\\n # # redirect to the login page\\n # return redirect(url_for('auth.login'))\\n\\n # load registration template\\n return render_template('auth/register.html', form=form, title='Update')\",\n \"def admin_edit_admins():\\n return user_management_handler(\\\"show_admin_edit_admins\\\", \\\"new_admins\\\", True)\",\n \"def save_edit(self):\\r\\n self.driver.find_element_by_xpath('//*[@id=\\\"vnos\\\"]/span[1]/div/a').click()\",\n \"def edit_user(user_id):\\n\\n user = User.query.get_or_404(user_id)\\n return render_template('users/edit.html', user=user)\",\n \"def is_edit(self):\\n return self._tag == 'edit'\",\n \"def admin_update_preview():\\n return user_management_handler(\\\"show_admin\\\", \\\"\\\", False)\",\n \"def form_valid(self, form):\\n address_form = AddressInlineFormSet(self.request.POST, instance = self.object)\\n if form.is_valid() and address_form.is_valid():\\n form.save()\\n address_form.save()\\n return HttpResponseRedirect(self.get_success_url())\\n else:\\n context = {\\n 'form' : form,\\n 'address_form' : address_form\\n }\\n return self.render_to_response(context)\",\n \"def isEditLink(self, rel, type = None): #$NON-NLS-1$\\r\\n return self._isInRelList(rel, ZAtomRelTypes.ATOM_EDIT_LINK_REL_LIST)\",\n \"def admin_user_area():\\n session_id = request.args.get('session-id', None)\\n user_id = request.args.get('user-id', None)\\n edit = request.args.get('edit', None)\\n today = datetime.date.today()\\n reservations_list = get_user_reservations_list(user_id)\\n cars_reservations_list = get_cars_user_reservations_list(reservations_list)\\n reservations_status_list = get_reservations_status_list(reservations_list)\\n if edit == \\\"true\\\":\\n edit_mode = True\\n else:\\n edit_mode = False\\n user = get_user_by_id(user_id)\\n if check_authentication(session_id, user_id) and is_admin_user(user_id):\\n return render_template('user_area.html', user=user_id, session_id=session_id, edit_mode=edit_mode,\\n surname=user.surname, name=user.name, birthdate=user.birthdate, today=today,\\n reservations_list=reservations_list, cars_reservations_list=cars_reservations_list,\\n reservations_status_list=reservations_status_list, admin=True)\\n else:\\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__(), del_session_cookie=True)\",\n \"def _editClickedSlot(self):\\r\\n\\r\\n index = self.propertiesTableView.selectionModel().currentIndex()\\r\\n if index.isValid():\\r\\n self.propertiesTableView.edit(index)\",\n \"def model_admin_url(self, instance: BaseModel, name: str = None) -> str:\\n url = resolve_url(\\n admin_urlname(instance._meta, SafeText(\\\"change\\\")), instance.pk\\n )\\n return format_html('<a href=\\\"{}\\\">{}</a>', url, name or str(instance))\",\n \"def _get_address_bar(parent_handle):\\n childwins = []\\n win32gui.EnumChildWindows(parent_handle, _enumWindowsCallback,\\n childwins)\\n for win in childwins:\\n child_handle = win[0]\\n class_name = win[1]\\n if 'Edit' in class_name:\\n edit_text = _get_edit_text(child_handle)\\n if 'http://' in edit_text or 'https://' in edit_text:\\n return child_handle # then this must be it...\",\n \"def admin_edit_users():\\n return user_management_handler(\\\"show_admin_edit_users\\\", \\\"new_users\\\", False)\",\n \"def show_fresh_address(self):\\n\\t\\treturn self.__fresh_account()[\\\"address\\\"]\",\n \"def edit(tesserae, tessera_id):\\n try:\\n return tesserae.edit(tessera_id)\\n except TesseraError, e:\\n sys.stderr.write(\\\"Error: %s\\\\n\\\", str(e))\\n return False\",\n \"def editar_empresa(id):\\n cadastrando_empresa = False\\n\\n empresa = Empresa.query.get_or_404(id)\\n form = EditarEmpresaForm(obj=empresa)\\n\\n if form.validate_on_submit():\\n empresa.nome = form.nome.data\\n empresa.simbolo = form.simbolo.data\\n empresa.regiao = form.regiao.data\\n empresa.tipo = form.tipo.data\\n empresa.abertura = form.abertura.data\\n empresa.fechamento = form.fechamento.data\\n empresa.zona = form.zona.data\\n empresa.moeda = form.moeda.data\\n db.session.commit()\\n flash('Empresa editada com sucesso!')\\n\\n return redirect(url_for('home.listar_empresas'))\\n\\n form.nome.data = empresa.nome\\n form.simbolo.data = empresa.abertura \\n form.regiao.data = empresa.regiao\\n form.tipo.data = empresa.tipo\\n form.abertura = empresa.abertura\\n form.fechamento = empresa.fechamento\\n form.zona.data = empresa.zona\\n form.moeda.data = empresa.moeda\\n\\n\\n return render_template('home/empresa.html', action=\\\"Edit\\\",\\n cadastrando_empresa=cadastrando_empresa, form=form,\\n empresa=empresa, title=\\\"Editar empresa\\\")\",\n \"def test_edit_view(self):\\n self.client.post(reverse('misago:admin:users:bans:new'), data={\\n 'check_type': '0',\\n 'banned_value': 'Admin',\\n })\\n\\n test_ban = Ban.objects.get(banned_value='admin')\\n form_link = reverse('misago:admin:users:bans:edit', kwargs={'pk': test_ban.pk})\\n\\n response = self.client.post(form_link, data={\\n 'check_type': '1',\\n 'banned_value': 'test@test.com',\\n 'user_message': 'Lorem ipsum dolor met',\\n 'staff_message': 'Sit amet elit',\\n 'expires_on': '',\\n })\\n self.assertEqual(response.status_code, 302)\\n\\n response = self.client.get(reverse('misago:admin:users:bans:index'))\\n response = self.client.get(response['location'])\\n self.assertEqual(response.status_code, 200)\\n self.assertContains(response, 'test@test.com')\",\n \"def edit(self, *args, **kw):\\n\\t\\t\\ttmpl_context.widget = self.edit_form\\n\\t\\t\\tpks \\t\\t= self.provider.get_primary_fields(self.model)\\n\\t\\t\\tkw \\t\\t\\t= {}\\n\\n\\t\\t\\tfor i, pk in enumerate(pks):\\n\\t\\t\\t\\tkw[pk] \\t\\t= args[i]\\n\\n\\t\\t\\tvalue \\t\\t= self.edit_filler.get_value(kw)\\n\\t\\t\\tvalue['_method'] \\t= 'PUT'\\n\\n\\t\\t\\treturn dict(value = value, model = self.model.__name__, pk_count = len(pks))\",\n \"def allow_to_edit(user):\\n return allow_to_edit_well(user)\",\n \"def office_view(request, slug, id):\\n company =get_object_or_404(Company,slug=slug)\\n edit = validate_user_company_access_or_redirect(request,company)\\n office_reference = get_object_or_404(Office, id=id,company=company)\\n\\n return render_to_response('office_form.html', \\n {'details': office_reference,'info':office_reference},\\n context_instance=RequestContext(request))\",\n \"def test_update_route_has_populated_form(testapp, fill_the_db):\\n response = testapp.get('/journal/1/edit-entry', status=200)\\n title = response.html.form.input[\\\"value\\\"]\\n body = response.html.form.textarea.contents[0]\\n assert title == ENTRIES[0][\\\"title\\\"]\\n assert body == ENTRIES[0][\\\"body\\\"]\",\n \"def startNewAddressTool(self):\\n self.iface.mapCanvas().setMapTool(self._createnewaddresstool)\\n self._createnewaddresstool.setEnabled(True)\",\n \"def edit(self,item=None):\\r\\n raise AbstractError\\r\\n return False\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7748994","0.6355615","0.5770656","0.5743798","0.5656257","0.55580795","0.55527085","0.55411637","0.5522946","0.5501704","0.5454978","0.5401237","0.5389246","0.5380006","0.53368807","0.53319603","0.5306126","0.52961576","0.52745545","0.52547807","0.5243811","0.5242433","0.52371204","0.52199036","0.5215948","0.5209036","0.52000123","0.51821035","0.513429","0.51050574","0.51001364","0.5094042","0.50936365","0.507857","0.5076266","0.50678056","0.50556934","0.5045548","0.50056046","0.49972856","0.499551","0.49889207","0.4982295","0.4946047","0.4941043","0.4937125","0.49214903","0.49205768","0.4918814","0.49059254","0.48899147","0.48888195","0.48853394","0.4876813","0.48636302","0.48604658","0.48203564","0.48188213","0.48171434","0.48042086","0.48018605","0.47950184","0.4794343","0.4789267","0.47846258","0.47730145","0.4772723","0.47661215","0.47634205","0.47619274","0.47514093","0.47501644","0.4748515","0.47483164","0.47293496","0.47076443","0.47075817","0.4706267","0.4694961","0.46930483","0.4691633","0.46915576","0.46914512","0.4688384","0.46828955","0.46799543","0.4674472","0.4668519","0.46661648","0.4665612","0.46622357","0.46562812","0.46455318","0.46454215","0.46448848","0.46432877","0.46431202","0.46404362","0.4622876","0.46182817"],"string":"[\n \"0.7748994\",\n \"0.6355615\",\n \"0.5770656\",\n \"0.5743798\",\n \"0.5656257\",\n \"0.55580795\",\n \"0.55527085\",\n \"0.55411637\",\n \"0.5522946\",\n \"0.5501704\",\n \"0.5454978\",\n \"0.5401237\",\n \"0.5389246\",\n \"0.5380006\",\n \"0.53368807\",\n \"0.53319603\",\n \"0.5306126\",\n \"0.52961576\",\n \"0.52745545\",\n \"0.52547807\",\n \"0.5243811\",\n \"0.5242433\",\n \"0.52371204\",\n \"0.52199036\",\n \"0.5215948\",\n \"0.5209036\",\n \"0.52000123\",\n \"0.51821035\",\n \"0.513429\",\n \"0.51050574\",\n \"0.51001364\",\n \"0.5094042\",\n \"0.50936365\",\n \"0.507857\",\n \"0.5076266\",\n \"0.50678056\",\n \"0.50556934\",\n \"0.5045548\",\n \"0.50056046\",\n \"0.49972856\",\n \"0.499551\",\n \"0.49889207\",\n \"0.4982295\",\n \"0.4946047\",\n \"0.4941043\",\n \"0.4937125\",\n \"0.49214903\",\n \"0.49205768\",\n \"0.4918814\",\n \"0.49059254\",\n \"0.48899147\",\n \"0.48888195\",\n \"0.48853394\",\n \"0.4876813\",\n \"0.48636302\",\n \"0.48604658\",\n \"0.48203564\",\n \"0.48188213\",\n \"0.48171434\",\n \"0.48042086\",\n \"0.48018605\",\n \"0.47950184\",\n \"0.4794343\",\n \"0.4789267\",\n \"0.47846258\",\n \"0.47730145\",\n \"0.4772723\",\n \"0.47661215\",\n \"0.47634205\",\n \"0.47619274\",\n \"0.47514093\",\n \"0.47501644\",\n \"0.4748515\",\n \"0.47483164\",\n \"0.47293496\",\n \"0.47076443\",\n \"0.47075817\",\n \"0.4706267\",\n \"0.4694961\",\n \"0.46930483\",\n \"0.4691633\",\n \"0.46915576\",\n \"0.46914512\",\n \"0.4688384\",\n \"0.46828955\",\n \"0.46799543\",\n \"0.4674472\",\n \"0.4668519\",\n \"0.46661648\",\n \"0.4665612\",\n \"0.46622357\",\n \"0.46562812\",\n \"0.46455318\",\n \"0.46454215\",\n \"0.46448848\",\n \"0.46432877\",\n \"0.46431202\",\n \"0.46404362\",\n \"0.4622876\",\n \"0.46182817\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.81699383"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":94697,"cells":{"query":{"kind":"string","value":"Run the algorithm for the scores in the axis layer (0 for x, 1 for y). This score and the associated ranking are reliable, the ranking of the opposite score is not."},"document":{"kind":"string","value":"def run(self, axis, gamma):\n \n # Trajectories of the main score to compute and the opposite one\n traj_s, traj_o = [_np.ones(self.d[axis])], [_np.ones(self.d[1-axis])]\n # Ranked indices of the scores\n rank_s, rank_o = _np.array([], dtype=int), _np.array([], dtype=int)\n # List of node indices that have reached the zero threshold\n zeros_s, zeros_o = _np.array([], dtype=int), _np.array([], dtype=int)\n \n # Main loop\n for t in range(int(self.params['t_max'])):\n \n # Computing the opposite score without approx\n o = self._one_step(gamma, 1-axis, traj_s[-1])\n rank_o, zeros_o = self._update_zero_rank(o, zeros_o, rank_o)\n traj_o = _np.concatenate((traj_o, [o]))\n \n # Computing the main score (given the opposite one) without approx\n s = self._one_step(gamma, axis, o)\n rank_s, zeros_s = self._update_zero_rank(s, zeros_s, rank_s)\n # Imposing the threshold to the score\n s[zeros_s] = self.params['low_bound']\n traj_s = _np.concatenate((traj_s, [s]))\n \n # Checking the convergence\n if self._converg_check(axis, t, traj_s):\n break\n\n # Finalize the ranking of the positive scores\n rank_s = _np.append(rank_s, _np.argsort(s)[len(zeros_s):])[::-1]\n rank_o = _np.append(rank_o, _np.argsort(o)[len(zeros_o):])[::-1]\n\n # Update the class variables\n self._update_vars(axis, traj_s, traj_o, rank_s, rank_o, t)\n \n if self.params['print_info']:\n print (\"Convergence in \" + str(t) + \" time steps.\")\n if t >= self.params['t_max']:\n print(\"Warning. Stationary state not reached.\")"},"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 _check_run(self, axis):\n if (self.x_traj, self.y_traj)[axis] is None:\n if (self.inverse_x_traj, self.inverse_y_traj)[axis] is None:\n raise Exception('The algorithm has not been run.')\n else:\n if self.params['print_info']:\n print('Warning: you are using the opposite score. It can contain errors if any score is a zero below threshold.')\n return (self.inverse_x_traj, self.inverse_y_traj)[axis], (self.inverse_x_ranking, self.inverse_y_ranking)[axis]\n return (self.x_traj, self.y_traj)[axis], (self.x_ranking, self.y_ranking)[axis]","def score(self, X, y=...):\n ...","def score(self, X, y):\n ...","def score(self, X, y):\n raise NotImplementedError('Abstract method \"score\" must be '\n 'specialised!')","def rank(self,others):\n self.__verify(others)\n \n #construct the n evaluation criteria + classes in an extensible way\n #evalFn = [AP,R] in the standard format -> column with as many rows as replicates\n numClasses = others[0].eval['APBCI'].shape[2]\n\n iouType = others[0].params.iouType\n if iouType in [\"segm\",\"bbox\"]:\n evalFunctions = [ \\\n lambda AP,R: np.nanmean(AP[:,:,:,0,-1],axis=(0,2)),\n lambda AP,R: np.nanmean(AP[0,:,:,0,-1],axis=(1)),\n lambda AP,R: np.nanmean(AP[5,:,:,0,-1],axis=(1)),\n lambda AP,R: np.nanmean(AP[:,:,:,1,-1],axis=(0,2)),\n lambda AP,R: np.nanmean(AP[:,:,:,2,-1],axis=(0,2)),\n lambda AP,R: np.nanmean(AP[:,:,:,3,-1],axis=(0,2)),\n lambda AP,R: np.nanmean(R[:,:,:,0,0],axis=(0,2)),\n lambda AP,R: np.nanmean(R[:,:,:,0,1],axis=(0,2)),\n lambda AP,R: np.nanmean(R[:,:,:,0,2],axis=(0,2)),\n lambda AP,R: np.nanmean(R[:,:,:,1,2],axis=(0,2)),\n lambda AP,R: np.nanmean(R[:,:,:,2,2],axis=(0,2)),\n lambda AP,R: np.nanmean(R[:,:,:,3,2],axis=(0,2))]\n\n evfAP = lambda c: (lambda AP,R: np.nanmean(AP[:,:,c,0,-1],axis=0))\n for i in range(numClasses):\n evalFunctions.append(evfAP(i))\n\n else:\n evalFunctions = [ \\\n lambda AP,R: np.nanmean(AP[:,:,:,0,0],axis=(0,2)),\n lambda AP,R: np.nanmean(AP[0,:,:,0,0],axis=(1)),\n lambda AP,R: np.nanmean(AP[5,:,:,0,0],axis=(1)),\n lambda AP,R: np.nanmean(AP[:,:,:,1,0],axis=(0,2)),\n lambda AP,R: np.nanmean(AP[:,:,:,2,0],axis=(0,2)),\n lambda AP,R: np.nanmean(R[:,:,:,0,0],axis=(0,2)),\n lambda AP,R: np.nanmean(R[0,:,:,0,0],axis=(1)),\n lambda AP,R: np.nanmean(R[5,:,:,0,0],axis=(1)),\n lambda AP,R: np.nanmean(R[:,:,:,1,0],axis=(0,2)),\n lambda AP,R: np.nanmean(R[:,:,:,2,0],axis=(0,2))]\n\n numReplicates = others[0].eval['APBCI'].shape[1]\n numInstances = len(others)\n numEvals = len(evalFunctions)\n\n replicateStats = np.zeros((numReplicates,numInstances))\n\n outperformMatrix = np.zeros((numInstances,numInstances,numEvals))\n rankCI = np.zeros((numInstances,3,numEvals))\n ranks = np.zeros((numInstances,numEvals,numReplicates))\n\n for evi,evf in enumerate(evalFunctions):\n for oi,o in enumerate(others):\n replicateStats[:,oi] = evf(o.eval['APBCI'],o.eval['RBCI'])\n\n for oi in range(len(others)):\n for oj in range(len(others)):\n outperformMatrix[oi,oj,evi] = np.mean(replicateStats[:,oi]>replicateStats[:,oj])\n\n for bci in range(numReplicates):\n ranks[:,evi,bci] = stats.rankdata(-replicateStats[bci,:],method='min')\n\n for oi in range(len(others)): \n rankCI[oi,0,evi] = np.mean(ranks[oi,evi,:])\n #use simple percentile method; the bias correction misbehaves \n rankCI[oi,1:,evi] = np.percentile(ranks[oi,evi,:],[100*(self.params.bootstrapAlpha/2),100*(1-self.params.bootstrapAlpha/2)])\n\n return rankCI, outperformMatrix, ranks","def run(self):\n if self.pb.xvalEN and not self.isXvalMain:\n dt = np.ones(self.pb.total_exam_no,\n dtype=\"float32\") / self.train_exam_no\n dt[self.val_indices] = 0.0\n else:\n dt = np.ones(self.pb.total_exam_no,\n dtype=\"float32\") / self.pb.total_exam_no\n\n val = np.zeros(8,dtype=\"float32\")-1\n boosting = None\n wl = None\n if self.pb.algorithm == 'conf-rated':\n boosting = ConfidenceRated(self)\n wl = ConfidenceRatedWL(self)\n elif self.pb.algorithm == 'adaboost':\n boosting = AdaBoost(self)\n wl = AdaBoostWL(self)\n elif self.pb.algorithm == 'adaboost-fast':\n boosting = AdaBoostFast(self)\n wl = AdaBoostFastWLMPI(self)\n elif self.pb.algorithm == 'rankboost':\n boosting = RankBoost(self)\n wl = ConfidenceRatedWL(self)\n elif self.pb.algorithm == 'rankboost-fast':\n boosting = RankBoost(self)\n wl = AdaBoostFastWL(self)\n else:\n raise Exception(\"Unknown Boosting Algorithm\")\n \n for r in range(self.pb.rounds):\n tree = wl.run(dt)\n dt = boosting.run(dt = dt,\n r = r,\n tree = tree)\n \n if self.isXvalMain:\n boosting.finalize()\n \n \"\"\"Sync the predictions and save them to a file\"\"\"\n if self.pb.isLeader:\n if self.pb.xvalEN and not self.isXvalMain:\n val_predictions = boosting.get_val_predictions()\n hypotheses = boosting.get_hypotheses()\n np.savez(self.out_fp,\n val_predictions = val_predictions,\n hypotheses = hypotheses,\n )\n if self.pb.testEN and self.isXvalMain:\n train_predictions = np.zeros([self.pb.total_exam_no,self.pb.rounds],\n dtype=\"float32\")\n test_predictions = np.zeros([self.pb.test_exam_no,self.pb.rounds],\n dtype=\"float32\")\n for slv in np.arange(self.pb.comm_size):\n tr_i1 = self.pb.train_partition[slv]\n tr_i2 = self.pb.train_partition[slv+1]\n te_i1 = self.pb.test_partition[slv]\n te_i2 = self.pb.test_partition[slv+1]\n if slv == 0:\n train_predictions[tr_i1:tr_i2,:] = boosting.get_train_predictions()\n test_predictions[te_i1:te_i2,:] = boosting.get_test_predictions()\n else:\n train_predictions[tr_i1:tr_i2,:] = self.pb.comm.recv(source=slv,tag=11)\n test_predictions[te_i1:te_i2,:] = self.pb.comm.recv(source=slv,tag=12)\n hypotheses = boosting.get_hypotheses()\n np.savez(self.out_fp,\n train_predictions = train_predictions,\n test_predictions = test_predictions,\n hypotheses = hypotheses,\n )\n if not self.pb.testEN and self.isXvalMain:\n train_predictions = np.zeros([self.pb.total_exam_no,self.pb.rounds],\n dtype=\"float32\")\n for slv in np.arange(self.pb.comm_size):\n tr_i1 = self.pb.train_partition[slv]\n tr_i2 = self.pb.train_partition[slv+1]\n if slv == 0:\n train_predictions[tr_i1:tr_i2,:] = boosting.get_train_predictions()\n else:\n train_predictions[tr_i1:tr_i2,:] = self.pb.comm.recv(source=slv,tag=11)\n hypotheses = boosting.get_hypotheses()\n np.savez(self.out_fp,\n train_predictions = train_predictions,\n hypotheses = hypotheses,\n )\n else:\n if self.pb.testEN and self.isXvalMain:\n train_predictions = boosting.get_train_predictions()\n self.pb.comm.send(train_predictions,dest = 0,tag=11)\n test_predictions = boosting.get_test_predictions()\n self.pb.comm.send(test_predictions,dest = 0,tag=12)\n if not self.pb.testEN and self.isXvalMain:\n train_predictions = boosting.get_train_predictions()\n self.pb.comm.send(train_predictions,dest = 0,tag=11)","def set_rank_order(self):\n for k in self._run:\n self._run[k].sort(key=lambda x:x.get_rank(),reverse=False)\n tot_res = len(self._run[k])\n for r in self._run[k]:\n r.set_score(tot_res - int(r.get_rank()) + 1)\n print r.get_str()","def update_score(self, red_alliance, blue_alliance, prediction, red_score, blue_score):\n score = self.get_score(red_score, blue_score)\n\n for team in red_alliance:\n self.scores[team] = self.el.update(self.scores[team], prediction, score)\n\n for team in blue_alliance:\n self.scores[team] = self.el.update(self.scores[team], 1 - prediction, score)","def run(self):\n if self.pb.xvalEN and not self.isXvalMain:\n dt = np.ones(self.pb.total_exam_no,\n dtype=\"float32\") / self.train_exam_no\n dt[self.val_indices] = 0.0\n else:\n dt = np.ones(self.pb.total_exam_no,\n dtype=\"float32\") / self.pb.total_exam_no\n\n val = np.zeros(8,dtype=\"float32\")-1\n boosting = None\n wl = None\n if self.pb.algorithm == 'conf-rated':\n boosting = ConfidenceRated(self)\n wl = ConfidenceRatedWL(self)\n elif self.pb.algorithm == 'adaboost':\n boosting = AdaBoost(self)\n wl = AdaBoostWL(self)\n elif self.pb.algorithm == 'adaboost-fast':\n boosting = AdaBoostFast(self)\n wl = AdaBoostFastWL(self)\n elif self.pb.algorithm == 'rankboost':\n boosting = RankBoost(self)\n wl = ConfidenceRatedWL(self)\n elif self.pb.algorithm == 'rankboost-fast':\n boosting = RankBoost(self)\n wl = AdaBoostFastWL(self)\n else:\n raise Exception(\"Unknown Boosting Algorithm\")\n \n for r in range(self.pb.rounds):\n tree = wl.run(dt)\n dt = boosting.run(dt = dt,\n r = r,\n tree = tree)\n \n if self.isXvalMain:\n boosting.finalize()\n \n \"\"\"Sync the predictions and save them to a file\"\"\"\n if self.pb.isLeader:\n if self.pb.xvalEN and not self.isXvalMain:\n val_predictions = boosting.get_val_predictions()\n hypotheses = boosting.get_hypotheses()\n np.savez(self.out_fp,\n val_predictions = val_predictions,\n hypotheses = hypotheses,\n )\n if self.pb.testEN and self.isXvalMain:\n train_predictions = boosting.get_train_predictions()\n test_predictions = boosting.get_test_predictions()\n hypotheses = boosting.get_hypotheses()\n np.savez(self.out_fp,\n train_predictions = train_predictions,\n test_predictions = test_predictions,\n hypotheses = hypotheses,\n )\n if not self.pb.testEN and self.isXvalMain:\n train_predictions = boosting.get_train_predictions()\n hypotheses = boosting.get_hypotheses()\n np.savez(self.out_fp,\n train_predictions = train_predictions,\n hypotheses = hypotheses,\n )","def evaluate_ranking_loss(eval_model, x_, y_, df_, loss_fn, mode='val'):\n\n qm_tensors_, db_mask_, blink_mask_ = convert_values_to_tensors(df_)\n dataset = TensorDataset(x_, y_, qm_tensors_, db_mask_, blink_mask_)\n question_sampler = QuestionSampler(torch.utils.data.SequentialSampler(range(len(qm_tensors_))),\n qm_tensors_, False)\n loader = DataLoader(dataset, batch_sampler=question_sampler, shuffle=False)\n\n eval_model.eval()\n with torch.no_grad():\n pred_ = []\n for x_, y_, qm_, db_mask, blink_mask in loader:\n xbatch_list = []\n xbatch_idxes = []\n for mask in [db_mask, blink_mask]:\n idxes = get_idxes_from_mask(mask)\n xbatch_idxes.append(idxes)\n x_pick, y_pick, qm_pick = x_[idxes], y_[idxes], qm_[idxes]\n\n if len(x_pick) == 0:\n xbatch_list.append(torch.tensor([]))\n continue\n # elif len(x_pick) <= 1:\n # xbatch_list.append(torch.tensor([]))\n # continue\n x_batch = x_pick\n xbatch_list.append(x_batch)\n if (len(xbatch_list[0]) == 0) and (len(xbatch_list[1]) == 0):\n print(x_, y_, qm_, db_mask, blink_mask)\n print(xbatch_idxes)\n yhat = eval_model(xbatch_list[0], xbatch_list[1])\n # get individual instance predicted prob\n yhat_in_order1 = torch.zeros(len(x_), 1)\n yhat_in_order2 = torch.zeros(len(x_), 1)\n yhat_in_order1[xbatch_idxes[0]] = yhat[:len(xbatch_idxes[0])]\n yhat_in_order2[xbatch_idxes[1]] = yhat[len(xbatch_idxes[0]):]\n yhat_in_order = torch.max(torch.cat((yhat_in_order1, yhat_in_order2), 1), 1)[0].reshape(-1,1)\n # print(yhat_in_order)\n pred_.append(yhat_in_order)\n pred_ = torch.cat(pred_, 0)\n prec, recall, f1, df_output = compute_qald_metrics(pred_, df_, gold_file_name=args.gold_file_name)\n avg_loss, _ = compute_loss(eval_model, loader, loss_fn, optimizer=None)\n print(\"Current {} -- prec {}; recall {}; f1 {}, loss {}\".format(mode, prec, recall, f1, avg_loss))\n\n return avg_loss, f1, pred_, df_output","def scoring(self):\n pass","def score(self, x, y, **kwargs):\n kwargs = self.filter_sk_params(Sequential.evaluate, kwargs)\n loss = self.model.evaluate(x, y, **kwargs)\n if isinstance(loss, list):\n return loss[0]\n return loss","def score(self, X, y):\r\n n_feature, _ = self.check_model()\r\n _, n_label = y.shape\r\n y = self.check_X_y_weights(X, y)\r\n\r\n if X.shape[1] == (n_feature + 1):\r\n X = X[:, 1:]\r\n\r\n assert (X.shape[1] == n_feature), \"X is of the wrong shape\"\r\n\r\n if self.scoring_func is None:\r\n y_pred = self.forward(X)\r\n\r\n loss = self.loss_func(torch.from_numpy(y_pred).float(),\r\n torch.from_numpy(y).float())\r\n loss = torch.mean(torch.sum(loss, 1)).numpy()\r\n\r\n return - loss\r\n else:\r\n y_pred = self.predict(X)\r\n return self.scoring_func(y_pred, y)","def scores(model,X_train,y_train, X_test,y_test,inside):\r\n \r\n model.best_apply(X_test,y_test)\r\n y_pred = model.forward(X_test).cpu()\r\n y_pred = np.asarray(torch.argmax(y_pred, dim = 1).squeeze())\r\n y_test_ = np.asarray(torch.argmax(y_test.cpu(), dim = 1))\r\n acc1 = accuracy_score(y_test_,y_pred)\r\n model.best_apply(X_train,y_train)\r\n y_pred = model.forward(X_train).cpu()\r\n y_pred = np.asarray(torch.argmax(y_pred, dim = 1).squeeze())\r\n y_train_ = np.asarray(torch.argmax(y_train.cpu(), dim = 1))\r\n acc2 = accuracy_score(y_train_,y_pred)\r\n if inside :\r\n print(\"###### Results ##########\")\r\n print(\"The Validation Acuuracy is : \",acc1)\r\n print(\"The Training Acuuracy is : \",acc2)\r\n return [acc1,acc2]","def score(self,x,**kwargs):\r\n if self.kfun != 'matrix' and len(self.sv): \r\n k = self.kfun(x,self.sv,**self.cparam)\r\n #print \"Kernel after test: \", k\r\n else:\r\n k = x\r\n \r\n \r\n self.W=self.alphas \r\n self.mat=self.kfun(np.array([self.sv[1]]), self.sv,**self.cparam) \r\n self.bias=self.svLabels[1]- np.dot((self.alphas*self.svLabels).T,self.mat.T) \r\n z=np.dot((self.alphas*self.svLabels).T,k.T)+self.bias\r\n \r\n #print \"bias: \", self.bias, \"\\nZ: \",z\r\n \r\n \r\n return z","def pre_step(x, y):\r\n feed_dict = {\r\n cnn.input_x: x,\r\n cnn.input_y: y,\r\n cnn.dropout_keep_prob: 1.0\r\n }\r\n scores = sess.run([cnn.scores], feed_dict)\r\n return scores","def r_point(self):\n self.r_score += 1\n self.update_scoreboard()","def score(self, X, y, *args, **kwargs):\n y_t = self.target_transform.transform(y)\n\n if hasattr(self, 'ml_score') and self.ml_score:\n log.info('Using custom score')\n return r2_score(y_true=y_t,\n y_pred=self._notransform_predict(\n X, *args, **kwargs))\n else:\n return super().score(X, y, *args, **kwargs)","def score(self, y_true, y_pred):\r\n pass","def update_scores(self):\n self.score[0] = (-1)*sum(self.board[self.board == -1])\n self.score[1] = sum(self.board[self.board == 1])\n #self.score[i] = sum(1 for j in range(len(stones_on_board)) if stones_on_board[j] == i)","def score(self) -> int:\n return self.function(self.x, self.y)","def score(self, epochs=None, y=None):\n import sklearn.metrics\n from sklearn.base import is_classifier\n from sklearn.metrics import accuracy_score, mean_squared_error\n if check_version('sklearn', '0.17'):\n from sklearn.base import is_regressor\n else:\n def is_regressor(clf):\n return False\n\n # Run predictions if not already done\n if epochs is not None:\n self.predict(epochs)\n else:\n if not hasattr(self, 'y_pred_'):\n raise RuntimeError('Please predict() epochs first or pass '\n 'epochs to score()')\n\n # Check scorer\n if self.score_mode not in ('fold-wise', 'mean-fold-wise',\n 'mean-sample-wise'):\n raise ValueError(\"score_mode must be 'fold-wise', \"\n \"'mean-fold-wise' or 'mean-sample-wise'. \"\n \"Got %s instead'\" % self.score_mode)\n score_mode = self.score_mode\n if (self.predict_mode == 'mean-prediction' and\n self.score_mode != 'mean-sample-wise'):\n warn(\"score_mode changed from %s set to 'mean-sample-wise' because\"\n \" predict_mode is 'mean-prediction'.\" % self.score_mode)\n score_mode = 'mean-sample-wise'\n self.scorer_ = self.scorer\n if self.scorer_ is None:\n # Try to guess which scoring metrics should be used\n if self.predict_method == \"predict\":\n if is_classifier(self.clf):\n self.scorer_ = accuracy_score\n elif is_regressor(self.clf):\n self.scorer_ = mean_squared_error\n\n elif isinstance(self.scorer_, str):\n if hasattr(sklearn.metrics, '%s_score' % self.scorer_):\n self.scorer_ = getattr(sklearn.metrics, '%s_score' %\n self.scorer_)\n else:\n raise KeyError(\"{0} scorer Doesn't appear to be valid a \"\n \"scikit-learn scorer.\".format(self.scorer_))\n if not self.scorer_:\n raise ValueError('Could not find a scoring metric for clf=%s '\n ' and predict_method=%s. Manually define scorer'\n '.' % (self.clf, self.predict_method))\n\n # If no regressor is passed, use default epochs events\n if y is None:\n if self.predict_mode == 'cross-validation':\n y = self.y_train_\n else:\n if epochs is not None:\n y = epochs.events[:, 2]\n else:\n raise RuntimeError('y is undefined because '\n 'predict_mode=\"mean-prediction\" and '\n 'epochs are missing. You need to '\n 'explicitly specify y.')\n if not np.all(np.unique(y) == np.unique(self.y_train_)):\n raise ValueError('Classes (y) passed differ from classes used '\n 'for training. Please explicitly pass your y '\n 'for scoring.')\n elif isinstance(y, list):\n y = np.array(y)\n\n # Clean attributes\n for att in ['scores_', 'y_true_']:\n if hasattr(self, att):\n delattr(self, att)\n\n self.y_true_ = y # to be compared with y_pred for scoring\n\n # Preprocessing for parallelization across training times; to avoid\n # overheads, we divide them in large chunks.\n n_jobs = min(len(self.y_pred_[0][0]), check_n_jobs(self.n_jobs))\n parallel, p_func, n_jobs = parallel_func(_score_slices, n_jobs)\n n_estimators = len(self.train_times_['slices'])\n n_chunks = min(n_estimators, n_jobs)\n chunks = np.array_split(range(len(self.train_times_['slices'])),\n n_chunks)\n scores = parallel(p_func(\n self.y_true_, [self.y_pred_[train] for train in chunk],\n self.scorer_, score_mode, self._cv_splits)\n for chunk in chunks)\n # TODO: np.array scores from initialization JRK\n self.scores_ = np.array([score for chunk in scores for score in chunk])\n return self.scores_","def preprocess_lab_data(self):\n print(\"Processing dataset from the labs ...\")\n self.full_dataset[\"score\"] = self.full_dataset[\"score\"].apply(lambda x: -1 if x==0 else 1)","def compute_scores(self, score_type, adjust = None, verbose = False):\n if score_type == 'coupling':\n scores = self.coupling\n elif score_type == 'barycentric':\n ot_emd = ot.da.EMDTransport()\n ot_emd.xs_ = self.xs\n ot_emd.xt_ = self.xt\n ot_emd.coupling_= self.coupling\n xt_s = ot_emd.inverse_transform(Xt=self.xt) # Maps target to source space\n scores = -sp.spatial.distance.cdist(self.xs, xt_s, metric = self.metric) #FIXME: should this be - dist?\n elif score_type == 'distance':\n # For baselines that only use distances without OT\n scores = -sp.spatial.distance.cdist(self.xs,self.xt, metric = self.metric)\n elif score_type == 'projected':\n # Uses projection mapping, computes distance in projected space\n scores = -sp.spatial.distance.cdist(self.xs,self.xt@self.mapping.T, metric = self.metric)\n\n if adjust == 'csls':\n scores = csls(scores, knn = 10)\n #print('here')\n elif adjust == 'isf':\n raise NotImplementedError('Inverted Softmax not implemented yet')\n\n self.scores = scores\n\n if verbose:\n plt.figure()\n plt.imshow(scores, cmap='jet')\n plt.colorbar()\n plt.show()","def rank_yx(self, rankyx, rank_to_yx=1):\r\n if rank_to_yx == 1:\r\n x = int(rankyx) % int(self.shapes[1])\r\n y = (rankyx - x) / int(self.shapes[1])\r\n return [y, x] # More convenient to return y, x\r\n \r\n if rank_to_yx == 0: # that means transform yx to rank, expecting rankyx to be a list, may not be necessary\r\n rankyx = rankyx[0] * int(self.shapes[1]) + rankyx[1]\r\n return rankyx # returns back a float\r","def compute_positions(scores, layers, cells, direction):\n prior = 1/np.arange(3, layers+3)\n prior = prior/prior.sum()\n x = np.linspace(-5, 5, layers)[::-1] \n prior = 1/(1 + np.exp(-x))\n prior = prior/prior.sum()\n\n # Compute the probability depending on the direction in which we want to know where the change is\n if direction == \"lr\":\n col_scores = np.array([scores[np.arange(cells[0])*cells[1]+j, np.arange(cells[0])*cells[1]+j+1].sum() for j in range(layers)])\n elif direction == \"rl\":\n col_scores = np.array([scores[np.arange(cells[0])*cells[1]+cells[1]-1-j, np.arange(cells[0])*cells[1]+cells[1]-1-j-1].sum() for j in range(layers)])\n elif direction == \"tb\":\n col_scores = np.array([scores[np.arange(cells[1])+cells[1]*j, np.arange(cells[1])+cells[1]*(j+1)].sum() for j in range(layers)])\n elif direction == \"bt\":\n col_scores = np.array([scores[np.arange(cells[1])+cells[1]*(cells[0]-1-j), np.arange(cells[1])+cells[1]*(cells[0]-1-j-1)].sum() for j in range(layers)])\n \n # Apply softmax + multiply by prior -> Then get the most likely position\n col_scores = sm(col_scores)\n position = np.argmax(col_scores*prior)\n return position","def _run(output_dir_name):\n\n file_system_utils.mkdir_recursive_if_necessary(\n directory_name=output_dir_name\n )\n\n # Housekeeping.\n y_tick_labels = [\n '{0:s}'.format(s) for s in MODEL_DEPTH_WIDTH_STRINGS\n ]\n x_tick_labels = [\n '{0:d}'.format(c) for c in FIRST_LAYER_CHANNEL_COUNTS\n ]\n\n y_axis_label = 'NN depth, width'\n x_axis_label = 'Spectral complexity'\n\n # Plot grid for U-net++ without deep supervision.\n dimensions_3d = (\n len(MODEL_WIDTHS), len(MODEL_DEPTHS), len(FIRST_LAYER_CHANNEL_COUNTS)\n )\n num_weights_matrix = numpy.reshape(NUM_WEIGHTS_ARRAY_PLUSPLUS, dimensions_3d)\n num_weights_matrix = numpy.swapaxes(num_weights_matrix, 0, 1)\n\n num_depth_width_combos = len(MODEL_DEPTH_WIDTH_STRINGS)\n num_channel_counts = len(FIRST_LAYER_CHANNEL_COUNTS)\n dimensions_2d = (num_depth_width_combos, num_channel_counts)\n num_weights_matrix = numpy.reshape(num_weights_matrix, dimensions_2d)\n\n all_weights = numpy.concatenate((\n NUM_WEIGHTS_ARRAY_PLUSPLUS, NUM_WEIGHTS_ARRAY_PLUSPLUSPLUS,\n NUM_WEIGHTS_ARRAY_PLUSPLUS_DEEP, NUM_WEIGHTS_ARRAY_PLUSPLUSPLUS_DEEP\n ), axis=0)\n\n min_colour_value = numpy.min(numpy.log10(all_weights))\n max_colour_value = numpy.max(numpy.log10(all_weights))\n\n figure_object, axes_object = _plot_scores_2d(\n score_matrix=numpy.log10(num_weights_matrix),\n min_colour_value=min_colour_value,\n max_colour_value=max_colour_value,\n x_tick_labels=x_tick_labels, y_tick_labels=y_tick_labels\n )\n\n figure_width_px = (\n figure_object.get_size_inches()[0] * figure_object.dpi\n )\n marker_size_px = figure_width_px * (\n MARKER_SIZE_GRID_CELLS / num_weights_matrix.shape[1]\n )\n\n if SELECTED_MARKER_INDICES[0] == 0:\n axes_object.plot(\n SELECTED_MARKER_INDICES[2], SELECTED_MARKER_INDICES[1],\n linestyle='None', marker=MARKER_TYPE,\n markersize=marker_size_px, markeredgewidth=0,\n markerfacecolor=MARKER_COLOUR,\n markeredgecolor=MARKER_COLOUR\n )\n\n axes_object.set_xlabel(x_axis_label)\n axes_object.set_ylabel(y_axis_label)\n axes_object.set_title(NN_TYPE_STRINGS_FANCY[0])\n\n panel_file_names = [\n '{0:s}/num_weights_log10_{1:s}.jpg'.format(\n output_dir_name, NN_TYPE_STRINGS[0].replace('_', '-')\n )\n ]\n\n print('Saving figure to: \"{0:s}\"...'.format(panel_file_names[-1]))\n figure_object.savefig(\n panel_file_names[-1], dpi=FIGURE_RESOLUTION_DPI,\n pad_inches=0, bbox_inches='tight'\n )\n pyplot.close(figure_object)\n\n # Plot grid for U-net++ with deep supervision.\n num_weights_matrix = numpy.reshape(\n NUM_WEIGHTS_ARRAY_PLUSPLUS_DEEP, dimensions_3d\n )\n num_weights_matrix = numpy.swapaxes(num_weights_matrix, 0, 1)\n num_weights_matrix = numpy.reshape(num_weights_matrix, dimensions_2d)\n\n figure_object, axes_object = _plot_scores_2d(\n score_matrix=numpy.log10(num_weights_matrix),\n min_colour_value=min_colour_value,\n max_colour_value=max_colour_value,\n x_tick_labels=x_tick_labels, y_tick_labels=y_tick_labels\n )\n\n if SELECTED_MARKER_INDICES[0] == 1:\n axes_object.plot(\n SELECTED_MARKER_INDICES[2], SELECTED_MARKER_INDICES[1],\n linestyle='None', marker=MARKER_TYPE,\n markersize=marker_size_px, markeredgewidth=0,\n markerfacecolor=MARKER_COLOUR,\n markeredgecolor=MARKER_COLOUR\n )\n\n axes_object.set_xlabel(x_axis_label)\n axes_object.set_ylabel(y_axis_label)\n axes_object.set_title(NN_TYPE_STRINGS_FANCY[1])\n\n panel_file_names.append(\n '{0:s}/num_weights_log10_{1:s}.jpg'.format(\n output_dir_name, NN_TYPE_STRINGS[1].replace('_', '-')\n )\n )\n\n print('Saving figure to: \"{0:s}\"...'.format(panel_file_names[-1]))\n figure_object.savefig(\n panel_file_names[-1], dpi=FIGURE_RESOLUTION_DPI,\n pad_inches=0, bbox_inches='tight'\n )\n pyplot.close(figure_object)\n\n # Plot grid for U-net3+ without deep supervision.\n num_weights_matrix = numpy.reshape(\n NUM_WEIGHTS_ARRAY_PLUSPLUSPLUS, dimensions_3d\n )\n num_weights_matrix = numpy.swapaxes(num_weights_matrix, 0, 1)\n num_weights_matrix = numpy.reshape(num_weights_matrix, dimensions_2d)\n\n figure_object, axes_object = _plot_scores_2d(\n score_matrix=numpy.log10(num_weights_matrix),\n min_colour_value=min_colour_value,\n max_colour_value=max_colour_value,\n x_tick_labels=x_tick_labels, y_tick_labels=y_tick_labels\n )\n\n if SELECTED_MARKER_INDICES[0] == 2:\n axes_object.plot(\n SELECTED_MARKER_INDICES[2], SELECTED_MARKER_INDICES[1],\n linestyle='None', marker=MARKER_TYPE,\n markersize=marker_size_px, markeredgewidth=0,\n markerfacecolor=MARKER_COLOUR,\n markeredgecolor=MARKER_COLOUR\n )\n\n axes_object.set_xlabel(x_axis_label)\n axes_object.set_ylabel(y_axis_label)\n axes_object.set_title(NN_TYPE_STRINGS_FANCY[2])\n\n panel_file_names.append(\n '{0:s}/num_weights_log10_{1:s}.jpg'.format(\n output_dir_name, NN_TYPE_STRINGS[2].replace('_', '-')\n )\n )\n\n print('Saving figure to: \"{0:s}\"...'.format(panel_file_names[-1]))\n figure_object.savefig(\n panel_file_names[-1], dpi=FIGURE_RESOLUTION_DPI,\n pad_inches=0, bbox_inches='tight'\n )\n pyplot.close(figure_object)\n\n # Plot grid for U-net3+ with deep supervision.\n num_weights_matrix = numpy.reshape(\n NUM_WEIGHTS_ARRAY_PLUSPLUSPLUS_DEEP, dimensions_3d\n )\n num_weights_matrix = numpy.swapaxes(num_weights_matrix, 0, 1)\n num_weights_matrix = numpy.reshape(num_weights_matrix, dimensions_2d)\n\n figure_object, axes_object = _plot_scores_2d(\n score_matrix=numpy.log10(num_weights_matrix),\n min_colour_value=min_colour_value,\n max_colour_value=max_colour_value,\n x_tick_labels=x_tick_labels, y_tick_labels=y_tick_labels\n )\n\n if SELECTED_MARKER_INDICES[0] == 3:\n axes_object.plot(\n SELECTED_MARKER_INDICES[2], SELECTED_MARKER_INDICES[1],\n linestyle='None', marker=MARKER_TYPE,\n markersize=marker_size_px, markeredgewidth=0,\n markerfacecolor=MARKER_COLOUR,\n markeredgecolor=MARKER_COLOUR\n )\n\n axes_object.set_xlabel(x_axis_label)\n axes_object.set_ylabel(y_axis_label)\n axes_object.set_title(NN_TYPE_STRINGS_FANCY[3])\n\n panel_file_names.append(\n '{0:s}/num_weights_log10_{1:s}.jpg'.format(\n output_dir_name, NN_TYPE_STRINGS[3].replace('_', '-')\n )\n )\n\n print('Saving figure to: \"{0:s}\"...'.format(panel_file_names[-1]))\n figure_object.savefig(\n panel_file_names[-1], dpi=FIGURE_RESOLUTION_DPI,\n pad_inches=0, bbox_inches='tight'\n )\n pyplot.close(figure_object)\n\n # Concatenate panels into one figure.\n concat_file_name = '{0:s}/num_weights_log10.jpg'.format(output_dir_name)\n print('Concatenating panels to: \"{0:s}\"...'.format(concat_file_name))\n imagemagick_utils.concatenate_images(\n input_file_names=panel_file_names,\n output_file_name=concat_file_name,\n num_panel_rows=2, num_panel_columns=2\n )\n imagemagick_utils.resize_image(\n input_file_name=concat_file_name,\n output_file_name=concat_file_name, output_size_pixels=int(1e7)\n )","def _update_vars(self, axis, traj_s, traj_o, rank_s, rank_o, t):\n if axis == 0:\n self.x_traj = traj_s\n self.x_ranking = rank_s\n self.x_scores = traj_s[-1]\n self.inverse_y_traj = traj_o\n self.inverse_y_ranking = rank_o\n self.inverse_y_scores = traj_o[-1]\n if axis == 1:\n self.y_traj = traj_s\n self.y_ranking = rank_s\n self.y_scores = traj_s[-1]\n self.inverse_x_traj = traj_o\n self.inverse_x_ranking = rank_o\n self.inverse_x_scores = traj_o[-1]","def run(pos_data,ball_data,match,ranking_type='A'):\n roles = ['home','guest']\n sections = ['1st','2nd']\n result = {'home':[0]*2, 'guest':[0]*2, 'ball':[0]*2}\n \n # switch for l2r switching mode\n l2r_section = 0\n\n # processing player position data first \n for sec in sections:\n home_direction = 'r2l'\n for role in roles:\n print('Processing: %s-%s...' % (role,sec))\n sorted_pos_data = sort_position_data(pos_data[role][sec], ranking_type)\n stitched_data = stitch_position_data(sorted_pos_data,ball_data[sec!='1st'])\n if role == 'home':\n home_direction = determine_playing_direction(stitched_data[:,0:2])\n if home_direction == 'l2r':\n switch_playing_direction(stitched_data)\n l2r_section = 0 if sec=='1st' else 1\n rescale_playing_coords(stitched_data,match['stadium'])\n result[role][0 if sec=='1st' else 1] = stitched_data\n print('done')\n \n # processing ball data\n print('Processing ball...')\n switch_playing_direction(ball_data[l2r_section][:,1:3])\n for i in [0,1]:\n rescale_playing_coords(ball_data[i][:,1:3],match['stadium'])\n result['ball'][0] = ball_data[0][:,1:3]\n result['ball'][1] = ball_data[1][:,1:3]\n\n #correct value ranges.\n print('clamping values.')\n clamp_values(result)\n print('done.')\n return result","def score(self, X, y):\n X_pp = self.preprocessor.transform(X)\n # Score the model on the data here\n return(self.estimator.score(X_pp, y))","def score(self, x, y, verbose=False):\n y_pred = self.forward(x).T\n\n y_p = np.argmax(y_pred, axis=-1)\n y_t = np.argmax(y, axis=-1)\n fpr, tpr, thresholds = roc_curve(y_t, y_p, pos_label=2)\n\n metricas = {\n 'accuracy': accuracy_score(y_t, y_p),\n 'f1':f1_score(y_t, y_p, average='macro'),\n 'balanced accuracy': balanced_accuracy_score(y_t, y_p),\n 'auc': auc(fpr, tpr)\n }\n\n if verbose:\n print('Accuracy', metricas['accuracy'])\n print('F1-Macro', metricas['f1'])\n print('Balanced accuracy', metricas['balanced accuracy'])\n print('AUC', metricas['auc'])\n\n return metricas","def score(self, X, y):\n\n stuff = self._vectorizer.transform(X)\n result = self._classifier.score(stuff,y)\n\n return result\n pass","def evaluate_ranking_loss(eval_model, x_, y_, m_labels_, ques_, loss_fn, mode='val'):\n eval_model.eval()\n\n dataset_ = TensorDataset(x_, y_)\n question_sampler = QuestionSampler(torch.utils.data.SequentialSampler(range(len(y_))), y_, False)\n loader = DataLoader(dataset_, batch_sampler=question_sampler, shuffle=False)\n total_loss = 0.0\n\n with torch.no_grad():\n pred_ = eval_model(x_, m_labels_)\n\n batch_num = 0\n for xb, yb in loader:\n if yb.shape[0] == 1:\n # print(xb, yb)\n continue\n yhat = eval_model(xb, yb)\n yb = yb.reshape(-1, 1)\n yhat_pos = yhat[-1].repeat((len(yhat) - 1), 1)\n yhat_neg = yhat[:-1]\n loss = loss_fn(yhat_pos, yhat_neg, torch.ones((len(yhat) - 1), 1).to(device))\n total_loss += loss.item() * (yb.shape[0] - 1)\n batch_num += 1\n avg_loss = total_loss / batch_num\n prec, recall, f1, _ = get_qald_metrics(pred_, m_labels_, ques_, mode=mode) # train and val both use 'val' mode\n\n return avg_loss, f1, pred_","def evaluate(self, test_x, test_y):\n score = self._model.evaluate(test_x, test_y, verbose=self._verbose)\n print(\"Test score: \", score[0])\n print(\"Test accuracy: \", score[1])","def custom_scoring(y_te, y_pred):\n #weights computed with training data set\n w = np.array([0.02409584, 0.00787456, 0.03685528, 0.01760536, 0.04589969, 0.8483942 , 0.01724058, 0.00203449]);\n \n ## F1 SCORES\n #evaluate F1 score, precision and recall for each label, \n #along with custom proportionally weighted F1 score\n #and built in weighted and macro F1 scores\n F1_tab, Ptab, Rtab, pf1 = F1_score(y_te, y_pred, w)\n f = F1Score(8, threshold = 0.5, average = 'weighted')\n f.update_state(y_te, y_pred)\n wf1 = f.result().numpy() #weighted f1 score\n f.reset_states()\n f = F1Score(8, threshold = 0.5, average = 'macro')\n f.update_state(y_te, y_pred)\n mf1 = f.result().numpy() #macro f1 score\n f.reset_states()\n\n ##EDIT DISTANCE\n #edit_dist_av = LevDistMultilabels(y_true, y_pred)\n\n ##ACCURACY\n #evaluate accuracy per label\n acc_tab = Acc(y_te, y_pred)\n\n return wf1, mf1, pf1, F1_tab, Ptab, Rtab, acc_tab","def update(self, rank):\n # calculate MR and MRR\n self.mr += rank\n self.mrr += 1 / rank\n # calculate Hits@k\n if rank <= 1:\n self.hits1 += 1\n self.hits3 += 1\n self.hits5 += 1\n self.hits10 += 1\n elif rank <= 3:\n self.hits3 += 1\n self.hits5 += 1\n self.hits10 += 1\n elif rank <= 5:\n self.hits5 += 1\n self.hits10 += 1\n elif rank <= 10:\n self.hits10 += 1","def positive_scores(self, axis, th_stat=10**(-2)):\n \n traj, rank = self._check_run(axis)\n \n low_boundary = _np.exp(_np.log(self.params['low_bound'])/2)\n t = len(traj)-1\n stat = 0\n \n # Iteration over the trajectories\n for i in range(len(traj[0])):\n # above the low boundary\n if traj[t][i] > low_boundary:\n # stationary (change less than stat_th)\n if abs(_np.log(traj[t][i]) - _np.log(traj[t-1][i])) < th_stat:\n stat += 1\n return stat","def validate_average_rank(self) -> float:\n logger.info('Average rank validation ...')\n\n args = self.args\n self.biencoder.eval()\n distributed_factor = self.distributed_factor\n\n if args.use_dict_input:\n data_iterator = self.get_dict_data_iterator(args.dev_psgs_file, args.dev_queries_file,\n args.dev_qrels_file, args.dev_trec_file,\n args.dev_batch_size,\n shuffle=False, split=False)\n else:\n data_iterator = self.get_data_iterator(args.dev_file, args.dev_batch_size, shuffle=False)\n\n sub_batch_size = args.val_av_rank_bsz\n sim_score_f = ClusterNllLoss.get_similarity_function()\n q_represenations = []\n ctx_represenations = []\n positive_idx_per_question = []\n\n num_hard_negatives = args.val_av_rank_hard_neg\n num_other_negatives = args.val_av_rank_other_neg\n\n log_result_step = args.log_batch_step\n\n for i, samples_batch in enumerate(data_iterator.iterate_data()):\n # samples += 1\n if len(q_represenations) > args.val_av_rank_max_qs / distributed_factor:\n break\n\n biencoder_input = ClusterBertEncoder.create_biencoder_input(samples_batch, self.tensorizer,\n True,\n max_query_length=args.query_sequence_length,\n max_doc_length=args.sequence_length,\n num_hard_negatives=num_hard_negatives,\n num_other_negatives=num_other_negatives,\n shuffle=False)\n total_ctxs = len(ctx_represenations)\n ctxs_ids = biencoder_input.context_ids\n ctxs_segments = biencoder_input.ctx_segments\n bsz = ctxs_ids.size(0)\n\n # split contexts batch into sub batches since it is supposed to be too large to be processed in one batch\n for j, batch_start in enumerate(range(0, bsz, sub_batch_size)):\n\n q_ids, q_segments = (biencoder_input.question_ids, biencoder_input.question_segments) if j == 0 \\\n else (None, None)\n\n if j == 0 and args.n_gpu > 1 and q_ids.size(0) == 1:\n # if we are in DP (but not in DDP) mode, all model input tensors should have batch size >1 or 0,\n # otherwise the other input tensors will be split but only the first split will be called\n continue\n\n ctx_ids_batch = ctxs_ids[batch_start:batch_start + sub_batch_size]\n ctx_seg_batch = ctxs_segments[batch_start:batch_start + sub_batch_size]\n\n q_attn_mask = self.tensorizer.get_attn_mask(q_ids)\n ctx_attn_mask = self.tensorizer.get_attn_mask(ctx_ids_batch)\n with torch.no_grad():\n q_dense, ctx_dense = self.biencoder(q_ids, q_segments, q_attn_mask, ctx_ids_batch, ctx_seg_batch,\n ctx_attn_mask)\n\n if q_dense is not None:\n q_represenations.extend(q_dense.cpu().split(1, dim=0))\n\n ctx_represenations.extend(ctx_dense.cpu().split(1, dim=0))\n\n batch_positive_idxs = biencoder_input.is_positive\n positive_idx_per_question.extend([total_ctxs + v for v in batch_positive_idxs])\n\n if (i + 1) % log_result_step == 0:\n logger.info('Av.rank validation: step %d, computed ctx_vectors %d, q_vectors %d', i,\n len(ctx_represenations), len(q_represenations))\n\n ctx_represenations = torch.cat(ctx_represenations, dim=0)\n q_represenations = torch.cat(q_represenations, dim=0)\n\n logger.info('Av.rank validation: total q_vectors size=%s', q_represenations.size())\n logger.info('Av.rank validation: total ctx_vectors size=%s', ctx_represenations.size())\n\n q_num = q_represenations.size(0)\n assert q_num == len(positive_idx_per_question)\n\n scores = sim_score_f(q_represenations, ctx_represenations)\n values, indices = torch.sort(scores, dim=1, descending=True)\n\n rank = 0\n for i, idx in enumerate(positive_idx_per_question):\n # aggregate the rank of the known gold passage in the sorted results for each question\n gold_idx = (indices[i] == idx).nonzero()\n rank += gold_idx.item()\n\n if distributed_factor > 1:\n # each node calcuated its own rank, exchange the information between node and calculate the \"global\" average rank\n # NOTE: the set of passages is still unique for every node\n eval_stats = all_gather_list([rank, q_num], max_size=1000)\n for i, item in enumerate(eval_stats):\n remote_rank, remote_q_num = item\n if i != args.local_rank:\n rank += remote_rank\n q_num += remote_q_num\n\n av_rank = float(rank / q_num)\n logger.info('Av.rank validation: average rank %s, total questions=%d', av_rank, q_num)\n return av_rank","def custom_score_2(game, player):\n if game.is_loser(player):\n return float(\"-inf\")\n\n if game.is_winner(player):\n return float(\"inf\")\n \n # First obtain the base information to calculate player & opponent\n # feature values\n player_legal_moves = game.get_legal_moves(player)\n opponent = game.get_opponent(player)\n opponent_legal_moves = game.get_legal_moves(opponent)\n if len(player_legal_moves) != len(opponent_legal_moves):\n return float(len(player_legal_moves) - len(opponent_legal_moves))\n \n # Get_center_coordinates and opponent. Then set the list of participants\n center_coordinates = center_y, center_x = get_center_coordinates(game)\n participants = [player, opponent]\n \n # Then, for each participant obtain his/her feature values \n for participant in participants:\n if participant == player:\n p_legal_moves = player_legal_moves\n player_either = player\n participant_coordinates = p_y, p_x = \\\n game.get_player_location(participant)\n player_legal_move_count, player_start_center_distance, \\\n player_total_next_moves, player_max_path_length, \\\n player_path_count, player_min_center_diff \\\n = \\\n get_player_feature_values(game, center_coordinates, \\\n player_either,participant_coordinates, p_legal_moves)\n else:\n p_legal_moves = opponent_legal_moves\n player_either = opponent\n participant_coordinates = p_y, p_x \\\n = game.get_player_location(participant)\n opponent_legal_move_count, opponent_start_center_distance, \\\n opponent_total_next_moves, opponent_max_path_length, \\\n opponent_path_count, opponent_min_center_diff \\\n = \\\n get_player_feature_values(game, center_coordinates, \\\n player_either, participant_coordinates, p_legal_moves)\n \n # Place each participant's feature values in a tuple/vector surrogate \n pro_player_vector = \\\n (player_legal_move_count, player_start_center_distance, \\\n player_total_next_moves, player_max_path_length, player_path_count, \\\n opponent_min_center_diff)\n pro_opponent_vector = \\\n (opponent_legal_move_count, opponent_start_center_distance, \\\n opponent_total_next_moves, opponent_max_path_length, \\\n opponent_path_count, player_min_center_diff)\n \n # Provide a weighting vector for the features \n weight_vector = (1.5,0.1,1.0,0.001,0.001,0.001)\n \n # Calculate the return value = weighted difference of players' features\n weighted_difference_dot_product = sum(p*(q-r ) for p,q,r \\\n in zip(weight_vector, pro_player_vector, pro_opponent_vector))\n \n return float(weighted_difference_dot_product)","def reset_score(self):\n self.x_score = 0\n self.o_score = 0","def classification_score(self, x, y):\t\n\t\tpass","def run(self, x):\n \"*** YOUR CODE HERE question 1 ***\"\n score = nn.DotProduct(self.w, x)\n\n return score","def evaluation(self):\n rows_list = []\n for name in self.single_classifier_best.keys():\n row = {}\n row['algorithm'] = name \n row[self.scoring_metric] = self.single_classifier_best[name].best_score_\n rows_list.append(row)\n \n scoring_df = pd.DataFrame(rows_list)\n scoring_sorted = scoring_df.sort_values(self.scoring_metric, ascending=False)\n print()\n print('*'*shutil.get_terminal_size().columns)\n print(scoring_sorted)\n print('*'*shutil.get_terminal_size().columns)\n self.evaluation_scores = scoring_sorted","def evaluate(self, X_test, Y_test):\n \n test_data = zip(X_test, Y_test)\n test_results = [(np.argmax(self.feedforward(x)), y)\n for (x, y) in test_data]\n \n# Updated for the testing\n# ========================\n return (sum(int(x == y) for (x, y) in test_results) / 100)","def evaluate(self, batch_x, batch_y):\n raise NotImplementedError()","def score(self, x, y):\n y_hat = (self.model['b1'] * x) + self.model['b0']\n return R2(y, y_hat)","def run(self):\n dataset = self.make_dataset()\n preprocess_mask = self.preprocess()\n cnn1_scores = self.run_cnn(dataset, 'cnn1')\n cnn2_scores = self.run_cnn(dataset, 'cnn2')\n return self.score_stamps(preprocess_mask, cnn1_scores, cnn2_scores)","def do_score_prediction(self):\n \n import iread.myio as mio\n from igui.score_canvas import ScoreCanvas\n exp_name = 'JPS_act_12_exp_4_accv_half_fc_j2'\n exp_name_base = 'ASM_act_12_exp_4'\n exp_base_folder = '/opt/visal/tmp/for_sijin/Data/H36M/H36MExp'\n exp_path = iu.fullfile(exp_base_folder, 'folder_%s' % exp_name, 'batches.meta')\n meta_base_path = iu.fullfile(exp_base_folder, 'folder_%s' % exp_name_base, 'batches.meta')\n meta = mio.unpickle(exp_path)\n meta_base = mio.unpickle(meta_base_path)\n images_path = meta_base['images_path']\n \n pred_pose = meta['feature_list'][0]\n gt_pose = meta['random_feature_list'][0]\n ntotal = gt_pose.shape[-1]\n print 'gt_pose_shape',gt_pose.shape\n print 'pred_pose_shape', pred_pose.shape\n ref_frame = 7600 # This is the index in test range\n ## ref_frame = 2600 # This is the index in test range\n test_range = self.test_data_provider.feature_range\n ref_idx = test_range[ref_frame]\n \n n_to_show = 1000\n \n idx_to_show = np.random.choice(ntotal, n_to_show - 1)\n idx_to_show = [ref_idx] + idx_to_show.tolist() \n idx_to_show = np.asarray(idx_to_show, dtype=np.int).flatten()\n \n ref_pose = pred_pose[...,ref_idx].reshape((-1,1),order='F') \n pose_to_eval =gt_pose[...,idx_to_show]\n output_feature_name = 'fc_2' # <------------------Parameter\n output_layer_idx = self.get_layer_idx(output_feature_name)\n\n # do it once <------------- Maybe it can support multiple batch in the future\n data_dim = self.model_state['layers'][output_layer_idx]['outputs']\n print 'data_dim', data_dim\n \n cur_data = [np.require(np.tile(ref_pose, [1,n_to_show]), \\\n dtype=np.single,requirements='C'), \\\n np.require(pose_to_eval.reshape((-1,n_to_show),order='F'),\\\n dtype=np.single,requirements='C'), \\\n np.require(np.zeros((1,n_to_show),dtype=np.single), \\\n requirements='C'),\n np.require(np.zeros((n_to_show,data_dim),dtype=np.single), \\\n requirements='C')]\n residuals = cur_data[1][...,0].reshape((-1,1),order='F') - cur_data[1]\n dp = self.test_data_provider\n mpjpe = dutils.calc_mpjpe_from_residual(residuals, dp.num_joints)\n\n gt_score = dp.calc_score(mpjpe, dp.mpjpe_factor/dp.max_depth,\\\n dp.mpjpe_offset/dp.max_depth).reshape((1,n_to_show)).flatten()\n self.libmodel.startFeatureWriter(cur_data, output_layer_idx)\n self.finish_batch()\n score = cur_data[-1].T\n print 'dim score', score.shape, 'dim gt_score', gt_score.shape\n score = score.flatten()\n # score = gt_score.flatten()\n def my_sort_f(k):\n if k == 0:\n return 10000000\n else:\n return score[k]\n sorted_idx = sorted(range(n_to_show), key=my_sort_f,reverse=True)\n s_to_show = [idx_to_show[k] for k in sorted_idx]\n sorted_score = np.asarray( [score[k] for k in sorted_idx])\n \n pose_to_plot = self.convert_relskel2rel(cur_data[1])\n sorted_pose = pose_to_plot[...,sorted_idx]\n class ScorePoseCanvas(ScoreCanvas):\n def __init__(self,data_dic):\n import iread.h36m_hmlpe as h36m\n ScoreCanvas.__init__(self,data_dic)\n self.pose_data = data_dic['pose_data']\n self.limbs = h36m.part_idx\n self.tmp = 0\n def show_image(self,ax):\n # ScoreCanvas.show_image(self,ax)\n # return\n import Image\n idx =self.cur_data_idx\n if idx == 0:\n self.tmp = self.tmp + 1\n if self.tmp == 1:\n img = self.load_image(idx)\n ax.imshow(np.asarray(img))\n return\n print 'Current data idx %d ' % self.cur_data_idx\n # params = {'elev':-89, 'azim':-107}\n # params = {'elev':-69, 'azim':-107}\n params = {'elev':-81, 'azim':-91} # frontal view\n fig = plt.figure(100)\n from mpl_toolkits.mplot3d import Axes3D\n import imgproc\n # new_ax = self.fig.add_axes( rng_rel,projection='polar')\n new_ax = fig.add_subplot(111,projection='3d')\n imgproc.turn_off_axis(new_ax)\n cur_pose = self.pose_data[...,idx].reshape((3,-1),order='F')\n dutils.show_3d_skeleton(cur_pose.T,\\\n self.limbs, params)\n xmin,xmax = np.min(cur_pose[0]),np.max(cur_pose[0])\n ymin,ymax = np.min(cur_pose[1]),np.max(cur_pose[1])\n zmin,zmax = np.min(cur_pose[2]),np.max(cur_pose[2])\n def extent(x,y,ratio):\n x = x + (x-y) * ratio\n y = y + (y-x) * ratio\n return -0.4,0.4\n r = 0.1\n new_ax.set_xlim(extent(xmin,xmax,r))\n new_ax.set_ylim(extent(ymin,ymax,r))\n new_ax.set_ylim(extent(zmin,zmax,r))\n tmp_folder = '/public/sijinli2/ibuffer/2014-CVPR2015/tmp/images'\n save_path = iu.fullfile(tmp_folder, 'tmp_image.png')\n plt.savefig(save_path)\n img = Image.open(save_path)\n plt.close(100)\n img_arr = np.asarray(img)\n s = np.int(img_arr.shape[0]/5.0)\n e = np.int(img_arr.shape[0] - s)\n s = 0\n e = img_arr.shape[0]\n img_arr = img_arr[s:e,:,:]\n ax.imshow(np.asarray(img_arr))\n # ax.plot([1,0,0],[0,1,0],[0,0,1])\n\n\n sc = ScorePoseCanvas({'x': np.asarray(range(len(idx_to_show))), 'y':sorted_score,\\\n 'images_path': [images_path[k] for k in s_to_show], \\\n 'pose_data':sorted_pose})\n sc.start()\n print 'max score is ' , sorted_score.max()\n gt_sort_idx = sorted(range(n_to_show), key=lambda k:gt_score[k], reverse=True)\n sorted_gt_score = np.asarray([gt_score[k] for k in gt_sort_idx])\n sorted_score_by_gt = [score[k] for k in gt_sort_idx]\n pl.plot(np.asarray(range(n_to_show)), sorted_gt_score, 'r', label='gt_score')\n pl.plot(np.asarray(range(n_to_show)), sorted_score_by_gt, 'g', label='pred_score')","def _forward(self, x):\n # Computing: `x * w^T`\n score = CArray(x.dot(self.w.T)).todense().ravel() + self.b\n\n scores = CArray.ones(shape=(x.shape[0], self.n_classes))\n scores[:, 0] = -score.ravel().T\n scores[:, 1] = score.ravel().T\n\n return scores","def score(self):","def calculate(self, prev_scores):\n self.set_scores(prev_scores)\n for match in self.week.matches:\n if match.played:\n # Fetch old scores\n winner_score = float(prev_scores[match.winner])\n loser_score = float(prev_scores[match.loser])\n\n # Update this ranking's scores\n score_delta = loser_score * 0.1\n self.score[match.winner] = winner_score + score_delta\n self.score[match.loser] = loser_score - score_delta","def get_prediction_scores(self, X_te, Y_te, scoring):\n X_te = X_te - self.Xm\n if self.scale_X:\n X_te = X_te / self.Xs\n M_te = X_te @ self.Q\n\n scores = torch.zeros(Y_te.shape[1], len(self.ls), dtype=X_te.dtype)\n for j, Y_te_j in enumerate(Y_te.t()):\n N_te_j = None\n for k, l in enumerate(self.ls):\n Yhat_te_j, N_te_j = self._predict_single(\n X_te, M_te, j, l, N_te_j\n )\n scores[j, k] = scoring(Y_te_j, Yhat_te_j).item()\n return scores","def calculate_scores(self):\n # Prediction based scores\n #self.report = classification_report(self.y_test, self.y_pred)\n self.accuracy = accuracy_score(self.y_real, self.y_pred)\n self.precision = precision_score(self.y_real, self.y_pred)\n self.recall = recall_score(self.y_real, self.y_pred)\n self.f1 = f1_score(self.y_real, self.y_pred)\n \n # Probability based scores\n self.fpr, self.tpr, _ = roc_curve(self.y_real, self.y_proba)\n self.average_precision = average_precision_score(self.y_real, self.y_proba)\n self.brier_loss = brier_score_loss(self.y_real, self.y_proba)\n self.roc_auc = roc_auc_score(self.y_real, self.y_proba)\n self.prec_cur, self.recall_cur, _ = precision_recall_curve(self.y_real, self.y_proba)","def evalrank(model, data, split='dev'):\n print 'Loading dataset'\n if split == 'dev':\n X = load_dataset(data, load_train=False)[1]\n else:\n X = load_dataset(data, load_train=False)[2]\n\n print 'Computing results...'\n train = load_dataset('CAD', load_train=True)[0]\n vectors = encode_sentences(model, train[0], verbose=False)\n # demo.retrieve_captions(model, net, train[0], vectors, 'image.jpg', k=5)\n ls = encode_sentences(model, X[0])\n lim = encode_images(model, X[1])\n\n (r1, r5, r10) = i2t(lim, X[0], train[0], vectors)\n print \"Image to text: %.1f, %.1f, %.1f\" % (r1, r5, r10)\n # (r1i, r5i, r10i, medri) = t2i(lim, ls)\n # print \"Text to image: %.1f, %.1f, %.1f, %.1f\" % (r1i, r5i, r10i, medri)","def custom_score_2(game, player):\n # TODO: finish this function!\n if game.is_loser(player):\n #print(\"You lose!\")\n return -math.inf\n if game.is_winner(player):\n #print(\"You win\")\n return math.inf\n\n # center\n width = game.width / 2\n height = game.height / 2\n\n # Opponent\n opponent = game.get_opponent(player)\n opp_y_coord, opp_x_coord = game.get_player_location(opponent)\n opp_x_eval = (width - float(opp_x_coord)) ** 2\n opp_y_eval = (height - float(opp_y_coord)) ** 2\n opp_center_eval = float(opp_x_eval + opp_y_eval)\n\n # Remaining spaces left on the board\n rem_spaces = len(game.get_blank_spaces())\n\n # number of agent's available moves\n no_moves = len(game.get_legal_moves(player))\n\n # number of opponent's available moves\n opp_moves = len(game.get_legal_moves(opponent))\n\n # evaluation of board \"goodness\"\n # using moves available to both players\n # Idea is player chooses moves with scores that maximise whilst minimizing\n # evaluate board states and positions as scores\n opp_score = opp_moves * 2 - opp_center_eval\n score = no_moves - opp_score/rem_spaces\n return float(score)","def _evaluate(self, x, y):\n raise NotImplementedError()","def _score_to_decision(self, score):","def _score(self, x, seq):\n pass","def scoreEvaluationFunction(currentGameState):\n return currentGameState.getScore()","def scoreEvaluationFunction(currentGameState):\n return currentGameState.getScore()","def scoreEvaluationFunction(currentGameState):\n return currentGameState.getScore()","def scoreEvaluationFunction(currentGameState):\n return currentGameState.getScore()","def scoreEvaluationFunction(currentGameState):\n return currentGameState.getScore()","def scoreEvaluationFunction(currentGameState):\n return currentGameState.getScore()","def run_prediction(self):\r\n self.get_prediction_indices()\r\n self.walk_forward_prediction()","def update_score():\n pass","def score(self, X, y):\n out = None\n ### YOUR CODE HERE\n pred = self.predict(X)\n assert pred.shape == y.shape\n out = ((pred-y)**2).mean()\n ### END CODE\n return out","def score_batch(self, pipelines, X, y, objectives):\n check_all_pipeline_names_unique(pipelines)","def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:\n score = multi_scale_ssim(x=x, y=y, kernel_size=self.kernel_size, kernel_sigma=self.kernel_sigma,\n data_range=self.data_range, reduction=self.reduction, scale_weights=self.scale_weights,\n k1=self.k1, k2=self.k2)\n return torch.ones_like(score) - score","def find_ranking(comparisons, equal_width=0.2, max_rank=-1, verbose=False):\n # remove unnecessary variables\n comparisons = {(i, j) if i < j else (j, i): value if i < j else 1 - value\n for (i, j), value in comparisons.items()}\n nodes = np.unique(\n [i for ij in comparisons.keys() for i in ij])\n\n # define variables\n model = Model('comparison')\n model.setParam('OutputFlag', verbose)\n values = np.fromiter(comparisons.values(), dtype=float)\n assert values.max() <= 1 and values.min() >= 0\n # variables to encode the error of comparisons\n E_ij = model.addVars(comparisons.keys(), name='e_ij', vtype=GRB.CONTINUOUS,\n ub=1.0-values, lb=-values)\n # variables to encode hard choice of >=, <=, ==\n Ge_ij = model.addVars(comparisons.keys(), name='ge_ij', vtype=GRB.BINARY)\n Le_ij = model.addVars(comparisons.keys(), name='le_ij', vtype=GRB.BINARY)\n Eq_ij = model.addVars(comparisons.keys(), name='eq_ij', vtype=GRB.BINARY)\n # variables to help with transitivity in non-fully connected graphs\n if max_rank < 1:\n max_rank = len(nodes)\n R_i = model.addVars(nodes, name='r_i', vtype=GRB.CONTINUOUS, lb=0,\n ub=max_rank)\n # variables to emulate abs\n T_ij_pos = {}\n T_ij_neg = {}\n index = (values != 1) & (values != 0)\n T_ij_pos = model.addVars(\n (ij for ij, value in comparisons.items() if value not in [0.0, 1.0]),\n vtype=GRB.CONTINUOUS, name='T_ij_pos', lb=0, ub=1-values[index])\n T_ij_neg = model.addVars(\n (ij for ij, value in comparisons.items() if value not in [0.0, 1.0]),\n vtype=GRB.CONTINUOUS, name='T_ij_neg', lb=0, ub=values[index])\n model.update()\n\n # emulate abs for non-binary comparisons: E_ij = T_ij_pos - T_ij_neg\n model.addConstrs(\n (E_ij[ij] == T_ij_pos[ij] - T_ij_neg[ij] for ij in T_ij_pos),\n 'E_ij = T_ij_pos - T_ij_neg')\n\n # hard decision of >=, <=, and ==\n lower_bound = 0.5 - equal_width / 2.0\n upper_bound = 0.5 + equal_width / 2.0\n # <=\n model.addConstrs(\n (E_ij[ij] + comparisons[ij] - upper_bound <= ge_ij\n for ij, ge_ij in Ge_ij.items()), 'ge_ij_lower_bound')\n model.addConstrs(\n (E_ij[ij] + comparisons[ij] - upper_bound >= -1 + ge_ij\n for ij, ge_ij in Ge_ij.items()), 'ge_ij_upper_bound')\n # >=\n model.addConstrs(\n (E_ij[ij] + comparisons[ij] - lower_bound >= -le_ij\n for ij, le_ij in Le_ij.items()), 'le_ij_lower_bound')\n model.addConstrs(\n (E_ij[ij] + comparisons[ij] - lower_bound <= 1 - le_ij\n for ij, le_ij in Le_ij.items()), 'le_ij_upper_bound')\n # ==\n model.addConstrs(\n (le + eq + ge == 1 for le, eq, ge in zip(\n Le_ij.values(), Eq_ij.values(), Ge_ij.values())), 'eq_ij')\n\n # transitivity\n for (i, j), eq_a in Eq_ij.items():\n le_a = Le_ij[i, j]\n ge_a = Ge_ij[i, j]\n for k in nodes:\n j_, k_ = j, k\n if j > k:\n j_, k_ = k, j\n eq_b = Eq_ij.get((j_, k_), None)\n if eq_b is None:\n continue\n else:\n le_b = Le_ij[j_, k_]\n ge_b = Ge_ij[j_, k_]\n if j_ != j:\n le_b, ge_b = ge_b, le_b\n\n i_, k_ = i, k\n if i > k:\n i_, k_ = k, i\n eq_c = Eq_ij.get((i_, k_), None)\n if eq_c is None:\n continue\n else:\n le_c = Le_ij[i_, k_]\n ge_c = Ge_ij[i_, k_]\n if i_ != i:\n le_c, ge_c = ge_c, le_c\n\n # a <= b and b <= c -> a <= c\n model.addLConstr(\n ge_a + ge_b, GRB.LESS_EQUAL, 1 + ge_c,\n f'transitivity_ge_{i},{j},{k}')\n # a >= b and b >= c -> a >= c\n model.addLConstr(\n le_a + le_b, GRB.LESS_EQUAL, 1 + le_c,\n f'transitivity_le_{i},{j},{k}')\n # a <= b and b == c -> a <= c\n model.addLConstr(\n le_a + eq_b, GRB.LESS_EQUAL, 1 + le_c,\n f'transitivity_leeq_{i},{j},{k}')\n # a == b and b <= c -> a <= c\n model.addLConstr(\n eq_a + le_b, GRB.LESS_EQUAL, 1 + le_c,\n f'transitivity_eqle_{i},{j},{k}')\n # a >= b and b == c --> a >= c\n model.addLConstr(\n ge_a + eq_b, GRB.LESS_EQUAL, 1 + ge_c,\n f'transitivity_geeq_{i},{j},{k}')\n # a == b and b >= c --> a >= c\n model.addLConstr(\n eq_a + ge_b, GRB.LESS_EQUAL, 1 + ge_c,\n f'transitivity_eqge_{i},{j},{k}')\n # a == b and b == c --> a == c\n model.addLConstr(\n eq_a + eq_b, GRB.LESS_EQUAL, 1 + eq_c,\n f'transitivity_eq_{i},{j},{k}')\n\n # transitivity helper (for not-fully connected graphs)\n # also provides a latent rank\n big_m = max_rank\n model.addConstrs(\n ((1 - ge_ij) * big_m + R_i[i] >= R_i[j] + 1 for (i, j), ge_ij in Ge_ij.items()),\n 'rank_transitivity_larger')\n model.addConstrs(\n ((1 - le_ij) * big_m + R_i[j] >= R_i[i] + 1 for (i, j), le_ij in Le_ij.items()),\n 'rank_transitivity_smaller')\n model.addConstrs(\n ((1 - eq_ij) * big_m + R_i[j] >= R_i[i] for (i, j), eq_ij in Eq_ij.items()),\n 'rank_transitivity_equal1')\n model.addConstrs(\n ((1 - eq_ij) * big_m + R_i[i] >= R_i[j] for (i, j), eq_ij in Eq_ij.items()),\n 'rank_transitivity_equal2')\n\n # objective function\n objective = LinExpr()\n for ij, value in comparisons.items():\n if value == 1.0:\n objective += -E_ij[ij]\n elif value == 0.0:\n objective += E_ij[ij]\n else:\n objective += T_ij_pos[ij] + T_ij_neg[ij]\n model.setObjective(objective, GRB.MINIMIZE)\n\n # solve\n model.optimize()\n\n # verify abs emulation: one T_ij has to be 0\n for ij, value in T_ij_pos.items():\n assert value.X == 0 or T_ij_neg[ij] == 0, \\\n f'T_{ij} pos {value.X} neg {T_ij_neg[ij]}'\n\n # find minimal Rs\n model_ = Model('comparison')\n model_.setParam('OutputFlag', verbose)\n R_i = model_.addVars(nodes, name='r_i', vtype=GRB.CONTINUOUS, lb=0,\n ub=len(nodes))\n for ((i, j), ge_ij), le_ij in zip(Ge_ij.items(), Le_ij.values()):\n if ge_ij.x == 1:\n model_.addConstr(R_i[i] >= R_i[j] + 1)\n elif le_ij.x == 1:\n model_.addConstr(R_i[j] >= R_i[i] + 1)\n else:\n model_.addConstr(R_i[j] == R_i[i])\n model_.setObjective(R_i.sum(), GRB.MINIMIZE)\n model_.optimize()\n\n return [model_.getVarByName(f'r_i[{i}]').X for i in range(len(nodes))], \\\n model.objVal","def predict(self, x):\n if(self.score(x) > 0):\n return 1\n return -1","def predict(self, x):\n if(self.score(x) > 0):\n return 1\n return -1","def predict(self, x):\n if(self.score(x) > 0):\n return 1\n return -1","def predict(self, x):\n if(self.score(x) > 0):\n return 1\n return -1","def score(self,A,B,method=\"mse\"):\n if method ==\"mse\":\n loss = self._reconstruction_loss\n elif method == \"mae\":\n loss = self._mae\n data={self._xphA: A,\n self._xphB: B,\n K.learning_phase():False}\n return self.sess.run(loss,\n feed_dict=data)","def scoreEvaluationFunction(currentGameState):\r\n return currentGameState.getScore()","def main():\n args = get_args().parse_args()\n\n (wins, losses) = args.record.split('-')\n print wins, losses\n \n is_winning_streak = args.streak[0] == 'W'\n streak_length = args.streak[1:]\n print is_winning_streak, streak_length\n\n (recent_wins, recent_losses) = args.last.split('-')\n print recent_wins, recent_losses\n\n power_ranking = calculate_power_ranking(int(wins), int(losses), is_winning_streak,\n int(streak_length), int(recent_wins),\n int(recent_losses))\n print power_ranking","def rank_vars(xTrain, yTrain, scoreFunc):\r\n funcsDic = {\r\n 'pearsonr': [np.arange(xTrain.shape[1]), 1], \r\n 'mutual_info_score': np.arange(xTrain.shape[0]),\r\n 'ttest_ind': [np.arange(xTrain.shape[1]), 1], \r\n }\r\n \r\n scores = list()\r\n for feat in np.arange(xTrain.shape[1]):\r\n if scoreFunc.func_name == 'pearsonr':\r\n scores.append(scoreFunc(xTrain[:, feat], yTrain))\r\n elif scoreFunc.func_name == 'ttest_ind':\r\n scores.append(scoreFunc(xTrain[yTrain == 1, feat], xTrain[yTrain==-1, feat]))\r\n \r\n scores = np.asarray(scores)\r\n pvals = scores[funcsDic[scoreFunc.func_name]]\r\n sortedIndices = [i[0] for i in sorted(enumerate(pvals), key=lambda x:x[1])]\r\n return sortedIndices","def disp_score():","def score(self, X, y=None) -> float:\n self.check_is_fitted()\n X = self._check_clusterer_input(X)\n return self._score(X, y)","def _run(evaluation_dir_name, smoothing_radius_grid_cells,\n score_colour_map_name, num_ex_colour_map_name, max_colour_percentile,\n output_dir_name):\n\n if smoothing_radius_grid_cells <= 0:\n smoothing_radius_grid_cells = None\n\n score_colour_map_object = pyplot.get_cmap(score_colour_map_name)\n num_ex_colour_map_object = pyplot.get_cmap(num_ex_colour_map_name)\n error_checking.assert_is_geq(max_colour_percentile, 90.)\n error_checking.assert_is_leq(max_colour_percentile, 100.)\n\n grid_metafile_name = grids.find_equidistant_metafile(\n directory_name=evaluation_dir_name, raise_error_if_missing=True\n )\n\n print('Reading grid metadata from: \"{0:s}\"...'.format(grid_metafile_name))\n grid_metadata_dict = grids.read_equidistant_metafile(grid_metafile_name)\n print(SEPARATOR_STRING)\n\n num_grid_rows = len(grid_metadata_dict[grids.Y_COORDS_KEY])\n num_grid_columns = len(grid_metadata_dict[grids.X_COORDS_KEY])\n\n auc_matrix = numpy.full((num_grid_rows, num_grid_columns), numpy.nan)\n csi_matrix = numpy.full((num_grid_rows, num_grid_columns), numpy.nan)\n pod_matrix = numpy.full((num_grid_rows, num_grid_columns), numpy.nan)\n far_matrix = numpy.full((num_grid_rows, num_grid_columns), numpy.nan)\n num_examples_matrix = numpy.full(\n (num_grid_rows, num_grid_columns), 0, dtype=int\n )\n num_positive_examples_matrix = numpy.full(\n (num_grid_rows, num_grid_columns), 0, dtype=int\n )\n\n for i in range(num_grid_rows):\n for j in range(num_grid_columns):\n this_eval_file_name = model_eval.find_file(\n directory_name=evaluation_dir_name, grid_row=i, grid_column=j,\n raise_error_if_missing=False)\n\n if not os.path.isfile(this_eval_file_name):\n warning_string = (\n 'Cannot find file (this may or may not be a problem). '\n 'Expected at: \"{0:s}\"'\n ).format(this_eval_file_name)\n\n warnings.warn(warning_string)\n continue\n\n print('Reading data from: \"{0:s}\"...'.format(this_eval_file_name))\n this_evaluation_dict = model_eval.read_evaluation(\n this_eval_file_name)\n\n num_examples_matrix[i, j] = len(\n this_evaluation_dict[model_eval.OBSERVED_LABELS_KEY]\n )\n num_positive_examples_matrix[i, j] = numpy.sum(\n this_evaluation_dict[model_eval.OBSERVED_LABELS_KEY]\n )\n\n this_evaluation_table = this_evaluation_dict[\n model_eval.EVALUATION_TABLE_KEY]\n\n auc_matrix[i, j] = numpy.nanmean(\n this_evaluation_table[model_eval.AUC_KEY].values\n )\n csi_matrix[i, j] = numpy.nanmean(\n this_evaluation_table[model_eval.CSI_KEY].values\n )\n pod_matrix[i, j] = numpy.nanmean(\n this_evaluation_table[model_eval.POD_KEY].values\n )\n far_matrix[i, j] = 1. - numpy.nanmean(\n this_evaluation_table[model_eval.SUCCESS_RATIO_KEY].values\n )\n\n print(SEPARATOR_STRING)\n\n auc_matrix[num_positive_examples_matrix == 0] = numpy.nan\n csi_matrix[num_positive_examples_matrix == 0] = numpy.nan\n pod_matrix[num_positive_examples_matrix == 0] = numpy.nan\n far_matrix[num_positive_examples_matrix == 0] = numpy.nan\n\n if smoothing_radius_grid_cells is not None:\n print((\n 'Applying Gaussian smoother with e-folding radius of {0:.1f} grid '\n 'cells...'\n ).format(\n smoothing_radius_grid_cells\n ))\n\n orig_num_examples_matrix = num_examples_matrix + 0\n num_examples_matrix = general_utils.apply_gaussian_filter(\n input_matrix=num_examples_matrix.astype(float),\n e_folding_radius_grid_cells=smoothing_radius_grid_cells\n )\n num_examples_matrix = numpy.round(num_examples_matrix).astype(int)\n num_examples_matrix[orig_num_examples_matrix == 0] = 0 # HACK\n\n num_positive_examples_matrix = general_utils.apply_gaussian_filter(\n input_matrix=num_positive_examples_matrix.astype(float),\n e_folding_radius_grid_cells=smoothing_radius_grid_cells\n )\n num_positive_examples_matrix = (\n numpy.round(num_positive_examples_matrix).astype(int)\n )\n num_positive_examples_matrix[num_examples_matrix == 0] = 0\n\n auc_matrix = general_utils.apply_gaussian_filter(\n input_matrix=ge_utils.fill_nans(auc_matrix),\n e_folding_radius_grid_cells=smoothing_radius_grid_cells\n )\n csi_matrix = general_utils.apply_gaussian_filter(\n input_matrix=ge_utils.fill_nans(csi_matrix),\n e_folding_radius_grid_cells=smoothing_radius_grid_cells\n )\n pod_matrix = general_utils.apply_gaussian_filter(\n input_matrix=ge_utils.fill_nans(pod_matrix),\n e_folding_radius_grid_cells=smoothing_radius_grid_cells\n )\n far_matrix = general_utils.apply_gaussian_filter(\n input_matrix=ge_utils.fill_nans(far_matrix),\n e_folding_radius_grid_cells=smoothing_radius_grid_cells\n )\n\n auc_matrix[num_positive_examples_matrix == 0] = numpy.nan\n csi_matrix[num_positive_examples_matrix == 0] = numpy.nan\n pod_matrix[num_positive_examples_matrix == 0] = numpy.nan\n far_matrix[num_positive_examples_matrix == 0] = numpy.nan\n\n panel_file_names = []\n file_system_utils.mkdir_recursive_if_necessary(\n directory_name=output_dir_name)\n\n # Plot number of examples.\n this_data_matrix = numpy.maximum(numpy.log10(num_examples_matrix), 0.)\n this_data_matrix[this_data_matrix == 0] = numpy.nan\n max_colour_value = numpy.nanpercentile(\n this_data_matrix, max_colour_percentile)\n\n figure_object, axes_object = _plot_one_value(\n data_matrix=this_data_matrix, grid_metadata_dict=grid_metadata_dict,\n colour_map_object=num_ex_colour_map_object,\n min_colour_value=0., max_colour_value=max_colour_value,\n plot_cbar_min_arrow=False, plot_cbar_max_arrow=True, log_scale=True)\n\n axes_object.set_title(r'Number of examples')\n plotting_utils.label_axes(axes_object=axes_object, label_string='(a)')\n\n panel_file_names.append('{0:s}/num_examples.jpg'.format(output_dir_name))\n print('Saving figure to: \"{0:s}\"...'.format(panel_file_names[-1]))\n\n figure_object.savefig(\n panel_file_names[-1], dpi=FIGURE_RESOLUTION_DPI, pad_inches=0,\n bbox_inches='tight')\n pyplot.close(figure_object)\n\n # Plot number of positive examples.\n this_data_matrix = num_positive_examples_matrix.astype(float)\n this_data_matrix[this_data_matrix == 0] = numpy.nan\n\n max_colour_value = numpy.nanpercentile(\n this_data_matrix, max_colour_percentile)\n min_colour_value = numpy.nanpercentile(\n this_data_matrix, 100. - max_colour_percentile)\n\n figure_object, axes_object = _plot_one_value(\n data_matrix=this_data_matrix, grid_metadata_dict=grid_metadata_dict,\n colour_map_object=num_ex_colour_map_object,\n min_colour_value=min_colour_value, max_colour_value=max_colour_value,\n plot_cbar_min_arrow=True, plot_cbar_max_arrow=True)\n\n axes_object.set_title('Number of tornadic examples')\n plotting_utils.label_axes(axes_object=axes_object, label_string='(b)')\n\n panel_file_names.append(\n '{0:s}/num_positive_examples.jpg'.format(output_dir_name)\n )\n print('Saving figure to: \"{0:s}\"...'.format(panel_file_names[-1]))\n\n figure_object.savefig(\n panel_file_names[-1], dpi=FIGURE_RESOLUTION_DPI, pad_inches=0,\n bbox_inches='tight')\n pyplot.close(figure_object)\n\n # Plot AUC.\n max_colour_value = numpy.nanpercentile(auc_matrix, max_colour_percentile)\n min_colour_value = numpy.maximum(\n numpy.nanpercentile(auc_matrix, 100. - max_colour_percentile),\n 0.5\n )\n\n figure_object, axes_object = _plot_one_value(\n data_matrix=auc_matrix, grid_metadata_dict=grid_metadata_dict,\n colour_map_object=score_colour_map_object,\n min_colour_value=min_colour_value, max_colour_value=max_colour_value,\n plot_cbar_min_arrow=True, plot_cbar_max_arrow=max_colour_value < 1.)\n\n axes_object.set_title('AUC (area under ROC curve)')\n plotting_utils.label_axes(axes_object=axes_object, label_string='(c)')\n\n panel_file_names.append('{0:s}/auc.jpg'.format(output_dir_name))\n print('Saving figure to: \"{0:s}\"...'.format(panel_file_names[-1]))\n\n figure_object.savefig(\n panel_file_names[-1], dpi=FIGURE_RESOLUTION_DPI, pad_inches=0,\n bbox_inches='tight')\n pyplot.close(figure_object)\n\n # Plot CSI.\n max_colour_value = numpy.nanpercentile(csi_matrix, max_colour_percentile)\n min_colour_value = numpy.nanpercentile(\n csi_matrix, 100. - max_colour_percentile)\n\n figure_object, axes_object = _plot_one_value(\n data_matrix=csi_matrix, grid_metadata_dict=grid_metadata_dict,\n colour_map_object=score_colour_map_object,\n min_colour_value=min_colour_value, max_colour_value=max_colour_value,\n plot_cbar_min_arrow=min_colour_value > 0.,\n plot_cbar_max_arrow=max_colour_value < 1.)\n\n axes_object.set_title('CSI (critical success index)')\n plotting_utils.label_axes(axes_object=axes_object, label_string='(d)')\n\n panel_file_names.append('{0:s}/csi.jpg'.format(output_dir_name))\n print('Saving figure to: \"{0:s}\"...'.format(panel_file_names[-1]))\n\n figure_object.savefig(\n panel_file_names[-1], dpi=FIGURE_RESOLUTION_DPI, pad_inches=0,\n bbox_inches='tight')\n pyplot.close(figure_object)\n\n # Plot POD.\n max_colour_value = numpy.nanpercentile(pod_matrix, max_colour_percentile)\n min_colour_value = numpy.nanpercentile(\n pod_matrix, 100. - max_colour_percentile)\n\n figure_object, axes_object = _plot_one_value(\n data_matrix=pod_matrix, grid_metadata_dict=grid_metadata_dict,\n colour_map_object=score_colour_map_object,\n min_colour_value=min_colour_value, max_colour_value=max_colour_value,\n plot_cbar_min_arrow=min_colour_value > 0.,\n plot_cbar_max_arrow=max_colour_value < 1.)\n\n axes_object.set_title('POD (probability of detection)')\n plotting_utils.label_axes(axes_object=axes_object, label_string='(e)')\n\n panel_file_names.append('{0:s}/pod.jpg'.format(output_dir_name))\n print('Saving figure to: \"{0:s}\"...'.format(panel_file_names[-1]))\n\n figure_object.savefig(\n panel_file_names[-1], dpi=FIGURE_RESOLUTION_DPI, pad_inches=0,\n bbox_inches='tight')\n pyplot.close(figure_object)\n\n # Plot FAR.\n max_colour_value = numpy.nanpercentile(far_matrix, max_colour_percentile)\n min_colour_value = numpy.nanpercentile(\n far_matrix, 100. - max_colour_percentile)\n\n figure_object, axes_object = _plot_one_value(\n data_matrix=far_matrix, grid_metadata_dict=grid_metadata_dict,\n colour_map_object=score_colour_map_object,\n min_colour_value=min_colour_value, max_colour_value=max_colour_value,\n plot_cbar_min_arrow=min_colour_value > 0.,\n plot_cbar_max_arrow=max_colour_value < 1.)\n\n axes_object.set_title('FAR (false-alarm ratio)')\n plotting_utils.label_axes(axes_object=axes_object, label_string='(f)')\n\n panel_file_names.append('{0:s}/far.jpg'.format(output_dir_name))\n print('Saving figure to: \"{0:s}\"...'.format(panel_file_names[-1]))\n\n figure_object.savefig(\n panel_file_names[-1], dpi=FIGURE_RESOLUTION_DPI, pad_inches=0,\n bbox_inches='tight')\n pyplot.close(figure_object)\n\n # Concatenate panels.\n concat_file_name = '{0:s}/spatially_subset_evaluation.jpg'.format(\n output_dir_name)\n print('Concatenating panels to: \"{0:s}\"...'.format(concat_file_name))\n\n imagemagick_utils.concatenate_images(\n input_file_names=panel_file_names, output_file_name=concat_file_name,\n num_panel_rows=NUM_PANEL_ROWS, num_panel_columns=NUM_PANEL_COLUMNS)\n\n imagemagick_utils.resize_image(\n input_file_name=concat_file_name, output_file_name=concat_file_name,\n output_size_pixels=CONCAT_FIGURE_SIZE_PX)","def new_evaluate(board):\n\n #Logic for new_evaluate function:\n #1)Traverse through each of the columns\n #2)For each of the columns, find the top most element.\n\t #If the topmost element = Current Player\n\t\t \t#3)Find the possible number of continuous elements of the same type in all the 4 directions from that cell(Horizontal,vertical and two diagonals)\n\t\t\t #Take the max of these lengths and this becomes the score for that column and it will stored as a POSITIVE value\n\t #Else\n\t\t \t#4)Find the possible number of continuous elements of the same type in all the 4 directions from that cell(Horizontal,vertical and two diagonals)\n\t\t\t #Take the max of these lengths and this becomes the score for that column and it will stored as a NEGATIVE value\n #5)Sort these Positive and Negative scores\n #6)IF the highest negative score is greater than the highest positive score, then it means that the opposition has MORE chances to WIN.\n #So, that has to be blocked and so we will return that HIGHEST NEGATIVE value as the score for that board\n #7)ELSE we go ahead and return the HIGHEST POSITIVE value as the score for that board\n #->This logic has increasing the AGGRESSION of the player a lot and it makes senses we hope.\n\n posdict = {}\n negdict = {}\n for col in range(7):\n if(board.get_top_elt_in_column(col)==board.get_current_player_id()) :\n rowValue = board.get_height_of_column(col)\n score = board._max_length_from_cell(rowValue,col)\n posdict[col]=score\n elif(board.get_top_elt_in_column(col)==board.get_other_player_id()) :\n rowValue = board.get_height_of_column(col)\n score = -(board._max_length_from_cell(rowValue,col))\n negdict[col]=score\n\n\n sorted(posdict.values(),reverse= True)\n sorted(negdict.values())\n if((bool(posdict))and (bool(negdict))):\n if(abs(negdict.values()[0]) >= ((posdict.values()[0]))):\n return negdict[negdict.keys()[0]]\n else:\n return posdict[posdict.keys()[0]]\n elif(bool(posdict)):\n return posdict[posdict.keys()[0]]\n elif(bool(negdict)):\n return negdict[negdict.keys()[0]]\n else:\n return 0","def score_fn(self):\n raise NotImplementedError()","def score(self, X, y, batch_size=50):\n features = self.encode(X, batch_size=batch_size)\n return self.classifier.score(features, y)","def score(self, x: np.ndarray) -> np.ndarray:\n score = self.backend.score(self.backend._to_backend_dtype(x))\n return self.backend._to_frontend_dtype(score)","def score(self, X: TwoDimArray, y: OneDimArray = None) -> float:\n\n check_is_fitted(self, '_glasso')\n\n return self._glasso.score(X)","def forward(ctx, scores):\n idx = argmax(scores, dim=-1)\n scores_net = eye(scores.size(-1), device=scores.device)\n return scores_net[idx]","def l_point(self):\n self.l_score += 1\n self.update_scoreboard()","def scoring(self):\n return -100 if self.loss_condition() else 0","def __call__(self, score, model):\n if self.best_score is None:\n # assign the best score and save the model at the end of the first epoch\n self.best_score = score\n self.save_checkpoint(model)\n elif score < self.best_score + self.delta:\n # if the score not increase of at least delta, increment the counter and if it reach the patience early stops\n self.counter += 1\n if self.counter >= self.patience:\n self.early_stop = True\n else:\n # otherwise the score is better that the saved one, so replace the best score and save the model\n self.best_score = score\n self.save_checkpoint(model)\n self.counter = 0","def auto_evaluation(model,x_train,y_train,x_test,y_test):\n\n y_train_prediction=model.predict(x_train)\n y_test_prediction=model.predict(x_test)\n\n plt.scatter(y_train,y_train_prediction,c=\"b\",s=1,alpha=0.5)\n plt.scatter(y_test,y_test_prediction,c=\"r\",s=2,alpha=0.5)\n plt.xlabel(\"actual\")\n plt.ylabel(\"predicted\")\n\n print(\"tr R2: {:.2f}\".format(r2_score(y_train_prediction,y_train)))\n print(\"te R2: {:.2f}\".format(r2_score(y_test_prediction,y_test))) \n \n return y_train_prediction,y_test_prediction","def custom_score(game, player):\n \n if game.is_loser(player):\n return float(\"-inf\")\n\n if game.is_winner(player):\n return float(\"inf\")\n \n # First obtain the improved score\n player_legal_moves = game.get_legal_moves(player)\n opponent = game.get_opponent(player)\n opponent_legal_moves = game.get_legal_moves(opponent)\n improved = len(player_legal_moves) - len(opponent_legal_moves)\n if improved != 0:\n return float(improved)\n \n # Second get differences from center\n center_coordinates = center_y, center_x = get_center_coordinates(game)\n player_coordinates = game.get_player_location(player)\n opponent_coordinates = game.get_player_location(opponent)\n player_center_dist = get_distances_from_center(center_coordinates, player_coordinates)\n opponent_center_dist = get_distances_from_center(center_coordinates, opponent_coordinates)\n center_dist_diff = player_center_dist - opponent_center_dist\n \n # Third obtain next_moves\n player_next_moves = [get_next_moves(game, move, list(move)) for move in player_legal_moves]\n opponent_next_moves = [get_next_moves(game, move, list(move)) for move in opponent_legal_moves] \n improved_next = len(player_next_moves) - len(opponent_next_moves)\n \n # Put player and opponent feature differences in a tuple/vector surrogoate\n feature_diff_vector = (improved, center_dist_diff, improved_next)\n \n # Provide a weighting vector for the features of each player-participant\n weight_vector = (1.5,0.1,1.0)\n # Calculate the return value = weighted difference of players' features\n weighted_difference_dot_product = sum(p*q for p,q, \\\n in zip(feature_diff_vector, weight_vector))\n \n return float(weighted_difference_dot_product)","def r_point(self):\n self.r_score += 1\n self.update()","def rank(m, axis=0, method='average', ascending=False, reverse=False):\n if isinstance(m, list):\n m = np.array(m)\n if ascending == reverse: # greater is better (descending order)\n m = -m # take the opposite to inverse rank\n r = np.apply_along_axis(rankdata, axis, m, method=method) # convert values to ranking in all rows or columns\n return process_vote(m, r)","def get_metrics(cfg, model, X_anchor, y_anchor, X_gal, y_gal, annoy_index, vec_dim):\n rank10_acc = 0\n rank5_acc = 0\n rank1_acc = 0\n avg_acc = 0\n vote_res = 0\n\n l2 = []\n for anchor in range(0, len(X_anchor)):\n res = get_result(get_image_features(cfg, model, X_anchor[anchor]), annoy_index)\n vote = defaultdict(int)\n # Accuracy\n correct = 0\n for i in res[:10]:\n vote[y_gal[i]] += 1\n\n max_key = max(vote, key=vote.get)\n if max_key == y_anchor[anchor]:\n vote_res += 1\n \n\n for recomm in res[:10]:\n if y_gal[recomm] == y_anchor[anchor]:\n correct += 1 \n\n avg_acc += correct/len(res)\n\n # Mean Average Precision\n l1 = []\n for recomm in res[:10]:\n if y_gal[recomm] == y_anchor[anchor]:\n correct += 1\n l1.append(1)\n else:\n l1.append(0)\n l2.append(l1) \n\n # Rank10 Accuracy\n for each_val in res[:10]:\n if y_gal[each_val] == y_anchor[anchor]:\n rank10_acc += 1\n break\n \n # Rank5 Accuracy\n for each_val in res[:5]:\n if y_gal[each_val] == y_anchor[anchor]:\n rank5_acc += 1\n break\n\n # Rank1 Accuracy\n for each_val in res[:1]:\n if y_gal[each_val] == y_anchor[anchor]:\n rank1_acc += 1\n break\n\n print(\"Avg acc is :: {avg_acc}\".format(avg_acc = avg_acc/len(X_anchor)))\n print(\"Rank 10 acc is :: {rank10_acc}\".format(rank10_acc = rank10_acc/len(X_anchor)))\n print(\"Rank 5 acc is :: {rank5_acc}\".format(rank5_acc = rank5_acc/len(X_anchor)))\n print(\"Rank 1 acc is :: {rank1_acc}\".format(rank1_acc = rank1_acc/len(X_anchor)))\n print(\"Mean Avg Precision is :: {mAP}\".format(mAP=mean_average_precision(l2)))\n print(\"Vote res :: \", vote_res/len(X_anchor))\n\n return rank1_acc/len(X_anchor), mean_average_precision(l2)","def predict(self, x_val, y1_test=None, y2_test=None):\n if y1_test is None and y2_test is None:\n y1_test = np.ones(len(x_val))\n y2_test = y1_test\n score_model = False\n else:\n score_model = True\n\n self.stage_one.predict(x_val, y1_test)\n if score_model:\n stage_one_score = self.stage_one.score\n\n # This section will not generalize well beyond this specific dataset.\n if score_model:\n df = pd.DataFrame(data=[y1_test, self.stage_one.pred],\n columns=['y1_test', 'stage_one.pred'])\n df = pd.DataFrame(y1_test).rename(columns={'class_second': 'y1_test'})\n df['stage_one.pred'] = self.stage_one.pred\n df['tweets'] = x_val\n df['y2_test'] = y2_test\n\n # x2_val = df['tweets'].loc[df['stage_one.pred'] == 0]\n # y2_test_ = df['y2_test'].loc[df['stage_one.pred'] == 0]\n x2_val = df['tweets'].loc[df['stage_one.pred'] == 1]\n y2_test_ = df['y2_test'].loc[df['stage_one.pred'] == 1]\n else:\n # x2_val = x_val[self.stage_one.pred == 0]\n x2_val = x_val[self.stage_one.pred == 1]\n y2_test_ = np.ones(len(x2_val))\n\n self.stage_two.predict(x2_val, y2_test_)\n if score_model:\n stage_two_score = self.stage_two.score\n return (stage_one_score, stage_two_score, stage_one_score*stage_two_score)\n else:\n # return tweets that contain hate speech\n return x2_val[self.stage_two.pred == 0]","def get_polyscore(self,X_test=None,y_test=None,metric='adjusted_r2'):\n X = self.get_points()\n y_pred = self.get_polyfit(X)\n train_score = score(self._model_evaluations, y_pred,metric, X=X)\n if X_test is not None and y_test is not None:\n y_pred_test = self.get_polyfit(X_test)\n test_score = score(y_test,y_pred_test,metric,X=X_test)\n return train_score, test_score\n else:\n return train_score","def evaluate(self, test_data):\r\n test_results = [(np.argmax(self.feedforward(x)), y)\r\n for (x, y) in test_data]\r\n #print(self.feedforward(test_data[0][0]))\r\n #print(test_data[0][1])\r\n return sum(int(x == y) for (x, y) in test_results)","def main():\n graph_alg_eq()\n graph_points()\n graph_smooth_from_pts()\n\n return GOOD_RET # success","def styblinskitankfcn(x: np.ndarray) -> np.ndarray:\n n = x.shape[1]\n scores = np.zeros((x.shape[0], 1))\n for i in range(n):\n scores += x[:, i] ** 4 - 16 * x[:, i] ** 2 + 5 * x[:, i]\n scores *= 0.5\n return scores"],"string":"[\n \"def _check_run(self, axis):\\n if (self.x_traj, self.y_traj)[axis] is None:\\n if (self.inverse_x_traj, self.inverse_y_traj)[axis] is None:\\n raise Exception('The algorithm has not been run.')\\n else:\\n if self.params['print_info']:\\n print('Warning: you are using the opposite score. It can contain errors if any score is a zero below threshold.')\\n return (self.inverse_x_traj, self.inverse_y_traj)[axis], (self.inverse_x_ranking, self.inverse_y_ranking)[axis]\\n return (self.x_traj, self.y_traj)[axis], (self.x_ranking, self.y_ranking)[axis]\",\n \"def score(self, X, y=...):\\n ...\",\n \"def score(self, X, y):\\n ...\",\n \"def score(self, X, y):\\n raise NotImplementedError('Abstract method \\\"score\\\" must be '\\n 'specialised!')\",\n \"def rank(self,others):\\n self.__verify(others)\\n \\n #construct the n evaluation criteria + classes in an extensible way\\n #evalFn = [AP,R] in the standard format -> column with as many rows as replicates\\n numClasses = others[0].eval['APBCI'].shape[2]\\n\\n iouType = others[0].params.iouType\\n if iouType in [\\\"segm\\\",\\\"bbox\\\"]:\\n evalFunctions = [ \\\\\\n lambda AP,R: np.nanmean(AP[:,:,:,0,-1],axis=(0,2)),\\n lambda AP,R: np.nanmean(AP[0,:,:,0,-1],axis=(1)),\\n lambda AP,R: np.nanmean(AP[5,:,:,0,-1],axis=(1)),\\n lambda AP,R: np.nanmean(AP[:,:,:,1,-1],axis=(0,2)),\\n lambda AP,R: np.nanmean(AP[:,:,:,2,-1],axis=(0,2)),\\n lambda AP,R: np.nanmean(AP[:,:,:,3,-1],axis=(0,2)),\\n lambda AP,R: np.nanmean(R[:,:,:,0,0],axis=(0,2)),\\n lambda AP,R: np.nanmean(R[:,:,:,0,1],axis=(0,2)),\\n lambda AP,R: np.nanmean(R[:,:,:,0,2],axis=(0,2)),\\n lambda AP,R: np.nanmean(R[:,:,:,1,2],axis=(0,2)),\\n lambda AP,R: np.nanmean(R[:,:,:,2,2],axis=(0,2)),\\n lambda AP,R: np.nanmean(R[:,:,:,3,2],axis=(0,2))]\\n\\n evfAP = lambda c: (lambda AP,R: np.nanmean(AP[:,:,c,0,-1],axis=0))\\n for i in range(numClasses):\\n evalFunctions.append(evfAP(i))\\n\\n else:\\n evalFunctions = [ \\\\\\n lambda AP,R: np.nanmean(AP[:,:,:,0,0],axis=(0,2)),\\n lambda AP,R: np.nanmean(AP[0,:,:,0,0],axis=(1)),\\n lambda AP,R: np.nanmean(AP[5,:,:,0,0],axis=(1)),\\n lambda AP,R: np.nanmean(AP[:,:,:,1,0],axis=(0,2)),\\n lambda AP,R: np.nanmean(AP[:,:,:,2,0],axis=(0,2)),\\n lambda AP,R: np.nanmean(R[:,:,:,0,0],axis=(0,2)),\\n lambda AP,R: np.nanmean(R[0,:,:,0,0],axis=(1)),\\n lambda AP,R: np.nanmean(R[5,:,:,0,0],axis=(1)),\\n lambda AP,R: np.nanmean(R[:,:,:,1,0],axis=(0,2)),\\n lambda AP,R: np.nanmean(R[:,:,:,2,0],axis=(0,2))]\\n\\n numReplicates = others[0].eval['APBCI'].shape[1]\\n numInstances = len(others)\\n numEvals = len(evalFunctions)\\n\\n replicateStats = np.zeros((numReplicates,numInstances))\\n\\n outperformMatrix = np.zeros((numInstances,numInstances,numEvals))\\n rankCI = np.zeros((numInstances,3,numEvals))\\n ranks = np.zeros((numInstances,numEvals,numReplicates))\\n\\n for evi,evf in enumerate(evalFunctions):\\n for oi,o in enumerate(others):\\n replicateStats[:,oi] = evf(o.eval['APBCI'],o.eval['RBCI'])\\n\\n for oi in range(len(others)):\\n for oj in range(len(others)):\\n outperformMatrix[oi,oj,evi] = np.mean(replicateStats[:,oi]>replicateStats[:,oj])\\n\\n for bci in range(numReplicates):\\n ranks[:,evi,bci] = stats.rankdata(-replicateStats[bci,:],method='min')\\n\\n for oi in range(len(others)): \\n rankCI[oi,0,evi] = np.mean(ranks[oi,evi,:])\\n #use simple percentile method; the bias correction misbehaves \\n rankCI[oi,1:,evi] = np.percentile(ranks[oi,evi,:],[100*(self.params.bootstrapAlpha/2),100*(1-self.params.bootstrapAlpha/2)])\\n\\n return rankCI, outperformMatrix, ranks\",\n \"def run(self):\\n if self.pb.xvalEN and not self.isXvalMain:\\n dt = np.ones(self.pb.total_exam_no,\\n dtype=\\\"float32\\\") / self.train_exam_no\\n dt[self.val_indices] = 0.0\\n else:\\n dt = np.ones(self.pb.total_exam_no,\\n dtype=\\\"float32\\\") / self.pb.total_exam_no\\n\\n val = np.zeros(8,dtype=\\\"float32\\\")-1\\n boosting = None\\n wl = None\\n if self.pb.algorithm == 'conf-rated':\\n boosting = ConfidenceRated(self)\\n wl = ConfidenceRatedWL(self)\\n elif self.pb.algorithm == 'adaboost':\\n boosting = AdaBoost(self)\\n wl = AdaBoostWL(self)\\n elif self.pb.algorithm == 'adaboost-fast':\\n boosting = AdaBoostFast(self)\\n wl = AdaBoostFastWLMPI(self)\\n elif self.pb.algorithm == 'rankboost':\\n boosting = RankBoost(self)\\n wl = ConfidenceRatedWL(self)\\n elif self.pb.algorithm == 'rankboost-fast':\\n boosting = RankBoost(self)\\n wl = AdaBoostFastWL(self)\\n else:\\n raise Exception(\\\"Unknown Boosting Algorithm\\\")\\n \\n for r in range(self.pb.rounds):\\n tree = wl.run(dt)\\n dt = boosting.run(dt = dt,\\n r = r,\\n tree = tree)\\n \\n if self.isXvalMain:\\n boosting.finalize()\\n \\n \\\"\\\"\\\"Sync the predictions and save them to a file\\\"\\\"\\\"\\n if self.pb.isLeader:\\n if self.pb.xvalEN and not self.isXvalMain:\\n val_predictions = boosting.get_val_predictions()\\n hypotheses = boosting.get_hypotheses()\\n np.savez(self.out_fp,\\n val_predictions = val_predictions,\\n hypotheses = hypotheses,\\n )\\n if self.pb.testEN and self.isXvalMain:\\n train_predictions = np.zeros([self.pb.total_exam_no,self.pb.rounds],\\n dtype=\\\"float32\\\")\\n test_predictions = np.zeros([self.pb.test_exam_no,self.pb.rounds],\\n dtype=\\\"float32\\\")\\n for slv in np.arange(self.pb.comm_size):\\n tr_i1 = self.pb.train_partition[slv]\\n tr_i2 = self.pb.train_partition[slv+1]\\n te_i1 = self.pb.test_partition[slv]\\n te_i2 = self.pb.test_partition[slv+1]\\n if slv == 0:\\n train_predictions[tr_i1:tr_i2,:] = boosting.get_train_predictions()\\n test_predictions[te_i1:te_i2,:] = boosting.get_test_predictions()\\n else:\\n train_predictions[tr_i1:tr_i2,:] = self.pb.comm.recv(source=slv,tag=11)\\n test_predictions[te_i1:te_i2,:] = self.pb.comm.recv(source=slv,tag=12)\\n hypotheses = boosting.get_hypotheses()\\n np.savez(self.out_fp,\\n train_predictions = train_predictions,\\n test_predictions = test_predictions,\\n hypotheses = hypotheses,\\n )\\n if not self.pb.testEN and self.isXvalMain:\\n train_predictions = np.zeros([self.pb.total_exam_no,self.pb.rounds],\\n dtype=\\\"float32\\\")\\n for slv in np.arange(self.pb.comm_size):\\n tr_i1 = self.pb.train_partition[slv]\\n tr_i2 = self.pb.train_partition[slv+1]\\n if slv == 0:\\n train_predictions[tr_i1:tr_i2,:] = boosting.get_train_predictions()\\n else:\\n train_predictions[tr_i1:tr_i2,:] = self.pb.comm.recv(source=slv,tag=11)\\n hypotheses = boosting.get_hypotheses()\\n np.savez(self.out_fp,\\n train_predictions = train_predictions,\\n hypotheses = hypotheses,\\n )\\n else:\\n if self.pb.testEN and self.isXvalMain:\\n train_predictions = boosting.get_train_predictions()\\n self.pb.comm.send(train_predictions,dest = 0,tag=11)\\n test_predictions = boosting.get_test_predictions()\\n self.pb.comm.send(test_predictions,dest = 0,tag=12)\\n if not self.pb.testEN and self.isXvalMain:\\n train_predictions = boosting.get_train_predictions()\\n self.pb.comm.send(train_predictions,dest = 0,tag=11)\",\n \"def set_rank_order(self):\\n for k in self._run:\\n self._run[k].sort(key=lambda x:x.get_rank(),reverse=False)\\n tot_res = len(self._run[k])\\n for r in self._run[k]:\\n r.set_score(tot_res - int(r.get_rank()) + 1)\\n print r.get_str()\",\n \"def update_score(self, red_alliance, blue_alliance, prediction, red_score, blue_score):\\n score = self.get_score(red_score, blue_score)\\n\\n for team in red_alliance:\\n self.scores[team] = self.el.update(self.scores[team], prediction, score)\\n\\n for team in blue_alliance:\\n self.scores[team] = self.el.update(self.scores[team], 1 - prediction, score)\",\n \"def run(self):\\n if self.pb.xvalEN and not self.isXvalMain:\\n dt = np.ones(self.pb.total_exam_no,\\n dtype=\\\"float32\\\") / self.train_exam_no\\n dt[self.val_indices] = 0.0\\n else:\\n dt = np.ones(self.pb.total_exam_no,\\n dtype=\\\"float32\\\") / self.pb.total_exam_no\\n\\n val = np.zeros(8,dtype=\\\"float32\\\")-1\\n boosting = None\\n wl = None\\n if self.pb.algorithm == 'conf-rated':\\n boosting = ConfidenceRated(self)\\n wl = ConfidenceRatedWL(self)\\n elif self.pb.algorithm == 'adaboost':\\n boosting = AdaBoost(self)\\n wl = AdaBoostWL(self)\\n elif self.pb.algorithm == 'adaboost-fast':\\n boosting = AdaBoostFast(self)\\n wl = AdaBoostFastWL(self)\\n elif self.pb.algorithm == 'rankboost':\\n boosting = RankBoost(self)\\n wl = ConfidenceRatedWL(self)\\n elif self.pb.algorithm == 'rankboost-fast':\\n boosting = RankBoost(self)\\n wl = AdaBoostFastWL(self)\\n else:\\n raise Exception(\\\"Unknown Boosting Algorithm\\\")\\n \\n for r in range(self.pb.rounds):\\n tree = wl.run(dt)\\n dt = boosting.run(dt = dt,\\n r = r,\\n tree = tree)\\n \\n if self.isXvalMain:\\n boosting.finalize()\\n \\n \\\"\\\"\\\"Sync the predictions and save them to a file\\\"\\\"\\\"\\n if self.pb.isLeader:\\n if self.pb.xvalEN and not self.isXvalMain:\\n val_predictions = boosting.get_val_predictions()\\n hypotheses = boosting.get_hypotheses()\\n np.savez(self.out_fp,\\n val_predictions = val_predictions,\\n hypotheses = hypotheses,\\n )\\n if self.pb.testEN and self.isXvalMain:\\n train_predictions = boosting.get_train_predictions()\\n test_predictions = boosting.get_test_predictions()\\n hypotheses = boosting.get_hypotheses()\\n np.savez(self.out_fp,\\n train_predictions = train_predictions,\\n test_predictions = test_predictions,\\n hypotheses = hypotheses,\\n )\\n if not self.pb.testEN and self.isXvalMain:\\n train_predictions = boosting.get_train_predictions()\\n hypotheses = boosting.get_hypotheses()\\n np.savez(self.out_fp,\\n train_predictions = train_predictions,\\n hypotheses = hypotheses,\\n )\",\n \"def evaluate_ranking_loss(eval_model, x_, y_, df_, loss_fn, mode='val'):\\n\\n qm_tensors_, db_mask_, blink_mask_ = convert_values_to_tensors(df_)\\n dataset = TensorDataset(x_, y_, qm_tensors_, db_mask_, blink_mask_)\\n question_sampler = QuestionSampler(torch.utils.data.SequentialSampler(range(len(qm_tensors_))),\\n qm_tensors_, False)\\n loader = DataLoader(dataset, batch_sampler=question_sampler, shuffle=False)\\n\\n eval_model.eval()\\n with torch.no_grad():\\n pred_ = []\\n for x_, y_, qm_, db_mask, blink_mask in loader:\\n xbatch_list = []\\n xbatch_idxes = []\\n for mask in [db_mask, blink_mask]:\\n idxes = get_idxes_from_mask(mask)\\n xbatch_idxes.append(idxes)\\n x_pick, y_pick, qm_pick = x_[idxes], y_[idxes], qm_[idxes]\\n\\n if len(x_pick) == 0:\\n xbatch_list.append(torch.tensor([]))\\n continue\\n # elif len(x_pick) <= 1:\\n # xbatch_list.append(torch.tensor([]))\\n # continue\\n x_batch = x_pick\\n xbatch_list.append(x_batch)\\n if (len(xbatch_list[0]) == 0) and (len(xbatch_list[1]) == 0):\\n print(x_, y_, qm_, db_mask, blink_mask)\\n print(xbatch_idxes)\\n yhat = eval_model(xbatch_list[0], xbatch_list[1])\\n # get individual instance predicted prob\\n yhat_in_order1 = torch.zeros(len(x_), 1)\\n yhat_in_order2 = torch.zeros(len(x_), 1)\\n yhat_in_order1[xbatch_idxes[0]] = yhat[:len(xbatch_idxes[0])]\\n yhat_in_order2[xbatch_idxes[1]] = yhat[len(xbatch_idxes[0]):]\\n yhat_in_order = torch.max(torch.cat((yhat_in_order1, yhat_in_order2), 1), 1)[0].reshape(-1,1)\\n # print(yhat_in_order)\\n pred_.append(yhat_in_order)\\n pred_ = torch.cat(pred_, 0)\\n prec, recall, f1, df_output = compute_qald_metrics(pred_, df_, gold_file_name=args.gold_file_name)\\n avg_loss, _ = compute_loss(eval_model, loader, loss_fn, optimizer=None)\\n print(\\\"Current {} -- prec {}; recall {}; f1 {}, loss {}\\\".format(mode, prec, recall, f1, avg_loss))\\n\\n return avg_loss, f1, pred_, df_output\",\n \"def scoring(self):\\n pass\",\n \"def score(self, x, y, **kwargs):\\n kwargs = self.filter_sk_params(Sequential.evaluate, kwargs)\\n loss = self.model.evaluate(x, y, **kwargs)\\n if isinstance(loss, list):\\n return loss[0]\\n return loss\",\n \"def score(self, X, y):\\r\\n n_feature, _ = self.check_model()\\r\\n _, n_label = y.shape\\r\\n y = self.check_X_y_weights(X, y)\\r\\n\\r\\n if X.shape[1] == (n_feature + 1):\\r\\n X = X[:, 1:]\\r\\n\\r\\n assert (X.shape[1] == n_feature), \\\"X is of the wrong shape\\\"\\r\\n\\r\\n if self.scoring_func is None:\\r\\n y_pred = self.forward(X)\\r\\n\\r\\n loss = self.loss_func(torch.from_numpy(y_pred).float(),\\r\\n torch.from_numpy(y).float())\\r\\n loss = torch.mean(torch.sum(loss, 1)).numpy()\\r\\n\\r\\n return - loss\\r\\n else:\\r\\n y_pred = self.predict(X)\\r\\n return self.scoring_func(y_pred, y)\",\n \"def scores(model,X_train,y_train, X_test,y_test,inside):\\r\\n \\r\\n model.best_apply(X_test,y_test)\\r\\n y_pred = model.forward(X_test).cpu()\\r\\n y_pred = np.asarray(torch.argmax(y_pred, dim = 1).squeeze())\\r\\n y_test_ = np.asarray(torch.argmax(y_test.cpu(), dim = 1))\\r\\n acc1 = accuracy_score(y_test_,y_pred)\\r\\n model.best_apply(X_train,y_train)\\r\\n y_pred = model.forward(X_train).cpu()\\r\\n y_pred = np.asarray(torch.argmax(y_pred, dim = 1).squeeze())\\r\\n y_train_ = np.asarray(torch.argmax(y_train.cpu(), dim = 1))\\r\\n acc2 = accuracy_score(y_train_,y_pred)\\r\\n if inside :\\r\\n print(\\\"###### Results ##########\\\")\\r\\n print(\\\"The Validation Acuuracy is : \\\",acc1)\\r\\n print(\\\"The Training Acuuracy is : \\\",acc2)\\r\\n return [acc1,acc2]\",\n \"def score(self,x,**kwargs):\\r\\n if self.kfun != 'matrix' and len(self.sv): \\r\\n k = self.kfun(x,self.sv,**self.cparam)\\r\\n #print \\\"Kernel after test: \\\", k\\r\\n else:\\r\\n k = x\\r\\n \\r\\n \\r\\n self.W=self.alphas \\r\\n self.mat=self.kfun(np.array([self.sv[1]]), self.sv,**self.cparam) \\r\\n self.bias=self.svLabels[1]- np.dot((self.alphas*self.svLabels).T,self.mat.T) \\r\\n z=np.dot((self.alphas*self.svLabels).T,k.T)+self.bias\\r\\n \\r\\n #print \\\"bias: \\\", self.bias, \\\"\\\\nZ: \\\",z\\r\\n \\r\\n \\r\\n return z\",\n \"def pre_step(x, y):\\r\\n feed_dict = {\\r\\n cnn.input_x: x,\\r\\n cnn.input_y: y,\\r\\n cnn.dropout_keep_prob: 1.0\\r\\n }\\r\\n scores = sess.run([cnn.scores], feed_dict)\\r\\n return scores\",\n \"def r_point(self):\\n self.r_score += 1\\n self.update_scoreboard()\",\n \"def score(self, X, y, *args, **kwargs):\\n y_t = self.target_transform.transform(y)\\n\\n if hasattr(self, 'ml_score') and self.ml_score:\\n log.info('Using custom score')\\n return r2_score(y_true=y_t,\\n y_pred=self._notransform_predict(\\n X, *args, **kwargs))\\n else:\\n return super().score(X, y, *args, **kwargs)\",\n \"def score(self, y_true, y_pred):\\r\\n pass\",\n \"def update_scores(self):\\n self.score[0] = (-1)*sum(self.board[self.board == -1])\\n self.score[1] = sum(self.board[self.board == 1])\\n #self.score[i] = sum(1 for j in range(len(stones_on_board)) if stones_on_board[j] == i)\",\n \"def score(self) -> int:\\n return self.function(self.x, self.y)\",\n \"def score(self, epochs=None, y=None):\\n import sklearn.metrics\\n from sklearn.base import is_classifier\\n from sklearn.metrics import accuracy_score, mean_squared_error\\n if check_version('sklearn', '0.17'):\\n from sklearn.base import is_regressor\\n else:\\n def is_regressor(clf):\\n return False\\n\\n # Run predictions if not already done\\n if epochs is not None:\\n self.predict(epochs)\\n else:\\n if not hasattr(self, 'y_pred_'):\\n raise RuntimeError('Please predict() epochs first or pass '\\n 'epochs to score()')\\n\\n # Check scorer\\n if self.score_mode not in ('fold-wise', 'mean-fold-wise',\\n 'mean-sample-wise'):\\n raise ValueError(\\\"score_mode must be 'fold-wise', \\\"\\n \\\"'mean-fold-wise' or 'mean-sample-wise'. \\\"\\n \\\"Got %s instead'\\\" % self.score_mode)\\n score_mode = self.score_mode\\n if (self.predict_mode == 'mean-prediction' and\\n self.score_mode != 'mean-sample-wise'):\\n warn(\\\"score_mode changed from %s set to 'mean-sample-wise' because\\\"\\n \\\" predict_mode is 'mean-prediction'.\\\" % self.score_mode)\\n score_mode = 'mean-sample-wise'\\n self.scorer_ = self.scorer\\n if self.scorer_ is None:\\n # Try to guess which scoring metrics should be used\\n if self.predict_method == \\\"predict\\\":\\n if is_classifier(self.clf):\\n self.scorer_ = accuracy_score\\n elif is_regressor(self.clf):\\n self.scorer_ = mean_squared_error\\n\\n elif isinstance(self.scorer_, str):\\n if hasattr(sklearn.metrics, '%s_score' % self.scorer_):\\n self.scorer_ = getattr(sklearn.metrics, '%s_score' %\\n self.scorer_)\\n else:\\n raise KeyError(\\\"{0} scorer Doesn't appear to be valid a \\\"\\n \\\"scikit-learn scorer.\\\".format(self.scorer_))\\n if not self.scorer_:\\n raise ValueError('Could not find a scoring metric for clf=%s '\\n ' and predict_method=%s. Manually define scorer'\\n '.' % (self.clf, self.predict_method))\\n\\n # If no regressor is passed, use default epochs events\\n if y is None:\\n if self.predict_mode == 'cross-validation':\\n y = self.y_train_\\n else:\\n if epochs is not None:\\n y = epochs.events[:, 2]\\n else:\\n raise RuntimeError('y is undefined because '\\n 'predict_mode=\\\"mean-prediction\\\" and '\\n 'epochs are missing. You need to '\\n 'explicitly specify y.')\\n if not np.all(np.unique(y) == np.unique(self.y_train_)):\\n raise ValueError('Classes (y) passed differ from classes used '\\n 'for training. Please explicitly pass your y '\\n 'for scoring.')\\n elif isinstance(y, list):\\n y = np.array(y)\\n\\n # Clean attributes\\n for att in ['scores_', 'y_true_']:\\n if hasattr(self, att):\\n delattr(self, att)\\n\\n self.y_true_ = y # to be compared with y_pred for scoring\\n\\n # Preprocessing for parallelization across training times; to avoid\\n # overheads, we divide them in large chunks.\\n n_jobs = min(len(self.y_pred_[0][0]), check_n_jobs(self.n_jobs))\\n parallel, p_func, n_jobs = parallel_func(_score_slices, n_jobs)\\n n_estimators = len(self.train_times_['slices'])\\n n_chunks = min(n_estimators, n_jobs)\\n chunks = np.array_split(range(len(self.train_times_['slices'])),\\n n_chunks)\\n scores = parallel(p_func(\\n self.y_true_, [self.y_pred_[train] for train in chunk],\\n self.scorer_, score_mode, self._cv_splits)\\n for chunk in chunks)\\n # TODO: np.array scores from initialization JRK\\n self.scores_ = np.array([score for chunk in scores for score in chunk])\\n return self.scores_\",\n \"def preprocess_lab_data(self):\\n print(\\\"Processing dataset from the labs ...\\\")\\n self.full_dataset[\\\"score\\\"] = self.full_dataset[\\\"score\\\"].apply(lambda x: -1 if x==0 else 1)\",\n \"def compute_scores(self, score_type, adjust = None, verbose = False):\\n if score_type == 'coupling':\\n scores = self.coupling\\n elif score_type == 'barycentric':\\n ot_emd = ot.da.EMDTransport()\\n ot_emd.xs_ = self.xs\\n ot_emd.xt_ = self.xt\\n ot_emd.coupling_= self.coupling\\n xt_s = ot_emd.inverse_transform(Xt=self.xt) # Maps target to source space\\n scores = -sp.spatial.distance.cdist(self.xs, xt_s, metric = self.metric) #FIXME: should this be - dist?\\n elif score_type == 'distance':\\n # For baselines that only use distances without OT\\n scores = -sp.spatial.distance.cdist(self.xs,self.xt, metric = self.metric)\\n elif score_type == 'projected':\\n # Uses projection mapping, computes distance in projected space\\n scores = -sp.spatial.distance.cdist(self.xs,self.xt@self.mapping.T, metric = self.metric)\\n\\n if adjust == 'csls':\\n scores = csls(scores, knn = 10)\\n #print('here')\\n elif adjust == 'isf':\\n raise NotImplementedError('Inverted Softmax not implemented yet')\\n\\n self.scores = scores\\n\\n if verbose:\\n plt.figure()\\n plt.imshow(scores, cmap='jet')\\n plt.colorbar()\\n plt.show()\",\n \"def rank_yx(self, rankyx, rank_to_yx=1):\\r\\n if rank_to_yx == 1:\\r\\n x = int(rankyx) % int(self.shapes[1])\\r\\n y = (rankyx - x) / int(self.shapes[1])\\r\\n return [y, x] # More convenient to return y, x\\r\\n \\r\\n if rank_to_yx == 0: # that means transform yx to rank, expecting rankyx to be a list, may not be necessary\\r\\n rankyx = rankyx[0] * int(self.shapes[1]) + rankyx[1]\\r\\n return rankyx # returns back a float\\r\",\n \"def compute_positions(scores, layers, cells, direction):\\n prior = 1/np.arange(3, layers+3)\\n prior = prior/prior.sum()\\n x = np.linspace(-5, 5, layers)[::-1] \\n prior = 1/(1 + np.exp(-x))\\n prior = prior/prior.sum()\\n\\n # Compute the probability depending on the direction in which we want to know where the change is\\n if direction == \\\"lr\\\":\\n col_scores = np.array([scores[np.arange(cells[0])*cells[1]+j, np.arange(cells[0])*cells[1]+j+1].sum() for j in range(layers)])\\n elif direction == \\\"rl\\\":\\n col_scores = np.array([scores[np.arange(cells[0])*cells[1]+cells[1]-1-j, np.arange(cells[0])*cells[1]+cells[1]-1-j-1].sum() for j in range(layers)])\\n elif direction == \\\"tb\\\":\\n col_scores = np.array([scores[np.arange(cells[1])+cells[1]*j, np.arange(cells[1])+cells[1]*(j+1)].sum() for j in range(layers)])\\n elif direction == \\\"bt\\\":\\n col_scores = np.array([scores[np.arange(cells[1])+cells[1]*(cells[0]-1-j), np.arange(cells[1])+cells[1]*(cells[0]-1-j-1)].sum() for j in range(layers)])\\n \\n # Apply softmax + multiply by prior -> Then get the most likely position\\n col_scores = sm(col_scores)\\n position = np.argmax(col_scores*prior)\\n return position\",\n \"def _run(output_dir_name):\\n\\n file_system_utils.mkdir_recursive_if_necessary(\\n directory_name=output_dir_name\\n )\\n\\n # Housekeeping.\\n y_tick_labels = [\\n '{0:s}'.format(s) for s in MODEL_DEPTH_WIDTH_STRINGS\\n ]\\n x_tick_labels = [\\n '{0:d}'.format(c) for c in FIRST_LAYER_CHANNEL_COUNTS\\n ]\\n\\n y_axis_label = 'NN depth, width'\\n x_axis_label = 'Spectral complexity'\\n\\n # Plot grid for U-net++ without deep supervision.\\n dimensions_3d = (\\n len(MODEL_WIDTHS), len(MODEL_DEPTHS), len(FIRST_LAYER_CHANNEL_COUNTS)\\n )\\n num_weights_matrix = numpy.reshape(NUM_WEIGHTS_ARRAY_PLUSPLUS, dimensions_3d)\\n num_weights_matrix = numpy.swapaxes(num_weights_matrix, 0, 1)\\n\\n num_depth_width_combos = len(MODEL_DEPTH_WIDTH_STRINGS)\\n num_channel_counts = len(FIRST_LAYER_CHANNEL_COUNTS)\\n dimensions_2d = (num_depth_width_combos, num_channel_counts)\\n num_weights_matrix = numpy.reshape(num_weights_matrix, dimensions_2d)\\n\\n all_weights = numpy.concatenate((\\n NUM_WEIGHTS_ARRAY_PLUSPLUS, NUM_WEIGHTS_ARRAY_PLUSPLUSPLUS,\\n NUM_WEIGHTS_ARRAY_PLUSPLUS_DEEP, NUM_WEIGHTS_ARRAY_PLUSPLUSPLUS_DEEP\\n ), axis=0)\\n\\n min_colour_value = numpy.min(numpy.log10(all_weights))\\n max_colour_value = numpy.max(numpy.log10(all_weights))\\n\\n figure_object, axes_object = _plot_scores_2d(\\n score_matrix=numpy.log10(num_weights_matrix),\\n min_colour_value=min_colour_value,\\n max_colour_value=max_colour_value,\\n x_tick_labels=x_tick_labels, y_tick_labels=y_tick_labels\\n )\\n\\n figure_width_px = (\\n figure_object.get_size_inches()[0] * figure_object.dpi\\n )\\n marker_size_px = figure_width_px * (\\n MARKER_SIZE_GRID_CELLS / num_weights_matrix.shape[1]\\n )\\n\\n if SELECTED_MARKER_INDICES[0] == 0:\\n axes_object.plot(\\n SELECTED_MARKER_INDICES[2], SELECTED_MARKER_INDICES[1],\\n linestyle='None', marker=MARKER_TYPE,\\n markersize=marker_size_px, markeredgewidth=0,\\n markerfacecolor=MARKER_COLOUR,\\n markeredgecolor=MARKER_COLOUR\\n )\\n\\n axes_object.set_xlabel(x_axis_label)\\n axes_object.set_ylabel(y_axis_label)\\n axes_object.set_title(NN_TYPE_STRINGS_FANCY[0])\\n\\n panel_file_names = [\\n '{0:s}/num_weights_log10_{1:s}.jpg'.format(\\n output_dir_name, NN_TYPE_STRINGS[0].replace('_', '-')\\n )\\n ]\\n\\n print('Saving figure to: \\\"{0:s}\\\"...'.format(panel_file_names[-1]))\\n figure_object.savefig(\\n panel_file_names[-1], dpi=FIGURE_RESOLUTION_DPI,\\n pad_inches=0, bbox_inches='tight'\\n )\\n pyplot.close(figure_object)\\n\\n # Plot grid for U-net++ with deep supervision.\\n num_weights_matrix = numpy.reshape(\\n NUM_WEIGHTS_ARRAY_PLUSPLUS_DEEP, dimensions_3d\\n )\\n num_weights_matrix = numpy.swapaxes(num_weights_matrix, 0, 1)\\n num_weights_matrix = numpy.reshape(num_weights_matrix, dimensions_2d)\\n\\n figure_object, axes_object = _plot_scores_2d(\\n score_matrix=numpy.log10(num_weights_matrix),\\n min_colour_value=min_colour_value,\\n max_colour_value=max_colour_value,\\n x_tick_labels=x_tick_labels, y_tick_labels=y_tick_labels\\n )\\n\\n if SELECTED_MARKER_INDICES[0] == 1:\\n axes_object.plot(\\n SELECTED_MARKER_INDICES[2], SELECTED_MARKER_INDICES[1],\\n linestyle='None', marker=MARKER_TYPE,\\n markersize=marker_size_px, markeredgewidth=0,\\n markerfacecolor=MARKER_COLOUR,\\n markeredgecolor=MARKER_COLOUR\\n )\\n\\n axes_object.set_xlabel(x_axis_label)\\n axes_object.set_ylabel(y_axis_label)\\n axes_object.set_title(NN_TYPE_STRINGS_FANCY[1])\\n\\n panel_file_names.append(\\n '{0:s}/num_weights_log10_{1:s}.jpg'.format(\\n output_dir_name, NN_TYPE_STRINGS[1].replace('_', '-')\\n )\\n )\\n\\n print('Saving figure to: \\\"{0:s}\\\"...'.format(panel_file_names[-1]))\\n figure_object.savefig(\\n panel_file_names[-1], dpi=FIGURE_RESOLUTION_DPI,\\n pad_inches=0, bbox_inches='tight'\\n )\\n pyplot.close(figure_object)\\n\\n # Plot grid for U-net3+ without deep supervision.\\n num_weights_matrix = numpy.reshape(\\n NUM_WEIGHTS_ARRAY_PLUSPLUSPLUS, dimensions_3d\\n )\\n num_weights_matrix = numpy.swapaxes(num_weights_matrix, 0, 1)\\n num_weights_matrix = numpy.reshape(num_weights_matrix, dimensions_2d)\\n\\n figure_object, axes_object = _plot_scores_2d(\\n score_matrix=numpy.log10(num_weights_matrix),\\n min_colour_value=min_colour_value,\\n max_colour_value=max_colour_value,\\n x_tick_labels=x_tick_labels, y_tick_labels=y_tick_labels\\n )\\n\\n if SELECTED_MARKER_INDICES[0] == 2:\\n axes_object.plot(\\n SELECTED_MARKER_INDICES[2], SELECTED_MARKER_INDICES[1],\\n linestyle='None', marker=MARKER_TYPE,\\n markersize=marker_size_px, markeredgewidth=0,\\n markerfacecolor=MARKER_COLOUR,\\n markeredgecolor=MARKER_COLOUR\\n )\\n\\n axes_object.set_xlabel(x_axis_label)\\n axes_object.set_ylabel(y_axis_label)\\n axes_object.set_title(NN_TYPE_STRINGS_FANCY[2])\\n\\n panel_file_names.append(\\n '{0:s}/num_weights_log10_{1:s}.jpg'.format(\\n output_dir_name, NN_TYPE_STRINGS[2].replace('_', '-')\\n )\\n )\\n\\n print('Saving figure to: \\\"{0:s}\\\"...'.format(panel_file_names[-1]))\\n figure_object.savefig(\\n panel_file_names[-1], dpi=FIGURE_RESOLUTION_DPI,\\n pad_inches=0, bbox_inches='tight'\\n )\\n pyplot.close(figure_object)\\n\\n # Plot grid for U-net3+ with deep supervision.\\n num_weights_matrix = numpy.reshape(\\n NUM_WEIGHTS_ARRAY_PLUSPLUSPLUS_DEEP, dimensions_3d\\n )\\n num_weights_matrix = numpy.swapaxes(num_weights_matrix, 0, 1)\\n num_weights_matrix = numpy.reshape(num_weights_matrix, dimensions_2d)\\n\\n figure_object, axes_object = _plot_scores_2d(\\n score_matrix=numpy.log10(num_weights_matrix),\\n min_colour_value=min_colour_value,\\n max_colour_value=max_colour_value,\\n x_tick_labels=x_tick_labels, y_tick_labels=y_tick_labels\\n )\\n\\n if SELECTED_MARKER_INDICES[0] == 3:\\n axes_object.plot(\\n SELECTED_MARKER_INDICES[2], SELECTED_MARKER_INDICES[1],\\n linestyle='None', marker=MARKER_TYPE,\\n markersize=marker_size_px, markeredgewidth=0,\\n markerfacecolor=MARKER_COLOUR,\\n markeredgecolor=MARKER_COLOUR\\n )\\n\\n axes_object.set_xlabel(x_axis_label)\\n axes_object.set_ylabel(y_axis_label)\\n axes_object.set_title(NN_TYPE_STRINGS_FANCY[3])\\n\\n panel_file_names.append(\\n '{0:s}/num_weights_log10_{1:s}.jpg'.format(\\n output_dir_name, NN_TYPE_STRINGS[3].replace('_', '-')\\n )\\n )\\n\\n print('Saving figure to: \\\"{0:s}\\\"...'.format(panel_file_names[-1]))\\n figure_object.savefig(\\n panel_file_names[-1], dpi=FIGURE_RESOLUTION_DPI,\\n pad_inches=0, bbox_inches='tight'\\n )\\n pyplot.close(figure_object)\\n\\n # Concatenate panels into one figure.\\n concat_file_name = '{0:s}/num_weights_log10.jpg'.format(output_dir_name)\\n print('Concatenating panels to: \\\"{0:s}\\\"...'.format(concat_file_name))\\n imagemagick_utils.concatenate_images(\\n input_file_names=panel_file_names,\\n output_file_name=concat_file_name,\\n num_panel_rows=2, num_panel_columns=2\\n )\\n imagemagick_utils.resize_image(\\n input_file_name=concat_file_name,\\n output_file_name=concat_file_name, output_size_pixels=int(1e7)\\n )\",\n \"def _update_vars(self, axis, traj_s, traj_o, rank_s, rank_o, t):\\n if axis == 0:\\n self.x_traj = traj_s\\n self.x_ranking = rank_s\\n self.x_scores = traj_s[-1]\\n self.inverse_y_traj = traj_o\\n self.inverse_y_ranking = rank_o\\n self.inverse_y_scores = traj_o[-1]\\n if axis == 1:\\n self.y_traj = traj_s\\n self.y_ranking = rank_s\\n self.y_scores = traj_s[-1]\\n self.inverse_x_traj = traj_o\\n self.inverse_x_ranking = rank_o\\n self.inverse_x_scores = traj_o[-1]\",\n \"def run(pos_data,ball_data,match,ranking_type='A'):\\n roles = ['home','guest']\\n sections = ['1st','2nd']\\n result = {'home':[0]*2, 'guest':[0]*2, 'ball':[0]*2}\\n \\n # switch for l2r switching mode\\n l2r_section = 0\\n\\n # processing player position data first \\n for sec in sections:\\n home_direction = 'r2l'\\n for role in roles:\\n print('Processing: %s-%s...' % (role,sec))\\n sorted_pos_data = sort_position_data(pos_data[role][sec], ranking_type)\\n stitched_data = stitch_position_data(sorted_pos_data,ball_data[sec!='1st'])\\n if role == 'home':\\n home_direction = determine_playing_direction(stitched_data[:,0:2])\\n if home_direction == 'l2r':\\n switch_playing_direction(stitched_data)\\n l2r_section = 0 if sec=='1st' else 1\\n rescale_playing_coords(stitched_data,match['stadium'])\\n result[role][0 if sec=='1st' else 1] = stitched_data\\n print('done')\\n \\n # processing ball data\\n print('Processing ball...')\\n switch_playing_direction(ball_data[l2r_section][:,1:3])\\n for i in [0,1]:\\n rescale_playing_coords(ball_data[i][:,1:3],match['stadium'])\\n result['ball'][0] = ball_data[0][:,1:3]\\n result['ball'][1] = ball_data[1][:,1:3]\\n\\n #correct value ranges.\\n print('clamping values.')\\n clamp_values(result)\\n print('done.')\\n return result\",\n \"def score(self, X, y):\\n X_pp = self.preprocessor.transform(X)\\n # Score the model on the data here\\n return(self.estimator.score(X_pp, y))\",\n \"def score(self, x, y, verbose=False):\\n y_pred = self.forward(x).T\\n\\n y_p = np.argmax(y_pred, axis=-1)\\n y_t = np.argmax(y, axis=-1)\\n fpr, tpr, thresholds = roc_curve(y_t, y_p, pos_label=2)\\n\\n metricas = {\\n 'accuracy': accuracy_score(y_t, y_p),\\n 'f1':f1_score(y_t, y_p, average='macro'),\\n 'balanced accuracy': balanced_accuracy_score(y_t, y_p),\\n 'auc': auc(fpr, tpr)\\n }\\n\\n if verbose:\\n print('Accuracy', metricas['accuracy'])\\n print('F1-Macro', metricas['f1'])\\n print('Balanced accuracy', metricas['balanced accuracy'])\\n print('AUC', metricas['auc'])\\n\\n return metricas\",\n \"def score(self, X, y):\\n\\n stuff = self._vectorizer.transform(X)\\n result = self._classifier.score(stuff,y)\\n\\n return result\\n pass\",\n \"def evaluate_ranking_loss(eval_model, x_, y_, m_labels_, ques_, loss_fn, mode='val'):\\n eval_model.eval()\\n\\n dataset_ = TensorDataset(x_, y_)\\n question_sampler = QuestionSampler(torch.utils.data.SequentialSampler(range(len(y_))), y_, False)\\n loader = DataLoader(dataset_, batch_sampler=question_sampler, shuffle=False)\\n total_loss = 0.0\\n\\n with torch.no_grad():\\n pred_ = eval_model(x_, m_labels_)\\n\\n batch_num = 0\\n for xb, yb in loader:\\n if yb.shape[0] == 1:\\n # print(xb, yb)\\n continue\\n yhat = eval_model(xb, yb)\\n yb = yb.reshape(-1, 1)\\n yhat_pos = yhat[-1].repeat((len(yhat) - 1), 1)\\n yhat_neg = yhat[:-1]\\n loss = loss_fn(yhat_pos, yhat_neg, torch.ones((len(yhat) - 1), 1).to(device))\\n total_loss += loss.item() * (yb.shape[0] - 1)\\n batch_num += 1\\n avg_loss = total_loss / batch_num\\n prec, recall, f1, _ = get_qald_metrics(pred_, m_labels_, ques_, mode=mode) # train and val both use 'val' mode\\n\\n return avg_loss, f1, pred_\",\n \"def evaluate(self, test_x, test_y):\\n score = self._model.evaluate(test_x, test_y, verbose=self._verbose)\\n print(\\\"Test score: \\\", score[0])\\n print(\\\"Test accuracy: \\\", score[1])\",\n \"def custom_scoring(y_te, y_pred):\\n #weights computed with training data set\\n w = np.array([0.02409584, 0.00787456, 0.03685528, 0.01760536, 0.04589969, 0.8483942 , 0.01724058, 0.00203449]);\\n \\n ## F1 SCORES\\n #evaluate F1 score, precision and recall for each label, \\n #along with custom proportionally weighted F1 score\\n #and built in weighted and macro F1 scores\\n F1_tab, Ptab, Rtab, pf1 = F1_score(y_te, y_pred, w)\\n f = F1Score(8, threshold = 0.5, average = 'weighted')\\n f.update_state(y_te, y_pred)\\n wf1 = f.result().numpy() #weighted f1 score\\n f.reset_states()\\n f = F1Score(8, threshold = 0.5, average = 'macro')\\n f.update_state(y_te, y_pred)\\n mf1 = f.result().numpy() #macro f1 score\\n f.reset_states()\\n\\n ##EDIT DISTANCE\\n #edit_dist_av = LevDistMultilabels(y_true, y_pred)\\n\\n ##ACCURACY\\n #evaluate accuracy per label\\n acc_tab = Acc(y_te, y_pred)\\n\\n return wf1, mf1, pf1, F1_tab, Ptab, Rtab, acc_tab\",\n \"def update(self, rank):\\n # calculate MR and MRR\\n self.mr += rank\\n self.mrr += 1 / rank\\n # calculate Hits@k\\n if rank <= 1:\\n self.hits1 += 1\\n self.hits3 += 1\\n self.hits5 += 1\\n self.hits10 += 1\\n elif rank <= 3:\\n self.hits3 += 1\\n self.hits5 += 1\\n self.hits10 += 1\\n elif rank <= 5:\\n self.hits5 += 1\\n self.hits10 += 1\\n elif rank <= 10:\\n self.hits10 += 1\",\n \"def positive_scores(self, axis, th_stat=10**(-2)):\\n \\n traj, rank = self._check_run(axis)\\n \\n low_boundary = _np.exp(_np.log(self.params['low_bound'])/2)\\n t = len(traj)-1\\n stat = 0\\n \\n # Iteration over the trajectories\\n for i in range(len(traj[0])):\\n # above the low boundary\\n if traj[t][i] > low_boundary:\\n # stationary (change less than stat_th)\\n if abs(_np.log(traj[t][i]) - _np.log(traj[t-1][i])) < th_stat:\\n stat += 1\\n return stat\",\n \"def validate_average_rank(self) -> float:\\n logger.info('Average rank validation ...')\\n\\n args = self.args\\n self.biencoder.eval()\\n distributed_factor = self.distributed_factor\\n\\n if args.use_dict_input:\\n data_iterator = self.get_dict_data_iterator(args.dev_psgs_file, args.dev_queries_file,\\n args.dev_qrels_file, args.dev_trec_file,\\n args.dev_batch_size,\\n shuffle=False, split=False)\\n else:\\n data_iterator = self.get_data_iterator(args.dev_file, args.dev_batch_size, shuffle=False)\\n\\n sub_batch_size = args.val_av_rank_bsz\\n sim_score_f = ClusterNllLoss.get_similarity_function()\\n q_represenations = []\\n ctx_represenations = []\\n positive_idx_per_question = []\\n\\n num_hard_negatives = args.val_av_rank_hard_neg\\n num_other_negatives = args.val_av_rank_other_neg\\n\\n log_result_step = args.log_batch_step\\n\\n for i, samples_batch in enumerate(data_iterator.iterate_data()):\\n # samples += 1\\n if len(q_represenations) > args.val_av_rank_max_qs / distributed_factor:\\n break\\n\\n biencoder_input = ClusterBertEncoder.create_biencoder_input(samples_batch, self.tensorizer,\\n True,\\n max_query_length=args.query_sequence_length,\\n max_doc_length=args.sequence_length,\\n num_hard_negatives=num_hard_negatives,\\n num_other_negatives=num_other_negatives,\\n shuffle=False)\\n total_ctxs = len(ctx_represenations)\\n ctxs_ids = biencoder_input.context_ids\\n ctxs_segments = biencoder_input.ctx_segments\\n bsz = ctxs_ids.size(0)\\n\\n # split contexts batch into sub batches since it is supposed to be too large to be processed in one batch\\n for j, batch_start in enumerate(range(0, bsz, sub_batch_size)):\\n\\n q_ids, q_segments = (biencoder_input.question_ids, biencoder_input.question_segments) if j == 0 \\\\\\n else (None, None)\\n\\n if j == 0 and args.n_gpu > 1 and q_ids.size(0) == 1:\\n # if we are in DP (but not in DDP) mode, all model input tensors should have batch size >1 or 0,\\n # otherwise the other input tensors will be split but only the first split will be called\\n continue\\n\\n ctx_ids_batch = ctxs_ids[batch_start:batch_start + sub_batch_size]\\n ctx_seg_batch = ctxs_segments[batch_start:batch_start + sub_batch_size]\\n\\n q_attn_mask = self.tensorizer.get_attn_mask(q_ids)\\n ctx_attn_mask = self.tensorizer.get_attn_mask(ctx_ids_batch)\\n with torch.no_grad():\\n q_dense, ctx_dense = self.biencoder(q_ids, q_segments, q_attn_mask, ctx_ids_batch, ctx_seg_batch,\\n ctx_attn_mask)\\n\\n if q_dense is not None:\\n q_represenations.extend(q_dense.cpu().split(1, dim=0))\\n\\n ctx_represenations.extend(ctx_dense.cpu().split(1, dim=0))\\n\\n batch_positive_idxs = biencoder_input.is_positive\\n positive_idx_per_question.extend([total_ctxs + v for v in batch_positive_idxs])\\n\\n if (i + 1) % log_result_step == 0:\\n logger.info('Av.rank validation: step %d, computed ctx_vectors %d, q_vectors %d', i,\\n len(ctx_represenations), len(q_represenations))\\n\\n ctx_represenations = torch.cat(ctx_represenations, dim=0)\\n q_represenations = torch.cat(q_represenations, dim=0)\\n\\n logger.info('Av.rank validation: total q_vectors size=%s', q_represenations.size())\\n logger.info('Av.rank validation: total ctx_vectors size=%s', ctx_represenations.size())\\n\\n q_num = q_represenations.size(0)\\n assert q_num == len(positive_idx_per_question)\\n\\n scores = sim_score_f(q_represenations, ctx_represenations)\\n values, indices = torch.sort(scores, dim=1, descending=True)\\n\\n rank = 0\\n for i, idx in enumerate(positive_idx_per_question):\\n # aggregate the rank of the known gold passage in the sorted results for each question\\n gold_idx = (indices[i] == idx).nonzero()\\n rank += gold_idx.item()\\n\\n if distributed_factor > 1:\\n # each node calcuated its own rank, exchange the information between node and calculate the \\\"global\\\" average rank\\n # NOTE: the set of passages is still unique for every node\\n eval_stats = all_gather_list([rank, q_num], max_size=1000)\\n for i, item in enumerate(eval_stats):\\n remote_rank, remote_q_num = item\\n if i != args.local_rank:\\n rank += remote_rank\\n q_num += remote_q_num\\n\\n av_rank = float(rank / q_num)\\n logger.info('Av.rank validation: average rank %s, total questions=%d', av_rank, q_num)\\n return av_rank\",\n \"def custom_score_2(game, player):\\n if game.is_loser(player):\\n return float(\\\"-inf\\\")\\n\\n if game.is_winner(player):\\n return float(\\\"inf\\\")\\n \\n # First obtain the base information to calculate player & opponent\\n # feature values\\n player_legal_moves = game.get_legal_moves(player)\\n opponent = game.get_opponent(player)\\n opponent_legal_moves = game.get_legal_moves(opponent)\\n if len(player_legal_moves) != len(opponent_legal_moves):\\n return float(len(player_legal_moves) - len(opponent_legal_moves))\\n \\n # Get_center_coordinates and opponent. Then set the list of participants\\n center_coordinates = center_y, center_x = get_center_coordinates(game)\\n participants = [player, opponent]\\n \\n # Then, for each participant obtain his/her feature values \\n for participant in participants:\\n if participant == player:\\n p_legal_moves = player_legal_moves\\n player_either = player\\n participant_coordinates = p_y, p_x = \\\\\\n game.get_player_location(participant)\\n player_legal_move_count, player_start_center_distance, \\\\\\n player_total_next_moves, player_max_path_length, \\\\\\n player_path_count, player_min_center_diff \\\\\\n = \\\\\\n get_player_feature_values(game, center_coordinates, \\\\\\n player_either,participant_coordinates, p_legal_moves)\\n else:\\n p_legal_moves = opponent_legal_moves\\n player_either = opponent\\n participant_coordinates = p_y, p_x \\\\\\n = game.get_player_location(participant)\\n opponent_legal_move_count, opponent_start_center_distance, \\\\\\n opponent_total_next_moves, opponent_max_path_length, \\\\\\n opponent_path_count, opponent_min_center_diff \\\\\\n = \\\\\\n get_player_feature_values(game, center_coordinates, \\\\\\n player_either, participant_coordinates, p_legal_moves)\\n \\n # Place each participant's feature values in a tuple/vector surrogate \\n pro_player_vector = \\\\\\n (player_legal_move_count, player_start_center_distance, \\\\\\n player_total_next_moves, player_max_path_length, player_path_count, \\\\\\n opponent_min_center_diff)\\n pro_opponent_vector = \\\\\\n (opponent_legal_move_count, opponent_start_center_distance, \\\\\\n opponent_total_next_moves, opponent_max_path_length, \\\\\\n opponent_path_count, player_min_center_diff)\\n \\n # Provide a weighting vector for the features \\n weight_vector = (1.5,0.1,1.0,0.001,0.001,0.001)\\n \\n # Calculate the return value = weighted difference of players' features\\n weighted_difference_dot_product = sum(p*(q-r ) for p,q,r \\\\\\n in zip(weight_vector, pro_player_vector, pro_opponent_vector))\\n \\n return float(weighted_difference_dot_product)\",\n \"def reset_score(self):\\n self.x_score = 0\\n self.o_score = 0\",\n \"def classification_score(self, x, y):\\t\\n\\t\\tpass\",\n \"def run(self, x):\\n \\\"*** YOUR CODE HERE question 1 ***\\\"\\n score = nn.DotProduct(self.w, x)\\n\\n return score\",\n \"def evaluation(self):\\n rows_list = []\\n for name in self.single_classifier_best.keys():\\n row = {}\\n row['algorithm'] = name \\n row[self.scoring_metric] = self.single_classifier_best[name].best_score_\\n rows_list.append(row)\\n \\n scoring_df = pd.DataFrame(rows_list)\\n scoring_sorted = scoring_df.sort_values(self.scoring_metric, ascending=False)\\n print()\\n print('*'*shutil.get_terminal_size().columns)\\n print(scoring_sorted)\\n print('*'*shutil.get_terminal_size().columns)\\n self.evaluation_scores = scoring_sorted\",\n \"def evaluate(self, X_test, Y_test):\\n \\n test_data = zip(X_test, Y_test)\\n test_results = [(np.argmax(self.feedforward(x)), y)\\n for (x, y) in test_data]\\n \\n# Updated for the testing\\n# ========================\\n return (sum(int(x == y) for (x, y) in test_results) / 100)\",\n \"def evaluate(self, batch_x, batch_y):\\n raise NotImplementedError()\",\n \"def score(self, x, y):\\n y_hat = (self.model['b1'] * x) + self.model['b0']\\n return R2(y, y_hat)\",\n \"def run(self):\\n dataset = self.make_dataset()\\n preprocess_mask = self.preprocess()\\n cnn1_scores = self.run_cnn(dataset, 'cnn1')\\n cnn2_scores = self.run_cnn(dataset, 'cnn2')\\n return self.score_stamps(preprocess_mask, cnn1_scores, cnn2_scores)\",\n \"def do_score_prediction(self):\\n \\n import iread.myio as mio\\n from igui.score_canvas import ScoreCanvas\\n exp_name = 'JPS_act_12_exp_4_accv_half_fc_j2'\\n exp_name_base = 'ASM_act_12_exp_4'\\n exp_base_folder = '/opt/visal/tmp/for_sijin/Data/H36M/H36MExp'\\n exp_path = iu.fullfile(exp_base_folder, 'folder_%s' % exp_name, 'batches.meta')\\n meta_base_path = iu.fullfile(exp_base_folder, 'folder_%s' % exp_name_base, 'batches.meta')\\n meta = mio.unpickle(exp_path)\\n meta_base = mio.unpickle(meta_base_path)\\n images_path = meta_base['images_path']\\n \\n pred_pose = meta['feature_list'][0]\\n gt_pose = meta['random_feature_list'][0]\\n ntotal = gt_pose.shape[-1]\\n print 'gt_pose_shape',gt_pose.shape\\n print 'pred_pose_shape', pred_pose.shape\\n ref_frame = 7600 # This is the index in test range\\n ## ref_frame = 2600 # This is the index in test range\\n test_range = self.test_data_provider.feature_range\\n ref_idx = test_range[ref_frame]\\n \\n n_to_show = 1000\\n \\n idx_to_show = np.random.choice(ntotal, n_to_show - 1)\\n idx_to_show = [ref_idx] + idx_to_show.tolist() \\n idx_to_show = np.asarray(idx_to_show, dtype=np.int).flatten()\\n \\n ref_pose = pred_pose[...,ref_idx].reshape((-1,1),order='F') \\n pose_to_eval =gt_pose[...,idx_to_show]\\n output_feature_name = 'fc_2' # <------------------Parameter\\n output_layer_idx = self.get_layer_idx(output_feature_name)\\n\\n # do it once <------------- Maybe it can support multiple batch in the future\\n data_dim = self.model_state['layers'][output_layer_idx]['outputs']\\n print 'data_dim', data_dim\\n \\n cur_data = [np.require(np.tile(ref_pose, [1,n_to_show]), \\\\\\n dtype=np.single,requirements='C'), \\\\\\n np.require(pose_to_eval.reshape((-1,n_to_show),order='F'),\\\\\\n dtype=np.single,requirements='C'), \\\\\\n np.require(np.zeros((1,n_to_show),dtype=np.single), \\\\\\n requirements='C'),\\n np.require(np.zeros((n_to_show,data_dim),dtype=np.single), \\\\\\n requirements='C')]\\n residuals = cur_data[1][...,0].reshape((-1,1),order='F') - cur_data[1]\\n dp = self.test_data_provider\\n mpjpe = dutils.calc_mpjpe_from_residual(residuals, dp.num_joints)\\n\\n gt_score = dp.calc_score(mpjpe, dp.mpjpe_factor/dp.max_depth,\\\\\\n dp.mpjpe_offset/dp.max_depth).reshape((1,n_to_show)).flatten()\\n self.libmodel.startFeatureWriter(cur_data, output_layer_idx)\\n self.finish_batch()\\n score = cur_data[-1].T\\n print 'dim score', score.shape, 'dim gt_score', gt_score.shape\\n score = score.flatten()\\n # score = gt_score.flatten()\\n def my_sort_f(k):\\n if k == 0:\\n return 10000000\\n else:\\n return score[k]\\n sorted_idx = sorted(range(n_to_show), key=my_sort_f,reverse=True)\\n s_to_show = [idx_to_show[k] for k in sorted_idx]\\n sorted_score = np.asarray( [score[k] for k in sorted_idx])\\n \\n pose_to_plot = self.convert_relskel2rel(cur_data[1])\\n sorted_pose = pose_to_plot[...,sorted_idx]\\n class ScorePoseCanvas(ScoreCanvas):\\n def __init__(self,data_dic):\\n import iread.h36m_hmlpe as h36m\\n ScoreCanvas.__init__(self,data_dic)\\n self.pose_data = data_dic['pose_data']\\n self.limbs = h36m.part_idx\\n self.tmp = 0\\n def show_image(self,ax):\\n # ScoreCanvas.show_image(self,ax)\\n # return\\n import Image\\n idx =self.cur_data_idx\\n if idx == 0:\\n self.tmp = self.tmp + 1\\n if self.tmp == 1:\\n img = self.load_image(idx)\\n ax.imshow(np.asarray(img))\\n return\\n print 'Current data idx %d ' % self.cur_data_idx\\n # params = {'elev':-89, 'azim':-107}\\n # params = {'elev':-69, 'azim':-107}\\n params = {'elev':-81, 'azim':-91} # frontal view\\n fig = plt.figure(100)\\n from mpl_toolkits.mplot3d import Axes3D\\n import imgproc\\n # new_ax = self.fig.add_axes( rng_rel,projection='polar')\\n new_ax = fig.add_subplot(111,projection='3d')\\n imgproc.turn_off_axis(new_ax)\\n cur_pose = self.pose_data[...,idx].reshape((3,-1),order='F')\\n dutils.show_3d_skeleton(cur_pose.T,\\\\\\n self.limbs, params)\\n xmin,xmax = np.min(cur_pose[0]),np.max(cur_pose[0])\\n ymin,ymax = np.min(cur_pose[1]),np.max(cur_pose[1])\\n zmin,zmax = np.min(cur_pose[2]),np.max(cur_pose[2])\\n def extent(x,y,ratio):\\n x = x + (x-y) * ratio\\n y = y + (y-x) * ratio\\n return -0.4,0.4\\n r = 0.1\\n new_ax.set_xlim(extent(xmin,xmax,r))\\n new_ax.set_ylim(extent(ymin,ymax,r))\\n new_ax.set_ylim(extent(zmin,zmax,r))\\n tmp_folder = '/public/sijinli2/ibuffer/2014-CVPR2015/tmp/images'\\n save_path = iu.fullfile(tmp_folder, 'tmp_image.png')\\n plt.savefig(save_path)\\n img = Image.open(save_path)\\n plt.close(100)\\n img_arr = np.asarray(img)\\n s = np.int(img_arr.shape[0]/5.0)\\n e = np.int(img_arr.shape[0] - s)\\n s = 0\\n e = img_arr.shape[0]\\n img_arr = img_arr[s:e,:,:]\\n ax.imshow(np.asarray(img_arr))\\n # ax.plot([1,0,0],[0,1,0],[0,0,1])\\n\\n\\n sc = ScorePoseCanvas({'x': np.asarray(range(len(idx_to_show))), 'y':sorted_score,\\\\\\n 'images_path': [images_path[k] for k in s_to_show], \\\\\\n 'pose_data':sorted_pose})\\n sc.start()\\n print 'max score is ' , sorted_score.max()\\n gt_sort_idx = sorted(range(n_to_show), key=lambda k:gt_score[k], reverse=True)\\n sorted_gt_score = np.asarray([gt_score[k] for k in gt_sort_idx])\\n sorted_score_by_gt = [score[k] for k in gt_sort_idx]\\n pl.plot(np.asarray(range(n_to_show)), sorted_gt_score, 'r', label='gt_score')\\n pl.plot(np.asarray(range(n_to_show)), sorted_score_by_gt, 'g', label='pred_score')\",\n \"def _forward(self, x):\\n # Computing: `x * w^T`\\n score = CArray(x.dot(self.w.T)).todense().ravel() + self.b\\n\\n scores = CArray.ones(shape=(x.shape[0], self.n_classes))\\n scores[:, 0] = -score.ravel().T\\n scores[:, 1] = score.ravel().T\\n\\n return scores\",\n \"def score(self):\",\n \"def calculate(self, prev_scores):\\n self.set_scores(prev_scores)\\n for match in self.week.matches:\\n if match.played:\\n # Fetch old scores\\n winner_score = float(prev_scores[match.winner])\\n loser_score = float(prev_scores[match.loser])\\n\\n # Update this ranking's scores\\n score_delta = loser_score * 0.1\\n self.score[match.winner] = winner_score + score_delta\\n self.score[match.loser] = loser_score - score_delta\",\n \"def get_prediction_scores(self, X_te, Y_te, scoring):\\n X_te = X_te - self.Xm\\n if self.scale_X:\\n X_te = X_te / self.Xs\\n M_te = X_te @ self.Q\\n\\n scores = torch.zeros(Y_te.shape[1], len(self.ls), dtype=X_te.dtype)\\n for j, Y_te_j in enumerate(Y_te.t()):\\n N_te_j = None\\n for k, l in enumerate(self.ls):\\n Yhat_te_j, N_te_j = self._predict_single(\\n X_te, M_te, j, l, N_te_j\\n )\\n scores[j, k] = scoring(Y_te_j, Yhat_te_j).item()\\n return scores\",\n \"def calculate_scores(self):\\n # Prediction based scores\\n #self.report = classification_report(self.y_test, self.y_pred)\\n self.accuracy = accuracy_score(self.y_real, self.y_pred)\\n self.precision = precision_score(self.y_real, self.y_pred)\\n self.recall = recall_score(self.y_real, self.y_pred)\\n self.f1 = f1_score(self.y_real, self.y_pred)\\n \\n # Probability based scores\\n self.fpr, self.tpr, _ = roc_curve(self.y_real, self.y_proba)\\n self.average_precision = average_precision_score(self.y_real, self.y_proba)\\n self.brier_loss = brier_score_loss(self.y_real, self.y_proba)\\n self.roc_auc = roc_auc_score(self.y_real, self.y_proba)\\n self.prec_cur, self.recall_cur, _ = precision_recall_curve(self.y_real, self.y_proba)\",\n \"def evalrank(model, data, split='dev'):\\n print 'Loading dataset'\\n if split == 'dev':\\n X = load_dataset(data, load_train=False)[1]\\n else:\\n X = load_dataset(data, load_train=False)[2]\\n\\n print 'Computing results...'\\n train = load_dataset('CAD', load_train=True)[0]\\n vectors = encode_sentences(model, train[0], verbose=False)\\n # demo.retrieve_captions(model, net, train[0], vectors, 'image.jpg', k=5)\\n ls = encode_sentences(model, X[0])\\n lim = encode_images(model, X[1])\\n\\n (r1, r5, r10) = i2t(lim, X[0], train[0], vectors)\\n print \\\"Image to text: %.1f, %.1f, %.1f\\\" % (r1, r5, r10)\\n # (r1i, r5i, r10i, medri) = t2i(lim, ls)\\n # print \\\"Text to image: %.1f, %.1f, %.1f, %.1f\\\" % (r1i, r5i, r10i, medri)\",\n \"def custom_score_2(game, player):\\n # TODO: finish this function!\\n if game.is_loser(player):\\n #print(\\\"You lose!\\\")\\n return -math.inf\\n if game.is_winner(player):\\n #print(\\\"You win\\\")\\n return math.inf\\n\\n # center\\n width = game.width / 2\\n height = game.height / 2\\n\\n # Opponent\\n opponent = game.get_opponent(player)\\n opp_y_coord, opp_x_coord = game.get_player_location(opponent)\\n opp_x_eval = (width - float(opp_x_coord)) ** 2\\n opp_y_eval = (height - float(opp_y_coord)) ** 2\\n opp_center_eval = float(opp_x_eval + opp_y_eval)\\n\\n # Remaining spaces left on the board\\n rem_spaces = len(game.get_blank_spaces())\\n\\n # number of agent's available moves\\n no_moves = len(game.get_legal_moves(player))\\n\\n # number of opponent's available moves\\n opp_moves = len(game.get_legal_moves(opponent))\\n\\n # evaluation of board \\\"goodness\\\"\\n # using moves available to both players\\n # Idea is player chooses moves with scores that maximise whilst minimizing\\n # evaluate board states and positions as scores\\n opp_score = opp_moves * 2 - opp_center_eval\\n score = no_moves - opp_score/rem_spaces\\n return float(score)\",\n \"def _evaluate(self, x, y):\\n raise NotImplementedError()\",\n \"def _score_to_decision(self, score):\",\n \"def _score(self, x, seq):\\n pass\",\n \"def scoreEvaluationFunction(currentGameState):\\n return currentGameState.getScore()\",\n \"def scoreEvaluationFunction(currentGameState):\\n return currentGameState.getScore()\",\n \"def scoreEvaluationFunction(currentGameState):\\n return currentGameState.getScore()\",\n \"def scoreEvaluationFunction(currentGameState):\\n return currentGameState.getScore()\",\n \"def scoreEvaluationFunction(currentGameState):\\n return currentGameState.getScore()\",\n \"def scoreEvaluationFunction(currentGameState):\\n return currentGameState.getScore()\",\n \"def run_prediction(self):\\r\\n self.get_prediction_indices()\\r\\n self.walk_forward_prediction()\",\n \"def update_score():\\n pass\",\n \"def score(self, X, y):\\n out = None\\n ### YOUR CODE HERE\\n pred = self.predict(X)\\n assert pred.shape == y.shape\\n out = ((pred-y)**2).mean()\\n ### END CODE\\n return out\",\n \"def score_batch(self, pipelines, X, y, objectives):\\n check_all_pipeline_names_unique(pipelines)\",\n \"def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:\\n score = multi_scale_ssim(x=x, y=y, kernel_size=self.kernel_size, kernel_sigma=self.kernel_sigma,\\n data_range=self.data_range, reduction=self.reduction, scale_weights=self.scale_weights,\\n k1=self.k1, k2=self.k2)\\n return torch.ones_like(score) - score\",\n \"def find_ranking(comparisons, equal_width=0.2, max_rank=-1, verbose=False):\\n # remove unnecessary variables\\n comparisons = {(i, j) if i < j else (j, i): value if i < j else 1 - value\\n for (i, j), value in comparisons.items()}\\n nodes = np.unique(\\n [i for ij in comparisons.keys() for i in ij])\\n\\n # define variables\\n model = Model('comparison')\\n model.setParam('OutputFlag', verbose)\\n values = np.fromiter(comparisons.values(), dtype=float)\\n assert values.max() <= 1 and values.min() >= 0\\n # variables to encode the error of comparisons\\n E_ij = model.addVars(comparisons.keys(), name='e_ij', vtype=GRB.CONTINUOUS,\\n ub=1.0-values, lb=-values)\\n # variables to encode hard choice of >=, <=, ==\\n Ge_ij = model.addVars(comparisons.keys(), name='ge_ij', vtype=GRB.BINARY)\\n Le_ij = model.addVars(comparisons.keys(), name='le_ij', vtype=GRB.BINARY)\\n Eq_ij = model.addVars(comparisons.keys(), name='eq_ij', vtype=GRB.BINARY)\\n # variables to help with transitivity in non-fully connected graphs\\n if max_rank < 1:\\n max_rank = len(nodes)\\n R_i = model.addVars(nodes, name='r_i', vtype=GRB.CONTINUOUS, lb=0,\\n ub=max_rank)\\n # variables to emulate abs\\n T_ij_pos = {}\\n T_ij_neg = {}\\n index = (values != 1) & (values != 0)\\n T_ij_pos = model.addVars(\\n (ij for ij, value in comparisons.items() if value not in [0.0, 1.0]),\\n vtype=GRB.CONTINUOUS, name='T_ij_pos', lb=0, ub=1-values[index])\\n T_ij_neg = model.addVars(\\n (ij for ij, value in comparisons.items() if value not in [0.0, 1.0]),\\n vtype=GRB.CONTINUOUS, name='T_ij_neg', lb=0, ub=values[index])\\n model.update()\\n\\n # emulate abs for non-binary comparisons: E_ij = T_ij_pos - T_ij_neg\\n model.addConstrs(\\n (E_ij[ij] == T_ij_pos[ij] - T_ij_neg[ij] for ij in T_ij_pos),\\n 'E_ij = T_ij_pos - T_ij_neg')\\n\\n # hard decision of >=, <=, and ==\\n lower_bound = 0.5 - equal_width / 2.0\\n upper_bound = 0.5 + equal_width / 2.0\\n # <=\\n model.addConstrs(\\n (E_ij[ij] + comparisons[ij] - upper_bound <= ge_ij\\n for ij, ge_ij in Ge_ij.items()), 'ge_ij_lower_bound')\\n model.addConstrs(\\n (E_ij[ij] + comparisons[ij] - upper_bound >= -1 + ge_ij\\n for ij, ge_ij in Ge_ij.items()), 'ge_ij_upper_bound')\\n # >=\\n model.addConstrs(\\n (E_ij[ij] + comparisons[ij] - lower_bound >= -le_ij\\n for ij, le_ij in Le_ij.items()), 'le_ij_lower_bound')\\n model.addConstrs(\\n (E_ij[ij] + comparisons[ij] - lower_bound <= 1 - le_ij\\n for ij, le_ij in Le_ij.items()), 'le_ij_upper_bound')\\n # ==\\n model.addConstrs(\\n (le + eq + ge == 1 for le, eq, ge in zip(\\n Le_ij.values(), Eq_ij.values(), Ge_ij.values())), 'eq_ij')\\n\\n # transitivity\\n for (i, j), eq_a in Eq_ij.items():\\n le_a = Le_ij[i, j]\\n ge_a = Ge_ij[i, j]\\n for k in nodes:\\n j_, k_ = j, k\\n if j > k:\\n j_, k_ = k, j\\n eq_b = Eq_ij.get((j_, k_), None)\\n if eq_b is None:\\n continue\\n else:\\n le_b = Le_ij[j_, k_]\\n ge_b = Ge_ij[j_, k_]\\n if j_ != j:\\n le_b, ge_b = ge_b, le_b\\n\\n i_, k_ = i, k\\n if i > k:\\n i_, k_ = k, i\\n eq_c = Eq_ij.get((i_, k_), None)\\n if eq_c is None:\\n continue\\n else:\\n le_c = Le_ij[i_, k_]\\n ge_c = Ge_ij[i_, k_]\\n if i_ != i:\\n le_c, ge_c = ge_c, le_c\\n\\n # a <= b and b <= c -> a <= c\\n model.addLConstr(\\n ge_a + ge_b, GRB.LESS_EQUAL, 1 + ge_c,\\n f'transitivity_ge_{i},{j},{k}')\\n # a >= b and b >= c -> a >= c\\n model.addLConstr(\\n le_a + le_b, GRB.LESS_EQUAL, 1 + le_c,\\n f'transitivity_le_{i},{j},{k}')\\n # a <= b and b == c -> a <= c\\n model.addLConstr(\\n le_a + eq_b, GRB.LESS_EQUAL, 1 + le_c,\\n f'transitivity_leeq_{i},{j},{k}')\\n # a == b and b <= c -> a <= c\\n model.addLConstr(\\n eq_a + le_b, GRB.LESS_EQUAL, 1 + le_c,\\n f'transitivity_eqle_{i},{j},{k}')\\n # a >= b and b == c --> a >= c\\n model.addLConstr(\\n ge_a + eq_b, GRB.LESS_EQUAL, 1 + ge_c,\\n f'transitivity_geeq_{i},{j},{k}')\\n # a == b and b >= c --> a >= c\\n model.addLConstr(\\n eq_a + ge_b, GRB.LESS_EQUAL, 1 + ge_c,\\n f'transitivity_eqge_{i},{j},{k}')\\n # a == b and b == c --> a == c\\n model.addLConstr(\\n eq_a + eq_b, GRB.LESS_EQUAL, 1 + eq_c,\\n f'transitivity_eq_{i},{j},{k}')\\n\\n # transitivity helper (for not-fully connected graphs)\\n # also provides a latent rank\\n big_m = max_rank\\n model.addConstrs(\\n ((1 - ge_ij) * big_m + R_i[i] >= R_i[j] + 1 for (i, j), ge_ij in Ge_ij.items()),\\n 'rank_transitivity_larger')\\n model.addConstrs(\\n ((1 - le_ij) * big_m + R_i[j] >= R_i[i] + 1 for (i, j), le_ij in Le_ij.items()),\\n 'rank_transitivity_smaller')\\n model.addConstrs(\\n ((1 - eq_ij) * big_m + R_i[j] >= R_i[i] for (i, j), eq_ij in Eq_ij.items()),\\n 'rank_transitivity_equal1')\\n model.addConstrs(\\n ((1 - eq_ij) * big_m + R_i[i] >= R_i[j] for (i, j), eq_ij in Eq_ij.items()),\\n 'rank_transitivity_equal2')\\n\\n # objective function\\n objective = LinExpr()\\n for ij, value in comparisons.items():\\n if value == 1.0:\\n objective += -E_ij[ij]\\n elif value == 0.0:\\n objective += E_ij[ij]\\n else:\\n objective += T_ij_pos[ij] + T_ij_neg[ij]\\n model.setObjective(objective, GRB.MINIMIZE)\\n\\n # solve\\n model.optimize()\\n\\n # verify abs emulation: one T_ij has to be 0\\n for ij, value in T_ij_pos.items():\\n assert value.X == 0 or T_ij_neg[ij] == 0, \\\\\\n f'T_{ij} pos {value.X} neg {T_ij_neg[ij]}'\\n\\n # find minimal Rs\\n model_ = Model('comparison')\\n model_.setParam('OutputFlag', verbose)\\n R_i = model_.addVars(nodes, name='r_i', vtype=GRB.CONTINUOUS, lb=0,\\n ub=len(nodes))\\n for ((i, j), ge_ij), le_ij in zip(Ge_ij.items(), Le_ij.values()):\\n if ge_ij.x == 1:\\n model_.addConstr(R_i[i] >= R_i[j] + 1)\\n elif le_ij.x == 1:\\n model_.addConstr(R_i[j] >= R_i[i] + 1)\\n else:\\n model_.addConstr(R_i[j] == R_i[i])\\n model_.setObjective(R_i.sum(), GRB.MINIMIZE)\\n model_.optimize()\\n\\n return [model_.getVarByName(f'r_i[{i}]').X for i in range(len(nodes))], \\\\\\n model.objVal\",\n \"def predict(self, x):\\n if(self.score(x) > 0):\\n return 1\\n return -1\",\n \"def predict(self, x):\\n if(self.score(x) > 0):\\n return 1\\n return -1\",\n \"def predict(self, x):\\n if(self.score(x) > 0):\\n return 1\\n return -1\",\n \"def predict(self, x):\\n if(self.score(x) > 0):\\n return 1\\n return -1\",\n \"def score(self,A,B,method=\\\"mse\\\"):\\n if method ==\\\"mse\\\":\\n loss = self._reconstruction_loss\\n elif method == \\\"mae\\\":\\n loss = self._mae\\n data={self._xphA: A,\\n self._xphB: B,\\n K.learning_phase():False}\\n return self.sess.run(loss,\\n feed_dict=data)\",\n \"def scoreEvaluationFunction(currentGameState):\\r\\n return currentGameState.getScore()\",\n \"def main():\\n args = get_args().parse_args()\\n\\n (wins, losses) = args.record.split('-')\\n print wins, losses\\n \\n is_winning_streak = args.streak[0] == 'W'\\n streak_length = args.streak[1:]\\n print is_winning_streak, streak_length\\n\\n (recent_wins, recent_losses) = args.last.split('-')\\n print recent_wins, recent_losses\\n\\n power_ranking = calculate_power_ranking(int(wins), int(losses), is_winning_streak,\\n int(streak_length), int(recent_wins),\\n int(recent_losses))\\n print power_ranking\",\n \"def rank_vars(xTrain, yTrain, scoreFunc):\\r\\n funcsDic = {\\r\\n 'pearsonr': [np.arange(xTrain.shape[1]), 1], \\r\\n 'mutual_info_score': np.arange(xTrain.shape[0]),\\r\\n 'ttest_ind': [np.arange(xTrain.shape[1]), 1], \\r\\n }\\r\\n \\r\\n scores = list()\\r\\n for feat in np.arange(xTrain.shape[1]):\\r\\n if scoreFunc.func_name == 'pearsonr':\\r\\n scores.append(scoreFunc(xTrain[:, feat], yTrain))\\r\\n elif scoreFunc.func_name == 'ttest_ind':\\r\\n scores.append(scoreFunc(xTrain[yTrain == 1, feat], xTrain[yTrain==-1, feat]))\\r\\n \\r\\n scores = np.asarray(scores)\\r\\n pvals = scores[funcsDic[scoreFunc.func_name]]\\r\\n sortedIndices = [i[0] for i in sorted(enumerate(pvals), key=lambda x:x[1])]\\r\\n return sortedIndices\",\n \"def disp_score():\",\n \"def score(self, X, y=None) -> float:\\n self.check_is_fitted()\\n X = self._check_clusterer_input(X)\\n return self._score(X, y)\",\n \"def _run(evaluation_dir_name, smoothing_radius_grid_cells,\\n score_colour_map_name, num_ex_colour_map_name, max_colour_percentile,\\n output_dir_name):\\n\\n if smoothing_radius_grid_cells <= 0:\\n smoothing_radius_grid_cells = None\\n\\n score_colour_map_object = pyplot.get_cmap(score_colour_map_name)\\n num_ex_colour_map_object = pyplot.get_cmap(num_ex_colour_map_name)\\n error_checking.assert_is_geq(max_colour_percentile, 90.)\\n error_checking.assert_is_leq(max_colour_percentile, 100.)\\n\\n grid_metafile_name = grids.find_equidistant_metafile(\\n directory_name=evaluation_dir_name, raise_error_if_missing=True\\n )\\n\\n print('Reading grid metadata from: \\\"{0:s}\\\"...'.format(grid_metafile_name))\\n grid_metadata_dict = grids.read_equidistant_metafile(grid_metafile_name)\\n print(SEPARATOR_STRING)\\n\\n num_grid_rows = len(grid_metadata_dict[grids.Y_COORDS_KEY])\\n num_grid_columns = len(grid_metadata_dict[grids.X_COORDS_KEY])\\n\\n auc_matrix = numpy.full((num_grid_rows, num_grid_columns), numpy.nan)\\n csi_matrix = numpy.full((num_grid_rows, num_grid_columns), numpy.nan)\\n pod_matrix = numpy.full((num_grid_rows, num_grid_columns), numpy.nan)\\n far_matrix = numpy.full((num_grid_rows, num_grid_columns), numpy.nan)\\n num_examples_matrix = numpy.full(\\n (num_grid_rows, num_grid_columns), 0, dtype=int\\n )\\n num_positive_examples_matrix = numpy.full(\\n (num_grid_rows, num_grid_columns), 0, dtype=int\\n )\\n\\n for i in range(num_grid_rows):\\n for j in range(num_grid_columns):\\n this_eval_file_name = model_eval.find_file(\\n directory_name=evaluation_dir_name, grid_row=i, grid_column=j,\\n raise_error_if_missing=False)\\n\\n if not os.path.isfile(this_eval_file_name):\\n warning_string = (\\n 'Cannot find file (this may or may not be a problem). '\\n 'Expected at: \\\"{0:s}\\\"'\\n ).format(this_eval_file_name)\\n\\n warnings.warn(warning_string)\\n continue\\n\\n print('Reading data from: \\\"{0:s}\\\"...'.format(this_eval_file_name))\\n this_evaluation_dict = model_eval.read_evaluation(\\n this_eval_file_name)\\n\\n num_examples_matrix[i, j] = len(\\n this_evaluation_dict[model_eval.OBSERVED_LABELS_KEY]\\n )\\n num_positive_examples_matrix[i, j] = numpy.sum(\\n this_evaluation_dict[model_eval.OBSERVED_LABELS_KEY]\\n )\\n\\n this_evaluation_table = this_evaluation_dict[\\n model_eval.EVALUATION_TABLE_KEY]\\n\\n auc_matrix[i, j] = numpy.nanmean(\\n this_evaluation_table[model_eval.AUC_KEY].values\\n )\\n csi_matrix[i, j] = numpy.nanmean(\\n this_evaluation_table[model_eval.CSI_KEY].values\\n )\\n pod_matrix[i, j] = numpy.nanmean(\\n this_evaluation_table[model_eval.POD_KEY].values\\n )\\n far_matrix[i, j] = 1. - numpy.nanmean(\\n this_evaluation_table[model_eval.SUCCESS_RATIO_KEY].values\\n )\\n\\n print(SEPARATOR_STRING)\\n\\n auc_matrix[num_positive_examples_matrix == 0] = numpy.nan\\n csi_matrix[num_positive_examples_matrix == 0] = numpy.nan\\n pod_matrix[num_positive_examples_matrix == 0] = numpy.nan\\n far_matrix[num_positive_examples_matrix == 0] = numpy.nan\\n\\n if smoothing_radius_grid_cells is not None:\\n print((\\n 'Applying Gaussian smoother with e-folding radius of {0:.1f} grid '\\n 'cells...'\\n ).format(\\n smoothing_radius_grid_cells\\n ))\\n\\n orig_num_examples_matrix = num_examples_matrix + 0\\n num_examples_matrix = general_utils.apply_gaussian_filter(\\n input_matrix=num_examples_matrix.astype(float),\\n e_folding_radius_grid_cells=smoothing_radius_grid_cells\\n )\\n num_examples_matrix = numpy.round(num_examples_matrix).astype(int)\\n num_examples_matrix[orig_num_examples_matrix == 0] = 0 # HACK\\n\\n num_positive_examples_matrix = general_utils.apply_gaussian_filter(\\n input_matrix=num_positive_examples_matrix.astype(float),\\n e_folding_radius_grid_cells=smoothing_radius_grid_cells\\n )\\n num_positive_examples_matrix = (\\n numpy.round(num_positive_examples_matrix).astype(int)\\n )\\n num_positive_examples_matrix[num_examples_matrix == 0] = 0\\n\\n auc_matrix = general_utils.apply_gaussian_filter(\\n input_matrix=ge_utils.fill_nans(auc_matrix),\\n e_folding_radius_grid_cells=smoothing_radius_grid_cells\\n )\\n csi_matrix = general_utils.apply_gaussian_filter(\\n input_matrix=ge_utils.fill_nans(csi_matrix),\\n e_folding_radius_grid_cells=smoothing_radius_grid_cells\\n )\\n pod_matrix = general_utils.apply_gaussian_filter(\\n input_matrix=ge_utils.fill_nans(pod_matrix),\\n e_folding_radius_grid_cells=smoothing_radius_grid_cells\\n )\\n far_matrix = general_utils.apply_gaussian_filter(\\n input_matrix=ge_utils.fill_nans(far_matrix),\\n e_folding_radius_grid_cells=smoothing_radius_grid_cells\\n )\\n\\n auc_matrix[num_positive_examples_matrix == 0] = numpy.nan\\n csi_matrix[num_positive_examples_matrix == 0] = numpy.nan\\n pod_matrix[num_positive_examples_matrix == 0] = numpy.nan\\n far_matrix[num_positive_examples_matrix == 0] = numpy.nan\\n\\n panel_file_names = []\\n file_system_utils.mkdir_recursive_if_necessary(\\n directory_name=output_dir_name)\\n\\n # Plot number of examples.\\n this_data_matrix = numpy.maximum(numpy.log10(num_examples_matrix), 0.)\\n this_data_matrix[this_data_matrix == 0] = numpy.nan\\n max_colour_value = numpy.nanpercentile(\\n this_data_matrix, max_colour_percentile)\\n\\n figure_object, axes_object = _plot_one_value(\\n data_matrix=this_data_matrix, grid_metadata_dict=grid_metadata_dict,\\n colour_map_object=num_ex_colour_map_object,\\n min_colour_value=0., max_colour_value=max_colour_value,\\n plot_cbar_min_arrow=False, plot_cbar_max_arrow=True, log_scale=True)\\n\\n axes_object.set_title(r'Number of examples')\\n plotting_utils.label_axes(axes_object=axes_object, label_string='(a)')\\n\\n panel_file_names.append('{0:s}/num_examples.jpg'.format(output_dir_name))\\n print('Saving figure to: \\\"{0:s}\\\"...'.format(panel_file_names[-1]))\\n\\n figure_object.savefig(\\n panel_file_names[-1], dpi=FIGURE_RESOLUTION_DPI, pad_inches=0,\\n bbox_inches='tight')\\n pyplot.close(figure_object)\\n\\n # Plot number of positive examples.\\n this_data_matrix = num_positive_examples_matrix.astype(float)\\n this_data_matrix[this_data_matrix == 0] = numpy.nan\\n\\n max_colour_value = numpy.nanpercentile(\\n this_data_matrix, max_colour_percentile)\\n min_colour_value = numpy.nanpercentile(\\n this_data_matrix, 100. - max_colour_percentile)\\n\\n figure_object, axes_object = _plot_one_value(\\n data_matrix=this_data_matrix, grid_metadata_dict=grid_metadata_dict,\\n colour_map_object=num_ex_colour_map_object,\\n min_colour_value=min_colour_value, max_colour_value=max_colour_value,\\n plot_cbar_min_arrow=True, plot_cbar_max_arrow=True)\\n\\n axes_object.set_title('Number of tornadic examples')\\n plotting_utils.label_axes(axes_object=axes_object, label_string='(b)')\\n\\n panel_file_names.append(\\n '{0:s}/num_positive_examples.jpg'.format(output_dir_name)\\n )\\n print('Saving figure to: \\\"{0:s}\\\"...'.format(panel_file_names[-1]))\\n\\n figure_object.savefig(\\n panel_file_names[-1], dpi=FIGURE_RESOLUTION_DPI, pad_inches=0,\\n bbox_inches='tight')\\n pyplot.close(figure_object)\\n\\n # Plot AUC.\\n max_colour_value = numpy.nanpercentile(auc_matrix, max_colour_percentile)\\n min_colour_value = numpy.maximum(\\n numpy.nanpercentile(auc_matrix, 100. - max_colour_percentile),\\n 0.5\\n )\\n\\n figure_object, axes_object = _plot_one_value(\\n data_matrix=auc_matrix, grid_metadata_dict=grid_metadata_dict,\\n colour_map_object=score_colour_map_object,\\n min_colour_value=min_colour_value, max_colour_value=max_colour_value,\\n plot_cbar_min_arrow=True, plot_cbar_max_arrow=max_colour_value < 1.)\\n\\n axes_object.set_title('AUC (area under ROC curve)')\\n plotting_utils.label_axes(axes_object=axes_object, label_string='(c)')\\n\\n panel_file_names.append('{0:s}/auc.jpg'.format(output_dir_name))\\n print('Saving figure to: \\\"{0:s}\\\"...'.format(panel_file_names[-1]))\\n\\n figure_object.savefig(\\n panel_file_names[-1], dpi=FIGURE_RESOLUTION_DPI, pad_inches=0,\\n bbox_inches='tight')\\n pyplot.close(figure_object)\\n\\n # Plot CSI.\\n max_colour_value = numpy.nanpercentile(csi_matrix, max_colour_percentile)\\n min_colour_value = numpy.nanpercentile(\\n csi_matrix, 100. - max_colour_percentile)\\n\\n figure_object, axes_object = _plot_one_value(\\n data_matrix=csi_matrix, grid_metadata_dict=grid_metadata_dict,\\n colour_map_object=score_colour_map_object,\\n min_colour_value=min_colour_value, max_colour_value=max_colour_value,\\n plot_cbar_min_arrow=min_colour_value > 0.,\\n plot_cbar_max_arrow=max_colour_value < 1.)\\n\\n axes_object.set_title('CSI (critical success index)')\\n plotting_utils.label_axes(axes_object=axes_object, label_string='(d)')\\n\\n panel_file_names.append('{0:s}/csi.jpg'.format(output_dir_name))\\n print('Saving figure to: \\\"{0:s}\\\"...'.format(panel_file_names[-1]))\\n\\n figure_object.savefig(\\n panel_file_names[-1], dpi=FIGURE_RESOLUTION_DPI, pad_inches=0,\\n bbox_inches='tight')\\n pyplot.close(figure_object)\\n\\n # Plot POD.\\n max_colour_value = numpy.nanpercentile(pod_matrix, max_colour_percentile)\\n min_colour_value = numpy.nanpercentile(\\n pod_matrix, 100. - max_colour_percentile)\\n\\n figure_object, axes_object = _plot_one_value(\\n data_matrix=pod_matrix, grid_metadata_dict=grid_metadata_dict,\\n colour_map_object=score_colour_map_object,\\n min_colour_value=min_colour_value, max_colour_value=max_colour_value,\\n plot_cbar_min_arrow=min_colour_value > 0.,\\n plot_cbar_max_arrow=max_colour_value < 1.)\\n\\n axes_object.set_title('POD (probability of detection)')\\n plotting_utils.label_axes(axes_object=axes_object, label_string='(e)')\\n\\n panel_file_names.append('{0:s}/pod.jpg'.format(output_dir_name))\\n print('Saving figure to: \\\"{0:s}\\\"...'.format(panel_file_names[-1]))\\n\\n figure_object.savefig(\\n panel_file_names[-1], dpi=FIGURE_RESOLUTION_DPI, pad_inches=0,\\n bbox_inches='tight')\\n pyplot.close(figure_object)\\n\\n # Plot FAR.\\n max_colour_value = numpy.nanpercentile(far_matrix, max_colour_percentile)\\n min_colour_value = numpy.nanpercentile(\\n far_matrix, 100. - max_colour_percentile)\\n\\n figure_object, axes_object = _plot_one_value(\\n data_matrix=far_matrix, grid_metadata_dict=grid_metadata_dict,\\n colour_map_object=score_colour_map_object,\\n min_colour_value=min_colour_value, max_colour_value=max_colour_value,\\n plot_cbar_min_arrow=min_colour_value > 0.,\\n plot_cbar_max_arrow=max_colour_value < 1.)\\n\\n axes_object.set_title('FAR (false-alarm ratio)')\\n plotting_utils.label_axes(axes_object=axes_object, label_string='(f)')\\n\\n panel_file_names.append('{0:s}/far.jpg'.format(output_dir_name))\\n print('Saving figure to: \\\"{0:s}\\\"...'.format(panel_file_names[-1]))\\n\\n figure_object.savefig(\\n panel_file_names[-1], dpi=FIGURE_RESOLUTION_DPI, pad_inches=0,\\n bbox_inches='tight')\\n pyplot.close(figure_object)\\n\\n # Concatenate panels.\\n concat_file_name = '{0:s}/spatially_subset_evaluation.jpg'.format(\\n output_dir_name)\\n print('Concatenating panels to: \\\"{0:s}\\\"...'.format(concat_file_name))\\n\\n imagemagick_utils.concatenate_images(\\n input_file_names=panel_file_names, output_file_name=concat_file_name,\\n num_panel_rows=NUM_PANEL_ROWS, num_panel_columns=NUM_PANEL_COLUMNS)\\n\\n imagemagick_utils.resize_image(\\n input_file_name=concat_file_name, output_file_name=concat_file_name,\\n output_size_pixels=CONCAT_FIGURE_SIZE_PX)\",\n \"def new_evaluate(board):\\n\\n #Logic for new_evaluate function:\\n #1)Traverse through each of the columns\\n #2)For each of the columns, find the top most element.\\n\\t #If the topmost element = Current Player\\n\\t\\t \\t#3)Find the possible number of continuous elements of the same type in all the 4 directions from that cell(Horizontal,vertical and two diagonals)\\n\\t\\t\\t #Take the max of these lengths and this becomes the score for that column and it will stored as a POSITIVE value\\n\\t #Else\\n\\t\\t \\t#4)Find the possible number of continuous elements of the same type in all the 4 directions from that cell(Horizontal,vertical and two diagonals)\\n\\t\\t\\t #Take the max of these lengths and this becomes the score for that column and it will stored as a NEGATIVE value\\n #5)Sort these Positive and Negative scores\\n #6)IF the highest negative score is greater than the highest positive score, then it means that the opposition has MORE chances to WIN.\\n #So, that has to be blocked and so we will return that HIGHEST NEGATIVE value as the score for that board\\n #7)ELSE we go ahead and return the HIGHEST POSITIVE value as the score for that board\\n #->This logic has increasing the AGGRESSION of the player a lot and it makes senses we hope.\\n\\n posdict = {}\\n negdict = {}\\n for col in range(7):\\n if(board.get_top_elt_in_column(col)==board.get_current_player_id()) :\\n rowValue = board.get_height_of_column(col)\\n score = board._max_length_from_cell(rowValue,col)\\n posdict[col]=score\\n elif(board.get_top_elt_in_column(col)==board.get_other_player_id()) :\\n rowValue = board.get_height_of_column(col)\\n score = -(board._max_length_from_cell(rowValue,col))\\n negdict[col]=score\\n\\n\\n sorted(posdict.values(),reverse= True)\\n sorted(negdict.values())\\n if((bool(posdict))and (bool(negdict))):\\n if(abs(negdict.values()[0]) >= ((posdict.values()[0]))):\\n return negdict[negdict.keys()[0]]\\n else:\\n return posdict[posdict.keys()[0]]\\n elif(bool(posdict)):\\n return posdict[posdict.keys()[0]]\\n elif(bool(negdict)):\\n return negdict[negdict.keys()[0]]\\n else:\\n return 0\",\n \"def score_fn(self):\\n raise NotImplementedError()\",\n \"def score(self, X, y, batch_size=50):\\n features = self.encode(X, batch_size=batch_size)\\n return self.classifier.score(features, y)\",\n \"def score(self, x: np.ndarray) -> np.ndarray:\\n score = self.backend.score(self.backend._to_backend_dtype(x))\\n return self.backend._to_frontend_dtype(score)\",\n \"def score(self, X: TwoDimArray, y: OneDimArray = None) -> float:\\n\\n check_is_fitted(self, '_glasso')\\n\\n return self._glasso.score(X)\",\n \"def forward(ctx, scores):\\n idx = argmax(scores, dim=-1)\\n scores_net = eye(scores.size(-1), device=scores.device)\\n return scores_net[idx]\",\n \"def l_point(self):\\n self.l_score += 1\\n self.update_scoreboard()\",\n \"def scoring(self):\\n return -100 if self.loss_condition() else 0\",\n \"def __call__(self, score, model):\\n if self.best_score is None:\\n # assign the best score and save the model at the end of the first epoch\\n self.best_score = score\\n self.save_checkpoint(model)\\n elif score < self.best_score + self.delta:\\n # if the score not increase of at least delta, increment the counter and if it reach the patience early stops\\n self.counter += 1\\n if self.counter >= self.patience:\\n self.early_stop = True\\n else:\\n # otherwise the score is better that the saved one, so replace the best score and save the model\\n self.best_score = score\\n self.save_checkpoint(model)\\n self.counter = 0\",\n \"def auto_evaluation(model,x_train,y_train,x_test,y_test):\\n\\n y_train_prediction=model.predict(x_train)\\n y_test_prediction=model.predict(x_test)\\n\\n plt.scatter(y_train,y_train_prediction,c=\\\"b\\\",s=1,alpha=0.5)\\n plt.scatter(y_test,y_test_prediction,c=\\\"r\\\",s=2,alpha=0.5)\\n plt.xlabel(\\\"actual\\\")\\n plt.ylabel(\\\"predicted\\\")\\n\\n print(\\\"tr R2: {:.2f}\\\".format(r2_score(y_train_prediction,y_train)))\\n print(\\\"te R2: {:.2f}\\\".format(r2_score(y_test_prediction,y_test))) \\n \\n return y_train_prediction,y_test_prediction\",\n \"def custom_score(game, player):\\n \\n if game.is_loser(player):\\n return float(\\\"-inf\\\")\\n\\n if game.is_winner(player):\\n return float(\\\"inf\\\")\\n \\n # First obtain the improved score\\n player_legal_moves = game.get_legal_moves(player)\\n opponent = game.get_opponent(player)\\n opponent_legal_moves = game.get_legal_moves(opponent)\\n improved = len(player_legal_moves) - len(opponent_legal_moves)\\n if improved != 0:\\n return float(improved)\\n \\n # Second get differences from center\\n center_coordinates = center_y, center_x = get_center_coordinates(game)\\n player_coordinates = game.get_player_location(player)\\n opponent_coordinates = game.get_player_location(opponent)\\n player_center_dist = get_distances_from_center(center_coordinates, player_coordinates)\\n opponent_center_dist = get_distances_from_center(center_coordinates, opponent_coordinates)\\n center_dist_diff = player_center_dist - opponent_center_dist\\n \\n # Third obtain next_moves\\n player_next_moves = [get_next_moves(game, move, list(move)) for move in player_legal_moves]\\n opponent_next_moves = [get_next_moves(game, move, list(move)) for move in opponent_legal_moves] \\n improved_next = len(player_next_moves) - len(opponent_next_moves)\\n \\n # Put player and opponent feature differences in a tuple/vector surrogoate\\n feature_diff_vector = (improved, center_dist_diff, improved_next)\\n \\n # Provide a weighting vector for the features of each player-participant\\n weight_vector = (1.5,0.1,1.0)\\n # Calculate the return value = weighted difference of players' features\\n weighted_difference_dot_product = sum(p*q for p,q, \\\\\\n in zip(feature_diff_vector, weight_vector))\\n \\n return float(weighted_difference_dot_product)\",\n \"def r_point(self):\\n self.r_score += 1\\n self.update()\",\n \"def rank(m, axis=0, method='average', ascending=False, reverse=False):\\n if isinstance(m, list):\\n m = np.array(m)\\n if ascending == reverse: # greater is better (descending order)\\n m = -m # take the opposite to inverse rank\\n r = np.apply_along_axis(rankdata, axis, m, method=method) # convert values to ranking in all rows or columns\\n return process_vote(m, r)\",\n \"def get_metrics(cfg, model, X_anchor, y_anchor, X_gal, y_gal, annoy_index, vec_dim):\\n rank10_acc = 0\\n rank5_acc = 0\\n rank1_acc = 0\\n avg_acc = 0\\n vote_res = 0\\n\\n l2 = []\\n for anchor in range(0, len(X_anchor)):\\n res = get_result(get_image_features(cfg, model, X_anchor[anchor]), annoy_index)\\n vote = defaultdict(int)\\n # Accuracy\\n correct = 0\\n for i in res[:10]:\\n vote[y_gal[i]] += 1\\n\\n max_key = max(vote, key=vote.get)\\n if max_key == y_anchor[anchor]:\\n vote_res += 1\\n \\n\\n for recomm in res[:10]:\\n if y_gal[recomm] == y_anchor[anchor]:\\n correct += 1 \\n\\n avg_acc += correct/len(res)\\n\\n # Mean Average Precision\\n l1 = []\\n for recomm in res[:10]:\\n if y_gal[recomm] == y_anchor[anchor]:\\n correct += 1\\n l1.append(1)\\n else:\\n l1.append(0)\\n l2.append(l1) \\n\\n # Rank10 Accuracy\\n for each_val in res[:10]:\\n if y_gal[each_val] == y_anchor[anchor]:\\n rank10_acc += 1\\n break\\n \\n # Rank5 Accuracy\\n for each_val in res[:5]:\\n if y_gal[each_val] == y_anchor[anchor]:\\n rank5_acc += 1\\n break\\n\\n # Rank1 Accuracy\\n for each_val in res[:1]:\\n if y_gal[each_val] == y_anchor[anchor]:\\n rank1_acc += 1\\n break\\n\\n print(\\\"Avg acc is :: {avg_acc}\\\".format(avg_acc = avg_acc/len(X_anchor)))\\n print(\\\"Rank 10 acc is :: {rank10_acc}\\\".format(rank10_acc = rank10_acc/len(X_anchor)))\\n print(\\\"Rank 5 acc is :: {rank5_acc}\\\".format(rank5_acc = rank5_acc/len(X_anchor)))\\n print(\\\"Rank 1 acc is :: {rank1_acc}\\\".format(rank1_acc = rank1_acc/len(X_anchor)))\\n print(\\\"Mean Avg Precision is :: {mAP}\\\".format(mAP=mean_average_precision(l2)))\\n print(\\\"Vote res :: \\\", vote_res/len(X_anchor))\\n\\n return rank1_acc/len(X_anchor), mean_average_precision(l2)\",\n \"def predict(self, x_val, y1_test=None, y2_test=None):\\n if y1_test is None and y2_test is None:\\n y1_test = np.ones(len(x_val))\\n y2_test = y1_test\\n score_model = False\\n else:\\n score_model = True\\n\\n self.stage_one.predict(x_val, y1_test)\\n if score_model:\\n stage_one_score = self.stage_one.score\\n\\n # This section will not generalize well beyond this specific dataset.\\n if score_model:\\n df = pd.DataFrame(data=[y1_test, self.stage_one.pred],\\n columns=['y1_test', 'stage_one.pred'])\\n df = pd.DataFrame(y1_test).rename(columns={'class_second': 'y1_test'})\\n df['stage_one.pred'] = self.stage_one.pred\\n df['tweets'] = x_val\\n df['y2_test'] = y2_test\\n\\n # x2_val = df['tweets'].loc[df['stage_one.pred'] == 0]\\n # y2_test_ = df['y2_test'].loc[df['stage_one.pred'] == 0]\\n x2_val = df['tweets'].loc[df['stage_one.pred'] == 1]\\n y2_test_ = df['y2_test'].loc[df['stage_one.pred'] == 1]\\n else:\\n # x2_val = x_val[self.stage_one.pred == 0]\\n x2_val = x_val[self.stage_one.pred == 1]\\n y2_test_ = np.ones(len(x2_val))\\n\\n self.stage_two.predict(x2_val, y2_test_)\\n if score_model:\\n stage_two_score = self.stage_two.score\\n return (stage_one_score, stage_two_score, stage_one_score*stage_two_score)\\n else:\\n # return tweets that contain hate speech\\n return x2_val[self.stage_two.pred == 0]\",\n \"def get_polyscore(self,X_test=None,y_test=None,metric='adjusted_r2'):\\n X = self.get_points()\\n y_pred = self.get_polyfit(X)\\n train_score = score(self._model_evaluations, y_pred,metric, X=X)\\n if X_test is not None and y_test is not None:\\n y_pred_test = self.get_polyfit(X_test)\\n test_score = score(y_test,y_pred_test,metric,X=X_test)\\n return train_score, test_score\\n else:\\n return train_score\",\n \"def evaluate(self, test_data):\\r\\n test_results = [(np.argmax(self.feedforward(x)), y)\\r\\n for (x, y) in test_data]\\r\\n #print(self.feedforward(test_data[0][0]))\\r\\n #print(test_data[0][1])\\r\\n return sum(int(x == y) for (x, y) in test_results)\",\n \"def main():\\n graph_alg_eq()\\n graph_points()\\n graph_smooth_from_pts()\\n\\n return GOOD_RET # success\",\n \"def styblinskitankfcn(x: np.ndarray) -> np.ndarray:\\n n = x.shape[1]\\n scores = np.zeros((x.shape[0], 1))\\n for i in range(n):\\n scores += x[:, i] ** 4 - 16 * x[:, i] ** 2 + 5 * x[:, i]\\n scores *= 0.5\\n return scores\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6627804","0.6555331","0.6459971","0.5922805","0.5655154","0.5596297","0.5581356","0.5572477","0.55473816","0.5538019","0.5492573","0.5474258","0.5454743","0.54495645","0.54365635","0.5428014","0.54214275","0.54198104","0.5418108","0.5414656","0.5410967","0.5396969","0.5384275","0.53795254","0.5376827","0.5349952","0.534903","0.5332553","0.5322696","0.5318197","0.53151476","0.52902913","0.5271636","0.5260204","0.5260108","0.52508485","0.5247232","0.5243922","0.5240747","0.5239355","0.52388096","0.5233807","0.52275246","0.5221788","0.5218996","0.5218726","0.5211635","0.52049625","0.5203669","0.51912093","0.51877534","0.517864","0.5169333","0.516902","0.51686835","0.5158324","0.5152412","0.51491857","0.51437145","0.51437145","0.51437145","0.51437145","0.51437145","0.51437145","0.5139344","0.5137981","0.51378465","0.5131359","0.51246274","0.5114021","0.5108517","0.5108517","0.5108517","0.5108517","0.5105084","0.5101936","0.5101859","0.51005816","0.5099474","0.50923896","0.5091486","0.5086944","0.5084811","0.5080646","0.5078523","0.507327","0.5068066","0.5066557","0.50658745","0.5064409","0.50638884","0.5054698","0.50546724","0.5052691","0.50503534","0.50444937","0.5041456","0.5041132","0.504014","0.50391644"],"string":"[\n \"0.6627804\",\n \"0.6555331\",\n \"0.6459971\",\n \"0.5922805\",\n \"0.5655154\",\n \"0.5596297\",\n \"0.5581356\",\n \"0.5572477\",\n \"0.55473816\",\n \"0.5538019\",\n \"0.5492573\",\n \"0.5474258\",\n \"0.5454743\",\n \"0.54495645\",\n \"0.54365635\",\n \"0.5428014\",\n \"0.54214275\",\n \"0.54198104\",\n \"0.5418108\",\n \"0.5414656\",\n \"0.5410967\",\n \"0.5396969\",\n \"0.5384275\",\n \"0.53795254\",\n \"0.5376827\",\n \"0.5349952\",\n \"0.534903\",\n \"0.5332553\",\n \"0.5322696\",\n \"0.5318197\",\n \"0.53151476\",\n \"0.52902913\",\n \"0.5271636\",\n \"0.5260204\",\n \"0.5260108\",\n \"0.52508485\",\n \"0.5247232\",\n \"0.5243922\",\n \"0.5240747\",\n \"0.5239355\",\n \"0.52388096\",\n \"0.5233807\",\n \"0.52275246\",\n \"0.5221788\",\n \"0.5218996\",\n \"0.5218726\",\n \"0.5211635\",\n \"0.52049625\",\n \"0.5203669\",\n \"0.51912093\",\n \"0.51877534\",\n \"0.517864\",\n \"0.5169333\",\n \"0.516902\",\n \"0.51686835\",\n \"0.5158324\",\n \"0.5152412\",\n \"0.51491857\",\n \"0.51437145\",\n \"0.51437145\",\n \"0.51437145\",\n \"0.51437145\",\n \"0.51437145\",\n \"0.51437145\",\n \"0.5139344\",\n \"0.5137981\",\n \"0.51378465\",\n \"0.5131359\",\n \"0.51246274\",\n \"0.5114021\",\n \"0.5108517\",\n \"0.5108517\",\n \"0.5108517\",\n \"0.5108517\",\n \"0.5105084\",\n \"0.5101936\",\n \"0.5101859\",\n \"0.51005816\",\n \"0.5099474\",\n \"0.50923896\",\n \"0.5091486\",\n \"0.5086944\",\n \"0.5084811\",\n \"0.5080646\",\n \"0.5078523\",\n \"0.507327\",\n \"0.5068066\",\n \"0.5066557\",\n \"0.50658745\",\n \"0.5064409\",\n \"0.50638884\",\n \"0.5054698\",\n \"0.50546724\",\n \"0.5052691\",\n \"0.50503534\",\n \"0.50444937\",\n \"0.5041456\",\n \"0.5041132\",\n \"0.504014\",\n \"0.50391644\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.66810125"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":94698,"cells":{"query":{"kind":"string","value":"Gets the fraction of positive stationary scores."},"document":{"kind":"string","value":"def positive_scores(self, axis, th_stat=10**(-2)):\n \n traj, rank = self._check_run(axis)\n \n low_boundary = _np.exp(_np.log(self.params['low_bound'])/2)\n t = len(traj)-1\n stat = 0\n \n # Iteration over the trajectories\n for i in range(len(traj[0])):\n # above the low boundary\n if traj[t][i] > low_boundary:\n # stationary (change less than stat_th)\n if abs(_np.log(traj[t][i]) - _np.log(traj[t-1][i])) < th_stat:\n stat += 1\n return stat"},"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 positive_share(self) -> float:\n pos = self.tsdf.pct_change()[1:][self.tsdf.pct_change()[1:].values > 0.0].count()\n tot = self.tsdf.pct_change()[1:].count()\n return float(pos / tot)","def game_score(self):\n score = self.score.quantize(Decimal('0.001'))\n return score if score > 0 else 0","def get_average(self) -> float:\n return sum(self._scores) / len(self._scores)","def get_score(self):\n return float(self._score)","def getPercent(self):\n if isinstance(self.score,numbers.Number) and self.getMaximum():\n return (1.0*self.score/self.getMaximum())\n return None","def get_f_score(self):\n return self.get_g_score() + self.get_h_score()","def get_percentage_sf_votes(self):\n\n votes_f = self.get_num_f_votes()\n votes_sf = self.get_num_sf_votes()\n\n # avoid dividing by zero\n if votes_f + votes_sf == 0:\n return 0\n else:\n ratio = float(votes_sf)/(votes_f + votes_sf)\n return round(ratio * 100, 1)","def get_mean_score(rating_scores):\n return sum(rating_scores) / len(rating_scores)","def get_real_rating(self):\n if not (self.votes and self.score):\n return 0\n return float(self.score)/self.votes","def getAverage(self):\n return sum(self.scores) / len(self.scores)","def g_score(self):\n _, _, I_CK = self._entropies()\n return 2.0 * I_CK","def get_percent(self):\n if not (self.votes and self.score):\n return 0\n return 100 * (self.get_rating() / self.field.range)","def get_rating(self):\n if not (self.votes and self.score):\n return 0\n return float(self.score)/(self.votes+self.field.weight)","def score(self):\n self.set_idx()\n if self.idx:\n diffs = self.diffs()\n weights = self.weights\n return np.sum(weights * diffs) / np.sum(weights)\n else:\n return 0.0","def null_score(game, player):\n\n if game.is_loser(player):\n return float(\"-inf\")\n\n if game.is_winner(player):\n return float(\"inf\")\n\n return 0.","def z_score(self) -> float:\n return float((self.tsdf.pct_change().iloc[-1] - self.tsdf.pct_change().mean()) / self.tsdf.pct_change().std())","def get_real_percent(self):\n if not (self.votes and self.score):\n return 0\n return 100 * (self.get_real_rating() / self.field.range)","def drift_score(self):\n if self.measured_val is None:\n return 0.0\n\n if self.rebalance_type == self.REBALANCE_TYPE_ABSOLUTE:\n return (self.measured_val - self.configured_val) / self.rebalance_thr\n else:\n return ((self.measured_val - self.configured_val) / self.configured_val) / self.rebalance_thr","def result(self):\n tp = self.true_positive\n fp = self.false_positive\n return math_ops.div_no_nan(tp, tp + fp)","def get_g_score(self):\n return self._g_score","def worst(self) -> float:\n return float(self.tsdf.pct_change().min())","def rate(self) -> float:\n return self.success_cnt / self.total_cnt if self.total_cnt > 0 else 1.0","def get_score(self, solution: np.array) -> float:\n pass","def mapd(self) -> float:\n a = np.sum(np.abs(self.predicted - self.true))\n b = np.sum(np.abs(self.true))\n return float(a / b)","def get_average_presence_of_students(students):\n total_presence = 0\n for row in students:\n total_presence += int(row[6])\n average_presence = str(total_presence/len(students))\n if average_presence.find(\".5\") != -1:\n average_presence = float(average_presence) + 1\n else: \n average_presence = float(average_presence)\n\n return int(average_presence)","def calculate_sentiment(positive, negative):\n denominator = (positive - negative)\n numerator = (positive + negative)\n if numerator == 0:\n return 0\n return 0.268 * (denominator / numerator)","def denominator(self):\n return 1","def get_percentage_f_votes(self):\n\n votes_f = self.get_num_f_votes()\n votes_sf = self.get_num_sf_votes()\n\n # avoid dividing by zero\n if votes_f + votes_sf == 0:\n return 0\n else:\n ratio = float(votes_f)/(votes_f + votes_sf)\n return round(ratio * 100, 1)","def rmspe(self) -> float:\n return float(np.sqrt(np.mean(np.square(((self.true - self.predicted) / self.true)), axis=0)))","def fraction_nonzero(module):\n return count_params_nonzero(module) / count_params(module)","def get_score(p):\n temp = path[round(p[0], 1), round(p[1], 1)] / a_star\n return (clip(1 - temp, a_min=0, a_max=1) + clip(1 - temp, a_min=0, a_max=1) ** 2) / 2","def getScore(self, node):\n return self.getGravityScoreFromNode(node) or 0","def rmdspe(self) -> float:\n return float(np.sqrt(np.median(np.square(self._percentage_error()))) * 100.0)","def rmsse(self, seasonality: int = 1) -> float:\n q = np.abs(self._error()) / self.mae(self.true[seasonality:], self._naive_prognose(seasonality))\n return float(np.sqrt(np.mean(np.square(q))))","def getScore(self):\n tempscore = 1000 - 0.01*self.timeDriving \n tempscore -= 0.1*getDist(self.maze.checkpoints[self.checkpoint].getMid(),self.pos)\n tempscore += self.checkpoint *1000\n tempscore += self.laps * 1000 * len(self.maze.checkpoints)\n return tempscore","def get_score(self):\n return self.score","def get_score(self):\n return self.score","def get_score(self):\n return self.score","def dire_score(self):\n return self._get(\"dire_score\")","def score(self):\n return self.aggregate(Sum('score')).values()[0] or 0","def get_score(self):\n return self._score","def get_score(self):\n return self._score","def get_score(self):\n return self._score","def div(self):\n a = self.nums()\n x = LibraryFunctions.per(a, 0.9) - LibraryFunctions.per(a, 0.1)\n return x / 2.58","def mape(self) -> float:\n return float(np.mean(np.abs((self.true - self.predicted) / self.true)) * 100)","def train_frac(self):\n return self._train_frac","def pss(self):\n return (self.table[0, 0] * self.table[1, 1] - self.table[0, 1] * self.table[1, 0]) / \\\n ((self.table[0, 0] + self.table[1, 0]) * (self.table[0, 1] + self.table[1, 1]))","def radiant_score(self):\n return self._get(\"radiant_score\")","def value(self):\n if len(self.fscore_history) == 0:\n return 0\n else:\n return np.mean(self.fscore_history)","def get_score(self):\r\n return None","def gpa(self):\n try:\n return sum(self.courses.values()) / len(self.courses)\n except ZeroDivisionError:\n return 0","def get_win_percentage(self) -> float:\n if self.wins == 0:\n return 0.0\n else:\n return round((self.wins / (self.wins + self.losses)) * 100, 2)","def get_score(self):\n rewards, resets = self.runner.get_rewards_resets()\n self.runner.clear_rewards_resets()\n assert rewards.ndim == 1 and resets.ndim == 1, (rewards.ndim, resets.ndim)\n assert rewards.shape[0] == resets.shape[0], (rewards.shape, resets.shape)\n scores = [0]\n for t in reversed(range(rewards.shape[0])):\n if resets[t]:\n scores.append(0)\n scores[-1] += rewards[t]\n return np.mean(scores)","def get_b(self):\n return ((self.s_pos / self.n_pos) + (self.s_neg / self.n_neg)) / 2","def smape(self) -> float:\n _temp = np.sum(2 * np.abs(self.predicted - self.true) / (np.abs(self.true) + np.abs(self.predicted)))\n return float(100 / len(self.true) * _temp)","def get_score(self):\n\n return self._score","def get_score(self) -> int:\n return self.rstate.score()","def get_score(self):\n return self.__score","def compute_f_score(alpha, true_positves, false_positives, false_negatives):\n num = true_positves\n den = np.float64(alpha * (true_positves + false_positives) +\\\n (1 - alpha) * (true_positves + false_negatives))\n with np.errstate(divide='ignore', invalid='ignore'):\n return num / den","def pct(self):\n\t\treturn self.bottle.pct()","def result(self):\n tp = self.true_positive.read_value()\n fn = self.false_negative.read_value()\n return math_ops.div_no_nan(tp, tp + fn)","def value_ret(self) -> float:\n if float(self.tsdf.iloc[0]) == 0.0:\n raise Exception('First data point == 0.0')\n return float(self.tsdf.iloc[-1] / self.tsdf.iloc[0] - 1)","def get_as_float(self):\n return float(self.numerator / self.denominator)","def getScore(self):\r\n return self._score","def samp_frac(self):\n return self._samp_frac","def fractionPassing(self):\n return self.cut.entries / self.entries","def getScore(self):\n return self._score","def percent_passing(self) -> float:\n num_meas = Enumerable(self.mlc_meas).select_many(lambda m: m.passed).count()\n num_pass = (\n Enumerable(self.mlc_meas)\n .select_many(lambda m: m.passed)\n .count(lambda p: bool(p) is True)\n )\n return float(100 * num_pass / num_meas)","def _calculate_score(predictions: np.ndarray, correct: np.ndarray) -> float:\n return np.sqrt(np.sum(np.square(np.log(predictions + 1) - np.log(correct + 1))) / len(correct))","def rmsle(self) -> float:\n return float(np.sqrt(np.mean(np.power(np.log1p(self.predicted) - np.log1p(self.true), 2))))","def winning_percentage(self):\n return float(len(self.wins))/len((self.wins+self.losses))","def custom_score_3(game, player):\n if game.is_loser(player):\n return float(\"-inf\")\n\n if game.is_winner(player):\n return float(\"inf\")\n\n own_moves = number_moves(game, player)\n if own_moves == 0:\n return float(\"-inf\")\n\n opp_moves = number_moves(game, game.get_opponent(player))\n if opp_moves == 0:\n return float(\"inf\")\n\n #Between 1-8\n return own_moves / opp_moves","def adv_ratio(self): # XXX\r\n bw = StatsRouter.global_bw_mean\r\n if bw == 0.0: return 0\r\n else: return self.bw/bw","def getScore(self):\n return sum(self.field)","def total_score(self):\n return _projected_site_total_score(self)","def get_probability_loss(self):\n return sum(self._loss)/len(self._loss)","def getScore(self, gameState):\n\n if (self.red):\n return gameState.getScore()\n else:\n return gameState.getScore() * -1","def score( self ):\r\n result = 0.0\r\n for rr in self.ee.getRsrcs( ):\r\n value = self.scoreRsrc( rr )\r\n result += value\r\n print( \"INFO: Value for the schedule is %s \" % ( rr, result ) )\r\n return( result )","def scoring(self):\n return -100 if self.loss_condition() else 0","def possession(self, good):\n return float(self._haves[good])","def S(self):\n Ae = 1.0/float(len(self.K)) \n return (self.avg_Ao() - Ae)/(1.0 - Ae)","def matthewscc(self):\n if not self.total_examples:\n return 0.\n\n true_pos = float(self.true_positives)\n false_pos = float(self.false_positives)\n false_neg = float(self.false_negatives)\n true_neg = float(self.true_negatives)\n terms = [(true_pos + false_pos),\n (true_pos + false_neg),\n (true_neg + false_pos),\n (true_neg + false_neg)]\n denom = 1.\n for t in filter(lambda t: t != 0., terms):\n denom *= t\n return ((true_pos * true_neg) - (false_pos * false_neg)) / math.sqrt(denom)","def get_mean(self):\n try:\n return sum(self.speakers.values()) / len(self.speakers)\n except (ZeroDivisionError):\n return 0.0","def calculate_score(self):\n try:\n self.score = self.__total_comment_score / float(self.num_comments)\n except ZeroDivisionError:\n self.score = float(0)","def score(cur_ven, ven):\r\n try:\r\n alpha = 750\r\n numerator = (ven[\"rating\"] * 0.75) + (2.5 * (1- eulers**(-ven[\"ratingSignals\"]/144)))\r\n cur_coord = (cur_ven[\"location\"][\"lat\"], cur_ven[\"location\"][\"lng\"])\r\n ven_coord = (ven[\"location\"][\"lat\"], ven[\"location\"][\"lng\"])\r\n denominator = vincenty(cur_coord, ven_coord).meters + alpha\r\n except Exception as e:\r\n print \"{}, \\n has produced an error from {}\".format(ven[\"name\"], e)\r\n return float(\"-inf\")\r\n return numerator / denominator","def cronbach_alpha(self) -> float:\n itemscores = np.stack([self.true, self.predicted])\n itemvars = itemscores.var(axis=1, ddof=1)\n tscores = itemscores.sum(axis=0)\n nitems = len(itemscores)\n return float(nitems / (nitems - 1.) * (1 - itemvars.sum() / tscores.var(ddof=1)))","def get_r_score(self):\n return self.r_score","def calculate(self):\n\n gt = self.ground_truth.flatten().astype(np.int8)\n seg = self.segmentation.flatten().astype(np.int8)\n\n probability_difference = np.absolute(gt - seg).sum()\n probability_joint = (gt * seg).sum()\n\n if probability_joint != 0:\n return probability_difference / (2. * probability_joint)\n else:\n return -1","def get_value(\n self\n ) -> float:\n\n return self.average","def getavgvel(self):\n if self.total_time:\n return (6.28)/(self.total_time)","def ratio_local_cons(self):\n if self.current_energy_consumed == 0.0:\n return 1.0\n else:\n return self.local_cons / self.current_energy_consumed","def calculate_positron_fraction(\n positron_energy, isotope_energy, isotope_intensity\n):\n return positron_energy / np.sum(isotope_energy * isotope_intensity)","def _get_average(self):\n norm = 1.0\n for pos, idx in enumerate(self.idx):\n norm *= (self.high[pos] - self.low[pos])\n return 1.0/norm","def acceptance_fraction(self):\n return self.sampler.acceptance_fraction","def acceptance_fraction(self):\n return float(self._accepted / self.num_iterations)","def elution_score(self):\n return self.score","def improved_score(game, player):\n if game.is_loser(player):\n return float(\"-inf\")\n\n if game.is_winner(player):\n return float(\"inf\")\n\n own_moves = len(game.get_legal_moves(player))\n opp_moves = len(game.get_legal_moves(game.get_opponent(player)))\n return float(own_moves - opp_moves)","def smdape(self) -> float:\n return float(np.median(2.0 * self._ae() / ((np.abs(self.true) + np.abs(self.predicted)) + EPS)))","def acceptance_rate(self):\n total = float(self.accepted + self.rejected)\n return self.accepted / total","def get_score(self):\r\n return self.lcp.get_score()","def compute_fraction( poi_messages, all_messages ):\n import math\n if poi_messages == 0 or all_messages == 0 or math.isnan(float(poi_messages)) or math.isnan(float(all_messages)) :\n return 0.\n fraction = 0.\n fraction = float(poi_messages) / float(all_messages) \n return fraction"],"string":"[\n \"def positive_share(self) -> float:\\n pos = self.tsdf.pct_change()[1:][self.tsdf.pct_change()[1:].values > 0.0].count()\\n tot = self.tsdf.pct_change()[1:].count()\\n return float(pos / tot)\",\n \"def game_score(self):\\n score = self.score.quantize(Decimal('0.001'))\\n return score if score > 0 else 0\",\n \"def get_average(self) -> float:\\n return sum(self._scores) / len(self._scores)\",\n \"def get_score(self):\\n return float(self._score)\",\n \"def getPercent(self):\\n if isinstance(self.score,numbers.Number) and self.getMaximum():\\n return (1.0*self.score/self.getMaximum())\\n return None\",\n \"def get_f_score(self):\\n return self.get_g_score() + self.get_h_score()\",\n \"def get_percentage_sf_votes(self):\\n\\n votes_f = self.get_num_f_votes()\\n votes_sf = self.get_num_sf_votes()\\n\\n # avoid dividing by zero\\n if votes_f + votes_sf == 0:\\n return 0\\n else:\\n ratio = float(votes_sf)/(votes_f + votes_sf)\\n return round(ratio * 100, 1)\",\n \"def get_mean_score(rating_scores):\\n return sum(rating_scores) / len(rating_scores)\",\n \"def get_real_rating(self):\\n if not (self.votes and self.score):\\n return 0\\n return float(self.score)/self.votes\",\n \"def getAverage(self):\\n return sum(self.scores) / len(self.scores)\",\n \"def g_score(self):\\n _, _, I_CK = self._entropies()\\n return 2.0 * I_CK\",\n \"def get_percent(self):\\n if not (self.votes and self.score):\\n return 0\\n return 100 * (self.get_rating() / self.field.range)\",\n \"def get_rating(self):\\n if not (self.votes and self.score):\\n return 0\\n return float(self.score)/(self.votes+self.field.weight)\",\n \"def score(self):\\n self.set_idx()\\n if self.idx:\\n diffs = self.diffs()\\n weights = self.weights\\n return np.sum(weights * diffs) / np.sum(weights)\\n else:\\n return 0.0\",\n \"def null_score(game, player):\\n\\n if game.is_loser(player):\\n return float(\\\"-inf\\\")\\n\\n if game.is_winner(player):\\n return float(\\\"inf\\\")\\n\\n return 0.\",\n \"def z_score(self) -> float:\\n return float((self.tsdf.pct_change().iloc[-1] - self.tsdf.pct_change().mean()) / self.tsdf.pct_change().std())\",\n \"def get_real_percent(self):\\n if not (self.votes and self.score):\\n return 0\\n return 100 * (self.get_real_rating() / self.field.range)\",\n \"def drift_score(self):\\n if self.measured_val is None:\\n return 0.0\\n\\n if self.rebalance_type == self.REBALANCE_TYPE_ABSOLUTE:\\n return (self.measured_val - self.configured_val) / self.rebalance_thr\\n else:\\n return ((self.measured_val - self.configured_val) / self.configured_val) / self.rebalance_thr\",\n \"def result(self):\\n tp = self.true_positive\\n fp = self.false_positive\\n return math_ops.div_no_nan(tp, tp + fp)\",\n \"def get_g_score(self):\\n return self._g_score\",\n \"def worst(self) -> float:\\n return float(self.tsdf.pct_change().min())\",\n \"def rate(self) -> float:\\n return self.success_cnt / self.total_cnt if self.total_cnt > 0 else 1.0\",\n \"def get_score(self, solution: np.array) -> float:\\n pass\",\n \"def mapd(self) -> float:\\n a = np.sum(np.abs(self.predicted - self.true))\\n b = np.sum(np.abs(self.true))\\n return float(a / b)\",\n \"def get_average_presence_of_students(students):\\n total_presence = 0\\n for row in students:\\n total_presence += int(row[6])\\n average_presence = str(total_presence/len(students))\\n if average_presence.find(\\\".5\\\") != -1:\\n average_presence = float(average_presence) + 1\\n else: \\n average_presence = float(average_presence)\\n\\n return int(average_presence)\",\n \"def calculate_sentiment(positive, negative):\\n denominator = (positive - negative)\\n numerator = (positive + negative)\\n if numerator == 0:\\n return 0\\n return 0.268 * (denominator / numerator)\",\n \"def denominator(self):\\n return 1\",\n \"def get_percentage_f_votes(self):\\n\\n votes_f = self.get_num_f_votes()\\n votes_sf = self.get_num_sf_votes()\\n\\n # avoid dividing by zero\\n if votes_f + votes_sf == 0:\\n return 0\\n else:\\n ratio = float(votes_f)/(votes_f + votes_sf)\\n return round(ratio * 100, 1)\",\n \"def rmspe(self) -> float:\\n return float(np.sqrt(np.mean(np.square(((self.true - self.predicted) / self.true)), axis=0)))\",\n \"def fraction_nonzero(module):\\n return count_params_nonzero(module) / count_params(module)\",\n \"def get_score(p):\\n temp = path[round(p[0], 1), round(p[1], 1)] / a_star\\n return (clip(1 - temp, a_min=0, a_max=1) + clip(1 - temp, a_min=0, a_max=1) ** 2) / 2\",\n \"def getScore(self, node):\\n return self.getGravityScoreFromNode(node) or 0\",\n \"def rmdspe(self) -> float:\\n return float(np.sqrt(np.median(np.square(self._percentage_error()))) * 100.0)\",\n \"def rmsse(self, seasonality: int = 1) -> float:\\n q = np.abs(self._error()) / self.mae(self.true[seasonality:], self._naive_prognose(seasonality))\\n return float(np.sqrt(np.mean(np.square(q))))\",\n \"def getScore(self):\\n tempscore = 1000 - 0.01*self.timeDriving \\n tempscore -= 0.1*getDist(self.maze.checkpoints[self.checkpoint].getMid(),self.pos)\\n tempscore += self.checkpoint *1000\\n tempscore += self.laps * 1000 * len(self.maze.checkpoints)\\n return tempscore\",\n \"def get_score(self):\\n return self.score\",\n \"def get_score(self):\\n return self.score\",\n \"def get_score(self):\\n return self.score\",\n \"def dire_score(self):\\n return self._get(\\\"dire_score\\\")\",\n \"def score(self):\\n return self.aggregate(Sum('score')).values()[0] or 0\",\n \"def get_score(self):\\n return self._score\",\n \"def get_score(self):\\n return self._score\",\n \"def get_score(self):\\n return self._score\",\n \"def div(self):\\n a = self.nums()\\n x = LibraryFunctions.per(a, 0.9) - LibraryFunctions.per(a, 0.1)\\n return x / 2.58\",\n \"def mape(self) -> float:\\n return float(np.mean(np.abs((self.true - self.predicted) / self.true)) * 100)\",\n \"def train_frac(self):\\n return self._train_frac\",\n \"def pss(self):\\n return (self.table[0, 0] * self.table[1, 1] - self.table[0, 1] * self.table[1, 0]) / \\\\\\n ((self.table[0, 0] + self.table[1, 0]) * (self.table[0, 1] + self.table[1, 1]))\",\n \"def radiant_score(self):\\n return self._get(\\\"radiant_score\\\")\",\n \"def value(self):\\n if len(self.fscore_history) == 0:\\n return 0\\n else:\\n return np.mean(self.fscore_history)\",\n \"def get_score(self):\\r\\n return None\",\n \"def gpa(self):\\n try:\\n return sum(self.courses.values()) / len(self.courses)\\n except ZeroDivisionError:\\n return 0\",\n \"def get_win_percentage(self) -> float:\\n if self.wins == 0:\\n return 0.0\\n else:\\n return round((self.wins / (self.wins + self.losses)) * 100, 2)\",\n \"def get_score(self):\\n rewards, resets = self.runner.get_rewards_resets()\\n self.runner.clear_rewards_resets()\\n assert rewards.ndim == 1 and resets.ndim == 1, (rewards.ndim, resets.ndim)\\n assert rewards.shape[0] == resets.shape[0], (rewards.shape, resets.shape)\\n scores = [0]\\n for t in reversed(range(rewards.shape[0])):\\n if resets[t]:\\n scores.append(0)\\n scores[-1] += rewards[t]\\n return np.mean(scores)\",\n \"def get_b(self):\\n return ((self.s_pos / self.n_pos) + (self.s_neg / self.n_neg)) / 2\",\n \"def smape(self) -> float:\\n _temp = np.sum(2 * np.abs(self.predicted - self.true) / (np.abs(self.true) + np.abs(self.predicted)))\\n return float(100 / len(self.true) * _temp)\",\n \"def get_score(self):\\n\\n return self._score\",\n \"def get_score(self) -> int:\\n return self.rstate.score()\",\n \"def get_score(self):\\n return self.__score\",\n \"def compute_f_score(alpha, true_positves, false_positives, false_negatives):\\n num = true_positves\\n den = np.float64(alpha * (true_positves + false_positives) +\\\\\\n (1 - alpha) * (true_positves + false_negatives))\\n with np.errstate(divide='ignore', invalid='ignore'):\\n return num / den\",\n \"def pct(self):\\n\\t\\treturn self.bottle.pct()\",\n \"def result(self):\\n tp = self.true_positive.read_value()\\n fn = self.false_negative.read_value()\\n return math_ops.div_no_nan(tp, tp + fn)\",\n \"def value_ret(self) -> float:\\n if float(self.tsdf.iloc[0]) == 0.0:\\n raise Exception('First data point == 0.0')\\n return float(self.tsdf.iloc[-1] / self.tsdf.iloc[0] - 1)\",\n \"def get_as_float(self):\\n return float(self.numerator / self.denominator)\",\n \"def getScore(self):\\r\\n return self._score\",\n \"def samp_frac(self):\\n return self._samp_frac\",\n \"def fractionPassing(self):\\n return self.cut.entries / self.entries\",\n \"def getScore(self):\\n return self._score\",\n \"def percent_passing(self) -> float:\\n num_meas = Enumerable(self.mlc_meas).select_many(lambda m: m.passed).count()\\n num_pass = (\\n Enumerable(self.mlc_meas)\\n .select_many(lambda m: m.passed)\\n .count(lambda p: bool(p) is True)\\n )\\n return float(100 * num_pass / num_meas)\",\n \"def _calculate_score(predictions: np.ndarray, correct: np.ndarray) -> float:\\n return np.sqrt(np.sum(np.square(np.log(predictions + 1) - np.log(correct + 1))) / len(correct))\",\n \"def rmsle(self) -> float:\\n return float(np.sqrt(np.mean(np.power(np.log1p(self.predicted) - np.log1p(self.true), 2))))\",\n \"def winning_percentage(self):\\n return float(len(self.wins))/len((self.wins+self.losses))\",\n \"def custom_score_3(game, player):\\n if game.is_loser(player):\\n return float(\\\"-inf\\\")\\n\\n if game.is_winner(player):\\n return float(\\\"inf\\\")\\n\\n own_moves = number_moves(game, player)\\n if own_moves == 0:\\n return float(\\\"-inf\\\")\\n\\n opp_moves = number_moves(game, game.get_opponent(player))\\n if opp_moves == 0:\\n return float(\\\"inf\\\")\\n\\n #Between 1-8\\n return own_moves / opp_moves\",\n \"def adv_ratio(self): # XXX\\r\\n bw = StatsRouter.global_bw_mean\\r\\n if bw == 0.0: return 0\\r\\n else: return self.bw/bw\",\n \"def getScore(self):\\n return sum(self.field)\",\n \"def total_score(self):\\n return _projected_site_total_score(self)\",\n \"def get_probability_loss(self):\\n return sum(self._loss)/len(self._loss)\",\n \"def getScore(self, gameState):\\n\\n if (self.red):\\n return gameState.getScore()\\n else:\\n return gameState.getScore() * -1\",\n \"def score( self ):\\r\\n result = 0.0\\r\\n for rr in self.ee.getRsrcs( ):\\r\\n value = self.scoreRsrc( rr )\\r\\n result += value\\r\\n print( \\\"INFO: Value for the schedule is %s \\\" % ( rr, result ) )\\r\\n return( result )\",\n \"def scoring(self):\\n return -100 if self.loss_condition() else 0\",\n \"def possession(self, good):\\n return float(self._haves[good])\",\n \"def S(self):\\n Ae = 1.0/float(len(self.K)) \\n return (self.avg_Ao() - Ae)/(1.0 - Ae)\",\n \"def matthewscc(self):\\n if not self.total_examples:\\n return 0.\\n\\n true_pos = float(self.true_positives)\\n false_pos = float(self.false_positives)\\n false_neg = float(self.false_negatives)\\n true_neg = float(self.true_negatives)\\n terms = [(true_pos + false_pos),\\n (true_pos + false_neg),\\n (true_neg + false_pos),\\n (true_neg + false_neg)]\\n denom = 1.\\n for t in filter(lambda t: t != 0., terms):\\n denom *= t\\n return ((true_pos * true_neg) - (false_pos * false_neg)) / math.sqrt(denom)\",\n \"def get_mean(self):\\n try:\\n return sum(self.speakers.values()) / len(self.speakers)\\n except (ZeroDivisionError):\\n return 0.0\",\n \"def calculate_score(self):\\n try:\\n self.score = self.__total_comment_score / float(self.num_comments)\\n except ZeroDivisionError:\\n self.score = float(0)\",\n \"def score(cur_ven, ven):\\r\\n try:\\r\\n alpha = 750\\r\\n numerator = (ven[\\\"rating\\\"] * 0.75) + (2.5 * (1- eulers**(-ven[\\\"ratingSignals\\\"]/144)))\\r\\n cur_coord = (cur_ven[\\\"location\\\"][\\\"lat\\\"], cur_ven[\\\"location\\\"][\\\"lng\\\"])\\r\\n ven_coord = (ven[\\\"location\\\"][\\\"lat\\\"], ven[\\\"location\\\"][\\\"lng\\\"])\\r\\n denominator = vincenty(cur_coord, ven_coord).meters + alpha\\r\\n except Exception as e:\\r\\n print \\\"{}, \\\\n has produced an error from {}\\\".format(ven[\\\"name\\\"], e)\\r\\n return float(\\\"-inf\\\")\\r\\n return numerator / denominator\",\n \"def cronbach_alpha(self) -> float:\\n itemscores = np.stack([self.true, self.predicted])\\n itemvars = itemscores.var(axis=1, ddof=1)\\n tscores = itemscores.sum(axis=0)\\n nitems = len(itemscores)\\n return float(nitems / (nitems - 1.) * (1 - itemvars.sum() / tscores.var(ddof=1)))\",\n \"def get_r_score(self):\\n return self.r_score\",\n \"def calculate(self):\\n\\n gt = self.ground_truth.flatten().astype(np.int8)\\n seg = self.segmentation.flatten().astype(np.int8)\\n\\n probability_difference = np.absolute(gt - seg).sum()\\n probability_joint = (gt * seg).sum()\\n\\n if probability_joint != 0:\\n return probability_difference / (2. * probability_joint)\\n else:\\n return -1\",\n \"def get_value(\\n self\\n ) -> float:\\n\\n return self.average\",\n \"def getavgvel(self):\\n if self.total_time:\\n return (6.28)/(self.total_time)\",\n \"def ratio_local_cons(self):\\n if self.current_energy_consumed == 0.0:\\n return 1.0\\n else:\\n return self.local_cons / self.current_energy_consumed\",\n \"def calculate_positron_fraction(\\n positron_energy, isotope_energy, isotope_intensity\\n):\\n return positron_energy / np.sum(isotope_energy * isotope_intensity)\",\n \"def _get_average(self):\\n norm = 1.0\\n for pos, idx in enumerate(self.idx):\\n norm *= (self.high[pos] - self.low[pos])\\n return 1.0/norm\",\n \"def acceptance_fraction(self):\\n return self.sampler.acceptance_fraction\",\n \"def acceptance_fraction(self):\\n return float(self._accepted / self.num_iterations)\",\n \"def elution_score(self):\\n return self.score\",\n \"def improved_score(game, player):\\n if game.is_loser(player):\\n return float(\\\"-inf\\\")\\n\\n if game.is_winner(player):\\n return float(\\\"inf\\\")\\n\\n own_moves = len(game.get_legal_moves(player))\\n opp_moves = len(game.get_legal_moves(game.get_opponent(player)))\\n return float(own_moves - opp_moves)\",\n \"def smdape(self) -> float:\\n return float(np.median(2.0 * self._ae() / ((np.abs(self.true) + np.abs(self.predicted)) + EPS)))\",\n \"def acceptance_rate(self):\\n total = float(self.accepted + self.rejected)\\n return self.accepted / total\",\n \"def get_score(self):\\r\\n return self.lcp.get_score()\",\n \"def compute_fraction( poi_messages, all_messages ):\\n import math\\n if poi_messages == 0 or all_messages == 0 or math.isnan(float(poi_messages)) or math.isnan(float(all_messages)) :\\n return 0.\\n fraction = 0.\\n fraction = float(poi_messages) / float(all_messages) \\n return fraction\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6627993","0.66167814","0.66115385","0.65545696","0.64541936","0.6416723","0.63477427","0.63358057","0.63094145","0.6304095","0.628694","0.62657887","0.62262195","0.61896664","0.6168893","0.61631715","0.61393696","0.6111311","0.6108707","0.6084148","0.60761714","0.6073406","0.6073316","0.60536677","0.605203","0.60387444","0.602564","0.60252386","0.5993697","0.5978535","0.5958823","0.5955687","0.5944775","0.59443057","0.59356964","0.59263736","0.59263736","0.59263736","0.5913756","0.59115237","0.5908968","0.5908968","0.5908968","0.5907544","0.5902421","0.59011996","0.58813137","0.58730155","0.587213","0.58668137","0.58583164","0.58567435","0.585564","0.58503413","0.5849997","0.5842663","0.5840269","0.5837454","0.5812161","0.58035535","0.5800793","0.57902026","0.57829887","0.5781378","0.5780975","0.57726806","0.5772235","0.57574","0.5756556","0.5754479","0.5752282","0.574795","0.5746013","0.5742877","0.5740939","0.5738643","0.573851","0.57369703","0.573584","0.57284933","0.57241964","0.5723374","0.5723305","0.5719764","0.571459","0.57143533","0.5710754","0.5703422","0.569309","0.5688714","0.5682666","0.5682548","0.5677757","0.5675936","0.5675464","0.5672092","0.566654","0.56651217","0.56630164","0.56630003","0.56476545"],"string":"[\n \"0.6627993\",\n \"0.66167814\",\n \"0.66115385\",\n \"0.65545696\",\n \"0.64541936\",\n \"0.6416723\",\n \"0.63477427\",\n \"0.63358057\",\n \"0.63094145\",\n \"0.6304095\",\n \"0.628694\",\n \"0.62657887\",\n \"0.62262195\",\n \"0.61896664\",\n \"0.6168893\",\n \"0.61631715\",\n \"0.61393696\",\n \"0.6111311\",\n \"0.6108707\",\n \"0.6084148\",\n \"0.60761714\",\n \"0.6073406\",\n \"0.6073316\",\n \"0.60536677\",\n \"0.605203\",\n \"0.60387444\",\n \"0.602564\",\n \"0.60252386\",\n \"0.5993697\",\n \"0.5978535\",\n \"0.5958823\",\n \"0.5955687\",\n \"0.5944775\",\n \"0.59443057\",\n \"0.59356964\",\n \"0.59263736\",\n \"0.59263736\",\n \"0.59263736\",\n \"0.5913756\",\n \"0.59115237\",\n \"0.5908968\",\n \"0.5908968\",\n \"0.5908968\",\n \"0.5907544\",\n \"0.5902421\",\n \"0.59011996\",\n \"0.58813137\",\n \"0.58730155\",\n \"0.587213\",\n \"0.58668137\",\n \"0.58583164\",\n \"0.58567435\",\n \"0.585564\",\n \"0.58503413\",\n \"0.5849997\",\n \"0.5842663\",\n \"0.5840269\",\n \"0.5837454\",\n \"0.5812161\",\n \"0.58035535\",\n \"0.5800793\",\n \"0.57902026\",\n \"0.57829887\",\n \"0.5781378\",\n \"0.5780975\",\n \"0.57726806\",\n \"0.5772235\",\n \"0.57574\",\n \"0.5756556\",\n \"0.5754479\",\n \"0.5752282\",\n \"0.574795\",\n \"0.5746013\",\n \"0.5742877\",\n \"0.5740939\",\n \"0.5738643\",\n \"0.573851\",\n \"0.57369703\",\n \"0.573584\",\n \"0.57284933\",\n \"0.57241964\",\n \"0.5723374\",\n \"0.5723305\",\n \"0.5719764\",\n \"0.571459\",\n \"0.57143533\",\n \"0.5710754\",\n \"0.5703422\",\n \"0.569309\",\n \"0.5688714\",\n \"0.5682666\",\n \"0.5682548\",\n \"0.5677757\",\n \"0.5675936\",\n \"0.5675464\",\n \"0.5672092\",\n \"0.566654\",\n \"0.56651217\",\n \"0.56630164\",\n \"0.56630003\",\n \"0.56476545\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":94699,"cells":{"query":{"kind":"string","value":"Compute the extinction area given the last ranking obtained through the run method"},"document":{"kind":"string","value":"def ext_area(self, axis):\n traj, rank = self._check_run(axis)\n return _ext_area(axis, rank, self._neighb[1], self._neighb[0])"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _overlap_energy(self, this, that):\n if not this.overlaps(that):\n return 0.0\n\n return min(10.0 / this.rank, 10.0 / that.rank)","def _ext_area(axis, ranking, row_ind_at_col, col_ind_at_row):\n if axis == 0:\n indexes_lists = _np.array([col_ind_at_row[:], row_ind_at_col[:]])\n else:\n indexes_lists = _np.array([row_ind_at_col[:], col_ind_at_row[:]])\n if len(ranking) != len(indexes_lists[0]):\n print ('Dimensions do not match')\n return\n \n # Counting the already extincted columns. They are the ones whose list of\n # associated row indexes is empty. In that case the extinction counter is\n # increased and a -1 is added to the indexes list.\n ext_nodes = 0\n for c in range(len(indexes_lists[1])):\n if len(indexes_lists[1][c]) == 0:\n ext_nodes += 1\n indexes_lists[1][c] = _np.append(indexes_lists[1][c], -1)\n \n ext_curve = [ext_nodes]\n # Iteration over the ranked nodes to remove, r\n for r in ranking[:-1]:\n # Iter over the connected nodes in the other layer, r\n for c in indexes_lists[0][r]:\n # Removing the ranked node from the neighbours of c\n indexes_lists[1][c] = indexes_lists[1][c][indexes_lists[1][c] != r]\n \n # If the neighbours of c is empty, then c gets extincted\n if len(indexes_lists[1][c]) == 0:\n ext_nodes += 1\n indexes_lists[1][c] = _np.append(indexes_lists[1][c], -1)\n \n ext_curve.append(ext_nodes)\n \n # Returning the area below the extinction curve\n return sum(ext_curve) / float(len(indexes_lists[0]) * len(indexes_lists[1]))","def _calculate_fitness(self):\n pass","def lowest_rank_approx(A,e):","def __rank__(self) -> int:","def _calc_neighborhood_func(self, curr_it: int, mode: str) -> float:\n return decreasing_rate(\n self.radius_max_,\n self.radius_min_,\n iteration_max=self.max_iterations_,\n iteration=curr_it,\n mode=mode,\n )","def overall_reduction(self):\n return 84","def get_absolute_regret(self):\n values = self.stats['return_stats']['episode_totals']\n first_episode = self.get_convergence_episode()\n final_return = self.get_final_return()\n regret = np.sum(final_return - values[:first_episode])\n return regret","def evaluate(self) -> int:","def rank(self,others):\n self.__verify(others)\n \n #construct the n evaluation criteria + classes in an extensible way\n #evalFn = [AP,R] in the standard format -> column with as many rows as replicates\n numClasses = others[0].eval['APBCI'].shape[2]\n\n iouType = others[0].params.iouType\n if iouType in [\"segm\",\"bbox\"]:\n evalFunctions = [ \\\n lambda AP,R: np.nanmean(AP[:,:,:,0,-1],axis=(0,2)),\n lambda AP,R: np.nanmean(AP[0,:,:,0,-1],axis=(1)),\n lambda AP,R: np.nanmean(AP[5,:,:,0,-1],axis=(1)),\n lambda AP,R: np.nanmean(AP[:,:,:,1,-1],axis=(0,2)),\n lambda AP,R: np.nanmean(AP[:,:,:,2,-1],axis=(0,2)),\n lambda AP,R: np.nanmean(AP[:,:,:,3,-1],axis=(0,2)),\n lambda AP,R: np.nanmean(R[:,:,:,0,0],axis=(0,2)),\n lambda AP,R: np.nanmean(R[:,:,:,0,1],axis=(0,2)),\n lambda AP,R: np.nanmean(R[:,:,:,0,2],axis=(0,2)),\n lambda AP,R: np.nanmean(R[:,:,:,1,2],axis=(0,2)),\n lambda AP,R: np.nanmean(R[:,:,:,2,2],axis=(0,2)),\n lambda AP,R: np.nanmean(R[:,:,:,3,2],axis=(0,2))]\n\n evfAP = lambda c: (lambda AP,R: np.nanmean(AP[:,:,c,0,-1],axis=0))\n for i in range(numClasses):\n evalFunctions.append(evfAP(i))\n\n else:\n evalFunctions = [ \\\n lambda AP,R: np.nanmean(AP[:,:,:,0,0],axis=(0,2)),\n lambda AP,R: np.nanmean(AP[0,:,:,0,0],axis=(1)),\n lambda AP,R: np.nanmean(AP[5,:,:,0,0],axis=(1)),\n lambda AP,R: np.nanmean(AP[:,:,:,1,0],axis=(0,2)),\n lambda AP,R: np.nanmean(AP[:,:,:,2,0],axis=(0,2)),\n lambda AP,R: np.nanmean(R[:,:,:,0,0],axis=(0,2)),\n lambda AP,R: np.nanmean(R[0,:,:,0,0],axis=(1)),\n lambda AP,R: np.nanmean(R[5,:,:,0,0],axis=(1)),\n lambda AP,R: np.nanmean(R[:,:,:,1,0],axis=(0,2)),\n lambda AP,R: np.nanmean(R[:,:,:,2,0],axis=(0,2))]\n\n numReplicates = others[0].eval['APBCI'].shape[1]\n numInstances = len(others)\n numEvals = len(evalFunctions)\n\n replicateStats = np.zeros((numReplicates,numInstances))\n\n outperformMatrix = np.zeros((numInstances,numInstances,numEvals))\n rankCI = np.zeros((numInstances,3,numEvals))\n ranks = np.zeros((numInstances,numEvals,numReplicates))\n\n for evi,evf in enumerate(evalFunctions):\n for oi,o in enumerate(others):\n replicateStats[:,oi] = evf(o.eval['APBCI'],o.eval['RBCI'])\n\n for oi in range(len(others)):\n for oj in range(len(others)):\n outperformMatrix[oi,oj,evi] = np.mean(replicateStats[:,oi]>replicateStats[:,oj])\n\n for bci in range(numReplicates):\n ranks[:,evi,bci] = stats.rankdata(-replicateStats[bci,:],method='min')\n\n for oi in range(len(others)): \n rankCI[oi,0,evi] = np.mean(ranks[oi,evi,:])\n #use simple percentile method; the bias correction misbehaves \n rankCI[oi,1:,evi] = np.percentile(ranks[oi,evi,:],[100*(self.params.bootstrapAlpha/2),100*(1-self.params.bootstrapAlpha/2)])\n\n return rankCI, outperformMatrix, ranks","def rank():\n return 0","def fitness(self):\r\n history = self.history\r\n return sum(history) / len(history)","def worst_score(self):\r\n pass","def fitness(self):\n # TO BE DECIDED\n return 1","def getScore(self):\n tempscore = 1000 - 0.01*self.timeDriving \n tempscore -= 0.1*getDist(self.maze.checkpoints[self.checkpoint].getMid(),self.pos)\n tempscore += self.checkpoint *1000\n tempscore += self.laps * 1000 * len(self.maze.checkpoints)\n return tempscore","def e(self):\n\n ylow = self.e_min\n yhigh = self._e\n\n xlow = 0\n xhigh = self.anneal_max\n\n steep_mult = 8\n\n steepness = steep_mult / (xhigh - xlow)\n offset = (xhigh + xlow) / 2\n midpoint = yhigh - ylow\n\n x = np.clip(self.avg_score, 0, xhigh)\n x = steepness * (x - offset)\n e = ylow + midpoint / (1 + np.exp(x))\n return e","def completing_evalution(self, *args):\n self.calculate_risk_tol(*args)\n graham_picks = key_ratios(\"GrahamScore\", total_score)\n lynch_picks = key_ratios(\"LynchScore\", total_score)\n return (graham_picks, lynch_picks)","def evaluate(self, mode=0):\r\n winner = self.determine_winner()\r\n if winner:\r\n return winner * self.WIN_SCORE\r\n\r\n if mode == 1:\r\n return self.centre_priority_evaluate()\r\n elif mode == 2:\r\n return 0.5 * (self.centre_priority_evaluate() + self.piece_evaluate())\r\n else:\r\n return self.piece_evaluate()","def update_measure(self):\r\n lam = len(self.fit)\r\n idx = np.argsort(self.fit + self.fitre)\r\n ranks = np.argsort(idx).reshape((2, lam))\r\n rankDelta = ranks[0] - ranks[1] - np.sign(ranks[0] - ranks[1])\r\n\r\n # compute rank change limits using both ranks[0] and ranks[1]\r\n r = np.arange(1, 2 * lam) # 2 * lam - 2 elements\r\n limits = [0.5 * (Mh.prctile(np.abs(r - (ranks[0,i] + 1 - (ranks[0,i] > ranks[1,i]))),\r\n self.theta*50) +\r\n Mh.prctile(np.abs(r - (ranks[1,i] + 1 - (ranks[1,i] > ranks[0,i]))),\r\n self.theta*50))\r\n for i in self.idx]\r\n # compute measurement\r\n # max: 1 rankchange in 2*lambda is always fine\r\n s = np.abs(rankDelta[self.idx]) - Mh.amax(limits, 1) # lives roughly in 0..2*lambda\r\n self.noiseS += self.cum * (np.mean(s) - self.noiseS)\r\n return self.noiseS, s","def calculate_fitness(self, obs):\n feature_units = obs.observation.feature_units\n self.fitness = self.initial_enemy_hit_points + self.calculate_hitpoints(\n feature_units, _PLAYER_SELF) - self.calculate_hitpoints(feature_units, _PLAYER_ENEMY)\n max_fitness = self.max_fitness\n return self.fitness / max_fitness","def evaluate_fitness(self, pos):\n _, index = self.tree.query(pos)\n return 1 - (self.fitness_function[index // self.resolution][index % self.resolution] - self.min) / (self.max - self.min)","def calculate(self):\n avg = self.sum / self.n if self.n != 0 else 0\n self.running_avg.append(avg)\n return avg","def get_final_return(self):\n values = self.stats['return_stats']['episode_totals']\n _, y, (_, _) = self._moving_average(values, window=_ROLLING_WINDOW,\n p=_CONFIDENCE_LEVEL)\n return y[-1]","def calculate_fitness(self, obs):\n feature_units = obs.observation.feature_units\n self.fitness = self.initial_enemy_hit_points + self.calculate_hitpoints(\n feature_units, _PLAYER_SELF) - self.calculate_hitpoints(feature_units, _PLAYER_ENEMY)\n return self.fitness / 950","def calculate_fitness(self, obs):\n feature_units = obs.observation.feature_units\n total_damage_dealt = self.initial_enemy_hit_points - \\\n self.calculate_hitpoints(feature_units, _PLAYER_ENEMY)\n hit_point_loss = self.initial_self_hit_points - \\\n self.calculate_hitpoints(feature_units, _PLAYER_SELF)\n self.fitness = (total_damage_dealt - hit_point_loss) / \\\n self.initial_self_hit_points + 1\n return self.fitness","def get_estimated_rank(self):\n # At the moment the rank returned by this function is normally too high for either\n # my machine or the tensorly library to handle, therefore I have made it just return 1 for right now\n\n I = len(self.tdt[0])\n J = len(self.tdt[0][0])\n K = len(self.tdt)\n\n if I == 1 or J == 1 or K == 1:\n return 1\n elif I == J == K == 2:\n return 2\n elif I == J == 3 and K == 2:\n return 3\n elif I == 5 and J == K == 3:\n return 5\n elif I >= 2*J and K == 2:\n return 2*J\n elif 2*J > I > J and K ==2:\n return I\n elif I == J and K == 2:\n return I\n elif I >= J*K:\n return J*K\n elif J*K - J < I < J*K:\n return I\n elif I == J*K - I:\n return I\n else:\n print(I, J, K, \"did not have an exact estimation\")\n return min(I*J, I*K, J*K)","def calcFitness (self) :\n fitnessArray = [[8, 4, 2, 1],\n [16, 8, 4, 2],\n [32, 16, 8, 4],\n [64, 32, 16, 8]]\n # fitnessArray = [[160, 80, 5, 4],\n # [320, 40, 4, 3],\n # [640, 20, 3, 2],\n # [1280, 10, 2, 1]]\n fitness = 0\n for k in range(4) :\n for i in range (4) :\n fitness += self.grid[k,i] * fitnessArray[k][i]\n return (fitness / 100)","def percenter(rank, max_rank):\n\treturn 100 * (rank/(max_rank or 1))","def compute(self):\n return self.elbo_component / self.get_intervals_recorded()","def get_convergence_episode(self):\n values = self.stats['return_stats']['episode_totals']\n _, y, (y_lower, _) = self._moving_average(\n values, window=_ROLLING_WINDOW, p=_CONFIDENCE_LEVEL)\n # The convergence is established as the first time the average return\n # is above the lower bounds of the final return.\n first_episode = max(np.argmax(y >= y_lower[-1]), 1)\n return first_episode","def get_max_score(self):\r\n return sum(self.maxpoints.values())","def evaluate(self):\r\n\r\n fitness = 0\r\n\r\n for i in range(len(self.representation)):\r\n # Calculates full distance, including from last city\r\n # to first, to terminate the trip\r\n fitness += distance_matrix[self.representation[i - 1]][self.representation[i]]\r\n\r\n return int(fitness)","def validate_average_rank(self) -> float:\n logger.info('Average rank validation ...')\n\n args = self.args\n self.biencoder.eval()\n distributed_factor = self.distributed_factor\n\n if args.use_dict_input:\n data_iterator = self.get_dict_data_iterator(args.dev_psgs_file, args.dev_queries_file,\n args.dev_qrels_file, args.dev_trec_file,\n args.dev_batch_size,\n shuffle=False, split=False)\n else:\n data_iterator = self.get_data_iterator(args.dev_file, args.dev_batch_size, shuffle=False)\n\n sub_batch_size = args.val_av_rank_bsz\n sim_score_f = ClusterNllLoss.get_similarity_function()\n q_represenations = []\n ctx_represenations = []\n positive_idx_per_question = []\n\n num_hard_negatives = args.val_av_rank_hard_neg\n num_other_negatives = args.val_av_rank_other_neg\n\n log_result_step = args.log_batch_step\n\n for i, samples_batch in enumerate(data_iterator.iterate_data()):\n # samples += 1\n if len(q_represenations) > args.val_av_rank_max_qs / distributed_factor:\n break\n\n biencoder_input = ClusterBertEncoder.create_biencoder_input(samples_batch, self.tensorizer,\n True,\n max_query_length=args.query_sequence_length,\n max_doc_length=args.sequence_length,\n num_hard_negatives=num_hard_negatives,\n num_other_negatives=num_other_negatives,\n shuffle=False)\n total_ctxs = len(ctx_represenations)\n ctxs_ids = biencoder_input.context_ids\n ctxs_segments = biencoder_input.ctx_segments\n bsz = ctxs_ids.size(0)\n\n # split contexts batch into sub batches since it is supposed to be too large to be processed in one batch\n for j, batch_start in enumerate(range(0, bsz, sub_batch_size)):\n\n q_ids, q_segments = (biencoder_input.question_ids, biencoder_input.question_segments) if j == 0 \\\n else (None, None)\n\n if j == 0 and args.n_gpu > 1 and q_ids.size(0) == 1:\n # if we are in DP (but not in DDP) mode, all model input tensors should have batch size >1 or 0,\n # otherwise the other input tensors will be split but only the first split will be called\n continue\n\n ctx_ids_batch = ctxs_ids[batch_start:batch_start + sub_batch_size]\n ctx_seg_batch = ctxs_segments[batch_start:batch_start + sub_batch_size]\n\n q_attn_mask = self.tensorizer.get_attn_mask(q_ids)\n ctx_attn_mask = self.tensorizer.get_attn_mask(ctx_ids_batch)\n with torch.no_grad():\n q_dense, ctx_dense = self.biencoder(q_ids, q_segments, q_attn_mask, ctx_ids_batch, ctx_seg_batch,\n ctx_attn_mask)\n\n if q_dense is not None:\n q_represenations.extend(q_dense.cpu().split(1, dim=0))\n\n ctx_represenations.extend(ctx_dense.cpu().split(1, dim=0))\n\n batch_positive_idxs = biencoder_input.is_positive\n positive_idx_per_question.extend([total_ctxs + v for v in batch_positive_idxs])\n\n if (i + 1) % log_result_step == 0:\n logger.info('Av.rank validation: step %d, computed ctx_vectors %d, q_vectors %d', i,\n len(ctx_represenations), len(q_represenations))\n\n ctx_represenations = torch.cat(ctx_represenations, dim=0)\n q_represenations = torch.cat(q_represenations, dim=0)\n\n logger.info('Av.rank validation: total q_vectors size=%s', q_represenations.size())\n logger.info('Av.rank validation: total ctx_vectors size=%s', ctx_represenations.size())\n\n q_num = q_represenations.size(0)\n assert q_num == len(positive_idx_per_question)\n\n scores = sim_score_f(q_represenations, ctx_represenations)\n values, indices = torch.sort(scores, dim=1, descending=True)\n\n rank = 0\n for i, idx in enumerate(positive_idx_per_question):\n # aggregate the rank of the known gold passage in the sorted results for each question\n gold_idx = (indices[i] == idx).nonzero()\n rank += gold_idx.item()\n\n if distributed_factor > 1:\n # each node calcuated its own rank, exchange the information between node and calculate the \"global\" average rank\n # NOTE: the set of passages is still unique for every node\n eval_stats = all_gather_list([rank, q_num], max_size=1000)\n for i, item in enumerate(eval_stats):\n remote_rank, remote_q_num = item\n if i != args.local_rank:\n rank += remote_rank\n q_num += remote_q_num\n\n av_rank = float(rank / q_num)\n logger.info('Av.rank validation: average rank %s, total questions=%d', av_rank, q_num)\n return av_rank","def find_max_score_location(grid, shape):","def eval(self, x, y):\n return 100 * (1 - self._root.eval(x, y) / x.shape[0])","def _distance_last_evaluations(self):\n if self.X.shape[0] < 2:\n # less than 2 evaluations\n return np.inf\n return np.sqrt(np.sum((self.X[-1, :] - self.X[-2, :]) ** 2))","def score( self ):\r\n result = 0.0\r\n for rr in self.ee.getRsrcs( ):\r\n value = self.scoreRsrc( rr )\r\n result += value\r\n print( \"INFO: Value for the schedule is %s \" % ( rr, result ) )\r\n return( result )","def extinction_efficiency(self):\r\n n = np.arange(1, self.n + 1)\r\n return 2*np.sum((2*n+1)*np.real(self.a + self.b))/self.x**2","def get_max_score(location_list, grid, shape):","def __call__(self, X, fit, func, ask=None, args=()):\r\n self.evaluations_just_done = 0\r\n if not self.maxevals or self.lam_reeval == 0:\r\n return 1.0\r\n res = self.reeval(X, fit, func, ask, args)\r\n if not len(res):\r\n return 1.0\r\n self.update_measure()\r\n return self.treat()","def mbrae(self, benchmark: np.ndarray = None) -> float:\n return float(np.mean(self._bounded_relative_error(benchmark)))","def get_fitness(self) -> float:\n return self.fitness","def __calculate_estimation(self):\n estimation = 0.0\n for index_cluster in range(0, len(self.__clusters)):\n cluster = self.__clusters[index_cluster]\n index_medoid = self.__current[index_cluster]\n for index_point in cluster:\n estimation += euclidean_distance_square(\n self.__pointer_data[index_point],\n self.__pointer_data[index_medoid],\n )\n\n return estimation","def gmrae(self, benchmark: np.ndarray = None) -> float:\n return _geometric_mean(np.abs(self._relative_error(benchmark)))","def ml_result(self, var, e):\n\t\tdist = self.enumerate_ask(var, e)\n\t\treturn max(dist.items(), key=lambda x:x[1])[0]","def get_total_risk_level(self) -> int:\n origin = Point(0, 0)\n bounded_points = self.grid.get_bounded_points(origin, self.target)\n return sum(self.get_region(point) for point, _ in bounded_points)","def __calculate_estimation(self):\r\n estimation = 0.0\r\n for index_cluster in range(0, len(self.__clusters)):\r\n cluster = self.__clusters[index_cluster]\r\n index_medoid = self.__current[index_cluster]\r\n for index_point in cluster:\r\n estimation += euclidean_distance_square(self.__pointer_data[index_point], self.__pointer_data[index_medoid])\r\n\r\n return estimation","def balancedEE(leaf):\n # determine the exploitation and exploration value for the leaf\n leafExploitation = medianDominationCount(leaf)\n leafExploration = leaf.n / np.product(leaf.ub-leaf.lb)\n # determine the exploitation and exploration rank for each leafs (not efficient, need to outside)\n leafNodes = leaf.leafNodes()\n leafExploitationRank = 0\n leafExplorationRank = 0\n for _leaf in leafNodes:\n _exploitation = medianDominationCount(_leaf)\n _exploration = _leaf.n / np.product(_leaf.ub-_leaf.lb)\n leafExploitationRank += _exploitation < leafExploitation\n leafExplorationRank += _exploration < leafExploration\n # determine the number of total observed samples\n N = np.nanmax([sum([l.n for l in leaf.root.leafNodes()]),1]) \n # calculate the balanced value with adaptive weight on the exploration part\n balancedV = leafExploitationRank + leafExplorationRank\n print(leafExploitationRank, leafExplorationRank)\n return balancedV","def max_score(self):\n return self.points","def score(self) -> int:\n return self.function(self.x, self.y)","def evaluate(self, state):\n _n = len(state)\n _t = np.ceil(self.t_pct*_n)\n\n # Calculate head and tail values\n tail_0 = self.tail(0, state)\n head_1 = self.head(1, state)\n\n # Calculate R(X, T)\n if (tail_0 > _t and head_1 > _t):\n _r = _n\n else:\n _r = 0\n\n # Evaluate function\n fitness = max(tail_0, head_1) + _r\n self.num_evals += 1\n return fitness","def compute_average(self, error=None):\n\n nbjobs = len(self)\n if not nbjobs:\n return\n max_xsec = max(one.xsec for one in self)\n min_xsec = min(one.xsec for one in self)\n self.axsec = sum([one.axsec for one in self]) / nbjobs\n self.xsec = sum([one.xsec for one in self]) /nbjobs\n self.xerrc = sum([one.xerrc for one in self]) /nbjobs\n self.xerru = math.sqrt(sum([one.xerru**2 for one in self])) /nbjobs\n if error:\n self.xerrc = error\n self.xerru = error\n\n self.nevents = sum([one.nevents for one in self])\n self.nw = 0#sum([one.nw for one in self])\n self.maxit = 0#len(self.yerr_iter) # \n self.nunwgt = sum([one.nunwgt for one in self]) \n self.wgt = 0\n self.luminosity = sum([one.luminosity for one in self])\n self.ysec_iter = []\n self.yerr_iter = []\n self.th_maxwgt = 0.0\n self.th_nunwgt = 0 \n for result in self:\n self.ysec_iter+=result.ysec_iter\n self.yerr_iter+=result.yerr_iter\n self.yasec_iter += result.yasec_iter\n self.eff_iter += result.eff_iter\n self.maxwgt_iter += result.maxwgt_iter\n\n #check full consistency\n onefail = False\n for one in list(self):\n if one.xsec < (self.xsec - 25* one.xerru):\n if not onefail:\n logger.debug('multi run are inconsistent: %s < %s - 25* %s: assign error %s', one.xsec, self.xsec, one.xerru, error if error else max_xsec-min_xsec)\n onefail = True\n self.remove(one)\n if onefail:\n if error:\n return self.compute_average(error)\n else:\n return self.compute_average((max_xsec-min_xsec)/2.)","def compute_cost(self, r):\n self.r_max = 1\n return np.exp(1 / (np.power(r, 2) - np.power(self.r_max, 2))) if r < self.r_max else 0","def upper_bound(self) -> float:\n ...","def result(self):\r\n # TODO: how about xcurrent?\r\n return self.best.get() + (\r\n self.countevals, self.countiter, self.gp.pheno(self.mean), self.gp.scales * self.sigma * self.sigma_vec * self.dC**0.5)","def fitness(self):\n pass","def _area(self):\n self.area = 0.0\n for sail in self.sails:\n self.area += sail.area","def calculate_fitness(self):\n self.fitness = distMat[self.tour[-1]][self.tour[0]]\n for i in range(self.dimension):\n self.fitness += distMat[self.tour[i]][self.tour[i - 1]]","def _get_rank(self,fitness):\n # infact you can get the order or rank by only once sort.\n rank=fitness[:,0].argsort().argsort() # [n]\n return rank","def compute(self, pred: torch.Tensor, target: torch.Tensor) \\\n -> torch.Tensor:\n # If 2-dimensional, select the highest score in each row\n if len(target.size()) == 2:\n target = target.argmax(dim=1)\n\n ranked_scores = torch.argsort(pred, dim=1)[:, -self.top_k:]\n recalled = torch.sum((target.unsqueeze(1) == ranked_scores).float(), dim=1)\n return recalled.mean()","def max_evidence(self):\n self.A = np.linalg.inv(self.Sn)\n A_eigval = np.linalg.eigvals(self.A)\n gamma = 0\n for i in range(len(A_eigval)):\n gamma += A_eigval[i]/(self.alpha + A_eigval[i])\n new_alpha = gamma/(self.mn.T@self.mn)\n\n sum = 0\n for i in range(self.n):\n sum +=(self.t[i]-self.mn.T@self.design_matrix[i])**2\n new_beta = 1/((1/(self.n-gamma))*sum)\n\n return new_alpha, new_beta","def calculate_fitness(self, obs):\n feature_units = obs.observation.feature_units\n self.fitness = self.initial_enemy_hit_points + self.calculate_hitpoints(\n feature_units, _PLAYER_SELF) - self.calculate_hitpoints(feature_units, _PLAYER_ENEMY)\n return self.fitness","def score(self):\n xg, yg = self.goal\n xe, ye = self.empty_node()\n score = len(self.history) + 4*(xg + yg)\n if xg == 1:\n score -= 3\n if ye > 1:\n score += ye - 1\n dx = abs(xe - xg + 1)\n if xg and dx:\n score += dx\n return score","def get_rmax(self):\n return self.rmax","def centre_priority_evaluate(self):\r\n evaluation = 0\r\n for player in range(2):\r\n player_sign = player * 2 - 1\r\n for i in range(4):\r\n score = i + 1\r\n evaluation += player_sign * score * count_bits(self.bitboard_king[player] &\r\n self.CENTRE_PRIORITY_BITMASKS[i])\r\n evaluation += player_sign * score * count_bits(self.bitboard_pawns[player] &\r\n self.CENTRE_PRIORITY_BITMASKS[i])\r\n return evaluation","def _compute_results(self):\n self.Y_best = best_value(self.Y)\n self.x_opt = self.X[np.argmin(self.Y),:]\n self.fx_opt = np.min(self.Y)\n self.distance = self._compute_distance_betw_consecutive_x()","def argmaxY( self ):\n max = -1e30\n for i in range( 0, self.GetN() ):\n p = ( ROOT.Double(), ROOT.Double() )\n self.GetPoint( i, p[0], p[1] )\n if p[1] > max: max = p[1]\n return max","def betterEvaluationFunction(currentGameState: GameState):\n \"*** YOUR CODE HERE ***\"\n ghostScore : float = 1\n nearGhosts : float = 0\n foodScore : float = 0\n curScore = currentGameState.getScore()\n\n nearestFood = [(0, 0), float('inf')]\n pacPos = currentGameState.getPacmanPosition()\n foodPoss= currentGameState.getFood().asList()\n capsulePoss = currentGameState.getCapsules()\n ghostPoss = currentGameState.getGhostPositions()\n\n for foodPos in foodPoss:\n val = manhattanDistance(foodPos, pacPos)\n if val < nearestFood[1]:\n nearestFood[1] = val\n nearestFood[0] = foodPos\n foodScore = nearestFood[1]\n \n for gpos in ghostPoss:\n val = manhattanDistance(pacPos, gpos)\n if val <= 1:\n nearGhosts += (1-val)\n ghostScore += val\n\n return curScore - (1/ghostScore) + (1/foodScore) - nearGhosts - len(capsulePoss)","def area(self):\n return 0.5*np.abs(np.dot(self.x,np.roll(self.y,1))-np.dot(self.y,np.roll(self.x,1)))","def get_number_of_evaluation(self):\n return self.n_eval","def rae(self) -> float:\n return float(np.sum(self._ae()) / (np.sum(np.abs(self.true - np.mean(self.true))) + EPS))","def betterEvaluationFunction(currentGameState):\n \"*** YOUR CODE HERE ***\"\n\n if currentGameState.getFood().asList() == []: # Null list catch if there is no food on the board\n return currentGameState.getScore()\n else:\n return max([manhattanDistance(currentGameState.getPacmanPosition(),x) * -1\n for x in currentGameState.getFood().asList()]) + currentGameState.getScore()","def expected_return(self, n_step):\r\n value = 0\r\n n_experiences = 50\r\n for i in range(n_experiences):\r\n trajectory = self.domain_exploration(n_step)\r\n value += self.compute_j(trajectory)\r\n return value/n_experiences","def total_area(self):\n return numpy.prod([r[1] - r[0] for r in self.range_])","def getReward(self):\n# def evaluateFitness(self):\n fitness = 0.0\n distance = self.env.getDistance()\n speed = self.env.getSpeed()\n theta = self.env.getOrientation()\n\n ## implementation 101\n timeBonus = (self.maxTime - self.t)/self.maxTime\n alpha = 1.0/((1+distance)*(1+fabs(theta))*(speed+1));\n if distance < 0.5*self.env.init_distance :\n if(distance < self.env.vicinity_distance and\n abs(theta) < self.env.vicinity_orientation and\n speed < self.env.vicinity_speed ):\n fitness = 1 + timeBonus; \n else:\n fitness = alpha;\n else: fitness = 0\n self.lastFitness = fitness\n if fitness > self.bestFitness : \n self.bestFitness = fitness \n\n return fitness","def elution_score(self):\n return self.score","def eval_func(self, game, player):\n if game.is_loser(player):\n return float(\"-inf\")\n \n if game.is_winner(player):\n return float(\"inf\")\n \n own_moves = len(game.get_legal_moves(player))\n opp_moves = len(game.get_legal_moves(game.get_opponent(player)))\n if game.move_count < ((game.height * game.width)/2):\n return float(self.weights[0] * own_moves - \n self.weights[1] * opp_moves - \n self.weights[2] * __distance_from_center__(game, player))\n else:\n return float(self.weights[3] * own_moves - \n self.weights[4] * opp_moves - \n self.weights[5] * __distance_from_center__(game, player))","def calculate(self):\n\n tp = self.confusion_matrix.tp\n tn = self.confusion_matrix.tn\n fp = self.confusion_matrix.fp\n fn = self.confusion_matrix.fn\n\n n = tp + tn + fp + fn\n e1 = (fn * (fn + 2 * tp) / (tp + fn) + fp * (fp + 2 * tn) / (tn + fp)) / n\n e2 = (fp * (fp + 2 * tp) / (tp + fp) + fn * (fn + 2 * tn) / (tn + fn)) / n\n\n return min(e1, e2)","def custom_score_2(game, player):\n if game.is_loser(player):\n return float(\"-inf\")\n\n if game.is_winner(player):\n return float(\"inf\")\n\n own_moves = len(game.get_legal_moves(player))\n\n if game.move_count < 15:\n opp_moves = len(game.get_legal_moves(game.get_opponent(player)))\n return float(own_moves - opp_moves)\n\n delta = 0\n\n moves = game.get_legal_moves()\n initial_moves_count = len(moves)\n indexes = np.random.permutation(initial_moves_count)\n\n for i in range(0, min(4, initial_moves_count)):\n first_level = True\n simulation = game.copy()\n\n while True:\n moves = simulation.get_legal_moves()\n moves_count = len(moves)\n if moves_count == 0:\n if simulation.is_winner(player):\n delta = delta + 1\n else:\n delta = delta - 1\n break\n if first_level:\n selected_move = indexes[i]\n first_level = False\n else:\n selected_move = random.randint(0, moves_count - 1)\n\n simulation.apply_move(moves[selected_move])\n\n return float(own_moves + delta) #float(own_moves - opp_moves + 5 * delta)\n\n #return float(own_moves - opp_moves + free_area_score(game, player) - free_area_score(game, game.get_opponent(player)))","def mrr(self, ranking):\n return np.mean(ranking)","def right(self) -> float:\n points = self.get_adjusted_points()\n x_points = [point[0] for point in points]\n return max(x_points)","def mrae(self, benchmark: np.ndarray = None):\n return float(np.mean(np.abs(self._relative_error(benchmark))))","def _fitness(individual, X, y):\n yhat = individual.evaluate(X)\n return ((y - yhat) ** 2).sum()","def update(self,x): #update the estimate of rewards and number of esteps run\n\t\tself.N += 1\n\t\tself.estimate_mean = (1.0-1.0/self.N)*self.estimate_mean + (1.0/self.N)*x #recurence relation for averages","def calculate(self):\n\n return self._calculate_area(self.ground_truth, self.slice_number)","def leafScore(self) :\n return 0","def annealing(self, precision=None, print_steps=True):\n if self.max_temp is None:\n self.get_max_temp()\n state = self.individuals[0]\n self._temp = self.max_temp\n generation = 0\n mins = []\n while self.evaluations < self.eval_limit:\n equilibrium = False\n while not equilibrium:\n solution = self.get_neighbour(state.solution)\n value = self.get_solutions_value(solution)\n fitness = self.get_fitness(value)\n if self.acceptance_probability(state.value, value, self._temp) > np.random.random():\n equilibrium = True\n if self.evaluations % 100 == 0:\n mins += [self.best_individual.value]\n if self.evaluations > self.eval_limit:\n break\n state.solution = solution\n state.value = value\n state.fitness = fitness\n self.update_temperature()\n if state.fitness > self.best_individual.fitness:\n self.best_individual = copy.deepcopy(state)\n if print_steps:\n print(\"Generation \", generation, \"; Evaluations: \", self.evaluations,\n \"; Temperature: \", self._temp, \"; \", state)\n if precision is not None and self.solution_precision(precision):\n break\n generation += 1\n return self.best_individual, mins","def __call__(self, ret):\n \n ce = None\n for reli in itertools.product([False, True], repeat = len(ret)):\n\n mean, var = self.learner.updated_prediction({ i : 1 if r else -1 for i, r in zip(ret, reli) }, self.learner.candidates, cov_mode = 'diag')\n \n prob_irrel = scipy.stats.norm.cdf(0, mean, np.sqrt(var))\n prob_rel = 1. - prob_irrel\n cur_ce = np.sum(prob_irrel * np.log(prob_irrel + self.eps) + prob_rel * np.log(prob_rel + self.eps))\n \n if (ce is None) or (cur_ce < ce):\n ce = cur_ce\n \n return ce","def getEll(self):\n\n\t\tellx = fftengine.fftfreq(self.data.shape[0])*2.0*np.pi / self.resolution.to(u.rad).value\n\t\telly = fftengine.rfftfreq(self.data.shape[0])*2.0*np.pi / self.resolution.to(u.rad).value\n\t\treturn np.sqrt(ellx[:,None]**2 + elly[None,:]**2)","def cronbach_alpha(self) -> float:\n itemscores = np.stack([self.true, self.predicted])\n itemvars = itemscores.var(axis=1, ddof=1)\n tscores = itemscores.sum(axis=0)\n nitems = len(itemscores)\n return float(nitems / (nitems - 1.) * (1 - itemvars.sum() / tscores.var(ddof=1)))","def evaltr(x_solution): \n \n large = 10.0**30\n pred = np.zeros(cfg.ntrain)\n e0 = 0.0 # mean of observed values\n y=0.0\n for i in range(cfg.ntrain): # Computation of correct piece\n e0 += cfg.a_unscaled[i][-1]\n pind = 0\n ipbest = 0\n pbest = -large # for max\n \n for j1 in range(cfg.nomax):\n ipmin=pind\n pmin=large # for min\n for _ in range(cfg.jk[j1]):\n piece=x_solution[(pind+1)*cfg.nfea-1] \n for j3 in range(cfg.nfea-1): #\n piece += x_solution[pind*cfg.nfea+j3]*cfg.a_unscaled[i][j3]\n if piece < pmin:\n ipmin = pind\n pmin = piece\n pind += 1 \n \n if pmin > pbest:\n ipbest = ipmin\n pbest = pmin\n \n pred[i] = x_solution[(ipbest+1)*cfg.nfea-1] # Computation of prediction\n for j1 in range(cfg.nfea-1):\n pred[i] += x_solution[ipbest*cfg.nfea+j1]*cfg.a_unscaled[i][j1]\n y += pred[i]\n \n y = y/cfg.ntrain \n e0 = e0/cfg.ntrain\n \n # Computation of indices\n rmse = 0.0\n mae = 0.0\n e1 = 0.0\n for i in range(cfg.ntrain):\n rmse += (pred[i]-cfg.a_unscaled[i][-1])**2\n mae += np.abs(pred[i]-cfg.a_unscaled[i][-1]) \n e1 += (cfg.a_unscaled[i][-1] - e0)**2\n ce = 1.0 - rmse/e1 \n rmse = np.sqrt(rmse/cfg.ntrain)\n mae = mae/cfg.ntrain \n\n if cfg.ntrain > 1:\n sx=0.0\n sy=0.0\n rcor=0.0\n for i in range(cfg.ntrain):\n sx += (pred[i]-y)**2\n sy += (cfg.a_unscaled[i][-1]-e0)**2 \n rcor += (pred[i]-y) * (cfg.a_unscaled[i][-1]-e0) \n\n r = rcor/np.sqrt(sx*sy)\n \n return rmse,mae,ce,r","def get_binary_rf_area(self):\n\n if self.thr is None:\n raise LookupError('To th area, the receptive field should be thresholded!!')\n\n alt_step = abs(np.mean(np.diff(self.altPos).astype(np.float)))\n azi_step = abs(np.mean(np.diff(self.aziPos).astype(np.float)))\n\n return len(self.weights) * alt_step * azi_step","def rank(self):\n rank = 0\n rho = self.array_form[:]\n n = self.size - 1\n size = n + 1\n psize = int(ifac(n))\n for j in xrange(size - 1):\n rank += rho[j]*psize\n for i in xrange(j + 1, size):\n if rho[i] > rho[j]:\n rho[i] -= 1\n psize //= n\n n -= 1\n return rank","def computeX0 (self):\n self.m_x0 = np.sum(self.m_arr, axis=0)\n \"\"\" Subtract the point for which f(x) is max \"\"\"\n self.m_x0 -= self.m_arr[self.m_sorted[-1], :]\n \"\"\" Compute average \"\"\"\n self.m_x0 /= self.m_dim\n _debugPrint(\"Centroid: %s\" %self.m_x0)","def scoreEvaluationFunction(currentGameState, index):\n if currentGameState.isLose():\n return -float(\"inf\")\n \n if currentGameState.isWin():\n return float(\"inf\")\n\n # ghost function\n def ghostScore(gameState):\n if len(gameState.getGhostStates()) == 0:\n return 0\n score = 0\n newGhostPos = gameState.getGhostPositions()\n newGhostStates = gameState.getGhostStates()\n for pacman in gameState.getPacmanPositions():\n for i in range(len(newGhostPos)):\n if newGhostStates[i].scaredTimer > 0:\n score += ((max(4 - euclidDistance(pacman, newGhostPos[i]), 0)) ** 2)\n else:\n score -= ((max(4 - euclidDistance(pacman, newGhostPos[i]), 0)) ** 2)\n if manhattanDistance(pacman, newGhostPos[i]) < 2:\n return -float(\"inf\")\n \n return score\n \n # food function\n def foodScore(gameState):\n score = 0\n for pacman in gameState.getPacmanPositions():\n pacScore = []\n for foodCoord in gameState.getFood().asList():\n pacScore.append(euclidDistance(foodCoord, pacman))\n score = min(pacScore)\n \n score = score * -2\n score -= len(gameState.getFood().asList()) * 15\n return score\n\n\n totalScore = currentGameState.getScore()[0]\n totalScore += ghostScore(currentGameState) \n totalScore += foodScore(currentGameState)\n return totalScore","def betterEvaluationFunction(currentGameState):\n \"*** YOUR CODE HERE ***\"\n \n newPos = currentGameState.getPacmanPosition()\n newFood = currentGameState.getFood()\n newGhostStates = currentGameState.getGhostStates()\n GhostLocs = currentGameState.getGhostPositions()\n newScaredTimes = [ghostState.scaredTimer for ghostState in newGhostStates]\n capsuleLocations = currentGameState.getCapsules()\n Hueristic = 0.0\n \n if currentGameState.isWin():\n return 10000\n if currentGameState.isLose():\n return -10000\n\n FoodDistances = []\n foodLocations = newFood.asList()\n for food in foodLocations:\n FoodDistances.append(manhattanDistance(newPos,food))\n closestFood = min(FoodDistances)\n closestFoodLocation = foodLocations[FoodDistances.index(closestFood)]\n\n GhostsToMe = []\n GhostsToFood = []\n for ghost in GhostLocs:\n GhostsToMe.append(manhattanDistance(newPos,ghost))\n GhostsToFood.append(manhattanDistance(closestFoodLocation,ghost))\n closestGhostToMe = min(GhostsToMe)\n closestGhostToClosestFood = min(GhostsToFood)\n closestGhostLocation = GhostLocs[GhostsToMe.index(closestGhostToMe)]\n\n if newPos in currentGameState.getCapsules():\n capsule = 100\n else: \n capsule = 0\n \n if closestGhostToClosestFood < closestFood:\n if closestGhostToMe > 4:\n Hueristic = (1.0/(closestFood+1.0))*20 - len(foodLocations)*50 - (1/closestGhostToMe)*5\n else:\n Hueristic = (-1/closestGhostToMe)*50\n else:\n Hueristic = (1.0/(closestFood+1.0))*20 - len(foodLocations)*50 - (1/closestGhostToMe)*5\n return Hueristic","def get_score(location, grid, shape):","def Ethanol(self) -> int:\n return self.raw_measure()[1]","def area(self) -> float:\n raise NotImplementedError","def est(self):\n self.min_key = 'average'\n if self.iteration >= 3:\n # Poll estimated times from different algorithms\n lin_est_time = self.lin_est_no_outlier()\n lin_no_est_time = self.lin_est_outlier()\n average_est_time = self.avg_est_no_outlier()\n average_no_est_time = self.avg_est_outlier()\n\n # Record discrepancies between the estimated delta t's and the\n # actual delta t.\n if self.iteration > 8:\n self.err_rec()\n\n # Review the choice of algorithm after every 15 jobs and switch\n # to a better one if necessary.\n if not self.override:\n if self.iteration % 5 == 0 and self.iteration > 8:\n self.least_err()\n\n # Return the time associated with the algorithm that offers the\n # highest accuracy.\n if self.min_key is 'average':\n est_time = average_est_time\n if self.min_key is 'average_no':\n est_time = average_no_est_time\n elif self.min_key is 'lin':\n est_time = lin_est_time\n elif self.min_key is 'lin_no':\n est_time = lin_no_est_time\n\n est_time = int(round(est_time))\n else:\n est_time = 0\n\n # Bypasses negative estimates occasionally generated by the linear\n # algorithm and huge numbers occasionally generated by the positive\n # exponential algorithm. 3.2e7 is a little over a year.\n if est_time < 0:\n est_time = self.est_time\n if not self.override:\n self.min_key = 'average'\n else:\n self.est_time = est_time\n\n return est_time"],"string":"[\n \"def _overlap_energy(self, this, that):\\n if not this.overlaps(that):\\n return 0.0\\n\\n return min(10.0 / this.rank, 10.0 / that.rank)\",\n \"def _ext_area(axis, ranking, row_ind_at_col, col_ind_at_row):\\n if axis == 0:\\n indexes_lists = _np.array([col_ind_at_row[:], row_ind_at_col[:]])\\n else:\\n indexes_lists = _np.array([row_ind_at_col[:], col_ind_at_row[:]])\\n if len(ranking) != len(indexes_lists[0]):\\n print ('Dimensions do not match')\\n return\\n \\n # Counting the already extincted columns. They are the ones whose list of\\n # associated row indexes is empty. In that case the extinction counter is\\n # increased and a -1 is added to the indexes list.\\n ext_nodes = 0\\n for c in range(len(indexes_lists[1])):\\n if len(indexes_lists[1][c]) == 0:\\n ext_nodes += 1\\n indexes_lists[1][c] = _np.append(indexes_lists[1][c], -1)\\n \\n ext_curve = [ext_nodes]\\n # Iteration over the ranked nodes to remove, r\\n for r in ranking[:-1]:\\n # Iter over the connected nodes in the other layer, r\\n for c in indexes_lists[0][r]:\\n # Removing the ranked node from the neighbours of c\\n indexes_lists[1][c] = indexes_lists[1][c][indexes_lists[1][c] != r]\\n \\n # If the neighbours of c is empty, then c gets extincted\\n if len(indexes_lists[1][c]) == 0:\\n ext_nodes += 1\\n indexes_lists[1][c] = _np.append(indexes_lists[1][c], -1)\\n \\n ext_curve.append(ext_nodes)\\n \\n # Returning the area below the extinction curve\\n return sum(ext_curve) / float(len(indexes_lists[0]) * len(indexes_lists[1]))\",\n \"def _calculate_fitness(self):\\n pass\",\n \"def lowest_rank_approx(A,e):\",\n \"def __rank__(self) -> int:\",\n \"def _calc_neighborhood_func(self, curr_it: int, mode: str) -> float:\\n return decreasing_rate(\\n self.radius_max_,\\n self.radius_min_,\\n iteration_max=self.max_iterations_,\\n iteration=curr_it,\\n mode=mode,\\n )\",\n \"def overall_reduction(self):\\n return 84\",\n \"def get_absolute_regret(self):\\n values = self.stats['return_stats']['episode_totals']\\n first_episode = self.get_convergence_episode()\\n final_return = self.get_final_return()\\n regret = np.sum(final_return - values[:first_episode])\\n return regret\",\n \"def evaluate(self) -> int:\",\n \"def rank(self,others):\\n self.__verify(others)\\n \\n #construct the n evaluation criteria + classes in an extensible way\\n #evalFn = [AP,R] in the standard format -> column with as many rows as replicates\\n numClasses = others[0].eval['APBCI'].shape[2]\\n\\n iouType = others[0].params.iouType\\n if iouType in [\\\"segm\\\",\\\"bbox\\\"]:\\n evalFunctions = [ \\\\\\n lambda AP,R: np.nanmean(AP[:,:,:,0,-1],axis=(0,2)),\\n lambda AP,R: np.nanmean(AP[0,:,:,0,-1],axis=(1)),\\n lambda AP,R: np.nanmean(AP[5,:,:,0,-1],axis=(1)),\\n lambda AP,R: np.nanmean(AP[:,:,:,1,-1],axis=(0,2)),\\n lambda AP,R: np.nanmean(AP[:,:,:,2,-1],axis=(0,2)),\\n lambda AP,R: np.nanmean(AP[:,:,:,3,-1],axis=(0,2)),\\n lambda AP,R: np.nanmean(R[:,:,:,0,0],axis=(0,2)),\\n lambda AP,R: np.nanmean(R[:,:,:,0,1],axis=(0,2)),\\n lambda AP,R: np.nanmean(R[:,:,:,0,2],axis=(0,2)),\\n lambda AP,R: np.nanmean(R[:,:,:,1,2],axis=(0,2)),\\n lambda AP,R: np.nanmean(R[:,:,:,2,2],axis=(0,2)),\\n lambda AP,R: np.nanmean(R[:,:,:,3,2],axis=(0,2))]\\n\\n evfAP = lambda c: (lambda AP,R: np.nanmean(AP[:,:,c,0,-1],axis=0))\\n for i in range(numClasses):\\n evalFunctions.append(evfAP(i))\\n\\n else:\\n evalFunctions = [ \\\\\\n lambda AP,R: np.nanmean(AP[:,:,:,0,0],axis=(0,2)),\\n lambda AP,R: np.nanmean(AP[0,:,:,0,0],axis=(1)),\\n lambda AP,R: np.nanmean(AP[5,:,:,0,0],axis=(1)),\\n lambda AP,R: np.nanmean(AP[:,:,:,1,0],axis=(0,2)),\\n lambda AP,R: np.nanmean(AP[:,:,:,2,0],axis=(0,2)),\\n lambda AP,R: np.nanmean(R[:,:,:,0,0],axis=(0,2)),\\n lambda AP,R: np.nanmean(R[0,:,:,0,0],axis=(1)),\\n lambda AP,R: np.nanmean(R[5,:,:,0,0],axis=(1)),\\n lambda AP,R: np.nanmean(R[:,:,:,1,0],axis=(0,2)),\\n lambda AP,R: np.nanmean(R[:,:,:,2,0],axis=(0,2))]\\n\\n numReplicates = others[0].eval['APBCI'].shape[1]\\n numInstances = len(others)\\n numEvals = len(evalFunctions)\\n\\n replicateStats = np.zeros((numReplicates,numInstances))\\n\\n outperformMatrix = np.zeros((numInstances,numInstances,numEvals))\\n rankCI = np.zeros((numInstances,3,numEvals))\\n ranks = np.zeros((numInstances,numEvals,numReplicates))\\n\\n for evi,evf in enumerate(evalFunctions):\\n for oi,o in enumerate(others):\\n replicateStats[:,oi] = evf(o.eval['APBCI'],o.eval['RBCI'])\\n\\n for oi in range(len(others)):\\n for oj in range(len(others)):\\n outperformMatrix[oi,oj,evi] = np.mean(replicateStats[:,oi]>replicateStats[:,oj])\\n\\n for bci in range(numReplicates):\\n ranks[:,evi,bci] = stats.rankdata(-replicateStats[bci,:],method='min')\\n\\n for oi in range(len(others)): \\n rankCI[oi,0,evi] = np.mean(ranks[oi,evi,:])\\n #use simple percentile method; the bias correction misbehaves \\n rankCI[oi,1:,evi] = np.percentile(ranks[oi,evi,:],[100*(self.params.bootstrapAlpha/2),100*(1-self.params.bootstrapAlpha/2)])\\n\\n return rankCI, outperformMatrix, ranks\",\n \"def rank():\\n return 0\",\n \"def fitness(self):\\r\\n history = self.history\\r\\n return sum(history) / len(history)\",\n \"def worst_score(self):\\r\\n pass\",\n \"def fitness(self):\\n # TO BE DECIDED\\n return 1\",\n \"def getScore(self):\\n tempscore = 1000 - 0.01*self.timeDriving \\n tempscore -= 0.1*getDist(self.maze.checkpoints[self.checkpoint].getMid(),self.pos)\\n tempscore += self.checkpoint *1000\\n tempscore += self.laps * 1000 * len(self.maze.checkpoints)\\n return tempscore\",\n \"def e(self):\\n\\n ylow = self.e_min\\n yhigh = self._e\\n\\n xlow = 0\\n xhigh = self.anneal_max\\n\\n steep_mult = 8\\n\\n steepness = steep_mult / (xhigh - xlow)\\n offset = (xhigh + xlow) / 2\\n midpoint = yhigh - ylow\\n\\n x = np.clip(self.avg_score, 0, xhigh)\\n x = steepness * (x - offset)\\n e = ylow + midpoint / (1 + np.exp(x))\\n return e\",\n \"def completing_evalution(self, *args):\\n self.calculate_risk_tol(*args)\\n graham_picks = key_ratios(\\\"GrahamScore\\\", total_score)\\n lynch_picks = key_ratios(\\\"LynchScore\\\", total_score)\\n return (graham_picks, lynch_picks)\",\n \"def evaluate(self, mode=0):\\r\\n winner = self.determine_winner()\\r\\n if winner:\\r\\n return winner * self.WIN_SCORE\\r\\n\\r\\n if mode == 1:\\r\\n return self.centre_priority_evaluate()\\r\\n elif mode == 2:\\r\\n return 0.5 * (self.centre_priority_evaluate() + self.piece_evaluate())\\r\\n else:\\r\\n return self.piece_evaluate()\",\n \"def update_measure(self):\\r\\n lam = len(self.fit)\\r\\n idx = np.argsort(self.fit + self.fitre)\\r\\n ranks = np.argsort(idx).reshape((2, lam))\\r\\n rankDelta = ranks[0] - ranks[1] - np.sign(ranks[0] - ranks[1])\\r\\n\\r\\n # compute rank change limits using both ranks[0] and ranks[1]\\r\\n r = np.arange(1, 2 * lam) # 2 * lam - 2 elements\\r\\n limits = [0.5 * (Mh.prctile(np.abs(r - (ranks[0,i] + 1 - (ranks[0,i] > ranks[1,i]))),\\r\\n self.theta*50) +\\r\\n Mh.prctile(np.abs(r - (ranks[1,i] + 1 - (ranks[1,i] > ranks[0,i]))),\\r\\n self.theta*50))\\r\\n for i in self.idx]\\r\\n # compute measurement\\r\\n # max: 1 rankchange in 2*lambda is always fine\\r\\n s = np.abs(rankDelta[self.idx]) - Mh.amax(limits, 1) # lives roughly in 0..2*lambda\\r\\n self.noiseS += self.cum * (np.mean(s) - self.noiseS)\\r\\n return self.noiseS, s\",\n \"def calculate_fitness(self, obs):\\n feature_units = obs.observation.feature_units\\n self.fitness = self.initial_enemy_hit_points + self.calculate_hitpoints(\\n feature_units, _PLAYER_SELF) - self.calculate_hitpoints(feature_units, _PLAYER_ENEMY)\\n max_fitness = self.max_fitness\\n return self.fitness / max_fitness\",\n \"def evaluate_fitness(self, pos):\\n _, index = self.tree.query(pos)\\n return 1 - (self.fitness_function[index // self.resolution][index % self.resolution] - self.min) / (self.max - self.min)\",\n \"def calculate(self):\\n avg = self.sum / self.n if self.n != 0 else 0\\n self.running_avg.append(avg)\\n return avg\",\n \"def get_final_return(self):\\n values = self.stats['return_stats']['episode_totals']\\n _, y, (_, _) = self._moving_average(values, window=_ROLLING_WINDOW,\\n p=_CONFIDENCE_LEVEL)\\n return y[-1]\",\n \"def calculate_fitness(self, obs):\\n feature_units = obs.observation.feature_units\\n self.fitness = self.initial_enemy_hit_points + self.calculate_hitpoints(\\n feature_units, _PLAYER_SELF) - self.calculate_hitpoints(feature_units, _PLAYER_ENEMY)\\n return self.fitness / 950\",\n \"def calculate_fitness(self, obs):\\n feature_units = obs.observation.feature_units\\n total_damage_dealt = self.initial_enemy_hit_points - \\\\\\n self.calculate_hitpoints(feature_units, _PLAYER_ENEMY)\\n hit_point_loss = self.initial_self_hit_points - \\\\\\n self.calculate_hitpoints(feature_units, _PLAYER_SELF)\\n self.fitness = (total_damage_dealt - hit_point_loss) / \\\\\\n self.initial_self_hit_points + 1\\n return self.fitness\",\n \"def get_estimated_rank(self):\\n # At the moment the rank returned by this function is normally too high for either\\n # my machine or the tensorly library to handle, therefore I have made it just return 1 for right now\\n\\n I = len(self.tdt[0])\\n J = len(self.tdt[0][0])\\n K = len(self.tdt)\\n\\n if I == 1 or J == 1 or K == 1:\\n return 1\\n elif I == J == K == 2:\\n return 2\\n elif I == J == 3 and K == 2:\\n return 3\\n elif I == 5 and J == K == 3:\\n return 5\\n elif I >= 2*J and K == 2:\\n return 2*J\\n elif 2*J > I > J and K ==2:\\n return I\\n elif I == J and K == 2:\\n return I\\n elif I >= J*K:\\n return J*K\\n elif J*K - J < I < J*K:\\n return I\\n elif I == J*K - I:\\n return I\\n else:\\n print(I, J, K, \\\"did not have an exact estimation\\\")\\n return min(I*J, I*K, J*K)\",\n \"def calcFitness (self) :\\n fitnessArray = [[8, 4, 2, 1],\\n [16, 8, 4, 2],\\n [32, 16, 8, 4],\\n [64, 32, 16, 8]]\\n # fitnessArray = [[160, 80, 5, 4],\\n # [320, 40, 4, 3],\\n # [640, 20, 3, 2],\\n # [1280, 10, 2, 1]]\\n fitness = 0\\n for k in range(4) :\\n for i in range (4) :\\n fitness += self.grid[k,i] * fitnessArray[k][i]\\n return (fitness / 100)\",\n \"def percenter(rank, max_rank):\\n\\treturn 100 * (rank/(max_rank or 1))\",\n \"def compute(self):\\n return self.elbo_component / self.get_intervals_recorded()\",\n \"def get_convergence_episode(self):\\n values = self.stats['return_stats']['episode_totals']\\n _, y, (y_lower, _) = self._moving_average(\\n values, window=_ROLLING_WINDOW, p=_CONFIDENCE_LEVEL)\\n # The convergence is established as the first time the average return\\n # is above the lower bounds of the final return.\\n first_episode = max(np.argmax(y >= y_lower[-1]), 1)\\n return first_episode\",\n \"def get_max_score(self):\\r\\n return sum(self.maxpoints.values())\",\n \"def evaluate(self):\\r\\n\\r\\n fitness = 0\\r\\n\\r\\n for i in range(len(self.representation)):\\r\\n # Calculates full distance, including from last city\\r\\n # to first, to terminate the trip\\r\\n fitness += distance_matrix[self.representation[i - 1]][self.representation[i]]\\r\\n\\r\\n return int(fitness)\",\n \"def validate_average_rank(self) -> float:\\n logger.info('Average rank validation ...')\\n\\n args = self.args\\n self.biencoder.eval()\\n distributed_factor = self.distributed_factor\\n\\n if args.use_dict_input:\\n data_iterator = self.get_dict_data_iterator(args.dev_psgs_file, args.dev_queries_file,\\n args.dev_qrels_file, args.dev_trec_file,\\n args.dev_batch_size,\\n shuffle=False, split=False)\\n else:\\n data_iterator = self.get_data_iterator(args.dev_file, args.dev_batch_size, shuffle=False)\\n\\n sub_batch_size = args.val_av_rank_bsz\\n sim_score_f = ClusterNllLoss.get_similarity_function()\\n q_represenations = []\\n ctx_represenations = []\\n positive_idx_per_question = []\\n\\n num_hard_negatives = args.val_av_rank_hard_neg\\n num_other_negatives = args.val_av_rank_other_neg\\n\\n log_result_step = args.log_batch_step\\n\\n for i, samples_batch in enumerate(data_iterator.iterate_data()):\\n # samples += 1\\n if len(q_represenations) > args.val_av_rank_max_qs / distributed_factor:\\n break\\n\\n biencoder_input = ClusterBertEncoder.create_biencoder_input(samples_batch, self.tensorizer,\\n True,\\n max_query_length=args.query_sequence_length,\\n max_doc_length=args.sequence_length,\\n num_hard_negatives=num_hard_negatives,\\n num_other_negatives=num_other_negatives,\\n shuffle=False)\\n total_ctxs = len(ctx_represenations)\\n ctxs_ids = biencoder_input.context_ids\\n ctxs_segments = biencoder_input.ctx_segments\\n bsz = ctxs_ids.size(0)\\n\\n # split contexts batch into sub batches since it is supposed to be too large to be processed in one batch\\n for j, batch_start in enumerate(range(0, bsz, sub_batch_size)):\\n\\n q_ids, q_segments = (biencoder_input.question_ids, biencoder_input.question_segments) if j == 0 \\\\\\n else (None, None)\\n\\n if j == 0 and args.n_gpu > 1 and q_ids.size(0) == 1:\\n # if we are in DP (but not in DDP) mode, all model input tensors should have batch size >1 or 0,\\n # otherwise the other input tensors will be split but only the first split will be called\\n continue\\n\\n ctx_ids_batch = ctxs_ids[batch_start:batch_start + sub_batch_size]\\n ctx_seg_batch = ctxs_segments[batch_start:batch_start + sub_batch_size]\\n\\n q_attn_mask = self.tensorizer.get_attn_mask(q_ids)\\n ctx_attn_mask = self.tensorizer.get_attn_mask(ctx_ids_batch)\\n with torch.no_grad():\\n q_dense, ctx_dense = self.biencoder(q_ids, q_segments, q_attn_mask, ctx_ids_batch, ctx_seg_batch,\\n ctx_attn_mask)\\n\\n if q_dense is not None:\\n q_represenations.extend(q_dense.cpu().split(1, dim=0))\\n\\n ctx_represenations.extend(ctx_dense.cpu().split(1, dim=0))\\n\\n batch_positive_idxs = biencoder_input.is_positive\\n positive_idx_per_question.extend([total_ctxs + v for v in batch_positive_idxs])\\n\\n if (i + 1) % log_result_step == 0:\\n logger.info('Av.rank validation: step %d, computed ctx_vectors %d, q_vectors %d', i,\\n len(ctx_represenations), len(q_represenations))\\n\\n ctx_represenations = torch.cat(ctx_represenations, dim=0)\\n q_represenations = torch.cat(q_represenations, dim=0)\\n\\n logger.info('Av.rank validation: total q_vectors size=%s', q_represenations.size())\\n logger.info('Av.rank validation: total ctx_vectors size=%s', ctx_represenations.size())\\n\\n q_num = q_represenations.size(0)\\n assert q_num == len(positive_idx_per_question)\\n\\n scores = sim_score_f(q_represenations, ctx_represenations)\\n values, indices = torch.sort(scores, dim=1, descending=True)\\n\\n rank = 0\\n for i, idx in enumerate(positive_idx_per_question):\\n # aggregate the rank of the known gold passage in the sorted results for each question\\n gold_idx = (indices[i] == idx).nonzero()\\n rank += gold_idx.item()\\n\\n if distributed_factor > 1:\\n # each node calcuated its own rank, exchange the information between node and calculate the \\\"global\\\" average rank\\n # NOTE: the set of passages is still unique for every node\\n eval_stats = all_gather_list([rank, q_num], max_size=1000)\\n for i, item in enumerate(eval_stats):\\n remote_rank, remote_q_num = item\\n if i != args.local_rank:\\n rank += remote_rank\\n q_num += remote_q_num\\n\\n av_rank = float(rank / q_num)\\n logger.info('Av.rank validation: average rank %s, total questions=%d', av_rank, q_num)\\n return av_rank\",\n \"def find_max_score_location(grid, shape):\",\n \"def eval(self, x, y):\\n return 100 * (1 - self._root.eval(x, y) / x.shape[0])\",\n \"def _distance_last_evaluations(self):\\n if self.X.shape[0] < 2:\\n # less than 2 evaluations\\n return np.inf\\n return np.sqrt(np.sum((self.X[-1, :] - self.X[-2, :]) ** 2))\",\n \"def score( self ):\\r\\n result = 0.0\\r\\n for rr in self.ee.getRsrcs( ):\\r\\n value = self.scoreRsrc( rr )\\r\\n result += value\\r\\n print( \\\"INFO: Value for the schedule is %s \\\" % ( rr, result ) )\\r\\n return( result )\",\n \"def extinction_efficiency(self):\\r\\n n = np.arange(1, self.n + 1)\\r\\n return 2*np.sum((2*n+1)*np.real(self.a + self.b))/self.x**2\",\n \"def get_max_score(location_list, grid, shape):\",\n \"def __call__(self, X, fit, func, ask=None, args=()):\\r\\n self.evaluations_just_done = 0\\r\\n if not self.maxevals or self.lam_reeval == 0:\\r\\n return 1.0\\r\\n res = self.reeval(X, fit, func, ask, args)\\r\\n if not len(res):\\r\\n return 1.0\\r\\n self.update_measure()\\r\\n return self.treat()\",\n \"def mbrae(self, benchmark: np.ndarray = None) -> float:\\n return float(np.mean(self._bounded_relative_error(benchmark)))\",\n \"def get_fitness(self) -> float:\\n return self.fitness\",\n \"def __calculate_estimation(self):\\n estimation = 0.0\\n for index_cluster in range(0, len(self.__clusters)):\\n cluster = self.__clusters[index_cluster]\\n index_medoid = self.__current[index_cluster]\\n for index_point in cluster:\\n estimation += euclidean_distance_square(\\n self.__pointer_data[index_point],\\n self.__pointer_data[index_medoid],\\n )\\n\\n return estimation\",\n \"def gmrae(self, benchmark: np.ndarray = None) -> float:\\n return _geometric_mean(np.abs(self._relative_error(benchmark)))\",\n \"def ml_result(self, var, e):\\n\\t\\tdist = self.enumerate_ask(var, e)\\n\\t\\treturn max(dist.items(), key=lambda x:x[1])[0]\",\n \"def get_total_risk_level(self) -> int:\\n origin = Point(0, 0)\\n bounded_points = self.grid.get_bounded_points(origin, self.target)\\n return sum(self.get_region(point) for point, _ in bounded_points)\",\n \"def __calculate_estimation(self):\\r\\n estimation = 0.0\\r\\n for index_cluster in range(0, len(self.__clusters)):\\r\\n cluster = self.__clusters[index_cluster]\\r\\n index_medoid = self.__current[index_cluster]\\r\\n for index_point in cluster:\\r\\n estimation += euclidean_distance_square(self.__pointer_data[index_point], self.__pointer_data[index_medoid])\\r\\n\\r\\n return estimation\",\n \"def balancedEE(leaf):\\n # determine the exploitation and exploration value for the leaf\\n leafExploitation = medianDominationCount(leaf)\\n leafExploration = leaf.n / np.product(leaf.ub-leaf.lb)\\n # determine the exploitation and exploration rank for each leafs (not efficient, need to outside)\\n leafNodes = leaf.leafNodes()\\n leafExploitationRank = 0\\n leafExplorationRank = 0\\n for _leaf in leafNodes:\\n _exploitation = medianDominationCount(_leaf)\\n _exploration = _leaf.n / np.product(_leaf.ub-_leaf.lb)\\n leafExploitationRank += _exploitation < leafExploitation\\n leafExplorationRank += _exploration < leafExploration\\n # determine the number of total observed samples\\n N = np.nanmax([sum([l.n for l in leaf.root.leafNodes()]),1]) \\n # calculate the balanced value with adaptive weight on the exploration part\\n balancedV = leafExploitationRank + leafExplorationRank\\n print(leafExploitationRank, leafExplorationRank)\\n return balancedV\",\n \"def max_score(self):\\n return self.points\",\n \"def score(self) -> int:\\n return self.function(self.x, self.y)\",\n \"def evaluate(self, state):\\n _n = len(state)\\n _t = np.ceil(self.t_pct*_n)\\n\\n # Calculate head and tail values\\n tail_0 = self.tail(0, state)\\n head_1 = self.head(1, state)\\n\\n # Calculate R(X, T)\\n if (tail_0 > _t and head_1 > _t):\\n _r = _n\\n else:\\n _r = 0\\n\\n # Evaluate function\\n fitness = max(tail_0, head_1) + _r\\n self.num_evals += 1\\n return fitness\",\n \"def compute_average(self, error=None):\\n\\n nbjobs = len(self)\\n if not nbjobs:\\n return\\n max_xsec = max(one.xsec for one in self)\\n min_xsec = min(one.xsec for one in self)\\n self.axsec = sum([one.axsec for one in self]) / nbjobs\\n self.xsec = sum([one.xsec for one in self]) /nbjobs\\n self.xerrc = sum([one.xerrc for one in self]) /nbjobs\\n self.xerru = math.sqrt(sum([one.xerru**2 for one in self])) /nbjobs\\n if error:\\n self.xerrc = error\\n self.xerru = error\\n\\n self.nevents = sum([one.nevents for one in self])\\n self.nw = 0#sum([one.nw for one in self])\\n self.maxit = 0#len(self.yerr_iter) # \\n self.nunwgt = sum([one.nunwgt for one in self]) \\n self.wgt = 0\\n self.luminosity = sum([one.luminosity for one in self])\\n self.ysec_iter = []\\n self.yerr_iter = []\\n self.th_maxwgt = 0.0\\n self.th_nunwgt = 0 \\n for result in self:\\n self.ysec_iter+=result.ysec_iter\\n self.yerr_iter+=result.yerr_iter\\n self.yasec_iter += result.yasec_iter\\n self.eff_iter += result.eff_iter\\n self.maxwgt_iter += result.maxwgt_iter\\n\\n #check full consistency\\n onefail = False\\n for one in list(self):\\n if one.xsec < (self.xsec - 25* one.xerru):\\n if not onefail:\\n logger.debug('multi run are inconsistent: %s < %s - 25* %s: assign error %s', one.xsec, self.xsec, one.xerru, error if error else max_xsec-min_xsec)\\n onefail = True\\n self.remove(one)\\n if onefail:\\n if error:\\n return self.compute_average(error)\\n else:\\n return self.compute_average((max_xsec-min_xsec)/2.)\",\n \"def compute_cost(self, r):\\n self.r_max = 1\\n return np.exp(1 / (np.power(r, 2) - np.power(self.r_max, 2))) if r < self.r_max else 0\",\n \"def upper_bound(self) -> float:\\n ...\",\n \"def result(self):\\r\\n # TODO: how about xcurrent?\\r\\n return self.best.get() + (\\r\\n self.countevals, self.countiter, self.gp.pheno(self.mean), self.gp.scales * self.sigma * self.sigma_vec * self.dC**0.5)\",\n \"def fitness(self):\\n pass\",\n \"def _area(self):\\n self.area = 0.0\\n for sail in self.sails:\\n self.area += sail.area\",\n \"def calculate_fitness(self):\\n self.fitness = distMat[self.tour[-1]][self.tour[0]]\\n for i in range(self.dimension):\\n self.fitness += distMat[self.tour[i]][self.tour[i - 1]]\",\n \"def _get_rank(self,fitness):\\n # infact you can get the order or rank by only once sort.\\n rank=fitness[:,0].argsort().argsort() # [n]\\n return rank\",\n \"def compute(self, pred: torch.Tensor, target: torch.Tensor) \\\\\\n -> torch.Tensor:\\n # If 2-dimensional, select the highest score in each row\\n if len(target.size()) == 2:\\n target = target.argmax(dim=1)\\n\\n ranked_scores = torch.argsort(pred, dim=1)[:, -self.top_k:]\\n recalled = torch.sum((target.unsqueeze(1) == ranked_scores).float(), dim=1)\\n return recalled.mean()\",\n \"def max_evidence(self):\\n self.A = np.linalg.inv(self.Sn)\\n A_eigval = np.linalg.eigvals(self.A)\\n gamma = 0\\n for i in range(len(A_eigval)):\\n gamma += A_eigval[i]/(self.alpha + A_eigval[i])\\n new_alpha = gamma/(self.mn.T@self.mn)\\n\\n sum = 0\\n for i in range(self.n):\\n sum +=(self.t[i]-self.mn.T@self.design_matrix[i])**2\\n new_beta = 1/((1/(self.n-gamma))*sum)\\n\\n return new_alpha, new_beta\",\n \"def calculate_fitness(self, obs):\\n feature_units = obs.observation.feature_units\\n self.fitness = self.initial_enemy_hit_points + self.calculate_hitpoints(\\n feature_units, _PLAYER_SELF) - self.calculate_hitpoints(feature_units, _PLAYER_ENEMY)\\n return self.fitness\",\n \"def score(self):\\n xg, yg = self.goal\\n xe, ye = self.empty_node()\\n score = len(self.history) + 4*(xg + yg)\\n if xg == 1:\\n score -= 3\\n if ye > 1:\\n score += ye - 1\\n dx = abs(xe - xg + 1)\\n if xg and dx:\\n score += dx\\n return score\",\n \"def get_rmax(self):\\n return self.rmax\",\n \"def centre_priority_evaluate(self):\\r\\n evaluation = 0\\r\\n for player in range(2):\\r\\n player_sign = player * 2 - 1\\r\\n for i in range(4):\\r\\n score = i + 1\\r\\n evaluation += player_sign * score * count_bits(self.bitboard_king[player] &\\r\\n self.CENTRE_PRIORITY_BITMASKS[i])\\r\\n evaluation += player_sign * score * count_bits(self.bitboard_pawns[player] &\\r\\n self.CENTRE_PRIORITY_BITMASKS[i])\\r\\n return evaluation\",\n \"def _compute_results(self):\\n self.Y_best = best_value(self.Y)\\n self.x_opt = self.X[np.argmin(self.Y),:]\\n self.fx_opt = np.min(self.Y)\\n self.distance = self._compute_distance_betw_consecutive_x()\",\n \"def argmaxY( self ):\\n max = -1e30\\n for i in range( 0, self.GetN() ):\\n p = ( ROOT.Double(), ROOT.Double() )\\n self.GetPoint( i, p[0], p[1] )\\n if p[1] > max: max = p[1]\\n return max\",\n \"def betterEvaluationFunction(currentGameState: GameState):\\n \\\"*** YOUR CODE HERE ***\\\"\\n ghostScore : float = 1\\n nearGhosts : float = 0\\n foodScore : float = 0\\n curScore = currentGameState.getScore()\\n\\n nearestFood = [(0, 0), float('inf')]\\n pacPos = currentGameState.getPacmanPosition()\\n foodPoss= currentGameState.getFood().asList()\\n capsulePoss = currentGameState.getCapsules()\\n ghostPoss = currentGameState.getGhostPositions()\\n\\n for foodPos in foodPoss:\\n val = manhattanDistance(foodPos, pacPos)\\n if val < nearestFood[1]:\\n nearestFood[1] = val\\n nearestFood[0] = foodPos\\n foodScore = nearestFood[1]\\n \\n for gpos in ghostPoss:\\n val = manhattanDistance(pacPos, gpos)\\n if val <= 1:\\n nearGhosts += (1-val)\\n ghostScore += val\\n\\n return curScore - (1/ghostScore) + (1/foodScore) - nearGhosts - len(capsulePoss)\",\n \"def area(self):\\n return 0.5*np.abs(np.dot(self.x,np.roll(self.y,1))-np.dot(self.y,np.roll(self.x,1)))\",\n \"def get_number_of_evaluation(self):\\n return self.n_eval\",\n \"def rae(self) -> float:\\n return float(np.sum(self._ae()) / (np.sum(np.abs(self.true - np.mean(self.true))) + EPS))\",\n \"def betterEvaluationFunction(currentGameState):\\n \\\"*** YOUR CODE HERE ***\\\"\\n\\n if currentGameState.getFood().asList() == []: # Null list catch if there is no food on the board\\n return currentGameState.getScore()\\n else:\\n return max([manhattanDistance(currentGameState.getPacmanPosition(),x) * -1\\n for x in currentGameState.getFood().asList()]) + currentGameState.getScore()\",\n \"def expected_return(self, n_step):\\r\\n value = 0\\r\\n n_experiences = 50\\r\\n for i in range(n_experiences):\\r\\n trajectory = self.domain_exploration(n_step)\\r\\n value += self.compute_j(trajectory)\\r\\n return value/n_experiences\",\n \"def total_area(self):\\n return numpy.prod([r[1] - r[0] for r in self.range_])\",\n \"def getReward(self):\\n# def evaluateFitness(self):\\n fitness = 0.0\\n distance = self.env.getDistance()\\n speed = self.env.getSpeed()\\n theta = self.env.getOrientation()\\n\\n ## implementation 101\\n timeBonus = (self.maxTime - self.t)/self.maxTime\\n alpha = 1.0/((1+distance)*(1+fabs(theta))*(speed+1));\\n if distance < 0.5*self.env.init_distance :\\n if(distance < self.env.vicinity_distance and\\n abs(theta) < self.env.vicinity_orientation and\\n speed < self.env.vicinity_speed ):\\n fitness = 1 + timeBonus; \\n else:\\n fitness = alpha;\\n else: fitness = 0\\n self.lastFitness = fitness\\n if fitness > self.bestFitness : \\n self.bestFitness = fitness \\n\\n return fitness\",\n \"def elution_score(self):\\n return self.score\",\n \"def eval_func(self, game, player):\\n if game.is_loser(player):\\n return float(\\\"-inf\\\")\\n \\n if game.is_winner(player):\\n return float(\\\"inf\\\")\\n \\n own_moves = len(game.get_legal_moves(player))\\n opp_moves = len(game.get_legal_moves(game.get_opponent(player)))\\n if game.move_count < ((game.height * game.width)/2):\\n return float(self.weights[0] * own_moves - \\n self.weights[1] * opp_moves - \\n self.weights[2] * __distance_from_center__(game, player))\\n else:\\n return float(self.weights[3] * own_moves - \\n self.weights[4] * opp_moves - \\n self.weights[5] * __distance_from_center__(game, player))\",\n \"def calculate(self):\\n\\n tp = self.confusion_matrix.tp\\n tn = self.confusion_matrix.tn\\n fp = self.confusion_matrix.fp\\n fn = self.confusion_matrix.fn\\n\\n n = tp + tn + fp + fn\\n e1 = (fn * (fn + 2 * tp) / (tp + fn) + fp * (fp + 2 * tn) / (tn + fp)) / n\\n e2 = (fp * (fp + 2 * tp) / (tp + fp) + fn * (fn + 2 * tn) / (tn + fn)) / n\\n\\n return min(e1, e2)\",\n \"def custom_score_2(game, player):\\n if game.is_loser(player):\\n return float(\\\"-inf\\\")\\n\\n if game.is_winner(player):\\n return float(\\\"inf\\\")\\n\\n own_moves = len(game.get_legal_moves(player))\\n\\n if game.move_count < 15:\\n opp_moves = len(game.get_legal_moves(game.get_opponent(player)))\\n return float(own_moves - opp_moves)\\n\\n delta = 0\\n\\n moves = game.get_legal_moves()\\n initial_moves_count = len(moves)\\n indexes = np.random.permutation(initial_moves_count)\\n\\n for i in range(0, min(4, initial_moves_count)):\\n first_level = True\\n simulation = game.copy()\\n\\n while True:\\n moves = simulation.get_legal_moves()\\n moves_count = len(moves)\\n if moves_count == 0:\\n if simulation.is_winner(player):\\n delta = delta + 1\\n else:\\n delta = delta - 1\\n break\\n if first_level:\\n selected_move = indexes[i]\\n first_level = False\\n else:\\n selected_move = random.randint(0, moves_count - 1)\\n\\n simulation.apply_move(moves[selected_move])\\n\\n return float(own_moves + delta) #float(own_moves - opp_moves + 5 * delta)\\n\\n #return float(own_moves - opp_moves + free_area_score(game, player) - free_area_score(game, game.get_opponent(player)))\",\n \"def mrr(self, ranking):\\n return np.mean(ranking)\",\n \"def right(self) -> float:\\n points = self.get_adjusted_points()\\n x_points = [point[0] for point in points]\\n return max(x_points)\",\n \"def mrae(self, benchmark: np.ndarray = None):\\n return float(np.mean(np.abs(self._relative_error(benchmark))))\",\n \"def _fitness(individual, X, y):\\n yhat = individual.evaluate(X)\\n return ((y - yhat) ** 2).sum()\",\n \"def update(self,x): #update the estimate of rewards and number of esteps run\\n\\t\\tself.N += 1\\n\\t\\tself.estimate_mean = (1.0-1.0/self.N)*self.estimate_mean + (1.0/self.N)*x #recurence relation for averages\",\n \"def calculate(self):\\n\\n return self._calculate_area(self.ground_truth, self.slice_number)\",\n \"def leafScore(self) :\\n return 0\",\n \"def annealing(self, precision=None, print_steps=True):\\n if self.max_temp is None:\\n self.get_max_temp()\\n state = self.individuals[0]\\n self._temp = self.max_temp\\n generation = 0\\n mins = []\\n while self.evaluations < self.eval_limit:\\n equilibrium = False\\n while not equilibrium:\\n solution = self.get_neighbour(state.solution)\\n value = self.get_solutions_value(solution)\\n fitness = self.get_fitness(value)\\n if self.acceptance_probability(state.value, value, self._temp) > np.random.random():\\n equilibrium = True\\n if self.evaluations % 100 == 0:\\n mins += [self.best_individual.value]\\n if self.evaluations > self.eval_limit:\\n break\\n state.solution = solution\\n state.value = value\\n state.fitness = fitness\\n self.update_temperature()\\n if state.fitness > self.best_individual.fitness:\\n self.best_individual = copy.deepcopy(state)\\n if print_steps:\\n print(\\\"Generation \\\", generation, \\\"; Evaluations: \\\", self.evaluations,\\n \\\"; Temperature: \\\", self._temp, \\\"; \\\", state)\\n if precision is not None and self.solution_precision(precision):\\n break\\n generation += 1\\n return self.best_individual, mins\",\n \"def __call__(self, ret):\\n \\n ce = None\\n for reli in itertools.product([False, True], repeat = len(ret)):\\n\\n mean, var = self.learner.updated_prediction({ i : 1 if r else -1 for i, r in zip(ret, reli) }, self.learner.candidates, cov_mode = 'diag')\\n \\n prob_irrel = scipy.stats.norm.cdf(0, mean, np.sqrt(var))\\n prob_rel = 1. - prob_irrel\\n cur_ce = np.sum(prob_irrel * np.log(prob_irrel + self.eps) + prob_rel * np.log(prob_rel + self.eps))\\n \\n if (ce is None) or (cur_ce < ce):\\n ce = cur_ce\\n \\n return ce\",\n \"def getEll(self):\\n\\n\\t\\tellx = fftengine.fftfreq(self.data.shape[0])*2.0*np.pi / self.resolution.to(u.rad).value\\n\\t\\telly = fftengine.rfftfreq(self.data.shape[0])*2.0*np.pi / self.resolution.to(u.rad).value\\n\\t\\treturn np.sqrt(ellx[:,None]**2 + elly[None,:]**2)\",\n \"def cronbach_alpha(self) -> float:\\n itemscores = np.stack([self.true, self.predicted])\\n itemvars = itemscores.var(axis=1, ddof=1)\\n tscores = itemscores.sum(axis=0)\\n nitems = len(itemscores)\\n return float(nitems / (nitems - 1.) * (1 - itemvars.sum() / tscores.var(ddof=1)))\",\n \"def evaltr(x_solution): \\n \\n large = 10.0**30\\n pred = np.zeros(cfg.ntrain)\\n e0 = 0.0 # mean of observed values\\n y=0.0\\n for i in range(cfg.ntrain): # Computation of correct piece\\n e0 += cfg.a_unscaled[i][-1]\\n pind = 0\\n ipbest = 0\\n pbest = -large # for max\\n \\n for j1 in range(cfg.nomax):\\n ipmin=pind\\n pmin=large # for min\\n for _ in range(cfg.jk[j1]):\\n piece=x_solution[(pind+1)*cfg.nfea-1] \\n for j3 in range(cfg.nfea-1): #\\n piece += x_solution[pind*cfg.nfea+j3]*cfg.a_unscaled[i][j3]\\n if piece < pmin:\\n ipmin = pind\\n pmin = piece\\n pind += 1 \\n \\n if pmin > pbest:\\n ipbest = ipmin\\n pbest = pmin\\n \\n pred[i] = x_solution[(ipbest+1)*cfg.nfea-1] # Computation of prediction\\n for j1 in range(cfg.nfea-1):\\n pred[i] += x_solution[ipbest*cfg.nfea+j1]*cfg.a_unscaled[i][j1]\\n y += pred[i]\\n \\n y = y/cfg.ntrain \\n e0 = e0/cfg.ntrain\\n \\n # Computation of indices\\n rmse = 0.0\\n mae = 0.0\\n e1 = 0.0\\n for i in range(cfg.ntrain):\\n rmse += (pred[i]-cfg.a_unscaled[i][-1])**2\\n mae += np.abs(pred[i]-cfg.a_unscaled[i][-1]) \\n e1 += (cfg.a_unscaled[i][-1] - e0)**2\\n ce = 1.0 - rmse/e1 \\n rmse = np.sqrt(rmse/cfg.ntrain)\\n mae = mae/cfg.ntrain \\n\\n if cfg.ntrain > 1:\\n sx=0.0\\n sy=0.0\\n rcor=0.0\\n for i in range(cfg.ntrain):\\n sx += (pred[i]-y)**2\\n sy += (cfg.a_unscaled[i][-1]-e0)**2 \\n rcor += (pred[i]-y) * (cfg.a_unscaled[i][-1]-e0) \\n\\n r = rcor/np.sqrt(sx*sy)\\n \\n return rmse,mae,ce,r\",\n \"def get_binary_rf_area(self):\\n\\n if self.thr is None:\\n raise LookupError('To th area, the receptive field should be thresholded!!')\\n\\n alt_step = abs(np.mean(np.diff(self.altPos).astype(np.float)))\\n azi_step = abs(np.mean(np.diff(self.aziPos).astype(np.float)))\\n\\n return len(self.weights) * alt_step * azi_step\",\n \"def rank(self):\\n rank = 0\\n rho = self.array_form[:]\\n n = self.size - 1\\n size = n + 1\\n psize = int(ifac(n))\\n for j in xrange(size - 1):\\n rank += rho[j]*psize\\n for i in xrange(j + 1, size):\\n if rho[i] > rho[j]:\\n rho[i] -= 1\\n psize //= n\\n n -= 1\\n return rank\",\n \"def computeX0 (self):\\n self.m_x0 = np.sum(self.m_arr, axis=0)\\n \\\"\\\"\\\" Subtract the point for which f(x) is max \\\"\\\"\\\"\\n self.m_x0 -= self.m_arr[self.m_sorted[-1], :]\\n \\\"\\\"\\\" Compute average \\\"\\\"\\\"\\n self.m_x0 /= self.m_dim\\n _debugPrint(\\\"Centroid: %s\\\" %self.m_x0)\",\n \"def scoreEvaluationFunction(currentGameState, index):\\n if currentGameState.isLose():\\n return -float(\\\"inf\\\")\\n \\n if currentGameState.isWin():\\n return float(\\\"inf\\\")\\n\\n # ghost function\\n def ghostScore(gameState):\\n if len(gameState.getGhostStates()) == 0:\\n return 0\\n score = 0\\n newGhostPos = gameState.getGhostPositions()\\n newGhostStates = gameState.getGhostStates()\\n for pacman in gameState.getPacmanPositions():\\n for i in range(len(newGhostPos)):\\n if newGhostStates[i].scaredTimer > 0:\\n score += ((max(4 - euclidDistance(pacman, newGhostPos[i]), 0)) ** 2)\\n else:\\n score -= ((max(4 - euclidDistance(pacman, newGhostPos[i]), 0)) ** 2)\\n if manhattanDistance(pacman, newGhostPos[i]) < 2:\\n return -float(\\\"inf\\\")\\n \\n return score\\n \\n # food function\\n def foodScore(gameState):\\n score = 0\\n for pacman in gameState.getPacmanPositions():\\n pacScore = []\\n for foodCoord in gameState.getFood().asList():\\n pacScore.append(euclidDistance(foodCoord, pacman))\\n score = min(pacScore)\\n \\n score = score * -2\\n score -= len(gameState.getFood().asList()) * 15\\n return score\\n\\n\\n totalScore = currentGameState.getScore()[0]\\n totalScore += ghostScore(currentGameState) \\n totalScore += foodScore(currentGameState)\\n return totalScore\",\n \"def betterEvaluationFunction(currentGameState):\\n \\\"*** YOUR CODE HERE ***\\\"\\n \\n newPos = currentGameState.getPacmanPosition()\\n newFood = currentGameState.getFood()\\n newGhostStates = currentGameState.getGhostStates()\\n GhostLocs = currentGameState.getGhostPositions()\\n newScaredTimes = [ghostState.scaredTimer for ghostState in newGhostStates]\\n capsuleLocations = currentGameState.getCapsules()\\n Hueristic = 0.0\\n \\n if currentGameState.isWin():\\n return 10000\\n if currentGameState.isLose():\\n return -10000\\n\\n FoodDistances = []\\n foodLocations = newFood.asList()\\n for food in foodLocations:\\n FoodDistances.append(manhattanDistance(newPos,food))\\n closestFood = min(FoodDistances)\\n closestFoodLocation = foodLocations[FoodDistances.index(closestFood)]\\n\\n GhostsToMe = []\\n GhostsToFood = []\\n for ghost in GhostLocs:\\n GhostsToMe.append(manhattanDistance(newPos,ghost))\\n GhostsToFood.append(manhattanDistance(closestFoodLocation,ghost))\\n closestGhostToMe = min(GhostsToMe)\\n closestGhostToClosestFood = min(GhostsToFood)\\n closestGhostLocation = GhostLocs[GhostsToMe.index(closestGhostToMe)]\\n\\n if newPos in currentGameState.getCapsules():\\n capsule = 100\\n else: \\n capsule = 0\\n \\n if closestGhostToClosestFood < closestFood:\\n if closestGhostToMe > 4:\\n Hueristic = (1.0/(closestFood+1.0))*20 - len(foodLocations)*50 - (1/closestGhostToMe)*5\\n else:\\n Hueristic = (-1/closestGhostToMe)*50\\n else:\\n Hueristic = (1.0/(closestFood+1.0))*20 - len(foodLocations)*50 - (1/closestGhostToMe)*5\\n return Hueristic\",\n \"def get_score(location, grid, shape):\",\n \"def Ethanol(self) -> int:\\n return self.raw_measure()[1]\",\n \"def area(self) -> float:\\n raise NotImplementedError\",\n \"def est(self):\\n self.min_key = 'average'\\n if self.iteration >= 3:\\n # Poll estimated times from different algorithms\\n lin_est_time = self.lin_est_no_outlier()\\n lin_no_est_time = self.lin_est_outlier()\\n average_est_time = self.avg_est_no_outlier()\\n average_no_est_time = self.avg_est_outlier()\\n\\n # Record discrepancies between the estimated delta t's and the\\n # actual delta t.\\n if self.iteration > 8:\\n self.err_rec()\\n\\n # Review the choice of algorithm after every 15 jobs and switch\\n # to a better one if necessary.\\n if not self.override:\\n if self.iteration % 5 == 0 and self.iteration > 8:\\n self.least_err()\\n\\n # Return the time associated with the algorithm that offers the\\n # highest accuracy.\\n if self.min_key is 'average':\\n est_time = average_est_time\\n if self.min_key is 'average_no':\\n est_time = average_no_est_time\\n elif self.min_key is 'lin':\\n est_time = lin_est_time\\n elif self.min_key is 'lin_no':\\n est_time = lin_no_est_time\\n\\n est_time = int(round(est_time))\\n else:\\n est_time = 0\\n\\n # Bypasses negative estimates occasionally generated by the linear\\n # algorithm and huge numbers occasionally generated by the positive\\n # exponential algorithm. 3.2e7 is a little over a year.\\n if est_time < 0:\\n est_time = self.est_time\\n if not self.override:\\n self.min_key = 'average'\\n else:\\n self.est_time = est_time\\n\\n return est_time\"\n]","isConversational":false},"negative_scores":{"kind":"list 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\"0.5964079\",\n \"0.59478605\",\n \"0.5941593\",\n \"0.59182143\",\n \"0.5786603\",\n \"0.5778234\",\n \"0.57550836\",\n \"0.5740219\",\n \"0.5736216\",\n \"0.5681183\",\n \"0.5669513\",\n \"0.56531614\",\n \"0.56521064\",\n \"0.56079173\",\n \"0.55998206\",\n \"0.5591468\",\n \"0.5575538\",\n \"0.55385804\",\n \"0.5520699\",\n \"0.55159914\",\n \"0.551502\",\n \"0.55144674\",\n \"0.55141747\",\n \"0.5506697\",\n \"0.5498125\",\n \"0.54953456\",\n \"0.5491692\",\n \"0.5471989\",\n \"0.54712856\",\n \"0.5467187\",\n \"0.54644704\",\n \"0.5449209\",\n \"0.54318404\",\n \"0.5425441\",\n \"0.5414447\",\n \"0.5412684\",\n \"0.54107934\",\n \"0.54100734\",\n \"0.5402503\",\n \"0.5381907\",\n \"0.5380527\",\n \"0.5378784\",\n \"0.53712845\",\n \"0.5366297\",\n \"0.53628856\",\n \"0.5362532\",\n \"0.53565776\",\n \"0.53540426\",\n \"0.5349062\",\n \"0.53462297\",\n \"0.5344771\",\n \"0.5343933\",\n \"0.5343559\",\n \"0.53429276\",\n \"0.5337479\",\n \"0.5329334\",\n \"0.532878\",\n \"0.53220147\",\n \"0.53214103\",\n \"0.5320175\",\n \"0.5319016\",\n \"0.53178084\",\n \"0.5310642\",\n \"0.53013146\",\n \"0.52985865\",\n \"0.52962685\",\n \"0.5292655\",\n \"0.52854383\",\n \"0.5277143\",\n \"0.5268681\",\n \"0.52644706\",\n \"0.526178\",\n \"0.52615976\",\n \"0.526148\",\n \"0.52610755\",\n \"0.5259959\",\n \"0.52592653\",\n \"0.5259028\",\n \"0.5255163\",\n \"0.525285\",\n \"0.52527106\",\n \"0.5251454\",\n \"0.52497226\",\n \"0.5247169\",\n \"0.524496\",\n \"0.52440226\",\n \"0.52420336\",\n \"0.5240281\",\n \"0.5239418\",\n \"0.52391195\",\n \"0.5239108\",\n \"0.5237286\",\n \"0.5237132\",\n \"0.5231005\",\n \"0.5227787\",\n \"0.52260303\",\n \"0.5223432\",\n \"0.5212448\",\n \"0.5210709\",\n 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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 |
|---|---|---|---|---|---|---|
Private. Resets the traversal state of all nodes in preparation for a new traversal | def _reset_traversal_state(self):
for n in self.nodes.values():
n.reset_traversal_state() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def reset_tree(self):\n self.root = None\n self.action = None\n self.dist_probability = None",
"def reset_visited(self):\n self.__visited = False",
"def reset_graph(self):\n self.nodes = {}\n self.add_node(self.initial_state)\n self.add_node(self.final_state)",
... | [
"0.74285185",
"0.73662233",
"0.7337941",
"0.73155934",
"0.7201365",
"0.71484256",
"0.7138489",
"0.71265846",
"0.71129096",
"0.71065027",
"0.7018088",
"0.6959817",
"0.6881637",
"0.6879142",
"0.681779",
"0.6811828",
"0.6801169",
"0.67853063",
"0.67844963",
"0.6764829",
"0.67444... | 0.8812322 | 0 |
Perform a depthfirst traversal of the entire graph. The supplied visitor_function callable is called twice per | def depth_first_traversal(self, visitor_function=None):
self._reset_traversal_state()
self.time = 0
result = False
for n in self.nodes.values():
if NodeColor.WHITE == n.color:
stack = collections.deque()
stack.append(n)
while... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def traverse_depth_first(self, fn):\n queue = deque([self.root])\n while len(queue) > 0:\n node = queue.popleft()\n fn(node)\n queue.extendleft(reversed(node.children))",
"def _traverse_region_graph(root: Region, fun: Callable[[Region], None]) -> None:\n visited_... | [
"0.69131184",
"0.6532276",
"0.63960713",
"0.6223167",
"0.61571103",
"0.59844995",
"0.59705156",
"0.59194154",
"0.59114933",
"0.5801044",
"0.5797877",
"0.5770219",
"0.5733677",
"0.5723791",
"0.5710354",
"0.5705172",
"0.5666923",
"0.5657166",
"0.5648695",
"0.5614636",
"0.561195... | 0.69286025 | 0 |
Perform BFS, starting at the specified node. | def breadth_first_traversal(self, start_node, visitor_function=None, max_depth=None):
self._reset_traversal_state()
if isinstance(start_node, str):
start_node = self.nodes[start_node]
if not isinstance(start_node, ProcessNode):
raise TypeError('Expect start_node to eith... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def bfs(maze, current_node):\n q = collections.deque()\n\n q.append(current_node)\n\n while len(q) > 0:\n current_node = q.popleft()\n maze[current_node.row][current_node.cell] = 1\n yield maze\n\n for neighbour in get_neighbours(maze, current_node):\n if maze[neighb... | [
"0.712657",
"0.7019322",
"0.6977231",
"0.6904166",
"0.6873453",
"0.6778046",
"0.6764516",
"0.67611027",
"0.66524756",
"0.65571046",
"0.6479184",
"0.64744705",
"0.6444245",
"0.6440059",
"0.64398944",
"0.64012533",
"0.6394898",
"0.6370323",
"0.6339818",
"0.63252306",
"0.6316992... | 0.59082425 | 67 |
This lengthy, irritating piece of code returns the slice idcs for subcube (i,j) in a decomposition of an original map of shape (2HD_res[0],2HD_res[1]) with chunks of 2LD_res per sides, together with buffers[0],[1] buffers pixel on each side, fixed by the periodicity condition of the HD map. Nothing very subtle for the ... | def get_slices_chk_N(N, LD_res, HD_res, buffers, inverse=False):
assert len(LD_res) == 2 and len(HD_res) == 2
if np.all(LD_res == HD_res):
assert N == 0, N
assert buffers == (0, 0), buffers
sl0_LD = slice(0, 2 ** LD_res[0]) # Center of buffered cube
sl1_L... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _segments2slices(array_size, grid_segments, patch_segments):\n patch_slices = [slice(start, stop) for start, stop in patch_segments]\n array_slices = []\n\n for start, stop in grid_segments:\n segment_size = max(abs(start), abs(stop))\n k = int(ceil(float(segment_size) / array_size) + (-... | [
"0.60255563",
"0.5919896",
"0.58811724",
"0.5863269",
"0.585582",
"0.58138037",
"0.57283646",
"0.56544244",
"0.5653726",
"0.56497926",
"0.5631293",
"0.55874455",
"0.5561426",
"0.5546333",
"0.55354935",
"0.55143887",
"0.5499627",
"0.54738224",
"0.5452872",
"0.54172575",
"0.541... | 0.6117562 | 0 |
Function that raise exception of the public method area | def area(self):
raise Exception("area() is not implemented") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __call__(self):\r\n raise self",
"def __call__(self):\r\n raise self",
"def throw(self):\n pass",
"def exception(self, *args, **kwargs):",
"def unexpectedException(self):",
"def raise_(err):\n raise err",
"def exception(self):\n raise Exception(\"Exception tes... | [
"0.7519949",
"0.7519949",
"0.7479147",
"0.7351615",
"0.72099924",
"0.69999564",
"0.69876474",
"0.69554514",
"0.66960365",
"0.6632302",
"0.6583972",
"0.6538573",
"0.65098816",
"0.65000296",
"0.6460797",
"0.64351404",
"0.64302117",
"0.64289075",
"0.6414966",
"0.6404366",
"0.639... | 0.0 | -1 |
Function that integer validator to a name and value | def integer_validator(self, name, value):
self.name = name
self.value = value
if type(value) is not int:
raise TypeError("{} must be an integer".format(name))
if value <= 0:
raise ValueError("{} must be greater than 0".format(name))
self.value = value
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def integer_validator(self, name, value):\n if type(value) is not int:\n raise TypeError(name + \" must be an integer\")\n elif value <= 0 and name not in (\"x\", \"y\"):\n raise ValueError(name + \" must be > 0\")\n elif value < 0 and name in (\"x\", \"y\"):\n ... | [
"0.8130921",
"0.788335",
"0.7841091",
"0.7826758",
"0.7700402",
"0.7427867",
"0.7427867",
"0.7261612",
"0.7167946",
"0.70053536",
"0.693558",
"0.6867623",
"0.67775255",
"0.6776555",
"0.67418826",
"0.6563546",
"0.65442383",
"0.6446882",
"0.6382053",
"0.6289101",
"0.626554",
... | 0.8029562 | 1 |
This function is called to check if a username / password combination is valid. | def check_auth(username, password):
return username == USERNAME and password == PASSWORD | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def check_auth(username, password, expected_user, expected_pw):\n return username == expected_user and password == expected_pw",
"def validate_authentication(self, username, password):\n return self.user_table[username]['pwd'] == password",
"def check_valid(self, username, password):\n con... | [
"0.82211864",
"0.8146436",
"0.7962432",
"0.7885527",
"0.78588223",
"0.78382313",
"0.78288996",
"0.7815222",
"0.7798579",
"0.77856815",
"0.77700555",
"0.7760794",
"0.7754975",
"0.77548647",
"0.7750886",
"0.7749223",
"0.77322245",
"0.7715806",
"0.7687129",
"0.7685904",
"0.76736... | 0.7975914 | 2 |
Sends a 401 response that enables basic auth | def authenticate():
return Response(
'Could not verify your access level for that URL.\n'
'You have to login with proper credentials', 401,
{'WWW-Authenticate': 'Basic realm="Login Required"'}) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def authenticate():\n return Response('Not Authorized', 401, {'WWW-Authenticate': 'Basic realm=\"api\"'})",
"def authenticate():\n return Response(\n '', 401, {'WWW-Authenticate': 'Basic realm=\"Login Required\"'}\n )",
"def authenticate():\n return Response(\n 'You have to login with pro... | [
"0.8093736",
"0.80926424",
"0.7958403",
"0.7852648",
"0.7843114",
"0.7824419",
"0.7810746",
"0.7810746",
"0.7810746",
"0.7810746",
"0.7810746",
"0.7810746",
"0.7810746",
"0.7810746",
"0.7810746",
"0.7810746",
"0.7810746",
"0.7810746",
"0.7810746",
"0.7810746",
"0.7810746",
... | 0.774767 | 34 |
Set pin as high | def set_pin_high(pin):
HIGH_PINS.append(pin) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_high(pin):\n _write_value(HIGH, \"{0}/gpio{1}/value\".format(_path_prefix, pin))",
"def gpio_output_high(self, pin: int) -> None:\n self._pins[pin - 1] = \"OUTPUT_HIGH\"",
"def set_pin_low(pin):\n if pin in HIGH_PINS:\n HIGH_PINS.remove(pin)",
"def set_pin(self):\n GPIO.set... | [
"0.8202024",
"0.7684762",
"0.76168716",
"0.756904",
"0.74249405",
"0.720764",
"0.7134327",
"0.711949",
"0.71125674",
"0.7103002",
"0.70253396",
"0.69540894",
"0.68812466",
"0.6861089",
"0.6766074",
"0.67559886",
"0.6692666",
"0.66920656",
"0.66851836",
"0.6638315",
"0.6575943... | 0.85144776 | 0 |
Set pin as high | def set_pin_low(pin):
if pin in HIGH_PINS:
HIGH_PINS.remove(pin) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_pin_high(pin):\n HIGH_PINS.append(pin)",
"def set_high(pin):\n _write_value(HIGH, \"{0}/gpio{1}/value\".format(_path_prefix, pin))",
"def gpio_output_high(self, pin: int) -> None:\n self._pins[pin - 1] = \"OUTPUT_HIGH\"",
"def set_pin(self):\n GPIO.setwarnings(False)\n GPIO... | [
"0.85144776",
"0.8202024",
"0.7684762",
"0.756904",
"0.74249405",
"0.720764",
"0.7134327",
"0.711949",
"0.71125674",
"0.7103002",
"0.70253396",
"0.69540894",
"0.68812466",
"0.6861089",
"0.6766074",
"0.67559886",
"0.6692666",
"0.66920656",
"0.66851836",
"0.6638315",
"0.6575943... | 0.76168716 | 3 |
init a service group | def __init__(self, name):
self.name = name
self.elements = list() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def initService(self):",
"def _initGroups(self):\n defaults = self._getGroupDefaults()\n ddict = self._getDefaultGroupDict(defaults)\n\n for group in self._config.sections():\n ddict[\"_name\"] = group\n container = self.getGroupContainer(**ddict)\n self._pas... | [
"0.6977778",
"0.647504",
"0.6311481",
"0.6163518",
"0.6104335",
"0.6073847",
"0.60509956",
"0.6020045",
"0.59971154",
"0.59911984",
"0.5847313",
"0.58042663",
"0.5801854",
"0.57892656",
"0.5780774",
"0.5769546",
"0.5730015",
"0.5711347",
"0.5633376",
"0.56113166",
"0.5598102"... | 0.0 | -1 |
yield the values of the underlying objects | def __getattr__(self, item):
if item == 'value':
return [s.value for s in self.elements]
else:
raise AttributeError | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __iter__(self):\n for val in self.value:\n yield val",
"def __iter__(self):\n for value in self.__dict__.values():\n yield value",
"def __iter__(self):\n yield from chain.from_iterable(self.data.values())",
"def __iter__(self):\n for v in self._items:\n ... | [
"0.78900456",
"0.783961",
"0.76023287",
"0.75327784",
"0.7466362",
"0.7364445",
"0.7347672",
"0.73172176",
"0.72873604",
"0.72775656",
"0.71958953",
"0.7171008",
"0.7160181",
"0.70680296",
"0.7050591",
"0.7016633",
"0.70131326",
"0.7009068",
"0.7009068",
"0.7009068",
"0.70090... | 0.0 | -1 |
Return a related filter_name, using the filterset relationship if present. | def related(filterset, filter_name):
if not filterset.relationship:
return filter_name
return LOOKUP_SEP.join([filterset.relationship, filter_name]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_filter_name(self):\n pass",
"def get_param_filter_name(cls, param, rel=None):\n # check for empty param\n if not param:\n return param\n\n # strip the rel prefix from the param name.\n prefix = '%s%s' % (rel or '', LOOKUP_SEP)\n if rel and param.starts... | [
"0.6471144",
"0.62778616",
"0.58047044",
"0.56574893",
"0.56555384",
"0.56209326",
"0.5564204",
"0.55566245",
"0.5534012",
"0.5517095",
"0.5462722",
"0.54582113",
"0.5367023",
"0.5363491",
"0.53548676",
"0.53417313",
"0.53322023",
"0.5322664",
"0.526207",
"0.52405727",
"0.522... | 0.8743976 | 0 |
Resolve `AutoFilter`s into their perlookup filters. `AutoFilter`s are a declarative alternative to the `Meta.fields` dictionary syntax, and use the same machinery internally. | def expand_auto_filters(cls, new_class):
# get reference to opts/declared filters
orig_meta, orig_declared = new_class._meta, new_class.declared_filters
# override opts/declared filters w/ copies
new_class._meta = copy.deepcopy(new_class._meta)
new_class.declared_filters = new_c... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def loadFilters(ufo):\n preFilters, postFilters = [], []\n for filterDict in ufo.lib.get(FILTERS_KEY, []):\n namespace = filterDict.get(\"namespace\", \"ufo2ft.filters\")\n try:\n filterClass = getFilterClass(filterDict[\"name\"], namespace)\n except (ImportError, AttributeErr... | [
"0.5492552",
"0.5431763",
"0.5252897",
"0.52475154",
"0.5196505",
"0.5192317",
"0.5170377",
"0.5147656",
"0.5139516",
"0.50391585",
"0.4980402",
"0.49512303",
"0.49480322",
"0.49272498",
"0.49130383",
"0.49125457",
"0.489388",
"0.48330957",
"0.48206162",
"0.4809472",
"0.48018... | 0.5948139 | 0 |
Returns the subset of filters that should be initialized by the FilterSet, dependent on the requested `params`. This helps minimize the cost of initialization by reducing the number of deepcopy ops. The `rel` argument is used for related filtersets to strip the param of its relationship prefix. See `.get_param_filter_n... | def get_filter_subset(cls, params, rel=None):
# Determine names of filters from query params and remove empty values.
# param names that traverse relations are translated to just the local
# filter names. eg, `author__username` => `author`. Empty values are
# removed, as they indicate an... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_request_filters(self):\n # build the compiled set of all filters\n requested_filters = OrderedDict()\n for filter_name, f in self.filters.items():\n requested_filters[filter_name] = f\n\n # exclusion params\n exclude_name = '%s!' % filter_name\n ... | [
"0.6619125",
"0.6226987",
"0.6226987",
"0.60220295",
"0.56563276",
"0.557124",
"0.5495559",
"0.5387023",
"0.537955",
"0.5361835",
"0.5359296",
"0.53389424",
"0.53347725",
"0.5314394",
"0.53115654",
"0.52742255",
"0.5228215",
"0.5208534",
"0.51974314",
"0.5195309",
"0.5190603"... | 0.8191257 | 0 |
Disable filter subsetting, allowing the form to render the filterset. Note that this decreases performance and should only be used when rendering a form, such as with DRF's browsable API. | def disable_subset(cls, *, depth=0):
if not issubclass(cls, SubsetDisabledMixin):
cls = type('SubsetDisabled%s' % cls.__name__,
(SubsetDisabledMixin, cls), {})
# recursively disable subset for related filtersets
if depth > 0:
# shallow copy to prev... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def allow_filtering(self):\r\n clone = copy.deepcopy(self)\r\n clone._allow_filtering = True\r\n return clone",
"def non_hidden(self):\n return self.filter(hidden=False)",
"def non_hidden(self):\n return self.filter(hidden=False)",
"def __disableSearchEdit(self):\n s... | [
"0.6328865",
"0.61500955",
"0.61500955",
"0.61072856",
"0.5808935",
"0.57838905",
"0.57728505",
"0.5675368",
"0.56208265",
"0.5614127",
"0.55958295",
"0.55693483",
"0.5554618",
"0.5534325",
"0.5480112",
"0.5472331",
"0.5463367",
"0.5458596",
"0.5456048",
"0.54518133",
"0.5449... | 0.6411095 | 0 |
Get the filter name for the request data parameter. | def get_param_filter_name(cls, param, rel=None):
# check for empty param
if not param:
return param
# strip the rel prefix from the param name.
prefix = '%s%s' % (rel or '', LOOKUP_SEP)
if rel and param.startswith(prefix):
param = param[len(prefix):]
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_filter_name(self):\n pass",
"async def get_filter(self, **kwargs: Any) -> str:\n return self._telescope.filter_name",
"def name(self) -> StringFilter:\n return self.__name",
"def filter(self) -> Optional[str]:\n return pulumi.get(self, \"filter\")",
"def get_json_callbac... | [
"0.79879385",
"0.73243344",
"0.675019",
"0.65612745",
"0.6520124",
"0.6505116",
"0.64178914",
"0.6189231",
"0.6183338",
"0.61544156",
"0.6082367",
"0.60678583",
"0.6023081",
"0.60075915",
"0.6005047",
"0.59691584",
"0.59691584",
"0.587701",
"0.5817631",
"0.5786401",
"0.576572... | 0.6619432 | 3 |
Build a set of filters based on the request data. This currently includes only filter exclusion/negation. | def get_request_filters(self):
# build the compiled set of all filters
requested_filters = OrderedDict()
for filter_name, f in self.filters.items():
requested_filters[filter_name] = f
# exclusion params
exclude_name = '%s!' % filter_name
if relate... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_filters(self, req):\n filters = {}\n properties = {}\n\n for param in req.params:\n if param in SUPPORTED_FILTERS:\n filters[param] = req.params.get(param)\n if param.startswith('property-'):\n _param = param[9:]\n pro... | [
"0.7212947",
"0.70940274",
"0.7063467",
"0.70396554",
"0.70150787",
"0.7010616",
"0.68755764",
"0.67024094",
"0.67008877",
"0.6694236",
"0.6660822",
"0.6646979",
"0.65543395",
"0.65318733",
"0.64761287",
"0.64659613",
"0.6453026",
"0.64314073",
"0.6378158",
"0.63564056",
"0.6... | 0.7715377 | 0 |
Get the related filterset instances for all related filters. | def get_related_filtersets(self):
related_filtersets = OrderedDict()
for related_name in self.related_filters:
if related_name not in self.filters:
continue
f = self.filters[related_name]
related_filtersets[related_name] = f.filterset(
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def filter_related_filtersets(self, queryset):\n for related_name, related_filterset in self.related_filtersets.items():\n # Related filtersets should only be applied if they had data.\n prefix = '%s%s' % (related(self, related_name), LOOKUP_SEP)\n if not any(value.startswit... | [
"0.7343028",
"0.67154413",
"0.61035365",
"0.60354567",
"0.5970218",
"0.59653723",
"0.5931315",
"0.58990955",
"0.5755272",
"0.5717431",
"0.5715828",
"0.5692265",
"0.5688741",
"0.5616253",
"0.5609072",
"0.5583161",
"0.55796957",
"0.5577063",
"0.55623674",
"0.55400354",
"0.55383... | 0.82383394 | 0 |
Filter the provided `queryset` by the `related_filtersets`. It is recommended that you override this method to change the filtering behavior across relationships. | def filter_related_filtersets(self, queryset):
for related_name, related_filterset in self.related_filtersets.items():
# Related filtersets should only be applied if they had data.
prefix = '%s%s' % (related(self, related_name), LOOKUP_SEP)
if not any(value.startswith(prefix)... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def apply_filters(self, queryset, applicable_filters=None, applicable_exclusions=None):\n if applicable_filters:\n queryset = queryset.filter(applicable_filters)\n if applicable_exclusions:\n queryset = queryset.exclude(applicable_exclusions)\n return queryset",
"def ge... | [
"0.67854434",
"0.6782465",
"0.66839457",
"0.6680913",
"0.646492",
"0.63564074",
"0.63288736",
"0.63143224",
"0.63010234",
"0.6240037",
"0.6125244",
"0.60945296",
"0.608679",
"0.6075394",
"0.6073214",
"0.60558784",
"0.60067743",
"0.60016656",
"0.5998127",
"0.59923685",
"0.5961... | 0.84336936 | 0 |
Callback function for metrics_btn A window will popup to display the user metadata of previous user | def display_metrics(self):
metrics = client.user_metrics(self.user_name.get())
messagebox.showinfo("Metrics", metrics) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def display_metrics3(self):\n messagebox.showinfo(\"Processed Image Metrics\", self.pro_metrics)",
"def display_metrics2(self):\n messagebox.showinfo(\"Original Image Metrics\", self.raw_metrics)",
"def user_labels_prev(*args):\n return _ida_hexrays.user_labels_prev(*args)",
"def action_sessio... | [
"0.5536531",
"0.5336233",
"0.5274754",
"0.52151805",
"0.5156828",
"0.51532024",
"0.51404095",
"0.509433",
"0.50761753",
"0.50675863",
"0.5057054",
"0.5030155",
"0.49895877",
"0.49638245",
"0.49565363",
"0.4928132",
"0.49049008",
"0.48959267",
"0.48698136",
"0.4867956",
"0.486... | 0.63189566 | 0 |
Callback function for im_metrics_btn1 A window will popup to display the original image metadata | def display_metrics2(self):
messagebox.showinfo("Original Image Metrics", self.raw_metrics) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def display_metrics3(self):\n messagebox.showinfo(\"Processed Image Metrics\", self.pro_metrics)",
"def win5_ManageData_Caption(event=None):\r\n\r\n clearwin()\r\n global IMAGES_FILE_PATH\r\n global i\r\n i = 0\r\n\r\n Descriptions = load()\r\n imgs = []\r\n if os.path.exists(IMAGES_F... | [
"0.69807404",
"0.62527585",
"0.58932143",
"0.58668804",
"0.58499783",
"0.58255225",
"0.5808261",
"0.57940733",
"0.5692784",
"0.56442815",
"0.5617792",
"0.5611394",
"0.55932826",
"0.5588301",
"0.5586624",
"0.5570289",
"0.555042",
"0.5538848",
"0.55376774",
"0.55155426",
"0.548... | 0.73585606 | 0 |
Callback function for im_metrics_btn2 A window will popup to display the processed image metadata | def display_metrics3(self):
messagebox.showinfo("Processed Image Metrics", self.pro_metrics) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def display_metrics2(self):\n messagebox.showinfo(\"Original Image Metrics\", self.raw_metrics)",
"def btn_display_hist_callback(self):\n self.show_as_waiting(True)\n ids = self.tbl_images.get_selected_ids()\n names = self.tbl_images.get_selected_names()\n\n for id, name in zip... | [
"0.7458454",
"0.616558",
"0.60215414",
"0.5975251",
"0.584566",
"0.582564",
"0.5801999",
"0.5760714",
"0.5755779",
"0.5739632",
"0.5733116",
"0.56553966",
"0.56404024",
"0.5639404",
"0.5581315",
"0.5552886",
"0.5552128",
"0.55366683",
"0.5528491",
"0.55033773",
"0.5477598",
... | 0.74962056 | 0 |
Open a popup window to let user choose file(s) The full filepath will be stored in the class | def import_file(self):
from tkinter import filedialog
self.filepath = filedialog.askopenfilenames(
initialdir="/", title="Select file",
filetypes=(("PNG files", "*.png"),
("JPEG files", "*.jpeg"),
("TIFF files", "*.tiff"),
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def file_popup(file) -> str:\n layout = [\n [sg.Text(f\"Select the action to perform on\\n\\n{file}\")],\n [sg.Button(\"Open File\", key=\"-APP-\"),\n sg.Button(\"Open in File Explorer\", key=\"-EXPLORER-\"),\n sg.Button(\"Delete File\", key=\"-DEl-\",\n ... | [
"0.81688225",
"0.80503184",
"0.78248763",
"0.77235496",
"0.7679591",
"0.76397675",
"0.76276994",
"0.7585482",
"0.7508304",
"0.74986696",
"0.7496582",
"0.7495827",
"0.74400616",
"0.7432761",
"0.7358748",
"0.7349472",
"0.7333362",
"0.73316485",
"0.7327523",
"0.7324655",
"0.7269... | 0.7110334 | 29 |
Control the load button Load a list of processed images in the database. User could choose one or more images to download. If only one image is chosen the resulted plot will be displayed in the GUI. If multiple file is chosen, the files will be zip to a zip archive and save to a designated path. | def load_function(self):
self.image_names = client.get_image_list(self.user_name.get())
# clear listbox
self.name_list.delete(0, END)
for i in self.image_names:
self.name_list.insert(END, i) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def download_function(self):\n # Ask user for directory and user ID\n savepath = filedialog.askdirectory()\n ID = self.user_name.get()\n\n self.msg2.set('Saving files to the designated folder')\n\n # Get selected filenames\n index = self.name_list.curselection()\n s... | [
"0.70843863",
"0.64637136",
"0.6282635",
"0.61812353",
"0.6077754",
"0.60075426",
"0.60023975",
"0.5985294",
"0.59840703",
"0.59838974",
"0.59453404",
"0.5897838",
"0.5861998",
"0.5781273",
"0.5778588",
"0.5764681",
"0.5759482",
"0.5740892",
"0.57235175",
"0.57107615",
"0.568... | 0.595108 | 10 |
Callback function that controls the download button | def download_function(self):
# Ask user for directory and user ID
savepath = filedialog.askdirectory()
ID = self.user_name.get()
self.msg2.set('Saving files to the designated folder')
# Get selected filenames
index = self.name_list.curselection()
select_files = ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def download_with_callback(self, url, path=None, filename=None, headers=None, force=False, func=None):",
"def click_download_button(self):\n self._basket.click_download_button()",
"def download_files(self):",
"def download(self):\n pass",
"def download(self):\n pass",
"def download_f... | [
"0.7018457",
"0.69721246",
"0.69597185",
"0.6953735",
"0.6953735",
"0.6816827",
"0.6681164",
"0.663355",
"0.66132164",
"0.65275913",
"0.6489063",
"0.6469115",
"0.64611053",
"0.6418321",
"0.6399592",
"0.637393",
"0.6368756",
"0.6359143",
"0.63438714",
"0.6293492",
"0.62759054"... | 0.6004468 | 68 |
Determine the image size of the images to be displayed in GUI The original width to height ratio will be preserved. Max width/height is set to be 300, and the other dimension will be adjusted accordingly. | def image_size(size):
l_max = max(size)
if l_max > 300:
num = l_max/300
else:
num = 1
w = round(size[0] / num)
h = round(size[1] / num)
new_size = [w, h]
return new_size | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_image_size(self):",
"def scale_widget_to_image_size(self):\n if self._image is not None:\n im = self._image.make_image()\n self.width = im.shape[1]\n self.height = im.shape[0]",
"def format_img_size(self, img, C):\n img_min_side = float(C.im_size)\... | [
"0.7428963",
"0.72670555",
"0.7153747",
"0.7006132",
"0.6961046",
"0.69432396",
"0.678366",
"0.6766124",
"0.6737722",
"0.66931385",
"0.6661223",
"0.6660257",
"0.6642184",
"0.6614157",
"0.66024864",
"0.66024864",
"0.65730685",
"0.656699",
"0.6548179",
"0.6547853",
"0.65275776"... | 0.6792414 | 6 |
Communicate with server and database to get original and processed images, pre and postprocessing histogram. | def get_image_pair(filename, ID):
r = client.get_image_file(ID, filename)
try:
check_r_type(r)
except TypeError:
return r
else:
pro_img_arr, method = client.get_image(r)
pro_img = Image.fromarray(pro_img_arr)
raw_img_name = filename.replace('_' + method, "")
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def process():\n config = read_config()\n \n\n img_dir = config['DEFAULT']['images_directory']\n results_dict = {}\n images = list(get_image_files(img_dir))\n for image in tqdm.tqdm(images):\n info = hash_file(image)\n if info == 0:\n continue\n\n hash_value = info... | [
"0.6015694",
"0.5810847",
"0.57723105",
"0.5640843",
"0.56245506",
"0.56011194",
"0.5577318",
"0.55666625",
"0.5558006",
"0.5541642",
"0.5531403",
"0.5502279",
"0.5499759",
"0.54302055",
"0.541875",
"0.5414848",
"0.5412454",
"0.541103",
"0.5408943",
"0.53958166",
"0.535821",
... | 0.0 | -1 |
Check if the output from server is a string of error message Raise error if it is string. It should be a dictionary. | def check_r_type(r):
if type(r) is str:
raise TypeError('Get Error message.') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def verify_rpc_value ( user_dict ):\n for key in user_dict:\n if not isinstance ( user_dict[ key ], str ):\n # Error code 422\n raise ValueError ( 'Value of {0} is not a string'.format ( key ) )",
"def is_error(response: str) -> bool:\n return \"ERROR\" in respo... | [
"0.67002916",
"0.6622685",
"0.65426445",
"0.64297044",
"0.6372873",
"0.62947875",
"0.61671525",
"0.604404",
"0.6020194",
"0.59307563",
"0.590339",
"0.5883814",
"0.5872144",
"0.58716655",
"0.58712596",
"0.5850643",
"0.5848179",
"0.5827672",
"0.58271694",
"0.58263636",
"0.58262... | 0.68892103 | 0 |
Extract filename and extension from filepath | def get_file_name(filepath): # need pytest
filename, extension = os.path.splitext(filepath.split('/')[-1])
return filename, extension | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def file_ext(path):\n result = os.path.splitext(path)[1]\n return result",
"def get_file_ext(path: str) -> str:\n return os.path.splitext(os.path.basename(path))[1]",
"def get_file_name_with_ext(path: str) -> str:\n return os.path.basename(path)",
"def _get_ext(self, path):\n return os.pat... | [
"0.80850315",
"0.7980222",
"0.7961075",
"0.79087883",
"0.7875789",
"0.7851262",
"0.7824376",
"0.7815243",
"0.7754425",
"0.7731678",
"0.7668338",
"0.7649159",
"0.76470023",
"0.7626894",
"0.7625784",
"0.76075673",
"0.7575197",
"0.7550891",
"0.75276417",
"0.7518576",
"0.74935955... | 0.79995126 | 1 |
Check if multiple or single file is chosen. | def check_multi_single(filenames):
num = len(filenames)
if num == 1:
single = bool(1)
else:
single = bool(0)
return single | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_multi_file(self):\n return 'files' in self.torrent['info']",
"def select_files():\n\n if not Settings.is_prompt(): return [File.get_random_file()]\n category = Settings.select_category()\n if not category: return File.select_file_upload_method()\n # if not Settings.confi... | [
"0.6862918",
"0.6714143",
"0.64036655",
"0.6360947",
"0.62420434",
"0.6179852",
"0.6168158",
"0.606886",
"0.6025578",
"0.5963181",
"0.59183747",
"0.5914388",
"0.59100074",
"0.59086585",
"0.5877628",
"0.5876562",
"0.5874005",
"0.58681023",
"0.5844574",
"0.5818032",
"0.5810063"... | 0.6930178 | 0 |
Call other function to run analysis and upload the results to the database It can both accept one single .zip file or one/multiple image file(s) | def run_analysis(filepath, ID, method):
filename, extension = get_file_name(filepath[0])
if extension == '.zip':
msg = run_zip_analysis(filepath, ID, method)
else:
msg = run_images_analysis(filepath, ID, method)
return msg | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def run_images_analysis(filepath, ID, method):\n for path in filepath:\n try:\n Image.open(path)\n except IOError:\n msg = 'Please import images files, or just a single zip archive'\n else:\n filename, extension = get_file_name(path)\n\n # Save ra... | [
"0.7374393",
"0.6846931",
"0.6268699",
"0.62572134",
"0.6137036",
"0.6073903",
"0.6000757",
"0.5980023",
"0.59539145",
"0.5949638",
"0.5929469",
"0.59196985",
"0.59115666",
"0.59078693",
"0.5898189",
"0.5814665",
"0.57921034",
"0.5774928",
"0.57512265",
"0.5742385",
"0.574088... | 0.68059576 | 2 |
Run image analysis for zip archive | def run_zip_analysis(filepath, ID, method):
with zipfile.ZipFile(filepath[0]) as zf:
for entry in zf.namelist():
if not entry.startswith("__"): # Get rid hidden files in zip
with zf.open(entry) as file:
data = file.read()
fh = io.BytesIO(d... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def run():\n\n today = datetime.now().strftime(\"%Y-%m-%d\")\n log_file = os.path.abspath(\"logs/{}.log\".format(today))\n logger = RsmasLogger(\"pipeline\", log_file)\n\n images = get_list_of_images()\n # LOG: list of images to process\n logger.log(loglevel.INFO, [img.key for img in images])\n\n... | [
"0.64381856",
"0.63635296",
"0.6358149",
"0.6324381",
"0.62869585",
"0.6232183",
"0.6155203",
"0.6140258",
"0.6044015",
"0.6022487",
"0.5966659",
"0.5904075",
"0.5880147",
"0.58736247",
"0.5790913",
"0.5768327",
"0.57619715",
"0.5741277",
"0.57412493",
"0.5739594",
"0.5689725... | 0.693419 | 0 |
Check if message contains Error or Warning for run_analysis functions. | def check_msg(msg):
err = bool(0)
if 'Error' in msg:
err = bool(1)
elif "Warning" in msg:
msg = 'Success! Warning: Image already exists. ' \
'Processing ran on existing image'
else:
msg = 'Image saved successfully'
return err, msg | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def error_check(self, message):\n matches = ERROR_SYNTAX.match(message)\n if matches:\n error_code = int(matches.group(1))\n error_message = matches.group(2)\n return error_code, error_message\n return None",
"def collect_errors_and_warnings(self) -> str:\n ... | [
"0.6047532",
"0.59931284",
"0.59822315",
"0.5976988",
"0.5970713",
"0.59450835",
"0.59394825",
"0.5925255",
"0.5906123",
"0.59012663",
"0.5873115",
"0.58316827",
"0.57754755",
"0.57746696",
"0.57093453",
"0.5681229",
"0.56606114",
"0.5603111",
"0.558577",
"0.5583142",
"0.5579... | 0.6277207 | 0 |
Check if message contains Error for get new_user_id function. Raise a ValueError if message contains "Error" | def check_user(msg):
if "Error" in msg:
raise ValueError('User already exists.') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def check_message_id(self, user, item_id, message_id):\n try:\n\n err_options = message_id.split(':', 2)\n msg_error_count = int(err_options[1])\n\n # check for token update\n if len(err_options) > 1 and err_options[2] != str(self.get_token(user)):\n ... | [
"0.67848724",
"0.61461526",
"0.6122",
"0.6017559",
"0.5941634",
"0.5908885",
"0.59077275",
"0.5874253",
"0.5843121",
"0.5823432",
"0.5823432",
"0.5823432",
"0.57579714",
"0.57539093",
"0.5725768",
"0.57183814",
"0.57163316",
"0.57006186",
"0.56851375",
"0.5680018",
"0.5669614... | 0.701784 | 0 |
Upload image(s) and run the required analysis for multiple or single image file(s) | def run_images_analysis(filepath, ID, method):
for path in filepath:
try:
Image.open(path)
except IOError:
msg = 'Please import images files, or just a single zip archive'
else:
filename, extension = get_file_name(path)
# Save raw image to dat... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main():\n \n # for inserting other images, add tem to /input folder and list them here\n images = (\n 'image-0',\n 'image-1',\n 'image-2'\n )\n\n for image_name in images:\n print(image_name, \"image:\")\n\n image = open_image(image_name)\n display_image(im... | [
"0.6907992",
"0.6606996",
"0.65919435",
"0.6470824",
"0.6414355",
"0.6368806",
"0.6355245",
"0.63245857",
"0.6265804",
"0.62025523",
"0.62011194",
"0.6152564",
"0.6149888",
"0.61378866",
"0.61241436",
"0.608345",
"0.60723394",
"0.606071",
"0.606038",
"0.6033568",
"0.6033372",... | 0.76435435 | 0 |
Download multiple processed images in a zip archive. | def download_multiple(select_files, savepath, id, ext):
with zipfile.ZipFile(savepath + '/processed_images.zip', mode='w') as zf:
for file in select_files:
pro_img, _, _, _, _ = get_image_pair(file, id)
output = io.BytesIO()
pro_img.save(output, format=ext)
f... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def downloadImages(self):\n\t\ti = 0\n\t\tfor im in self.images:\n\t\t\t# Let's get the file extension and file name and make the final file path. \n\t\t\t# We need to do this to slugify the file name and avoid errors when loading images\n\t\t\tfile_name, file_extension = os.path.splitext(im['url'])\n\t\t\tfile_na... | [
"0.76530296",
"0.72565246",
"0.68648124",
"0.673801",
"0.6725581",
"0.67049855",
"0.67010486",
"0.6687771",
"0.6672859",
"0.6647223",
"0.6634884",
"0.66337126",
"0.66298825",
"0.66295946",
"0.65946233",
"0.65826285",
"0.6576",
"0.65477145",
"0.6529526",
"0.6509803",
"0.650044... | 0.7829335 | 0 |
This method will store a MistAssociation object into mongodb after creating a MistAssociation with the same values as the Association provided. Secret will be encoded because it constantly produced errors with encoding. | def storeAssociation(self, server_url, association):
mist_association = MistAssociation()
mist_association.assoc_type = association.assoc_type
mist_association.handle = association.handle.hex()
mist_association.secret = association.secret.hex()
mist_association.lifetime = associ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def storeAssociation(self, server_url, assoc):\n assoc = models.Association(url=server_url,\n handle=assoc.handle,\n association=assoc.serialize())\n assoc.put()",
"def storeAssociation(self, server_url, association):\r\n entity = datastore.Entity('Associati... | [
"0.702098",
"0.6713939",
"0.56481075",
"0.53882486",
"0.5067821",
"0.50430053",
"0.5034432",
"0.493951",
"0.49269888",
"0.4900855",
"0.48810253",
"0.48749557",
"0.4837358",
"0.48064864",
"0.48010197",
"0.4800175",
"0.47752208",
"0.4773829",
"0.47659233",
"0.47503135",
"0.4745... | 0.705629 | 0 |
Gets a server url and the handle and finds a matching association that has not expired. Expired associations are deleted. The association returned is the one with the most recent issuing timestamp. | def getAssociation(self, server_url, handle=None):
query = {'server_url': server_url}
if handle:
query.update({'handle': handle.hex()})
try:
mist_associations = MistAssociation.objects(**query)
except me.DoesNotExist:
mist_associations = []
f... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getAssociation(self, server_url, handle=None):\n query = models.Association.all().filter('url', server_url)\n if handle:\n query.filter('handle', handle)\n\n results = query.fetch(1)\n if len(results) > 0:\n assoc = xAssociation.deserialize(results[0].association)\n if assoc.getExpir... | [
"0.77972186",
"0.76248354",
"0.61887425",
"0.6054781",
"0.568007",
"0.49513325",
"0.4506718",
"0.44599456",
"0.44545853",
"0.4377314",
"0.4343994",
"0.42867646",
"0.42596504",
"0.42539644",
"0.4183411",
"0.41812235",
"0.41506642",
"0.4147285",
"0.41288793",
"0.41181672",
"0.4... | 0.7585846 | 2 |
This method removes the matching association if it's found, and returns whether the association was removed or not. | def removeAssociation(self, server_url, handle):
try:
mist_associations = MistAssociation.objects(
server_url=server_url, handle=handle.hex())
except me.DoesNotExist:
return False
for assoc in mist_associations:
assoc.delete()
return... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def removeAssociation(self, *args):\n return _libsbml.FbcOr_removeAssociation(self, *args)",
"def removeAssociation(self, *args):\n return _libsbml.Association_removeAssociation(self, *args)",
"def removeAssociation(self, *args):\n return _libsbml.FbcAnd_removeAssociation(self, *args)",
... | [
"0.69179744",
"0.6731579",
"0.67246026",
"0.5947728",
"0.58918077",
"0.5864309",
"0.5859789",
"0.58411855",
"0.5758755",
"0.5709778",
"0.56879985",
"0.5626436",
"0.5593932",
"0.55795133",
"0.5561436",
"0.55258703",
"0.550188",
"0.5499768",
"0.54942656",
"0.5483335",
"0.545969... | 0.64031404 | 3 |
Called when using a nonce. This method should return C{True} if the nonce has not been used before, and store it for a while to make sure nobody tries to use the same value again. If the nonce has already been used or the timestamp is not current, return C{False}. You may use L{openid.store.nonce.SKEW} for your timesta... | def useNonce(self, server_url, timestamp, salt):
if is_nonce_old(timestamp):
return False
try:
mist_nonces = MistNonce.objects(server_url=server_url, salt=salt,
timestamp=timestamp)
except me.DoesNotExist:
mist_non... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def useNonce(self, nonce):\r\n query = datastore.Query('Nonce')\r\n query['nonce ='] = nonce\r\n query['created >='] = (datetime.datetime.now() -\r\n datetime.timedelta(hours=6))\r\n\r\n results = query.Get(1)\r\n if results:\r\n datastore.Delete(results[0].key())\r\n ... | [
"0.73136973",
"0.6600226",
"0.6195637",
"0.61672926",
"0.61513644",
"0.6094848",
"0.6070164",
"0.6020712",
"0.6004455",
"0.5996191",
"0.5996191",
"0.59095937",
"0.5881976",
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"0.5756545",
"0.569583",
"0.5590977",
"0.5563972",
"0.55559796",
"0.5550194",
"0.5542444",... | 0.76029676 | 0 |
Remove expired nonces from the store. Discards any nonce from storage that is old enough that its timestamp would not pass L{useNonce}. This method is not called in the normal operation of the library. It provides a way for store admins to keep their storage from filling up with expired data. | def cleanupNonces(self):
try:
mist_nonces = MistNonce.objects()
except me.DoesNotExist:
mist_nonces = []
counter = 0
for n in mist_nonces:
if n.is_old():
n.delete()
counter += 1
return counter | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _remove_expired(self):\n with self.__lock:\n is_changed = False\n for k in list(self._d.keys()):\n if self._d[k].is_expired():\n log.debug(\"removing expired item: {}\".format(self._d[k]))\n del self[k]\n is_ch... | [
"0.6667037",
"0.66389847",
"0.66271514",
"0.6594752",
"0.65522724",
"0.64164263",
"0.640086",
"0.62734115",
"0.6258594",
"0.6247243",
"0.6166327",
"0.6157293",
"0.6084007",
"0.6060935",
"0.60464877",
"0.5984581",
"0.592853",
"0.58712083",
"0.5768196",
"0.5718193",
"0.56924313... | 0.5977415 | 16 |
Remove expired associations from the store. This method is not called in the normal operation of the library. It provides a way for store admins to keep their storage from filling up with expired data. | def cleanupAssociations(self):
try:
mist_associations = MistAssociation.objects()
except me.DoesNotExist:
mist_associations = []
counter = 0
for assoc in mist_associations:
if assoc.is_expired():
assoc.delete()
counter ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _remove_expired(self):\n with self.__lock:\n is_changed = False\n for k in list(self._d.keys()):\n if self._d[k].is_expired():\n log.debug(\"removing expired item: {}\".format(self._d[k]))\n del self[k]\n is_ch... | [
"0.6875887",
"0.6844624",
"0.67177826",
"0.65542144",
"0.6533008",
"0.6387391",
"0.6350593",
"0.6350029",
"0.63188756",
"0.62800354",
"0.6261716",
"0.6261716",
"0.62265635",
"0.61934316",
"0.6192193",
"0.6032369",
"0.6010329",
"0.600182",
"0.59811556",
"0.5909272",
"0.5898087... | 0.68006694 | 2 |
Shortcut for C{L{cleanupNonces}()}, C{L{cleanupAssociations}()}. This method is not called in the normal operation of the library. It provides a way for store admins to keep their storage from filling up with expired data. | def cleanup(self):
return self.cleanupNonces(), self.cleanupAssociations() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cleanup(self, *args, **kwargs):",
"def _cleanup(self):\n pass",
"def cleanup():",
"def cleanup(*args, **kwargs): # real signature unknown\n pass",
"def cleanup(self):\n\n pass",
"def cleanup(self):\n raise NotImplementedError",
"def cleanup(self):\n pass",
"def cleanup(... | [
"0.7016629",
"0.69023955",
"0.66813576",
"0.6666886",
"0.66557264",
"0.65890634",
"0.6582056",
"0.6582056",
"0.6582056",
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"0.6582056",
"0.6582056",
"0.6582056",
"0.6582056",
"0.6582056",
"0.6582056",
"0.6582056",
"0.6575037",
"0.65191555",
"0.6517952",
"0.649991",... | 0.73417866 | 0 |
Return top level command handler. | def init():
@click.command()
@click.option('--cell',
callback=cli.handle_context_opt,
envvar='TREADMILL_CELL',
expose_value=False,
required=True)
@click.argument('app-or-svc')
@click.option('--host',
help='Hostnam... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_command_handler(self) -> Callable:\n try:\n return globals()[self.command_handler]\n except KeyError:\n logging.error(\"command_handler function '%s' for command '%s' not found in global scope\" %\n (self.command_handler, self... | [
"0.7392408",
"0.69905674",
"0.6935066",
"0.6817442",
"0.6468187",
"0.6460933",
"0.6309276",
"0.62970257",
"0.62815976",
"0.6280105",
"0.6223721",
"0.62009555",
"0.61951655",
"0.6180926",
"0.6155766",
"0.61281425",
"0.60346127",
"0.600372",
"0.6003017",
"0.59750646",
"0.584907... | 0.0 | -1 |
View application's service logs. | def logs(app_or_svc, host, uniq, service):
try:
app, uniq, logtype, logname = app_or_svc.split('/', 3)
except ValueError:
app, uniq, logtype, logname = app_or_svc, uniq, 'service', service
if any(param is None for param in [app, uniq, logtype, logname]):
cli.... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getLogs():",
"def getLogs():",
"def logs(self):\n return self.logger.logs()",
"def log(self):\n resp = requests.get(\"%s/api/log\"%self.urlbase, verify=False)\n return resp.json[\"log\"]",
"def logs(self, shell=False):\n if self.app_id:\n return self.yarn_api.logs... | [
"0.68098724",
"0.68098724",
"0.64053893",
"0.6395825",
"0.630246",
"0.6281809",
"0.6209214",
"0.62079066",
"0.611495",
"0.61128664",
"0.6103324",
"0.60686356",
"0.6053491",
"0.6024822",
"0.60207194",
"0.5982966",
"0.5934909",
"0.5930149",
"0.59281075",
"0.59259474",
"0.592594... | 0.6984841 | 0 |
Find nodeinfo endpoint on host | def _nodeinfo_endpoint(host):
zkclient = context.GLOBAL.zk.conn
nodeinfo_zk_path = '{}/{}'.format(z.ENDPOINTS, 'root')
for node in zkclient.get_children(nodeinfo_zk_path):
if 'nodeinfo' in node and host in node:
data, _metadata = zkclient.get(
'{}/{}'.format(nodeinfo_zk_p... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getHostInfo():",
"def get_node_details(self, node):\n node_details = self.parser.find_server_by_ip(node.get('ip')) or \\\n self.parser.find_server_by_hostname(node.get('host'))\n\n return node_details",
"def getHost():",
"def getHost():",
"def test_get_node_internal_... | [
"0.7210888",
"0.66543496",
"0.6595249",
"0.6595249",
"0.6384198",
"0.62400854",
"0.6236083",
"0.6164553",
"0.6050032",
"0.6024226",
"0.5987261",
"0.5953469",
"0.593182",
"0.5915911",
"0.59002453",
"0.5839691",
"0.58132106",
"0.57908964",
"0.5767918",
"0.5744465",
"0.57380223"... | 0.7731481 | 0 |
dropout + batch norm + l1_l2 | def architecture_CONV_FC_batch_norm_dropout_L1_l2(
X, nbclasses, nb_conv=1, nb_fc=1, kernel_initializer="random_normal"
):
# input size
width, height, depth = X.shape
input_shape = (height, depth)
# parameters of the architecture
l1_l2_rate = 1.0e-3
dropout_rate = 0.5
conv_kernel = 3
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def architecture_CONV_FC_batch_norm_dropout_L1_l2_LEAKY_ReLU(\n X, nbclasses, nb_conv=1, nb_fc=1\n):\n # input size\n width, height, depth = X.shape\n input_shape = (height, depth)\n\n # parameters of the architecture\n l1_l2_rate = 1.0e-3\n dropout_rate = 0.5\n conv_kernel = 3\n conv_fi... | [
"0.6057391",
"0.6015575",
"0.59682286",
"0.59510905",
"0.5895679",
"0.5895679",
"0.5812982",
"0.57391614",
"0.5654636",
"0.5644836",
"0.56329393",
"0.56230843",
"0.5617473",
"0.56083846",
"0.5606605",
"0.55982274",
"0.55822885",
"0.5554348",
"0.5552772",
"0.554574",
"0.554353... | 0.6199096 | 0 |
dropout + batch norm + l1_l2 | def architecture_CONV_FC_batch_norm_dropout_L1_l2_TANH(
X, nbclasses, nb_conv=1, nb_fc=1
):
# input size
width, height, depth = X.shape
input_shape = (height, depth)
# parameters of the architecture
l1_l2_rate = 1.0e-3
dropout_rate = 0.5
conv_kernel = 3
conv_filters = 64
nbunits... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def architecture_CONV_FC_batch_norm_dropout_L1_l2(\n X, nbclasses, nb_conv=1, nb_fc=1, kernel_initializer=\"random_normal\"\n):\n # input size\n width, height, depth = X.shape\n input_shape = (height, depth)\n\n # parameters of the architecture\n l1_l2_rate = 1.0e-3\n dropout_rate = 0.5\n c... | [
"0.61973625",
"0.605561",
"0.59693855",
"0.5950774",
"0.58955586",
"0.58955586",
"0.5811553",
"0.57387733",
"0.5654752",
"0.5646109",
"0.5632108",
"0.56242067",
"0.5616107",
"0.56091213",
"0.5604995",
"0.559818",
"0.5580515",
"0.55549634",
"0.5554276",
"0.5544025",
"0.554147"... | 0.6014573 | 2 |
dropout + batch norm + l1_l2 | def architecture_CONV_FC_batch_norm_dropout_L1_l2_SIGMOID(
X, nbclasses, nb_conv=1, nb_fc=1
):
# input size
width, height, depth = X.shape
input_shape = (height, depth)
# parameters of the architecture
l1_l2_rate = 1.0e-3
dropout_rate = 0.5
conv_kernel = 3
conv_filters = 64
nbun... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def architecture_CONV_FC_batch_norm_dropout_L1_l2(\n X, nbclasses, nb_conv=1, nb_fc=1, kernel_initializer=\"random_normal\"\n):\n # input size\n width, height, depth = X.shape\n input_shape = (height, depth)\n\n # parameters of the architecture\n l1_l2_rate = 1.0e-3\n dropout_rate = 0.5\n c... | [
"0.6197579",
"0.60561115",
"0.60153174",
"0.5969007",
"0.5949365",
"0.58943415",
"0.58943415",
"0.58114785",
"0.5737982",
"0.56550455",
"0.5645784",
"0.5630686",
"0.56221515",
"0.5616789",
"0.560882",
"0.56057966",
"0.5596632",
"0.55800354",
"0.55554694",
"0.5553616",
"0.5540... | 0.55448663 | 20 |
dropout + batch norm + l1_l2 | def architecture_CONV_FC_batch_norm_dropout_L1_l2_LEAKY_ReLU(
X, nbclasses, nb_conv=1, nb_fc=1
):
# input size
width, height, depth = X.shape
input_shape = (height, depth)
# parameters of the architecture
l1_l2_rate = 1.0e-3
dropout_rate = 0.5
conv_kernel = 3
conv_filters = 64
n... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def architecture_CONV_FC_batch_norm_dropout_L1_l2(\n X, nbclasses, nb_conv=1, nb_fc=1, kernel_initializer=\"random_normal\"\n):\n # input size\n width, height, depth = X.shape\n input_shape = (height, depth)\n\n # parameters of the architecture\n l1_l2_rate = 1.0e-3\n dropout_rate = 0.5\n c... | [
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"0.58950055",
"0.58950055",
"0.5811575",
"0.5737953",
"0.56548023",
"0.5646338",
"0.563125",
"0.5623065",
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"0.5609015",
"0.5605381",
"0.5597299",
"0.5579035",
"0.55545306",
"0.5553643",
"0.5545299",
"0.5540341",
... | 0.60564125 | 1 |
This first section contains the functions that make the program convert the different number systems | def calculator (menuWindow):
#This procedure accepts the parameter subProgram which will tell it which conversion function
#to call. These functions will then return a value to outputUpdate and
#set resultText to the appropriate message"""
def popUP(message):
pop = tk.Tk()
pop.title("Er... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _pettifor_numbers():\n return { \"Li\": 0.45,\n \"Be\": 1.5,\n \"B\": 2.0,\n \"C\": 2.5,\n \"N\": 3.0, \n \"O\": 3.5,\n \"F\": 4.0,\n \n \"Na\": 0.4,\n \"Mg\": 1.28,\n \"Al\": 1.66,\n ... | [
"0.6049695",
"0.57279676",
"0.56754863",
"0.56605375",
"0.56527585",
"0.56470346",
"0.56375796",
"0.5623967",
"0.56085277",
"0.56013507",
"0.56012034",
"0.55787975",
"0.5558308",
"0.5548418",
"0.5497168",
"0.5491952",
"0.54788685",
"0.54778224",
"0.54226166",
"0.54081273",
"0... | 0.0 | -1 |
Generates a trajectory of the Markov jump process. | def Execute(self,settings,IsStatusBar=False):
if settings.IsSeed:
np.random.seed(5)
self._IsInitial = True
self.settings = settings
self.sim_t = copy.copy(settings.starttime) # does not have to start at zero if we perform sequential simulations
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _generate_trajectory(self, current_pos, velocity, goal, dt, prediction_time):\n\n history = list(np.copy(self._reached_goals))\n\n\n\n\n out = []\n out.append(np.copy(current_pos))\n first_goal_idx = np.where(self._goals == goal)[0][0]\n selected_goal = goal\n reached_... | [
"0.6040567",
"0.6015634",
"0.60108757",
"0.590324",
"0.58615047",
"0.58486295",
"0.5742071",
"0.5694309",
"0.5661889",
"0.5657638",
"0.563613",
"0.5598414",
"0.5571726",
"0.5569394",
"0.55562425",
"0.5501541",
"0.5492893",
"0.5480169",
"0.54617375",
"0.54580027",
"0.5438285",... | 0.0 | -1 |
Calculates a time step of the Direct Method | def RunExactTimestep(self):
if self.sim_t == 0:
randoms = np.random.random(1000)
self.randoms_log = np.log(randoms)*-1
self.randoms = np.random.random(1000)
self.count = 0
elif self.count == 1000:
randoms = np.random.random(1000)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def compute_time_step():\n\n dt = Hydro.compute_time_step()\n\n return dt",
"def _STEPS2TIME(step):\n return step/1000.",
"def time(self, step: int) -> float:\n return self._start_time + self._parameters.dt*(step - self._start_step)",
"def time_step(self):\n\n rho_rel = np.abs(self.rho_d... | [
"0.73281395",
"0.72096485",
"0.7029386",
"0.70150405",
"0.6889623",
"0.68617964",
"0.68476254",
"0.6813826",
"0.68087006",
"0.66766346",
"0.6659325",
"0.6623326",
"0.660472",
"0.6590279",
"0.65758175",
"0.656104",
"0.6550675",
"0.6538886",
"0.6505917",
"0.649663",
"0.6495107"... | 0.0 | -1 |
r""" Find named groups in `pattern` and replace them with the group name. E.g., 1. ^(?P\w+)/b/(\w+)$ ==> ^/b/(\w+)$ 2. ^(?P\w+)/b/(?P\w+)/$ ==> ^/b//$ | def replace_named_groups(pattern):
named_group_indices = [
(m.start(0), m.end(0), m.group(1))
for m in named_group_matcher.finditer(pattern)
]
# Tuples of (named capture group pattern, group name).
group_pattern_and_name = []
# Loop over the groups and their start and end indices.
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def rename_regex_groups(pattern):\n\n grp_rewritten = ''\n mapping = dict()\n rmapping = dict()\n cnt = 1\n for item in _NAMED_GRP_PATTERN.split(pattern):\n if _NAMED_GRP_PATTERN.fullmatch(item):\n old_name = item[len(_NAMED_GRP_PREFIX):-len(_NAMED_GRP_S... | [
"0.7320233",
"0.67950296",
"0.605959",
"0.6014507",
"0.5789089",
"0.5741866",
"0.55717754",
"0.5548644",
"0.55015105",
"0.5437232",
"0.54301506",
"0.53881884",
"0.5342404",
"0.53323615",
"0.52918",
"0.5223952",
"0.520838",
"0.51812756",
"0.5165162",
"0.5128126",
"0.510275",
... | 0.7663291 | 0 |
r""" Find unnamed groups in `pattern` and replace them with ''. E.g., 1. ^(?P\w+)/b/(\w+)$ ==> ^(?P\w+)/b/$ 2. ^(?P\w+)/b/((x|y)\w+)$ ==> ^(?P\w+)/b/$ | def replace_unnamed_groups(pattern):
unnamed_group_indices = [m.start(0) for m in unnamed_group_matcher.finditer(pattern)]
# Indices of the start of unnamed capture groups.
group_indices = []
# Loop over the start indices of the groups.
for start in unnamed_group_indices:
# Handle nested par... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def remove_named_groups(pattern: str, noncapturing: bool = True) -> str:\n if noncapturing:\n new_parens = \"(?:\"\n else:\n new_parens = \"(\"\n\n return re.sub(r\"\\(\\?P<\\w+>\", new_parens, pattern)",
"def replace_named_groups(pattern):\n named_group_indices = [\n (m.start(0)... | [
"0.680426",
"0.6300317",
"0.60647875",
"0.58528376",
"0.5731375",
"0.56507546",
"0.56190664",
"0.5457598",
"0.5445742",
"0.5427602",
"0.5402431",
"0.5378804",
"0.531599",
"0.52673435",
"0.5245449",
"0.5193543",
"0.5173778",
"0.51385576",
"0.50884974",
"0.50674146",
"0.5060320... | 0.73646694 | 0 |
r""" Clean up urlpattern regexes into something more readable by humans. For example, turn "^(?P\w+)/athletes/(?P\w+)/$" into "//athletes//". | def simplify_regex(pattern):
pattern = replace_named_groups(pattern)
pattern = replace_unnamed_groups(pattern)
# clean up any outstanding regex-y characters.
pattern = pattern.replace('^', '').replace('$', '')
if not pattern.startswith('/'):
pattern = '/' + pattern
return pattern | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def clean(self, sub):\n sub = re.sub(r'^RT[\\s]+', '', sub)\n sub = re.sub(r'https?:\\/\\/.*[\\r\\n]*', '', sub)\n sub = re.sub(r'#', '', sub)\n sub = re.sub(r'@[A-Za-z0–9]+', '', sub) \n\n return sub",
"def url_removal(text):\n return re.sub(r'''(?i)\\b((?:https?://|www\\d{... | [
"0.6629905",
"0.6508251",
"0.638263",
"0.63161266",
"0.6314307",
"0.6314307",
"0.6314307",
"0.6314307",
"0.6314307",
"0.6314307",
"0.6314307",
"0.63040775",
"0.62951714",
"0.6283508",
"0.62794656",
"0.62432885",
"0.62392426",
"0.6236246",
"0.6177149",
"0.6127283",
"0.60598266... | 0.5762543 | 42 |
return a list of manifests that are applied on this machine | def fact():
manifests = [x for x in os.walk(manifests_dir)]
return { 'manifests': manifests } | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def available_manifest_strands(self):\n return self._available_manifest_strands",
"def list_manifests():\n import enaml\n with enaml.imports():\n from .pulses.manifest import PulsesManagerManifest\n from .tasks.manifest import PulsesTasksManifest\n from .measure.manifest import ... | [
"0.7041786",
"0.6661976",
"0.65691805",
"0.62815154",
"0.62224513",
"0.6135718",
"0.60951227",
"0.60727566",
"0.5942932",
"0.58993065",
"0.588448",
"0.58641076",
"0.58577496",
"0.5830976",
"0.58182544",
"0.58155537",
"0.58051246",
"0.5784909",
"0.5783124",
"0.57371914",
"0.57... | 0.5902238 | 9 |
Avoid using literal_eval for simple addition expressions. Returns sum of all positive numbers. | def split_and_sum(expression):
split_vals = expression.split('+')
float_vals = [float(v) for v in split_vals]
total = sum([v for v in float_vals if v > 0.0])
return total | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def eval_sum(parse_result):\r\n total = 0.0\r\n current_op = operator.add\r\n for token in parse_result:\r\n if token == '+':\r\n current_op = operator.add\r\n elif token == '-':\r\n current_op = operator.sub\r\n else:\r\n total = current_op(total, tok... | [
"0.71442914",
"0.70861304",
"0.62418014",
"0.62357926",
"0.6229933",
"0.62040615",
"0.6063164",
"0.60560673",
"0.6047006",
"0.5978517",
"0.59740245",
"0.59523994",
"0.59317786",
"0.59307003",
"0.5925646",
"0.5923283",
"0.5915873",
"0.5901256",
"0.58456707",
"0.5834098",
"0.58... | 0.65476674 | 2 |
Requests the last known production mix (in MW) of a given country | def fetch_production(zone_key='IN-GJ', session=None, target_datetime=None,
logger=getLogger('IN-GJ')):
session = session or requests.session()
if target_datetime:
raise NotImplementedError(
'This parser is not yet able to parse past dates')
value_map = fetch_data(zo... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_inflation_country():\n print(\">> Downloading WORLD BANK inflation / country data...\")\n url = source_config.inflation_data_url['latest']\n output_file = source_config.inflation_data_files['raw']['latest']\n download_insee_excel(url, output_file, check=False)",
"def get_country(self, country... | [
"0.56177616",
"0.54850656",
"0.5471012",
"0.5399314",
"0.526083",
"0.5193768",
"0.51925",
"0.51359487",
"0.50710964",
"0.5054442",
"0.49891022",
"0.49824235",
"0.4921711",
"0.488063",
"0.48740438",
"0.48602745",
"0.48566684",
"0.48416945",
"0.48135594",
"0.480547",
"0.4795513... | 0.45403993 | 55 |
Method to get consumption data of Gujarat | def fetch_consumption(zone_key='IN-GJ', session=None, target_datetime=None,
logger=getLogger('IN-GJ')):
session = session or requests.session()
if target_datetime:
raise NotImplementedError(
'This parser is not yet able to parse past dates')
value_map = fetch_data(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getEnergyUsage():\n energy_data = asyncio.run(plug.get_emeter_realtime())\n\n return energy_data",
"def getStockData():\n pass",
"def getCurentData(self):\n if not self.labExperiment:\n super().getCurentData()\n else:\n return np.array(self.connection.query(... | [
"0.658672",
"0.63099957",
"0.6295263",
"0.62537384",
"0.61207724",
"0.6117239",
"0.61045456",
"0.606737",
"0.6031213",
"0.6023843",
"0.5998073",
"0.598272",
"0.59735596",
"0.59629476",
"0.5935335",
"0.5935141",
"0.593349",
"0.5888127",
"0.58589953",
"0.5831155",
"0.5789991",
... | 0.0 | -1 |
Smoothed absolute function. Useful to compute an L1 smooth error. | def abs_smooth(net_loc, input_loc):
x = net_loc - input_loc
absx = tf.abs(x)
minx = tf.minimum(absx, 1)
r = 0.5 * ((absx - 1) * minx + absx)
return r | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _l1_smooth_loss(self, y_true, y_predicted):\n absolute_value_loss = tf.abs(y_true, y_predicted) - 0.5\n square_loss = 0.5 * (y_true - y_predicted)**2\n smooth_l1_condition = tf.less(absolute_value_loss, 1.0)\n l1_smooth_loss = tf.select(smooth_l1_condition,\n ... | [
"0.64517206",
"0.58765614",
"0.56680757",
"0.5649169",
"0.5616871",
"0.55822164",
"0.55743736",
"0.5557006",
"0.5528426",
"0.5517026",
"0.54865664",
"0.5475101",
"0.5430965",
"0.54217964",
"0.5417211",
"0.53726065",
"0.5364687",
"0.5338996",
"0.5331391",
"0.5327666",
"0.53195... | 0.5849999 | 2 |
if keys from dict occur in kw, pop them | def _kw_extract(kw,dict):
for key,value in dict.items():
dict[key]=kw.pop(key,value)
return kw,dict | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def mRemove(self, **kw):\n kw = copy_non_reserved_keywords(kw)\n for key, val in kw.items():\n # It would be easier on the eyes to write this using\n # \"continue\" statements whenever we finish processing an item,\n # but Python 1.5.2 apparently doesn't let you use \... | [
"0.659709",
"0.658242",
"0.64829236",
"0.6343324",
"0.62005615",
"0.602767",
"0.59886026",
"0.5980594",
"0.5973243",
"0.59430367",
"0.5851196",
"0.5803663",
"0.578763",
"0.5769544",
"0.5725139",
"0.5696665",
"0.564784",
"0.56437635",
"0.5620172",
"0.56135494",
"0.5593906",
... | 0.73164487 | 0 |
plot(f) allows f to be (x,y) tuple | def plot(f,**kw):
if type(f)==tuple:
x,y=f
pl.plot(x,y,**kw)
else:
pl.plot(f,**kw) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def make_plot(x,y):",
"def plot():\n pass",
"def plot_f(self, *args, **kwargs):\r\n kwargs['plot_raw'] = True\r\n self.plot(*args, **kwargs)",
"def __plot(self, x: list, y:list):\r\n # clear the figure\r\n self.figure.clear()\r\n # create an axis\r\n self.canvas.a... | [
"0.7233913",
"0.67269236",
"0.6719971",
"0.643809",
"0.63938314",
"0.63545954",
"0.62653416",
"0.61198306",
"0.6113964",
"0.6108317",
"0.60705554",
"0.6022874",
"0.5939718",
"0.59299445",
"0.59191865",
"0.5891549",
"0.58682585",
"0.58335316",
"0.5833347",
"0.5831018",
"0.5812... | 0.8709098 | 0 |
Plot values along direction dir={0,1,2}, through point pt=[x,y,z] | def plot_values_along(pp,pt=[0.5,0.5,0.5],**kw):
kv = {'dir':0, 'verbose':0, 'all':False, 'iv':0, 'i4':0, 'var':None}
kw,kv = _kw_extract(kw,kv)
plot(ds.values_along(pp,pt,iv=kv['iv'],dir=kv['dir'],all=kv['all']),**kw) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def draw_point(axis, pnt3d, color):\n xy = bc2xy(pnt3d)\n axis.scatter(xy[0], xy[1], c=color, marker='o')",
"def plot_direction(ax, phi_deg, psi_deg, \n color='black', marker='d', markersize=3,\n label=None, label_position='right', weight='normal'):\n phi_rad = phi_de... | [
"0.6262929",
"0.6006915",
"0.57216173",
"0.5702731",
"0.56610227",
"0.5660082",
"0.5647964",
"0.5641175",
"0.56411433",
"0.5629691",
"0.56232494",
"0.5620824",
"0.5591335",
"0.5566767",
"0.5546428",
"0.5539211",
"0.553336",
"0.553327",
"0.552634",
"0.5522603",
"0.55138886",
... | 0.605601 | 1 |
Plot values along direction dir={0,1,2}, through point pt=[x,y,z] | def plot_patch_values_along(pp_in,pt=[0.5,0.5,0.5],hold=False,**kw):
kv = {'dir':0, 'verbose':0, 'all':False, 'iv':0, 'i4':0, 'var':None}
kw,kv = _kw_extract(kw,kv)
pp = ds.patches_along(pp_in,pt,dir=kv['dir'],verbose=kv['verbose'])
xmin,xmax = ds.minmax_patches(pp,dir=kv['dir'])
if not hold:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def draw_point(axis, pnt3d, color):\n xy = bc2xy(pnt3d)\n axis.scatter(xy[0], xy[1], c=color, marker='o')",
"def plot_values_along(pp,pt=[0.5,0.5,0.5],**kw):\n kv = {'dir':0, 'verbose':0, 'all':False, 'iv':0, 'i4':0, 'var':None}\n kw,kv = _kw_extract(kw,kv)\n plot(ds.values_along(pp,pt,iv=kv['iv']... | [
"0.62647605",
"0.60552585",
"0.6005312",
"0.5723366",
"0.57031566",
"0.566268",
"0.5660064",
"0.56475013",
"0.56423193",
"0.5641629",
"0.563067",
"0.5624117",
"0.56221503",
"0.55907345",
"0.556801",
"0.55449003",
"0.5540896",
"0.5535063",
"0.5534622",
"0.5526678",
"0.55239534... | 0.0 | -1 |
plot power spectrum of 2D array | def power2d(f,*kw):
ft2=np.abs(np.fft.fft2(f))**2
m=f.shape
k=np.meshgrid(range(m[0]),range(m[1]))
k=np.sqrt(k[0]**2+k[1]**2)
a=2
k0=1.0/a**0.5
k1=1.0*a**0.5
power=[]
kk=[]
while(k1 <= m[0]//2):
kk.append((k0*k1)**0.5)
w=np.where((k>k0) & (k <= k1))
power.... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def plot_spectrum( data ):\n d = data[1]\n # rfft gives positive frequecies. Square to get power spectrum.\n fp = numpy.absolute( numpy.fft.rfft( d ) )**2\n freq = numpy.fft.fftfreq( d.shape[-1] )\n n = len(fp)\n\n # reshape stuff a bit. keep only positive freqs.\n fp = fp[1:-1]\n freq = fr... | [
"0.6865385",
"0.68452597",
"0.68080705",
"0.6768042",
"0.66995203",
"0.66993386",
"0.64395714",
"0.6430631",
"0.6409568",
"0.63501585",
"0.6320459",
"0.6320107",
"0.6272982",
"0.62168336",
"0.6190964",
"0.6128101",
"0.6125795",
"0.61254364",
"0.6112583",
"0.61059433",
"0.6100... | 0.0 | -1 |
raise a figure to the top | def show_plot(figure_id=None):
if figure_id is None:
fig = pl.gcf()
else:
# do this even if figure_id == 0
fig = pl.figure(num=figure_id)
pl.show()
pl.pause(1e-9)
fig.canvas.manager.window.activateWindow()
fig.canvas.manager.window.raise_() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def draw_top(self):\n return group()",
"def XPBringRootWidgetToFront(inWidget):\n pass",
"def autostop():",
"def raise_window(window):\n window.attributes('-topmost', 1)\n window.attributes('-topmost', 0)",
"def back_extra(self, ontop):\n\n self.frames[ontop].forget()\n self.e... | [
"0.62913567",
"0.61378044",
"0.5949495",
"0.5857847",
"0.58181804",
"0.5804387",
"0.57883143",
"0.5783538",
"0.5747582",
"0.5702409",
"0.5701583",
"0.56797063",
"0.5665179",
"0.5653984",
"0.56508195",
"0.5632079",
"0.5603762",
"0.5568151",
"0.5565534",
"0.5541218",
"0.5529163... | 0.0 | -1 |
replace the normal pause, w/o raising window() | def pause(time=1e-6):
pl.draw()
pl.gcf().canvas.start_event_loop(time) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def pause(self):\n\t\tpass",
"def pause(): # real signature unknown; restored from __doc__\n pass",
"def pause(self):\n pass",
"def pause(self):\n pass",
"def pause():\n click.pause()",
"def pause(self):\n \n self.pause = True",
"def pause(self):\n pass\n ... | [
"0.7750189",
"0.760757",
"0.7583673",
"0.7583673",
"0.7269366",
"0.71191716",
"0.70278",
"0.69974154",
"0.6884622",
"0.68308437",
"0.68308437",
"0.68252546",
"0.68184483",
"0.68105435",
"0.6808565",
"0.6798502",
"0.67898375",
"0.6775584",
"0.6741306",
"0.6734974",
"0.67122114... | 0.6827029 | 11 |
Plot the PDF of the density | def pdf_d(iout,run='',data='../data',iv='d',i4=0,nbin=100,xlim=[-4,3],lnd=False):
s = di.snapshot(iout,run=run,data=data)
n = nbin
bins = np.linspace(xlim[0],xlim[1],n+1)
htot = 0.0
i = 0
for p in s.patches:
i += 1
if i%1000==0:
print('{:.1f}%'.format(i/len(s.patches)... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def pdf(data, args):\n return Plot._dist(data, args)",
"def make_kde_plot(x, pdf):\n\n fig = plt.figure(figsize=(768/96, 400/96), dpi=9)\n ax = plt.gca()\n ax.plot(x, pdf)\n ax.fill_between(x, pdf, alpha=.5)\n\n # Formatting\n plt.xlabel('Hourly rate ($)', fontsize=18)\n plt.xticks(fo... | [
"0.73690754",
"0.71617067",
"0.71548754",
"0.6999526",
"0.6962633",
"0.6902345",
"0.690083",
"0.6887748",
"0.6878603",
"0.68121535",
"0.67572474",
"0.66559654",
"0.66133654",
"0.66133654",
"0.6585173",
"0.6584147",
"0.6578231",
"0.6572529",
"0.6552081",
"0.654156",
"0.6535898... | 0.5843463 | 98 |
Plot the output of the dispatch.pdf() procedure | def plot_pdf(pdf,**kwargs):
pl.hist(pdf.bins,bins=pdf.bins,weights=pdf.counts,**kwargs)
return pdf.time | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def Plot(self):\n\n ### Create the path names ###\n folder_string = self.params.folder+\"/plots/\"\n u_string = self.params.folder+\"/plots/u.pdf\"\n p_string = self.params.folder+\"/plots/p.pdf\"\n\n ### Check if folder exists ###\n if not os.path.exists(folder_string): o... | [
"0.70713794",
"0.69506466",
"0.65899605",
"0.6551687",
"0.65347886",
"0.6530138",
"0.65197337",
"0.6519003",
"0.6465785",
"0.6432504",
"0.63811475",
"0.6373067",
"0.6363945",
"0.63360846",
"0.6330689",
"0.63069654",
"0.62943375",
"0.62903255",
"0.6219337",
"0.62005454",
"0.61... | 0.591895 | 48 |
Delete matches category and all its channels. | async def delete_matches_category(self):
existing_categories = self.get_channels(
'matches', ChannelType.category)
for c in existing_categories:
try:
await asyncio.gather(*(chan.delete() for chan in c.channels))
await c.delete()
# We ca... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cleanup(self, channel=None):\n # falls `channel` angegeben wurden, werden nur diese bereinigt,\n # ansonsten wird alles bereinigt\n if channel:\n # ueberpruefe, ob `channel` eine Zahl ist und erstelle eventuell eine Liste nur mit dieser Zahl\n # dies ist wichtig, weil... | [
"0.64337945",
"0.6401022",
"0.6107181",
"0.6081878",
"0.5946473",
"0.5895014",
"0.587733",
"0.5871811",
"0.5821551",
"0.5815859",
"0.57719624",
"0.5770912",
"0.5759033",
"0.57166386",
"0.5705552",
"0.5697941",
"0.5684132",
"0.56386846",
"0.55753577",
"0.5554672",
"0.55487365"... | 0.8869049 | 0 |
Prevent both players from reporting at the same time. | def _add_to_recently_called(self, match, reporter):
if utils.istrcmp(match.player1_tag, reporter):
other = match.player2_tag
else:
other = match.player1_tag
self.recently_called[other] = time() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def still_deciding(self):\n for player in self.players:\n if isinstance(player, user.User):\n if not player.has_played:\n return True\n return False",
"def playerdefeated(self):\n globalvalues.gameover_combat()",
"def losesChance(self):\n ... | [
"0.61262506",
"0.60236615",
"0.5634644",
"0.56011015",
"0.5578245",
"0.5538049",
"0.5537875",
"0.55358166",
"0.5534296",
"0.5521882",
"0.5503166",
"0.55023617",
"0.5500676",
"0.5480155",
"0.5468607",
"0.54599667",
"0.5449898",
"0.54205763",
"0.5392923",
"0.5390785",
"0.537815... | 0.5070516 | 77 |
Check the participants list for players not on the server. | async def missing_tags(self, owner) -> List[str]:
dms = await utils.get_dms(owner)
missing = [player for player in await self.gar.get_players()
if not self.get_user(player)]
if not missing:
return []
message = ['Missing Discord accounts for the following pl... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def get_participants(self):\n for i in range(self.num):\n def check(m):\n if m.content.lower().strip() == \"i\" and m.author not in self.participants:\n return True\n\n return False\n\n # Wait with a timeout of 2 minutes and check ... | [
"0.6719684",
"0.63900346",
"0.6368742",
"0.62854147",
"0.6258194",
"0.6222326",
"0.6147451",
"0.61474484",
"0.6127953",
"0.60543185",
"0.598346",
"0.59729105",
"0.5960582",
"0.5942845",
"0.594048",
"0.58446205",
"0.5834904",
"0.5820176",
"0.5808948",
"0.58085364",
"0.5796258"... | 0.6249625 | 5 |
Gets our permissions on the server. | def permissions(self) -> discord.Permissions:
return self.channel.permissions_for(self.guild.me) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_permissions(self):\n return self.settings[\"permissions\"]",
"def octopus_permissions_get(self, msg, args):\r\n return self.permissions.get_permissions()",
"def permissions(self):\n return self.get_permissions()",
"def get_permissions():\n return config.get_cfg_storage(ID_... | [
"0.7986771",
"0.794256",
"0.7887418",
"0.78345644",
"0.77169865",
"0.76053184",
"0.7577209",
"0.75250983",
"0.74911094",
"0.7277559",
"0.72693956",
"0.72676855",
"0.723835",
"0.71908754",
"0.7177519",
"0.7157548",
"0.7146727",
"0.71430004",
"0.7129717",
"0.70936584",
"0.70712... | 0.7516241 | 8 |
Gets the user mention string. If the user isn't found, just return the username. | def mention_user(self, username: str) -> str:
member = self.get_user(username)
if member:
return member.mention
return username | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_user_mention(self, name: str) -> str:\n try:\n return self.known_user_ids[name]\n except KeyError:\n pass\n\n mention = name\n users = get_value_from_redis('slack-members') or []\n user = self._get_user(name, users)\n if user:\n me... | [
"0.78680474",
"0.75037277",
"0.698189",
"0.698189",
"0.68996614",
"0.6867837",
"0.67106825",
"0.6666238",
"0.66000545",
"0.6591795",
"0.6591795",
"0.65639687",
"0.65635896",
"0.65554214",
"0.65437853",
"0.64928955",
"0.64731586",
"0.6467594",
"0.6467354",
"0.6457515",
"0.6457... | 0.8424426 | 0 |
Get member by username. | def get_user(self, username: str) -> Optional[discord.Member]:
for m in self.guild.members:
if utils.istrcmp(m.display_name, username):
return m
return None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_user(self, username):\n\t\treturn self.users.get(username, None)",
"def get_member(self, user):\n for player in self.members:\n if player.uuid == user.id:\n return player\n return None",
"def Get(self, username):\n if self._users:\n return self._users.get... | [
"0.75246143",
"0.7495561",
"0.74683446",
"0.74049187",
"0.73253155",
"0.7299027",
"0.72175676",
"0.71334416",
"0.7068116",
"0.7049825",
"0.7039728",
"0.6946112",
"0.69360876",
"0.69183743",
"0.69003195",
"0.689256",
"0.68825346",
"0.6881717",
"0.68705404",
"0.68620247",
"0.68... | 0.8309965 | 0 |
Catch any method/attribute lookups that are not defined in this class and try to find them on the provided bridge object. | def __getattr__(self, name: str):
return getattr(self._client, name) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _lookup_method(self, call):\n raise Exception(\"_lookup_method must be implemented by subclasses.\")",
"def lookup(self, *args, **kwargs): # real signature unknown\n pass",
"def lookup(self, *args, **kwargs): # real signature unknown\n pass",
"def test_lookup_parameter_handler_object... | [
"0.5799882",
"0.56170505",
"0.56170505",
"0.5608421",
"0.52733755",
"0.51485467",
"0.5021419",
"0.49733004",
"0.48889658",
"0.48796782",
"0.4817488",
"0.47882497",
"0.478772",
"0.47650564",
"0.4761213",
"0.47604015",
"0.475525",
"0.47418138",
"0.47340906",
"0.47281703",
"0.46... | 0.0 | -1 |
Test names like Goat Cat | def test_first_last(self):
full_name = get_full_name("pony", "cat")
self.assertEqual(full_name, "Pony Cat")
full_name = get_full_name("goat", "cat")
self.assertEqual(full_name, "Goat Cat") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_title(names):",
"def test_name(self):\n\n self.check_search(\n dict(name=u'flamethrower'),\n [u'Flamethrower'],\n 'searching by name',\n exact=True,\n )\n\n self.check_search(\n dict(name=u'durp'),\n [],\n ... | [
"0.7272773",
"0.72304636",
"0.72284096",
"0.69994545",
"0.697421",
"0.69290376",
"0.689268",
"0.68515533",
"0.6850236",
"0.6830433",
"0.6828345",
"0.6685354",
"0.6678936",
"0.6674695",
"0.66445744",
"0.6638108",
"0.6600845",
"0.65993506",
"0.6574424",
"0.657173",
"0.65344596"... | 0.6196084 | 50 |
Test names like Goat Studman Cat | def test_first_last_middle(self):
full_name = get_full_name("pony", "cat", "sweetie")
self.assertEqual(full_name, "Pony Sweetie Cat")
full_name = get_full_name("goat", "cat", "studman")
self.assertEqual(full_name, "Goat Studman Cat") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_legal_names(self):\n names = [prod.name for prod in generate_products()]\n sep = [(name.split()[0], name.split()[1]) for name in names]\n for name in sep:\n self.assertIn(name[0], ADJS)\n self.assertIn(name[1], NOUNS)",
"def test_legal_names(self):\n tes... | [
"0.7357881",
"0.7340329",
"0.732584",
"0.72818434",
"0.72236216",
"0.720758",
"0.7120223",
"0.69911456",
"0.69444036",
"0.69189924",
"0.69121563",
"0.69082373",
"0.6899404",
"0.6823745",
"0.6816478",
"0.68021417",
"0.6771601",
"0.6714057",
"0.67046154",
"0.67022836",
"0.66533... | 0.64093536 | 49 |
Calculate the bake time remaining. | def bake_time_remaining(elapsed_bake_time=EXPECTED_BAKE_TIME):
time_remaining = EXPECTED_BAKE_TIME - elapsed_bake_time
return time_remaining | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def bake_time_remaining(elapsed_bake_time: int) -> int:\n return EXPECTED_BAKE_TIME - elapsed_bake_time",
"def bake_time_remaining(elapsed_bake_time):\n return EXPECTED_BAKE_TIME - elapsed_bake_time",
"def bake_time_remaining(elapsed_bake_time):\n return EXPECTED_BAKE_TIME - elapsed_bake_time",
"def... | [
"0.8687785",
"0.85834104",
"0.85834104",
"0.85834104",
"0.7155303",
"0.6970577",
"0.6957602",
"0.688031",
"0.67487776",
"0.6720861",
"0.67003495",
"0.66638684",
"0.6635681",
"0.6626661",
"0.6574791",
"0.65689373",
"0.6551115",
"0.65494686",
"0.653096",
"0.6519621",
"0.6506905... | 0.84728587 | 4 |
Calculate Time To Prepare All Layers. This funtion takes the lasagna number of layers as a parameter and mutiplies that value with the designated preparation time. | def preparation_time_in_minutes(number_of_layers):
layers_preparation_time = number_of_layers * PREPARATION_TIME
return layers_preparation_time | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def preparation_time_in_minutes(number_of_layers: int) -> int:\n return PREPARATION_TIME_PER_LAYER_IN_MINUTES * number_of_layers",
"def preparation_time_in_minutes(number_of_layers):\n return number_of_layers * 2",
"def preparation_time_in_minutes(number_of_layers):\n return PREPARATION_TIME * number_... | [
"0.74356353",
"0.7422801",
"0.73030597",
"0.7143981",
"0.5985816",
"0.5975828",
"0.5915438",
"0.5865488",
"0.57980657",
"0.5677244",
"0.5349525",
"0.5309606",
"0.5289832",
"0.52619296",
"0.5260173",
"0.5198084",
"0.51720953",
"0.5171042",
"0.5160975",
"0.5144867",
"0.51287425... | 0.7569402 | 0 |
Calculate Total Elapsed Time. This funtion takes the lasagna number of layers as a parameter and also the elapsed bake time and adds both values to aquire an accurate result hoe much time we have spent baking the lasagna. | def elapsed_time_in_minutes(number_of_layers, elapsed_bake_time):
preparation_time = preparation_time_in_minutes(number_of_layers)
total_bake_time = preparation_time + elapsed_bake_time
return total_bake_time | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def elapsed_time_in_minutes(no_of_layers, elapsed_bake_time):\n return preparation_time_in_minutes(no_of_layers) + elapsed_bake_time",
"def elapsed_time_in_minutes(number_of_layers: int, elapsed_bake_time: int) -> int:\n return preparation_time_in_minutes(number_of_layers) + elapsed_bake_time",
"def elap... | [
"0.75032663",
"0.7467269",
"0.74404085",
"0.73925406",
"0.64541507",
"0.6305446",
"0.6257169",
"0.6257169",
"0.6257169",
"0.6000466",
"0.5808721",
"0.5773526",
"0.5667682",
"0.5637501",
"0.5635833",
"0.55388767",
"0.552437",
"0.5441152",
"0.5426811",
"0.53834176",
"0.53707606... | 0.7721855 | 0 |
get address by id | def get_address_by_id(address_id: int):
try:
address = address_service.get_address_by_id(address_id)
current_app.logger.info("Return data for address_id: {}".format(address_id))
return jsonify({
"data": {
"address": address
}}), 200
except SQLCusto... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_bank_address_by_id(bank_id: int) -> str:\n # Open a new connection\n db, cursor = db_connector.cursor()\n\n query = \"select address from bank where name = '{}';\".format(bank_id)\n cursor.execute(query)\n data = cursor.fetchall()\n db.disconnect()\n return data[0][0]",
"def get_supp... | [
"0.7158677",
"0.70268",
"0.6940104",
"0.6777704",
"0.67587537",
"0.6659815",
"0.6643687",
"0.65674657",
"0.6503904",
"0.6471765",
"0.64163893",
"0.6403165",
"0.63863844",
"0.6383097",
"0.6337203",
"0.6270841",
"0.6270169",
"0.6261926",
"0.62596136",
"0.62596136",
"0.6199845",... | 0.75688535 | 0 |
delete address by id | def delete_address(address_id: int):
try:
current_app.logger.info("delete address : address_id: %s", address_id)
return jsonify({
"status": address_service.delete_address_by_id(address_id)
}), 200
except SQLCustomError as error:
current_app.logger.error("fail to delet... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def delete(self, request, *args, **kwargs):\n # validate address id and get object\n instance = self.get_object()\n\n # get last transaction save point id\n sid = transaction.savepoint()\n\n try:\n # soft delete address\n instance.delete_address(request.user... | [
"0.75762683",
"0.72528064",
"0.70932806",
"0.7015976",
"0.7014734",
"0.6979125",
"0.68229157",
"0.6805178",
"0.67385775",
"0.67281526",
"0.6688489",
"0.66123915",
"0.6570931",
"0.6570451",
"0.65646535",
"0.6552276",
"0.654469",
"0.6536282",
"0.6487301",
"0.6466477",
"0.645843... | 0.7932358 | 0 |
get all addresses list | def get_all_addresses():
try:
addresses = address_service.get_all_addresses()
current_app.logger.info("get all addresses")
return jsonify({
"data": {
"count": len(addresses),
"addresses": addresses
}}), 200
except SQLCustomError as ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_addrs(self) -> List[Multiaddr]:",
"def addresses(self) -> \"List[str]\":\n return self._attrs.get(\"addresses\")",
"def addresses(self) -> \"List[str]\":\n return self._attrs.get(\"addresses\")",
"def addresses(self) -> \"List[str]\":\n return self._attrs.get(\"addresses\")",
"... | [
"0.7885754",
"0.7816518",
"0.7816518",
"0.7816518",
"0.7325519",
"0.7184814",
"0.71506286",
"0.70696783",
"0.705442",
"0.70192426",
"0.7001219",
"0.6982373",
"0.6977537",
"0.69752955",
"0.69717383",
"0.6926956",
"0.69209373",
"0.69204617",
"0.68843",
"0.68579096",
"0.68555427... | 0.7560295 | 4 |
Get the XML representation as `ElementTree` object. | def get_xml(self):
profile = self.profile
version = self.version
#self.attribs['xmlns'] = "http://www.w3.org/2000/svg"
self.attribs['xmlns:xlink'] = "http://www.w3.org/1999/xlink"
self.attribs['xmlns:ev'] = "http://www.w3.org/2001/xml-events"
self.attribs['baseProfile'] = profile
self.attri... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_xml(self):\n with io.StringIO() as string:\n string.write(ET.tostring(self.root, encoding=\"unicode\"))\n return string.getvalue()",
"def get_xml(self):\n return etree.tostring(self.get_etree())",
"def get_tree():\n root = ET.fromstring(xmlstring)\n return ET.E... | [
"0.76870763",
"0.75312847",
"0.74353814",
"0.69993716",
"0.68324274",
"0.6808984",
"0.67279035",
"0.67279035",
"0.67270184",
"0.67270184",
"0.6613549",
"0.65791756",
"0.65375465",
"0.6401415",
"0.6362028",
"0.6343287",
"0.63272727",
"0.6314633",
"0.6282842",
"0.6246787",
"0.6... | 0.0 | -1 |
Get the XML representation as `ElementTree` object. | def get_xml(self):
xml = svgwrite.etree.etree.Element(self.elementname)
if self.debug:
self.validator.check_all_svg_attribute_values(self.elementname, self.attribs)
for attribute, value in self.attribs.items():
# filter 'None' values
if value is not None:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_xml(self):\n with io.StringIO() as string:\n string.write(ET.tostring(self.root, encoding=\"unicode\"))\n return string.getvalue()",
"def get_xml(self):\n return etree.tostring(self.get_etree())",
"def get_tree():\n root = ET.fromstring(xmlstring)\n return ET.E... | [
"0.7688683",
"0.75327605",
"0.7435341",
"0.6999228",
"0.6832689",
"0.68084466",
"0.67295486",
"0.67295486",
"0.67260617",
"0.67260617",
"0.66133803",
"0.65811235",
"0.65377057",
"0.64042026",
"0.63633126",
"0.63447636",
"0.6327719",
"0.6314219",
"0.6283882",
"0.6245147",
"0.6... | 0.58009404 | 39 |
This will create persuasions in bulk and will push it to ES API /persuasion/bulk/create [ { | def bulk_create():
logger.info("Creating persuasions in bulk")
try:
request_data = json.loads(request.data)
with concurrent.futures.ThreadPoolExecutor(max_workers=settings.MAX_WORKERS) as executor:
{executor.submit(PersuasionServices.create, data): data for data i... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def handle(self, *args, **options):\n self.create_indices()\n self.bulk()",
"def insert_to_elastic(elastic, paper_authors, papers, authors, index_name):\n helpers.bulk(elastic, merge_to_elastic(paper_authors, papers, authors, index_name))",
"def bulk_refresh():\n logger.info(\"Refreshin... | [
"0.60707426",
"0.6044942",
"0.6015414",
"0.5868079",
"0.58612776",
"0.58422595",
"0.57517433",
"0.5748778",
"0.5713514",
"0.56829447",
"0.56696784",
"0.5647391",
"0.5640021",
"0.56258446",
"0.5564406",
"0.5555156",
"0.5547321",
"0.5522877",
"0.5503289",
"0.5487169",
"0.544674... | 0.79547685 | 0 |
This will update the persuasion and will return it API /persuasion/refresh?push_to_es=true&push_to_inflow=false { | def refresh_persuasion():
try:
request_data = json.loads(request.data)
meta = request.args
persuasion = PersuasionServices.refresh(request_data, meta)
except Exception as e:
error_msg = "Getting persuasion details - " + repr(e)
logger.error(err... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def schedule_refresh():\n logger.info(\"Refreshing/Updating persuasions in bulk\")\n try:\n args = request.args\n if not args.get(\"type\") or not args.get(\"sub_type\"):\n return jsonify(\n dict(status=\"failure\", message=\"Invalid type/sub_ty... | [
"0.68004",
"0.65762883",
"0.6169109",
"0.6017237",
"0.58676904",
"0.58676904",
"0.5842514",
"0.58078766",
"0.571597",
"0.5683472",
"0.5669192",
"0.56609315",
"0.56607497",
"0.56477195",
"0.56477195",
"0.5646708",
"0.5581315",
"0.5581315",
"0.55265003",
"0.5518151",
"0.5514036... | 0.8352617 | 0 |
This will update persuasions in bulk and will push it to ES API /persuasion/bulk/refresh?push_to_es=true&push_to_inflow=false [ { | def bulk_refresh():
logger.info("Refreshing/Updating persuasions in bulk")
try:
request_data = json.loads(request.data)
args = request.args
with concurrent.futures.ThreadPoolExecutor(max_workers=settings.MAX_WORKERS) as executor:
{executor.submit(Persu... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def schedule_refresh():\n logger.info(\"Refreshing/Updating persuasions in bulk\")\n try:\n args = request.args\n if not args.get(\"type\") or not args.get(\"sub_type\"):\n return jsonify(\n dict(status=\"failure\", message=\"Invalid type/sub_ty... | [
"0.76656324",
"0.6743843",
"0.6214206",
"0.59055984",
"0.58515877",
"0.57441527",
"0.5547801",
"0.54766494",
"0.54745275",
"0.54507333",
"0.5444725",
"0.542627",
"0.53595006",
"0.5348997",
"0.5331371",
"0.52764916",
"0.527101",
"0.52681786",
"0.5237418",
"0.5237225",
"0.52309... | 0.8021315 | 0 |
This will also update persuasions in bulk based on type subtype and will push it to ES API /persuasion/schedule/refresh?push_to_es=true&push_to_inflow=false&type=quality_score&sub_type=inventory_depth | def schedule_refresh():
logger.info("Refreshing/Updating persuasions in bulk")
try:
args = request.args
if not args.get("type") or not args.get("sub_type"):
return jsonify(
dict(status="failure", message="Invalid type/sub_type"))
p... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def bulk_refresh():\n logger.info(\"Refreshing/Updating persuasions in bulk\")\n try:\n request_data = json.loads(request.data)\n args = request.args\n with concurrent.futures.ThreadPoolExecutor(max_workers=settings.MAX_WORKERS) as executor:\n {executor... | [
"0.5894394",
"0.552447",
"0.5422689",
"0.51684684",
"0.51551306",
"0.50729585",
"0.50314814",
"0.495626",
"0.49350718",
"0.48938638",
"0.48739257",
"0.48735148",
"0.48624405",
"0.48345262",
"0.48125353",
"0.47565222",
"0.47560182",
"0.47403848",
"0.47322214",
"0.46917525",
"0... | 0.6947471 | 0 |
Convert any text to a fernet key for encryption. | def any_text_to_fernet_key(self, text):
md5 = fingerprint.fingerprint.of_text(text)
fernet_key = base64.b64encode(md5.encode("utf-8"))
return fernet_key | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def text2PublicKey(text:str):\n return RSA.importKey(b58decode(text))",
"def encrypt(text):\r\n\r\n cipher = fuzz(text)\r\n return hexify(cipher)",
"def fernet_encript(key,message):\n\tf = Fernet(key)\n\treturn f.encrypt(message)",
"def caesar_encryption(text):\n result = ''\n for char in text:\n ... | [
"0.6408409",
"0.6371177",
"0.63681877",
"0.6212298",
"0.61476415",
"0.60205185",
"0.60133415",
"0.60062563",
"0.6004446",
"0.5816517",
"0.5788318",
"0.5727161",
"0.56803447",
"0.56624347",
"0.5623659",
"0.56033343",
"0.560295",
"0.55829906",
"0.5578667",
"0.5558498",
"0.55552... | 0.85045666 | 0 |
Manually enter a password for encryption on keyboard. | def input_password(self): # pragma: no cover
password = input("Please enter your secret key (case sensitive): ")
self.set_password(password) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def enter_password(self):",
"def enter_password(self):\n self.password.clear()\n self.password.click()\n self.password.send_keys(TestData.PASSWORD)\n sleep(TestData.DELAY)\n return self.password",
"async def password(self, ctx):\n pass",
"def prompt_pass():\n msg ... | [
"0.81982946",
"0.7473501",
"0.7234966",
"0.7115085",
"0.70761955",
"0.7038702",
"0.693298",
"0.68611926",
"0.68122035",
"0.67915016",
"0.67278564",
"0.67168885",
"0.6711128",
"0.67014295",
"0.66786796",
"0.66681784",
"0.66681784",
"0.66681784",
"0.66681784",
"0.66681784",
"0.... | 0.78404224 | 1 |
Set a new password for encryption. | def set_password(self, password):
self.__init__(password=password) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setpassword(self, pwd):\n pass",
"def set_password(self, value):\n # Salt need to be generated before set password\n m = hashlib.sha256()\n m.update('-'.join([\n str(datetime.now()),\n config.get('security.password_salt')\n ]))\n self.salt = m.h... | [
"0.8377578",
"0.8145898",
"0.8129737",
"0.80971813",
"0.8089785",
"0.80798155",
"0.80655986",
"0.8056996",
"0.8050682",
"0.7988545",
"0.7963893",
"0.7942522",
"0.7855977",
"0.7843381",
"0.7838417",
"0.78155446",
"0.78155446",
"0.78155446",
"0.78155446",
"0.7810031",
"0.778050... | 0.7456274 | 39 |
Open the deletion modal for an address row. | def delete_address(self) -> object:
self.delete_button.click()
return DeletionModal(self).wait_for_component_to_be_present() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def delete_address(self, address: dict) -> None:\n row = self.addresses_list.surface_address_row(address)\n\n row.open_kebab_menu()\n row.kebab_menu.delete_address()\n\n self.deletion_modal.confirm_address_deletion()",
"def DeleteRow(self, entry):\n for a_link in entry.link:\n ... | [
"0.70819616",
"0.61636835",
"0.6088303",
"0.5755534",
"0.5709963",
"0.55882573",
"0.55848145",
"0.5560167",
"0.55589795",
"0.55150014",
"0.5407896",
"0.5399962",
"0.5394524",
"0.53886646",
"0.5371516",
"0.5349296",
"0.5309787",
"0.53002477",
"0.529465",
"0.52785975",
"0.52676... | 0.77717626 | 0 |
Open the edit address form for an address row. A conditional check for whether the URL contains 'admin' allows this method to be used in both OnDemand Web and OnDemand Admin. | def edit_address(self) -> object:
self.edit_button.click()
if 'admin' not in self.driver.current_url:
return WebAddressForm(self).wait_for_component_to_be_present()
return AdminAddressForm(self).wait_for_component_to_be_present() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def open_edit_address(self, address: dict) -> None:\n row = self.addresses_list.surface_address_row(address)\n\n row.open_kebab_menu()\n row.kebab_menu.edit_address()",
"def __editAddress(self):\n idx = self.bookmarksTree.currentIndex()\n idx = idx.sibling(idx.row(), 1)\n ... | [
"0.7748994",
"0.6355615",
"0.5770656",
"0.5743798",
"0.5656257",
"0.55580795",
"0.55527085",
"0.55411637",
"0.5522946",
"0.5501704",
"0.5454978",
"0.5401237",
"0.5389246",
"0.5380006",
"0.53368807",
"0.53319603",
"0.5306126",
"0.52961576",
"0.52745545",
"0.52547807",
"0.52438... | 0.81699383 | 0 |
Run the algorithm for the scores in the axis layer (0 for x, 1 for y). This score and the associated ranking are reliable, the ranking of the opposite score is not. | def run(self, axis, gamma):
# Trajectories of the main score to compute and the opposite one
traj_s, traj_o = [_np.ones(self.d[axis])], [_np.ones(self.d[1-axis])]
# Ranked indices of the scores
rank_s, rank_o = _np.array([], dtype=int), _np.array([], dtype=int)
# List of nod... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _check_run(self, axis):\n if (self.x_traj, self.y_traj)[axis] is None:\n if (self.inverse_x_traj, self.inverse_y_traj)[axis] is None:\n raise Exception('The algorithm has not been run.')\n else:\n if self.params['print_info']:\n prin... | [
"0.6627804",
"0.6555331",
"0.6459971",
"0.5922805",
"0.5655154",
"0.5596297",
"0.5581356",
"0.5572477",
"0.55473816",
"0.5538019",
"0.5492573",
"0.5474258",
"0.5454743",
"0.54495645",
"0.54365635",
"0.5428014",
"0.54214275",
"0.54198104",
"0.5418108",
"0.5414656",
"0.5410967"... | 0.66810125 | 0 |
Gets the fraction of positive stationary scores. | def positive_scores(self, axis, th_stat=10**(-2)):
traj, rank = self._check_run(axis)
low_boundary = _np.exp(_np.log(self.params['low_bound'])/2)
t = len(traj)-1
stat = 0
# Iteration over the trajectories
for i in range(len(traj[0])):
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def positive_share(self) -> float:\n pos = self.tsdf.pct_change()[1:][self.tsdf.pct_change()[1:].values > 0.0].count()\n tot = self.tsdf.pct_change()[1:].count()\n return float(pos / tot)",
"def game_score(self):\n score = self.score.quantize(Decimal('0.001'))\n return score if... | [
"0.6627993",
"0.66167814",
"0.66115385",
"0.65545696",
"0.64541936",
"0.6416723",
"0.63477427",
"0.63358057",
"0.63094145",
"0.6304095",
"0.628694",
"0.62657887",
"0.62262195",
"0.61896664",
"0.6168893",
"0.61631715",
"0.61393696",
"0.6111311",
"0.6108707",
"0.6084148",
"0.60... | 0.0 | -1 |
Compute the extinction area given the last ranking obtained through the run method | def ext_area(self, axis):
traj, rank = self._check_run(axis)
return _ext_area(axis, rank, self._neighb[1], self._neighb[0]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _overlap_energy(self, this, that):\n if not this.overlaps(that):\n return 0.0\n\n return min(10.0 / this.rank, 10.0 / that.rank)",
"def _ext_area(axis, ranking, row_ind_at_col, col_ind_at_row):\n if axis == 0:\n indexes_lists = _np.array([col_ind_at_row[:], row_ind_at_col[:... | [
"0.5964079",
"0.59478605",
"0.5941593",
"0.59182143",
"0.5786603",
"0.5778234",
"0.57550836",
"0.5740219",
"0.5736216",
"0.5681183",
"0.5669513",
"0.56531614",
"0.56521064",
"0.56079173",
"0.55998206",
"0.5591468",
"0.5575538",
"0.55385804",
"0.5520699",
"0.55159914",
"0.5515... | 0.55559903 | 17 |
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